IPython Documentation

Table Of Contents

Previous topic

Using MPI with IPython

Next topic

Security details of IPython

This Page

IPython’s Task Database

The IPython Hub stores all task requests and results in a database. Currently supported backends are: MongoDB, SQLite (the default), and an in-memory DictDB. The most common use case for this is clients requesting results for tasks they did not submit, via:

In [1]: rc.get_result(task_id)

However, since we have this DB backend, we provide a direct query method in the client for users who want deeper introspection into their task history. The db_query() method of the Client is modeled after MongoDB queries, so if you have used MongoDB it should look familiar. In fact, when the MongoDB backend is in use, the query is relayed directly. However, when using other backends, the interface is emulated and only a subset of queries is possible.

See also

MongoDB query docs: http://www.mongodb.org/display/DOCS/Querying

Client.db_query() takes a dictionary query object, with keys from the TaskRecord key list, and values of either exact values to test, or MongoDB queries, which are dicts of The form: {'operator' : 'argument(s)'}. There is also an optional keys argument, that specifies which subset of keys should be retrieved. The default is to retrieve all keys excluding the request and result buffers. db_query() returns a list of TaskRecord dicts. Also like MongoDB, the msg_id key will always be included, whether requested or not.

TaskRecord keys:

Key Type Description
msg_id uuid(bytes) The msg ID
header dict The request header
content dict The request content (likely empty)
buffers list(bytes) buffers containing serialized request objects
submitted datetime timestamp for time of submission (set by client)
client_uuid uuid(bytes) IDENT of client’s socket
engine_uuid uuid(bytes) IDENT of engine’s socket
started datetime time task began execution on engine
completed datetime time task finished execution (success or failure) on engine
resubmitted datetime time of resubmission (if applicable)
result_header dict header for result
result_content dict content for result
result_buffers list(bytes) buffers containing serialized request objects
queue bytes The name of the queue for the task (‘mux’ or ‘task’)
pyin <unused> Python input (unused)
pyout <unused> Python output (unused)
pyerr <unused> Python traceback (unused)
stdout str Stream of stdout data
stderr str Stream of stderr data

MongoDB operators we emulate on all backends:

Operator Python equivalent
‘$in’ in
‘$nin’ not in
‘$eq’ ==
‘$ne’ !=
‘$ge’ >
‘$gte’ >=
‘$le’ <
‘$lte’ <=

The DB Query is useful for two primary cases:

  1. deep polling of task status or metadata
  2. selecting a subset of tasks, on which to perform a later operation (e.g. wait on result, purge records, resubmit,...)

Example Queries

To get all msg_ids that are not completed, only retrieving their ID and start time:

In [1]: incomplete = rc.db_query({'complete' : None}, keys=['msg_id', 'started'])

All jobs started in the last hour by me:

In [1]: from datetime import datetime, timedelta

In [2]: hourago = datetime.now() - timedelta(1./24)

In [3]: recent = rc.db_query({'started' : {'$gte' : hourago },
                                'client_uuid' : rc.session.session})

All jobs started more than an hour ago, by clients other than me:

In [3]: recent = rc.db_query({'started' : {'$le' : hourago },
                                'client_uuid' : {'$ne' : rc.session.session}})

Result headers for all jobs on engine 3 or 4:

In [1]: uuids = map(rc._engines.get, (3,4))

In [2]: hist34 = rc.db_query({'engine_uuid' : {'$in' : uuids }, keys='result_header')