This post was brought to you by Dominik Schulz, Head of Infrastructure.
#Prometheus postgres exporter free
We hope you enjoy this little tool like we do and if you haveĪny suggestions feel free to open an Issues or PR on the GitHub project.
That is available to SQL queries to Prometheus in a very straight-forward, We showed how one can easily export system metrics, KPIs, and basically everything Server can easily be overwhelmed by a huge number of timeseries. May suffer from too expensive queries running too often and the Prometheus It’s very easy to overload either your Database or Prometheus. While this exporter comes with great flexibility it requires some caution. My database professor at the university always used to say There is no such thing as a free lunch. The repository contains everything to get started, including example Alerts We deploy the SQL Exporter in Kubernetes and use Grafana to display the metrics. Metrics name will look like this: sql_pg_statio_user_tables = 57.0 Grafanadb | dashboard | 57 | 312 | 115 | 65Īs we defined the columns schemaname and relname as labels the fully qualified Running this query on one of our stage databases yields the following ouput (edited for brevity): schemaname | relname | heap_blks_read | heap_blks_hit | idx_blks_read | idx_blks_hit In this example the exported metric will be named sql_pg_statio_user_tables. Of different queries that will be executed sequentially on all connection URLs.Įach query produces an metric named after the query name with an sql_ prefix. The most interesting part are of course the queries. The different runs and the possibility to defined multiple connection strings Two properties of theĬonfig struct come in handy here: Being able to terminate the connection between To each database in turn and collect all of the metrics.
These tables are different in each database, so we need to connect This configuration defines one job to export the PostgreSQL I/O statistics tables
#Prometheus postgres exporter series
We’ll use the followingĬonfig to explain the available options and how the time series are built. To better explain how this works let’s use an example. Simple as writing a SQL query that exposes the labels of a time series as textįields and any metrics as float fields. In the end, our SQL exporter makes monitoring any metric you can imagine as The core concept of this exporter is based on the idea that a proper SQL queryĬan easily be mapped onto a set of labels and one or more numbers that make up Rules and Grafana dashboards proved to be very helpful. It’s a very small codebase but itĪllows for very flexible configuration and together with the right recording Out of this need the SQL Exporter was created. Us to export any KPI from SQL to Prometheus. What we needed was more of a general approach that would allow They were either not working up to spec or too focused on the systems There were some existing exporters for this task, but they didn’t really fit our In order to keep our data safe we need to monitor the status of these databases. Versatile one for many of our use cases is still PostgreSQL. There are some specialized ones like ElasticSearch and AeroSpike, but the most At JustWatch we rely on different databases to store and process our data.
0 kommentar(er)
