| SQLITE-UTILS(1) | sqlite-utils | SQLITE-UTILS(1) |
sqlite-utils - sqlite-utils documentation
CLI tool and Python utility functions for manipulating SQLite databases
This library and command-line utility helps create SQLite databases from an existing collection of data.
Most of the functionality is available as either a Python API or through the sqlite-utils command-line tool.
sqlite-utils is not intended to be a full ORM: the focus is utility helpers to make creating the initial database and populating it with data as productive as possible.
It is designed as a useful complement to Datasette.
Cleaning data with sqlite-utils and Datasette provides a tutorial introduction (and accompanying ten minute video) about using this tool.
sqlite-utils is tested on Linux, macOS and Windows.
The sqlite-utils command-line tool can be installed on macOS using Homebrew:
brew install sqlite-utils
If you have it installed and want to upgrade to the most recent release, you can run:
brew upgrade sqlite-utils
Then run sqlite-utils --version to confirm the installed version.
The sqlite-utils package on PyPI includes both the sqlite_utils Python library and the sqlite-utils command-line tool. You can install them using pip like so:
pip install sqlite-utils
pipx is a tool for installing Python command-line applications in their own isolated environments. You can use pipx to install the sqlite-utils command-line tool like this:
pipx install sqlite-utils
The sqlite-utils command-line tool can be used to manipulate SQLite databases in a number of different ways.
Once installed the tool should be available as sqlite-utils. It can also be run using python -m sqlite_utils.
The sqlite-utils query command lets you run queries directly against a SQLite database file. This is the default subcommand, so the following two examples work the same way:
$ sqlite-utils query dogs.db "select * from dogs" $ sqlite-utils dogs.db "select * from dogs"
NOTE:
The default format returned for queries is JSON:
$ sqlite-utils dogs.db "select * from dogs"
[{"id": 1, "age": 4, "name": "Cleo"},
{"id": 2, "age": 2, "name": "Pancakes"}]
Use --nl to get back newline-delimited JSON objects:
$ sqlite-utils dogs.db "select * from dogs" --nl
{"id": 1, "age": 4, "name": "Cleo"}
{"id": 2, "age": 2, "name": "Pancakes"}
You can use --arrays to request arrays instead of objects:
$ sqlite-utils dogs.db "select * from dogs" --arrays [[1, 4, "Cleo"],
[2, 2, "Pancakes"]]
You can also combine --arrays and --nl:
$ sqlite-utils dogs.db "select * from dogs" --arrays --nl [1, 4, "Cleo"] [2, 2, "Pancakes"]
If you want to pretty-print the output further, you can pipe it through python -mjson.tool:
$ sqlite-utils dogs.db "select * from dogs" | python -mjson.tool [
{
"id": 1,
"age": 4,
"name": "Cleo"
},
{
"id": 2,
"age": 2,
"name": "Pancakes"
} ]
Binary strings are not valid JSON, so BLOB columns containing binary data will be returned as a JSON object containing base64 encoded data, that looks like this:
$ sqlite-utils dogs.db "select name, content from images" | python -mjson.tool [
{
"name": "transparent.gif",
"content": {
"$base64": true,
"encoded": "R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7"
}
} ]
If one of your columns contains JSON, by default it will be returned as an escaped string:
$ sqlite-utils dogs.db "select * from dogs" | python -mjson.tool [
{
"id": 1,
"name": "Cleo",
"friends": "[{\"name\": \"Pancakes\"}, {\"name\": \"Bailey\"}]"
} ]
You can use the --json-cols option to automatically detect these JSON columns and output them as nested JSON data:
$ sqlite-utils dogs.db "select * from dogs" --json-cols | python -mjson.tool [
{
"id": 1,
"name": "Cleo",
"friends": [
{
"name": "Pancakes"
},
{
"name": "Bailey"
}
]
} ]
You can use the --csv option to return results as CSV:
$ sqlite-utils dogs.db "select * from dogs" --csv id,age,name 1,4,Cleo 2,2,Pancakes
This will default to including the column names as a header row. To exclude the headers, use --no-headers:
$ sqlite-utils dogs.db "select * from dogs" --csv --no-headers 1,4,Cleo 2,2,Pancakes
Use --tsv instead of --csv to get back tab-separated values:
$ sqlite-utils dogs.db "select * from dogs" --tsv id age name 1 4 Cleo 2 2 Pancakes
You can use the --table option (or -t shortcut) to output query results as a table:
$ sqlite-utils dogs.db "select * from dogs" --table
id age name ---- ----- --------
1 4 Cleo
2 2 Pancakes
You can use the --fmt option to specify different table formats, for example rst for reStructuredText:
$ sqlite-utils dogs.db "select * from dogs" --fmt rst ==== ===== ========
id age name ==== ===== ========
1 4 Cleo
2 2 Pancakes ==== ===== ========
Available --fmt options are:
This list can also be found by running sqlite-utils query --help.
If your table contains binary data in a BLOB you can use the --raw option to output specific columns directly to standard out.
For example, to retrieve a binary image from a BLOB column and store it in a file you can use the following:
$ sqlite-utils photos.db "select contents from photos where id=1" --raw > myphoto.jpg
You can pass named parameters to the query using -p:
$ sqlite-utils query dogs.db "select :num * :num2" -p num 5 -p num2 6
[{":num * :num2": 30}]
These will be correctly quoted and escaped in the SQL query, providing a safe way to combine other values with SQL.
If you execute an UPDATE, INSERT or DELETE query the command will return the number of affected rows:
$ sqlite-utils dogs.db "update dogs set age = 5 where name = 'Cleo'"
[{"rows_affected": 1}]
You can use the --functions option to pass a block of Python code that defines additional functions which can then be called by your SQL query.
This example defines a function which extracts the domain from a URL:
$ sqlite-utils query sites.db "select url, domain(url) from urls" --functions ' from urllib.parse import urlparse def domain(url):
return urlparse(url).netloc '
Every callable object defined in the block will be registered as a SQL function with the same name, with the exception of functions with names that begin with an underscore.
You can load SQLite extension modules using the --load-extension option, see Loading SQLite extensions.
$ sqlite-utils dogs.db "select spatialite_version()" --load-extension=spatialite
[{"spatialite_version()": "4.3.0a"}]
SQLite supports cross-database SQL queries, which can join data from tables in more than one database file.
You can attach one or more additional databases using the --attach option, providing an alias to use for that database and the path to the SQLite file on disk.
This example attaches the books.db database under the alias books and then runs a query that combines data from that database with the default dogs.db database:
sqlite-utils dogs.db --attach books books.db \
'select * from sqlite_master union all select * from books.sqlite_master'
NOTE:
The sqlite-utils memory command works similar to sqlite-utils query, but allows you to execute queries against an in-memory database.
You can also pass this command CSV or JSON files which will be loaded into a temporary in-memory table, allowing you to execute SQL against that data without a separate step to first convert it to SQLite.
Without any extra arguments, this command executes SQL against the in-memory database directly:
$ sqlite-utils memory 'select sqlite_version()'
[{"sqlite_version()": "3.35.5"}]
It takes all of the same output formatting options as sqlite-utils query: --csv and --csv and --table and --nl:
$ sqlite-utils memory 'select sqlite_version()' --csv sqlite_version() 3.35.5 $ sqlite-utils memory 'select sqlite_version()' --fmt grid +--------------------+ | sqlite_version() | +====================+ | 3.35.5 | +--------------------+
If you have data in CSV or JSON format you can load it into an in-memory SQLite database and run queries against it directly in a single command using sqlite-utils memory like this:
$ sqlite-utils memory data.csv "select * from data"
You can pass multiple files to the command if you want to run joins between data from different files:
$ sqlite-utils memory one.csv two.json "select * from one join two on one.id = two.other_id"
If your data is JSON it should be the same format supported by the sqlite-utils insert command - so either a single JSON object (treated as a single row) or a list of JSON objects.
CSV data can be comma- or tab- delimited.
The in-memory tables will be named after the files without their extensions. The tool also sets up aliases for those tables (using SQL views) as t1, t2 and so on, or you can use the alias t to refer to the first table:
$ sqlite-utils memory example.csv "select * from t"
If two files have the same name they will be assigned a numeric suffix:
$ sqlite-utils memory foo/data.csv bar/data.csv "select * from data_2"
To read from standard input, use either - or stdin as the filename - then use stdin or t or t1 as the table name:
$ cat example.csv | sqlite-utils memory - "select * from stdin"
Incoming CSV data will be assumed to use utf-8. If your data uses a different character encoding you can specify that with --encoding:
$ cat example.csv | sqlite-utils memory - "select * from stdin" --encoding=latin-1
If you are joining across multiple CSV files they must all use the same encoding.
Column types will be automatically detected in CSV or TSV data, using the same mechanism as --detect-types described in Inserting CSV or TSV data. You can pass the --no-detect-types option to disable this automatic type detection and treat all CSV and TSV columns as TEXT.
By default, sqlite-utils memory will attempt to detect the incoming data format (JSON, TSV or CSV) automatically.
You can instead specify an explicit format by adding a :csv, :tsv, :json or :nl (for newline-delimited JSON) suffix to the filename. For example:
$ sqlite-utils memory one.dat:csv two.dat:nl "select * from one union select * from two"
Here the contents of one.dat will be treated as CSV and the contents of two.dat will be treated as newline-delimited JSON.
To explicitly specify the format for data piped into the tool on standard input, use stdin:format - for example:
$ cat one.dat | sqlite-utils memory stdin:csv "select * from stdin"
The attach option can be used to attach database files to the in-memory connection, enabling joins between in-memory data loaded from a file and tables in existing SQLite database files. An example:
$ echo "id\n1\n3\n5" | sqlite-utils memory - --attach trees trees.db \
"select * from trees.trees where rowid in (select id from stdin)"
Here the --attach trees trees.db option makes the trees.db database available with an alias of trees.
select * from trees.trees where ... can then query the trees table in that database.
The CSV data that was piped into the script is available in the stdin table, so ... where rowid in (select id from stdin) can be used to return rows from the trees table that match IDs that were piped in as CSV content.
To see the in-memory database schema that would be used for a file or for multiple files, use --schema:
% sqlite-utils memory dogs.csv --schema CREATE TABLE [dogs] (
[id] INTEGER,
[age] INTEGER,
[name] TEXT ); CREATE VIEW t1 AS select * from [dogs]; CREATE VIEW t AS select * from [dogs];
You can run the equivalent of the analyze-tables command using --analyze:
% sqlite-utils memory dogs.csv --analyze dogs.id: (1/3)
Total rows: 2
Null rows: 0
Blank rows: 0
Distinct values: 2 dogs.name: (2/3)
Total rows: 2
Null rows: 0
Blank rows: 0
Distinct values: 2 dogs.age: (3/3)
Total rows: 2
Null rows: 0
Blank rows: 0
Distinct values: 2
You can output SQL that will both create the tables and insert the full data used to populate the in-memory database using --dump:
% sqlite-utils memory dogs.csv --dump BEGIN TRANSACTION; CREATE TABLE [dogs] (
[id] INTEGER,
[age] INTEGER,
[name] TEXT ); INSERT INTO "dogs" VALUES('1','4','Cleo'); INSERT INTO "dogs" VALUES('2','2','Pancakes'); CREATE VIEW t1 AS select * from [dogs]; CREATE VIEW t AS select * from [dogs]; COMMIT;
Passing --save other.db will instead use that SQL to populate a new database file:
% sqlite-utils memory dogs.csv --save dogs.db
These features are mainly intended as debugging tools - for much more finely grained control over how data is inserted into a SQLite database file see Inserting JSON data and Inserting CSV or TSV data.
You can return every row in a specified table using the rows command:
$ sqlite-utils rows dogs.db dogs
[{"id": 1, "age": 4, "name": "Cleo"},
{"id": 2, "age": 2, "name": "Pancakes"}]
This command accepts the same output options as query - so you can pass --nl, --csv, --tsv, --no-headers, --table and --fmt.
You can use the -c option to specify a subset of columns to return:
$ sqlite-utils rows dogs.db dogs -c age -c name
[{"age": 4, "name": "Cleo"},
{"age": 2, "name": "Pancakes"}]
You can filter rows using a where clause with the --where option:
$ sqlite-utils rows dogs.db dogs -c name --where 'name = "Cleo"'
[{"name": "Cleo"}]
Or pass named parameters using --where in combination with -p:
$ sqlite-utils rows dogs.db dogs -c name --where 'name = :name' -p name Cleo
[{"name": "Cleo"}]
You can define a sort order using --order column or --order 'column desc'.
Use --limit N to only return the first N rows. Use --offset N to return rows starting from the specified offset.
NOTE:
You can list the names of tables in a database using the tables command:
$ sqlite-utils tables mydb.db
[{"table": "dogs"},
{"table": "cats"},
{"table": "chickens"}]
You can output this list in CSV using the --csv or --tsv options:
$ sqlite-utils tables mydb.db --csv --no-headers dogs cats chickens
If you just want to see the FTS4 tables, you can use --fts4 (or --fts5 for FTS5 tables):
$ sqlite-utils tables docs.db --fts4
[{"table": "docs_fts"}]
Use --counts to include a count of the number of rows in each table:
$ sqlite-utils tables mydb.db --counts
[{"table": "dogs", "count": 12},
{"table": "cats", "count": 332},
{"table": "chickens", "count": 9}]
Use --columns to include a list of columns in each table:
$ sqlite-utils tables dogs.db --counts --columns
[{"table": "Gosh", "count": 0, "columns": ["c1", "c2", "c3"]},
{"table": "Gosh2", "count": 0, "columns": ["c1", "c2", "c3"]},
{"table": "dogs", "count": 2, "columns": ["id", "age", "name"]}]
Use --schema to include the schema of each table:
$ sqlite-utils tables dogs.db --schema --table table schema ------- ----------------------------------------------- Gosh CREATE TABLE Gosh (c1 text, c2 text, c3 text) Gosh2 CREATE TABLE Gosh2 (c1 text, c2 text, c3 text) dogs CREATE TABLE [dogs] (
[id] INTEGER,
[age] INTEGER,
[name] TEXT)
The --nl, --csv, --tsv, --table and --fmt options are also available.
NOTE:
The views command shows any views defined in the database:
$ sqlite-utils views sf-trees.db --table --counts --columns --schema view count columns schema --------- ------- -------------------- -------------------------------------------------------------- demo_view 189144 ['qSpecies'] CREATE VIEW demo_view AS select qSpecies from Street_Tree_List hello 1 ['sqlite_version()'] CREATE VIEW hello as select sqlite_version()
It takes the same options as the tables command:
NOTE:
The indexes command lists any indexes configured for the database:
$ sqlite-utils indexes covid.db --table table index_name seqno cid name desc coll key -------------------------------- ------------------------------------------------------ ------- ----- ----------------- ------ ------ ----- johns_hopkins_csse_daily_reports idx_johns_hopkins_csse_daily_reports_combined_key 0 12 combined_key 0 BINARY 1 johns_hopkins_csse_daily_reports idx_johns_hopkins_csse_daily_reports_country_or_region 0 1 country_or_region 0 BINARY 1 johns_hopkins_csse_daily_reports idx_johns_hopkins_csse_daily_reports_province_or_state 0 2 province_or_state 0 BINARY 1 johns_hopkins_csse_daily_reports idx_johns_hopkins_csse_daily_reports_day 0 0 day 0 BINARY 1 ny_times_us_counties idx_ny_times_us_counties_date 0 0 date 1 BINARY 1 ny_times_us_counties idx_ny_times_us_counties_fips 0 3 fips 0 BINARY 1 ny_times_us_counties idx_ny_times_us_counties_county 0 1 county 0 BINARY 1 ny_times_us_counties idx_ny_times_us_counties_state 0 2 state 0 BINARY 1
It shows indexes across all tables. To see indexes for specific tables, list those after the database:
$ sqlite-utils indexes covid.db johns_hopkins_csse_daily_reports --table
The command defaults to only showing the columns that are explicitly part of the index. To also include auxiliary columns use the --aux option - these columns will be listed with a key of 0.
The command takes the same format options as the tables and views commands.
NOTE:
The triggers command shows any triggers configured for the database:
$ sqlite-utils triggers global-power-plants.db --table name table sql --------------- --------- ----------------------------------------------------------------- plants_insert plants CREATE TRIGGER [plants_insert] AFTER INSERT ON [plants]
BEGIN
INSERT OR REPLACE INTO [_counts]
VALUES (
'plants',
COALESCE(
(SELECT count FROM [_counts] WHERE [table] = 'plants'),
0
) + 1
);
END
It defaults to showing triggers for all tables. To see triggers for one or more specific tables pass their names as arguments:
$ sqlite-utils triggers global-power-plants.db plants
The command takes the same format options as the tables and views commands.
NOTE:
The sqlite-utils schema command shows the full SQL schema for the database:
$ sqlite-utils schema dogs.db CREATE TABLE "dogs" (
[id] INTEGER PRIMARY KEY,
[name] TEXT );
This will show the schema for every table and index in the database. To view the schema just for a specified subset of tables pass those as additional arguments:
$ sqlite-utils schema dogs.db dogs chickens ...
NOTE:
When working with a new database it can be useful to get an idea of the shape of the data. The sqlite-utils analyze-tables command inspects specified tables (or all tables) and calculates some useful details about each of the columns in those tables.
To inspect the tags table in the github.db database, run the following:
$ sqlite-utils analyze-tables github.db tags tags.repo: (1/3)
Total rows: 261
Null rows: 0
Blank rows: 0
Distinct values: 14
Most common:
88: 107914493
75: 140912432
27: 206156866
Least common:
1: 209590345
2: 206649770
2: 303218369 tags.name: (2/3)
Total rows: 261
Null rows: 0
Blank rows: 0
Distinct values: 175
Most common:
10: 0.2
9: 0.1
7: 0.3
Least common:
1: 0.1.1
1: 0.11.1
1: 0.1a2 tags.sha: (3/3)
Total rows: 261
Null rows: 0
Blank rows: 0
Distinct values: 261
For each column this tool displays the number of null rows, the number of blank rows (rows that contain an empty string), the number of distinct values and, for columns that are not entirely distinct, the most common and least common values.
If you do not specify any tables every table in the database will be analyzed:
$ sqlite-utils analyze-tables github.db
If you wish to analyze one or more specific columns, use the -c option:
$ sqlite-utils analyze-tables github.db tags -c sha
analyze-tables can take quite a while to run for large database files. You can save the results of the analysis to a database table called _analyze_tables_ using the --save option:
$ sqlite-utils analyze-tables github.db --save
The _analyze_tables_ table has the following schema:
CREATE TABLE [_analyze_tables_] (
[table] TEXT,
[column] TEXT,
[total_rows] INTEGER,
[num_null] INTEGER,
[num_blank] INTEGER,
[num_distinct] INTEGER,
[most_common] TEXT,
[least_common] TEXT,
PRIMARY KEY ([table], [column]) );
The most_common and least_common columns will contain nested JSON arrays of the most common and least common values that look like this:
[
["Del Libertador, Av", 5068],
["Alberdi Juan Bautista Av.", 4612],
["Directorio Av.", 4552],
["Rivadavia, Av", 4532],
["Yerbal", 4512],
["Cosquín", 4472],
["Estado Plurinacional de Bolivia", 4440],
["Gordillo Timoteo", 4424],
["Montiel", 4360],
["Condarco", 4288] ]
You can create a new empty database file using the create-database command:
$ sqlite-utils create-database empty.db
To enable WAL mode on the newly created database add the --enable-wal option:
$ sqlite-utils create-database empty.db --enable-wal
To enable SpatiaLite metadata on a newly created database, add the --init-spatialite flag:
$ sqlite-utils create-database empty.db --init-spatialite
That will look for SpatiaLite in a set of predictable locations. To load it from somewhere else, use the --load-extension option:
$ sqlite-utils create-database empty.db --init-spatialite --load-extension /path/to/spatialite.so
If you have data as JSON, you can use sqlite-utils insert tablename to insert it into a database. The table will be created with the correct (automatically detected) columns if it does not already exist.
You can pass in a single JSON object or a list of JSON objects, either as a filename or piped directly to standard-in (by using - as the filename).
Here's the simplest possible example:
$ echo '{"name": "Cleo", "age": 4}' | sqlite-utils insert dogs.db dogs -
To specify a column as the primary key, use --pk=column_name.
To create a compound primary key across more than one column, use --pk multiple times.
If you feed it a JSON list it will insert multiple records. For example, if dogs.json looks like this:
[
{
"id": 1,
"name": "Cleo",
"age": 4
},
{
"id": 2,
"name": "Pancakes",
"age": 2
},
{
"id": 3,
"name": "Toby",
"age": 6
} ]
You can import all three records into an automatically created dogs table and set the id column as the primary key like so:
$ sqlite-utils insert dogs.db dogs dogs.json --pk=id
You can skip inserting any records that have a primary key that already exists using --ignore:
$ sqlite-utils insert dogs.db dogs dogs.json --ignore
You can delete all the existing rows in the table before inserting the new records using --truncate:
$ sqlite-utils insert dogs.db dogs dogs.json --truncate
You can add the --analyze option to run ANALYZE against the table after the rows have been inserted.
You can insert binary data into a BLOB column by first encoding it using base64 and then structuring it like this:
[
{
"name": "transparent.gif",
"content": {
"$base64": true,
"encoded": "R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7"
}
} ]
You can also import newline-delimited JSON using the --nl option:
$ echo '{"id": 1, "name": "Cleo"}
{"id": 2, "name": "Suna"}' | sqlite-utils insert creatures.db creatures - --nl
Newline-delimited JSON consists of full JSON objects separated by newlines.
If you are processing data using jq you can use the jq -c option to output valid newline-delimited JSON.
Since Datasette can export newline-delimited JSON, you can combine the Datasette and sqlite-utils like so:
$ curl -L "https://latest.datasette.io/fixtures/facetable.json?_shape=array&_nl=on" \
| sqlite-utils insert nl-demo.db facetable - --pk=id --nl
You can also pipe sqlite-utils together to create a new SQLite database file containing the results of a SQL query against another database:
$ sqlite-utils sf-trees.db \
"select TreeID, qAddress, Latitude, Longitude from Street_Tree_List" --nl \
| sqlite-utils insert saved.db trees - --nl # This creates saved.db with a single table called trees: $ sqlite-utils saved.db "select * from trees limit 5" --csv TreeID,qAddress,Latitude,Longitude 141565,501X Baker St,37.7759676911831,-122.441396661871 232565,940 Elizabeth St,37.7517102172731,-122.441498017841 119263,495X Lakeshore Dr,, 207368,920 Kirkham St,37.760210314285,-122.47073935813 188702,1501 Evans Ave,37.7422086702947,-122.387293152263
sqlite-utils insert and sqlite-utils memory both expect incoming JSON data to consist of an array of JSON objects, where the top-level keys of each object will become columns in the created database table.
If your data is nested you can use the --flatten option to create columns that are derived from the nested data.
Consider this example document, in a file called log.json:
{
"httpRequest": {
"latency": "0.112114537s",
"requestMethod": "GET",
"requestSize": "534",
"status": 200
},
"insertId": "6111722f000b5b4c4d4071e2",
"labels": {
"service": "datasette-io"
}
}
Inserting this into a table using sqlite-utils insert logs.db logs log.json will create a table with the following schema:
CREATE TABLE [logs] (
[httpRequest] TEXT,
[insertId] TEXT,
[labels] TEXT );
With the --flatten option columns will be created using topkey_nextkey column names - so running sqlite-utils insert logs.db logs log.json --flatten will create the following schema instead:
CREATE TABLE [logs] (
[httpRequest_latency] TEXT,
[httpRequest_requestMethod] TEXT,
[httpRequest_requestSize] TEXT,
[httpRequest_status] INTEGER,
[insertId] TEXT,
[labels_service] TEXT );
If your data is in CSV format, you can insert it using the --csv option:
$ sqlite-utils insert dogs.db dogs dogs.csv --csv
For tab-delimited data, use --tsv:
$ sqlite-utils insert dogs.db dogs dogs.tsv --tsv
Data is expected to be encoded as Unicode UTF-8. If your data is an another character encoding you can specify it using the --encoding option:
$ sqlite-utils insert dogs.db dogs dogs.tsv --tsv --encoding=latin-1
A progress bar is displayed when inserting data from a file. You can hide the progress bar using the --silent option.
By default every column inserted from a CSV or TSV file will be of type TEXT. To automatically detect column types - resulting in a mix of TEXT, INTEGER and FLOAT columns, use the --detect-types option (or its shortcut -d).
For example, given a creatures.csv file containing this:
name,age,weight Cleo,6,45.5 Dori,1,3.5
The following command:
$ sqlite-utils insert creatures.db creatures creatures.csv --csv --detect-types
Will produce this schema:
$ sqlite-utils schema creatures.db CREATE TABLE "creatures" (
[name] TEXT,
[age] INTEGER,
[weight] FLOAT );
You can set the SQLITE_UTILS_DETECT_TYPES environment variable if you want --detect-types to be the default behavior:
$ export SQLITE_UTILS_DETECT_TYPES=1
If your file uses a delimiter other than , or a quote character other than " you can attempt to detect delimiters or you can specify them explicitly.
The --sniff option can be used to attempt to detect the delimiters:
sqlite-utils insert dogs.db dogs dogs.csv --sniff
Alternatively, you can specify them using the --delimiter and --quotechar options.
Here's a CSV file that uses ; for delimiters and the | symbol for quote characters:
name;description Cleo;|Very fine; a friendly dog| Pancakes;A local corgi
You can import that using:
$ sqlite-utils insert dogs.db dogs dogs.csv --delimiter=";" --quotechar="|"
Passing --delimiter, --quotechar or --sniff implies --csv, so you can omit the --csv option.
The first row of any CSV or TSV file is expected to contain the names of the columns in that file.
If your file does not include this row, you can use the --no-headers option to specify that the tool should not use that fist row as headers.
If you do this, the table will be created with column names called untitled_1 and untitled_2 and so on. You can then rename them using the sqlite-utils transform ... --rename command, see Transforming tables.
If you have an unstructured file you can insert its contents into a table with a single line column containing each line from the file using --lines. This can be useful if you intend to further analyze those lines using SQL string functions or sqlite-utils convert:
$ sqlite-utils insert logs.db loglines logfile.log --lines
This will produce the following schema:
CREATE TABLE [loglines] (
[line] TEXT );
You can also insert the entire contents of the file into a single column called text using --text:
$ sqlite-utils insert content.db content file.txt --text
The schema here will be:
CREATE TABLE [content] (
[text] TEXT );
The --convert option can be used to apply a Python conversion function to imported data before it is inserted into the database. It works in a similar way to sqlite-utils convert.
Your Python function will be passed a dictionary called row for each item that is being imported. You can modify that dictionary and return it - or return a fresh dictionary - to change the data that will be inserted.
Given a JSON file called dogs.json containing this:
[
{"id": 1, "name": "Cleo"},
{"id": 2, "name": "Pancakes"} ]
The following command will insert that data and add an is_good column set to 1 for each dog:
$ sqlite-utils insert dogs.db dogs dogs.json --convert 'row["is_good"] = 1'
The --convert option also works with the --csv, --tsv and --nl insert options.
As with sqlite-utils convert you can use --import to import additional Python modules, see Importing additional modules for details.
You can also pass code that runs some initialization steps and defines a convert(value) function, see Using a convert() function to execute initialization.
Things work slightly differently when combined with the --lines or --text options.
With --lines, instead of being passed a row dictionary your function will be passed a line string representing each line of the input. Given a file called access.log containing the following:
INFO: 127.0.0.1:60581 - GET / HTTP/1.1 200 OK INFO: 127.0.0.1:60581 - GET /foo/-/static/app.css?cead5a HTTP/1.1 200 OK
You could convert it into structured data like so:
$ sqlite-utils insert logs.db loglines access.log --convert '
type, source, _, verb, path, _, status, _ = line.split()
return {
"type": type,
"source": source,
"verb": verb,
"path": path,
"status": status,
}' --lines
The resulting table would look like this:
| type | source | verb | path | status |
| INFO: | 127.0.0.1:60581 | GET | / | 200 |
| INFO: | 127.0.0.1:60581 | GET | /foo/-/static/app.css?cead5a | 200 |
With --text the entire input to the command will be made available to the function as a variable called text.
The function can return a single dictionary which will be inserted as a single row, or it can return a list or iterator of dictionaries, each of which will be inserted.
Here's how to use --convert and --text to insert one record per word in the input:
$ echo 'A bunch of words' | sqlite-utils insert words.db words - \
--text --convert '({"word": w} for w in text.split())'
The result looks like this:
$ sqlite-utils dump words.db BEGIN TRANSACTION; CREATE TABLE [words] (
[word] TEXT ); INSERT INTO "words" VALUES('A'); INSERT INTO "words" VALUES('bunch'); INSERT INTO "words" VALUES('of'); INSERT INTO "words" VALUES('words'); COMMIT;
The --replace option to insert causes any existing records with the same primary key to be replaced entirely by the new records.
To replace a dog with in ID of 2 with a new record, run the following:
$ echo '{"id": 2, "name": "Pancakes", "age": 3}' | \
sqlite-utils insert dogs.db dogs - --pk=id --replace
Upserting is update-or-insert. If a row exists with the specified primary key the provided columns will be updated. If no row exists that row will be created.
Unlike insert --replace, an upsert will ignore any column values that exist but are not present in the upsert document.
For example:
$ echo '{"id": 2, "age": 4}' | \
sqlite-utils upsert dogs.db dogs - --pk=id
This will update the dog with an ID of 2 to have an age of 4, creating a new record (with a null name) if one does not exist. If a row DOES exist the name will be left as-is.
The command will fail if you reference columns that do not exist on the table. To automatically create missing columns, use the --alter option.
NOTE:
If you have a JSON, newline-delimited JSON, CSV or TSV file you can execute a bulk SQL query using each of the records in that file using the sqlite-utils bulk command.
The command takes the database file, the SQL to be executed and the file containing records to be used when evaluating the SQL query.
The SQL query should include :named parameters that match the keys in the records.
For example, given a chickens.csv CSV file containing the following:
id,name 1,Blue 2,Snowy 3,Azi 4,Lila 5,Suna 6,Cardi
You could insert those rows into a pre-created chickens table like so:
$ sqlite-utils bulk chickens.db \
'insert into chickens (id, name) values (:id, :name)' \
chickens.csv --csv
This command takes the same options as the sqlite-utils insert command - so it defaults to expecting JSON but can accept other formats using --csv or --tsv or --nl or other options described above.
By default all of the SQL queries will be executed in a single transaction. To commit every 20 records, use --batch-size 20.
The insert-files command can be used to insert the content of files, along with their metadata, into a SQLite table.
Here's an example that inserts all of the GIF files in the current directory into a gifs.db database, placing the file contents in an images table:
$ sqlite-utils insert-files gifs.db images *.gif
You can also pass one or more directories, in which case every file in those directories will be added recursively:
$ sqlite-utils insert-files gifs.db images path/to/my-gifs
By default this command will create a table with the following schema:
CREATE TABLE [images] (
[path] TEXT PRIMARY KEY,
[content] BLOB,
[size] INTEGER );
Content will be treated as binary by default and stored in a BLOB column. You can use the --text option to store that content in a TEXT column instead.
You can customize the schema using one or more -c options. For a table schema that includes just the path, MD5 hash and last modification time of the file, you would use this:
$ sqlite-utils insert-files gifs.db images *.gif -c path -c md5 -c mtime --pk=path
This will result in the following schema:
CREATE TABLE [images] (
[path] TEXT PRIMARY KEY,
[md5] TEXT,
[mtime] FLOAT );
Note that there's no content column here at all - if you specify custom columns using -c you need to include -c content to create that column.
You can change the name of one of these columns using a -c colname:coldef parameter. To rename the mtime column to last_modified you would use this:
$ sqlite-utils insert-files gifs.db images *.gif \
-c path -c md5 -c last_modified:mtime --pk=path
You can pass --replace or --upsert to indicate what should happen if you try to insert a file with an existing primary key. Pass --alter to cause any missing columns to be added to the table.
The full list of column definitions you can use is as follows:
You can insert data piped from standard input like this:
cat dog.jpg | sqlite-utils insert-files dogs.db pics - --name=dog.jpg
The - argument indicates data should be read from standard input. The string passed using the --name option will be used for the file name and path values.
When inserting data from standard input only the following column definitions are supported: name, path, content, content_text, sha256, md5 and size.
The convert command can be used to transform the data in a specified column - for example to parse a date string into an ISO timestamp, or to split a string of tags into a JSON array.
The command accepts a database, table, one or more columns and a string of Python code to be executed against the values from those columns. The following example would replace the values in the headline column in the articles table with an upper-case version:
$ sqlite-utils convert content.db articles headline 'value.upper()'
The Python code is passed as a string. Within that Python code the value variable will be the value of the current column.
The code you provide will be compiled into a function that takes value as a single argument. If you break your function body into multiple lines the last line should be a return statement:
$ sqlite-utils convert content.db articles headline ' value = str(value) return value.upper()'
Your code will be automatically wrapped in a function, but you can also define a function called convert(value) which will be called, if available:
$ sqlite-utils convert content.db articles headline ' def convert(value):
return value.upper()'
Use a CODE value of - to read from standard input:
$ cat mycode.py | sqlite-utils convert content.db articles headline -
Where mycode.py contains a fragment of Python code that looks like this:
def convert(value):
return value.upper()
The conversion will be applied to every row in the specified table. You can limit that to just rows that match a WHERE clause using --where:
$ sqlite-utils convert content.db articles headline 'value.upper()' \
--where "headline like '%cat%'"
You can include named parameters in your where clause and populate them using one or more --param options:
$ sqlite-utils convert content.db articles headline 'value.upper()' \
--where "headline like :query" \
--param query '%cat%'
The --dry-run option will output a preview of the conversion against the first ten rows, without modifying the database.
You can specify Python modules that should be imported and made available to your code using one or more --import options. This example uses the textwrap module to wrap the content column at 100 characters:
$ sqlite-utils convert content.db articles content \
'"\n".join(textwrap.wrap(value, 100))' \
--import=textwrap
This supports nested imports as well, for example to use ElementTree:
$ sqlite-utils convert content.db articles content \
'xml.etree.ElementTree.fromstring(value).attrib["title"]' \
--import=xml.etree.ElementTree
In some cases you may need to execute one-off initialization code at the start of the run. You can do that by providing code that runs before defining your convert(value) function.
The following example adds a new score column, then updates it to list a random number - after first seeding the random number generator to ensure that multiple runs produce the same results:
$ sqlite-utils add-column content.db articles score float --not-null-default 1.0 $ sqlite-utils convert content.db articles score ' import random random.seed(10) def convert(value):
return random.random() '
Various built-in recipe functions are available for common operations. These are:
The delimiter parameter can be used to specify a different delimiter.
The type parameter can be set to float or int to produce a JSON array of different types, for example if the column's string value was 1.2,3,4.5 the following:
r.jsonsplit(value, type=float)
Would produce an array like this: [1.2, 3.0, 4.5]
In the case of dates such as 03/04/05 U.S. MM/DD/YY format is assumed - you can use dayfirst=True or yearfirst=True to change how these ambiguous dates are interpreted.
Use the errors= parameter to specify what should happen if a value cannot be parsed.
By default, if any value cannot be parsed an error will be occurred and all values will be left as they were.
Set errors=r.IGNORE to ignore any values that cannot be parsed, leaving them unchanged.
Set errors=r.SET_NULL to set any values that cannot be parsed to null.
These recipes can be used in the code passed to sqlite-utils convert like this:
$ sqlite-utils convert my.db mytable mycolumn \
'r.jsonsplit(value)'
To use any of the documented parameters, do this:
$ sqlite-utils convert my.db mytable mycolumn \
'r.jsonsplit(value, delimiter=":")'
The --output and --output-type options can be used to save the result of the conversion to a separate column, which will be created if that column does not already exist:
$ sqlite-utils convert content.db articles headline 'value.upper()' \
--output headline_upper
The type of the created column defaults to text, but a different column type can be specified using --output-type. This example will create a new floating point column called id_as_a_float with a copy of each item's ID increased by 0.5:
$ sqlite-utils convert content.db articles id 'float(value) + 0.5' \
--output id_as_a_float \
--output-type float
You can drop the original column at the end of the operation by adding --drop.
Sometimes you may wish to convert a single column into multiple derived columns. For example, you may have a location column containing latitude,longitude values which you wish to split out into separate latitude and longitude columns.
You can achieve this using the --multi option to sqlite-utils convert. This option expects your Python code to return a Python dictionary: new columns well be created and populated for each of the keys in that dictionary.
For the latitude,longitude example you would use the following:
$ sqlite-utils convert demo.db places location \
'bits = value.split(",")
return {
"latitude": float(bits[0]),
"longitude": float(bits[1]),
}' --multi
The type of the returned values will be taken into account when creating the new columns. In this example, the resulting database schema will look like this:
CREATE TABLE [places] (
[location] TEXT,
[latitude] FLOAT,
[longitude] FLOAT );
The code function can also return None, in which case its output will be ignored. You can drop the original column at the end of the operation by adding --drop.
Most of the time creating tables by inserting example data is the quickest approach. If you need to create an empty table in advance of inserting data you can do so using the create-table command:
$ sqlite-utils create-table mydb.db mytable id integer name text --pk=id
This will create a table called mytable with two columns - an integer id column and a text name column. It will set the id column to be the primary key.
You can pass as many column-name column-type pairs as you like. Valid types are integer, text, float and blob.
You can specify columns that should be NOT NULL using --not-null colname. You can specify default values for columns using --default colname defaultvalue.
$ sqlite-utils create-table mydb.db mytable \
id integer \
name text \
age integer \
is_good integer \
--not-null name \
--not-null age \
--default is_good 1 \
--pk=id $ sqlite-utils tables mydb.db --schema -t table schema ------- -------------------------------- mytable CREATE TABLE [mytable] (
[id] INTEGER PRIMARY KEY,
[name] TEXT NOT NULL,
[age] INTEGER NOT NULL,
[is_good] INTEGER DEFAULT '1'
)
You can specify foreign key relationships between the tables you are creating using --fk colname othertable othercolumn:
$ sqlite-utils create-table books.db authors \
id integer \
name text \
--pk=id $ sqlite-utils create-table books.db books \
id integer \
title text \
author_id integer \
--pk=id \
--fk author_id authors id $ sqlite-utils tables books.db --schema -t table schema ------- ------------------------------------------------- authors CREATE TABLE [authors] (
[id] INTEGER PRIMARY KEY,
[name] TEXT
) books CREATE TABLE [books] (
[id] INTEGER PRIMARY KEY,
[title] TEXT,
[author_id] INTEGER REFERENCES [authors]([id])
)
If a table with the same name already exists, you will get an error. You can choose to silently ignore this error with --ignore, or you can replace the existing table with a new, empty table using --replace.
You can also pass --transform to transform the existing table to match the new schema. See Explicitly creating a table in the Python library documentation for details of how this option works.
The duplicate command duplicates a table - creating a new table with the same schema and a copy of all of the rows:
$ sqlite-utils duplicate books.db authors authors_copy
You can drop a table using the drop-table command:
$ sqlite-utils drop-table mydb.db mytable
Use --ignore to ignore the error if the table does not exist.
The transform command allows you to apply complex transformations to a table that cannot be implemented using a regular SQLite ALTER TABLE command. See Transforming a table for details of how this works.
$ sqlite-utils transform mydb.db mytable \
--drop column1 \
--rename column2 column_renamed
Every option for this table (with the exception of --pk-none) can be specified multiple times. The options are as follows:
If you want to see the SQL that will be executed to make the change without actually executing it, add the --sql flag. For example:
$ sqlite-utils transform fixtures.db roadside_attractions \
--rename pk id \
--default name Untitled \
--column-order id \
--column-order longitude \
--column-order latitude \
--drop address \
--sql CREATE TABLE [roadside_attractions_new_4033a60276b9] (
[id] INTEGER PRIMARY KEY,
[longitude] FLOAT,
[latitude] FLOAT,
[name] TEXT DEFAULT 'Untitled' ); INSERT INTO [roadside_attractions_new_4033a60276b9] ([longitude], [latitude], [id], [name])
SELECT [longitude], [latitude], [pk], [name] FROM [roadside_attractions]; DROP TABLE [roadside_attractions]; ALTER TABLE [roadside_attractions_new_4033a60276b9] RENAME TO [roadside_attractions];
SQLite tables that are created without an explicit primary key are created as rowid tables. They still have a numeric primary key which is available in the rowid column, but that column is not included in the output of select *. Here's an example:
% echo '[{"name": "Azi"}, {"name": "Suna"}]' | \
sqlite-utils insert chickens.db chickens -
% sqlite-utils schema chickens.db
CREATE TABLE [chickens] (
[name] TEXT
);
% sqlite-utils chickens.db 'select * from chickens'
[{"name": "Azi"},
{"name": "Suna"}]
% sqlite-utils chickens.db 'select rowid, * from chickens'
[{"rowid": 1, "name": "Azi"},
{"rowid": 2, "name": "Suna"}]
You can use sqlite-utils transform ... --pk id to add a primary key column called id to the table. The primary key will be created as an INTEGER PRIMARY KEY and the existing rowid values will be copied across to it. It will automatically increment as new rows are added to the table:
% sqlite-utils transform chickens.db chickens --pk id % sqlite-utils schema chickens.db CREATE TABLE "chickens" (
[id] INTEGER PRIMARY KEY,
[name] TEXT ); % sqlite-utils chickens.db 'select * from chickens' [{"id": 1, "name": "Azi"},
{"id": 2, "name": "Suna"}] % echo '{"name": "Cardi"}' | sqlite-utils insert chickens.db chickens - % sqlite-utils chickens.db 'select * from chickens' [{"id": 1, "name": "Azi"},
{"id": 2, "name": "Suna"},
{"id": 3, "name": "Cardi"}]
The sqlite-utils extract command can be used to extract specified columns into a separate table.
Take a look at the Python API documentation for Extracting columns into a separate table for a detailed description of how this works, including examples of table schemas before and after running an extraction operation.
The command takes a database, table and one or more columns that should be extracted. To extract the species column from the trees table you would run:
$ sqlite-utils extract my.db trees species
This would produce the following schema:
CREATE TABLE "trees" (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[species_id] INTEGER,
FOREIGN KEY(species_id) REFERENCES species(id) ); CREATE TABLE [species] (
[id] INTEGER PRIMARY KEY,
[species] TEXT ); CREATE UNIQUE INDEX [idx_species_species]
ON [species] ([species]);
The command takes the following options:
Here's a more complex example that makes use of these options. It converts this CSV file full of global power plants into SQLite, then extracts the country and country_long columns into a separate countries table:
wget 'https://github.com/wri/global-power-plant-database/blob/232a6666/output_database/global_power_plant_database.csv?raw=true' sqlite-utils insert global.db power_plants \
'global_power_plant_database.csv?raw=true' --csv # Extract those columns: sqlite-utils extract global.db power_plants country country_long \
--table countries \
--fk-column country_id \
--rename country_long name
After running the above, the command sqlite-utils schema global.db reveals the following schema:
CREATE TABLE [countries] (
[id] INTEGER PRIMARY KEY,
[country] TEXT,
[name] TEXT ); CREATE TABLE "power_plants" (
[country_id] INTEGER,
[name] TEXT,
[gppd_idnr] TEXT,
[capacity_mw] TEXT,
[latitude] TEXT,
[longitude] TEXT,
[primary_fuel] TEXT,
[other_fuel1] TEXT,
[other_fuel2] TEXT,
[other_fuel3] TEXT,
[commissioning_year] TEXT,
[owner] TEXT,
[source] TEXT,
[url] TEXT,
[geolocation_source] TEXT,
[wepp_id] TEXT,
[year_of_capacity_data] TEXT,
[generation_gwh_2013] TEXT,
[generation_gwh_2014] TEXT,
[generation_gwh_2015] TEXT,
[generation_gwh_2016] TEXT,
[generation_gwh_2017] TEXT,
[generation_data_source] TEXT,
[estimated_generation_gwh] TEXT,
FOREIGN KEY([country_id]) REFERENCES [countries]([id]) ); CREATE UNIQUE INDEX [idx_countries_country_name]
ON [countries] ([country], [name]);
You can create a view using the create-view command:
$ sqlite-utils create-view mydb.db version "select sqlite_version()"
$ sqlite-utils mydb.db "select * from version"
[{"sqlite_version()": "3.31.1"}]
Use --replace to replace an existing view of the same name, and --ignore to do nothing if a view already exists.
You can drop a view using the drop-view command:
$ sqlite-utils drop-view myview
Use --ignore to ignore the error if the view does not exist.
You can add a column using the add-column command:
$ sqlite-utils add-column mydb.db mytable nameofcolumn text
The last argument here is the type of the column to be created. You can use one of text, integer, float or blob. If you leave it off, text will be used.
You can add a column that is a foreign key reference to another table using the --fk option:
$ sqlite-utils add-column mydb.db dogs species_id --fk species
This will automatically detect the name of the primary key on the species table and use that (and its type) for the new column.
You can explicitly specify the column you wish to reference using --fk-col:
$ sqlite-utils add-column mydb.db dogs species_id --fk species --fk-col ref
You can set a NOT NULL DEFAULT 'x' constraint on the new column using --not-null-default:
$ sqlite-utils add-column mydb.db dogs friends_count integer --not-null-default 0
You can use the --alter option to automatically add new columns if the data you are inserting or upserting is of a different shape:
$ sqlite-utils insert dogs.db dogs new-dogs.json --pk=id --alter
The add-foreign-key command can be used to add new foreign key references to an existing table - something which SQLite's ALTER TABLE command does not support.
To add a foreign key constraint pointing the books.author_id column to authors.id in another table, do this:
$ sqlite-utils add-foreign-key books.db books author_id authors id
If you omit the other table and other column references sqlite-utils will attempt to guess them - so the above example could instead look like this:
$ sqlite-utils add-foreign-key books.db books author_id
Add --ignore to ignore an existing foreign key (as opposed to returning an error):
$ sqlite-utils add-foreign-key books.db books author_id --ignore
See Adding foreign key constraints in the Python API documentation for further details, including how the automatic table guessing mechanism works.
Adding a foreign key requires a VACUUM. On large databases this can be an expensive operation, so if you are adding multiple foreign keys you can combine them into one operation (and hence one VACUUM) using add-foreign-keys:
$ sqlite-utils add-foreign-keys books.db \
books author_id authors id \
authors country_id countries id
When you are using this command each foreign key needs to be defined in full, as four arguments - the table, column, other table and other column.
If you want to ensure that every foreign key column in your database has a corresponding index, you can do so like this:
$ sqlite-utils index-foreign-keys books.db
You can use the --not-null and --default options (to both insert and upsert) to specify columns that should be NOT NULL or to set database defaults for one or more specific columns:
$ sqlite-utils insert dogs.db dogs_with_scores dogs-with-scores.json \
--not-null=age \
--not-null=name \
--default age 2 \
--default score 5
You can add an index to an existing table using the create-index command:
$ sqlite-utils create-index mydb.db mytable col1 [col2...]
This can be used to create indexes against a single column or multiple columns.
The name of the index will be automatically derived from the table and columns. To specify a different name, use --name=name_of_index.
Use the --unique option to create a unique index.
Use --if-not-exists to avoid attempting to create the index if one with that name already exists.
To add an index on a column in descending order, prefix the column with a hyphen. Since this can be confused for a command-line option you need to construct that like this:
$ sqlite-utils create-index mydb.db mytable -- col1 -col2 col3
This will create an index on that table on (col1, col2 desc, col3).
If your column names are already prefixed with a hyphen you'll need to manually execute a CREATE INDEX SQL statement to add indexes to them rather than using this tool.
Add the --analyze option to run ANALYZE against the index after it has been created.
You can enable SQLite full-text search on a table and a set of columns like this:
$ sqlite-utils enable-fts mydb.db documents title summary
This will use SQLite's FTS5 module by default. Use --fts4 if you want to use FTS4:
$ sqlite-utils enable-fts mydb.db documents title summary --fts4
The enable-fts command will populate the new index with all existing documents. If you later add more documents you will need to use populate-fts to cause them to be indexed as well:
$ sqlite-utils populate-fts mydb.db documents title summary
A better solution here is to use database triggers. You can set up database triggers to automatically update the full-text index using the --create-triggers option when you first run enable-fts:
$ sqlite-utils enable-fts mydb.db documents title summary --create-triggers
To set a custom FTS tokenizer, e.g. to enable Porter stemming, use --tokenize=:
$ sqlite-utils populate-fts mydb.db documents title summary --tokenize=porter
To remove the FTS tables and triggers you created, use disable-fts:
$ sqlite-utils disable-fts mydb.db documents
To rebuild one or more FTS tables (see Rebuilding a full-text search table), use rebuild-fts:
$ sqlite-utils rebuild-fts mydb.db documents
You can rebuild every FTS table by running rebuild-fts without passing any table names:
$ sqlite-utils rebuild-fts mydb.db
Once you have configured full-text search for a table, you can search it using sqlite-utils search:
$ sqlite-utils search mydb.db documents searchterm
This command accepts the same output options as sqlite-utils query: --table, --csv, --tsv, --nl etc.
By default it shows the most relevant matches first. You can specify a different sort order using the -o option, which can take a column or a column followed by desc:
# Sort by rowid $ sqlite-utils search mydb.db documents searchterm -o rowid # Sort by created in descending order $ sqlite-utils search mydb.db documents searchterm -o 'created desc'
SQLite advanced search syntax is enabled by default. To run a search with automatic quoting applied to the terms to avoid them being potentially interpreted as advanced search syntax use the --quote option.
You can specify a subset of columns to be returned using the -c option one or more times:
$ sqlite-utils search mydb.db documents searchterm -c title -c created
By default all search results will be returned. You can use --limit 20 to return just the first 20 results.
Use the --sql option to output the SQL that would be executed, rather than running the query:
$ sqlite-utils search mydb.db documents searchterm --sql with original as (
select
rowid,
*
from [documents] ) select
[original].* from
[original]
join [documents_fts] on [original].rowid = [documents_fts].rowid where
[documents_fts] match :query order by
[documents_fts].rank
select count(*) queries can take a long time against large tables. sqlite-utils can speed these up by adding triggers to maintain a _counts table, see Cached table counts using triggers for details.
The sqlite-utils enable-counts command can be used to configure these triggers, either for every table in the database or for specific tables.
# Configure triggers for every table in the database $ sqlite-utils enable-counts mydb.db # Configure triggers just for specific tables $ sqlite-utils enable-counts mydb.db table1 table2
If the _counts table ever becomes out-of-sync with the actual table counts you can repair it using the reset-counts command:
$ sqlite-utils reset-counts mydb.db
The SQLite ANALYZE command builds a table of statistics which the query planner can use to make better decisions about which indexes to use for a given query.
You should run ANALYZE if your database is large and you do not think your indexes are being efficiently used.
To run ANALYZE against every index in a database, use this:
$ sqlite-utils analyze mydb.db
You can run it against specific tables, or against specific named indexes, by passing them as optional arguments:
$ sqlite-utils analyze mydb.db mytable idx_mytable_name
You can also run ANALYZE as part of another command using the --analyze option. This is supported by the create-index, insert and upsert commands.
You can run VACUUM to optimize your database like so:
$ sqlite-utils vacuum mydb.db
The optimize command can dramatically reduce the size of your database if you are using SQLite full-text search. It runs OPTIMIZE against all of your FTS4 and FTS5 tables, then runs VACUUM.
If you just want to run OPTIMIZE without the VACUUM, use the --no-vacuum flag.
# Optimize all FTS tables and then VACUUM $ sqlite-utils optimize mydb.db # Optimize but skip the VACUUM $ sqlite-utils optimize --no-vacuum mydb.db
To optimize specific tables rather than every FTS table, pass those tables as extra arguments:
$ sqlite-utils optimize mydb.db table_1 table_2
You can enable Write-Ahead Logging for a database file using the enable-wal command:
$ sqlite-utils enable-wal mydb.db
You can disable WAL mode using disable-wal:
$ sqlite-utils disable-wal mydb.db
Both of these commands accept one or more database files as arguments.
The dump command outputs a SQL dump of the schema and full contents of the specified database file:
$ sqlite-utils dump mydb.db BEGIN TRANSACTION; CREATE TABLE ... ... COMMIT;
Many of these commands have the ability to load additional SQLite extensions using the --load-extension=/path/to/extension option - use --help to check for support, e.g. sqlite-utils rows --help.
This option can be applied multiple times to load multiple extensions.
Since SpatiaLite is commonly used with SQLite, the value spatialite is special: it will search for SpatiaLite in the most common installation locations, saving you from needing to remember exactly where that module is located:
$ sqlite-utils memory "select spatialite_version()" --load-extension=spatialite
[{"spatialite_version()": "4.3.0a"}]
SpatiaLite adds geographic capability to SQLite (similar to how PostGIS builds on PostgreSQL). The SpatiaLite cookbook is a good resource for learning what's possible with it.
You can convert an existing table to a geographic table by adding a geometry column, use the sqlite-utils add-geometry-column command:
$ sqlite-utils add-geometry-column spatial.db locations geometry --type POLYGON --srid 4326
The table (locations in the example above) must already exist before adding a geometry column. Use sqlite-utils create-table first, then add-geometry-column.
Use the --type option to specify a geometry type. By default, add-geometry-column uses a generic GEOMETRY, which will work with any type, though it may not be supported by some desktop GIS applications.
Eight (case-insensitive) types are allowed:
Once you have a geometry column, you can speed up bounding box queries by adding a spatial index:
$ sqlite-utils create-spatial-index spatial.db locations geometry
See this SpatiaLite Cookbook recipe for examples of how to use a spatial index.
The convert command and the insert -\-convert and query -\-functions options can be provided with a Python script that imports additional modules from the sqlite-utils environment.
You can install packages from PyPI directly into the correct environment using sqlite-utils install <package>. This is a wrapper around pip install.
$ sqlite-utils install beautifulsoup4
Use -U to upgrade an existing package.
You can uninstall packages that were installed using sqlite-utils install with sqlite-utils uninstall <package>:
$ sqlite-utils uninstall beautifulsoup4
Use -y to skip the request for confirmation.
Here's how to create a new SQLite database file containing a new chickens table, populated with four records:
from sqlite_utils import Database
db = Database("chickens.db")
db["chickens"].insert_all([{
"name": "Azi",
"color": "blue",
}, {
"name": "Lila",
"color": "blue",
}, {
"name": "Suna",
"color": "gold",
}, {
"name": "Cardi",
"color": "black",
}])
You can loop through those rows like this:
for row in db["chickens"].rows:
print(row)
Which outputs the following:
{'name': 'Azi', 'color': 'blue'}
{'name': 'Lila', 'color': 'blue'}
{'name': 'Suna', 'color': 'gold'}
{'name': 'Cardi', 'color': 'black'}
To run a SQL query, use db.query():
for row in db.query("""
select color, count(*)
from chickens group by color
order by count(*) desc
"""):
print(row)
Which outputs:
{'color': 'blue', 'count(*)': 2}
{'color': 'gold', 'count(*)': 1}
{'color': 'black', 'count(*)': 1}
Database objects are constructed by passing in either a path to a file on disk or an existing SQLite3 database connection:
from sqlite_utils import Database
db = Database("my_database.db")
This will create my_database.db if it does not already exist.
If you want to recreate a database from scratch (first removing the existing file from disk if it already exists) you can use the recreate=True argument:
db = Database("my_database.db", recreate=True)
Instead of a file path you can pass in an existing SQLite connection:
import sqlite3
db = Database(sqlite3.connect("my_database.db"))
If you want to create an in-memory database, you can do so like this:
db = Database(memory=True)
You can also create a named in-memory database. Unlike regular memory databases these can be accessed by multiple threads, provided at least one reference to the database still exists. del db will clear the database from memory.
db = Database(memory_name="my_shared_database")
Connections use PRAGMA recursive_triggers=on by default. If you don't want to use recursive triggers you can turn them off using:
db = Database(memory=True, recursive_triggers=False)
SQLite supports cross-database SQL queries, which can join data from tables in more than one database file.
You can attach an additional database using the .attach() method, providing an alias to use for that database and the path to the SQLite file on disk.
db = Database("first.db")
db.attach("second", "second.db")
# Now you can run queries like this one:
print(db.query("""
select * from table_in_first
union all
select * from second.table_in_second
"""))
You can reference tables in the attached database using the alias value you passed to db.attach(alias, filepath) as a prefix, for example the second.table_in_second reference in the SQL query above.
You can use the tracer mechanism to see SQL queries that are being executed by SQLite. A tracer is a function that you provide which will be called with sql and params arguments every time SQL is executed, for example:
def tracer(sql, params):
print("SQL: {} - params: {}".format(sql, params))
You can pass this function to the Database() constructor like so:
db = Database(memory=True, tracer=tracer)
You can also turn on a tracer function temporarily for a block of code using the with db.tracer(...) context manager:
db = Database(memory=True) # ... later with db.tracer(print):
db["dogs"].insert({"name": "Cleo"})
This example will print queries only for the duration of the with block.
The Database class offers several methods for directly executing SQL queries.
The db.query(sql) function executes a SQL query and returns an iterator over Python dictionaries representing the resulting rows:
db = Database(memory=True)
db["dogs"].insert_all([{"name": "Cleo"}, {"name": "Pancakes"}])
for row in db.query("select * from dogs"):
print(row)
# Outputs:
# {'name': 'Cleo'}
# {'name': 'Pancakes'}
The db.execute() and db.executescript() methods provide wrappers around .execute() and .executescript() on the underlying SQLite connection. These wrappers log to the tracer function if one has been registered.
db.execute(sql) returns a sqlite3.Cursor that was used to execute the SQL.
db = Database(memory=True)
db["dogs"].insert({"name": "Cleo"})
cursor = db.execute("update dogs set name = 'Cleopaws'")
print(cursor.rowcount)
# Outputs the number of rows affected by the update
# In this case 2
Other cursor methods such as .fetchone() and .fetchall() are also available, see the standard library documentation.
Both db.query() and db.execute() accept an optional second argument for parameters to be passed to the SQL query.
This can take the form of either a tuple/list or a dictionary, depending on the type of parameters used in the query. Values passed in this way will be correctly quoted and escaped, helping avoid SQL injection vulnerabilities.
? parameters in the SQL query can be filled in using a list:
db.execute("update dogs set name = ?", ["Cleopaws"])
# This will rename ALL dogs to be called "Cleopaws"
Named parameters using :name can be filled using a dictionary:
dog = next(db.query(
"select rowid, name from dogs where name = :name",
{"name": "Cleopaws"} )) # dog is now {'rowid': 1, 'name': 'Cleopaws'}
In this example next() is used to retrieve the first result in the iterator returned by the db.query() method.
Tables are accessed using the indexing operator, like so:
table = db["my_table"]
If the table does not yet exist, it will be created the first time you attempt to insert or upsert data into it.
You can also access tables using the .table() method like so:
table = db.table("my_table")
Using this factory function allows you to set Table configuration options.
You can list the names of tables in a database using the .table_names() method:
>>> db.table_names() ['dogs']
To see just the FTS4 tables, use .table_names(fts4=True). For FTS5, use .table_names(fts5=True).
You can also iterate through the table objects themselves using the .tables property:
>>> db.tables [<Table dogs>]
.view_names() shows you a list of views in the database:
>>> db.view_names() ['good_dogs']
You can iterate through view objects using the .views property:
>>> db.views [<View good_dogs>]
View objects are similar to Table objects, except that any attempts to insert or update data will throw an error. The full list of methods and properties available on a view object is as follows:
To iterate through dictionaries for each of the rows in a table, use .rows:
>>> db = sqlite_utils.Database("dogs.db")
>>> for row in db["dogs"].rows:
... print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}
{'id': 2, 'age': 2, 'name': 'Pancakes'}
You can filter rows by a WHERE clause using .rows_where(where, where_args):
>>> db = sqlite_utils.Database("dogs.db")
>>> for row in db["dogs"].rows_where("age > ?", [3]):
... print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}
The first argument is a fragment of SQL. The second, optional argument is values to be passed to that fragment - you can use ? placeholders and pass an array, or you can use :named parameters and pass a dictionary, like this:
>>> for row in db["dogs"].rows_where("age > :age", {"age": 3}):
... print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}
To return custom columns (instead of the default that uses select *) pass select="column1, column2":
>>> db = sqlite_utils.Database("dogs.db")
>>> for row in db["dogs"].rows_where(select='name, age'):
... print(row)
{'name': 'Cleo', 'age': 4}
To specify an order, use the order_by= argument:
>>> for row in db["dogs"].rows_where("age > 1", order_by="age"):
... print(row)
{'id': 2, 'age': 2, 'name': 'Pancakes'}
{'id': 1, 'age': 4, 'name': 'Cleo'}
You can use order_by="age desc" for descending order.
You can order all records in the table by excluding the where argument:
>>> for row in db["dogs"].rows_where(order_by="age desc"):
... print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}
{'id': 2, 'age': 2, 'name': 'Pancakes'}
This method also accepts offset= and limit= arguments, for specifying an OFFSET and a LIMIT for the SQL query:
>>> for row in db["dogs"].rows_where(order_by="age desc", limit=1):
... print(row)
{'id': 1, 'age': 4, 'name': 'Cleo'}
To count the number of rows that would be returned by a where filter, use .count_where(where, where_args):
>>> db["dogs"].count_where("age > ?", [1])
2
Sometimes it can be useful to retrieve the primary key along with each row, in order to pass that key (or primary key tuple) to the .get() or .update() methods.
The .pks_and_rows_where() method takes the same signature as .rows_where() (with the exception of the select= parameter) but returns a generator that yields pairs of (primary key, row dictionary).
The primary key value will usually be a single value but can also be a tuple if the table has a compound primary key.
If the table is a rowid table (with no explicit primary key column) then that ID will be returned.
>>> db = sqlite_utils.Database(memory=True)
>>> db["dogs"].insert({"name": "Cleo"})
>>> for pk, row in db["dogs"].pks_and_rows_where():
... print(pk, row)
1 {'rowid': 1, 'name': 'Cleo'}
>>> db["dogs_with_pk"].insert({"id": 5, "name": "Cleo"}, pk="id")
>>> for pk, row in db["dogs_with_pk"].pks_and_rows_where():
... print(pk, row)
5 {'id': 5, 'name': 'Cleo'}
>>> db["dogs_with_compound_pk"].insert(
... {"species": "dog", "id": 3, "name": "Cleo"},
... pk=("species", "id")
... )
>>> for pk, row in db["dogs_with_compound_pk"].pks_and_rows_where():
... print(pk, row)
('dog', 3) {'species': 'dog', 'id': 3, 'name': 'Cleo'}
You can retrieve a record by its primary key using table.get():
>>> db = sqlite_utils.Database("dogs.db")
>>> print(db["dogs"].get(1))
{'id': 1, 'age': 4, 'name': 'Cleo'}
If the table has a compound primary key you can pass in the primary key values as a tuple:
>>> db["compound_dogs"].get(("mixed", 3))
If the record does not exist a NotFoundError will be raised:
from sqlite_utils.db import NotFoundError try:
row = db["dogs"].get(5) except NotFoundError:
print("Dog not found")
The db.schema property returns the full SQL schema for the database as a string:
>>> db = sqlite_utils.Database("dogs.db")
>>> print(db.schema)
CREATE TABLE "dogs" (
[id] INTEGER PRIMARY KEY,
[name] TEXT
);
The easiest way to create a new table is to insert a record into it:
from sqlite_utils import Database
import sqlite3
db = Database("dogs.db")
dogs = db["dogs"]
dogs.insert({
"name": "Cleo",
"twitter": "cleopaws",
"age": 3,
"is_good_dog": True,
})
This will automatically create a new table called "dogs" with the following schema:
CREATE TABLE dogs (
name TEXT,
twitter TEXT,
age INTEGER,
is_good_dog INTEGER )
You can also specify a primary key by passing the pk= parameter to the .insert() call. This will only be obeyed if the record being inserted causes the table to be created:
dogs.insert({
"id": 1,
"name": "Cleo",
"twitter": "cleopaws",
"age": 3,
"is_good_dog": True,
}, pk="id")
After inserting a row like this, the dogs.last_rowid property will return the SQLite rowid assigned to the most recently inserted record.
The dogs.last_pk property will return the last inserted primary key value, if you specified one. This can be very useful when writing code that creates foreign keys or many-to-many relationships.
The order of the columns in the table will be derived from the order of the keys in the dictionary, provided you are using Python 3.6 or later.
If you want to explicitly set the order of the columns you can do so using the column_order= parameter:
db["dogs"].insert({
"id": 1,
"name": "Cleo",
"twitter": "cleopaws",
"age": 3,
"is_good_dog": True,
}, pk="id", column_order=("id", "twitter", "name"))
You don't need to pass all of the columns to the column_order parameter. If you only pass a subset of the columns the remaining columns will be ordered based on the key order of the dictionary.
Column types are detected based on the example data provided. Sometimes you may find you need to over-ride these detected types - to create an integer column for data that was provided as a string for example, or to ensure that a table where the first example was None is created as an INTEGER rather than a TEXT column. You can do this using the columns= parameter:
db["dogs"].insert({
"id": 1,
"name": "Cleo",
"age": "5",
}, pk="id", columns={"age": int, "weight": float})
This will create a table with the following schema:
CREATE TABLE [dogs] (
[id] INTEGER PRIMARY KEY,
[name] TEXT,
[age] INTEGER,
[weight] FLOAT )
You can directly create a new table without inserting any data into it using the .create() method:
db["cats"].create({
"id": int,
"name": str,
"weight": float,
}, pk="id")
The first argument here is a dictionary specifying the columns you would like to create. Each column is paired with a Python type indicating the type of column. See Adding columns for full details on how these types work.
This method takes optional arguments pk=, column_order=, foreign_keys=, not_null=set() and defaults=dict() - explained below.
A sqlite_utils.utils.sqlite3.OperationalError will be raised if a table of that name already exists.
To do nothing if the table already exists, add if_not_exists=True:
db["cats"].create({
"id": int,
"name": str,
}, pk="id", if_not_exists=True)
You can also pass transform=True to have any existing tables transformed to match your new table specification. This is a dangerous operation as it will drop columns that are no longer listed in your call to .create(), so be careful when running this.
db["cats"].create({
"id": int,
"name": str,
"weight": float,
}, pk="id", transform=True)
The transform=True option will update the table schema if any of the following have changed:
Changes to foreign_keys= are not currently detected and applied by transform=True.
If you want to create a table with a compound primary key that spans multiple columns, you can do so by passing a tuple of column names to any of the methods that accept a pk= parameter. For example:
db["cats"].create({
"id": int,
"breed": str,
"name": str,
"weight": float,
}, pk=("breed", "id"))
This also works for the .insert(), .insert_all(), .upsert() and .upsert_all() methods.
Any operation that can create a table (.create(), .insert(), .insert_all(), .upsert() and .upsert_all()) accepts an optional foreign_keys= argument which can be used to set up foreign key constraints for the table that is being created.
If you are using your database with Datasette, Datasette will detect these constraints and use them to generate hyperlinks to associated records.
The foreign_keys argument takes a list that indicates which foreign keys should be created. The list can take several forms. The simplest is a list of columns:
foreign_keys=["author_id"]
The library will guess which tables you wish to reference based on the column names using the rules described in Adding foreign key constraints.
You can also be more explicit, by passing in a list of tuples:
foreign_keys=[
("author_id", "authors", "id") ]
This means that the author_id column should be a foreign key that references the id column in the authors table.
You can leave off the third item in the tuple to have the referenced column automatically set to the primary key of that table. A full example:
db["authors"].insert_all([
{"id": 1, "name": "Sally"},
{"id": 2, "name": "Asheesh"} ], pk="id") db["books"].insert_all([
{"title": "Hedgehogs of the world", "author_id": 1},
{"title": "How to train your wolf", "author_id": 2}, ], foreign_keys=[
("author_id", "authors") ])
The .insert(), .upsert(), .insert_all() and .upsert_all() methods each take a number of keyword arguments, some of which influence what happens should they cause a table to be created and some of which affect the behavior of those methods.
You can set default values for these methods by accessing the table through the db.table(...) method (instead of using db["table_name"]), like so:
table = db.table(
"authors",
pk="id",
not_null={"name", "score"},
column_order=("id", "name", "score", "url") ) # Now you can call .insert() like so: table.insert({"id": 1, "name": "Tracy", "score": 5})
The configuration options that can be specified in this way are pk, foreign_keys, column_order, not_null, defaults, batch_size, hash_id, hash_id_columns, alter, ignore, replace, extracts, conversions, columns. These are all documented below.
Each of the methods that can cause a table to be created take optional arguments not_null=set() and defaults=dict(). The methods that take these optional arguments are:
You can use not_null= to pass a set of column names that should have a NOT NULL constraint set on them when they are created.
You can use defaults= to pass a dictionary mapping columns to the default value that should be specified in the CREATE TABLE statement.
Here's an example that uses these features:
db["authors"].insert_all(
[{"id": 1, "name": "Sally", "score": 2}],
pk="id",
not_null={"name", "score"},
defaults={"score": 1}, ) db["authors"].insert({"name": "Dharma"}) list(db["authors"].rows) # Outputs: # [{'id': 1, 'name': 'Sally', 'score': 2}, # {'id': 3, 'name': 'Dharma', 'score': 1}] print(db["authors"].schema) # Outputs: # CREATE TABLE [authors] ( # [id] INTEGER PRIMARY KEY, # [name] TEXT NOT NULL, # [score] INTEGER NOT NULL DEFAULT 1 # )
The table.duplicate() method creates a copy of the table, copying both the table schema and all of the rows in that table:
db["authors"].duplicate("authors_copy")
The new authors_copy table will now contain a duplicate copy of the data from authors.
This method raises sqlite_utils.db.NoTable if the table does not exist.
If you have more than one record to insert, the insert_all() method is a much more efficient way of inserting them. Just like insert() it will automatically detect the columns that should be created, but it will inspect the first batch of 100 items to help decide what those column types should be.
Use it like this:
db["dogs"].insert_all([{
"id": 1,
"name": "Cleo",
"twitter": "cleopaws",
"age": 3,
"is_good_dog": True,
}, {
"id": 2,
"name": "Marnie",
"twitter": "MarnieTheDog",
"age": 16,
"is_good_dog": True,
}], pk="id", column_order=("id", "twitter", "name"))
The column types used in the CREATE TABLE statement are automatically derived from the types of data in that first batch of rows. Any additional columns in subsequent batches will cause a sqlite3.OperationalError exception to be raised unless the alter=True argument is supplied, in which case the new columns will be created.
The function can accept an iterator or generator of rows and will commit them according to the batch size. The default batch size is 100, but you can specify a different size using the batch_size parameter:
db["big_table"].insert_all(({
"id": 1,
"name": "Name {}".format(i),
} for i in range(10000)), batch_size=1000)
You can skip inserting any records that have a primary key that already exists using ignore=True. This works with both .insert({...}, ignore=True) and .insert_all([...], ignore=True).
You can delete all the existing rows in the table before inserting the new records using truncate=True. This is useful if you want to replace the data in the table.
Pass analyze=True to run ANALYZE against the table after inserting the new records.
If you try to insert data using a primary key that already exists, the .insert() or .insert_all() method will raise a sqlite3.IntegrityError exception.
This example that catches that exception:
from sqlite_utils.utils import sqlite3 try:
db["dogs"].insert({"id": 1, "name": "Cleo"}, pk="id") except sqlite3.IntegrityError:
print("Record already exists with that primary key")
Importing from sqlite_utils.utils.sqlite3 ensures your code continues to work even if you are using the pysqlite3 library instead of the Python standard library sqlite3 module.
Use the ignore=True parameter to ignore this error:
# This fails silently if a record with id=1 already exists
db["dogs"].insert({"id": 1, "name": "Cleo"}, pk="id", ignore=True)
To replace any existing records that have a matching primary key, use the replace=True parameter to .insert() or .insert_all():
db["dogs"].insert_all([{
"id": 1,
"name": "Cleo",
"twitter": "cleopaws",
"age": 3,
"is_good_dog": True,
}, {
"id": 2,
"name": "Marnie",
"twitter": "MarnieTheDog",
"age": 16,
"is_good_dog": True,
}], pk="id", replace=True)
NOTE:
You can update a record by its primary key using table.update():
>>> db = sqlite_utils.Database("dogs.db")
>>> print(db["dogs"].get(1))
{'id': 1, 'age': 4, 'name': 'Cleo'}
>>> db["dogs"].update(1, {"age": 5})
>>> print(db["dogs"].get(1))
{'id': 1, 'age': 5, 'name': 'Cleo'}
The first argument to update() is the primary key. This can be a single value, or a tuple if that table has a compound primary key:
>>> db["compound_dogs"].update((5, 3), {"name": "Updated"})
The second argument is a dictionary of columns that should be updated, along with their new values.
You can cause any missing columns to be added automatically using alter=True:
>>> db["dogs"].update(1, {"breed": "Mutt"}, alter=True)
You can delete a record using table.delete():
>>> db = sqlite_utils.Database("dogs.db")
>>> db["dogs"].delete(1)
The delete() method takes the primary key of the record. This can be a tuple of values if the row has a compound primary key:
>>> db["compound_dogs"].delete((5, 3))
You can delete all records in a table that match a specific WHERE statement using table.delete_where():
>>> db = sqlite_utils.Database("dogs.db")
>>> # Delete every dog with age less than 3
>>> db["dogs"].delete_where("age < ?", [3])
Calling table.delete_where() with no other arguments will delete every row in the table.
Pass analyze=True to run ANALYZE against the table after deleting the rows.
Upserting allows you to insert records if they do not exist and update them if they DO exist, based on matching against their primary key.
For example, given the dogs database you could upsert the record for Cleo like so:
db["dogs"].upsert({
"id": 1,
"name": "Cleo",
"twitter": "cleopaws",
"age": 4,
"is_good_dog": True,
}, pk="id", column_order=("id", "twitter", "name"))
If a record exists with id=1, it will be updated to match those fields. If it does not exist it will be created.
Any existing columns that are not referenced in the dictionary passed to .upsert() will be unchanged. If you want to replace a record entirely, use .insert(doc, replace=True) instead.
Note that the pk and column_order parameters here are optional if you are certain that the table has already been created. You should pass them if the table may not exist at the time the first upsert is performed.
An upsert_all() method is also available, which behaves like insert_all() but performs upserts instead.
NOTE:
The table.convert(...) method can be used to apply a conversion function to the values in a column, either to update that column or to populate new columns. It is the Python library equivalent of the sqlite-utils convert command.
This feature works by registering a custom SQLite function that applies a Python transformation, then running a SQL query equivalent to UPDATE table SET column = convert_value(column);
To transform a specific column to uppercase, you would use the following:
db["dogs"].convert("name", lambda value: value.upper())
You can pass a list of columns, in which case the transformation will be applied to each one:
db["dogs"].convert(["name", "twitter"], lambda value: value.upper())
To save the output to of the transformation to a different column, use the output= parameter:
db["dogs"].convert("name", lambda value: value.upper(), output="name_upper")
This will add the new column, if it does not already exist. You can pass output_type=int or some other type to control the type of the new column - otherwise it will default to text.
If you want to drop the original column after saving the results in a separate output column, pass drop=True.
You can create multiple new columns from a single input column by passing multi=True and a conversion function that returns a Python dictionary. This example creates new upper and lower columns populated from the single title column:
table.convert(
"title", lambda v: {"upper": v.upper(), "lower": v.lower()}, multi=True )
The .convert() method accepts optional where= and where_args= parameters which can be used to apply the conversion to a subset of rows specified by a where clause. Here's how to apply the conversion only to rows with an id that is higher than 20:
table.convert("title", lambda v: v.upper(), where="id > :id", where_args={"id": 20})
These behave the same as the corresponding parameters to the .rows_where() method, so you can use ? placeholders and a list of values instead of :named placeholders with a dictionary.
A useful pattern when populating large tables in to break common values out into lookup tables. Consider a table of Trees, where each tree has a species. Ideally these species would be split out into a separate Species table, with each one assigned an integer primary key that can be referenced from the Trees table species_id column.
Calling db["Species"].lookup({"name": "Palm"}) creates a table called Species (if one does not already exist) with two columns: id and name. It sets up a unique constraint on the name column to guarantee it will not contain duplicate rows. It then inserts a new row with the name set to Palm and returns the new integer primary key value.
If the Species table already exists, it will insert the new row and return the primary key. If a row with that name already exists, it will return the corresponding primary key value directly.
If you call .lookup() against an existing table without the unique constraint it will attempt to add the constraint, raising an IntegrityError if the constraint cannot be created.
If you pass in a dictionary with multiple values, both values will be used to insert or retrieve the corresponding ID and any unique constraint that is created will cover all of those columns, for example:
db["Trees"].insert({
"latitude": 49.1265976,
"longitude": 2.5496218,
"species": db["Species"].lookup({
"common_name": "Common Juniper",
"latin_name": "Juniperus communis"
})
})
The .lookup() method has an optional second argument which can be used to populate other columns in the table but only if the row does not exist yet. These columns will not be included in the unique index.
To create a species record with a note on when it was first seen, you can use this:
db["Species"].lookup({"name": "Palm"}, {"first_seen": "2021-03-04"})
The first time this is called the record will be created for name="Palm". Any subsequent calls with that name will ignore the second argument, even if it includes different values.
.lookup() also accepts keyword arguments, which are passed through to the insert() method and can be used to influence the shape of the created table. Supported parameters are:
A more efficient way to work with lookup tables is to define them using the extracts= parameter, which is accepted by .insert(), .upsert(), .insert_all(), .upsert_all() and by the .table(...) factory function.
extracts= specifies columns which should be "extracted" out into a separate lookup table during the data insertion.
It can be either a list of column names, in which case the extracted table names will match the column names exactly, or it can be a dictionary mapping column names to the desired name of the extracted table.
To extract the species column out to a separate Species table, you can do this:
# Using the table factory
trees = db.table("Trees", extracts={"species": "Species"})
trees.insert({
"latitude": 49.1265976,
"longitude": 2.5496218,
"species": "Common Juniper"
})
# If you want the table to be called 'species', you can do this:
trees = db.table("Trees", extracts=["species"])
# Using .insert() directly
db["Trees"].insert({
"latitude": 49.1265976,
"longitude": 2.5496218,
"species": "Common Juniper"
}, extracts={"species": "Species"})
sqlite-utils includes a shortcut for creating records using many-to-many relationships in the form of the table.m2m(...) method.
Here's how to create two new records and connect them via a many-to-many table in a single line of code:
db["dogs"].insert({"id": 1, "name": "Cleo"}, pk="id").m2m(
"humans", {"id": 1, "name": "Natalie"}, pk="id"
)
Running this example actually creates three tables: dogs, humans and a many-to-many dogs_humans table. It will insert a record into each of those tables.
The .m2m() method executes against the last record that was affected by .insert() or .update() - the record identified by the table.last_pk property. To execute .m2m() against a specific record you can first select it by passing its primary key to .update():
db["dogs"].update(1).m2m(
"humans", {"id": 2, "name": "Simon"}, pk="id" )
The first argument to .m2m() can be either the name of a table as a string or it can be the table object itself.
The second argument can be a single dictionary record or a list of dictionaries. These dictionaries will be passed to .upsert() against the specified table.
Here's alternative code that creates the dog record and adds two people to it:
db = Database(memory=True)
dogs = db.table("dogs", pk="id")
humans = db.table("humans", pk="id")
dogs.insert({"id": 1, "name": "Cleo"}).m2m(
humans, [
{"id": 1, "name": "Natalie"},
{"id": 2, "name": "Simon"}
]
)
The method will attempt to find an existing many-to-many table by looking for a table that has foreign key relationships against both of the tables in the relationship.
If it cannot find such a table, it will create a new one using the names of the two tables - dogs_humans in this example. You can customize the name of this table using the m2m_table= argument to .m2m().
It it finds multiple candidate tables with foreign keys to both of the specified tables it will raise a sqlite_utils.db.NoObviousTable exception. You can avoid this error by specifying the correct table using m2m_table=.
The .m2m() method also takes an optional pk= argument to specify the primary key that should be used if the table is created, and an optional alter=True argument to specify that any missing columns of an existing table should be added if they are needed.
You can work with (or create) lookup tables as part of a call to .m2m() using the lookup= parameter. This accepts the same argument as table.lookup() does - a dictionary of values that should be used to lookup or create a row in the lookup table.
This example creates a dogs table, populates it, creates a characteristics table, populates that and sets up a many-to-many relationship between the two. It chains .m2m() twice to create two associated characteristics:
db = Database(memory=True)
dogs = db.table("dogs", pk="id")
dogs.insert({"id": 1, "name": "Cleo"}).m2m(
"characteristics", lookup={
"name": "Playful"
}
).m2m(
"characteristics", lookup={
"name": "Opinionated"
}
)
You can inspect the database to see the results like this:
>>> db.table_names()
['dogs', 'characteristics', 'characteristics_dogs']
>>> list(db["dogs"].rows)
[{'id': 1, 'name': 'Cleo'}]
>>> list(db["characteristics"].rows)
[{'id': 1, 'name': 'Playful'}, {'id': 2, 'name': 'Opinionated'}]
>>> list(db["characteristics_dogs"].rows)
[{'characteristics_id': 1, 'dogs_id': 1}, {'characteristics_id': 2, 'dogs_id': 1}]
>>> print(db["characteristics_dogs"].schema)
CREATE TABLE [characteristics_dogs] (
[characteristics_id] INTEGER REFERENCES [characteristics]([id]),
[dogs_id] INTEGER REFERENCES [dogs]([id]),
PRIMARY KEY ([characteristics_id], [dogs_id])
)
The table.analyze_column(column, common_limit=10, value_truncate=None) method is used by the analyze-tables CLI command. It returns a ColumnDetails named tuple with the following fields:
N defaults to 10, or you can pass a custom N using the common_limit parameter.
You can use the value_truncate parameter to truncate values in the most_common and least_common lists to a specified number of characters.
You can add a new column to a table using the .add_column(col_name, col_type) method:
db["dogs"].add_column("instagram", str)
db["dogs"].add_column("weight", float)
db["dogs"].add_column("dob", datetime.date)
db["dogs"].add_column("image", "BLOB")
db["dogs"].add_column("website") # str by default
You can specify the col_type argument either using a SQLite type as a string, or by directly passing a Python type e.g. str or float.
The col_type is optional - if you omit it the type of TEXT will be used.
SQLite types you can specify are "TEXT", "INTEGER", "FLOAT" or "BLOB".
If you pass a Python type, it will be mapped to SQLite types as shown here:
float: "FLOAT" int: "INTEGER" bool: "INTEGER" str: "TEXT" bytes: "BLOB" datetime.datetime: "TEXT" datetime.date: "TEXT" datetime.time: "TEXT" # If numpy is installed np.int8: "INTEGER" np.int16: "INTEGER" np.int32: "INTEGER" np.int64: "INTEGER" np.uint8: "INTEGER" np.uint16: "INTEGER" np.uint32: "INTEGER" np.uint64: "INTEGER" np.float16: "FLOAT" np.float32: "FLOAT" np.float64: "FLOAT"
You can also add a column that is a foreign key reference to another table using the fk parameter:
db["dogs"].add_column("species_id", fk="species")
This will automatically detect the name of the primary key on the species table and use that (and its type) for the new column.
You can explicitly specify the column you wish to reference using fk_col:
db["dogs"].add_column("species_id", fk="species", fk_col="ref")
You can set a NOT NULL DEFAULT 'x' constraint on the new column using not_null_default:
db["dogs"].add_column("friends_count", int, not_null_default=0)
You can insert or update data that includes new columns and have the table automatically altered to fit the new schema using the alter=True argument. This can be passed to all four of .insert(), .upsert(), .insert_all() and .upsert_all(), or it can be passed to db.table(table_name, alter=True) to enable it by default for all method calls against that table instance.
db["new_table"].insert({"name": "Gareth"})
# This will throw an exception:
db["new_table"].insert({"name": "Gareth", "age": 32})
# This will succeed and add a new "age" integer column:
db["new_table"].insert({"name": "Gareth", "age": 32}, alter=True)
# You can see confirm the new column like so:
print(db["new_table"].columns_dict)
# Outputs this:
# {'name': <class 'str'>, 'age': <class 'int'>}
# This works too:
new_table = db.table("new_table", alter=True)
new_table.insert({"name": "Gareth", "age": 32, "shoe_size": 11})
The SQLite ALTER TABLE statement doesn't have the ability to add foreign key references to an existing column.
It's possible to add these references through very careful manipulation of SQLite's sqlite_master table, using PRAGMA writable_schema.
sqlite-utils can do this for you, though there is a significant risk of data corruption if something goes wrong so it is advisable to create a fresh copy of your database file before attempting this.
Here's an example of this mechanism in action:
db["authors"].insert_all([
{"id": 1, "name": "Sally"},
{"id": 2, "name": "Asheesh"} ], pk="id") db["books"].insert_all([
{"title": "Hedgehogs of the world", "author_id": 1},
{"title": "How to train your wolf", "author_id": 2}, ]) db["books"].add_foreign_key("author_id", "authors", "id")
The table.add_foreign_key(column, other_table, other_column) method takes the name of the column, the table that is being referenced and the key column within that other table. If you omit the other_column argument the primary key from that table will be used automatically. If you omit the other_table argument the table will be guessed based on some simple rules:
This method first checks that the specified foreign key references tables and columns that exist and does not clash with an existing foreign key. It will raise a sqlite_utils.db.AlterError exception if these checks fail.
To ignore the case where the key already exists, use ignore=True:
db["books"].add_foreign_key("author_id", "authors", "id", ignore=True)
The final step in adding a new foreign key to a SQLite database is to run VACUUM, to ensure the new foreign key is available in future introspection queries.
VACUUM against a large (multi-GB) database can take several minutes or longer. If you are adding multiple foreign keys using table.add_foreign_key(...) these can quickly add up.
Instead, you can use db.add_foreign_keys(...) to add multiple foreign keys within a single transaction. This method takes a list of four-tuples, each one specifying a table, column, other_table and other_column.
Here's an example adding two foreign keys at once:
db.add_foreign_keys([
("dogs", "breed_id", "breeds", "id"),
("dogs", "home_town_id", "towns", "id") ])
This method runs the same checks as .add_foreign_keys() and will raise sqlite_utils.db.AlterError if those checks fail.
If you want to ensure that every foreign key column in your database has a corresponding index, you can do so like this:
db.index_foreign_keys()
You can drop a table or view using the .drop() method:
db["my_table"].drop()
Pass ignore=True if you want to ignore the error caused by the table or view not existing.
db["my_table"].drop(ignore=True)
The SQLite ALTER TABLE statement is limited. It can add and drop columns and rename tables, but it cannot change column types, change NOT NULL status or change the primary key for a table.
The table.transform() method can do all of these things, by implementing a multi-step pattern described in the SQLite documentation:
The .transform() method takes a number of parameters, all of which are optional.
To alter the type of a column, use the types= argument:
# Convert the 'age' column to an integer, and 'weight' to a float
table.transform(types={"age": int, "weight": float})
See Adding columns for a list of available types.
The rename= parameter can rename columns:
# Rename 'age' to 'initial_age':
table.transform(rename={"age": "initial_age"})
To drop columns, pass them in the drop= set:
# Drop the 'age' column:
table.transform(drop={"age"})
To change the primary key for a table, use pk=. This can be passed a single column for a regular primary key, or a tuple of columns to create a compound primary key. Passing pk=None will remove the primary key and convert the table into a rowid table.
# Make `user_id` the new primary key table.transform(pk="user_id")
You can change the NOT NULL status of columns by using not_null=. You can pass this a set of columns to make those columns NOT NULL:
# Make the 'age' and 'weight' columns NOT NULL
table.transform(not_null={"age", "weight"})
If you want to take existing NOT NULL columns and change them to allow null values, you can do so by passing a dictionary of true/false values instead:
# 'age' is NOT NULL but we want to allow NULL:
table.transform(not_null={"age": False})
# Make age allow NULL and switch weight to being NOT NULL:
table.transform(not_null={"age": False, "weight": True})
The defaults= parameter can be used to set or change the defaults for different columns:
# Set default age to 1:
table.transform(defaults={"age": 1})
# Now remove the default from that column:
table.transform(defaults={"age": None})
The column_order= parameter can be used to change the order of the columns. If you pass the names of a subset of the columns those will go first and columns you omitted will appear in their existing order after them.
# Change column order
table.transform(column_order=("name", "age", "id")
You can use .transform() to remove foreign key constraints from a table.
This example drops two foreign keys - the one from places.country to country.id and the one from places.continent to continent.id:
db["places"].transform(
drop_foreign_keys=("country", "continent") )
The .transform() method can handle most cases, but it does not automatically upgrade indexes, views or triggers associated with the table that is being transformed.
If you want to do something more advanced, you can call the table.transform_sql(...) method with the same arguments that you would have passed to table.transform(...).
This method will return a list of SQL statements that should be executed to implement the change. You can then make modifications to that SQL - or add additional SQL statements - before executing it yourself.
The table.extract() method can be used to extract specified columns into a separate table.
Imagine a Trees table that looks like this:
| id | TreeAddress | Species |
| 1 | 52 Vine St | Palm |
| 2 | 12 Draft St | Oak |
| 3 | 51 Dark Ave | Palm |
| 4 | 1252 Left St | Palm |
The Species column contains duplicate values. This database could be improved by extracting that column out into a separate Species table and pointing to it using a foreign key column.
The schema of the above table is:
CREATE TABLE [Trees] (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[Species] TEXT )
Here's how to extract the Species column using .extract():
db["Trees"].extract("Species")
After running this code the table schema now looks like this:
CREATE TABLE "Trees" (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[Species_id] INTEGER,
FOREIGN KEY(Species_id) REFERENCES Species(id) )
A new Species table will have been created with the following schema:
CREATE TABLE [Species] (
[id] INTEGER PRIMARY KEY,
[Species] TEXT )
The .extract() method defaults to creating a table with the same name as the column that was extracted, and adding a foreign key column called tablename_id.
You can specify a custom table name using table=, and a custom foreign key name using fk_column=. This example creates a table called tree_species and a foreign key column called tree_species_id:
db["Trees"].extract("Species", table="tree_species", fk_column="tree_species_id")
The resulting schema looks like this:
CREATE TABLE "Trees" (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[tree_species_id] INTEGER,
FOREIGN KEY(tree_species_id) REFERENCES tree_species(id) ) CREATE TABLE [tree_species] (
[id] INTEGER PRIMARY KEY,
[Species] TEXT )
You can also extract multiple columns into the same external table. Say for example you have a table like this:
| id | TreeAddress | CommonName | LatinName |
| 1 | 52 Vine St | Palm | Arecaceae |
| 2 | 12 Draft St | Oak | Quercus |
| 3 | 51 Dark Ave | Palm | Arecaceae |
| 4 | 1252 Left St | Palm | Arecaceae |
You can pass ["CommonName", "LatinName"] to .extract() to extract both of those columns:
db["Trees"].extract(["CommonName", "LatinName"])
This produces the following schema:
CREATE TABLE "Trees" (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[CommonName_LatinName_id] INTEGER,
FOREIGN KEY(CommonName_LatinName_id) REFERENCES CommonName_LatinName(id) ) CREATE TABLE [CommonName_LatinName] (
[id] INTEGER PRIMARY KEY,
[CommonName] TEXT,
[LatinName] TEXT )
The table name CommonName_LatinName is derived from the extract columns. You can use table= and fk_column= to specify custom names like this:
db["Trees"].extract(["CommonName", "LatinName"], table="Species", fk_column="species_id")
This produces the following schema:
CREATE TABLE "Trees" (
[id] INTEGER PRIMARY KEY,
[TreeAddress] TEXT,
[species_id] INTEGER,
FOREIGN KEY(species_id) REFERENCES Species(id) ) CREATE TABLE [Species] (
[id] INTEGER PRIMARY KEY,
[CommonName] TEXT,
[LatinName] TEXT )
You can use the rename= argument to rename columns in the lookup table. To create a Species table with columns called name and latin you can do this:
db["Trees"].extract(
["CommonName", "LatinName"],
table="Species",
fk_column="species_id",
rename={"CommonName": "name", "LatinName": "latin"} )
This produces a lookup table like so:
CREATE TABLE [Species] (
[id] INTEGER PRIMARY KEY,
[name] TEXT,
[latin] TEXT )
Sometimes you will find yourself working with a dataset that includes rows that do not have a provided obvious ID, but where you would like to assign one so that you can later upsert into that table without creating duplicate records.
In these cases, a useful technique is to create an ID that is derived from the sha1 hash of the row contents.
sqlite-utils can do this for you using the hash_id= option. For example:
db = sqlite_utils.Database("dogs.db")
db["dogs"].upsert({"name": "Cleo", "twitter": "cleopaws"}, hash_id="id")
print(list(db["dogs]))
Outputs:
[{'id': 'f501265970505d9825d8d9f590bfab3519fb20b1', 'name': 'Cleo', 'twitter': 'cleopaws'}]
If you are going to use that ID straight away, you can access it using last_pk:
dog_id = db["dogs"].upsert({
"name": "Cleo",
"twitter": "cleopaws"
}, hash_id="id").last_pk
# dog_id is now "f501265970505d9825d8d9f590bfab3519fb20b1"
The hash will be created using all of the column values. To create a hash using a subset of the columns, pass the hash_id_columns= parameter:
db["dogs"].upsert(
{"name": "Cleo", "twitter": "cleopaws", "age": 7},
hash_id_columns=("name", "twitter") )
The hash_id= parameter is optional if you specify hash_id_columns= - it will default to putting the hash in a column called id.
You can manually calculate these hashes using the hash_record(record, keys=...) utility function.
The .create_view() method on the database class can be used to create a view:
db.create_view("good_dogs", """
select * from dogs where is_good_dog = 1
""")
This will raise a sqlite_utils.utils.OperationalError if a view with that name already exists.
You can pass ignore=True to silently ignore an existing view and do nothing, or replace=True to replace an existing view with a new definition if your select statement differs from the current view:
db.create_view("good_dogs", """
select * from dogs where is_good_dog = 1
""", replace=True)
SQLite has excellent JSON support, and sqlite-utils can help you take advantage of this: if you attempt to insert a value that can be represented as a JSON list or dictionary, sqlite-utils will create TEXT column and store your data as serialized JSON. This means you can quickly store even complex data structures in SQLite and query them using JSON features.
For example:
db["niche_museums"].insert({
"name": "The Bigfoot Discovery Museum",
"url": "http://bigfootdiscoveryproject.com/"
"hours": {
"Monday": [11, 18],
"Wednesday": [11, 18],
"Thursday": [11, 18],
"Friday": [11, 18],
"Saturday": [11, 18],
"Sunday": [11, 18]
},
"address": {
"streetAddress": "5497 Highway 9",
"addressLocality": "Felton, CA",
"postalCode": "95018"
}
})
db.execute("""
select json_extract(address, '$.addressLocality')
from niche_museums
""").fetchall()
# Returns [('Felton, CA',)]
Sometimes it can be useful to run values through a SQL function prior to inserting them. A simple example might be converting a value to upper case while it is being inserted.
The conversions={...} parameter can be used to specify custom SQL to be used as part of a INSERT or UPDATE SQL statement.
You can specify an upper case conversion for a specific column like so:
db["example"].insert({
"name": "The Bigfoot Discovery Museum"
}, conversions={"name": "upper(?)"})
# list(db["example"].rows) now returns:
# [{'name': 'THE BIGFOOT DISCOVERY MUSEUM'}]
The dictionary key is the column name to be converted. The value is the SQL fragment to use, with a ? placeholder for the original value.
A more useful example: if you are working with SpatiaLite you may find yourself wanting to create geometry values from a WKT value. Code to do that could look like this:
import sqlite3
import sqlite_utils
from shapely.geometry import shape
import httpx
db = sqlite_utils.Database("places.db")
# Initialize SpatiaLite
db.init_spatialite()
# Use sqlite-utils to create a places table
places = db["places"].create({"id": int, "name": str})
# Add a SpatiaLite 'geometry' column
places.add_geometry_column("geometry", "MULTIPOLYGON")
# Fetch some GeoJSON from Who's On First:
geojson = httpx.get(
"https://raw.githubusercontent.com/whosonfirst-data/"
"whosonfirst-data-admin-gb/master/data/404/227/475/404227475.geojson"
).json()
# Convert to "Well Known Text" format using shapely
wkt = shape(geojson["geometry"]).wkt
# Insert the record, converting the WKT to a SpatiaLite geometry:
db["places"].insert(
{"name": "Wales", "geometry": wkt},
conversions={"geometry": "GeomFromText(?, 4326)"},
)
This example uses gographical data from [Who's On First](https://whosonfirst.org/) and depends on the [Shapely](https://shapely.readthedocs.io/en/stable/manual.html) and [HTTPX](https://www.python-httpx.org/) Python libraries.
The db.sqlite_version property returns a tuple of integers representing the version of SQLite used for that database object:
>>> db.sqlite_version (3, 36, 0)
If you have loaded an existing table or view, you can use introspection to find out more about it:
>>> db["PlantType"] <Table PlantType (id, value)>
The .exists() method can be used to find out if a table exists or not:
>>> db["PlantType"].exists() True >>> db["PlantType2"].exists() False
The .count property shows the current number of rows (select count(*) from table):
>>> db["PlantType"].count 3 >>> db["Street_Tree_List"].count 189144
This property will take advantage of Cached table counts using triggers if the use_counts_table property is set on the database. You can avoid that optimization entirely by calling table.count_where() instead of accessing the property.
The .columns property shows the columns in the table or view. It returns a list of Column(cid, name, type, notnull, default_value, is_pk) named tuples.
>>> db["PlantType"].columns [Column(cid=0, name='id', type='INTEGER', notnull=0, default_value=None, is_pk=1),
Column(cid=1, name='value', type='TEXT', notnull=0, default_value=None, is_pk=0)]
The .columns_dict property returns a dictionary version of the columns with just the names and Python types:
>>> db["PlantType"].columns_dict
{'id': <class 'int'>, 'value': <class 'str'>}
The .default_values property returns a dictionary of default values for each column that has a default:
>>> db["table_with_defaults"].default_values
{'score': 5}
The .pks property returns a list of strings naming the primary key columns for the table:
>>> db["PlantType"].pks ['id']
If a table has no primary keys but is a rowid table, this property will return ['rowid'].
Almost all SQLite tables have a rowid column, but a table with no explicitly defined primary keys must use that rowid as the primary key for identifying individual rows. The .use_rowid property checks to see if a table needs to use the rowid in this way - it returns True if the table has no explicitly defined primary keys and False otherwise.
>>> db["PlantType"].use_rowid False
The .foreign_keys property returns any foreign key relationships for the table, as a list of ForeignKey(table, column, other_table, other_column) named tuples. It is not available on views.
>>> db["Street_Tree_List"].foreign_keys [ForeignKey(table='Street_Tree_List', column='qLegalStatus', other_table='qLegalStatus', other_column='id'),
ForeignKey(table='Street_Tree_List', column='qCareAssistant', other_table='qCareAssistant', other_column='id'),
ForeignKey(table='Street_Tree_List', column='qSiteInfo', other_table='qSiteInfo', other_column='id'),
ForeignKey(table='Street_Tree_List', column='qSpecies', other_table='qSpecies', other_column='id'),
ForeignKey(table='Street_Tree_List', column='qCaretaker', other_table='qCaretaker', other_column='id'),
ForeignKey(table='Street_Tree_List', column='PlantType', other_table='PlantType', other_column='id')]
The .schema property outputs the table's schema as a SQL string:
>>> print(db["Street_Tree_List"].schema) CREATE TABLE "Street_Tree_List" ( "TreeID" INTEGER,
"qLegalStatus" INTEGER,
"qSpecies" INTEGER,
"qAddress" TEXT,
"SiteOrder" INTEGER,
"qSiteInfo" INTEGER,
"PlantType" INTEGER,
"qCaretaker" INTEGER,
"qCareAssistant" INTEGER,
"PlantDate" TEXT,
"DBH" INTEGER,
"PlotSize" TEXT,
"PermitNotes" TEXT,
"XCoord" REAL,
"YCoord" REAL,
"Latitude" REAL,
"Longitude" REAL,
"Location" TEXT , FOREIGN KEY ("PlantType") REFERENCES [PlantType](id),
FOREIGN KEY ("qCaretaker") REFERENCES [qCaretaker](id),
FOREIGN KEY ("qSpecies") REFERENCES [qSpecies](id),
FOREIGN KEY ("qSiteInfo") REFERENCES [qSiteInfo](id),
FOREIGN KEY ("qCareAssistant") REFERENCES [qCareAssistant](id),
FOREIGN KEY ("qLegalStatus") REFERENCES [qLegalStatus](id))
The .strict property identifies if the table is a SQLite STRICT table.
>>> db["ny_times_us_counties"].strict False
The .indexes property returns all indexes created for a table, as a list of Index(seq, name, unique, origin, partial, columns) named tuples. It is not available on views.
>>> db["Street_Tree_List"].indexes [Index(seq=0, name='"Street_Tree_List_qLegalStatus"', unique=0, origin='c', partial=0, columns=['qLegalStatus']),
Index(seq=1, name='"Street_Tree_List_qCareAssistant"', unique=0, origin='c', partial=0, columns=['qCareAssistant']),
Index(seq=2, name='"Street_Tree_List_qSiteInfo"', unique=0, origin='c', partial=0, columns=['qSiteInfo']),
Index(seq=3, name='"Street_Tree_List_qSpecies"', unique=0, origin='c', partial=0, columns=['qSpecies']),
Index(seq=4, name='"Street_Tree_List_qCaretaker"', unique=0, origin='c', partial=0, columns=['qCaretaker']),
Index(seq=5, name='"Street_Tree_List_PlantType"', unique=0, origin='c', partial=0, columns=['PlantType'])]
The .xindexes property returns more detailed information about the indexes on the table, using the SQLite PRAGMA index_xinfo() mechanism. It returns a list of XIndex(name, columns) named tuples, where columns is a list of XIndexColumn(seqno, cid, name, desc, coll, key) named tuples.
>>> db["ny_times_us_counties"].xindexes [
XIndex(
name='idx_ny_times_us_counties_date',
columns=[
XIndexColumn(seqno=0, cid=0, name='date', desc=1, coll='BINARY', key=1),
XIndexColumn(seqno=1, cid=-1, name=None, desc=0, coll='BINARY', key=0)
]
),
XIndex(
name='idx_ny_times_us_counties_fips',
columns=[
XIndexColumn(seqno=0, cid=3, name='fips', desc=0, coll='BINARY', key=1),
XIndexColumn(seqno=1, cid=-1, name=None, desc=0, coll='BINARY', key=0)
]
) ]
The .triggers property lists database triggers. It can be used on both database and table objects. It returns a list of Trigger(name, table, sql) named tuples.
>>> db["authors"].triggers [Trigger(name='authors_ai', table='authors', sql='CREATE TRIGGER [authors_ai] AFTER INSERT...'),
Trigger(name='authors_ad', table='authors', sql="CREATE TRIGGER [authors_ad] AFTER DELETE..."),
Trigger(name='authors_au', table='authors', sql="CREATE TRIGGER [authors_au] AFTER UPDATE")] >>> db.triggers ... similar output to db["authors"].triggers
The .triggers_dict property returns the triggers for that table as a dictionary mapping their names to their SQL definitions.
>>> db["authors"].triggers_dict
{'authors_ai': 'CREATE TRIGGER [authors_ai] AFTER INSERT...',
'authors_ad': 'CREATE TRIGGER [authors_ad] AFTER DELETE...',
'authors_au': 'CREATE TRIGGER [authors_au] AFTER UPDATE'}
The same property exists on the database, and will return all triggers across all tables:
>>> db.triggers_dict
{'authors_ai': 'CREATE TRIGGER [authors_ai] AFTER INSERT...',
'authors_ad': 'CREATE TRIGGER [authors_ad] AFTER DELETE...',
'authors_au': 'CREATE TRIGGER [authors_au] AFTER UPDATE'}
The detect_fts() method returns the associated SQLite FTS table name, if one exists for this table. If the table has not been configured for full-text search it returns None.
>>> db["authors"].detect_fts() "authors_fts"
The .virtual_table_using property reveals if a table is a virtual table. It returns None for regular tables and the upper case version of the type of virtual table otherwise. For example:
>>> db["authors"].enable_fts(["name"]) >>> db["authors_fts"].virtual_table_using "FTS5"
The .has_counts_triggers property shows if a table has been configured with triggers for updating a _counts table, as described in Cached table counts using triggers.
>>> db["authors"].has_counts_triggers False >>> db["authors"].enable_counts() >>> db["authors"].has_counts_triggers True
SQLite includes bundled extensions that implement powerful full-text search.
You can enable full-text search on a table using .enable_fts(columns):
db["dogs"].enable_fts(["name", "twitter"])
You can then run searches using the .search() method:
rows = list(db["dogs"].search("cleo"))
This method returns a generator that can be looped over to get dictionaries for each row, similar to Listing rows.
If you insert additional records into the table you will need to refresh the search index using populate_fts():
db["dogs"].insert({
"id": 2,
"name": "Marnie",
"twitter": "MarnieTheDog",
"age": 16,
"is_good_dog": True,
}, pk="id")
db["dogs"].populate_fts(["name", "twitter"])
A better solution is to use database triggers. You can set up database triggers to automatically update the full-text index using create_triggers=True:
db["dogs"].enable_fts(["name", "twitter"], create_triggers=True)
.enable_fts() defaults to using FTS5. If you wish to use FTS4 instead, use the following:
db["dogs"].enable_fts(["name", "twitter"], fts_version="FTS4")
You can customize the tokenizer configured for the table using the tokenize= parameter. For example, to enable Porter stemming, where English words like "running" will match stemmed alternatives such as "run", use tokenize="porter":
db["articles"].enable_fts(["headline", "body"], tokenize="porter")
The SQLite documentation has more on FTS5 tokenizers and FTS4 tokenizers. porter is a valid option for both.
If you attempt to configure a FTS table where one already exists, a sqlite3.OperationalError exception will be raised.
You can replace the existing table with a new configuration using replace=True:
db["articles"].enable_fts(["headline"], tokenize="porter", replace=True)
This will have no effect if the FTS table already exists, otherwise it will drop and recreate the table with the new settings. This takes into consideration the columns, the tokenizer, the FTS version used and whether or not the table has triggers.
To remove the FTS tables and triggers you created, use the disable_fts() table method:
db["dogs"].disable_fts()
SQLite supports advanced search query syntax. In some situations you may wish to disable this, since characters such as . may have special meaning that causes errors when searching for strings provided by your users.
The db.quote_fts(query) method returns the query with SQLite full-text search quoting applied such that the query should be safe to use in a search:
db.quote_fts("Search term.")
# Returns: '"Search" "term."'
The table.search(q) method returns a generator over Python dictionaries representing rows that match the search phrase q, ordered by relevance with the most relevant results first.
for article in db["articles"].search("jquery"):
print(article)
The .search() method also accepts the following optional parameters:
To return just the title and published columns for three matches for "dog" where the id is greater than 10 ordered by published with the most recent first, use the following:
for article in db["articles"].search(
"dog",
order_by="published desc",
limit=3,
where="id > :min_id",
where_args={"min_id": 10},
columns=["title", "published"] ):
print(article)
You can generate the SQL query that would be used for a search using the table.search_sql() method. It takes the same arguments as table.search(), with the exception of the search query and the where_args parameter, since those should be provided when the returned SQL is executed.
print(db["articles"].search_sql(columns=["title", "author"]))
Outputs:
with original as (
select
rowid,
[title],
[author]
from [articles] ) select
[original].[title],
[original].[author] from
[original]
join [articles_fts] on [original].rowid = [articles_fts].rowid where
[articles_fts] match :query order by
[articles_fts].rank
This method detects if a SQLite table uses FTS4 or FTS5, and outputs the correct SQL for ordering by relevance depending on the search type.
The FTS4 output looks something like this:
with original as (
select
rowid,
[title],
[author]
from [articles] ) select
[original].[title],
[original].[author] from
[original]
join [articles_fts] on [original].rowid = [articles_fts].rowid where
[articles_fts] match :query order by
rank_bm25(matchinfo([articles_fts], 'pcnalx'))
This uses the rank_bm25() custom SQL function from sqlite-fts4. You can register that custom function against a Database connection using this method:
db.register_fts4_bm25()
You can rebuild a table using the table.rebuild_fts() method. This is useful for if the table configuration changes or the indexed data has become corrupted in some way.
db["dogs"].rebuild_fts()
This method can be called on a table that has been configured for full-text search - dogs in this instance - or directly on a _fts table:
db["dogs_fts"].rebuild_fts()
This runs the following SQL:
INSERT INTO dogs_fts (dogs_fts) VALUES ("rebuild");
Once you have populated a FTS table you can optimize it to dramatically reduce its size like so:
db["dogs"].optimize()
This runs the following SQL:
INSERT INTO dogs_fts (dogs_fts) VALUES ("optimize");
The select count(*) query in SQLite requires a full scan of the primary key index, and can take an increasingly long time as the table grows larger.
The table.enable_counts() method can be used to configure triggers to continuously update a record in a _counts table. This value can then be used to quickly retrieve the count of rows in the associated table.
db["dogs"].enable_counts()
This will create the _counts table if it does not already exist, with the following schema:
CREATE TABLE [_counts] (
[table] TEXT PRIMARY KEY,
[count] INTEGER DEFAULT 0 )
You can enable cached counts for every table in a database (except for virtual tables and the _counts table itself) using the database enable_counts() method:
db.enable_counts()
Once enabled, table counts will be stored in the _counts table. The count records will be automatically kept up-to-date by the triggers when rows are added or deleted to the table.
To access these counts you can query the _counts table directly or you can use the db.cached_counts() method. This method returns a dictionary mapping tables to their counts:
>>> db.cached_counts()
{'global-power-plants': 33643,
'global-power-plants_fts_data': 136,
'global-power-plants_fts_idx': 199,
'global-power-plants_fts_docsize': 33643,
'global-power-plants_fts_config': 1}
You can pass a list of table names to this method to retrieve just those counts:
>>> db.cached_counts(["global-power-plants"])
{'global-power-plants': 33643}
The table.count property executes a select count(*) query by default, unless the db.use_counts_table property is set to True.
You can set use_counts_table to True when you instantiate the database object:
db = Database("global-power-plants.db", use_counts_table=True)
If the property is True any calls to the table.count property will first attempt to find the cached count in the _counts table, and fall back on a count(*) query if the value is not available or the table is missing.
Calling the .enable_counts() method on a database or table object will set use_counts_table to True for the lifetime of that database object.
If the _counts table ever becomes out-of-sync with the actual table counts you can repair it using the .reset_counts() method:
db.reset_counts()
You can create an index on a table using the .create_index(columns) method. The method takes a list of columns:
db["dogs"].create_index(["is_good_dog"])
By default the index will be named idx_{table-name}_{columns}. If you pass find_unique_name=True and the automatically derived name already exists, an available name will be found by incrementing a suffix number, for example idx_items_title_2.
You can customize the name of the created index by passing the index_name parameter:
db["dogs"].create_index(
["is_good_dog", "age"],
index_name="good_dogs_by_age" )
To create an index in descending order for a column, wrap the column name in db.DescIndex() like this:
from sqlite_utils.db import DescIndex db["dogs"].create_index(
["is_good_dog", DescIndex("age")],
index_name="good_dogs_by_age" )
You can create a unique index by passing unique=True:
db["dogs"].create_index(["name"], unique=True)
Use if_not_exists=True to do nothing if an index with that name already exists.
Pass analyze=True to run ANALYZE against the new index after creating it.
The SQLite ANALYZE command builds a table of statistics which the query planner can use to make better decisions about which indexes to use for a given query.
You should run ANALYZE if your database is large and you do not think your indexes are being efficiently used.
To run ANALYZE against every index in a database, use this:
db.analyze()
To run it just against a specific named index, pass the name of the index to that method:
db.analyze("idx_countries_country_name")
To run against all indexes attached to a specific table, you can either pass the table name to db.analyze(...) or you can call the method directly on the table, like this:
db["dogs"].analyze()
You can optimize your database by running VACUUM against it like so:
Database("my_database.db").vacuum()
You can enable Write-Ahead Logging for a database with .enable_wal():
Database("my_database.db").enable_wal()
You can disable WAL mode using .disable_wal():
Database("my_database.db").disable_wal()
You can check the current journal mode for a database using the journal_mode property:
journal_mode = Database("my_database.db").journal_mode
This will usually be wal or delete (meaning WAL is disabled), but can have other values - see the PRAGMA journal_mode documentation.
When you create a new table for a list of inserted or upserted Python dictionaries, those methods detect the correct types for the database columns based on the data you pass in.
In some situations you may need to intervene in this process, to customize the columns that are being created in some way - see Explicitly creating a table.
That table .create() method takes a dictionary mapping column names to the Python type they should store:
db["cats"].create({
"id": int,
"name": str,
"weight": float,
})
You can use the suggest_column_types() helper function to derive a dictionary of column names and types from a list of records, suitable to be passed to table.create().
For example:
from sqlite_utils import Database, suggest_column_types
cats = [{
"id": 1,
"name": "Snowflake"
}, {
"id": 2,
"name": "Crabtree",
"age": 4
}]
types = suggest_column_types(cats)
# types now looks like this:
# {"id": <class 'int'>,
# "name": <class 'str'>,
# "age": <class 'int'>}
# Manually add an extra field:
types["thumbnail"] = bytes
# types now looks like this:
# {"id": <class 'int'>,
# "name": <class 'str'>,
# "age": <class 'int'>,
# "thumbnail": <class 'bytes'>}
# Create the table
db = Database("cats.db")
db["cats"].create(types, pk="id")
# Insert the records
db["cats"].insert_all(cats)
# list(db["cats"].rows) now returns:
# [{"id": 1, "name": "Snowflake", "age": None, "thumbnail": None}
# {"id": 2, "name": "Crabtree", "age": 4, "thumbnail": None}]
# The table schema looks like this:
# print(db["cats"].schema)
# CREATE TABLE [cats] (
# [id] INTEGER PRIMARY KEY,
# [name] TEXT,
# [age] INTEGER,
# [thumbnail] BLOB
# )
SQLite supports registering custom SQL functions written in Python. The db.register_function() method lets you register these functions, and keeps track of functions that have already been registered.
If you use it as a method it will automatically detect the name and number of arguments needed by the function:
from sqlite_utils import Database db = Database(memory=True) def reverse_string(s):
return "".join(reversed(list(s))) db.register_function(reverse_string) print(db.execute('select reverse_string("hello")').fetchone()[0]) # This prints "olleh"
You can also use the method as a function decorator like so:
@db.register_function def reverse_string(s):
return "".join(reversed(list(s))) print(db.execute('select reverse_string("hello")').fetchone()[0])
By default, the name of the Python function will be used as the name of the SQL function. You can customize this with the name= keyword argument:
@db.register_function(name="rev") def reverse_string(s):
return "".join(reversed(list(s))) print(db.execute('select rev("hello")').fetchone()[0])
Python 3.8 added the ability to register deterministic SQLite functions, allowing you to indicate that a function will return the exact same result for any given inputs and hence allowing SQLite to apply some performance optimizations. You can mark a function as deterministic using deterministic=True, like this:
@db.register_function(deterministic=True) def reverse_string(s):
return "".join(reversed(list(s)))
If you run this on a version of Python prior to 3.8 your code will still work, but the deterministic=True parameter will be ignored.
By default registering a function with the same name and number of arguments will have no effect - the Database instance keeps track of functions that have already been registered and skips registering them if @db.register_function is called a second time.
If you want to deliberately replace the registered function with a new implementation, use the replace=True argument:
@db.register_function(deterministic=True, replace=True) def reverse_string(s):
return s[::-1]
Exceptions that occur inside a user-defined function default to returning the following error:
Unexpected error: user-defined function raised exception
You can cause sqlite3 to return more useful errors, including the traceback from the custom function, by executing the following before your custom functions are executed:
from sqlite_utils.utils import sqlite3 sqlite3.enable_callback_tracebacks(True)
In almost all cases you should pass values to your SQL queries using the optional parameters argument to db.query(), as described in Passing parameters.
If that option isn't relevant to your use-case you can to quote a string for use with SQLite using the db.quote() method, like so:
>>> db = Database(memory=True)
>>> db.quote("hello")
"'hello'"
>>> db.quote("hello'this'has'quotes")
"'hello''this''has''quotes'"
The sqlite_utils.utils.rows_from_file() helper function can read rows (a sequence of dictionaries) from CSV, TSV, JSON or newline-delimited JSON files.
Sometimes when working with CSV files that include extremely long fields you may see an error that looks like this:
_csv.Error: field larger than field limit (131072)
The Python standard library csv module enforces a field size limit. You can increase that limit using the csv.field_size_limit(new_limit) method (documented here) but if you don't want to pick a new level you may instead want to increase it to the maximum possible.
The maximum possible value for this is not documented, and varies between systems.
Calling sqlite_utils.utils.maximize_csv_field_size_limit() will set the value to the highest possible for the current system:
from sqlite_utils.utils import maximize_csv_field_size_limit maximize_csv_field_size_limit()
If you need to reset to the original value after calling this function you can do so like this:
from sqlite_utils.utils import ORIGINAL_CSV_FIELD_SIZE_LIMIT import csv csv.field_size_limit(ORIGINAL_CSV_FIELD_SIZE_LIMIT)
Sometimes you may find yourself working with data that lacks type information - data from a CSV file for example.
The TypeTracker class can be used to try to automatically identify the most likely types for data that is initially represented as strings.
Consider this example:
import csv, io
csv_file = io.StringIO("id,name\n1,Cleo\n2,Cardi")
rows = list(csv.DictReader(csv_file))
# rows is now this:
# [{'id': '1', 'name': 'Cleo'}, {'id': '2', 'name': 'Cardi'}]
If we insert this data directly into a table we will get a schema that is entirely TEXT columns:
from sqlite_utils import Database db = Database(memory=True) db["creatures"].insert_all(rows) print(db.schema) # Outputs: # CREATE TABLE [creatures] ( # [id] TEXT, # [name] TEXT # );
We can detect the best column types using a TypeTracker instance:
from sqlite_utils.utils import TypeTracker
tracker = TypeTracker()
db["creatures2"].insert_all(tracker.wrap(rows))
print(tracker.types)
# Outputs {'id': 'integer', 'name': 'text'}
We can then apply those types to our new table using the table.transform() method:
db["creatures2"].transform(types=tracker.types) print(db["creatures2"].schema) # Outputs: # CREATE TABLE [creatures2] ( # [id] INTEGER, # [name] TEXT # );
SpatiaLite is a geographic extension to SQLite (similar to PostgreSQL + PostGIS). Using requires finding, loading and initializing the extension, adding geometry columns to existing tables and optionally creating spatial indexes. The utilities here help streamline that setup.
Table and View are both subclasses of Queryable, providing access to the following methods:
This page lists the --help for every sqlite-utils CLI sub-command.
See Running SQL queries.
Usage: sqlite-utils query [OPTIONS] PATH SQL
Execute SQL query and return the results as JSON
Example:
sqlite-utils data.db \
"select * from chickens where age > :age" \
-p age 1 Options:
--attach <TEXT FILE>... Additional databases to attach - specify alias and
filepath
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira,
latex, latex_booktabs, latex_longtable, latex_raw,
mediawiki, mixed_grid, mixed_outline, moinmoin,
orgtbl, outline, pipe, plain, presto, pretty,
psql, rounded_grid, rounded_outline, rst, simple,
simple_grid, simple_outline, textile, tsv,
unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not
escaped strings
-r, --raw Raw output, first column of first row
-p, --param <TEXT TEXT>... Named :parameters for SQL query
--functions TEXT Python code defining one or more custom SQL
functions
--load-extension TEXT Path to SQLite extension, with optional
:entrypoint
-h, --help Show this message and exit.
See Querying data directly using an in-memory database.
Usage: sqlite-utils memory [OPTIONS] [PATHS]... SQL
Execute SQL query against an in-memory database, optionally populated by
imported data
To import data from CSV, TSV or JSON files pass them on the command-line:
sqlite-utils memory one.csv two.json \
"select * from one join two on one.two_id = two.id"
For data piped into the tool from standard input, use "-" or "stdin":
cat animals.csv | sqlite-utils memory - \
"select * from stdin where species = 'dog'"
The format of the data will be automatically detected. You can specify the
format explicitly using :json, :csv, :tsv or :nl (for newline-delimited JSON)
- for example:
cat animals.csv | sqlite-utils memory stdin:csv places.dat:nl \
"select * from stdin where place_id in (select id from places)"
Use --schema to view the SQL schema of any imported files:
sqlite-utils memory animals.csv --schema Options:
--functions TEXT Python code defining one or more custom SQL
functions
--attach <TEXT FILE>... Additional databases to attach - specify alias and
filepath
--flatten Flatten nested JSON objects, so {"foo": {"bar":
1}} becomes {"foo_bar": 1}
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira,
latex, latex_booktabs, latex_longtable, latex_raw,
mediawiki, mixed_grid, mixed_outline, moinmoin,
orgtbl, outline, pipe, plain, presto, pretty,
psql, rounded_grid, rounded_outline, rst, simple,
simple_grid, simple_outline, textile, tsv,
unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not
escaped strings
-r, --raw Raw output, first column of first row
-p, --param <TEXT TEXT>... Named :parameters for SQL query
--encoding TEXT Character encoding for CSV input, defaults to
utf-8
-n, --no-detect-types Treat all CSV/TSV columns as TEXT
--schema Show SQL schema for in-memory database
--dump Dump SQL for in-memory database
--save FILE Save in-memory database to this file
--analyze Analyze resulting tables and output results
--load-extension TEXT Path to SQLite extension, with optional
:entrypoint
-h, --help Show this message and exit.
See Inserting JSON data, Inserting CSV or TSV data, Inserting unstructured data with --lines and --text, Applying conversions while inserting data.
Usage: sqlite-utils insert [OPTIONS] PATH TABLE FILE
Insert records from FILE into a table, creating the table if it does not
already exist.
Example:
echo '{"name": "Lila"}' | sqlite-utils insert data.db chickens -
By default the input is expected to be a JSON object or array of objects.
- Use --nl for newline-delimited JSON objects
- Use --csv or --tsv for comma-separated or tab-separated input
- Use --lines to write each incoming line to a column called "line"
- Use --text to write the entire input to a column called "text"
You can also use --convert to pass a fragment of Python code that will be used
to convert each input.
Your Python code will be passed a "row" variable representing the imported
row, and can return a modified row.
This example uses just the name, latitude and longitude columns from a CSV
file, converting name to upper case and latitude and longitude to floating
point numbers:
sqlite-utils insert plants.db plants plants.csv --csv --convert '
return {
"name": row["name"].upper(),
"latitude": float(row["latitude"]),
"longitude": float(row["longitude"]),
}'
If you are using --lines your code will be passed a "line" variable, and for
--text a "text" variable.
When using --text your function can return an iterator of rows to insert. This
example inserts one record per word in the input:
echo 'A bunch of words' | sqlite-utils insert words.db words - \
--text --convert '({"word": w} for w in text.split())' Options:
--pk TEXT Columns to use as the primary key, e.g. id
--flatten Flatten nested JSON objects, so {"a": {"b": 1}}
becomes {"a_b": 1}
--nl Expect newline-delimited JSON
-c, --csv Expect CSV input
--tsv Expect TSV input
--lines Treat each line as a single value called 'line'
--text Treat input as a single value called 'text'
--convert TEXT Python code to convert each item
--import TEXT Python modules to import
--delimiter TEXT Delimiter to use for CSV files
--quotechar TEXT Quote character to use for CSV/TSV
--sniff Detect delimiter and quote character
--no-headers CSV file has no header row
--encoding TEXT Character encoding for input, defaults to utf-8
--batch-size INTEGER Commit every X records
--alter Alter existing table to add any missing columns
--not-null TEXT Columns that should be created as NOT NULL
--default <TEXT TEXT>... Default value that should be set for a column
-d, --detect-types Detect types for columns in CSV/TSV data
--analyze Run ANALYZE at the end of this operation
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
--silent Do not show progress bar
--ignore Ignore records if pk already exists
--replace Replace records if pk already exists
--truncate Truncate table before inserting records, if table
already exists
-h, --help Show this message and exit.
See Upserting data.
Usage: sqlite-utils upsert [OPTIONS] PATH TABLE FILE
Upsert records based on their primary key. Works like 'insert' but if an
incoming record has a primary key that matches an existing record the existing
record will be updated.
Example:
echo '[
{"id": 1, "name": "Lila"},
{"id": 2, "name": "Suna"}
]' | sqlite-utils upsert data.db chickens - --pk id Options:
--pk TEXT Columns to use as the primary key, e.g. id
[required]
--flatten Flatten nested JSON objects, so {"a": {"b": 1}}
becomes {"a_b": 1}
--nl Expect newline-delimited JSON
-c, --csv Expect CSV input
--tsv Expect TSV input
--lines Treat each line as a single value called 'line'
--text Treat input as a single value called 'text'
--convert TEXT Python code to convert each item
--import TEXT Python modules to import
--delimiter TEXT Delimiter to use for CSV files
--quotechar TEXT Quote character to use for CSV/TSV
--sniff Detect delimiter and quote character
--no-headers CSV file has no header row
--encoding TEXT Character encoding for input, defaults to utf-8
--batch-size INTEGER Commit every X records
--alter Alter existing table to add any missing columns
--not-null TEXT Columns that should be created as NOT NULL
--default <TEXT TEXT>... Default value that should be set for a column
-d, --detect-types Detect types for columns in CSV/TSV data
--analyze Run ANALYZE at the end of this operation
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
--silent Do not show progress bar
-h, --help Show this message and exit.
See Executing SQL in bulk.
Usage: sqlite-utils bulk [OPTIONS] PATH SQL FILE
Execute parameterized SQL against the provided list of documents.
Example:
echo '[
{"id": 1, "name": "Lila2"},
{"id": 2, "name": "Suna2"}
]' | sqlite-utils bulk data.db '
update chickens set name = :name where id = :id
' - Options:
--batch-size INTEGER Commit every X records
--functions TEXT Python code defining one or more custom SQL functions
--flatten Flatten nested JSON objects, so {"a": {"b": 1}} becomes
{"a_b": 1}
--nl Expect newline-delimited JSON
-c, --csv Expect CSV input
--tsv Expect TSV input
--lines Treat each line as a single value called 'line'
--text Treat input as a single value called 'text'
--convert TEXT Python code to convert each item
--import TEXT Python modules to import
--delimiter TEXT Delimiter to use for CSV files
--quotechar TEXT Quote character to use for CSV/TSV
--sniff Detect delimiter and quote character
--no-headers CSV file has no header row
--encoding TEXT Character encoding for input, defaults to utf-8
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Executing searches.
Usage: sqlite-utils search [OPTIONS] PATH DBTABLE Q
Execute a full-text search against this table
Example:
sqlite-utils search data.db chickens lila Options:
-o, --order TEXT Order by ('column' or 'column desc')
-c, --column TEXT Columns to return
--limit INTEGER Number of rows to return - defaults to everything
--sql Show SQL query that would be run
--quote Apply FTS quoting rules to search term
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira, latex,
latex_booktabs, latex_longtable, latex_raw, mediawiki,
mixed_grid, mixed_outline, moinmoin, orgtbl, outline,
pipe, plain, presto, pretty, psql, rounded_grid,
rounded_outline, rst, simple, simple_grid,
simple_outline, textile, tsv, unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not escaped
strings
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Transforming tables.
Usage: sqlite-utils transform [OPTIONS] PATH TABLE
Transform a table beyond the capabilities of ALTER TABLE
Example:
sqlite-utils transform mydb.db mytable \
--drop column1 \
--rename column2 column_renamed Options:
--type <TEXT CHOICE>... Change column type to INTEGER, TEXT, FLOAT or BLOB
--drop TEXT Drop this column
--rename <TEXT TEXT>... Rename this column to X
-o, --column-order TEXT Reorder columns
--not-null TEXT Set this column to NOT NULL
--not-null-false TEXT Remove NOT NULL from this column
--pk TEXT Make this column the primary key
--pk-none Remove primary key (convert to rowid table)
--default <TEXT TEXT>... Set default value for this column
--default-none TEXT Remove default from this column
--drop-foreign-key TEXT Drop foreign key constraint for this column
--sql Output SQL without executing it
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Extracting columns into a separate table.
Usage: sqlite-utils extract [OPTIONS] PATH TABLE COLUMNS...
Extract one or more columns into a separate table
Example:
sqlite-utils extract trees.db Street_Trees species Options:
--table TEXT Name of the other table to extract columns to
--fk-column TEXT Name of the foreign key column to add to the table
--rename <TEXT TEXT>... Rename this column in extracted table
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Showing the schema.
Usage: sqlite-utils schema [OPTIONS] PATH [TABLES]...
Show full schema for this database or for specified tables
Example:
sqlite-utils schema trees.db Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Inserting data from files.
Usage: sqlite-utils insert-files [OPTIONS] PATH TABLE FILE_OR_DIR...
Insert one or more files using BLOB columns in the specified table
Example:
sqlite-utils insert-files pics.db images *.gif \
-c name:name \
-c content:content \
-c content_hash:sha256 \
-c created:ctime_iso \
-c modified:mtime_iso \
-c size:size \
--pk name Options:
-c, --column TEXT Column definitions for the table
--pk TEXT Column to use as primary key
--alter Alter table to add missing columns
--replace Replace files with matching primary key
--upsert Upsert files with matching primary key
--name TEXT File name to use
--text Store file content as TEXT, not BLOB
--encoding TEXT Character encoding for input, defaults to utf-8
-s, --silent Don't show a progress bar
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Analyzing tables.
Usage: sqlite-utils analyze-tables [OPTIONS] PATH [TABLES]...
Analyze the columns in one or more tables
Example:
sqlite-utils analyze-tables data.db trees Options:
-c, --column TEXT Specific columns to analyze
--save Save results to _analyze_tables table
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Converting data in columns.
Usage: sqlite-utils convert [OPTIONS] DB_PATH TABLE COLUMNS... CODE
Convert columns using Python code you supply. For example:
sqlite-utils convert my.db mytable mycolumn \
'"\n".join(textwrap.wrap(value, 10))' \
--import=textwrap
"value" is a variable with the column value to be converted.
Use "-" for CODE to read Python code from standard input.
The following common operations are available as recipe functions:
r.jsonsplit(value, delimiter=',', type=<class 'str'>)
Convert a string like a,b,c into a JSON array ["a", "b", "c"]
r.parsedate(value, dayfirst=False, yearfirst=False, errors=None)
Parse a date and convert it to ISO date format: yyyy-mm-dd
?
- dayfirst=True: treat xx as the day in xx/yy/zz
- yearfirst=True: treat xx as the year in xx/yy/zz
- errors=r.IGNORE to ignore values that cannot be parsed
- errors=r.SET_NULL to set values that cannot be parsed to null
r.parsedatetime(value, dayfirst=False, yearfirst=False, errors=None)
Parse a datetime and convert it to ISO datetime format: yyyy-mm-ddTHH:MM:SS
?
- dayfirst=True: treat xx as the day in xx/yy/zz
- yearfirst=True: treat xx as the year in xx/yy/zz
- errors=r.IGNORE to ignore values that cannot be parsed
- errors=r.SET_NULL to set values that cannot be parsed to null
You can use these recipes like so:
sqlite-utils convert my.db mytable mycolumn \
'r.jsonsplit(value, delimiter=":")' Options:
--import TEXT Python modules to import
--dry-run Show results of running this against first 10
rows
--multi Populate columns for keys in returned
dictionary
--where TEXT Optional where clause
-p, --param <TEXT TEXT>... Named :parameters for where clause
--output TEXT Optional separate column to populate with the
output
--output-type [integer|float|blob|text]
Column type to use for the output column
--drop Drop original column afterwards
-s, --silent Don't show a progress bar
-h, --help Show this message and exit.
See Listing tables.
Usage: sqlite-utils tables [OPTIONS] PATH
List the tables in the database
Example:
sqlite-utils tables trees.db Options:
--fts4 Just show FTS4 enabled tables
--fts5 Just show FTS5 enabled tables
--counts Include row counts per table
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira, latex,
latex_booktabs, latex_longtable, latex_raw, mediawiki,
mixed_grid, mixed_outline, moinmoin, orgtbl, outline,
pipe, plain, presto, pretty, psql, rounded_grid,
rounded_outline, rst, simple, simple_grid,
simple_outline, textile, tsv, unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not escaped
strings
--columns Include list of columns for each table
--schema Include schema for each table
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Listing views.
Usage: sqlite-utils views [OPTIONS] PATH
List the views in the database
Example:
sqlite-utils views trees.db Options:
--counts Include row counts per view
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira, latex,
latex_booktabs, latex_longtable, latex_raw, mediawiki,
mixed_grid, mixed_outline, moinmoin, orgtbl, outline,
pipe, plain, presto, pretty, psql, rounded_grid,
rounded_outline, rst, simple, simple_grid,
simple_outline, textile, tsv, unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not escaped
strings
--columns Include list of columns for each view
--schema Include schema for each view
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Returning all rows in a table.
Usage: sqlite-utils rows [OPTIONS] PATH DBTABLE
Output all rows in the specified table
Example:
sqlite-utils rows trees.db Trees Options:
-c, --column TEXT Columns to return
--where TEXT Optional where clause
-o, --order TEXT Order by ('column' or 'column desc')
-p, --param <TEXT TEXT>... Named :parameters for where clause
--limit INTEGER Number of rows to return - defaults to everything
--offset INTEGER SQL offset to use
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira,
latex, latex_booktabs, latex_longtable, latex_raw,
mediawiki, mixed_grid, mixed_outline, moinmoin,
orgtbl, outline, pipe, plain, presto, pretty,
psql, rounded_grid, rounded_outline, rst, simple,
simple_grid, simple_outline, textile, tsv,
unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not
escaped strings
--load-extension TEXT Path to SQLite extension, with optional
:entrypoint
-h, --help Show this message and exit.
See Listing triggers.
Usage: sqlite-utils triggers [OPTIONS] PATH [TABLES]...
Show triggers configured in this database
Example:
sqlite-utils triggers trees.db Options:
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira, latex,
latex_booktabs, latex_longtable, latex_raw, mediawiki,
mixed_grid, mixed_outline, moinmoin, orgtbl, outline,
pipe, plain, presto, pretty, psql, rounded_grid,
rounded_outline, rst, simple, simple_grid,
simple_outline, textile, tsv, unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not escaped
strings
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Listing indexes.
Usage: sqlite-utils indexes [OPTIONS] PATH [TABLES]...
Show indexes for the whole database or specific tables
Example:
sqlite-utils indexes trees.db Trees Options:
--aux Include auxiliary columns
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--csv Output CSV
--tsv Output TSV
--no-headers Omit CSV headers
-t, --table Output as a formatted table
--fmt TEXT Table format - one of asciidoc, double_grid,
double_outline, fancy_grid, fancy_outline, github,
grid, heavy_grid, heavy_outline, html, jira, latex,
latex_booktabs, latex_longtable, latex_raw, mediawiki,
mixed_grid, mixed_outline, moinmoin, orgtbl, outline,
pipe, plain, presto, pretty, psql, rounded_grid,
rounded_outline, rst, simple, simple_grid,
simple_outline, textile, tsv, unsafehtml, youtrack
--json-cols Detect JSON cols and output them as JSON, not escaped
strings
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Creating an empty database.
Usage: sqlite-utils create-database [OPTIONS] PATH
Create a new empty database file
Example:
sqlite-utils create-database trees.db Options:
--enable-wal Enable WAL mode on the created database
--init-spatialite Enable SpatiaLite on the created database
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Creating tables.
Usage: sqlite-utils create-table [OPTIONS] PATH TABLE COLUMNS...
Add a table with the specified columns. Columns should be specified using
name, type pairs, for example:
sqlite-utils create-table my.db people \
id integer \
name text \
height float \
photo blob --pk id Options:
--pk TEXT Column to use as primary key
--not-null TEXT Columns that should be created as NOT NULL
--default <TEXT TEXT>... Default value that should be set for a column
--fk <TEXT TEXT TEXT>... Column, other table, other column to set as a
foreign key
--ignore If table already exists, do nothing
--replace If table already exists, replace it
--transform If table already exists, try to transform the schema
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Creating indexes.
Usage: sqlite-utils create-index [OPTIONS] PATH TABLE COLUMN...
Add an index to the specified table for the specified columns
Example:
sqlite-utils create-index chickens.db chickens name
To create an index in descending order:
sqlite-utils create-index chickens.db chickens -- -name Options:
--name TEXT Explicit name for the new index
--unique Make this a unique index
--if-not-exists, --ignore Ignore if index already exists
--analyze Run ANALYZE after creating the index
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Configuring full-text search.
Usage: sqlite-utils enable-fts [OPTIONS] PATH TABLE COLUMN...
Enable full-text search for specific table and columns"
Example:
sqlite-utils enable-fts chickens.db chickens name Options:
--fts4 Use FTS4
--fts5 Use FTS5
--tokenize TEXT Tokenizer to use, e.g. porter
--create-triggers Create triggers to update the FTS tables when the
parent table changes.
--replace Replace existing FTS configuration if it exists
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
Usage: sqlite-utils populate-fts [OPTIONS] PATH TABLE COLUMN...
Re-populate full-text search for specific table and columns
Example:
sqlite-utils populate-fts chickens.db chickens name Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
Usage: sqlite-utils rebuild-fts [OPTIONS] PATH [TABLES]...
Rebuild all or specific full-text search tables
Example:
sqlite-utils rebuild-fts chickens.db chickens Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
Usage: sqlite-utils disable-fts [OPTIONS] PATH TABLE
Disable full-text search for specific table
Example:
sqlite-utils disable-fts chickens.db chickens Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Optimize.
Usage: sqlite-utils optimize [OPTIONS] PATH [TABLES]...
Optimize all full-text search tables and then run VACUUM - should shrink the
database file
Example:
sqlite-utils optimize chickens.db Options:
--no-vacuum Don't run VACUUM
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Optimizing index usage with ANALYZE.
Usage: sqlite-utils analyze [OPTIONS] PATH [NAMES]...
Run ANALYZE against the whole database, or against specific named indexes and
tables
Example:
sqlite-utils analyze chickens.db Options:
-h, --help Show this message and exit.
See Vacuum.
Usage: sqlite-utils vacuum [OPTIONS] PATH
Run VACUUM against the database
Example:
sqlite-utils vacuum chickens.db Options:
-h, --help Show this message and exit.
See Dumping the database to SQL.
Usage: sqlite-utils dump [OPTIONS] PATH
Output a SQL dump of the schema and full contents of the database
Example:
sqlite-utils dump chickens.db Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Adding columns.
Usage: sqlite-utils add-column [OPTIONS] PATH TABLE COL_NAME
[[integer|float|blob|text|INTEGER|FLOAT|BLOB|TEXT]]
Add a column to the specified table
Example:
sqlite-utils add-column chickens.db chickens weight float Options:
--fk TEXT Table to reference as a foreign key
--fk-col TEXT Referenced column on that foreign key table - if
omitted will automatically use the primary key
--not-null-default TEXT Add NOT NULL DEFAULT 'TEXT' constraint
--ignore If column already exists, do nothing
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Adding foreign key constraints.
Usage: sqlite-utils add-foreign-key [OPTIONS] PATH TABLE COLUMN [OTHER_TABLE]
[OTHER_COLUMN]
Add a new foreign key constraint to an existing table
Example:
sqlite-utils add-foreign-key my.db books author_id authors id
WARNING: Could corrupt your database! Back up your database file first. Options:
--ignore If foreign key already exists, do nothing
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Adding multiple foreign keys at once.
Usage: sqlite-utils add-foreign-keys [OPTIONS] PATH [FOREIGN_KEY]...
Add multiple new foreign key constraints to a database
Example:
sqlite-utils add-foreign-keys my.db \
books author_id authors id \
authors country_id countries id Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Adding indexes for all foreign keys.
Usage: sqlite-utils index-foreign-keys [OPTIONS] PATH
Ensure every foreign key column has an index on it
Example:
sqlite-utils index-foreign-keys chickens.db Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See WAL mode.
Usage: sqlite-utils enable-wal [OPTIONS] PATH...
Enable WAL for database files
Example:
sqlite-utils enable-wal chickens.db Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
Usage: sqlite-utils disable-wal [OPTIONS] PATH...
Disable WAL for database files
Example:
sqlite-utils disable-wal chickens.db Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Enabling cached counts.
Usage: sqlite-utils enable-counts [OPTIONS] PATH [TABLES]...
Configure triggers to update a _counts table with row counts
Example:
sqlite-utils enable-counts chickens.db Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
Usage: sqlite-utils reset-counts [OPTIONS] PATH
Reset calculated counts in the _counts table
Example:
sqlite-utils reset-counts chickens.db Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
Usage: sqlite-utils duplicate [OPTIONS] PATH TABLE NEW_TABLE
Create a duplicate of this table, copying across the schema and all row data. Options:
--ignore If table does not exist, do nothing
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Dropping tables.
Usage: sqlite-utils drop-table [OPTIONS] PATH TABLE
Drop the specified table
Example:
sqlite-utils drop-table chickens.db chickens Options:
--ignore If table does not exist, do nothing
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Creating views.
Usage: sqlite-utils create-view [OPTIONS] PATH VIEW SELECT
Create a view for the provided SELECT query
Example:
sqlite-utils create-view chickens.db heavy_chickens \
'select * from chickens where weight > 3' Options:
--ignore If view already exists, do nothing
--replace If view already exists, replace it
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Dropping views.
Usage: sqlite-utils drop-view [OPTIONS] PATH VIEW
Drop the specified view
Example:
sqlite-utils drop-view chickens.db heavy_chickens Options:
--ignore If view does not exist, do nothing
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
See Installing packages.
Usage: sqlite-utils install [OPTIONS] PACKAGES...
Install packages from PyPI into the same environment as sqlite-utils Options:
-U, --upgrade Upgrade packages to latest version
-h, --help Show this message and exit.
See Uninstalling packages.
Usage: sqlite-utils uninstall [OPTIONS] PACKAGES...
Uninstall Python packages from the sqlite-utils environment Options:
-y, --yes Don't ask for confirmation
-h, --help Show this message and exit.
See SpatiaLite helpers.
Usage: sqlite-utils add-geometry-column [OPTIONS] DB_PATH TABLE COLUMN_NAME
Add a SpatiaLite geometry column to an existing table. Requires SpatiaLite
extension.
By default, this command will try to load the SpatiaLite extension from usual
paths. To load it from a specific path, use --load-extension. Options:
-t, --type [POINT|LINESTRING|POLYGON|MULTIPOINT|MULTILINESTRING|MULTIPOLYGON|GEOMETRYCOLLECTION|GEOMETRY]
Specify a geometry type for this column.
[default: GEOMETRY]
--srid INTEGER Spatial Reference ID. See
https://spatialreference.org for details on
specific projections. [default: 4326]
--dimensions TEXT Coordinate dimensions. Use XYZ for three-
dimensional geometries.
--not-null Add a NOT NULL constraint.
--load-extension TEXT Path to SQLite extension, with optional
:entrypoint
-h, --help Show this message and exit.
See Adding spatial indexes.
Usage: sqlite-utils create-spatial-index [OPTIONS] DB_PATH TABLE COLUMN_NAME
Create a spatial index on a SpatiaLite geometry column. The table and geometry
column must already exist before trying to add a spatial index.
By default, this command will try to load the SpatiaLite extension from usual
paths. To load it from a specific path, use --load-extension. Options:
--load-extension TEXT Path to SQLite extension, with optional :entrypoint
-h, --help Show this message and exit.
Development of sqlite-utils takes place in the sqlite-utils GitHub repository.
All improvements to the software should start with an issue. Read How I build a feature for a detailed description of the recommended process for building bug fixes or enhancements.
To work on this library locally, first checkout the code. Then create a new virtual environment:
git clone git@github.com:simonw/sqlite-utils cd sqlite-utils python3 -mvenv venv source venv/bin/activate
Or if you are using pipenv:
pipenv shell
Within the virtual environment running sqlite-utils should run your locally editable version of the tool. You can use which sqlite-utils to confirm that you are running the version that lives in your virtual environment.
To install the dependencies and test dependencies:
pip install -e '.[test]'
To run the tests:
pytest
To build the documentation, first install the documentation dependencies:
pip install -e '.[docs]'
Then run make livehtml from the docs/ directory to start a server on port 8000 that will serve the documentation and live-reload any time you make an edit to a .rst file:
cd docs make livehtml
The cog tool is used to maintain portions of the documentation. You can run it like so:
cog -r docs/*.rst
sqlite-utils uses Black for code formatting, and flake8 and mypy for linting and type checking.
Black is installed as part of pip install -e '.[test]' - you can then format your code by running it in the root of the project:
black .
To install mypy and flake8 run the following:
pip install -e '.[flake8,mypy]'
Both commands can then be run in the root of the project like this:
flake8 mypy sqlite_utils
All three of these tools are run by our CI mechanism against every commit and pull request.
If you install Just and pipenv you can use them to manage your local development environment.
To create a virtual environment and install all development dependencies, run:
cd sqlite-utils just init
To run all of the tests and linters:
just
To run tests, or run a specific test module or test by name:
just test # All tests just test tests/test_cli_memory.py # Just this module just test -k test_memory_no_detect_types # Just this test
To run just the linters:
just lint
To apply Black to your code:
just black
To update documentation using Cog:
just cog
To run the live documentation server (this will run Cog first):
just docs
And to list all available commands:
just -l
Releases are performed using tags. When a new release is published on GitHub, a GitHub Actions workflow will perform the following:
To deploy new releases you will need to have push access to the GitHub repository.
sqlite-utils follows Semantic Versioning:
major.minor.patch
We increment major for backwards-incompatible releases.
We increment minor for new features.
We increment patch for bugfix releass.
To release a new version, first create a commit that updates the version number in setup.py and the the changelog with highlights of the new version. An example commit can be seen here:
# Update changelog git commit -m " Release 3.29 Refs #423, #458, #467, #469, #470, #471, #472, #475" -a git push
Referencing the issues that are part of the release in the commit message ensures the name of the release shows up on those issue pages, e.g. here.
You can generate the list of issue references for a specific release by copying and pasting text from the release notes or GitHub changes-since-last-release view into this Extract issue numbers from pasted text tool.
To create the tag for the release, create a new release on GitHub matching the new version number. You can convert the release notes to Markdown by copying and pasting the rendered HTML into this Paste to Markdown tool.
See also the annotated release notes for this release.
Try that out using the Pyodide REPL:
>>> import micropip
>>> await micropip.install("sqlite-utils")
>>> import sqlite_utils
>>> db = sqlite_utils.Database(memory=True)
>>> list(db.query("select 3 * 5"))
[{'3 * 5': 15}]
This release introduces four new utility methods for working with SpatiaLite. Thanks, Chris Amico. (#385)
CLI and Python library improvements to help run ANALYZE after creating indexes or inserting rows, to gain better performance from the SQLite query planner when it runs against indexes.
Three new CLI commands: create-database, analyze and bulk.
More details and examples can be found in the annotated release notes.
This release introduces the new sqlite-utils convert command (#251) and corresponding table.convert(...) Python method (#302). These tools can be used to apply a Python conversion function to one or more columns of a table, either updating the column in place or using transformed data from that column to populate one or more other columns.
This command-line example uses the Python standard library textwrap module to wrap the content of the content column in the articles table to 100 characters:
$ sqlite-utils convert content.db articles content \
'"\n".join(textwrap.wrap(value, 100))' \
--import=textwrap
The same operation in Python code looks like this:
import sqlite_utils, textwrap
db = sqlite_utils.Database("content.db")
db["articles"].convert("content", lambda v: "\n".join(textwrap.wrap(v, 100)))
See the full documentation for the sqlite-utils convert command and the table.convert(...) Python method for more details.
Also in this release:
This release introduces the sqlite-utils memory command, which can be used to load CSV or JSON data into a temporary in-memory database and run SQL queries (including joins across multiple files) directly against that data.
Also new: sqlite-utils insert --detect-types, sqlite-utils dump, table.use_rowid plus some smaller fixes.
This example of sqlite-utils memory retrieves information about the all of the repositories in the Dogsheep organization on GitHub using this JSON API, sorts them by their number of stars and outputs a table of the top five (using -t):
$ curl -s 'https://api.github.com/users/dogsheep/repos' \
| sqlite-utils memory - '
select full_name, forks_count, stargazers_count
from stdin order by stargazers_count desc limit 5
' -t full_name forks_count stargazers_count --------------------------------- ------------- ------------------ dogsheep/twitter-to-sqlite 12 225 dogsheep/github-to-sqlite 14 139 dogsheep/dogsheep-photos 5 116 dogsheep/dogsheep.github.io 7 90 dogsheep/healthkit-to-sqlite 4 85
The tool works against files on disk as well. This example joins data from two CSV files:
$ cat creatures.csv species_id,name 1,Cleo 2,Bants 2,Dori 2,Azi $ cat species.csv id,species_name 1,Dog 2,Chicken $ sqlite-utils memory species.csv creatures.csv '
select * from creatures join species on creatures.species_id = species.id ' [{"species_id": 1, "name": "Cleo", "id": 1, "species_name": "Dog"},
{"species_id": 2, "name": "Bants", "id": 2, "species_name": "Chicken"},
{"species_id": 2, "name": "Dori", "id": 2, "species_name": "Chicken"},
{"species_id": 2, "name": "Azi", "id": 2, "species_name": "Chicken"}]
Here the species.csv file becomes the species table, the creatures.csv file becomes the creatures table and the output is JSON, the default output format.
You can also use the --attach option to attach existing SQLite database files to the in-memory database, in order to join data from CSV or JSON directly against your existing tables.
Full documentation of this new feature is available in Querying data directly using an in-memory database. (#272)
The sqlite-utils insert command can be used to insert data from JSON, CSV or TSV files into a SQLite database file. The new --detect-types option (shortcut -d), when used in conjunction with a CSV or TSV import, will automatically detect if columns in the file are integers or floating point numbers as opposed to treating everything as a text column and create the new table with the corresponding schema. See Inserting CSV or TSV data for details. (#282)
This release adds the ability to execute queries joining data from more than one database file - similar to the cross database querying feature introduced in Datasette 0.55.
This release introduces a new mechanism for speeding up count(*) queries using cached table counts, stored in a _counts table and updated by triggers. This mechanism is described in Cached table counts using triggers, and can be enabled using Python API methods or the new enable-counts CLI command. (#212)
This release introduces a new sqlite-utils search command for searching tables, see Executing searches. (#192)
The table.search() method has been redesigned, see Searching with table.search(). (#197)
The release includes minor backwards-incompatible changes, hence the version bump to 3.0. Those changes, which should not affect most users, are:
Also in this release:
Changes since the 3.0a0 alpha release:
This release introduces two key new capabilities: transform (#114) and extract (#42).
SQLite's ALTER TABLE has several documented limitations. The table.transform() Python method and sqlite-utils transform CLI command work around these limitations using a pattern where a new table with the desired structure is created, data is copied over to it and the old table is then dropped and replaced by the new one.
You can use these tools to change column types, rename columns, drop columns, add and remove NOT NULL and defaults, remove foreign key constraints and more. See the transforming tables (CLI) and transforming tables (Python library) documentation for full details of how to use them.
Sometimes a database table - especially one imported from a CSV file - will contain duplicate data. A Trees table may include a Species column with only a few dozen unique values, when the table itself contains thousands of rows.
The table.extract() method and sqlite-utils extract commands can extract a column - or multiple columns - out into a separate lookup table, and set up a foreign key relationship from the original table.
The Python library extract() documentation describes how extraction works in detail, and Extracting columns into a separate table in the CLI documentation includes a detailed example.
This release handles a bug where replacing rows in FTS tables could result in growing numbers of unnecessary rows in the associated *_fts_docsize table. (#149)
The theme of this release is better tools for working with binary data. The new insert-files command can be used to insert binary files directly into a database table, and other commands have been improved with better support for BLOB columns.
table.create_index() now works for columns that contain spaces. (#85)
table.exists() is now a method, not a property. This was not a documented part of the API before so I'm considering this a non-breaking change. (#83)
Fixed a bug where .upsert(..., hash_id="pk") threw an error (#84).
New feature: sqlite_utils.suggest_column_types([records]) returns the suggested column types for a list of records. See Suggesting column types. (#81).
This replaces the undocumented table.detect_column_types() method.
New feature: conversions={...} can be passed to the .insert() family of functions to specify SQL conversions that should be applied to values that are being inserted or updated. See Converting column values using SQL functions . (#77).
The .upsert() and .upsert_all() methods now raise a sqlite_utils.db.PrimaryKeyRequired exception if you call them without specifying the primary key column using pk= (#73).
This release changes the behaviour of upsert. It's a breaking change, hence 2.0.
The upsert command-line utility and the .upsert() and .upsert_all() Python API methods have had their behaviour altered. They used to completely replace the affected records: now, they update the specified values on existing records but leave other columns unaffected.
See Upserting data using the Python API and Upserting data using the CLI for full details.
If you want the old behaviour - where records were completely replaced - you can use $ sqlite-utils insert ... --replace on the command-line and .insert(..., replace=True) and .insert_all(..., replace=True) in the Python API. See Insert-replacing data using the Python API and Insert-replacing data using the CLI for more.
For full background on this change, see issue #66.
Python library utilities for deleting records (#62)
Option to create triggers to automatically keep FTS tables up-to-date with newly inserted, updated and deleted records. Thanks, Amjith Ramanujam! (#57)
Ability to introspect and run queries against views (#54)
See Listing views.
Support for lookup tables.
db["tablename"].insert(record, alter=True) - see Adding columns automatically using the Python API
New commands for enabling FTS against a table and columns:
sqlite-utils enable-fts db.db mytable col1 col2
See Configuring full-text search.
Handle datetime.date and datetime.time values.
New option for efficiently inserting rows from a CSV:
sqlite-utils insert db.db foo - --csv
Improved support for newline-delimited JSON.
sqlite-utils insert has two new command-line options:
In the Python API, the table.insert_all(...) method can now accept a generator as well as a list of objects. This will be efficiently used to populate the table no matter how many records are produced by the generator.
The Database() constructor can now accept a pathlib.Path object in addition to a string or an existing SQLite connection object.
Two new commands: sqlite-utils csv and sqlite-utils json
These commands execute a SQL query and return the results as CSV or JSON. See Returning CSV or TSV and Returning JSON for more details.
$ sqlite-utils json --help Usage: sqlite-utils json [OPTIONS] PATH SQL
Execute SQL query and return the results as JSON Options:
--nl Output newline-delimited JSON
--arrays Output rows as arrays instead of objects
--help Show this message and exit. $ sqlite-utils csv --help Usage: sqlite-utils csv [OPTIONS] PATH SQL
Execute SQL query and return the results as CSV Options:
--no-headers Exclude headers from CSV output
--help Show this message and exit.
This release implements the sqlite-utils command-line tool with a number of useful subcommands.
The two most useful subcommands are upsert and insert, which allow you to ingest JSON files with one or more records in them, creating the corresponding table with the correct columns if it does not already exist. See Inserting JSON data for more details.
One backwards incompatible change: the db["table"].table_names property is now a method:
A few other changes:
Simon Willison
2018-2022, Simon Willison
| November 1, 2022 |