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e2fyi-pyspark is an e2fyi namespaced python package with pyspark subpackage (i.e. e2fyi.pyspark) which holds a collections of useful functions for common but painful pyspark tasks.

API documentation can be found at https://e2fyi-pyspark.readthedocs.io/en/latest/.

Change logs are available in CHANGELOG.md.


pip install e2fyi-pyspark

Infer schema for unknown json strings inside a pyspark dataframe

e2fyi.pyspark.schema.infer_schema_from_rows is a util function to infer the schema of unknown json strings inside a pyspark dataframe - i.e. so that the schema can be subsequently used to parse the json string into a typed data structure in the dataframe (see ``pyspark.sql.functions.from_json` <https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.functions.from_json>`_).

import pyspark
from e2fyi.pyspark.schema import infer_schema_from_rows

# get spark session
spark = pyspark.sql.SparkSession.builder.getOrCreate()
# load a parquet (assume the parquet has a column "json_str", which
# contains a json str with unknown schema)
df = spark.read.parquet("s3://some-bucket/some-file.parquet")
# get 10% of the rows as sample (w/o replacement)
sample_rows = df.select("json_str").sample(False, 0.01).collect()
# infer the schema for json str in col "json_str" based on the sample rows
# NOTE: this is run locally (not in spark)
schema = infer_schema_from_rows(sample_rows, col="json_str")
# add a new column "data" which is the parsed json string with a inferred schema
df = df.withColumn("data", pyspark.sql.functions.from_json("json_str", schema))
# should have a column "data" with a proper schema

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