Writing JSONiq queries in Python

You can use RumbleDB from within Python programmes by running

pip install jsoniq

Important note: since the jsoniq package depends on pyspark 4, Java 17 or Java 21 is a requirement. If another version of Java is installed, the execution of a Python program attempting to create a RumbleSession will lead to an error message on stderr that contains explanations.

You can test that the Java version is correct by typing this in the command line:

java -version

High-level information on the library

A RumbleSession is a wrapper around a SparkSession that additionally makes sure the RumbleDB environment is in scope.

JSONiq queries are invoked with rumble.jsoniq() in a way similar to the way Spark SQL queries are invoked with spark.sql().

JSONiq variables can be bound to lists of JSON values (str, int, float, True, False, None, dict, list) or to Pyspark DataFrames. A JSONiq query can use as many variables as needed (for example, it can join between different collections).

It will later also be possible to read tables registered in the Hive metastore, similar to spark.sql(). Alternatively, the JSONiq query can also read many files of many different formats from many places (local drive, HTTP, S3, HDFS, ...) directly with simple builtin function calls such as json-lines(), text-file(), parquet-file(), csv-file(), etc.

The resulting sequence of items can be retrieved as a list of JSON values, as a Pyspark DataFrame, or, for advanced users, as an RDD or with a streaming iteration over the items using the RumbleDB Item API.

It is also possible to write the sequence of items to the local disk, to HDFS, to S3, etc in a way similar to how DataFrames are written back by Pyspark.

The design goal is that it is possible to chain DataFrames between JSONiq and Spark SQL queries seamlessly. For example, JSONiq can be used to clean up very messy data and turn it into a clean DataFrame, which can then be processed with Spark SQL, spark.ml, etc.

Any feedback or error reports are very welcome.