Processes
In Nextflow, a process is the basic processing primitive to execute a user script.
The process definition starts with the keyword process, followed by process name and finally the process body
delimited by curly brackets. The process body must contain a string which represents the command or, more generally,
a script that is executed by it. A basic process looks like the following example:
process sayHello {
"""
echo 'Hello world!' > file
"""
}
A process may contain any of the following definition blocks: directives, inputs, outputs, when clause, and the process script. The syntax is defined as follows:
process < name > {
[ directives ]
input:
< process inputs >
output:
< process outputs >
when:
< condition >
[script|shell|exec]:
< user script to be executed >
}
Script
The script block defines, as a string expression, the script that is executed by the process.
A process may contain only one script block, and it must be the final statement in the process block
(unless script: is explicitly declared).
The script string is executed as a Bash script in the host environment. It can be any command or script that you would normally execute on the command line or in a Bash script. Naturally, the script may only use commands that are available in the host environment.
The script block can be a simple string or a multi-line string. The latter approach makes it easier to write scripts with multiple commands spanning multiple lines. For example:
process doMoreThings {
"""
blastp -db $db -query query.fa -outfmt 6 > blast_result
cat blast_result | head -n 10 | cut -f 2 > top_hits
blastdbcmd -db $db -entry_batch top_hits > sequences
"""
}
As explained in the script tutorial section, strings can be defined using single-quotes or double-quotes, and multi-line strings are defined by three single-quote or three double-quote characters.
There is a subtle but important difference between them. Like in Bash, strings delimited by a " character support
variable substitutions, while strings delimited by ' do not.
In the above code fragment, the $db variable is replaced by the actual value defined elsewhere in the
pipeline script.
Warning
Since Nextflow uses the same Bash syntax for variable substitutions in strings, you must manage them carefully depending on whether you want to evaluate a Nextflow variable or a Bash variable.
When you need to access a system environment variable in your script, you have two options.
If you don’t need to access any Nextflow variables, you can define your script block with single-quotes:
process printPath {
'''
echo The path is: $PATH
'''
}
Otherwise, you can define your script with double-quotes and escape the system environment variables by
prefixing them with a back-slash \ character, as shown in the following example:
process doOtherThings {
"""
blastp -db \$DB -query query.fa -outfmt 6 > blast_result
cat blast_result | head -n $MAX | cut -f 2 > top_hits
blastdbcmd -db \$DB -entry_batch top_hits > sequences
"""
}
In this example, $MAX is a Nextflow variable that must be defined elsewhere in the pipeline script.
Nextflow replaces it with the actual value before executing the script. Meanwhile, $DB is a Bash variable
that must exist in the execution environment, and Bash will replace it with the actual value during execution.
Tip
Alternatively, you can use the Shell block definition, which allows a script to contain both Bash and Nextflow variables without having to escape the first.
Scripts à la carte
The process script is interpreted by Nextflow as a Bash script by default, but you are not limited to Bash.
You can use your favourite scripting language (Perl, Python, R, etc), or even mix them in the same pipeline.
A pipeline may be composed of processes that execute very different tasks. With Nextflow, you can choose the scripting language that best fits the task performed by a given process. For example, for some processes R might be more useful than Perl, whereas for others you may need to use Python because it provides better access to a library or an API, etc.
To use a language other than Bash, simply start your process script with the corresponding shebang. For example:
process perlStuff {
"""
#!/usr/bin/perl
print 'Hi there!' . '\n';
"""
}
process pythonStuff {
"""
#!/usr/bin/python
x = 'Hello'
y = 'world!'
print "%s - %s" % (x,y)
"""
}
Tip
Since the actual location of the interpreter binary file can differ across platforms,
it is wise to use the env command followed by the interpreter name, e.g.
#!/usr/bin/env perl, instead of the absolute path, in order to make your script
more portable.
Conditional scripts
So far, our script block has always been a simple string expression, but in reality, the script block is
just Groovy code that returns a string. This means that you can write arbitrary Groovy code to determine
the script to execute, as long as the final statement is a string (remember that the return keyword is optional in Groovy).
For example, you can use flow control statements (if, switch, etc) to execute a different script based on
the process inputs. The only difference here is that you must explicitly declare the script: block, whereas before
it was not required. Here is an example:
mode = 'tcoffee'
process align {
input:
path sequences
script:
if( mode == 'tcoffee' )
"""
t_coffee -in $sequences > out_file
"""
else if( mode == 'mafft' )
"""
mafft --anysymbol --parttree --quiet $sequences > out_file
"""
else if( mode == 'clustalo' )
"""
clustalo -i $sequences -o out_file
"""
else
error "Invalid alignment mode: ${mode}"
}
In the above example, the process will execute one of the script fragments depending on the value of the mode parameter.
By default it will execute the tcoffee command, but changing the mode variable will cause a different branch to be executed.
Template
Process scripts can be externalised to template files, which can be reused across different processes and tested independently from the overall pipeline execution.
A template is simply a shell script file that Nextflow is able to execute by using the template function
as shown below:
process templateExample {
input:
val STR
script:
template 'my_script.sh'
}
workflow {
Channel.of('this', 'that') | templateExample
}
By default, Nextflow looks for the my_script.sh template file in the templates directory located alongside the
Nextflow script and/or the module script in which the process is defined. Any other location can be specified by using
an absolute template path.
The template script may contain any code that can be executed by the underlying environment. For example:
#!/bin/bash
echo "process started at `date`"
echo $STR
echo "process completed"
Tip
The dollar character ($) is interpreted as a Nextflow variable when the script is run as a Nextflow template,
whereas it is evaluated as a Bash variable when run as a Bash script. This can be very useful for testing
your script independently from Nextflow execution. You only need to provide a Bash environment variable for each
of the Nextflow variables that are referenced in your script. For example, it would be possible to execute the above
script with the following command in the terminal: STR='foo' bash templates/my_script.sh
Tip
As a best practice, the template script should not contain any \$ escaped variables, because these variables
will not be evaluated properly when the script is executed directly.
Shell
The shell block is a string expression that defines the script that is executed by the process.
It is an alternative to the Script definition with one important difference: it uses
the exclamation mark ! character, instead of the usual dollar $ character, to denote Nextflow variables.
This way, it is possible to use both Nextflow and Bash variables in the same script without having to escape the latter, which makes process scripts easier to read and maintain. For example:
process myTask {
input:
val str
shell:
'''
echo "User $USER says !{str}"
'''
}
workflow {
Channel.of('Hello', 'Hola', 'Bonjour') | myTask
}
In the above example, $USER is treated as a Bash variable, while !{str} is treated as a Nextflow variable.
Note
Shell script definitions require the use of single-quote
'delimited strings. When using double-quote"delimited strings, dollar variables are interpreted as Nextflow variables as usual. See String interpolation.Variables prefixed with
!must always be enclosed in curly brackets, i.e.!{str}is a valid variable whereas!stris ignored.Shell scripts support the use of the Template mechanism. The same rules are applied to the variables defined in the script template.
Native execution
Nextflow processes can also execute native Groovy code as the task itself, using the exec block. Whereas the
script block defines a script to be executed, the exec block defines Groovy code to be executed directly.
For example:
process simpleSum {
input:
val x
exec:
println "Hello Mr. $x"
}
workflow {
Channel.of('a', 'b', 'c') | simpleSum
}
will display:
Hello Mr. b
Hello Mr. a
Hello Mr. c
Stub
Warning
This feature is experimental. It may change in future versions.
As of version 20.11.0-edge, you can define a command stub, which replaces the actual process command when
the -stub-run or -stub command line option:
process INDEX {
input:
path transcriptome
output:
path 'index'
script:
"""
salmon index --threads $task.cpus -t $transcriptome -i index
"""
stub:
"""
mkdir index
touch index/seq.bin
touch index/info.json
touch index/refseq.bin
"""
}
This feature makes it easier to quickly prototype the workflow logic without using the real commands. The developer can use it to provide a dummy script that mimics the execution of the real one in a quicker manner. In other words, it is a way to perform a dry-run.
Tip
The stub block can be defined before or after the script block.
When the pipeline is executed with the -stub-run option and a process’s stub
is not defined, the script block is executed.
Inputs
The input block allows you to define the input channels of a process, similar to function arguments.
A process may have at most one input block, and it must contain at least one input.
The input block follows the syntax shown below:
input:
<input qualifier> <input name>
An input definition consists of a qualifier and a name. The input qualifier defines the type of data to be received. This information is used by Nextflow to apply the semantic rules associated with each qualifier, and handle it properly depending on the target execution platform (grid, cloud, etc).
When a process is invoked in a workflow block, it must be provided a channel for each channel in the process input block, similar to calling a function with specific arguments. The examples provided in the following sections demonstrate how a process is invoked with input channels.
The available input qualifiers are listed in the following table:
Qualifier |
Semantic |
|---|---|
|
Access the input value by name in the process script. |
|
(DEPRECATED) Handle the input value as a file, staging it properly in the execution context. |
|
Handle the input value as a path, staging the file properly in the execution context. |
|
Use the input value to set an environment variable in the process script. |
|
Forward the input value to the process |
|
Handle a group of input values having any of the above qualifiers. |
|
Execute the process for each element in the input collection. |
Input type val
The val qualifier accepts any data type. It can be accessed in the process script
by using the specified input name, as shown in the following example:
process basicExample {
input:
val x
"echo process job $x"
}
workflow {
def num = Channel.of(1,2,3)
basicExample(num)
}
In the above example, the process is executed three times: once for each value emitted by the num channel.
The resulting output is similar to the one shown below:
process job 3
process job 1
process job 2
Note
While channels do emit items in the order that they are received, processes do not
necessarily process items in the order that they are received. In the above example,
the value 3 was processed before the others.
Note
When the process declares exactly one input, the pipe | operator can be used to provide inputs to the process,
instead of passing it as a parameter. Both methods have identical semantics:
process basicExample {
input:
val x
"echo process job $x"
}
workflow {
Channel.of(1,2,3) | basicExample
}
Input type file
Note
The file qualifier was the standard way to handle input files prior to Nextflow 19.10.0. In later versions
of Nextflow, the path qualifier should be preferred over file.
The file qualifier is identical to path, with one important difference. When a file input
receives a value that is not a file, it automatically converts the value to a string and saves it to a
temporary file. This behavior is useful in some cases, but tends to be confusing in general. The path
qualifier instead interprets string values as the path location of the input file and automatically
converts to a file object.
Input type path
The path qualifier allows you to provide input files to the process execution context. Nextflow will stage
the files into the process execution directory, and they can be accessed in the script by using the specified
input name. For example:
process blastThemAll {
input:
path query_file
"blastp -query ${query_file} -db nr"
}
workflow {
def proteins = Channel.fromPath( '/some/path/*.fa' )
blastThemAll(proteins)
}
In the above example, all the files ending with the suffix .fa are sent over the channel proteins.
These files are received by the process, which executes a BLAST query on each of them.
It’s worth noting that in the above example, the name of the file in the file-system is not used. You can access the file without even knowing its name, because you can reference it in the process script by the input name.
There may be cases where your task needs to use a file whose name is fixed, i.e. it does not have to change along
with the actual provided file. In this case, you can specify a fixed name with the name attribute in the
input file parameter definition, as shown in the following example:
input:
path query_file, name: 'query.fa'
or, using a shorter syntax:
input:
path 'query.fa'
The previous example can be re-written as shown below:
process blastThemAll {
input:
path 'query.fa'
"blastp -query query.fa -db nr"
}
workflow {
def proteins = Channel.fromPath( '/some/path/*.fa' )
blastThemAll(proteins)
}
In this example, each file received by the process is staged with the name query.fa
in a different execution context (i.e. the folder where a task is executed).
Tip
This feature allows you to execute the process command multiple times without worrying about the file names changing. In other words, Nextflow helps you write pipeline tasks that are self-contained and decoupled from the execution environment. As a best practice, you should avoid referencing files in your process script other than those defined in your input block.
Channel factories like Channel.fromPath produce file objects, but a path input can also
accept a string literal path. The string value should be an absolute path, i.e. it must be
prefixed with a / character or a supported URI protocol (file://, http://, s3://, etc),
and it cannot contain special characters (\n, etc).
process foo {
input:
path x
"""
your_command --in $x
"""
}
workflow {
foo('/some/data/file.txt')
}
The stageAs option allows you to control how the file should be named in the task work
directory. You can provide a specific name or a pattern as described in the Multiple input files
section:
process foo {
input:
path x, stageAs: 'data.txt'
"""
your_command --in data.txt
"""
}
workflow {
foo('/some/data/file.txt')
}
Multiple input files
A path input can also accept a collection of files instead of a single value.
In this case, the input variable will be a Groovy list, and you can use it as such.
When the input has a fixed file name and a collection of files is received by the process, the file name will be appended with a numerical suffix representing its ordinal position in the list. For example:
process blastThemAll {
input:
path 'seq'
"echo seq*"
}
workflow {
def fasta = Channel.fromPath( "/some/path/*.fa" ).buffer(size: 3)
blastThemAll(fasta)
}
will output:
seq1 seq2 seq3
seq1 seq2 seq3
...
The target input file name may contain the * and ? wildcards, which can be used
to control the name of staged files. The following table shows how the wildcards are
replaced depending on the cardinality of the received input collection.
Cardinality |
Name pattern |
Staged file names |
|---|---|---|
any |
|
named as the source file |
1 |
|
|
1 |
|
|
1 |
|
|
many |
|
|
many |
|
|
many |
|
|
many |
|
named as the source file, created in |
many |
|
named as the source file, created in a progressively indexed subdirectory e.g. |
many |
|
(as above) |
The following example shows how a wildcard can be used in the input file definition:
process blastThemAll {
input:
path 'seq?.fa'
"cat seq1.fa seq2.fa seq3.fa"
}
workflow {
def fasta = Channel.fromPath( "/some/path/*.fa" ).buffer(size: 3)
blastThemAll(fasta)
}
Note
Rewriting input file names according to a named pattern is an extra feature and not at all required.
The normal file input syntax introduced in the Input type path section is valid for collections of
multiple files as well. To handle multiple input files while preserving the original file names, use a variable
identifier or the * wildcard.
Dynamic input file names
When the input file name is specified by using the name option or a string literal, you
can also use other input values as variables in the file name string. For example:
process simpleCount {
input:
val x
path "${x}.fa"
"""
cat ${x}.fa | grep '>'
"""
}
In the above example, the input file name is determined by the current value of the x input value.
This approach allows input files to be staged in the task directory with a name that is coherent with the current execution context.
Tip
In most cases, you won’t need to use dynamic file names, because each task is executed in its own directory, and input files are automatically staged into this directory by Nextflow. This behavior guarantees that input files with the same name won’t overwrite each other.
An example of when you may have to deal with that is when you have many input files in a task,
and some of these files may have the same filename. In this case, a solution would be to use
the option stageAs.
Input type env
The env qualifier allows you to define an environment variable in the process execution context based
on the input value. For example:
process printEnv {
input:
env HELLO
'''
echo $HELLO world!
'''
}
workflow {
Channel.of('hello', 'hola', 'bonjour', 'ciao') | printEnv
}
hello world!
ciao world!
bonjour world!
hola world!
Input type stdin
The stdin qualifier allows you to forward the input value to the
standard input
of the process script. For example:
process printAll {
input:
stdin str
"""
cat -
"""
}
workflow {
Channel.of('hello', 'hola', 'bonjour', 'ciao')
| map { it + '\n' }
| printAll
}
will output:
hola
bonjour
ciao
hello
Input type set
Warning
The set input type has been deprecated. Use tuple instead.
Input type tuple
The tuple qualifier allows you to group multiple values into a single input definition. It can be useful
when a channel emits tuples of values that need to be handled separately. Each element in the tuple
is associated with a corresponding element in the tuple definition. For example:
process tupleExample {
input:
tuple val(x), path('latin.txt')
"""
echo "Processing $x"
cat - latin.txt > copy
"""
}
workflow {
Channel.of( [1, 'alpha'], [2, 'beta'], [3, 'delta'] ) | tupleExample
}
In the above example, the tuple input consists of the value x and the file latin.txt.
A tuple definition may contain any of the following qualifiers, as previously described:
val, env, path and stdin. Files specified with the path qualifier are treated
exactly the same as standalone path inputs.
Input repeaters (each)
The each qualifier allows you to repeat the execution of a process for each item in a collection,
each time a new value is received. For example:
process alignSequences {
input:
path seq
each mode
"""
t_coffee -in $seq -mode $mode > result
"""
}
workflow {
sequences = Channel.fromPath('*.fa')
methods = ['regular', 'expresso', 'psicoffee']
alignSequences(sequences, methods)
}
In the above example, each time a file of sequences is emitted from the sequences channel,
the process executes three tasks, each running a T-coffee alignment with a different value for
the mode parameter. This behavior is useful when you need to repeat the same task over a given
set of parameters.
Input repeaters can be applied to files as well. For example:
process alignSequences {
input:
path seq
each mode
each path(lib)
"""
t_coffee -in $seq -mode $mode -lib $lib > result
"""
}
workflow {
sequences = Channel.fromPath('*.fa')
methods = ['regular', 'expresso']
libraries = [ file('PQ001.lib'), file('PQ002.lib'), file('PQ003.lib') ]
alignSequences(sequences, methods, libraries)
}
In the above example, each sequence input file emitted by the sequences channel triggers six alignment tasks,
three with the regular method against each library file, and three with the expresso method.
Note
When multiple repeaters are defined, the process is executed for each combination of them.
Multiple input channels
A key feature of processes is the ability to handle inputs from multiple channels.
When two or more channels are declared as process inputs, the process waits until there is a complete input configuration, i.e. until it receives a value from each input channel. When this condition is satisfied, the process consumes a value from each channel and launches a new task, repeating this logic until one or more channels are empty.
As a result, channel values are consumed sequentially and any empty channel will cause the process to wait, even if the other channels have values.
For example:
process foo {
input:
val x
val y
script:
"""
echo $x and $y
"""
}
workflow {
x = Channel.of(1, 2)
y = Channel.of('a', 'b', 'c')
foo(x, y)
}
The process foo is executed two times because the x channel emits only two values, therefore
the c element is discarded. It outputs:
1 and a
2 and b
A different semantic is applied when using a value channel. This kind of channel is created by the Channel.value factory method or implicitly when a process is invoked with an argument that is not a channel. By definition, a value channel is bound to a single value and it can be read an unlimited number of times without consuming its content. Therefore, when mixing a value channel with one or more (queue) channels, it does not affect the process termination because the underlying value is applied repeatedly.
To better understand this behavior, compare the previous example with the following one:
process bar {
input:
val x
val y
script:
"""
echo $x and $y
"""
}
workflow {
x = Channel.value(1)
y = Channel.of('a', 'b', 'c')
foo(x, y)
}
The above example executes the bar process three times because x is a value channel, therefore
its value can be read as many times as needed. The process termination is determined by the contents of y.
It outputs:
1 and a
1 and b
1 and c
Note
In general, multiple input channels should be used to process combinations of different inputs,
using the each qualifier or value channels. Having multiple queue channels as inputs is equivalent
to using the merge operator, which is not recommended as it may lead to inputs being combined in
a non-deterministic way.
See also: Channel types.
Outputs
The output block allows you to define the output channels of a process, similar to function outputs.
A process may have at most one output block, and it must contain at least one output.
The output block follows the syntax shown below:
output:
<output qualifier> <output name> [, <option>: <option value>]
An output definition consists of a qualifier and a name. Some optional attributes can also be specified.
When a process is invoked, each process output is returned as a channel. The examples provided in the following sections demonstrate how to access the output channels of a process.
The available output qualifiers are listed in the following table:
Qualifier |
Semantic |
|---|---|
|
Emit the variable with the specified name. |
|
(DEPRECATED) Emit a file produced by the process with the specified name. |
|
Emit a file produced by the process with the specified name. |
|
Emit the variable defined in the process environment with the specified name. |
|
Emit the |
|
Emit multiple values. |
Output type val
The val qualifier allows you to output any Nextflow variable defined in the process. A common use case is to
output a variable that was defined in the input block, as shown in the following example:
process foo {
input:
each x
output:
val x
"""
echo $x > file
"""
}
workflow {
methods = ['prot', 'dna', 'rna']
receiver = foo(methods)
receiver.view { "Received: $it" }
}
The output value can be a value literal, an input variable, any other Nextflow variable in the process scope, or a value expression. For example:
process foo {
input:
path infile
output:
val x
val 'BB11'
val "${infile.baseName}.out"
script:
x = infile.name
"""
cat $x > file
"""
}
workflow {
ch_dummy = Channel.fromPath('*').first()
(ch_var, ch_str, ch_exp) = foo(ch_dummy)
ch_var.view { "ch_var: $it" }
ch_str.view { "ch_str: $it" }
ch_exp.view { "ch_exp: $it" }
}
Output type file
Note
The file qualifier was the standard way to handle input files prior to Nextflow 19.10.0.
In later versions of Nextflow, the path qualifier should be preferred over file.
The file qualifier is similar to path, but with some differences. The file qualifier
interprets : as a path separator, therefore file 'foo:bar' captures two files named foo
and bar, whereas path 'foo:bar' captures a single file named foo:bar. Additionally, file
does not support all of the extra options provided by path.
Output type path
The path qualifier allows you to output one or more files produced by the process. For example:
process randomNum {
output:
path 'result.txt'
'''
echo $RANDOM > result.txt
'''
}
workflow {
numbers = randomNum()
numbers.view { "Received: ${it.text}" }
}
In the above example, the randomNum process creates a file named result.txt which contains a random number.
Since a path output with the same name is declared, that file is emitted by the corresponding output channel.
A downstream process with a compatible input channel will be able to receive it.
A path output can be defined with any of the additional options defined in the following table.
Name |
Description |
|---|---|
|
When |
|
When |
|
When |
|
Type of paths returned, either |
|
Maximum number of directory levels to visit (default: no limit) |
|
When |
Multiple output files
When an output file name contains a * or ? wildcard character, it is interpreted as a glob path matcher.
This allows you to capture multiple files into a list and emit the list as a single value. For example:
process splitLetters {
output:
path 'chunk_*'
'''
printf 'Hola' | split -b 1 - chunk_
'''
}
workflow {
splitLetters
| flatten
| view { "File: ${it.name} => ${it.text}" }
}
It prints:
File: chunk_aa => H
File: chunk_ab => o
File: chunk_ac => l
File: chunk_ad => a
By default, all the files matching the specified glob pattern are emitted as a single list. However, as the above example demonstrates, the flatten operator can be used to transform the list of files into a channel that emits each file individually.
Some caveats on glob pattern behavior:
Input files are not included (unless
includeInputsistrue)Directories are included, unless the
**pattern is used to recurse through directories
Warning
Although the input files matching a glob output declaration are not included in the
resulting output channel, these files may still be transferred from the task scratch directory
to the original task work directory. Therefore, to avoid unnecessary file copies, avoid using
loose wildcards when defining output files, e.g. path '*'. Instead, use a prefix or a suffix
to restrict the set of matching files to only the expected ones, e.g. path 'prefix_*.sorted.bam'.
Read more about glob syntax at the following link What is a glob?
Dynamic output file names
When an output file name needs to be expressed dynamically, it is possible to define it using a dynamic
string which references variables in the input block or in the script global context.
For example:
process align {
input:
val species
path seq
output:
path "${species}.aln"
"""
t_coffee -in $seq > ${species}.aln
"""
}
In the above example, each process execution produces an alignment file whose name depends
on the actual value of the species input.
Tip
The management of output files in Nextflow is often misunderstood.
With other tools it is generally necessary to organize the output files into some kind of directory structure or to guarantee a unique file name scheme, so that result files don’t overwrite each other and so they can be referenced unequivocally by downstream tasks.
With Nextflow, in most cases, you don’t need to manage the naming of output files, because each task is executed in its own unique directory, so files produced by different tasks can’t overwrite each other. Also, metadata can be associated with outputs by using the tuple output qualifier, instead of including them in the output file name.
One example in which you’d need to manage the naming of output files is when you use the publishDir directive
to have output files also in a specific path of your choice. If two tasks have the same filename for their output and you want them
to be in the same path specified by publishDir, the last task to finish will overwrite the output of the task that finished before.
You can dynamically change that by adding the saveAs option to your publishDir directive.
To sum up, the use of output files with static names over dynamic ones is preferable whenever possible, because it will result in simpler and more portable code.
Output type env
The env qualifier allows you to output a variable defined in the process execution environment:
process myTask {
output:
env FOO
script:
'''
FOO=$(ls -la)
'''
}
workflow {
myTask | view { "directory contents: $it" }
}
Output type stdout
The stdout qualifier allows you to output the stdout of the executed process:
process sayHello {
output:
stdout
"""
echo Hello world!
"""
}
workflow {
sayHello | view { "I say... $it" }
}
Output type set
Warning
The set output type has been deprecated. Use tuple instead.
Output type tuple
The tuple qualifier allows you to output multiple values in a single channel. It is useful
when you need to associate outputs with metadata, for example:
process blast {
input:
val species
path query
output:
tuple val(species), path('result')
script:
"""
blast -db nr -query $query > result
"""
}
workflow {
ch_species = Channel.from('human', 'cow', 'horse')
ch_query = Channel.fromPath('*.fa')
blast(ch_species, ch_query)
}
In the above example, a blast task is executed for each pair of species and query that are received.
Each task produces a new tuple containing the value for species and the file result.
A tuple definition may contain any of the following qualifiers, as previously described:
val, path, env and stdout. Files specified with the path qualifier are treated
exactly the same as standalone path inputs.
Optional outputs
In most cases, a process is expected to produce an output for each output definition. However,
there are situations where it is valid for a process to not generate output. In these cases,
optional: true may be added to the output definition, which tells Nextflow not to fail the
process if the declared output is not produced:
output:
path("output.txt"), optional: true
In this example, the process is normally expected to produce an output.txt file, but in the
cases where the file is legitimately missing, the process does not fail. The output channel will
only contain values for those processes that produce output.txt.
When
The when block allows you to define a condition that must be satisfied in order to execute the process.
The condition can be any expression that returns a boolean value.
It can be useful to enable/disable the process execution depending on the state of various inputs and parameters. For example:
process find {
input:
path proteins
val dbtype
when:
proteins.name =~ /^BB11.*/ && dbtype == 'nr'
script:
"""
blastp -query $proteins -db nr
"""
}
Tip
As a best practice, it is better to define such control flow logic in the workflow block, i.e. with an if statement
or with channel operators, to make the process more portable.
Directives
Directives are optional settings that affect the execution of the current process.
They must be entered at the top of the process body, before any other declaration blocks (input, output, etc),
and have the following syntax:
name value [, value2 [,..]]
Some directives are generally available to all processes, while others depend on the executor currently defined.
accelerator
The accelerator directive allows you to specify the hardware accelerator requirement for the task execution
e.g. GPU processor. For example:
process foo {
accelerator 4, type: 'nvidia-tesla-k80'
script:
"""
your_gpu_enabled --command --line
"""
}
The above examples will request 4 GPUs of type nvidia-tesla-k80.
Note
This directive is only used by certain executors. Refer to the Executors page to see which executors support this directive.
Note
The accelerator type option depends on the target execution platform. Refer to the
platform-specific documentation for details on the available accelerators:
afterScript
The afterScript directive allows you to execute a custom (Bash) snippet immediately after the main process has run.
This may be useful to clean up your staging area.
Note
When combined with the container directive, the afterScript will be
executed outside the specified container. In other words, the afterScript is always executed in the host environment.
beforeScript
The beforeScript directive allows you to execute a custom (Bash) snippet before the main process script is run.
This may be useful to initialise the underlying cluster environment or for other custom initialisation.
For example:
process foo {
beforeScript 'source /cluster/bin/setup'
"""
echo bar
"""
}
Note
When combined with the container directive, the beforeScript will be
executed outside the specified container. In other words, the beforeScript is always executed in the host environment.
cache
The cache directive allows you to store the process results to a local cache. When the cache is enabled and
the pipeline is launched with the resume option, any following attempt to execute the process,
along with the same inputs, will cause the process execution to be skipped, producing the stored data as
the actual results.
The caching feature generates a unique key by indexing the process script and inputs. This key is used to identify univocally the outputs produced by the process execution.
The cache is enabled by default, you can disable it for a specific process by setting the cache
directive to false. For example:
process noCacheThis {
cache false
script:
<your command string here>
}
The cache directive possible values are shown in the following table:
Value |
Description |
|---|---|
|
Disable cache feature. |
|
Enable caching. Cache keys are created indexing input files meta-data information (name, size and last update timestamp attributes). |
|
Enable caching. Cache keys are created indexing input files content. |
|
Enable caching. Cache keys are created indexing input files path and size attributes (this policy provides a workaround for incorrect caching invalidation observed on shared file systems due to inconsistent files timestamps). |
clusterOptions
The clusterOptions directive allows the usage of any native configuration option accepted by your cluster submit command.
You can use it to request non-standard resources or use settings that are specific to your cluster and not supported
out of the box by Nextflow.
Note
This directive is only used by grid executors. Refer to the Executors page to see which executors support this directive.
conda
The conda directive allows for the definition of the process dependencies using the Conda
package manager.
Nextflow automatically sets up an environment for the given package names listed by in the conda directive.
For example:
process foo {
conda 'bwa=0.7.15'
'''
your_command --here
'''
}
Multiple packages can be specified separating them with a blank space eg. bwa=0.7.15 fastqc=0.11.5.
The name of the channel from where a specific package needs to be downloaded can be specified using the usual
Conda notation i.e. prefixing the package with the channel name as shown here bioconda::bwa=0.7.15.
The conda directory also allows the specification of a Conda environment file
path or the path of an existing environment directory. See the Conda environments page for further details.
container
The container directive allows you to execute the process script in a Docker container.
It requires the Docker daemon to be running in machine where the pipeline is executed, i.e. the local machine when using the local executor or the cluster nodes when the pipeline is deployed through a grid executor.
For example:
process runThisInDocker {
container 'dockerbox:tag'
"""
<your holy script here>
"""
}
Simply replace in the above script dockerbox:tag with the name of the Docker image you want to use.
Tip
Containers are a very useful way to execute your scripts in a reproducible self-contained environment or to run your pipeline in the cloud.
Note
This directive is ignored for processes that are executed natively.
containerOptions
The containerOptions directive allows you to specify any container execution option supported by the underlying
container engine (ie. Docker, Singularity, etc). This can be useful to provide container settings
only for a specific process e.g. mount a custom path:
process runThisWithDocker {
container 'busybox:latest'
containerOptions '--volume /data/db:/db'
output:
path 'output.txt'
'''
your_command --data /db > output.txt
'''
}
Warning
This feature is not supported by the Kubernetes and Google Life Sciences executors.
cpus
The cpus directive allows you to define the number of (logical) CPU required by the process’ task.
For example:
process big_job {
cpus 8
executor 'sge'
"""
blastp -query input_sequence -num_threads ${task.cpus}
"""
}
This directive is required for tasks that execute multi-process or multi-threaded commands/tools and it is meant to reserve enough CPUs when a pipeline task is executed through a cluster resource manager.
debug
By default the stdout produced by the commands executed in all processes is ignored.
By setting the debug directive to true, you can forward the process stdout to the current top
running process stdout file, showing it in the shell terminal.
For example:
process sayHello {
debug true
script:
"echo Hello"
}
Hello
Without specifying debug true, you won’t see the Hello string printed out when executing the above example.
disk
The disk directive allows you to define how much local disk storage the process is allowed to use. For example:
process big_job {
disk '2 GB'
executor 'cirrus'
"""
your task script here
"""
}
The following memory unit suffix can be used when specifying the disk value:
Unit |
Description |
|---|---|
B |
Bytes |
KB |
Kilobytes |
MB |
Megabytes |
GB |
Gigabytes |
TB |
Terabytes |
Note
This directive is only used by certain executors. Refer to the Executors page to see which executors support this directive.
See also: cpus, memory time, queue and Dynamic computing resources.
echo
As of version 22.04.0, echo has been deprecated and replaced by debug.
errorStrategy
The errorStrategy directive allows you to define how an error condition is managed by the process. By default when
an error status is returned by the executed script, the process stops immediately. This in turn forces the entire pipeline
to terminate.
Table of available error strategies:
Name |
Executor |
|---|---|
|
Terminates the execution as soon as an error condition is reported. Pending jobs are killed (default) |
|
Initiates an orderly pipeline shutdown when an error condition is raised, waiting the completion of any submitted job. |
|
Ignores processes execution errors. |
|
Re-submit for execution a process returning an error condition. |
When setting the errorStrategy directive to ignore the process doesn’t stop on an error condition,
it just reports a message notifying you of the error event.
For example:
process ignoreAnyError {
errorStrategy 'ignore'
script:
<your command string here>
}
Note
By definition, a command script fails when it ends with a non-zero exit status.
The retry error strategy allows you to re-submit for execution a process
returning an error condition. For example:
process retryIfFail {
errorStrategy 'retry'
script:
<your command string here>
}
The number of times a failing process is re-executed is defined by the maxRetries and maxErrors directives.
Tip
More complex strategies depending on the task exit status
or other parametric values can be defined using a dynamic errorStrategy.
See the Dynamic directives section for details.
See also: maxErrors, maxRetries and Dynamic computing resources.
executor
The executor defines the underlying system where processes are executed. By default a process uses the executor
defined globally in the nextflow.config file.
The executor directive allows you to configure what executor has to be used by the process, overriding the default
configuration. The following values can be used:
Name |
Executor |
|---|---|
|
The process is executed using the AWS Batch service. |
|
The process is executed using the Azure Batch service. |
|
The process is executed using the HTCondor job scheduler. |
|
The process is executed using the Google Genomics Pipelines service. |
|
The process is executed using the Apache Ignite cluster. |
|
The process is executed using the Kubernetes cluster. |
|
The process is executed in the computer where Nextflow is launched. |
|
The process is executed using the Platform LSF job scheduler. |
|
The process is executed using the Moab job scheduler. |
|
The process is executed using the NQSII job scheduler. |
|
Alias for the |
|
The process is executed using the PBS/Torque job scheduler. |
|
The process is executed using the PBS Pro job scheduler. |
|
The process is executed using the Sun Grid Engine / Open Grid Engine. |
|
The process is executed using the SLURM job scheduler. |
|
The process is executed using the GA4GH TES service. |
|
Alias for the |
The following example shows how to set the process’s executor:
process doSomething {
executor 'sge'
script:
<your script here>
}
Note
Each executor supports additional directives and executor configuration options. Refer to the
Executors page to see what each executor supports.
ext
The ext is a special directive used as namespace for user custom process directives. This can be useful for
advanced configuration options. For example:
process mapping {
container "biocontainers/star:${task.ext.version}"
input:
path genome
tuple val(sampleId), path(reads)
"""
STAR --genomeDir $genome --readFilesIn $reads
"""
}
In the above example, the process uses a container whose version is controlled by the ext.version property.
This can be defined in the nextflow.config file as shown below:
process.ext.version = '2.5.3'
label
The label directive allows the annotation of processes with mnemonic identifier of your choice.
For example:
process bigTask {
label 'big_mem'
'''
<task script>
'''
}
The same label can be applied to more than a process and multiple labels can be applied to the same
process using the label directive more than one time.
Note
A label must consist of alphanumeric characters or _, must start with an alphabetic character
and must end with an alphanumeric character.
Labels are useful to organise workflow processes in separate groups which can be referenced in the configuration file to select and configure subset of processes having similar computing requirements. See the Process selectors documentation for details.
See also: resourceLabels
machineType
The machineType can be used to specify a predefined Google Compute Platform machine type
when running using the Google Life Sciences executor.
This directive is optional and if specified overrides the cpus and memory directives:
process foo {
machineType 'n1-highmem-8'
"""
<your script here>
"""
}
Note
This feature requires Nextflow 19.07.0 or later.
maxErrors
The maxErrors directive allows you to specify the maximum number of times a process can fail when using the retry error strategy.
By default this directive is disabled, you can set it as shown in the example below:
process retryIfFail {
errorStrategy 'retry'
maxErrors 5
"""
echo 'do this as that .. '
"""
}
Note
This setting considers the total errors accumulated for a given process, across all instances. If you want
to control the number of times a process instance (aka task) can fail, use maxRetries.
See also: errorStrategy and maxRetries.
maxForks
The maxForks directive allows you to define the maximum number of process instances that can be executed in parallel.
By default this value is equals to the number of CPU cores available minus 1.
If you want to execute a process in a sequential manner, set this directive to one. For example:
process doNotParallelizeIt {
maxForks 1
'''
<your script here>
'''
}
maxRetries
The maxRetries directive allows you to define the maximum number of times a process instance can be
re-submitted in case of failure. This value is applied only when using the retry error strategy. By default
only one retry is allowed, you can increase this value as shown below:
process retryIfFail {
errorStrategy 'retry'
maxRetries 3
"""
echo 'do this as that .. '
"""
}
Note
There is a subtle but important difference between maxRetries and the maxErrors directive.
The latter defines the total number of errors that are allowed during the process execution (the same process can
launch different execution instances), while the maxRetries defines the maximum number of times the same process
execution can be retried in case of an error.
See also: errorStrategy and maxErrors.
memory
The memory directive allows you to define how much memory the process is allowed to use. For example:
process big_job {
memory '2 GB'
executor 'sge'
"""
your task script here
"""
}
The following memory unit suffix can be used when specifying the memory value:
Unit |
Description |
|---|---|
B |
Bytes |
KB |
Kilobytes |
MB |
Megabytes |
GB |
Gigabytes |
TB |
Terabytes |
See also: cpus, time, queue and Dynamic computing resources.
module
Environment Modules is a package manager that allows you to dynamically configure your execution environment and easily switch between multiple versions of the same software tool.
If it is available in your system you can use it with Nextflow in order to configure the processes execution environment in your pipeline.
In a process definition you can use the module directive to load a specific module version to be used in the
process execution environment. For example:
process basicExample {
module 'ncbi-blast/2.2.27'
"""
blastp -query <etc..>
"""
}
You can repeat the module directive for each module you need to load. Alternatively multiple modules
can be specified in a single module directive by separating all the module names by using a :
(colon) character as shown below:
process manyModules {
module 'ncbi-blast/2.2.27:t_coffee/10.0:clustalw/2.1'
"""
blastp -query <etc..>
"""
}
penv
The penv directive allows you to define the parallel environment to be used when submitting a parallel task to the
SGE resource manager. For example:
process big_job {
cpus 4
penv 'smp'
executor 'sge'
"""
blastp -query input_sequence -num_threads ${task.cpus}
"""
}
This configuration depends on the parallel environment provided by your grid engine installation. Refer to your cluster documentation or contact your admin to learn more about this.
pod
The pod directive allows the definition of pods specific settings, such as environment variables, secrets
and config maps when using the Kubernetes executor.
For example:
process your_task {
pod env: 'FOO', value: 'bar'
'''
echo $FOO
'''
}
The above snippet defines an environment variable named FOO which value is bar.
The pod directive allows the definition of the following options:
|
Defines a pod label with key |
|
Defines a pod annotation with key |
|
Defines an environment variable with name |
|
Defines an environment variable with name |
|
Defines an environment variable with name |
|
Defines an environment variable with name |
|
The content of the ConfigMap with name |
|
The content of the Secret with name |
|
Mounts a Persistent volume claim with name |
|
Specifies the strategy to be used to pull the container image e.g. |
|
Specifies the secret name to access a private container image registry. See Kubernetes documentation for details. |
|
Specifies the user ID to be used to run the container. Shortcut for the |
|
Specifies the pod security context. See Kubernetes security context for details. |
|
Specifies which node the process will run on. See Kubernetes nodeSelector for details. |
|
Specifies affinity for which nodes the process should run on. See Kubernetes affinity for details. |
|
Specifies whether to automount service account token into process pods. If |
|
Specifies the priority class name for pods. |
|
Specifies a toleration for a node taint. See Taints and Tolerations for details. |
|
Whenever the process task should run as a privileged container (default: |
When defined in the Nextflow configuration file, a pod setting can be defined using the canonical associative array syntax. For example:
process {
pod = [env: 'FOO', value: 'bar']
}
When more than one setting needs to be provides they must be enclosed in a list definition as shown below:
process {
pod = [ [env: 'FOO', value: 'bar'], [secret: 'my-secret/key1', mountPath: '/etc/file.txt'] ]
}
Some settings, including environment variables, configs, secrets, volume claims, and tolerations, can be specified multiple times for different values.
publishDir
The publishDir directive allows you to publish the process output files to a specified folder. For example:
process foo {
publishDir '/data/chunks'
output:
path 'chunk_*'
'''
printf 'Hola' | split -b 1 - chunk_
'''
}
The above example splits the string Hola into file chunks of a single byte. When complete the chunk_* output files
are published into the /data/chunks folder.
Note
Only files that match the declaration in the output: block are published, not all the outputs of the process.
Tip
The publishDir directive can be specified more than once in order to publish output files
to different target directories based on different rules.
By default files are published to the target folder creating a symbolic link for each process output that links
the file produced into the process working directory. This behavior can be modified using the mode parameter.
Table of optional parameters that can be used with the publishDir directive:
Name |
Description |
|---|---|
mode |
The file publishing method. See the following table for possible values. |
overwrite |
When |
pattern |
Specifies a glob file pattern that selects which files to publish from the overall set of output files. |
path |
Specifies the directory where files need to be published. Note: the syntax |
saveAs |
A closure which, given the name of the file being published, returns the actual file name or a full path where the file is required to be stored.
This can be used to rename or change the destination directory of the published files dynamically by using
a custom strategy.
Return the value |
enabled |
Enable or disable the publish rule depending on the boolean value specified (default: |
failOnError |
When |
contentType |
Allow specifying the media content type of the published file a.k.a. MIME type. If the boolean value |
tags |
Allow the association of arbitrary tags with the published file e.g. |
Table of publish modes:
Mode |
Description |
|---|---|
symlink |
Creates an absolute symbolic link in the published directory for each process output file (default). |
rellink |
Creates a relative symbolic link in the published directory for each process output file. |
link |
Creates a hard link in the published directory for each process output file. |
copy |
Copies the output files into the published directory. |
copyNoFollow |
Copies the output files into the published directory without following symlinks ie. copies the links themselves. |
move |
Moves the output files into the published directory. Note: this is only supposed to be used for a terminating process i.e. a process whose output is not consumed by any other downstream process. |
Note
The mode value must be specified as a string literal, i.e. in quotes. Multiple parameters
need to be separated by a colon character. For example:
process foo {
publishDir '/data/chunks', mode: 'copy', overwrite: false
output:
path 'chunk_*'
'''
printf 'Hola' | split -b 1 - chunk_
'''
}
Warning
Files are copied into the specified directory in an asynchronous manner, so they may not be immediately available in the published directory at the end of the process execution. For this reason, downstream processes should not try to access output files through the publish directory, but through channels.
queue
The queue directory allows you to set the queue where jobs are scheduled when using a grid based executor
in your pipeline. For example:
process grid_job {
queue 'long'
executor 'sge'
"""
your task script here
"""
}
Multiple queues can be specified by separating their names with a comma for example:
process grid_job {
queue 'short,long,cn-el6'
executor 'sge'
"""
your task script here
"""
}
Note
This directive is only used by certain executors. Refer to the Executors page to see which executors support this directive.
resourceLabels
The resourceLabels directive allows you to specify custom name-value pairs
that Nextflow applies to the computing resource used to carry out the process execution.
Resource labels can be specified using the syntax shown below:
process my_task {
resourceLabels region: 'some-region', user: 'some-username'
'''
<task script>
'''
}
The limits and the syntax of the corresponding cloud provider should be taken into consideration when using resource labels.
Note
Resource labels are currently only supported by the AWS Batch, Google Life Sciences, Google Cloud Batch and Kubernetes executors.
See also label.
scratch
The scratch directive allows you to execute the process in a temporary folder that is local to the execution node.
This is useful when your pipeline is launched by using a grid executor, because it allows you to decrease the NFS overhead by running the pipeline processes in a temporary directory in the local disk of the actual execution node. Only the files declared as output in the process definition will be copied in the pipeline working area.
In its basic form simply specify true at the directive value, as shown below:
process simpleTask {
scratch true
output:
path 'data_out'
'''
<task script>
'''
}
By doing this, it tries to execute the script in the directory defined by the variable $TMPDIR in the execution node.
If this variable does not exist, it will create a new temporary directory by using the Linux command mktemp.
A custom environment variable, other than $TMPDIR, can be specified by simply using it as the scratch value, for
example:
scratch '$MY_GRID_TMP'
Note, it must be wrapped by single quotation characters, otherwise the variable will be evaluated in the pipeline script context.
You can also provide a specific folder path as scratch value, for example:
scratch '/tmp/my/path'
By doing this, a new temporary directory will be created in the specified path each time a process is executed.
Finally, when the ram-disk string is provided as scratch value, the process will be execute in the node
RAM virtual disk.
Summary of allowed values:
scratch |
Description |
|---|---|
false |
Do not use the scratch folder. |
true |
Creates a scratch folder in the directory defined by the |
$YOUR_VAR |
Creates a scratch folder in the directory defined by the |
/my/tmp |
Creates a scratch folder in the specified directory. |
ram-disk |
Creates a scratch folder in the RAM disk |
storeDir
The storeDir directive allows you to define a directory that is used as a permanent cache for your process results.
In more detail, it affects the process execution in two main ways:
The process is executed only if the files declared in the
outputblock do not exist in the directory specified by thestoreDirdirective. When the files exist the process execution is skipped and these files are used as the actual process result.Whenever a process is successfully completed the files listed in the
outputblock are moved into the directory specified by thestoreDirdirective.
The following example shows how to use the storeDir directive to create a directory containing a BLAST database
for each species specified by an input parameter:
process formatBlastDatabases {
storeDir '/db/genomes'
input:
path species
output:
path "${dbName}.*"
script:
dbName = species.baseName
"""
makeblastdb -dbtype nucl -in ${species} -out ${dbName}
"""
}
Warning
The storeDir directive is meant for long-term process caching and should not be used to
publish output files or organize outputs into a semantic directory structure. In those cases, use
the publishDir directive instead.
Note
The use of AWS S3 paths is supported, however it requires the installation of the AWS CLI
(i.e. aws) in the target compute node.
stageInMode
The stageInMode directive defines how input files are staged-in to the process work directory. The following values
are allowed:
Value |
Description |
|---|---|
copy |
Input files are staged in the process work directory by creating a copy. |
link |
Input files are staged in the process work directory by creating an (hard) link for each of them. |
symlink |
Input files are staged in the process work directory by creating a symbolic link with an absolute path for each of them (default). |
rellink |
Input files are staged in the process work directory by creating a symbolic link with a relative path for each of them. |
stageOutMode
The stageOutMode directive defines how output files are staged-out from the scratch directory to the process work
directory. The following values are allowed:
Value |
Description |
|---|---|
copy |
Output files are copied from the scratch directory to the work directory. |
move |
Output files are moved from the scratch directory to the work directory. |
rsync |
Output files are copied from the scratch directory to the work directory by using the |
See also: scratch.
tag
The tag directive allows you to associate each process execution with a custom label, so that it will be easier
to identify them in the log file or in the trace execution report. For example:
process foo {
tag "$code"
input:
val code
"""
echo $code
"""
}
workflow {
Channel.of('alpha', 'gamma', 'omega') | foo
}
The above snippet will print a log similar to the following one, where process names contain the tag value:
[6e/28919b] Submitted process > foo (alpha)
[d2/1c6175] Submitted process > foo (gamma)
[1c/3ef220] Submitted process > foo (omega)
See also Trace execution report
time
The time directive allows you to define how long a process is allowed to run. For example:
process big_job {
time '1h'
"""
your task script here
"""
}
The following time unit suffixes can be used when specifying the duration value:
Unit |
Description |
|---|---|
|
Milliseconds |
|
Seconds |
|
Minutes |
|
Hours |
|
Days |
Multiple units can be used in a single declaration, for example: '1day 6hours 3minutes 30seconds'
Note
This directive is only used by certain executors. Refer to the Executors page to see which executors support this directive.
See also: cpus, memory, queue and Dynamic computing resources.
Dynamic directives
A directive can be assigned dynamically, during the process execution, so that its actual value can be evaluated based on the process inputs.
In order to be defined in a dynamic manner, the directive’s value needs to be expressed using a closure, as in the following example:
process foo {
executor 'sge'
queue { entries > 100 ? 'long' : 'short' }
input:
tuple val(entries), path('data.txt')
script:
"""
< your job here >
"""
}
In the above example, the queue directive is evaluated dynamically, depending on the input value entries. When it is
larger than 100, jobs will be submitted to the long queue, otherwise the short queue will be used.
All directives can be assigned a dynamic value except the following:
Tip
Assigning a string value with one or more variables is always resolved in a dynamic manner, and therefore is equivalent to the above syntax. For example, the above directive can also be written as:
queue "${ entries > 100 ? 'long' : 'short' }"
Note, however, that the latter syntax can be used both for a directive’s main argument (as in the above example) and for a directive’s optional named attributes, whereas the closure syntax is only resolved dynamically for a directive’s main argument.
Tip
You can retrieve the current value of a dynamic directive in the process script by using the implicit variable task,
which holds the directive values defined in the current task. For example:
process foo {
queue { entries > 100 ? 'long' : 'short' }
input:
tuple val(entries), path('data.txt')
script:
"""
echo Current queue: ${task.queue}
"""
}
Dynamic computing resources
It’s a very common scenario that different instances of the same process may have very different needs in terms of computing resources. In such situations requesting, for example, an amount of memory too low will cause some tasks to fail. Instead, using a higher limit that fits all the tasks in your execution could significantly decrease the execution priority of your jobs.
The Dynamic directives evaluation feature can be used to modify the amount of computing resources requested in case of a process failure and try to re-execute it using a higher limit. For example:
process foo {
memory { 2.GB * task.attempt }
time { 1.hour * task.attempt }
errorStrategy { task.exitStatus in 137..140 ? 'retry' : 'terminate' }
maxRetries 3
script:
<your job here>
}
In the above example the memory and execution time limits are defined dynamically. The first time the process
is executed the task.attempt is set to 1, thus it will request a two GB of memory and one hour of maximum execution
time.
If the task execution fail reporting an exit status in the range between 137 and 140, the task is re-submitted (otherwise terminates immediately).
This time the value of task.attempt is 2, thus increasing the amount of the memory to four GB and the time to 2 hours, and so on.
The directive maxRetries set the maximum number of time the same task can be re-executed.
Dynamic Retry with backoff
There are cases in which the required execution resources may be temporary unavailable e.g. network congestion. In these cases immediately re-executing the task will likely result in the identical error. A retry with an exponential backoff delay can better recover these error conditions:
process foo {
errorStrategy { sleep(Math.pow(2, task.attempt) * 200 as long); return 'retry' }
maxRetries 5
script:
'''
your_command --here
'''
}