A Workflow Management System geared towards scientific workflows. Cromwell is open sourced under the BSD 3-Clause license.
- Getting Help
- Requirements
- Building
- Installing
- Command Line Usage
- Getting Started with WDL
- Configuring Cromwell
- Backends
- Runtime Attributes
- Logging
- Workflow Options
- Call Caching
- REST API
- REST API Versions
- POST /api/workflows/:version
- POST /api/workflows/:version/batch
- GET /api/workflows/:version/query
- POST /api/workflows/:version/query
- GET /api/workflows/:version/:id/status
- GET /api/workflows/:version/:id/outputs
- GET /api/workflows/:version/:id/timing
- GET /api/workflows/:version/:id/logs
- GET /api/workflows/:version/:id/metadata
- POST /api/workflows/:version/:id/abort
- GET /api/workflows/:version/backends
- GET /api/engine/:version/stats
- Error handling
- Developer
The WDL website is the best place to go for more information on both WDL and Cromwell. In particular new users should check out the user guide which has many tutorials, examples and other bits to get you started.
If you have questions that aren't covered by the website you can ask them in the support forum.
There is a Cromwell gitter channel where people can discuss Cromwell and related topics with both the developers and user community.
The following is the toolchain used for development of Cromwell. Other versions may work, but these are recommended.
sbt assembly will build a runnable JAR in target/scala-2.11/
Tests are run via sbt test. Note that the tests do require Docker to be running. To test this out while downloading the Ubuntu image that is required for tests, run docker pull ubuntu:latest prior to running sbt test
OS X users can install Cromwell with Homebrew: brew install cromwell.
See the migration document for more details.
Run the JAR file with no arguments to get the usage message:
$ java -jar cromwell.jar
java -jar cromwell.jar <action> <parameters>
Actions:
run <WDL file> [<JSON inputs file> [<JSON workflow options>
[<OUTPUT workflow metadata>]]]
Given a WDL file and JSON file containing the value of the
workflow inputs, this will run the workflow locally and
print out the outputs in JSON format. The workflow
options file specifies some runtime configuration for the
workflow (see README for details). The workflow metadata
output is an optional file path to output the metadata.
Use a single dash ("-") to skip optional files. Ex:
run noinputs.wdl - - metadata.json
server
Starts a web server on port 8000. See the web server
documentation for more details about the API endpoints.
Given a WDL file and a JSON inputs file (see inputs subcommand), Run the workflow and print the outputs:
$ java -jar cromwell.jar run 3step.wdl inputs.json
... play-by-play output ...
{
"three_step.ps.procs": "/var/folders/kg/c7vgxnn902lc3qvc2z2g81s89xhzdz/T/stdout1272284837004786003.tmp",
"three_step.cgrep.count": 0,
"three_step.wc.count": 13
}
The JSON inputs can be left off if there's a file with the same name as the WDL file but with a .inputs extension. For example, this will assume that 3step.inputs exists:
$ java -jar cromwell.jar run 3step.wdl
If your workflow has no inputs, you can specify - as the value for the inputs parameter:
$ java -jar cromwell.jar run my_workflow.wdl -
The third, optional parameter to the 'run' subcommand is a JSON file of workflow options. By default, the command line will look for a file with the same name as the WDL file but with the extension .options. But one can also specify a value of - manually to specify that there are no workflow options.
See the section workflow options for more details.
$ java -jar cromwell.jar run my_jes_wf.wdl my_jes_wf.json wf_options.json
The fourth, optional parameter to the 'run' subcommand is a path where the workflow metadata will be written. By default, no workflow metadata will be written.
$ java -jar cromwell.jar run my_wf.wdl - - my_wf.metadata.json
... play-by-play output ...
$ cat my_wf.metadata.json
{
"workflowName": "w",
"calls": {
"w.x": [{
"executionStatus": "Done",
"stdout": "/Users/jdoe/projects/cromwell/cromwell-executions/w/a349534f-137b-4809-9425-1893ac272084/call-x/stdout",
"shardIndex": -1,
"outputs": {
"o": "local\nremote"
},
"runtimeAttributes": {
"failOnStderr": "false",
"continueOnReturnCode": "0"
},
"cache": {
"allowResultReuse": true
},
"inputs": {
"remote": "/Users/jdoe/remote.txt",
"local": "local.txt"
},
"returnCode": 0,
"backend": "Local",
"end": "2016-07-11T10:27:56.074-04:00",
"stderr": "/Users/jdoe/projects/cromwell/cromwell-executions/w/a349534f-137b-4809-9425-1893ac272084/call-x/stderr",
"callRoot": "cromwell-executions/w/a349534f-137b-4809-9425-1893ac272084/call-x",
"attempt": 1,
"start": "2016-07-11T10:27:55.992-04:00"
}]
},
"outputs": {
"w.x.o": "local\nremote"
},
"workflowRoot": "cromwell-executions/w/a349534f-137b-4809-9425-1893ac272084",
"id": "a349534f-137b-4809-9425-1893ac272084",
"inputs": {
"w.x.remote": "/Users/jdoe/remote.txt",
"w.x.local": "local.txt"
},
"submission": "2016-07-11T10:27:54.907-04:00",
"status": "Succeeded",
"end": "2016-07-11T10:27:56.108-04:00",
"start": "2016-07-11T10:27:54.919-04:00"
}
Start a server on port 8000, the API for the server is described in the REST API section.
For many examples on how to use WDL see the WDL site
Cromwell's default configuration file is located at src/main/resources/application.conf.
The configuration file is in Hocon which means the configuration file can specify configuration as JSON-like stanzas like:
webservice {
port = 8000
interface = 0.0.0.0
instance.name = "reference"
}Or, alternatively, as dot-separated values:
webservice.port = 8000
webservice.interface = 0.0.0.0
webservice.instance.name = "reference"This allows any value to be overridden on the command line:
java -Dwebservice.port=8080 cromwell.jar ...
It is recommended that one copies src/main/resources/application.conf, modify it, then link to it via:
java -Dconfig.file=/path/to/application.conf cromwell.jar ...
Cromwell has a configurable cap on the number of workflows running at a time. To set this value provide an integer value to the system.max-concurrent-workflows config value.
Cromwell will look for new workflows to start on a regular interval which can be modified by setting the system.new-workflow-poll-rate config value, which is the number of seconds between workflow launches. On every poll, Cromwell will take at most system.max-workflow-launch-count new submissions, provided there are new workflows to launch and the system.max-concurrent-workflows number has not been reached.
Cromwell uses either an in-memory or MySQL database to track the execution of workflows and store outputs of task invocations.
By default, Cromwell uses an in-memory database which will only live for the duration of the JVM. This provides a quick way to run workflows locally without having to set up MySQL, though it also makes workflow executions somewhat transient.
To configure Cromwell to instead point to a MySQL database, first create the empty database. In the example below, the database name is cromwell.
Then, edit the configuration file database stanza, as follows:
database {
config = main.mysql
main {
mysql {
db.url = "jdbc:mysql://localhost:3306/cromwell"
db.user = "root"
db.password = ""
db.driver = "com.mysql.jdbc.Driver"
db.connectionTimeout = 5000 # NOTE: The default 1000ms is often too short for production mysql use
driver = "slick.driver.MySQLDriver$"
}
}
test {
...
}
}
For backends that support aborting task invocations, Cromwell can be configured to automatically try to abort all currently running calls (and set their status to Aborted) when a SIGINT is sent to the Cromwell process. To turn this feature on, set the configuration option
system {
abort-jobs-on-terminate=true
}
Or, via -Dsystem.abort-jobs-on-terminate=true command line option.
A backend represents a way to run the user's command specified in the task section. Cromwell allows for backends conforming to
the Cromwell backend specification to be plugged into the Cromwell engine. Additionally, backends are included with the
Cromwell distribution:
- Local / GridEngine / LSF / etc. - Run jobs as subprocesses or via a dispatcher. Supports launching in Docker containers. Use
bash,qsub,bsub, etc. to run scripts. - Google JES - Launch jobs on Google Compute Engine through the Job Execution Service (JES).
- HtCondor - Allows to execute jobs using HTCondor.
- Spark - Adds support for execution of spark jobs.
Backends are specified in the backend configuration block under providers. Each backend has a configuration that looks like:
backend {
default = "Local"
providers {
BackendName {
actor-factory = "FQN of BackendLifecycleActorFactory instance"
config {
key = "value"
key2 = "value2"
...
}
}
}
}The structure within the config block will vary from one backend to another; it is the backend implementation's responsibility
to be able to interpret its configuration.
In the example below two backend types are named within the providers section here, so both
are available. The default backend is specified by backend.default and must match the name of one of the
configured backends:
backend {
default = "Local"
providers {
Local {
actor-factory = "cromwell.backend.impl.local.LocalBackendLifecycleActorFactory"
config {
root: "cromwell-executions"
filesystems = {
local {
localization: [
"hard-link", "soft-link", "copy"
]
}
gcs {
# References an auth scheme defined in the 'google' stanza.
auth = "application-default"
}
}
}
},
JES {
actor-factory = "cromwell.backend.impl.jes.JesBackendLifecycleActorFactory"
config {
project = "my-cromwell-workflows"
root = "gs://my-cromwell-workflows-bucket"
maximum-polling-interval = 600
dockerhub {
# account = ""
# token = ""
}
genomics {
# A reference to an auth defined in the 'google' stanza at the top. This auth is used to create
# Pipelines and manipulate auth JSONs.
auth = "application-default"
endpoint-url = "https://genomics.googleapis.com/"
}
filesystems = {
gcs {
# A reference to a potentially different auth for manipulating files via engine functions.
auth = "user-via-refresh"
}
}
}
}
]
}Each backend will utilize filesystems to store the directory structure of an executed workflow. Currently, the backends and the type of filesystems that the backend use are tightly coupled. In future versions of Cromwell, they may be more loosely coupled.
The backend/filesystem pairings are as follows:
- Local Backend and associated backends primarily use the Shared Local Filesystem.
- JES Backend uses the Google Cloud Storage Filesystem
Note that while Local, SGE, LSF, etc. backends use the local or network filesystem for the directory structure of a workflow, they are able to localize inputs from GCS paths if configured to use a GCS filesystem. See Google Cloud Storage Filesystem for more details.
For the local and Sun GridEngine backends, the following is required of the underlying filesystem:
Cromwell is configured with a root execution directory which is set in the configuration file under backend.providers.<backend_name>.config.root. This is called the cromwell_root and it is set to ./cromwell-executions by default. Relative paths are interpreted as relative to the current working directory of the Cromwell process.
When Cromwell runs a workflow, it first creates a directory <cromwell_root>/<workflow_uuid>. This is called the workflow_root and it is the root directory for all activity in this workflow.
Each call has its own subdirectory located at <workflow_root>/call-<call_name>. This is the <call_dir>. For example, having a stdout and stderr file is common among both backends and they both write a shell script file to the <call_dir> as well. See the descriptions below for details about backend-specific files that are written to these directories.
An example of a workflow output directory for a three-step WDL file might look like this:
cromwell-executions/
└── three_step
└── a59651fc-4d9a-4fed-99ba-f5e2c9d84bb4
├── call-cgrep
│  ├── Users
│  │  └── jdoe
│  │  └── projects
│  │  └── cromwell
│  │  └── cromwell-executions
│  │  └── three_step
│  │  └── a59651fc-4d9a-4fed-99ba-f5e2c9d84bb4
│  │  └── call-ps
│  │  └── stdout
│  ├── rc
│  ├── script
│  ├── stderr
│  └── stdout
├── call-ps
│  ├── rc
│  ├── script
│  ├── stderr
│  └── stdout
└── call-wc
├── Users
│  └── jdoe
│  └── projects
│  └── cromwell
│  └── cromwell-executions
│  └── three_step
│  └── a59651fc-4d9a-4fed-99ba-f5e2c9d84bb4
│  └── call-ps
│  └── stdout
├── rc
├── script
├── stderr
└── stdout
WDL File
task ps {
command {
ps
}
output {
File procs = stdout()
}
}
task cgrep {
String pattern
File in_file
command {
grep '${pattern}' ${in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
task wc {
File in_file
command {
cat ${in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
workflow three_step {
call ps
call cgrep {
input: in_file=ps.procs
}
call wc {
input: in_file=ps.procs
}
}In the above directory structure, you'll notice that the call-cgrep and call-wc sub-directories both contain a directory structure to point to the stdout file from the invocation of ps. In these cases, that stdout file is a localized version of the one within call-ps/stdout. By default both of those stdout files would be hard-links but they could also be symbolic links or copies of the file, depending on how Cromwell is configured (see below). The directory structure is nested so deeply to avoid collisions. For example, if either of these call invocations referenced two files called stdout, they'd collide if they were put into the same directory so the full directory structure is maintained.
Any input files to a call need to be localized into the <call_dir>. There are a few localization strategies that Cromwell will try until one works. Below is the default order specified in application.conf but this order can be overridden:
hard-link- This will create a hard link (not symbolic) link to the filesoft-link- Create a symbolic link to the file. This strategy is not applicable for tasks which specify a Docker image and will be ignored.copy- Make a copy the file
Shared filesystem localization is defined in the config section of each backend. The default stanza for the Local, SGE, and associated backends looks like this:
filesystems {
local {
localization: [
"hard-link", "soft-link", "copy"
]
}
}
On the JES backend the GCS (Google Cloud Storage) filesystem is used for the root of the workflow execution.
On the Local, SGE, and associated backends any GCS URI will be downloaded locally. For the JES backend the jes_gcs_root workflow option will take
precedence over the root specified at backend.providers.JES.config.root in the configuration file. Google Cloud Storage URIs are the only acceptable values for File inputs for
workflows using the JES backend.
The local backend will simply launch a subprocess for each task invocation and wait for it to produce its rc file.
This backend creates three files in the <call_dir> (see previous section):
script- A shell script of the job to be run. This contains the user's command from thecommandsection of the WDL code.stdout- The standard output of the processstderr- The standard error of the process
The script file contains:
#!/bin/sh
cd <container_call_root>
<user_command>
echo $? > rc
<container_call_root> would be equal to <call_dir> for non-Docker jobs, or it would be under /root/<workflow_uuid>/call-<call_name> if this is running in a Docker container.
When running without docker, the subprocess command that the local backend will launch is:
/bin/bash <script>"
When running with docker, the subprocess command that the local backend will launch is:
docker run --rm -v <cwd>:<docker_cwd> -i <docker_image> /bin/bash < <script>
NOTE: If you are using the local backend with Docker and Docker Machine on Mac OS X, by default Cromwell can only run from in any path under your home directory.
The
-vflag will only work if<cwd>is within your home directory because VirtualBox with Docker Machine only exposes the home directory by default. Any local path used in-vthat is not within the user's home directory will silently be interpreted as references to paths on the VirtualBox VM. This can manifest in Cromwell as tasks failing for odd reasons (like missing RC file)See https://docs.docker.com/engine/userguide/dockervolumes/ for more information on volume mounting in Docker.
The GridEngine and similar backends use programs such as qsub to launch a job and will poll the filesystem to determine if a job is completed.
The backend is specified via the actor factory ConfigBackendLifecycleActorFactory:
backend {
providers {
SGE {
config {
actor-factory = "cromwell.backend.impl.sfs.config.ConfigBackendLifecycleActorFactory"
# ... other configuration
}
}
}
}
This backend makes the same assumption about the filesystem that the local backend does: the Cromwell process and the jobs both have read/write access to the CWD of the job.
The CWD will contain a script.sh file which will contain the same contents as the Local backend:
#!/bin/sh
cd <container_call_root>
<user_command>
echo $? > rc
The job is launched with a configurable command such as:
qsub \
-terse \
-V \
-b n \
-N ${job_name} \
-wd ${cwd} \
-o ${out} \
-e ${err} \
-pe smp ${cpu} \
${"-l m_mem_free=" + memory_gb + "gb"} \
${"-q " + sge_queue} \
${"-P " + sge_project} \
${script}The SGE backend gets the job ID from parsing the submit.stdout text file.
Since the script.sh ends with echo $? > rc, the backend will wait for the existence of this file, parse out the return code and determine success or failure and then subsequently post-process.
The command used to submit the job is specified under the configuration key backend.providers.SGE.config.submit. It uses the same syntax as a command in WDL, and will be provided the variables:
script- A shell script of the job to be run. This contains the user's command from thecommandsection of the WDL code.cwd- The path where the script should be run.out- The path to the stdout.err- The path to the stderr.job_name- A unique name for the job.
backend {
providers {
SGE {
config {
# ... other configuration
submit = """
qsub \
-terse \
-V \
-b n \
-N ${job_name} \
-wd ${cwd} \
-o ${out} \
-e ${err} \
${script}
"""
}
}
}
}
If the backend supports docker, another optional configuration key backend.providers.<backend>.config.submit-docker may be specified. When the WDL contains a docker runtime attribute, this command will be provided the two additional variables:
docker- The docker image name.docker_cwd- The path wherecwdshould be mounted within the docker container.
backend {
providers {
SGE {
config {
# ... other configuration
submit-docker = """
qsub \
-terse \
-V \
-b n \
-N ${job_name} \
-wd ${cwd} \
-o ${out} \
-e ${err} \
-l docker,docker_images="${docker}"
-xdv ${cwd}:${docker_cwd}
${script}
"""
}
}
}
}
If the backend would like to support additional runtime attributes they may be specified in the configuration key backend.providers.<backend>.config.runtime-attributes. It uses the same syntax as specifying runtime attributes in a task in WDL.
There are two special runtime attribute configurations, cpu, and memory_<unit>.
When the runtime attribute configuration Int cpu is specified, it is always validated as a positive integer.
When the runtime attribute configuration Int memory_<unit> or Float memory_<unit> is specified, it is provided to submit by the runtime attribute in WDL memory.
For example, if the backend specifies the configuration for backend.providers.<backend>.config.runtime-attributes as:
backend {
providers {
SGE {
config {
# ... other configuration
runtime-attributes = "Float memory_mb"
}
}
}
}
And the WDL specifies a task with:
task hello_gigabyte {
command { echo "hello world" }
runtime { memory: "1 GB" }
}
Then for this call, the backend will be provided an additional variable memory_mb set to 1000.0.
Other runtime attributes may be defined by specifying them in under the runtime attributes configuration.
backend {
providers {
SGE {
config {
# ... other configuration
runtime-attributes = """
Float memory_mb
String sge_project
"""
}
}
}
}
These variables will then be passed from the WDL into the submit configuration. If one would like to have a default value, just like in WDL, the configuration may specify that the value have a default. The default must match the defined type or an error will be produced.
backend {
providers {
SGE {
config {
# ... other configuration
runtime-attributes = """
Float memory_mb = 2.0
String sge_project = "default"
"""
}
}
}
}
Optional values may also be used by appending ? to the type:
backend {
providers {
SGE {
config {
# ... other configuration
runtime-attributes = """
Float? memory_mb
String? sge_project
"""
}
}
}
}
The value will be passed to the submit configuration if provided, and omitted otherwise.
There are also configuration values related to how jobs are rechecked on startup and aborted.
The option is backend.providers.<backend>.config.run-in-background. When true the backend runs the submit configuration and records the unix process id (PID). To abort the job, the PID is stopped with the unix command kill. Upon a cromwell restart, the PID is checked via the unix command ps to see if it is still alive, before cromwell goes back to polling for the rc file.
When backend.providers.<backend>.config.run-in-background is false, the default, the backend must specify how read the job identifier from the stdout of the submit, how to kill the job, and how to check if the job is still running during a cromwell restart. These three configuration values are job-id-regex, kill, and check-alive, respectively:
backend {
providers {
SGE {
config {
# ... other configuration
job-id-regex = "(\\d+)"
kill = "qdel ${job_id}"
check-alive = "qstat -j ${job_id}"
"""
}
}
}
}
The job-id-regex should contain one capture group while matching against the whole line or stdout file. The check-alive should return zero if the job is still alive.
Allows to execute jobs using HTCondor which is a specialized workload management system for compute-intensive jobs created by the Center for High Throughput Computing in the Department of Computer Sciences at the University of Wisconsin-Madison (UW-Madison).
This backend creates six files in the <call_dir> (see previous section):
script- A shell script of the job to be run. This contains the user's command from thecommandsection of the WDL code.stdout- The standard output of the processstderr- The standard error of the processsubmitfile- A submit file that HtCondor understands in order to submit a jobsubmitfile.stdout- The standard output of the submit filesubmitfile.stderr- The standard error of the submit file
The script file contains:
cd <container_call_root>
<user_command>
echo $? > rc
The submitfile file contains:
executable=cromwell-executions/test/e950e07d-4132-4fe0-8d86-ab6925dd94ad/call-merge_files/script
output=cromwell-executions/test/e950e07d-4132-4fe0-8d86-ab6925dd94ad/call-merge_files/stdout
error=cromwell-executions/test/e950e07d-4132-4fe0-8d86-ab6925dd94ad/call-merge_files/stderr
log=cromwell-executions/test/e950e07d-4132-4fe0-8d86-ab6925dd94ad/call-merge_files/merge_files.log
queue
This implementation also add basic caching support. It relies in a cache provider to store successful job results. By default a MongoDB based cache implementation is provided but there is the option of implementing a new provider based on CacheActorFactory and CacheActor interfaces.
From application.conf file:
cache {
provider = "cromwell.backend.impl.htcondor.caching.provider.mongodb.MongoCacheActorFactory"
enabled = true
forceRewrite = false
db {
host = "127.0.0.1"
port = 27017
name = "htcondor"
collection = "cache"
}
}
- provider: it defines the provider to use based on CacheActorFactory and CacheActor interfaces.
- enabled: enables or disables cache.
- forceRewrite: it allows to invalidate the cache entry and store result again.
- db section: configuration related to MongoDB provider. It may not exist for other implementations.
This backend supports the following optional runtime attributes / workflow options for working with Docker:
- docker: Docker image to use such as "Ubuntu".
- dockerWorkingDir: defines the working directory in the container.
- dockerOutputDir: defiles the output directory in the container when there is the need to define a volume for outputs within the container. By default if this attribute is not set, dockerOutputDir will be the job working directory.
Inputs: HtCondor backend analyzes all inputs and do a distinct of the folders in order to mount input folders into the container.
Outputs: It will use dockerOutputDir runtime attribute / workflow option to resolve the folder in which the execution results will placed. If there is no dockerOutputDir defined it will use the current working directory.
This backend supports CPU, memory and disk size configuration through the use of the following runtime attributes / workflow options:
- cpu: defines the amount of CPU to use. Default value: 1. Type: Integer. Ex: 4.
- memory: defines the amount of memory to use. Default value: "512 MB". Type: String. Ex: "4 GB" or "4096 MB"
- disk: defines the amount of disk to use. Default value: "1024 MB". Type: String. Ex: "1 GB" or "1024 MB"
It they are not set, HtCondor backend will use default values.
This backend adds support for execution of spark jobs in a workflow using the existing wdl format.
It supports the following Spark deploy modes:
- Client deploy mode using the spark standalone cluster manager
- Cluster deploy mode using the spark standalone cluster manager
- Client deploy mode using Yarn resource manager
- Cluster deploy mode using Yarn resource manager
When using Spark backend uncomment the following Spark configuration in the application.conf file
Spark {
actor-factory = "cromwell.backend.impl.spark.SparkBackendFactory"
config {
# Root directory where Cromwell writes job results. This directory must be
# visible and writeable by the Cromwell process as well as the jobs that Cromwell
# launches.
root: "cromwell-executions"
filesystems {
local {
localization: [
"hard-link", "soft-link", "copy"
]
}
}
master: "local"
deployMode: "client"
}
}
and add backend provider as Spark.
backend {
default = "Spark"
providers {
....
Default configuration is as follows:
Spark {
......
master: "local"
deployMode: "client"
}
However to use Spark in standalone cluster mode change master: spark://hostname:6066 and deployMode: cluster similarly, for yarn change master: yarn and deployMode: cluster or deployMode: client to run in cluster or client mode respectively.
Supported runtime attributes for a Spark Job is as follows:
- executorCores (default value is 1)
- executorMemory (default value is "1 GB")
- appMainClass ( Spark app/job entry point)
- numberOfExecutors ( Specific to cluster deploy mode)
Sample usage :
task sparkjob_with_yarn_cluster {
.....
runtime {
appMainClass: "${entry_point}"
executorMemory: "4G"
executorCores: "2"
}
.....
}The Spark backend assumes Spark is already installed, and it constructs the spark submit command with the SPARK_HOME environment variable if set. Otherwise backend creates command spark-submit without a fully qualified path to spark-submit.
Supported File Systems as follows:
- Local File System
- Network File System
- Distributed file system
Next, create a Wdl, and it's json input like so:
task sparkjob_with_yarn_cluster {
File input_jar
String input_1
String output_base
String entry_point
Int cores
String memory
command {
${input_jar} ${input_1} ${output_base}
}
runtime {
appMainClass: "${entry_point}"
executorMemory: "${memory}"
executorCores: "${cores}"
}
output {
File out = "${output_base}"
}
}and its accompanying json input as:
{
"sparkWithYarnCluster.sparkjob_with_yarn_cluster.memory": "4G",
"sparkWithYarnCluster.sparkjob_with_yarn_cluster.output_base":"/mnt/lustre/hadoop/home/yarn_cluster_output",
"sparkWithYarnCluster.sparkjob_with_yarn_cluster.entry_point": "com.org.spark.poc.nfs.SparkVowelLine",
"sparkWithYarnCluster.sparkjob_with_yarn_cluster.cores": "12",
"sparkWithYarnCluster.sparkjob_with_yarn_cluster.input_1": "/mnt/lustre/hadoop/home/inputfiles/sample.txt",
"sparkWithYarnCluster.sparkjob_with_yarn_cluster.input_jar": "/mnt/lustre/hadoop/home/inputjars/spark_hdfs.jar"
}Google JES (Job Execution Service) is a Docker-as-a-service from Google.
You'll need the following things to get started:
- A Google Project (Manage/create projects here)
- A Google Cloud Storage bucket (View/create buckets in your project here)
On your Google project, open up the API Manager and enable the following APIs:
- Google Compute Engine
- Google Cloud Storage
- Genomics API
If your project is my-project your bucket is gs://my-bucket/, then update your Cromwell configuration file as follows:
backend {
default = "JES"
providers {
JES {
actor-factory = "cromwell.backend.impl.jes.JesBackendLifecycleActorFactory"
config {
project = "my-project"
root = "gs://my-bucket"
.
.
.
}
}
]
}The google stanza in the Cromwell configuration file defines how to authenticate to Google. There are four different
authentication schemes that might be used:
application_default- (default, recommended) Use application default credentials.service_account- Use a specific service account and key file (in PEM format) to authenticate.user_account- Authenticate as a user.refresh_token- Authenticate using a refresh token supplied in the workflow options.
The auths block in the google stanza defines the authorization schemes within a Cromwell deployment:
google {
application-name = "cromwell"
auths = [
{
name = "application-default"
scheme = "application_default"
},
{
name = "user-via-refresh"
scheme = "refresh_token"
client-id = "secret_id"
client-secret = "secret_secret"
},
{
name = "service-account"
scheme = "service_account"
service-account-id = "my-service-account"
pem-file = "/path/to/file.pem"
}
]
}These authorization schemes can be referenced by name within other portions of the configuration file. For example, both
the genomics and filesystems.gcs sections within a JES configuration block must reference an auth defined in this block.
The auth for the genomics section governs the interactions with JES itself, while filesystems.gcs governs the localization
of data into and out of GCE VMs.
By default, application default credentials will be used. There is no configuration required for application default
credentials, only name and scheme are required.
To authenticate, run the following commands from your command line (requires gcloud):
$ gcloud auth login
$ gcloud config set project my-project
First create a new service account through the API Credentials page. Go to Create credentials -> Service account key. Then in the Service account dropdown select New service account. Fill in a name (e.g. my-account), and select key type of JSON.
Creating the account will cause the JSON file to be downloaded. The structure of this file is roughly like this (account name is my-account):
{
"type": "service_account",
"project_id": "my-project",
"private_key_id": "OMITTED",
"private_key": "-----BEGIN PRIVATE KEY-----\nBASE64 ENCODED KEY WITH \n TO REPRESENT NEWLINES\n-----END PRIVATE KEY-----\n",
"client_email": "my-account@my-project.iam.gserviceaccount.com",
"client_id": "22377410244549202395",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://accounts.google.com/o/oauth2/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/my-account%40my-project.iam.gserviceaccount.com"
}
Most importantly, the value of the client_email field should go into the service-account-id field in the configuration (see below). The
private_key portion needs to be pulled into its own file (e.g. my-key.pem). The \ns in the string need to be converted to newline characters.
A refresh_token field must be specified in the workflow options when submitting the job. Omitting this field will cause the workflow to fail.
The refresh token is passed to JES along with the client-id and client-secret pair specified in the corresponding entry in auths.
It is possible to reference private docker images in DockerHub to be run on JES. However, in order for the image to be pulled, the docker credentials with access to this image must be provided in the configuration file.
backend {
default = "JES"
providers {
JES {
actor-factory = "cromwell.backend.impl.local.LocalBackendLifecycleActorFactory"
config {
dockerhub {
account = "mydockeraccount@mail.com"
token = "mydockertoken"
}
}
}
}
}
It is now possible to reference an image only this account has access to:
task mytask {
command {
...
}
runtime {
docker: "private_repo/image"
memory: "8 GB"
cpu: "1"
}
...
}
Note that if the docker image to be used is public there is no need to add this configuration.
In order to monitor metrics (CPU, Memory, Disk usage...) about the VM during Call Runtime, a workflow option can be used to specify the path to a script that will run in the background and write its output to a log file.
{
"monitoring_script": "gs://cromwell/monitoring/script.sh"
}
The output of this script will be written to a monitoring.log file that will be available in the call gcs bucket when the call completes. This feature is meant to run a script in the background during long-running processes. It's possible that if the task is very short that the log file does not flush before de-localization happens and you will end up with a zero byte file.
Runtime attributes are used to customize tasks. Within a task one can specify runtime attributes to customize the environment for the call.
For example:
task jes_task {
command {
echo "Hello JES!"
}
runtime {
docker: "ubuntu:latest"
memory: "4G"
cpu: "3"
zones: "us-central1-c us-central1-b"
disks: "/mnt/mnt1 3 SSD, /mnt/mnt2 500 HDD"
}
}
workflow jes_workflow {
call jes_task
}
This table lists the currently available runtime attributes for cromwell:
| Runtime Attribute | LOCAL | JES | SGE |
|---|---|---|---|
| continueOnReturnCode | x | x | x |
| cpu | x | x | |
| disks | x | ||
| zones | x | ||
| docker | x | x | x |
| failOnStderr | x | x | x |
| memory | x | x | |
| preemptible | x | ||
| bootDiskSizeGb | x |
Runtime attribute values are interpreted as expressions. This means that it is possible to express the value of a runtime attribute as a function of one of the task's inputs. For example:
task runtime_test {
String ubuntu_tag
Int memory_gb
command {
./my_binary
}
runtime {
docker: "ubuntu:" + ubuntu_tag
memory: memory_gb + "GB"
}
}
SGE and similar backends may define other configurable runtime attributes beyond the five listed. See Sun GridEngine for more information.
Default values for runtime attributes can be specified via workflow options. For example, consider this WDL file:
task first {
command { ... }
}
task second {
command {...}
runtime {
docker: "my_docker_image"
}
}
workflow w {
call first
call second
}And this set of workflow options:
{
"default_runtime_attributes": {
"docker": "ubuntu:latest",
"zones": "us-central1-c us-central1-b"
}
}Then these values for docker and zones will be used for any task that does not explicitly override them in the WDL file. So the effective runtime for task first is:
{
"docker": "ubuntu:latest",
"zones": "us-central1-c us-central1-b"
}
And the effective runtime for task second is:
{
"docker": "my_docker_image",
"zones": "us-central1-c us-central1-b"
}
Note how for task second, the WDL value for docker is used instead of the default provided in the workflow options.
When each task finishes it returns a code. Normally, a non-zero return code indicates a failure. However you can override this behavior by specifying the continueOnReturnCode attribute.
When set to false, any non-zero return code will be considered a failure. When set to true, all return codes will be considered successful.
runtime {
continueOnReturnCode: true
}
When set to an integer, or an array of integers, only those integers will be considered as successful return codes.
runtime {
continueOnReturnCode: 1
}
runtime {
continueOnReturnCode: [0, 1]
}
Defaults to "0".
Passed to JES: "The minimum number of cores to use."
Passed to SGE, etc.: Configurable, but usually a reservation and/or limit of number of cores.
runtime {
cpu: 2
}
Defaults to "1".
Passed to JES: "Disks to attach."
The disks are specified as a comma separated list of disks. Each disk is further separated as a space separated triplet of:
- Mount point (absolute path), or
local-diskto reference the mount point where JES will localize files and the task's current working directory will be - Disk size in GB (ignored for disk type LOCAL)
- Disk type. One of: "LOCAL", "SSD", or "HDD" (documentation)
All tasks launched on JES must have a local-disk. If one is not specified in the runtime section of the task, then a default of local-disk 10 SSD will be used. The local-disk will be mounted to /cromwell_root.
The Disk type must be one of "LOCAL", "SSD", or "HDD". When set to "LOCAL", the size of the drive is automatically provisioned by Google so any size specified in WDL will be ignored. All disks are set to auto-delete after the job completes.
Example 1: Changing the Localization Disk
runtime {
disks: "local-disk 100 SSD"
}
Example 2: Mounting an Additional Two Disks
runtime {
disks: "/mnt/my_mnt 3 SSD, /mnt/my_mnt2 500 HDD"
}
In addition to working disks, JES allows specification of a boot disk size. This is the disk where the docker image itself is booted, not the working directory of your task on the VM. Its primary purpose is to ensure that larger docker images can fit on the boot disk.
runtime {
# Yikes, we have a big OS in this docker image! Allow 50GB to hold it:
bootDiskSizeGb: 50
}
Since no local-disk entry is specified, Cromwell will automatically add local-disk 10 SSD to this list.
The ordered list of zone preference (see Region and Zones documentation for specifics)
The zones are specified as a space separated list, with no commas.
runtime {
zones: "us-central1-c us-central1-b"
}
Defaults to "us-central1-b"
When specified, cromwell will run your task within the specified Docker image.
runtime {
docker: "ubuntu:latest"
}
This attribute is mandatory when submitting tasks to JES. When running on other backends, they default to not running the process within Docker.
Some programs write to the standard error stream when there is an error, but still return a zero exit code. Set failOnStderr to true for these tasks, and it will be considered a failure if anything is written to the standard error stream.
runtime {
failOnStderr: true
}
Defaults to "false".
Passed to JES: "The minimum amount of RAM to use."
Passed to SGE, etc.: Configurable, but usually a reservation and/or limit of memory.
The memory size is specified as an amount and units of memory, for example "4 G".
runtime {
memory: "4G"
}
Defaults to "2G".
Passed to JES: "If applicable, preemptible machines may be used for the run."
Take an Int as a value that indicates the maximum number of times Cromwell should request a preemptible machine for this task before defaulting back to a non-preemptible one. eg. With a value of 1, Cromwell will request a preemptible VM, if the VM is preempted, the task will be retried with a non-preemptible VM. Note: If specified, this attribute overrides workflow options.
runtime {
preemptible: 1
}
Defaults to "false".
Cromwell accepts two Java Properties or Environment Variables for controlling logging:
LOG_MODE- Accepts eitherprettyorstandard(defaultpretty). Instandardmode, logs will be written without ANSI escape code coloring, with a layout more appropriate for server logs, versusprettythat is easier to read for a single workflow run.LOG_LEVEL- Level at which to log (defaultinfo).
Additionally, a directory may be set for writing per workflow logs. By default, the per workflow logs will be erased once the workflow completes.
// In application.conf or specified via system properties
workflow-options {
workflow-log-dir: "cromwell-workflow-logs"
workflow-log-temporary: true
}The usual case of generating the temporary per workflow logs is to copy them to a remote directory, while deleting the local copy to preserve local disk space. To specify the remote directory to copy the logs to use the separate workflow option final_workflow_log_dir.
When running a workflow from the command line or REST API, one may specify a JSON file that toggles various options for running the workflow. From the command line, the workflow options is passed in as the third positional parameter to the 'run' subcommand. From the REST API, it's an optional part in the multi-part POST request. See the respective sections for more details.
Example workflow options file:
{
"jes_gcs_root": "gs://my-bucket/workflows",
"google_project": "my_google_project",
"refresh_token": "1/Fjf8gfJr5fdfNf9dk26fdn23FDm4x"
}Valid keys and their meanings:
- Global (use with any backend)
- write_to_cache - Accepts values
trueorfalse. Iffalse, the completed calls from this workflow will not be added to the cache. See the Call Caching section for more details. - read_from_cache - Accepts values
trueorfalse. Iffalse, Cromwell will not search the cache when invoking a call (i.e. every call will be executed unconditionally). See the Call Caching section for more details. - final_workflow_log_dir - Specifies a path where per-workflow logs will be written. If this is not specified, per-workflow logs will not be copied out of the Cromwell workflow log temporary directory/path before they are deleted.
- final_workflow_outputs_dir - Specifies a path where final workflow outputs will be written. If this is not specified, workflow outputs will not be copied out of the Cromwell workflow execution directory/path.
- final_call_logs_dir - Specifies a path where final call logs will be written. If this is not specified, call logs will not be copied out of the Cromwell workflow execution directory/path.
- default_runtime_attributes - A JSON object where the keys are runtime attributes and the values are defaults that will be used through the workflow invocation. Individual tasks can choose to override these values. See the runtime attributes section for more information.
- continueOnReturnCode - Can accept a boolean value or a comma separated list of integers in a string. Defaults to false. If false, then only return code of 0 will be acceptable for a task invocation. If true, then any return code is valid. If the value is a list of comma-separated integers in a string, this is interpreted as the acceptable return codes for this task.
- workflow_failure_mode - What happens after a task fails. Choose from:
- ContinueWhilePossible - continues to start and process calls in the workflow, as long as they did not depend on the failing call
- NoNewCalls - no new calls are started but existing calls are allowed to finish
- The default is
NoNewCallsbut this can be changed using theworkflow-options.workflow-failure-modeconfiguration option.
- backend - Override the default backend specified in the Cromwell configuration for this workflow only.
- write_to_cache - Accepts values
- JES Backend Only
- jes_gcs_root - (JES backend only) Specifies where outputs of the workflow will be written. Expects this to be a GCS URL (e.g.
gs://my-bucket/workflows). If this is not set, this defaults to the value withinbackend.jes.rootin the configuration. - google_project - (JES backend only) Specifies which google project to execute this workflow.
- refresh_token - (JES backend only) Only used if
localizeWithRefreshTokenis specified in the configuration file. - auth_bucket - (JES backend only) defaults to the the value in jes_gcs_root. This should represent a GCS URL that only Cromwell can write to. The Cromwell account is determined by the
google.authScheme(and the correspondinggoogle.userAuthandgoogle.serviceAuth) - monitoring_script - (JES backend only) Specifies a GCS URL to a script that will be invoked prior to the WDL command being run. For example, if the value for monitoring_script is "gs://bucket/script.sh", it will be invoked as
./script.sh > monitoring.log &. The valuemonitoring.logfile will be automatically de-localized.
- jes_gcs_root - (JES backend only) Specifies where outputs of the workflow will be written. Expects this to be a GCS URL (e.g.
Call Caching allows Cromwell to detect when a job has been run in the past so it doesn't have to re-compute results. Cromwell searches the cache of previously run jobs for a one that has the exact same command and exact same inputs. If a previously run job is found in the cache, Cromwell will copy the results of the previous job instead of re-running it.
Cromwell's call cache is maintained in its database. For best mileage with call caching, configure Cromwell to point to a MySQL database instead of the default in-memory database. This way any invocation of Cromwell (either with run or server subcommands) will be able to utilize results from all calls that are in that database.
Call Caching is disabled by default. Once enabled, Cromwell will search the call cache for every call statement invocation, assuming read_from_cache is enabled (see below):
- If there was no cache hit, the
callwill be executed as normal. Once finished it will add itself to the cache, assumingread_from_cacheis enabled (see below) - If there was a cache hit, outputs are copied from the cached job to the new job's output directory
Note: If call caching is enabled, be careful not to change the contents of the output directory for any previously run job. Doing so might cause cache hits in Cromwell to copy over modified data and Cromwell currently does not check that the contents of the output directory changed.
To enable Call Caching, add the following to your Cromwell configuration:
call-caching {
enabled = true
}
When call-caching.enabled=true (default: false), Cromwell will be able to to copy results from previously run jobs (when appropriate).
Cromwell also accepts two workflow option related to call caching:
- If call caching is enabled, but one wishes to run a workflow but not add any of the calls into the call cache when they finish, the
write_to_cacheoption can be set tofalse. This value defaults totrue. - If call caching is enabled, but you don't want to check the cache for any
callinvocations, set the optionread_from_cachetofalse. This value also defaults totrue
Note: If call caching is disabled, the to workflow options
read_from_cacheandwrite_to_cachewill be ignored and the options will be treated as though they were 'false'.
The server subcommand on the executable JAR will start an HTTP server which can accept WDL files to run as well as check status and output of existing workflows.
The following sub-sections define which HTTP Requests the web server can accept and what they will return. Example HTTP requests are given in HTTPie and cURL
All web server requests include an API version in the url. The current version is v1.
This endpoint accepts a POST request with a multipart/form-data encoded body. The form fields that may be included are:
wdlSource- Required Contains the WDL file to submit for execution.workflowInputs- Optional JSON file containing the inputs. A skeleton file can be generated from wdltool using the "inputs" subcommand.workflowOptions- Optional JSON file containing options for this workflow execution. See the run CLI sub-command for some more information about this.
cURL:
$ curl -v "localhost:8000/api/workflows/v1" -F wdlSource=@src/main/resources/3step.wdl -F workflowInputs=@test.json
HTTPie:
$ http --print=hbHB --form POST localhost:8000/api/workflows/v1 wdlSource=@src/main/resources/3step.wdl workflowInputs@inputs.json
Request:
POST /api/workflows/v1 HTTP/1.1
Accept: */*
Accept-Encoding: gzip, deflate
Connection: keep-alive
Content-Length: 730
Content-Type: multipart/form-data; boundary=64128d499e9e4616adea7d281f695dca
Host: localhost:8000
User-Agent: HTTPie/0.9.2
--64128d499e9e4616adea7d281f695dca
Content-Disposition: form-data; name="wdlSource"
task ps {
command {
ps
}
output {
File procs = stdout()
}
}
task cgrep {
command {
grep '${pattern}' ${File in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
task wc {
command {
cat ${File in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
workflow three_step {
call ps
call cgrep {
input: in_file=ps.procs
}
call wc {
input: in_file=ps.procs
}
}
--64128d499e9e4616adea7d281f695dca
Content-Disposition: form-data; name="workflowInputs"; filename="inputs.json"
{
"three_step.cgrep.pattern": "..."
}
--64128d499e9e4616adea7d281f695dca--
Response:
HTTP/1.1 201 Created
Content-Length: 74
Content-Type: application/json; charset=UTF-8
Date: Tue, 02 Jun 2015 18:06:28 GMT
Server: spray-can/1.3.3
{
"id": "69d1d92f-3895-4a7b-880a-82535e9a096e",
"status": "Submitted"
}
To specify workflow options as well:
cURL:
$ curl -v "localhost:8000/api/workflows/v1" -F wdlSource=@wdl/jes0.wdl -F workflowInputs=@wdl/jes0.json -F workflowOptions=@options.json
HTTPie:
http --print=HBhb --form POST http://localhost:8000/api/workflows/v1 wdlSource=@wdl/jes0.wdl workflowInputs@wdl/jes0.json workflowOptions@options.json
Request (some parts truncated for brevity):
POST /api/workflows/v1 HTTP/1.1
Accept: */*
Accept-Encoding: gzip, deflate
Connection: keep-alive
Content-Length: 1472
Content-Type: multipart/form-data; boundary=f3fd038395644de596c460257626edd7
Host: localhost:8000
User-Agent: HTTPie/0.9.2
--f3fd038395644de596c460257626edd7
Content-Disposition: form-data; name="wdlSource"
task x { ... }
task y { ... }
task z { ... }
workflow myworkflow {
call x
call y
call z {
input: example="gs://my-bucket/cromwell-executions/myworkflow/example.txt", int=3000
}
}
--f3fd038395644de596c460257626edd7
Content-Disposition: form-data; name="workflowInputs"; filename="jes0.json"
{
"myworkflow.x.x": "100"
}
--f3fd038395644de596c460257626edd7
Content-Disposition: form-data; name="workflowOptions"; filename="options.json"
{
"jes_gcs_root": "gs://myworkflow-dev/workflows"
}
--f3fd038395644de596c460257626edd7--
This endpoint accepts a POST request with a multipart/form-data
encoded body. The form fields that may be included are:
wdlSource- Required Contains the WDL file to submit for execution.workflowInputs- Required JSON file containing the inputs in a JSON array. A skeleton file for a single inputs json element can be generated from wdltool using the "inputs" subcommand. The orderded endpoint responses will contain one workflow submission response for each input, respectively.workflowOptions- Optional JSON file containing options for this workflow execution. See the run CLI sub-command for some more information about this.
cURL:
$ curl -v "localhost:8000/api/workflows/v1/batch" -F wdlSource=@src/main/resources/3step.wdl -F workflowInputs=@test_array.json
HTTPie:
$ http --print=hbHB --form POST localhost:8000/api/workflows/v1/batch wdlSource=@src/main/resources/3step.wdl workflowInputs@inputs_array.json
Request:
POST /api/workflows/v1/batch HTTP/1.1
Accept: */*
Accept-Encoding: gzip, deflate
Connection: keep-alive
Content-Length: 750
Content-Type: multipart/form-data; boundary=64128d499e9e4616adea7d281f695dcb
Host: localhost:8000
User-Agent: HTTPie/0.9.2
--64128d499e9e4616adea7d281f695dcb
Content-Disposition: form-data; name="wdlSource"
task ps {
command {
ps
}
output {
File procs = stdout()
}
}
task cgrep {
command {
grep '${pattern}' ${File in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
task wc {
command {
cat ${File in_file} | wc -l
}
output {
Int count = read_int(stdout())
}
}
workflow three_step {
call ps
call cgrep {
input: in_file=ps.procs
}
call wc {
input: in_file=ps.procs
}
}
--64128d499e9e4616adea7d281f695dcb
Content-Disposition: form-data; name="workflowInputs"; filename="inputs_array.json"
[
{
"three_step.cgrep.pattern": "..."
},
{
"three_step.cgrep.pattern": "..."
}
]
--64128d499e9e4616adea7d281f695dcb--
Response:
HTTP/1.1 201 Created
Content-Length: 96
Content-Type: application/json; charset=UTF-8
Date: Tue, 02 Jun 2015 18:06:28 GMT
Server: spray-can/1.3.3
[
{
"id": "69d1d92f-3895-4a7b-880a-82535e9a096e",
"status": "Submitted"
},
{
"id": "69d1d92f-3895-4a7b-880a-82535e9a096f",
"status": "Submitted"
}
]
To specify workflow options as well:
cURL:
$ curl -v "localhost:8000/api/workflows/v1/batch" -F wdlSource=@wdl/jes0.wdl -F workflowInputs=@wdl/jes0_array.json -F workflowOptions=@options.json
HTTPie:
http --print=HBhb --form POST http://localhost:8000/api/workflows/v1/batch wdlSource=@wdl/jes0.wdl workflowInputs@wdl/jes0_array.json workflowOptions@options.json
Request (some parts truncated for brevity):
POST /api/workflows/v1/batch HTTP/1.1
Accept: */*
Accept-Encoding: gzip, deflate
Connection: keep-alive
Content-Length: 1492
Content-Type: multipart/form-data; boundary=f3fd038395644de596c460257626edd8
Host: localhost:8000
User-Agent: HTTPie/0.9.2
--f3fd038395644de596c460257626edd8
Content-Disposition: form-data; name="wdlSource"
task x { ... }
task y { ... }
task z { ... }
workflow myworkflow {
call x
call y
call z {
input: example="gs://my-bucket/cromwell-executions/myworkflow/example.txt", int=3000
}
}
--f3fd038395644de596c460257626edd8
Content-Disposition: form-data; name="workflowInputs"; filename="jes0_array.json"
[
{
"myworkflow.x.x": "100"
}, {
"myworkflow.x.x": "101"
}
]
--f3fd038395644de596c460257626edd8
Content-Disposition: form-data; name="workflowOptions"; filename="options.json"
{
"jes_gcs_root": "gs://myworkflow-dev/workflows"
}
--f3fd038395644de596c460257626edd8--
This endpoint allows for querying workflows based on the following criteria:
nameidstatusstart(start datetime with mandatory offset)end(end datetime with mandatory offset)page(page of results)pagesize(# of results per page)
Names, ids, and statuses can be given multiple times to include workflows with any of the specified names, ids, or statuses. When multiple names are specified, any workflow matching one of the names will be returned. The same is true for multiple ids or statuses. When different types of criteria are specified, for example names and statuses, the results must match both the one of the specified names and one of the statuses. Using page and pagesize will enable server side pagination.
Valid statuses are Submitted, Running, Aborting, Aborted, Failed, and Succeeded. start and end should
be in ISO8601 datetime format with mandatory offset and start cannot be after end.
cURL:
$ curl "http://localhost:8000/api/workflows/v1/query?start=2015-11-01T00%3A00%3A00-04%3A00&end=2015-11-04T00%3A00%3A00-04%3A00&status=Failed&status=Succeeded&page=1&pagesize=10"
HTTPie:
$ http "http://localhost:8000/api/workflows/v1/query?start=2015-11-01T00%3A00%3A00-04%3A00&end=2015-11-04T00%3A00%3A00-04%3A00&status=Failed&status=Succeeded&page=1&pagesize=10"
Response:
HTTP/1.1 200 OK
Content-Length: 133
Content-Type: application/json; charset=UTF-8
Date: Tue, 02 Jun 2015 18:06:56 GMT
Server: spray-can/1.3.3
{
"results": [
{
"name": "w",
"id": "fdfa8482-e870-4528-b639-73514b0469b2",
"status": "Succeeded",
"end": "2015-11-01T07:45:52.000-05:00",
"start": "2015-11-01T07:38:57.000-05:00"
},
{
"name": "hello",
"id": "e69895b1-42ed-40e1-b42d-888532c49a0f",
"status": "Succeeded",
"end": "2015-11-01T07:45:30.000-05:00",
"start": "2015-11-01T07:38:58.000-05:00"
},
{
"name": "crasher",
"id": "ed44cce4-d21b-4c42-b76d-9d145e4d3607",
"status": "Failed",
"end": "2015-11-01T07:45:44.000-05:00",
"start": "2015-11-01T07:38:59.000-05:00"
}
]
}
Query data is refreshed from raw data periodically according to the configuration value services.MetadataService.metadata-summary-refresh-interval.
This interval represents the duration between the end of one summary refresh sweep and the beginning of the next sweep. If not specified the
refresh interval will default to 2 seconds. To turn off metadata summary refresh, specify an infinite refresh interval value with "Inf".
services {
MetadataService {
metadata-summary-refresh-interval = "10 seconds"
}
}
This endpoint allows for querying workflows based on the same criteria as GET /api/workflows/:version/query.
Instead of specifying query parameters in the URL, the parameters
must be sent via the POST body. The request content type must be
application/json. The json should be a list of objects. Each json
object should contain a different criterion.
cURL:
$ curl -X POST --header "Content-Type: application/json" -d "[{\"start\": \"2015-11-01T00:00:00-04:00\"}, {\"end\": \"2015-11-04T00:00:00-04:00\"}, {\"status\": \"Failed\"}, {\"status\": \"Succeeded\"}, {\"page\": \"1\"}, {\"pagesize\": \"10\"}]" "http://localhost:8000/api/workflows/v1/query"
HTTPie:
$ echo "[{\"start\": \"2015-11-01T00:00:00-04:00\"}, {\"end\": \"2015-11-04T00:00:00-04:00\"}, {\"status\": \"Failed\"}, {\"status\": \"Succeeded\"}, {\"page\": \"1\"}, {\"pagesize\": \"10\"}]" | http "http://localhost:8000/api/workflows/v1/query"
Response:
HTTP/1.1 200 OK
Content-Length: 133
Content-Type: application/json; charset=UTF-8
Date: Tue, 02 Jun 2015 18:06:56 GMT
Server: spray-can/1.3.3
{
"results": [
{
"name": "w",
"id": "fdfa8482-e870-4528-b639-73514b0469b2",
"status": "Succeeded",
"end": "2015-11-01T07:45:52.000-05:00",
"start": "2015-11-01T07:38:57.000-05:00"
},
{
"name": "hello",
"id": "e69895b1-42ed-40e1-b42d-888532c49a0f",
"status": "Succeeded",
"end": "2015-11-01T07:45:30.000-05:00",
"start": "2015-11-01T07:38:58.000-05:00"
},
{
"name": "crasher",
"id": "ed44cce4-d21b-4c42-b76d-9d145e4d3607",
"status": "Failed",
"end": "2015-11-01T07:45:44.000-05:00",
"start": "2015-11-01T07:38:59.000-05:00"
}
]
}
cURL:
$ curl http://localhost:8000/api/workflows/v1/69d1d92f-3895-4a7b-880a-82535e9a096e/status
HTTPie:
$ http http://localhost:8000/api/workflows/v1/69d1d92f-3895-4a7b-880a-82535e9a096e/status
Response:
HTTP/1.1 200 OK
Content-Length: 74
Content-Type: application/json; charset=UTF-8
Date: Tue, 02 Jun 2015 18:06:56 GMT
Server: spray-can/1.3.3
{
"id": "69d1d92f-3895-4a7b-880a-82535e9a096e",
"status": "Succeeded"
}
cURL:
$ curl http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/outputs
HTTPie:
$ http http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/outputs
Response:
HTTP/1.1 200 OK
Content-Length: 241
Content-Type: application/json; charset=UTF-8
Date: Thu, 04 Jun 2015 12:15:33 GMT
Server: spray-can/1.3.3
{
"id": "e442e52a-9de1-47f0-8b4f-e6e565008cf1",
"outputs": {
"three_step.cgrep.count": 8,
"three_step.ps.procs": "/var/folders/kg/c7vgxnn902lc3qvc2z2g81s89xhzdz/T/stdout2814345504446060277.tmp",
"three_step.wc.count": 8
}
}
This endpoint is meant to be used in a web browser. It will show a Gantt Chart of a particular workflow. The bars in the chart represent start and end times for individual task invocations.
This will return paths to the standard out and standard error files that were generated during the execution of all calls in a workflow.
A call has one or more standard out and standard error logs, depending on if the call was scattered or not. In the latter case, one log is provided for each instance of the call that has been run.
cURL:
$ curl http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/logs
HTTPie:
$ http http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/logs
Response:
HTTP/1.1 200 OK
Content-Length: 379
Content-Type: application/json; charset=UTF-8
Date: Mon, 03 Aug 2015 17:11:28 GMT
Server: spray-can/1.3.3
{
"id": "b3e45584-9450-4e73-9523-fc3ccf749848",
"logs": {
"call.ps": [
{
"stderr": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-ps/stderr6126967977036995110.tmp",
"stdout": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-ps/stdout6128485235785447571.tmp"
}
],
"call.cgrep": [
{
"stderr": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-cgrep/stderr6126967977036995110.tmp",
"stdout": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-cgrep/stdout6128485235785447571.tmp"
}
],
"call.wc": [
{
"stderr": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-wc/stderr6126967977036995110.tmp",
"stdout": "/home/user/test/b3e45584-9450-4e73-9523-fc3ccf749848/call-wc/stdout6128485235785447571.tmp"
}
]
}
}
This endpoint returns a superset of the data from #get-workflowsversionidlogs in essentially the same format (i.e. shards are accounted for by an array of maps, in the same order as the shards). In addition to shards, every attempt that was made for this call will have its own object as well, in the same order as the attempts. Workflow metadata includes submission, start, and end datetimes, as well as status, inputs and outputs. Call-level metadata includes inputs, outputs, start and end datetime, backend-specific job id, return code, stdout and stderr. Date formats are ISO with milliseconds.
Accepted parameters are:
-
includeKeyOptional repeated string value, specifies what metadata keys to include in the output, matched as a prefix string. Keys that are not specified are filtered out. The call keysattemptandshardIndexwill always be included. May not be used withexcludeKey. -
excludeKeyOptional repeated string value, specifies what metadata keys to exclude from the output, matched as a prefix string. Keys that are specified are filtered out. The call keysattemptandshardIndexwill always be included. May not be used withincludeKey.
cURL:
$ curl http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/metadata
HTTPie:
$ http http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/metadata
Response:
HTTP/1.1 200 OK
Server spray-can/1.3.3 is not blacklisted
Server: spray-can/1.3.3
Date: Thu, 01 Oct 2015 22:18:07 GMT
Content-Type: application/json; charset=UTF-8
Content-Length: 7286
{
"workflowName": "sc_test",
"calls": {
"sc_test.do_prepare": [
{
"executionStatus": "Done",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/stdout",
"shardIndex": -1,
"outputs": {
"split_files": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_aa",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_ad"
]
},
"inputs": {
"input_file": "/home/jdoe/cromwell/11.txt"
},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"returnCode": 0,
"backend": "Local",
"end": "2016-02-04T13:47:56.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/stderr",
"attempt": 1,
"executionEvents": [],
"start": "2016-02-04T13:47:55.000-05:00"
}
],
"sc_test.do_scatter": [
{
"executionStatus": "Preempted",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/stdout",
"shardIndex": 0,
"outputs": {},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"inputs": {
"input_file": "f"
},
"backend": "Local",
"end": "2016-02-04T13:47:56.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/stderr",
"attempt": 1,
"executionEvents": [],
"start": "2016-02-04T13:47:56.000-05:00"
},
{
"executionStatus": "Done",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/stdout",
"shardIndex": 0,
"outputs": {
"count_file": "/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/output.txt"
},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"inputs": {
"input_file": "f"
},
"returnCode": 0,
"end": "2016-02-04T13:47:56.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/stderr",
"attempt": 2,
"executionEvents": [],
"start": "2016-02-04T13:47:56.000-05:00"
},
{
"executionStatus": "Done",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/stdout",
"shardIndex": 1,
"outputs": {
"count_file": "/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/output.txt"
},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"inputs": {
"input_file": "f"
},
"returnCode": 0,
"backend": "Local",
"end": "2016-02-04T13:47:56.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/stderr",
"attempt": 1,
"executionEvents": [],
"start": "2016-02-04T13:47:56.000-05:00"
}
],
"sc_test.do_gather": [
{
"executionStatus": "Done",
"stdout": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_gather/stdout",
"shardIndex": -1,
"outputs": {
"sum": 12
},
"runtimeAttributes": {
"failOnStderr": "true",
"continueOnReturnCode": "0"
},
"inputs": {
"input_files": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/output.txt"
]
},
"returnCode": 0,
"backend": "Local",
"end": "2016-02-04T13:47:57.000-05:00",
"stderr": "/home/jdoe/cromwell/cromwell-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_gather/stderr",
"attempt": 1,
"executionEvents": [],
"start": "2016-02-04T13:47:56.000-05:00"
}
]
},
"outputs": {
"sc_test.do_gather.sum": 12,
"sc_test.do_prepare.split_files": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_aa",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_ad"
],
"sc_test.do_scatter.count_file": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/output.txt"
]
},
"id": "8e592ed8-ebe5-4be0-8dcb-4073a41fe180",
"inputs": {
"sc_test.do_prepare.input_file": "/home/jdoe/cromwell/11.txt"
},
"submission": "2016-02-04T13:47:55.000-05:00",
"status": "Succeeded",
"end": "2016-02-04T13:47:57.000-05:00",
"start": "2016-02-04T13:47:55.000-05:00"
}
cURL:
$ curl "http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/metadata?includeKey=inputs&includeKey=outputs"
HTTPie:
$ http "http://localhost:8000/api/workflows/v1/b3e45584-9450-4e73-9523-fc3ccf749848/metadata?includeKey=inputs&includeKey=outputs"
Response:
HTTP/1.1 200 OK
Server spray-can/1.3.3 is not blacklisted
Server: spray-can/1.3.3
Date: Thu, 01 Oct 2015 22:19:07 GMT
Content-Type: application/json; charset=UTF-8
Content-Length: 4286
{
"calls": {
"sc_test.do_prepare": [
{
"shardIndex": -1,
"outputs": {
"split_files": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_aa",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_ad"
]
},
"inputs": {
"input_file": "/home/jdoe/cromwell/11.txt"
},
"attempt": 1
}
],
"sc_test.do_scatter": [
{
"shardIndex": 0,
"outputs": {},
"inputs": {
"input_file": "f"
},
"attempt": 1
},
{
"shardIndex": 0,
"outputs": {
"count_file": "/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/output.txt"
},
"inputs": {
"input_file": "f"
},
"attempt": 2
},
{
"shardIndex": 1,
"outputs": {
"count_file": "/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/output.txt"
},
"inputs": {
"input_file": "f"
},
"attempt": 1
}
],
"sc_test.do_gather": [
{
"shardIndex": -1,
"outputs": {
"sum": 12
}
"inputs": {
"input_files": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/output.txt"
]
},
"attempt": 1
}
]
},
"outputs": {
"sc_test.do_gather.sum": 12,
"sc_test.do_prepare.split_files": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_aa",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_prepare/temp_ad"
],
"sc_test.do_scatter.count_file": [
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-0/attempt-2/output.txt",
"/home/jdoe/cromwell/cromwell-test-executions/sc_test/8e592ed8-ebe5-4be0-8dcb-4073a41fe180/call-do_scatter/shard-1/output.txt"
]
},
"id": "8e592ed8-ebe5-4be0-8dcb-4073a41fe180",
"inputs": {
"sc_test.do_prepare.input_file": "/home/jdoe/cromwell/11.txt"
}
}
The call and workflow may optionally contain failures shaped like this:
"failures": [
{
"failure": "The failure message",
"timestamp": "2016-02-25T10:49:02.066-05:00"
}
]
cURL:
$ curl -X POST http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/abort
HTTPie:
$ http POST http://localhost:8000/api/workflows/v1/e442e52a-9de1-47f0-8b4f-e6e565008cf1/abort
Response:
HTTP/1.1 200 OK
Content-Length: 241
Content-Type: application/json; charset=UTF-8
Date: Thu, 04 Jun 2015 12:15:33 GMT
Server: spray-can/1.3.3
{
"id": "e442e52a-9de1-47f0-8b4f-e6e565008cf1",
"status": "Aborted"
}
This endpoint returns a list of the backends supported by the server as well as the default backend.
cURL:
$ curl http://localhost:8000/api/workflows/v1/backends
HTTPie:
$ http http://localhost:8000/api/workflows/v1/backends
Response:
HTTP/1.1 200 OK
Content-Length: 379
Content-Type: application/json; charset=UTF-8
Date: Mon, 03 Aug 2015 17:11:28 GMT
Server: spray-can/1.3.3
{
"supportedBackends": ["JES", "LSF", "Local", "SGE"],
"defaultBackend": "Local"
}
This endpoint returns some basic statistics on the current state of the engine. At the moment that includes the number of running workflows and the number of active jobs.
cURL:
$ curl http://localhost:8000/api/engine/v1/stats
HTTPie:
$ http http://localhost:8000/api/engine/v1/stats
Response:
"date": "Sun, 18 Sep 2016 14:38:11 GMT",
"server": "spray-can/1.3.3",
"content-length": "33",
"content-type": "application/json; charset=UTF-8"
{
"workflows": 3,
"jobs": 10
}
Requests that Cromwell can't process return a failure in the form of a JSON response respecting the following JSON schema:
{
"$schema": "http://json-schema.org/draft-04/schema#",
"description": "Error response schema",
"type": "object",
"properties": {
"status": {
"enum": [ "fail", "error"]
},
"message": {
"type": "string"
},
"errors": {
"type": "array",
"minItems": 1,
"items": { "type": "string" },
"uniqueItems": true
}
},
"required": ["status", "message"]
}
The status field can take two values:
"fail" means that the request was invalid and/or data validation failed. "fail" status is most likely returned with a 4xx HTTP Status code. e.g.
{
"status": "fail",
"message": "Workflow input processing failed.",
"errors": [
"Required workflow input 'helloworld.input' not specified."
]
}
"error" means that an error occurred while processing the request. "error" status is most likely returned with a 5xx HTTP Status code. e.g.
{
"status": "error",
"message": "Connection to the database failed."
}
The message field contains a short description of the error.
The errors field is optional and may contain additional information about why the request failed.
$ pip install mdtoc
$ mdtoc --check-links README.md
Essentially run sbt doc then commit the generated code into the gh-pages branch on this repository
$ sbt doc
$ git co gh-pages
$ mv target/scala-2.11/api scaladoc
$ git add scaladoc
$ git commit -m "API Docs"
$ git push origin gh-pages