The document provides an in-depth overview of Spark Streaming, highlighting its capabilities for large-scale stream processing through a fault-tolerant, stateful model. It covers key concepts such as discretized streams (DStreams), transformations, output operations, and fault-tolerance mechanisms, alongside examples demonstrating practical applications like hashtag counting and session tracking. Additionally, it discusses system architecture, checkpointing, and job scheduling within Spark Streaming for enhanced reliability and performance.

1. NRT: Deep Dive into Spark Streaming

2. Agenda
Spark Streaming Introduction
 Computing Model in Spark Streaming
 System Model & Architecture
 Fault-tolerance, Check pointing
 Comb on Spark Streaming

3. What is Spark Streaming?
• Extends Spark for doing large scale stream processing
• Efficient and fault-tolerant stateful stream processing
• Provides a simple batch-like API for implementing complex algorithms
Spark
Spark
SQL
Spark
Streaming
GraphX MLLib …

4. Why Spark Streaming
• Scales to hundreds of nodes
• Achieves low latency
• Efficiently recover from failures
• Integrates with batch and interactive processing

5. Spark Streaming
Run a streaming computation as a series of very small, deterministic
batch jobs
Spark
Spark
Streaming
batches of X
seconds
live data stream
processed
results
 Chop up the live stream into batches of X
seconds (Batch Size as low as 0.5 Second,
Latency is about 1 second)
 Spark treats each batch of data as RDDs
and processes them using RDD operations
 Finally, the processed results of the RDD
operations are returned in batches

6. Agenda
 Spark Streaming Introduction
Computing Model in Spark Streaming
 System Model & Architecture
 Fault-tolerance, Check pointing
 Comb on Spark Streaming

7. Spark Computing Model
• Discretized Stream (DStream)
Represents a stream of data
Implemented as a sequence of RDDs
• DStreams can be either …
Created from Streaming input sources
Created by applying transformations on existing DStreams

8. Spark Streaming - Key Concepts
 DStream – sequence of RDDs representing a stream of data
- Twitter, HDFS, Kafka, Flume, ZeroMQ, Akka Actor, TCP sockets
 Transformations – modify data from one DStream to another
- Standard RDD operations – map, countByValue, reduceByKey, join, …
- Stateful operations – window, countByValueAndWindow, …
 Output Operations – send data to external entity
- saveAsHadoopFiles – saves to HDFS
- foreach – do anything with each batch of results

9. Example 1 – Get hashtags from Twitter
val tweets = ssc.twitterStream(<Twitter username>, <Twitter password>)
DStream: a sequence of distributed datasets (RDDs)
representing a distributed stream of data
batch @ t+1batch @ t batch @ t+2
tweets DStream
stored in memory as an RDD
(immutable, distributed dataset)
Twitter Streaming API

10. Example 1 – Get hashtags from Twitter
val tweets = ssc.twitterStream(<Twitter username>, <Twitter password>)
val hashTags = tweets.flatMap (status => getTags(status))
flatMap flatMap flatMap
…
transformation: modify data in one DStream to create
another DStream
new DStream
new RDDs created
for every batch
batch @ t+1batch @ t batch @ t+2
tweets DStream
hashTags Dstream
[#cat, #dog, … ]

11. Example 1 – Get hashtags from Twitter
val tweets = ssc.twitterStream(<Twitter username>, <Twitter password>)
val hashTags = tweets.flatMap (status => getTags(status))
hashTags.saveAsHadoopFiles("hdfs://...")
output operation: to push data to external storage
flatMap flatMap flatMap
save save save
batch @ t+1batch @ t batch @ t+2
tweets DStream
hashTags DStream
every batch
saved to HDFS

12. DStream of data
Example 2 – Count the hashtags over last 1 min
val tweets = ssc.twitterStream(<Twitter username>, <Twitter password>)
val hashTags = tweets.flatMap (status => getTags(status))
val tagCounts = hashTags.window(Minutes(1), Seconds(1)).countByValue()
sliding window
operation
window length sliding interval
window length
sliding interval

13. tagCounts
Example 2 – Count the hashtags over last 1 min
val tagCounts = hashTags.window(Minutes(1), Seconds(1)).countByValue()
hashTags
t-1 t t+1 t+2 t+3
sliding window
countByValue
count over all
the data in the
window

14. Example 3 - Arbitrary Stateful Computations
 Maintain arbitrary state, track sessions
- Maintain per-user mood as state, and update it with his/her tweets
moods = tweets.updateStateByKey(tweet => updateMood(tweet))
updateMood(newTweets, lastMood) => newMood
tweets
t-1 t t+1 t+2 t+3
moods

15. Example 4 - Combine Batch and Stream Processing
 Do arbitrary Spark RDD computation within DStream
- Join incoming tweets with a spam file to filter out bad tweets
tweets.transform(tweetsRDD => {
tweetsRDD.join(spamHDFSFile).filter(...)
})
•Combine Live data Streams with Historical data
•Combine Streaming with MLLib, GraphX algos
•Query Streaming data using SQL

16. Agenda
 Spark Streaming Introduction
 Computing Model in Spark Streaming
System Model & Architecture
 Fault-tolerance, Check pointing
 Comb on Spark Streaming

17. System - Components
• Network Input Tracker – Keeps track of the data received by each network receiver and
maps them to the corresponding input DStreams
• Job Scheduler – Periodically queries the DStream graph to generate Spark jobs from
received data, and hands them to Job Manager for execution
• Job Manager – Maintains a job queue and executes the jobs in Spark
ssc = new StreamingContext
t = ssc.twitterStream(“…”)
t.filter(…).foreach(…)
Your program
DStream graph
Spark Context
Network Input Tracker
Job Manager
Job Scheduler
Spark Client
RDD graph Scheduler
Block manager Shuffle tracker
Spark Worker
Block
manager
Task threads
Cluster Manager

18. DStream Graph
Spark Streaming Program DStream Graph
t = ssc.twitterStream(“…”)
.map(…)
t.foreach(…)
t1 = ssc.twitterStream(“…”)
t2 = ssc.twitterStream(“…”)
t = t1.union(t2).map(…)
t.saveAsHadoopFiles(…)
t.map(…).foreach(…)
t.filter(…).foreach(…)
T
M
FE
Twitter Input DStream
Mapped DStream
Foreach DStream
T1
U
M
T2
M FFE
FE FE
Dummy DStream signifying
an output operation

19. DStream Graph -> RDD Graphs -> Spark Jobs
• Every interval, RDD graph is computed from DStream graph
• For each output operation, a Spark action is created
• For each action, a Spark job is created to compute it
T
U
M
T
M FFE
FE FE
DStream Graph
B
U
M
B
M FA
A A
RDD Graph
Spark actions
Block RDDs with data
received in last batch
interval
3 Spark jobs

20. Execution Model – Receiving Data
Spark Streaming + Driver Spark Workers
StreamingContext.start()
Network
Input
Tracker
Receiver
Data Received
Block Manager
Blocks replicated
Block Manager
Master
Block
Manager
Blocks pushed

21. Execution Model – Job Scheduling
Spark Streaming + Driver Spark Workers
Network
Input
Tracker
Job Scheduler
Spark’s
Schedulers
Receiver
Block
Manager
Block
Manager
Jobs executed on
worker nodes
DStream
Graph
Job
Manager
JobQueue
Jobs
Block IDsRDDs

22. Agenda
 Spark Streaming Introduction
 Computing Model in Spark Streaming
 System Model & Architecture
Fault-tolerance, Check pointing
 Comb on Spark Streaming

23. Fault-tolerance: Worker
• RDDs remember the operations that
created them
• Batches of input data are replicated in
memory for fault-tolerance
• Data lost due to worker failure, can be
recomputed from replicated input data
• All transformed data is fault-tolerant,
and exactly-once transformations
input data
replicated
in memory
flatMap
lost partitions
recomputed
on other
workers
tweets
RDD
hashTags
RDD

24. Fault-tolerance: Driver
• Master saves the state of the DStreams to a checkpoint file
• Checkpoint file saved to HDFS periodically
• If master fails, it can be restarted using the checkpoint file
• Automated master fault recovery

25. Check Pointing - RDD
Saving RDD to HDFS to prevent RDD graph from growing too large
 Done internally in Spark transparent to the user program
 Done lazily, saved to HDFS the first time it is computed
red_rdd.checkpoint()
HDFS file
Contents of red_rdd saved
to a HDFS file
transparent to
all child RDDs

26. Why is RDD checkpointing necessary?
Stateful DStream operators can have infinite lineages
Large lineages lead to …
 Large closure of the RDD object  large task sizes  high task launch times
 High recovery times under failure
data
t-1 t t+1 t+2 t+3
states

27. Check Pointing - Metadata
Saving of the information defining the streaming computation to fault-tolerant
storage like HDFS. Metadata includes:
 Configuration - The configuration that were used to create the streaming application.
 DStream operations - The set of DStream operations that define the streaming
application.
 Incomplete batches - Batches whose jobs are queued but have not completed yet.
This is used to recover from failure of the node running the driver of the streaming
application

28. Agenda
 Spark Streaming Introduction
 Computing Model in Spark Streaming
 System Model & Architecture
 Fault-tolerance, Check pointing
Comb on Spark Streaming

29. Data Processing – Window Operation
• Window based Computing
• countByWindow
• reduceByKeyAndWindow
• Scenarios
• App Metrics in time Window
• Cache/Persist events in time Window
• Notes:
• Checkpoint
• Performance tuning
val eventCount = inputs.countByWindow(Seconds(30), Seconds(10))
eventCount.print()
val idCount=lines.map(x=>(parser(x),1)).reduceByKey(_+_)
val idWindowCount=idCount.reduceByKeyAndWindow(
(a:Int,b:Int)=>(a+b),Seconds(30),Seconds(10))
idWindowCount.print()

30. Data Processing – Session Operation
• Sessionization: updateStateByKey
val events =inputs.map[(String, (Long, String))](event => {
val eventObj = event.getBondValue()
val id = eventObj.getAppInfo().getId()
val eventName = eventObj.getEventInfo.getName()
val time = System.currentTimeMillis()
(eventName, (time, id))
})
val latestSessions = events.map[(String, (Long, Long, Long))](imp => {
(imp._1, (imp._2._1, imp._2._1, 1))
})
.reduceByKey((imp1, imp2) => {
(Math.min(imp1._1, imp2._1), Math.max(imp1._2, imp2._2), imp1._3 + imp2._3)
})
.updateStateByKey(SessionObject.updateStatbyOfSessions)
1
1
2
How to Update
Session Status?

31. def updateStatbyOfSessions(newBatch: Seq[(Long, Long, Long)], session: Option[(Long, Long, Long)]
): Option[(Long, Long, Long)] = {
var result: Option[(Long, Long, Long)] = null
if (newBatch.isEmpty) {
if (System.currentTimeMillis() - session.get._2 > SESSION_TIMEOUT) {
result = None
} else {
result =session
}
}
newBatch.foreach(c => {
if (session.isEmpty || c._1 - session.get._2 > SESSION_TIMEOUT) {
result = Some(c)
} else {
result = Some((
Math.min(c._1, session.get._1), //newStartTime
Math.max(c._2, session.get._2), //newFinishTime
session.get._3 + c._3 //newSumOfPageView
))
}
})
result
}
Create New Session
• Previous Session is empty
OR
• Previous Session Timeout,
With new batches
Extend Going on Sessions
• Previous Session not empty
AND
• New Batch is not timed out to
previous Session
Retire Timed out Session
• Previous session is timed out

32. QA