Spark Structured Streaming

Fluss supports Spark Structured Streaming for both reading and writing data in a streaming fashion. This enables building real-time data pipelines with Apache Spark and Fluss.

Streaming Write

Fluss supports writing data to Fluss tables using Spark Structured Streaming. This is useful for continuously ingesting data into Fluss tables from streaming sources.

Write to Log Table

Spark Application

// Here we use MemoryStream to fake a streaming source.
val inputData = MemoryStream[(Int, String)]
val inputDF = inputData.toDS().toDF("k", "v")

val query = inputDF.writeStream
  .option("checkpointLocation", "/path/to/checkpoint")
  .toTable("fluss_catalog.fluss.log_table")

query.awaitTermination()

Write to Primary Key Table

When writing to a primary key table, if a record with the same primary key already exists, it will be updated (upsert semantics):

Spark Application

// Here we use MemoryStream to fake a streaming source.
val inputData = MemoryStream[(Int, String)]
val inputDF = inputData.toDS().toDF("k", "v")

val query = inputDF.writeStream
  .option("checkpointLocation", "/path/to/checkpoint")
  .toTable("fluss_catalog.fluss.pk_table")

query.awaitTermination()

note

Fluss supports exactly-once semantics for streaming writes through Spark's checkpoint mechanism. Make sure to specify a checkpointLocation for fault tolerance.

Streaming Read

Fluss supports reading data from Fluss tables using Spark Structured Streaming. The streaming source continuously reads new data as it arrives.

Limitations

• Streaming read currently only supports the latest startup mode. Other modes (full, earliest, timestamp) are not yet supported and will be available in a future release.

Read from Log Table

Spark Application

val df = spark.readStream
  .option("scan.startup.mode", "latest")
  .table("fluss_catalog.fluss.log_table")

val query = df.writeStream
  .format("console")
  .start()

query.awaitTermination()

Read from Primary Key Table

Spark Application

val df = spark.readStream
  .option("scan.startup.mode", "latest")
  .table("fluss_catalog.fluss.pk_table")

val query = df.writeStream
  .format("console")
  .start()

query.awaitTermination()

Trigger Modes

Fluss Spark streaming source supports the following Spark trigger modes:

Trigger Mode	Description
Default (micro-batch)	Processes data as soon as new data is available.
Trigger.ProcessingTime(interval)	Processes data at fixed time intervals.

Example:

Spark Application

import org.apache.spark.sql.streaming.Trigger

val df = spark.readStream
  .option("scan.startup.mode", "latest")
  .table("fluss_catalog.fluss.my_table")

// Processing time trigger (every 5 seconds)
val query = df.writeStream
  .format("console")
  .trigger(Trigger.ProcessingTime("5 seconds"))
  .start()

query.awaitTermination()

End-to-End Example

Here is a complete example that reads data from one Fluss table and writes to another:

Spark Application

import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
  .config("spark.sql.catalog.fluss_catalog", "org.apache.fluss.spark.SparkCatalog")
  .config("spark.sql.catalog.fluss_catalog.bootstrap.servers", "localhost:9123")
  .config("spark.sql.defaultCatalog", "fluss_catalog")
  .config("spark.sql.extensions", "org.apache.fluss.spark.FlussSparkSessionExtensions")
  .getOrCreate()

// Create source and sink tables
spark.sql("CREATE TABLE IF NOT EXISTS source_table (id INT, data STRING)")
spark.sql("""
  CREATE TABLE IF NOT EXISTS sink_table (id INT, data STRING)
  TBLPROPERTIES ('primary.key' = 'id')
""")

// Read from source table
val sourceDF = spark.readStream
  .option("scan.startup.mode", "latest")
  .table("fluss_catalog.fluss.source_table")

// Write to sink table
val query = sourceDF.writeStream
  .option("checkpointLocation", "/tmp/fluss-streaming-checkpoint")
  .toTable("fluss_catalog.fluss.sink_table")

query.awaitTermination()