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.

mysql observability
mysql observability - log config

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, PostgreSQL, and MongoDB.

You can also backfill old logs into Honeycomb to look at past data.

Getting Answers

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?
mysql observability - query chart example