Ceph s3 select

Overview

The purpose of the s3 select engine is to create an efficient pipe between user client and storage nodes (the engine should be close as possible to storage). It enables the selection of a restricted subset of (structured) data stored in an S3 object using an SQL-like syntax. It also enables for higher level analytic-applications (such as SPARK-SQL), using that feature to improve their latency and throughput.

For example, an s3-object of several GB (CSV file), a user needs to extract a single column filtered by another column. As the following query: select customer-id from s3Object where age>30 and age<65;

Currently the whole s3-object must be retrieved from OSD via RGW before filtering and extracting data. By “pushing down” the query into radosgw, it’s possible to save a lot of network and CPU(serialization / deserialization).

The bigger the object, and the more accurate the query, the better the performance.

Basic workflow

S3-select query is sent to RGW via AWS-CLI

It passes the authentication and permission process as an incoming message (POST). RGWSelectObj_ObjStore_S3::send_response_data is the “entry point”, it handles each fetched chunk according to input object-key. send_response_data is first handling the input query, it extracts the query and other CLI parameters.

Per each new fetched chunk (~4m), RGW executes an s3-select query on it. The current implementation supports CSV objects and since chunks are randomly “cutting” the CSV rows in the middle, those broken-lines (first or last per chunk) are skipped while processing the query. Those “broken” lines are stored and later merged with the next broken-line (belong to the next chunk), and finally processed.

Per each processed chunk an output message is formatted according to AWS specification and sent back to the client. RGW supports the following response: {:event-type,records} {:content-type,application/octet-stream} {:message-type,event}. For aggregation queries the last chunk should be identified as the end of input, following that the s3-select-engine initiates end-of-process and produces an aggregated result.

Basic functionalities

S3select has a definite set of functionalities compliant with AWS.

The implemented software architecture supports basic arithmetic expressions, logical and compare expressions, including nested function calls and casting operators, which enables the user great flexibility.

review the below s3-select-feature-table.

Error Handling

Any error occurs while the input query processing, i.e. parsing phase or execution phase, is returned to client as response error message.
Fatal severity (attached to the exception) will end query execution immediately, other error severity are counted, upon reaching 100, it ends query execution with an error message.

Features Support

Currently only part of AWS select command is implemented, table bellow describes what is currently supported.
The following table describes the current implementation for s3-select functionalities:

Feature

Detailed

Example

Arithmetic operators

^ * / + - ( )

select (int(_1)+int(_2))*int(_9) from stdin;

select ((1+2)*3.14) ^ 2 from stdin;

Compare operators

> < >= <= == !=

select _1,_2 from stdin where (int(1)+int(_3))>int(_5);

logical operator

AND OR

select count(*) from stdin where int(1)>123 and int(_5)<200;

casting operator

int(expression)

select int(_1),int( 1.2 + 3.4) from stdin;

float(expression)

select float(1.2) from stdin;

timestamp(…)

select timestamp(“1999:10:10-12:23:44”) from stdin;

Aggregation Function

sum

select sum(int(_1)) from stdin;

Aggregation Function

min

select min( int(_1) * int(_5) ) from stdin;

Aggregation Function

max

select max(float(_1)),min(int(_5)) from stdin;

Aggregation Function

count

select count(*) from stdin where (int(1)+int(_3))>int(_5);

Timestamp Functions

extract

select count(*) from stdin where extract(“year”,timestamp(_2)) > 1950 and extract(“year”,timestamp(_1)) < 1960;

Timestamp Functions

dateadd

select count(0) from stdin where datediff(“year”,timestamp(_1),dateadd(“day”,366,timestamp(_1))) == 1;

Timestamp Functions

datediff

select count(0) from stdin where datediff(“month”,timestamp(_1),timestamp(_2))) == 2;

Timestamp Functions

utcnow

select count(0) from stdin where datediff(“hours”,utcnow(),dateadd(“day”,1,utcnow())) == 24 ;

String Functions

substr

select count(0) from stdin where int(substr(_1,1,4))>1950 and int(substr(_1,1,4))<1960;

alias support

select int(_1) as a1, int(_2) as a2 , (a1+a2) as a3 from stdin where a3>100 and a3<300;

s3-select function interfaces

Timestamp functions

The timestamp functionalities is partially implemented.
the casting operator( timestamp( string ) ), converts string to timestamp basic type.
Currently it can convert the following pattern yyyy:mm:dd hh:mi:dd
extract( date-part , timestamp) : function return integer according to date-part extract from input timestamp.
supported date-part : year,month,week,day.
dateadd(date-part , integer,timestamp) : function return timestamp, a calculation results of input timestamp and date-part.
supported data-part : year,month,day.
datediff(date-part,timestamp,timestamp) : function return an integer, a calculated result for difference between 2 timestamps according to date-part.
supported date-part : year,month,day,hours.
utcnow() : return timestamp of current time.

Aggregation functions

count() : return integer according to number of rows matching condition(if such exist).
sum(expression) : return a summary of expression per all rows matching condition(if such exist).
max(expression) : return the maximal result for all expressions matching condition(if such exist).
min(expression) : return the minimal result for all expressions matching condition(if such exist).

String functions

substr(string,from,to) : return a string extract from input string according to from,to inputs.

Alias

Alias programming-construct is an essential part of s3-select language, it enables much better programming especially with objects containing many columns or in the case of complex queries.
Upon parsing the statement containing alias construct, it replaces alias with reference to correct projection column, on query execution time the reference is evaluated as any other expression.
There is a risk that self(or cyclic) reference may occur causing stack-overflow(endless-loop), for that concern upon evaluating an alias, it is validated for cyclic reference.
Alias also maintains result-cache, meaning upon using the same alias more than once, it’s not evaluating the same expression again(it will return the same result),instead it uses the result from cache.
Of Course, per each new row the cache is invalidated.

Sending Query to RGW

Any http-client can send s3-select request to RGW, it must be compliant with AWS Request syntax.
Sending s3-select request to RGW using AWS cli, should follow AWS command reference.
bellow is an example for it.
aws --endpoint-url http://localhost:8000 s3api select-object-content
 --bucket {BUCKET-NAME}
 --expression-type 'SQL'
 --input-serialization
 '{"CSV": {"FieldDelimiter": "," , "QuoteCharacter": "\"" , "RecordDelimiter" : "\n" , "QuoteEscapeCharacter" : "\\" , "FileHeaderInfo": "USE" }, "CompressionType": "NONE"}'
 --output-serialization '{"CSV": {}}'
 --key {OBJECT-NAME}
 --expression "select count(0) from stdin where int(_1)<10;" output.csv

Syntax

Input serialization (Implemented), it let the user define the CSV definitions; the default values are {\n} for row-delimiter {,} for field delimiter, {”} for quote, {\} for escape characters.
it handle the csv-header-info, the first row in input object containing the schema.
Output serialization is currently not implemented, the same for compression-type.
s3-select engine contain a CSV parser, which parse s3-objects as follows.
- each row ends with row-delimiter.
- field-separator separates between adjacent columns, successive field separator define NULL column.
- quote-character overrides field separator, meaning , field separator become as any character between quotes.
- escape character disables any special characters, except for row delimiter.
Below are examples for CSV parsing rules.

CSV parsing behavior

Feature

Description

input ==> tokens

NULL

successive field delimiter

,,1,,2, ==> {null}{null}{1}{null}{2}{null}

QUOTE

quote character overrides field delimiter

11,22,”a,b,c,d”,last ==> {11}{22}{“a,b,c,d”}{last}

Escape

escape char overrides meta-character. escape removed

11,22,str=\”abcd\”\,str2=\”123\”,last ==> {11}{22}{str=”abcd”,str2=”123”}{last}

row delimiter

no close quote, row delimiter is closing line

11,22,a=”str,44,55,66 ==> {11}{22}{a=”str,44,55,66}

csv header info

FileHeaderInfo tag

“USE” value means each token on first line is column-name, “IGNORE” value means to skip the first line

BOTO3

using BOTO3 is “natural” and easy due to AWS-cli support.
def run_s3select(bucket,key,query,column_delim=",",row_delim="\n",quot_char='"',esc_char='\\',csv_header_info="NONE"):
   s3 = boto3.client('s3',
       endpoint_url=endpoint,
       aws_access_key_id=access_key,
       region_name=region_name,
       aws_secret_access_key=secret_key)



   r = s3.select_object_content(
       Bucket=bucket,
       Key=key,
       ExpressionType='SQL',
       InputSerialization = {"CSV": {"RecordDelimiter" : row_delim, "FieldDelimiter" : column_delim,"QuoteEscapeCharacter": esc_char, "QuoteCharacter": quot_char, "FileHeaderInfo": csv_header_info}, "CompressionType": "NONE"},
       OutputSerialization = {"CSV": {}},
       Expression=query,)

   result = ""
   for event in r['Payload']:
       if 'Records' in event:
           records = event['Records']['Payload'].decode('utf-8')
           result += records

   return result




 run_s3select(
 "my_bucket",
 "my_csv_object",
 "select int(_1) as a1, int(_2) as a2 , (a1+a2) as a3 from stdin where a3>100 and a3<300;")