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Streaming

Spark-Redis supports streaming data from Stream and List data structures:

Redis Stream

To stream data from Redis Stream use createRedisXStream method (added in Spark-Redis 2.3.1):

import com.redislabs.provider.redis._
import com.redislabs.provider.redis.streaming.{ConsumerConfig, StreamItem}
import org.apache.spark.sql.SparkSession
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.{Seconds, StreamingContext}

val spark = SparkSession.builder.appName("Redis Stream Example")
  .master("local[*]")
  .config("spark.redis.host", "localhost")
  .config("spark.redis.port", "6379")
  .getOrCreate()

val ssc = new StreamingContext(spark.sparkContext, Seconds(1))

val stream = ssc.createRedisXStream(Seq(ConsumerConfig("my-stream", "my-consumer-group", "my-consumer-1")))
stream.print()

ssc.start()
ssc.awaitTermination()

It will automatically create a consumer group if it doesn't exist and will start listening for the messages in the stream.

Stream Offset

By default it pulls messages starting from the latest message. If you need to start from the earliest message or any specific position in the stream, specify the offset parameter:

ConsumerConfig("my-stream", "my-consumer-group", "my-consumer-1", offset = Earliest) // start from '0-0'
ConsumerConfig("my-stream", "my-consumer-group", "my-consumer-1", IdOffset(42, 0))   // start from '42-0'

Please note, Spark-Redis will attempt to create a consumer group with the specified offset, but if the consumer group already exists, it will use the existing offset. It means, for example, if you decide to re-process all the messages from the beginning, just changing the offset to Earliest may not be enough. You may need to either manually delete the consumer group with XGROUP DESTROY or modify the offset with XGROUP SETID.

Receiver reliability

The DStream is implemented with a Reliable Receiver that acknowledges after the data has been stored in Spark. As with any other Receiver to achieve strong fault-tolerance guarantees and ensure zero data loss, you have to enable write-ahead logs and checkpointing.

The received data is stored with StorageLevel.MEMORY_AND_DISK_2 by default. Storage level can be configured with storageLevel parameter, e.g.:

ssc.createRedisXStream(conf, storageLevel = StorageLevel.MEMORY_AND_DISK_SER_2)

Level of Parallelism

The createRedisXStream() takes a sequence of consumer configs, each consumer is started in a separate thread. This allows you, for example, to create a stream from multiple Redis Stream keys:

ssc.createRedisXStream(Seq(
  ConsumerConfig("my-stream-1", "my-consumer-group-1", "my-consumer-1"),
  ConsumerConfig("my-stream-2", "my-consumer-group-2", "my-consumer-1")
))

In this example we created an input DStream that corresponds to a single receiver running in a Spark executor. The receiver will create two threads pulling data from the streams in parallel. However if receiving data becomes a bottleneck, you may want to start multiple receivers in different executors (worker machines). This can be achieved by creating multiple input DStreams and using union to join them together. You can read more about about it here.

For example, the following will create two receivers pulling the data from my-stream and balancing the load:

val streams = Seq(
  ssc.createRedisXStream(Seq(ConsumerConfig("my-stream", "my-consumer-group", "my-consumer-1"))),
  ssc.createRedisXStream(Seq(ConsumerConfig("my-stream", "my-consumer-group", "my-consumer-2")))
)

val stream = ssc.union(streams)
stream.print()

Configuration

If the cluster is not large enough to process data as fast as it is being received, the receiving rate can be limited:

ConsumerConfig("stream", "group", "c-1", rateLimitPerConsumer = Some(100)) // 100 items per second

It defines the number of received items per second per consumer.

Another options you can configure are batchSize and block. They define the maximum number of pulled items and time in milliseconds to wait in a XREADGROUP call.

ConsumerConfig("stream", "group", "c-1", batchSize = 50, block = 200)

Redis List

The stream can be also created from Redis' List, the data is fetched with the blpop command. Users are required to provide an array which stores all the List names they are interested in. The storageLevel parameter is MEMORY_AND_DISK_SER_2 by default.

The method createRedisStream will create a (listName, value) stream, but if you don't care about which list feeds the value, you can use createRedisStreamWithoutListname to get the only value stream.

Use the following to get a (listName, value) stream from foo and bar list

import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.storage.StorageLevel
import com.redislabs.provider.redis._
val ssc = new StreamingContext(sc, Seconds(1))
val redisStream = ssc.createRedisStream(Array("foo", "bar"), storageLevel = StorageLevel.MEMORY_AND_DISK_2)
redisStream.print()
ssc.awaitTermination()

Use the following to get a value stream from foo and bar list

import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.storage.StorageLevel
import com.redislabs.provider.redis._
val ssc = new StreamingContext(sc, Seconds(1))
val redisStream = ssc.createRedisStreamWithoutListname(Array("foo", "bar"), storageLevel = StorageLevel.MEMORY_AND_DISK_2)
redisStream.print()
ssc.awaitTermination()