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Time series API built on top of Redis that can be used to store and query time series statistics. Multiple time granularities can be used to keep track of different time intervals.
.. image:: https://img.shields.io/pypi/v/redis_timeseries.svg :target: https://pypi.python.org/pypi/redis_timeseries
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To install Redis Timeseries, run this command in your terminal:
.. code-block:: console
$ pip install redis_timeseries
To initialize the TimeSeries class, you must pass a Redis client to access the database. You may also override the base key for the time series.
>>> import redis
>>> client = redis.StrictRedis()
>>> ts = TimeSeries(client, base_key='my_timeseries')
To customize the granularities, make sure each granularity has a ttl
and duration
in seconds. You can use the helper functions for
easier definitions.
>>> my_granularities = {
... '1minute': {'ttl': hours(1), 'duration': minutes(1)},
... '1hour': {'ttl': days(7), 'duration': hours(1)}
... }
>>> ts = TimeSeries(client, granularities=my_granularities)
.record_hit()
accepts a key and an optional timestamp and increment
count. It will record the data in all defined granularities.
>>> ts.record_hit('event:123')
>>> ts.record_hit('event:123', datetime(2017, 1, 1, 13, 5))
>>> ts.record_hit('event:123', count=5)
.record_hit()
will automatically execute when execute=True
. If you
set execute=False
, you can chain the commands into a single redis
pipeline. You must then execute the pipeline with .execute()
.
>>> ts.record_hit('event:123', execute=False)
>>> ts.record_hit('enter:123', execute=False)
>>> ts.record_hit('exit:123', execute=False)
>>> ts.execute()
.get_hits()
will query the database for the latest data in the
selected granularity. If you want to query the last 3 minutes, you
would query the 1minute
granularity with a count of 3. This will return
a list of tuples (bucket, count)
where the bucket is the rounded timestamp.
>>> ts.get_hits('event:123', '1minute', 3)
[(datetime(2017, 1, 1, 13, 5), 1), (datetime(2017, 1, 1, 13, 6), 0), (datetime(2017, 1, 1, 13, 7), 3)]
.get_total_hits()
will query the database and return only a sum of all
the buckets in the query.
>>> ts.get_total_hits('event:123', '1minute', 3)
4
.scan_keys()
will return a list of keys that could exist in the
selected range. You can pass a search string to limit the keys returned.
The search string should always have a *
to define the wildcard.
>>> ts.scan_keys('1minute', 10, 'event:*')
['event:123', 'event:456']
Algorithm copied from tonyskn/node-redis-timeseries
_
This package was created with Cookiecutter_ and the audreyr/cookiecutter-pypackage
_ project template.
.. _tonyskn/node-redis-timeseries
: https://github.com/tonyskn/node-redis-timeseries
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _audreyr/cookiecutter-pypackage
: https://github.com/audreyr/cookiecutter-pypackage
FAQs
Timeseries API built on top of Redis
We found that redis-timeseries demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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