This page provides you with instructions on how to extract data from PostgreSQL and analyze it in Grafana. (If the mechanics of extracting data from PostgreSQL seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is PostgreSQL?
PostgreSQL, also called Postgres, is an open source object-relational database management system that runs on all major operating systems. It's known for its stability and its ability to handle high volumes of transactions.
What is Grafana?
Grafana is an open source platform for time series analytics. It can run on-premises on all major operating systems or be hosted by Grafana Labs via GrafanaCloud. Grafana allows users to create, explore, and share dashboards to query, visualize, and alert on data.
Getting data out of PostgreSQL
Most people retrieve data from relational databases by writing SQL queries. If you're just looking to export data in bulk, however, you can use the command-line tool
pg_dump to export data from a PostgreSQL database as a CSV file or a script that you can run to restore the database on any PostgreSQL server.
Loading data into Grafana
Analyzing data in Grafana requires putting it into a format that Grafana can read. Grafana natively supports nine data sources, and offers plugins that provide access to more than 50 more. Generally, it's a good idea to move all your data into a data warehouse for analysis. MySQL, Microsoft SQL Server, and PostgreSQL are among the supported data sources, and because Amazon Redshift is built on PostgreSQL and Panoply is built on Redshift, those popular data warehouses are also supported. However, Snowflake and Google BigQuery are not currently supported.
Analyzing data in Grafana
Grafana provides a getting started guide that walks new users through the process of creating panels and dashboards. Panel data is powered by queries you build in Grafana's Query Editor. You can create graphs with as many metrics and series as you want. You can use variable strings within panel configuration to create template dashboards. Time ranges generally apply to an entire dashboard, but you can override them for individual panels.
Keeping PostgreSQL data up to date
The script you have now should satisfy all your data needs for PostgreSQL – right? Not yet. How do you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow; if latency is important to you, it's not a viable option.
Instead, you can identify some key fields that your script can use to bookmark its progression through the data, and pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in PostgreSQL.
From PostgreSQL to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing PostgreSQL data in Grafana is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites PostgreSQL to Redshift, PostgreSQL to BigQuery, PostgreSQL to Azure SQL Data Warehouse, PostgreSQL to PostgreSQL, PostgreSQL to Panoply, and PostgreSQL to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from PostgreSQL to Grafana automatically. With just a few clicks, Stitch starts extracting your PostgreSQL data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Grafana.