- From metrics to insight
- Subscribe to RSS
- Prometheus Querying - Breaking Down PromQL
- Monitoring Data in a SQL Table with Prometheus and Grafana
From metrics to insightRecently I set up a proof-of-concept to add monitoring and alerting on the results of a query against a Microsoft SQL Server database table. I know there are a lot of ways to do this in the SQL server ecosystembut I wanted to eventually be monitoring and alerting on metrics from many different sources - performance counters, Seq queries, and custom metrics exposed from a number of services. With this heterogeneity in mind I chose prometheus for this, and tacked on Grafana to give me some nice dashboards in the bargain. There were no pre-build binary for this, but building it is very straight-forward. I used prometheus-sql to periodically query SQL Server. You can configure how frequently the query is run, which is independent of how frequently prometheus collects data from this service. You can tune these two parameters to be displaying and alerting on the live-est data possible without putting too much load on your SQL Server. Failed queries are re-tried using a back-off mechanism. Queries are defined in a config file, along with the connection details for executing the query. Once again - no pre-built binaries. Integrating sql-agent and prometheus-sql and using integrated authentication would be a step in the right direction. Here is a very minimal queries. Like a lot of the tools from the golang ecosystem, Prometheus is beautifully simple to get up and running - download the binary for your platformunzip it, and start running. To add the prometheus-sql metrics to the set of metrics collected by Prometheus I added the following lines in the prometheus. Prometheus configures rules in a one or more rule files. I specified a single rule file called simple. As you can see from the example above you can do templating in your alert text, which can get richer and more complicated when you have faceted metrics, or the same metric tracked for different instances. Check the docs. Prometheus has an add-on component called AlertManager which is responsible for sending out alerts via different channels like e-mail, slack, HipChat etc, as well as silencing, inhibiting and aggregating alerts. Once again, getting alert-manager running is a simple case of downloading the right binary for your platform and running the executable.
Subscribe to RSS
Return a whole range of time in this case 5 minutes for the same vector, making it a range vector:. Note that an expression resulting in a range vector cannot be graphed directly, but viewed in the tabular "Console" view of the expression browser. Using regular expressions, you could select time series only for jobs whose name match a certain pattern, in this case, all jobs that end with server :. All regular expressions in Prometheus use RE2 syntax. This is an example of a nested subquery. The subquery for the deriv function uses the default resolution. Note that using subqueries unnecessarily is unwise. If we have two different metrics with the same dimensional labels, we can apply binary operators to them and elements on both sides with the same label set will get matched and propagated to the output. For example, this expression returns the unused memory in MiB for every instance on a fictional cluster scheduler exposing these metrics about the instances it runs :. If the same fictional cluster scheduler exposed CPU usage metrics like the following for every instance:. Assuming this metric contains one time series per running instance, you could count the number of running instances per application like this:. Simple time series selection Subquery Using functions, operators, etc.
Prometheus Querying - Breaking Down PromQL
Monitoring Data in a SQL Table with Prometheus and Grafana
GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Database agnostic SQL exporter for Prometheus. The collected metrics and the queries that produce them are entirely configuration defined. SQL queries are grouped into collectors -- logical groups of queries, e. Collectors may be DBMS-specific e. This means you can quickly and easily set up custom collectors to measure data quality, whatever that might mean in your specific case. If both the exporter and the DB server are on the same host, they will share the same failure domain: they will usually be either both up and running or both down. The configuration examples listed here only cover the core elements. You will find ready to use "standard" DBMS-specific collector definitions in the examples directory. You may contribute your own collector definitions and metric additions if you think they could be more widely useful, even if they are merely different takes on already covered DBMSs. Collectors may be defined inline, in the exporter configuration file, under collectorsor they may be defined in separate files and referenced in the exporter configuration by name, making them easy to share and reuse. However, because the Go sql library does not allow for automatic driver selection based on the DSN i. But what is the point of a configuration driven SQL exporter, if you're going to use it along with 2 more exporters with wholly different world views and configurations, because you also have MySQL and PostgreSQL instances to monitor? This is partly a philosophical issue, but practical issues are not all that difficult to imagine: jitter; duplicate data points; or collected but not scraped data points. The control they provide over which labels get applied is limited, and the base label set spammy. And finally, configurations are not easily reused without copy-pasting and editing across jobs and instances. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Go Makefile Dockerfile. Go Branch: master. Find file. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Service that generates basic metrics for SQL result sets and exposing them as Prometheus metrics. A queries file is required for the application to know which data source to query and which queries that shall be monitored. In the repository there is an example file that you can have a look at. The config file is optional and can defined some default values for queries and data sources which can be referenced by queries. The benefit of referencing a data source will be reduction of duplication of database connection information. See example config file here and queries file which utilizes the config information. If you want to separate database connection information etc you can do that by specifying data sources in separate file which you then can mount:. Alternately, use the docker-compose. The volumes section be added for mounting the queries. Read instructions how to contribute before you start. Additional options are set in the config. These are passed to the SQL Agent service which construct a DSN string to establish the connection on the backend alternately you can set the dsn key as the full string. As an example, a common gotcha when using Postgres in a development environment is to ignore SSL not being enabled on the server. This can be done by adding the sslmode: disable option in the connection map. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Service that exposes Prometheus metrics for a SQL result set. Go Makefile.