partitionBy (DataStreamWriter)

Partitions the output by the given columns on the file system. The output is laid out similar to Hive's partitioning scheme.

Syntax

partitionBy(*cols)

Parameters

Parameter	Type	Description
*cols	str or list	Names of the columns to partition by.

Returns

DataStreamWriter

Examples

Python

df = spark.readStream.format("rate").load()
df.writeStream.partitionBy("value")
# <...streaming.readwriter.DataStreamWriter object ...>

Partition a Rate source stream by timestamp and write to Parquet:

Python

import tempfile
import time
with tempfile.TemporaryDirectory(prefix="partitionBy1") as d:
    with tempfile.TemporaryDirectory(prefix="partitionBy2") as cp:
        df = spark.readStream.format("rate").option("rowsPerSecond", 10).load()
        q = df.writeStream.partitionBy(
            "timestamp").format("parquet").option("checkpointLocation", cp).start(d)
        time.sleep(5)
        q.stop()
        spark.read.schema(df.schema).parquet(d).show()