redis-metric-helper 0.7.6

A helper to make writing/reading metrics to Redis more convenient.

redis-metric-helper

WARNING: This is still under active development.

Handles some of the more tedious parts of logging and reading metrics that get logged to Redis. Counters, gauges and timeseries data. Requires RedisStack.

Why does this package exist?

A helper to make writing/reading metrics to Redis more convenient. Allows counters, gauges (including postive-only gauges) and timeseries data.

Quickstart

1. Install the package:

   pip install redis-metric-helper
   

2. Initialize the package:

   from metric_helper import metrics
   
   metrics.setup(
       connection_dict={
           'host': 'localhost', # Default
           'port': 6379, # Default
           'password': 'SuperS3kr!t',
           'socket_connect_timeout': 5, # Default
           'health_check_interval': 30, # Default
       },
       timezone='Africa/Johannesburg',
   )
   

3. Create/get a metric:

   timeseries = metrics.get(
       'http_requests', # Redis key
       'timeseries', # Default
       round_timestamp_to='second',
   )
   timeseries.add_sample(
       value=1,
       duplicate_policy='sum',
       round_timestamp_to='second',
   )
   # Equivalent to add_sample() with above kwargs.
   timeseries.incr()
   
   counter = metrics.get('http_requests_total_count', 'counter')
   counter.incr()
   
   gauge = metrics.get('my_gauge', 'gauge')
   gauge.incr()
   
   pos_gauge = metrics.get('my_pos_gauge', 'pos_gauge')
   pos_gauge.incr()
   pos_gauge.decr()
   

4. Query the metric:

   from datetime import datetime, timedelta
   
   end = datetime.now()
   start = end - timedelta(hours=24)
   results = timeseries.range(
       start=start, # Also allows "-"
       end=end, # Also allows "+"
       bucket_secs=3600, # Default
       empty=True, # Default
       agg_type='sum', # Default
       pipeline=None, # Default
   )
   
   count = counter.get()
   gauge_result = gauge.get()
   pos_gauge_result = pos_gauge.get()
   

5. Run commands in a Redis pipeline:

   from metric_helper import pipeline
   results = pipeline([
       timeseries.range(start='-', end='+', bucket_secs=3600, defer=True),
       timeseries.range(start=start, end=end, bucket_secs=3600, defer=True),
       timeseries.add_sample(value=1, defer=True),
       timeseries.add_sample(value=1, defer=True),
       timeseries.incr(defer=True),
       counter.incr(defer=True),
   ])
   

6. Add compaction rules. To create a compaction rule for an hourly aggregate:

   timeseries.add_rule(
       agg_type='sum',
       bucket_secs=3600,
       retention_days=120,
   )
   
   # If source key is named "http_requests", this will create a new key
   # named "http_requests--agg_3600_sum"
   source_key = 'http_requests'
   dest_key = f'{source_key}--agg_{bucket_secs}_{agg_type}'
   

   Or, optionally use the very opinionated auto_add_rules method:

   timeseries.auto_add_rules()
   

   auto_add_rules will create five compaction rules equal to the following:

   timeseries.add_rule(
       agg_type='sum',
       bucket_secs=60,
       retention_days=15,
   )
   timeseries.add_rule(
       agg_type='sum',
       bucket_secs=900,
       retention_days=31,
   )
   timeseries.add_rule(
       agg_type='sum',
       bucket_secs=3600,
       retention_days=367,
   )
   timeseries.add_rule(
       agg_type='sum',
       bucket_secs=86400,
       retention_days=367,
   )
   

Recommendations on metric naming conventions

These are really just suggestions but a possible naming convention could be something like this:

{prefix}:{metric_root_name}:{noun}:{noun_identifier}:{modifier_of_metric}

The prefix should be the package/component the metric is related to.

For example, for a component/app named "uploads" we might have a metric named "filesize":

uploads:filesize

Then, all the filesizes for a specific user's uploads:

uploads:filesize:user:{user_id}

And then perhaps the filesize of all uploads by that user that were identified as images:

uploads:filesize:user:{user_id}:images

For a metric named "failures":

uploads:failures

However, we might want to know how many timeouts occurred for any given user:

uploads:failures:user:{user_id}