Monitoring Tabnine
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This document will go over how Tabnine services deployed on-premise can be monitored and go over a few examples of monitoring our services locally. You can also enable Tabnine telemetry, which uses the principles shown in this document and reports the data to Tabnine’s servers.
As Tabnine’s self-hosted solution runs in a Kubernetes cluster, we rely on standard tools for our logs and metrics - logs are written to the stdout, and metrics are exposed using http endpoints in Prometheus format.
Note that as both writing logs to stdout and exposing metrics endpoints for scraping are industry standards when working in the Kubernetes ecosystem, there is an extensive collection of tools and platforms that support those formats. This document will go over the configuration options for scrapping metrics and will also provide examples for setting up a simple server for scraping the metrics and for the collection of the logs into a centralized endpoint.
All Tabnine services output their logs to the stdout. They are picked by and managed by Kubernetes, which allows integration with standard tools for log management and retention.
In Kubernetes, the standard way to deal with logs is to run a collection service, such as or , which collects the logs from the pods and forwards them to a centralized location. Cloud providers usually have an official way of integrating the logs with their native logging platforms. However, they all use FluentD or FluentBit under the hood.
When Tabnine’s telemetry is enabled, we install and use FluentD to forward logs from the cluster to Tabnine’s servers.
Log messages are in the following format:
How to send logs to an external log management system
Cloud providers
3rd party log management tools
In order to enable Tabnine metrics monitoring, edit the following sections in values.yaml.
Now that values.yaml is updated, it is time to install the chart on the cluster.
The following example adds a release=prom-example label to all PodMonitors and ServiceMonitor created by Tabnine as part of the installation.
The following configuration:
Scrapes only PodMonitors and ServiceMonitors with a release=prom-example label,
keeps the data for 14 days
requires 50GB of storage
requires 6G of RAM to operate
Tabnine services export Prometheus metrics and rely on having Prometheus Operator installed on the cluster. If you are unfamiliar with how to install a Prometheus Operator please follow article.
for full list of available configurations, please check the