This is a client for CrowdProcess's REST API.
It works in python 2.7 and 3.4+.
pip install crowdprocess
or
easy_install crowdprocess
>>> from crowdprocess import CrowdProcess
>>> crp = CrowdProcess('username', 'password')
>>> x2 = crp.job('function Run (d) { return d*2; }')
>>> results = x2(range(5)).results
>>> list(results)
[0, 2, 4, 6, 8, 10] # comes in a random order>>> from crowdprocess import CrowdProcess
>>> crp = CrowdProcess('[email protected]', 'password')To get those credentials you must register with CrowdProcess.
You can also instanciate it with a token instead of a username and password:
>>> crp = CrowdProcess(token='3c46d593-5435-47c5-92aa-1613ade978c2')With the CrowdProcess class instanciated above,
>>> program='function Run (d) { return d }'
>>> job = crp.job(program)
>>> job.id
'3c46d593-5435-47c5-92aa-1613ade978c2'Invoking crp.job with the program parameter automatically creates a job in CrowdProcess and returns an instanciated Job.
After you get a job.id, you can use it to get a Job again, without creating it:
>>> job = crp.job(id='3c46d593-5435-47c5-92aa-1613ade978c2')>>> crp.list_jobs()
[{u'status': u'active', u'failed': 0, u'bid': 1, u'created': u'2014-05-14T10:07:52.747503Z', u'modified': u'2014-05-14T10:07:53.716147Z', u'browserHours': 137, u'finished': 1000, u'lastResult': u'2014-05-14T10:07:59.06Z', u'total': 1000, u'id': u'3c46d593-5435-47c5-92aa-1613ade978c2'}]Prettier:
>>> jobs = crp.list_jobs()
>>> print(json.dumps(jobs, sort_keys=True, indent=2))
[
{
"bid": 1,
"browserHours": 137,
"created": "2014-05-14T10:07:52.747503Z",
"failed": 0,
"finished": 1000,
"id": "3c46d593-5435-47c5-92aa-1613ade978c2",
"lastResult": "2014-05-14T10:07:59.06Z",
"modified": "2014-05-14T10:07:53.716147Z",
"status": "active",
"total": 1000
}
]>>> job = crp.job(id='3c46d593-5435-47c5-92aa-1613ade978c2')
>>> job.delete()>>> crp.delete_jobs()After creating a job, you're all set to send it tasks and get back results.
tasks can be any iterable object, results will be a generator:
>>> job = crp.job('function Run (d) { return Math.pow(d, 2); }')
>>> tasks = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> results = job(tasks).results
>>> list(results)
[49, 64, 16, 25, 9, 36, 4, 81, 0, 1]which would be the same as,
>>> job = crp.job('function Run (d) { return Math.pow(d, 2); }')
>>> list(job(range(10)).results)
[49, 64, 16, 25, 9, 36, 4, 81, 0, 1]which would also be the same as,
>>> job = crp.job('function Run (d) { return Math.pow(d, 2); }')
>>> def tasks():
... for i in range(10):
... yield i
...
>>> list(job(tasks).results)
[25, 64, 49, 16, 36, 9, 0, 81, 1, 4]Notice that the results never come in order.
>>> multiply = crp.job('function Run (d) { return d*2 }')
>>> divide = crp.job('function Run (d) { return d/2 }')
>>> numbers = range(10)
>>> multiplied = multiply(numbers).results
>>> divided = divide(multiplied).results
>>> list(divided)
[7, 2, 6, 1, 5, 9, 8, 4, 3, 0]Sometimes your tasks will throw uncaught exceptions that you should know about, and you can get them the same way you get results:
>>> job = crp.job('function Run (d) { if (d === 4) { throw new Error("oh no, "+d) } return d; }')
>>> tasks = range(10)
>>> errors = job(tasks).errors
>>> for error in errors:
... print errorOnce again, tasks may be any iterable:
>>> multiply = crp.job('function Run (d) { return d*2 }')
>>> multiply.submit_tasks(range(10))>>> results = multiply.get_results()
>>> list(results)
[18, 8, 10, 4, 6, 16, 14, 0, 2, 12]This delivers all the job's computed results at the moment, but you should in fact get every result as soon as it's computed, in a stream:
You can also iterate through every result as soon as it comes in:
>>> expected_results = 10
>>> results = multiply.get_results_stream()
>>> for result in results:
... print(result)
... expected_results -= 1
... if expected_results == 0:
... breakThe stream does not know if or when a result might be computed and delivered, so you must count how many results you still expect to break the loop.
To use this properly you should start listening for streaming results before sending tasks, probably a separate thread:
>>> import threading
>>> job = crp.job("function Run(d) { return d; }")
>>> def get_results():
... expected_results = 10
... for result in job.get_results_stream():
... print(result)
... expected_results -= 1
... if expected_results == 0:
... break
...
>>> t = threading.Thread(target=get_results)
>>> t.start()
>>> job.submit_tasks(range(10))
>>> 7
9
6
2
3
8
1
4
0
5
>>> t.join()Sometimes your tasks will have uncaught exceptions and those will cause a result to not be delivered, so you must account for those as well to decrease your expected_results counter.
Sometimes your tasks throw uncaught exceptions, and you should get them:
>>> program = """
... function Run (d) {
... if (d === 4) {
... throw new Error("oops, it's "+d);
... } else {
... return d;
... }
... }
... """
>>> job = crp.job(program)
>>> job.submit_tasks(range(10))
>>> list(job.get_results())
[1, 6, 9, 8, 5, 7, 2, 3, 0] # oh no, 4 is missing...
>>> list(job.get_errors())
[{u'message': u"oops, it's 4", u'type': u'program', u'name': u'Error', u'stack': u'Run@blob:9a4029f7-fff7-4da8-b552-92507e341749:5\n[2]</</self.onmessage@blob:9a4029f7-fff7-4da8-b552-92507e341749:9\n'}]
>>> print(json.dumps(list(job.get_errors()), sort_keys=True, indent=2)) # prettier
[
{
"message": "oops, it's 4",
"name": "Error",
"stack": "Run@blob:9a4029f7-fff7-4da8-b552-92507e341749:5\n[2]</</self.onmessage@blob:9a4029f7-fff7-4da8-b552-92507e341749:9\n",
"type": "program"
}
]The same way you get streaming results, you can (and should) get streaming errors:
>>> errors = multiply.get_errors_stream()
>>> for error in errors:
... print(error)