Micromegas
Python analytics client for https://github.com/madesroches/micromegas/
📖 Complete Python API Documentation - Comprehensive guide with all methods, examples, and advanced patterns
Example usage
Query the 2 most recent log entries from the flightsql service
import datetime
import micromegas
# Connect to local server
client = micromegas.connect()
sql = """
SELECT time, process_id, level, target, msg
FROM log_entries
WHERE level <= 4
AND exe LIKE '%flight%'
ORDER BY time DESC
LIMIT 2
"""
now = datetime.datetime.now(datetime.timezone.utc)
begin = now - datetime.timedelta(minutes=2)
end = now
df = client.query(sql, begin, end)
print(df)
| time | process_id | level | target | msg | |
|---|---|---|---|---|---|
| 0 | 2024-10-03 18:17:56.087543714+00:00 | 1db06afc-1c88-47d1-81b3-f398c5f93616 | 4 | acme_telemetry::trace_middleware | response status=200 OK uri=/analytics/query |
| 1 | 2024-10-03 18:17:53.924037729+00:00 | 1db06afc-1c88-47d1-81b3-f398c5f93616 | 4 | micromegas_analytics::lakehouse::query | query sql= |
| SELECT time, process_id, level, target, msg | |||||
| FROM log_entries | |||||
| WHERE level <= 4 | |||||
| AND exe LIKE '%analytics%' | |||||
| ORDER BY time DESC | |||||
| LIMIT 2 |
Query the 10 slowest top level spans in a trace within a specified time window
import datetime
import micromegas
client = micromegas.connect()
# First find a stream ID
end = datetime.datetime.now(datetime.timezone.utc)
begin = end - datetime.timedelta(hours=1)
streams = client.query_streams(begin, end, limit=1)
if not streams.empty:
stream_id = streams['stream_id'].iloc[0]
sql = """
SELECT begin, end, duration, name
FROM view_instance('thread_spans', '{}')
WHERE depth=1
ORDER BY duration DESC
LIMIT 10
""".format(stream_id)
spans = client.query(sql, begin, end)
print(spans)
| begin | end | duration | name | |
|---|---|---|---|---|
| 0 | 2024-10-03 18:00:59.308952900+00:00 | 2024-10-03 18:00:59.371890+00:00 | 62937100 | FEngineLoop::Tick |
| 1 | 2024-10-03 18:00:58.752476800+00:00 | 2024-10-03 18:00:58.784389+00:00 | 31912200 | FEngineLoop::Tick |
| 2 | 2024-10-03 18:00:58.701507300+00:00 | 2024-10-03 18:00:58.731479500+00:00 | 29972200 | FEngineLoop::Tick |
| 3 | 2024-10-03 18:00:59.766343100+00:00 | 2024-10-03 18:00:59.792513700+00:00 | 26170600 | FEngineLoop::Tick |
| 4 | 2024-10-03 18:00:59.282902100+00:00 | 2024-10-03 18:00:59.308952500+00:00 | 26050400 | FEngineLoop::Tick |
| 5 | 2024-10-03 18:00:59.816034500+00:00 | 2024-10-03 18:00:59.841376900+00:00 | 25342400 | FEngineLoop::Tick |
| 6 | 2024-10-03 18:00:58.897813100+00:00 | 2024-10-03 18:00:58.922769700+00:00 | 24956600 | FEngineLoop::Tick |
| 7 | 2024-10-03 18:00:59.860637+00:00 | 2024-10-03 18:00:59.885523700+00:00 | 24886700 | FEngineLoop::Tick |
| 8 | 2024-10-03 18:00:58.630051300+00:00 | 2024-10-03 18:00:58.654871500+00:00 | 24820200 | FEngineLoop::Tick |
| 9 | 2024-10-03 18:00:57.952279800+00:00 | 2024-10-03 18:00:57.977024+00:00 | 24744200 | FEngineLoop::Tick |
Quick Start
For a complete getting started guide, see the Python API Documentation.
Schema Reference
For complete schema information including all available tables, columns, and data types, see the Schema Reference.
SQL Reference
The Micromegas analytics service is built on Apache DataFusion. For SQL syntax and functions, see the Apache DataFusion SQL Reference.
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