Time-Series Database (TSDB) Services

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Time-series databases (TSDBs) are used for storing time-series data — a series of time-based data points. The platform features enhanced built-in support for working with TSDBs, which includes a rich set of features for efficiently analyzing and storing time series data. The platform uses the Iguazio V3IO TSDB open-source library, which exposes a high-performance API for working with TSDBs — including creating and deleting TSDB instances (tables) and ingesting and consuming (querying) TSDB data. This API can be consumed in various ways:

  • Use the V3IO TSDB command-line interface (CLI) tool (tsdbctl), which is pre-deployed in the platform, to easily create, delete, and manage TSDB instances (tables) in the platform's data store, ingest metrics into such tables, and issue TSDB queries. The CLI can be run locally on a platform cluster — from a command-line shell interface, such as the web-based shell or a Jupyter terminal or notebook — or remotely from any computer with a network connection to the cluster. The platform's web shell and Jupyter terminal environments predefine a tsdbctl alias to the native CLI that preconfigures the URL of the web-APIs service and the authentication access key for the running user of the parent shell or Jupyter Notebook service.
  • Use the platform's TSDB Nuclio functions service to generate serverless V3IO TSDB Nuclio functions for ingesting and consuming TSDB data.
  • Use the Prometheus service service to run TSDB queries. Prometheus is an open-source systems monitoring and alerting toolkit that features a dimensional data model and a flexible query language. The platform's Prometheus service uses the pre-deployed Iguazio V3IO Prometheus distribution, which packages Prometheus with the V3IO TSDB library for a robust, scalable, and high-performance TSDB solution.

For more information and examples, see Time-Series Databases (TSDB) (including a TSDB CLI guide), as well as the TSDB section in the frames.ipynb tutorial Jupyter notebook.

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