- Spark Streaming allows processing of live data streams using Spark's batch processing engine by dividing streams into micro-batches. - A Spark Streaming application consists of input streams, transformations on those streams such as maps and filters, and output operations. The application runs continuously processing each micro-batch. - Key aspects of operationalizing Spark Streaming jobs include checkpointing to ensure fault tolerance, optimizing throughput by increasing parallelism, and debugging using Spark UI.

1. So you think you can Stream?
Vida Ha (@femineer)
Prakash Chockalingam (@prakash573)

2. Who am I ?
● Solutions Architect @ Databricks
● Formerly Square, Reporting & Analytics
● Formerly Google, Mobile Web Search & Speech
Recognition
2

3. Who am I ?
● Solutions Architect @ Databricks
● Formerly Netflix, Personalization
Infrastructure
3

4. About Databricks
Founded by creators of Spark in 2013
Cloud enterprise data platform
- Managed Spark clusters
- Interactive data science
- Production pipelines
- Data governance, security, …

5. Sign up for the Databricks Community Edition
Learn Apache Spark for free
● Mini 6GB cluster for learning Spark
● Interactive notebooks and dashboards
● Online learning resources
● Public environment to share your work
https://databricks.com/try-databricks

6. Agenda
• Introduction to Spark Streaming
• Lifecycle of a Spark streaming app
• Aggregations and best practices
• Operationalization tips
• Key benefits of Spark streaming

7. What is Spark Streaming?

8. Spark Streaming

10. How does it work?
● Receivers receive data streams and chops them in to batches.
● Spark processes the batches and pushes out the results

11. Word Count
val context = new StreamingContext(conf, Seconds(1))
val lines = context.socketTextStream(...)
Entry point Batch Interval
DStream: represents a
data stream

12. Word Count
val context = new StreamingContext(conf, Seconds(1))
val lines = context.socketTextStream(...)
val words = lines.flatMap(_.split(“ “))
Transformations: transform
data to create new DStreams

13. Word Count
val context = new StreamingContext(conf, Seconds(1))
val lines = context.socketTextStream(...)
val words = lines.flatMap(_.split(“ “))
val wordCounts = words.map(x => (x, 1)).reduceByKey(_+_)
wordCounts.print()
context.start()
Print the DStream contents on screen
Start the streaming job

14. Lifecycle of a streaming app

15. Execution in any Spark Application
Spark
Driver
User code runs in the
driver process
YARN / Mesos / Spark
Standalone cluster
Tasks sent to
executors for
processing data
Spark
Executor
Spark
Executor
Spark
Executor
Driver launches
executors in cluster

16. Execution in Spark Streaming: Receiving data
Executor
Executor
Driver runs receivers
as long running tasks
Receiver Data stream
Driver
Receiver divides stream into
blocks and keeps in memory
Data Blocks
Blocks also replicated
to another executor
Data Blocks

17. Execution in Spark Streaming: Processing data
Executor
Executor
Receiver
Data Blocks
Data Blocks
results
results
Data
store
Every batch interval,
driver launches tasks to
process the blocks
Driver

18. End-to-end view
18
t1 = ssc.socketStream(“…”)
t2 = ssc.socketStream(“…”)
t = t1.union(t2).map(…)
t.saveAsHadoopFiles(…)
t.map(…).foreach(…)
t.filter(…).foreach(…)
T
U
M
T
M FFE
FE FE
B
U
M
B
M F
Input
DStreams
Output
operations
RDD Actions /
Spark Jobs
BlockRDDs
DStreamGraph
DAG of RDDs
every interval
DAG of stages
every interval
Stage 1
Stage 2
Stage 3
Streaming app
Tasks
every interval
B
U
M
B
M F
B
U
M
B
M F
Stage 1
Stage 2
Stage 3
Stage 1
Stage 2
Stage 3
Spark Streaming
JobScheduler + JobGenerator
Spark
DAGScheduler
Spark
TaskScheduler
Executors
YOU write
this

19. Aggregations

20. Word count over a time window
val wordCounts = wordStream.reduceByKeyAndWindow((x:
Int, y:Int) => x+y, windowSize, slidingInterval)
Reduces over a time window

21. Word count over a time window
Scenario: Word count for the last 30 minutes
How to optimize for good performance?
● Increase batch interval, if possible
● Incremental aggregations with inverse reduce function
val wordCounts = wordStream.reduceByKeyAndWindow(
(x: Int, y:Int) => x+y, (x: Int, y: Int) =>
x-y, windowSize, slidingInterval)
● Checkpointing
wordStream.checkpoint(checkpointInterval)

22. Stateful: Global Aggregations
Scenario: Maintain a global state based on the input events coming
in. Ex: Word count from beginning of time.
updateStateByKey (Spark 1.5 and before)
● Performance is proportional to the size of the state.
mapWithState (Spark 1.6+)
● Performance is proportional to the size of the batch.

23. Stateful: Global Aggregations

24. Stateful: Global Aggregations
Key features of mapWithState:
● An initial state - Read from somewhere as a RDD
● # of partitions for the state - If you have a good estimate of the size of the
state, you can specify the # of partitions.
● Partitioner - Default: Hash partitioner. If you have a good understanding of
the key space, then you can provide a custom partitioner
● Timeout - Keys whose values are not updated within the specified timeout
period will be removed from the state.

25. Stateful: Global Aggregations (Word count)
val stateSpec = StateSpec.function(updateState _)
.initialState(initialRDD)
.numPartitions(100)
.partitioner(MyPartitioner())
.timeout(Minutes(120))
val wordCountState = wordStream.mapWithState(stateSpec)

26. Stateful: Global Aggregations (Word count)
def updateState(batchTime: Time,
key: String,
value: Option[Int],
state: State[Long])
: Option[(String, Long)]
Current batch time
A Word in the input stream
Current value (= 1)
Counts so far for the word
The word and its new count

27. Operationalization

28. Operationalization
Three main themes
● Checkpointing
● Achieving good throughput
● Debugging a streaming job

29. Checkpoint
Two types of checkpointing:
● Checkpointing Data
● Checkpointing Metadata

30. Checkpoint Data
● Checkpointing DStreams
• Primarily needed to cut long lineage on past batches
(updateStateByKey/reduceByKeyAndWindow).
• Example: wordStream.checkpoint(checkpointInterval)

31. Checkpoint Metadata
● Checkpointing Metadata
• All the configuration, DStream operations and incomplete batches are
checkpointed.
• Required for failure recovery if the driver process crashes.
• Example:
–streamingContext.checkpoint(directory)
–StreamingContext.getActiveOrCreate(directory, ...)

32. Achieving good throughput
context.socketStream(...)
.map(...)
.filter(...)
.saveAsHadoopFile(...)
Problem: There will be 1 receiver which receives all the data and
stores it in its executor and all the processing happens on that
executor. Adding more nodes doesn’t help.

33. Achieving good throughput
Solution: Increase the # of receivers and union them.
● Each receiver is run in 1 executor. Having 5 receivers will ensure
that the data gets received in parallel in 5 executors.
● Data gets distributed in 5 executors. So all the subsequent Spark
map/filter operations will be distributed
val numStreams = 5
val inputStreams = (1 to numStreams).map(i => context.
socketStream(...))
val fullStream = context.union(inputStreams)
fullStream.map(...).filter(...).saveAsHadoopFile(...)

34. Achieving good throughput
● In the case of direct receivers (like Kafka), set the appropriate #
of partitions in Kafka.
● Each kafka partition gets mapped to a Spark partition.
● More partitions in Kafka = More parallelism in Spark

35. Achieving good throughput
● Provide the right # of partitions based on your cluster size for
operations causing shuffles.
words.map(x => (x, 1)).reduceByKey(_+_, 100)
# of partitions

36. Debugging a Streaming application
Streaming tab in Spark UI

37. Debugging a Streaming application
Processing Time
● Make sure that the processing time < batch interval

38. Debugging a Streaming application

39. Debugging a Streaming application
Batch Details Page:
● Input to the batch
● Jobs that were run as part of the processing for the batch

40. Debugging a Streaming application
Job Details Page
● DAG Visualization
● Stages of a Spark job

41. Debugging a Streaming application
Task Details Page
Ensure that the tasks are executed on multiple executors (nodes) in your cluster to have enough parallelism while processing.
If you have a single receiver, sometimes only one executor might be doing all the work though you have more than one executor
in your cluster.

42. Key benefits of Spark streaming

43. Dynamic Load Balancing

44. Fast failure and Straggler recovery

45. Combine Batch and Stream Processing
Join data streams with static data sets
val dataset = sparkContext.hadoopFile(“file”)
…
kafkaStream.transform{ batchRdd =>
batchRdd.join(dataset).filter(...)
}

46. Combine ML and Stream Processing
Learn models offline, apply them online
val model = KMeans.train(dataset, …)
kakfaStream.map { event =>
model.predict(event.feature)
}

47. Combine SQL and Stream Processing
inputStream.foreachRDD{ rdd =>
val df = SQLContext.createDataframe(rdd)
df.select(...).where(...).groupBy(...)
}

48. Thank you.