Description:
pythonsjsclient 0.10.0
Features
Supports Spark Jobserver 0.6.0+
Library Installation
$ pip install python-sjsclient
Getting started
First create a client instance:
>>> from sjsclient import client
>>> sjs = client.Client("http://JOB_SERVER_URL:PORT")
Uploading a jar to Spark Jobserver:
>>> jar_file_path = os.path.join("path", "to", "jar")
>>> jar_blob = open(jar_file_path, 'rb').read()
>>> app = sjs.apps.create("test_app", jar_blob)
Uploading a python egg to Spark Jobserver:
>>> from sjsclient import app
>>> egg_file_path = os.path.join("path", "to", "egg")
>>> egg_blob = open(egg_file_path, 'rb').read()
>>> app = sjs.apps.create("test_python_app", egg_blob, app.AppType.PYTHON)
Listing available apps:
>>> for app in sjs.apps.list():
... print app.name
...
test_app
my_streaming_app
Creating an adhoc job:
>>> test_app = sjs.apps.get("test_app")
>>> class_path = "spark.jobserver.VeryShortDoubleJob"
>>> config = {"test_config": "test_config_value"}
>>> job = sjs.jobs.create(test_app, class_path, conf=config)
>>> print("Job Status: ", job.status)
Job Status: STARTED
Creating a synchronous adhoc job:
>>> job = sjs.jobs.create(test_app, class_path, conf=config, sync=True)
>>> print(job.result)
[2, 4, 6]
Polling for job status:
>>> job = sjs.jobs.create(...)
>>> while job.status != "FINISHED":
>>> time.sleep(2)
>>> job = sjs.jobs.get(job.jobId)
Getting job config:
>>> config = {"test_config": "test_config_value"}
>>> job = sjs.jobs.create(test_app, class_path, conf=config)
>>> job_config = job.get_config()
>>> print("test_config value: ", job_config["test_config"])
test_config_value: test_config_value
Listing jobs:
>>> for job in sjs.jobs.list():
... print job.jobId
...
8c5bd52f-6486-44ee-9ac3-a8327ee40494
24b67573-3115-49c7-983c-d0eff0499b71
99c8be9e-a0ec-42dd-8a2c-9a8680bc5051
bb82f712-d4b4-43a4-8e4d-e4bb272e85db
Limiting jobs list:
>>> for job in sjs.jobs.list(limit=1):
... print job.jobId
...
8c5bd52f-6486-44ee-9ac3-a8327ee40494
Creating a named context:
>>> ctx_config = {'num-cpu-cores': '1', 'memory-per-node': '512m'}
>>> ctx = sjs.contexts.create("test_context", ctx_config)
Running a job in a named context:
>>> test_app = sjs.apps.get("test_app")
>>> test_ctx = sjs.contexts.get("test_context")
>>> config = {"test_config": "test_config_value"}
>>> job = sjs.jobs.create(test_app, class_path, ctx=test_ctx, conf=config)
>>> print("Job Status: ", job.status)
Job Status: STARTED
Documentation
http://python-sjsclient.readthedocs.org
Discussion list
spark-jobserver google group: https://groups.google.com/forum/#!forum/spark-jobserver
Requirements
Python >= 2.7.0
License
python-sjsclient is offered under the Apache 2 license.
Source code
The latest developer version is available in a github repository:
https://github.com/spark-jobserver/python-sjsclient
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Files:
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