Database Observability
Honeycomb works directly with MySQL, MongoDB, or PostgreSQL in order to reliably track down the source(s) of any slowness or odd behavior in just a few clicks.
By consuming logs (or capture TCP and parsing them into structured data, Honeycomb makes it possible to answer deep questions about database operation instantly.
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Data Collection
The honeytail agent captures logs, extracts structure and transforms query-level events into JSON, and streams the data into Honeycomb as it’s written—gathering query-level details crucial to debugging struggling databases. We have a range of integration options for MySQL, and PostgreSQL.
You can also backfill old logs into Honeycomb to look at past data.
Query Log Analysis
With query-level database events in Honeycomb, you can ask questions like:
- What is the sum of all lock times held, over time? Grouped by normalized MySQL queries?
- What is the read lock percentage on the slowest MongoDB collection?
- Which set of queries are responsible for the most lock time held?
- What % of the write time is being consumed by any given user, app, shopping cart?
- What are 20 most commonly-run normalized query families (avg, 95th, 99th, 99.9th, 99.999th percentiles, and MAX duration), ordered by the slowest raw query?
- What is the scan efficiency–how many rows are being scanned by each normalized query family, relative to the number of rows in the table?
