Spark Streaming

You can write Hudi tables using spark’s structured streaming.

Scala

// spark-shell
// prepare to stream write to new table
import org.apache.spark.sql.streaming.Trigger
val streamingTableName = "hudi_trips_cow_streaming"
val baseStreamingPath = "file:///tmp/hudi_trips_cow_streaming"
val checkpointLocation = "file:///tmp/checkpoints/hudi_trips_cow_streaming"
// create streaming df
val df = spark.readStream.
        format("hudi").
        load(basePath)
// write stream to new hudi table
df.writeStream.format("hudi").
  options(getQuickstartWriteConfigs).
  option("hoodie.datasource.write.precombine.field", "ts").
  option("hoodie.datasource.write.recordkey.field", "uuid").
  option("hoodie.datasource.write.partitionpath.field", "partitionpath").
  option("hoodie.table.name", streamingTableName).
  outputMode("append").
  option("path", baseStreamingPath).
  option("checkpointLocation", checkpointLocation).
  trigger(Trigger.Once()).
  start()

Python

# pyspark
# prepare to stream write to new table
streamingTableName = "hudi_trips_cow_streaming"
baseStreamingPath = "file:///tmp/hudi_trips_cow_streaming"
checkpointLocation = "file:///tmp/checkpoints/hudi_trips_cow_streaming"
hudi_streaming_options = {
    'hoodie.table.name': streamingTableName,
    'hoodie.datasource.write.recordkey.field': 'uuid',
    'hoodie.datasource.write.partitionpath.field': 'partitionpath',
    'hoodie.datasource.write.table.name': streamingTableName,
    'hoodie.datasource.write.operation': 'upsert',
    'hoodie.datasource.write.precombine.field': 'ts',
    'hoodie.upsert.shuffle.parallelism': 2,
    'hoodie.insert.shuffle.parallelism': 2
}
# create streaming df
df = spark.readStream \
    .format("hudi") \
    .load(basePath)
# write stream to new hudi table
df.writeStream.format("hudi") \
    .options(**hudi_streaming_options) \
    .outputMode("append") \
    .option("path", baseStreamingPath) \
    .option("checkpointLocation", checkpointLocation) \
    .trigger(once=True) \
    .start()