Chronify¶
This package implements validation, mapping, and storage of time series data in support of Python-based modeling packages.
Features¶
Stores time series data in any database supported by SQLAlchemy.
Supports data ingestion in a variety of file formats and configurations.
Supports efficient retrieval of time series through SQL queries.
Validates consistency of timestamps and resolution.
Provides mappings between different time configurations.
Supported Backends¶
While chronify should work with any database supported by SQLAlchemy, it has been tested with the following:
DuckDB (default)
SQLite
Apache Spark through Apache Thrift Server
DuckDB and SQLite are fully supported.
Because of limitations in the backend software, chronify functionality with Spark is limited to the following:
Create a view into an existing Parquet file (or directory).
Perform time series checks.
Map between time configurations.
Write output data to Parquet files.
There is no support for creating tables and ingesting data with Spark.
How to use this guide¶
Refer to How Tos for step-by-step instructions for creating store and ingesting data.
Refer to Tutorials examples of ingesting different types of data and mapping between time configurations.
Refer to Reference for API reference material.
Refer to Explanation for descriptions and behaviors of the time series store.