This Python script generates graphs visualizing peer retention in Status app.
The script queries an ElasticSearch endpoint for logstash-* indices and aggregates counts of instances of log messages with set peer_id field.
This data is then analyzed using Pandas and graphed using Seaborn in the form of
This was built from a combination of a CSV generating script and a cohort analysis done by @jakubgs and @bgits.
The script provides a number of options:
Usage: main.py [options]
This generates a CSV with buckets of peer_ids for every day.
Options:
-h, --help show this help message and exit
-H ES_HOST, --host=ES_HOST
ElasticSearch host.
-P ES_PORT, --port=ES_PORT
ElasticSearch port.
-i INDEX_PATTERN, --index-pattern=INDEX_PATTERN
Patter for matching indices.
-f FIELD, --field=FIELD
Name of the field to count.
-F FLEET, --fleet=FLEET
Name of the fleet to query.
-m MAX_SIZE, --max-size=MAX_SIZE
Max number of counts to find.
-d IMAGE_DPI, --image-dpi=IMAGE_DPI
DPI of generated PNG images.
-o OUTPUT_DIR, --output-dir=OUTPUT_DIR
Dir into which images are generated.
Example: ./unique_count.py -i "logstash-2019.11.*" -f "peer_id"
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