By Kaarel Moppel – I could make good use of the “slow time” around the turn of the year and managed to push out another set of features for our Open Source PostgreSQL monitoring tool called pgwatch2 – so a quick overview on changes in this post. Continuing the tradition, I’m calling it “Feature Pack 4” as it’s mostly about new features. Git and Docker images carry version number 1.5.0. As our last pgwatch2-related blogpost covered only 1.4.0, I’ll include here also most important stuff from 1.4.5 minor feature release.
Highlight – Monitoring Postgres with Postgres
This is the biggest one this time – finally and quite appropriately for “Postgres-minded” people, there’s now a chance to store all the gathered metrics in Postgres! This of course doesn’t necessarily mean that Postgres is best for storing Time-Series Data although it performs very nicely thanks to JSONB…but in general it’s a good compromise – more disk space (~3-4x) at comparable query times to InfluxDB…but with full power of SQL! Meaning some saved time learning a new (and quite limited) query language. And after all, only a few people are running dozens and dozens of databases, so performance is mostly not an issue. And on the plus side we can now ask questions that were previously plainly not possible (no joins, remember) or were only possible by storing some extra columns of data (de-normalizing).
The new functionality is designed for the latest Postgres version of 11, but as people run all kinds of different versions and might not want to set up a new cluster, there is also a legacy mode, that will cost more IO though. In total there are 4 different “schema modes” so that people could optimize their IO based on needs:
- a separate normal table for each distinct metric (legacy mode)
- a separate partitioned table for each distinct metric + weekly partitions
- a separate partitioned table for each distinct metric + separate sub-table for each distinct monitored host + monthly partitions. Best for monitoring 50+ DB-s
- custom mode – all data inserted into a single table where it can be re-routed with a trigger for example.
For the partitioned modes there is also automatic “retention management” – by default 1 month of data is kept. Partitions live in a separate “subpartitions” schema, top level tables are in “public”.
To test it out fire up a Docker container with something like:
# assuming "postgres" is superuser and auto-creating metrics fetching helpers docker run --rm --name pw2 -p 3000:3000 -e PW2_ADHOC_CONN_STR="postgresql://firstname.lastname@example.org/mydb" -e PW2_ADHOC_CREATE_HELPERS=1 cybertec/pgwatch2-postgres # After 5min open up Grafana at 0.0.0.0:3000 and start evaluating what's going on in your database...
Call for feedback
And as always, please do let us know on GitHub if you’re still missing something in the tool or are experiencing difficulties – any feedback would be highly appreciated!
Most important changes for v1.5.0
- Postgres support for metrics storage
There are multiple (4) storage schema types supported so even legacy PG versions and custom needs should be covered. PG 11+ needed though to use time-based partitioning. Comes with automatic “retention policy” enforcement (given the gatherer is running).
- Test data generation
A by-product of testing Postgres metrics storage, it helps to quickly estimate metrics data volumes under real life conditions to see if your hardware or selected storage “schema type” can handle the amount of planned metrics. Metrics will be fetched one time from a user specified DB and then data inserted multiple times with correct intervals for the simulated host count. There are also some scripts provided to generate bigger data amounts faster and to test typical dashboard queries speed.
- Connection string password encryption/decryption with AES-GCM-256
Previously monitoring user passwords where stored in plain text for both “config DB” and “YAML” mode. Now an encryption key or keyfile can be provided to the Gatherer and the Web UI to transparently encrypt passwords. Default will remain “plain-text” as pgwatch2 is assumable mostly used in safe environments or for ad-hoc troubleshooting and it’s just more convenient so.
- Libpq SSLMODE-s ‘verify-ca’ and ‘verify-full’ support
Previously only ‘disabled’ and ‘require’ were supported. Certs need to be present on the machine where the gatherer is running.
- Support for declaring metrics as Standby-only or Master-only
For example, the “pg_stat_database_conflicts” view is always empty on a primary, so it makes no sense to query it there. This should result in less errors both in Postgres and pgwatch2 logs.
- De-coupling of metrics from the “public” schema
Previously “public” schema for extensions and “metric fetching helpers” was assumed, but now no such assumption is made, allowing any schema – user just needs to make sure that the monitoring role has it’s “search_path” correctly set. Also, no more “public” grants for helpers, only for the monitoring role.
- SystemD support + service files
Paths need to be adjusted manually both for the Gatherer and Web UI. Thanks @slardiere!
- Gatherer performance improvements
50% less internal message passing over channels meaning much better performance when monitoring 50+ DB-s.
- Reduced error messages when monitored Dbs are down
Also, when metric definitions are not found etc, one error per hour only.
- Various metrics fixes and additions
Some new metrics (“wal_size”) added and “db_size” split up from “db_stats”. Gathering intervals for some “not so used” metrics have also been increased in the preset configs.
Most important changes for v1.4.5
- Parallel metric fetching for a single monitored DB
Two different metric queries can now run simultaneously on a single monitored DB.
- New “Health-check” dashboard
All most important health indicators on a singe page, with green / yellow / red indicators.
- New “Index overview” dashboard
Top scanned, un-used, biggest, duplicate and invalid indexes overview.
- New “Tables top” dashboard
Top-N by size and growth/scan and INSERT/UPDATE/DELETE rates.
pgwatch2 is constantly being improved and new features are added. Learn more >>