- Prometheus Querying - Breaking Down PromQL
- Monitoring Data in a SQL Table with Prometheus and Grafana
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- From metrics to insight
Prometheus Querying - Breaking Down PromQLPrometheus provides a functional query language called PromQL Prometheus Query Language that lets the user select and aggregate time series data in real time. The result of an expression can either be shown as a graph, viewed as tabular data in Prometheus's expression browser, or consumed by external systems via the HTTP API. This document is meant as a reference. For learning, it might be easier to start with a couple of examples. In Prometheus's expression language, an expression or sub-expression can evaluate to one of four types:. Depending on the use-case e. For example, an expression that returns an instant vector is the only type that can be directly graphed. PromQL follows the same escaping rules as Go. No escaping is processed inside backticks. Unlike Go, Prometheus does not discard newlines inside backticks. Scalar float values can be literally written as numbers of the form [-] digits [. Instant vector selectors allow the selection of a set of time series and a single sample value for each at a given timestamp instant : in the simplest form, only a metric name is specified. This results in an instant vector containing elements for all time series that have this metric name. It is also possible to negatively match a label value, or to match label values against regular expressions. The following label matching operators exist:. Label matchers that match empty label values also select all time series that do not have the specific label set at all. Regex-matches are fully anchored. It is possible to have multiple matchers for the same label name. Vector selectors must either specify a name or at least one label matcher that does not match the empty string. The following expression is illegal:. In contrast, these expressions are valid as they both have a selector that does not match empty label values. The following expression selects all metrics that have a name starting with job: :. All regular expressions in Prometheus use RE2 syntax. Range vector literals work like instant vector literals, except that they select a range of samples back from the current instant. Syntactically, a range duration is appended in square brackets  at the end of a vector selector to specify how far back in time values should be fetched for each resulting range vector element. The offset modifier allows changing the time offset for individual instant and range vectors in a query. Note that the offset modifier always needs to follow the selector immediately, i. The same works for range vectors.
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. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit 6f96b0d Nov 27, Metric queries will run concurrently on multiple connections.
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Some functions have default arguments, e. This means that there is one argument v which is an instant vector, which if not provided it will default to the value of the expression vector time. This is useful for alerting on when no time series exist for a given metric name and label combination. In the first two examples, absent tries to be smart about deriving labels of the 1-element output vector from the input vector. This is useful for alerting on when no time series exist for a given metric name and label combination for a certain amount of time. For each input time series, changes v range-vector returns the number of times its value has changed within the provided time range as an instant vector. Returned values are from 1 to Returned values are from 0 to 6, where 0 means Sunday etc. Returned values are from 28 to The delta is extrapolated to cover the full time range as specified in the range vector selector, so that it is possible to get a non-integer result even if the sample values are all integers. The following example expression returns the difference in CPU temperature between now and 2 hours ago:. Special cases are:. The samples in b are the counts of observations in each bucket. Each sample must have a label le where the label value denotes the inclusive upper bound of the bucket. Samples without such a label are silently ignored. To calculate the 90th percentile of request durations over the last 10m, use the following expression:. To aggregate, use the sum aggregator around the rate function. The following expression aggregates the 90th percentile by job :. Otherwise, NaN is returned. If a quantile is located in the highest bucket, the upper bound of the second highest bucket is returned. A lower limit of the lowest bucket is assumed to be 0 if the upper bound of that bucket is greater than 0. In that case, the usual linear interpolation is applied within that bucket. Otherwise, the upper bound of the lowest bucket is returned for quantiles located in the lowest bucket. If b contains fewer than two buckets, NaN is returned. The lower the smoothing factor sfthe more importance is given to old data.
From metrics to insight
Recently 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. To provide the details for alert-manager to Prometheus I added the following section to the bottom of the prometheus. To publish my alerts to a slack channel via web hook I created an alertmanager. For a larger, more real-world setup, with multiple metrics, different levels of severity and channels for alerting SMS, e-mail, slack and different teams who should respond to those alerts this file would be much more complicated, but again for the proof-of-concept simplicity was all that was required. You can then save the dashboard. There are many additional customisations you can do to your Grafana charts to make them look nice. I was running this whole setup on a single Windows machine and without using much memory thanks to the niceties of GOhowever because GO is quite platform-agnostic these were just plain old executables. So my lovely monitoring and alerting setup continued to run after I logged off I I used NSSM to run all these executables as a bunch of services. Tools from the golang ecosystem are nice to work with because they have no run-time dependencies. After creating this proof-of-concept on one machine I was able to zip it up and move it to another, and have it up and running as fast as I could launch new console windows. The tools themselves are fast, and have a very small memory footprint. Some further work to secure all this properly is required, from the Grafana UI, to the HTTP services that expose the metrics and do the alerting, to the storage of the credentials. 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. This section contains defintions for databases to connect to. Key names are arbitrary and only used to reference databases in the queries section. See SQLAlchemy documentation for details on available engines. It's also possible to get the connection string from an environment variable e. This section contains Prometheus metrics definitions. Keys are used as metric names, and must therefore be valid metric identifiers. If specified, queries updating the metric must return rows that include values for each label in addition to the metric value. Column names must match metric and labels names. This section contains definitions for queries to perform. Key names are arbitrary and only used to identify queries in logs. Metrics are automatically tagged with the database label so that indipendent series are generated for each database that a query is run on. The value is interpreted as seconds if no suffix is specified; valid suffixes are smhd. Only integer values are accepted. If no value is specified or specified as nullthe query is only executed upon HTTP requests. If a query is specified with parameters in its sqlit will be run once for every set of parameters specified in this list, for every interval. Each parameter set must be a dictionary where keys must match parameters names from the query SQL e. The query must return columns with names that match those of the metrics defined in metricsplus those of labels if any for all these metrics. SQLAlchemy doesn't depend on specific Python database modules at installation. This means additional modules might need to be installed for engines in use. These can be installed as follows:. See supported databases for details. The snap provides both the query-exporter command and a deamon instance of the command, managed via a Systemd service.