This documentation is for a development version of IPython. There may be significant differences from the latest stable release.

IPython’s Task Database

Enabling a DB Backend

The IPython Hub can store all task requests and results in a database. Currently supported backends are: MongoDB, SQLite, and an in-memory DictDB.

The default is to store recent tasks in a dictionary in memory, which deletes old values if it gets too big, and only survives as long as the controller is running.

Using a real database is optional due to its potential Cost. You can enable one, either at the command-line:

$> ipcontroller --sqlitedb # or --mongodb or --nodb

or in your

c.HubFactory.db_class = "NoDB"
c.HubFactory.db_class = "DictDB" # default
c.HubFactory.db_class = "MongoDB"
c.HubFactory.db_class = "SQLiteDB"

Using the Task Database

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. When using other backends, the interface is emulated and only a subset of queries is possible.

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(ascii) 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(ascii) IDENT of client’s socket
engine_uuid uuid(ascii) 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 uuid(ascii) msg_id of resubmitted task (if applicable)
result_header dict header for result
result_content dict content for result
result_buffers list(bytes) buffers containing serialized request objects
queue str The name of the queue for the task (‘mux’ or ‘task’)
execute_input str Python input source
execute_result dict Python output (execute_result message content)
error dict Python traceback (error message content)
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({'completed' : None}, keys=['msg_id', 'started'])

All jobs started in the last hour by me:

In [1]: from datetime import datetime, timedelta

In [2]: hourago = - 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')


The advantage of the database backends is, of course, that large amounts of data can be stored that won’t fit in memory. The basic DictDB ‘backend’ is actually to just store all of this information in a Python dictionary. This is very fast, but will run out of memory quickly if you move a lot of data around, or your cluster is to run for a long time.

Unfortunately, the DB backends (SQLite and MongoDB) right now are rather slow, and can still consume large amounts of resources, particularly if large tasks or results are being created at a high frequency.

For this reason, we have added NoDB,a dummy backend that doesn’t actually store any information. When you use this database, nothing is stored, and any request for results will result in a KeyError. This obviously prevents later requests for results and task resubmission from functioning, but sometimes those nice features are not as useful as keeping Hub memory under control.