semvec/pypi-packages huggingface dataset:
event-metrics 0.2.0
An embedded, event-time metric collection library
Requires Python: ==3.*,>=3.5.0

Event Metrics
-------------

An embedded, event-time metric collection library built for serving system
--------------------------------------------------------------------------

Metric systems like prometheus aggregate metric at "processing time", whenever the scraper is
able to scrape the metric. ``event_metrics`` capture and record metrics at event time, when the
event happens.

Features
^^^^^^^^

Comparing to other metric system, the ``event_metrics`` library:


* Write data to sqlite3 database to keep low-memory footprint
* Minimal dependency (only requires numpy for numeric aggregation)
* Aggregate with the full data by default (no reservior sampling)
* Allow select from past duration with timedelta windowing
* Timestamp all observation by default
* Small API footprint: ``observe`` and ``query`` is all you need to know
* Compute raw timeseries with different aggregation strategies:

  * Scalers: last, min, max, mean, count, sum
  * Buckets for histogram
  * Percentiles for summary
  * Array and timestamps for native python wrangling

* Metrics can be labeled with arbitrary key value pair and querying supports
  multidimensional label matching.

Install
^^^^^^^


* Install from source: ``pip install -e .``
* PyPI package is work in progress

Usage
^^^^^

.. code-block:: python

   from event_metrics import MetricConnection

   conn = MetricConnection("/tmp/event_metrics_demo")

   conn.observe("latency", 1.2)
   conn.increment("counter", -1)

   # labeling
   conn.observe("latency", 2.0, labels={"service": "myapp", "other": "label"})

   # querying
   (conn.query("latency", labels={"service":"myapp"})
         # select from past duration using one of the following
        .from_beginning()
        .from_timestamp(...)
        .from_timedelta(...)

         # perform aggregation using one of the following
        .to_scaler(agg="last/min/max/mean/count/sum") # counter, guage
        .to_buckets(buckets=[1, 5, 10], cumulative=False) # histogram
        .to_percentiles(percnetiles=[50, 90, 99]) # summary
        .to_array() # -> value array
        .to_timestamps() # -> timestamp array
        .to_timestamps_array() # -> 2 array, (timestamp, value array)
   )

Speed
^^^^^

The library is *fast enough*. It can ingest about 34,000 data point
per seconds:

You can run ``pytest tests -k bench`` to generate benchmark on local hardware:

.. code-block::

   -------------------------------------------------- benchmark: 1 tests --------------------------------------------------
   Name (time in us)            Min       Max     Mean  StdDev   Median     IQR  Outliers  OPS (Kops/s)  Rounds  Iterations
   ------------------------------------------------------------------------------------------------------------------------
   test_bench_ingestion     25.3340  297.9320  28.8541  9.1582  26.8090  0.8650   521;814       34.6571    6496           1
   ------------------------------------------------------------------------------------------------------------------------