Independent of the source of data, the integration and analysis of event streams gets more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. In this session we compare two popular Streaming Analytics solutions: Spark Streaming and Kafka Streams. Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala. Kafka Streams is the stream processing solution which is part of Kafka. It is provided as a Java library and by that can be easily integrated with any Java application. This presentation shows how you can implement stream processing solutions with each of the two frameworks, discusses how they compare and highlights the differences and similarities.

1. BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF
HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH
Spark (Structured) Streaming vs.
Kafka Streams
Two stream processing platforms compared
Guido Schmutz
3.12.2018
@gschmutz guidoschmutz.wordpress.com

2. Agenda
1. Introducing Stream Processing
2. Spark Streaming vs. Kafka Streams – Overview
3. Spark Structured Streaming vs. Kafka Streams – in Action
4. Summary

3. Guido Schmutz
Working at Trivadis for more than 22 years
Oracle Groundbreaker Ambassador & Oracle ACE Director
Consultant, Trainer Software Architect for Java, Oracle, SOA and
Big Data / Fast Data
Head of Trivadis Architecture Board
Technology Manager @ Trivadis
More than 30 years of software development experience
Contact: guido.schmutz@trivadis.com
Blog: http://guidoschmutz.wordpress.com
Slideshare: http://www.slideshare.net/gschmutz
Twitter: gschmutz
135th edition

4. Introducing Stream Processing

5. “Data at Rest” vs. “Data in Motion”
Data at Rest Data in Motion
Store
Act
Analyze
StoreAct
Analyze
11101
01010
10110
11101
01010
10110
Architekturen von Big Data Anwendungen

6. Hadoop Clusterd
Hadoop Cluster
Big Data
Reference Architecture for Modern Data Analytics
Service
BI Tools
Enterprise Data
Warehouse
Search / Explore
File Import / SQL Import
Event
Hub
D
ata
Flow
D
ata
Flow
Change DataCapture Parallel
Processing
Storage
Storage
RawRefined
SQL
Export
Microservice State
{ }
API
Event
Stream
Event
Stream
Search
Service
Microservices
Enterprise Apps
Logic
{ }
API
Edge Node
Rules
Event Hub
Storage
Bulk Source
Event Source
Location
DB
Extract
File
IoT
Data
Mobile
Apps
Social
Event Stream
Telemetry
Stream
Processor
State
{ }
API
Stream Analytics
Results
DB

7. Two Types of Stream Processing
(from Gartner)
Stream Data Integration
• Primarily cover streaming ETL
• Integration of data source and data sinks
• Filter and transform data
• (Enrich data)
• Route data
Stream Analytics
• calculating aggregates & detecting patterns
to generate higher-level, more relevant
summary information (complex events =>
used to be CEP)
• Complex events may signify threats or
opportunities that require a response

8. Stream Processing & Analytics Ecosystem
Stream Analytics
Event Hub
Open Source Closed Source
Stream Data Integration
Source: adapted from Tibco
Edge
Introduction to Stream Processing

9. Stream Processing & Analytics Ecosystem
Stream Analytics
Event Hub
Open Source Closed Source
Stream Data Integration
Source: adapted from Tibco
Edge
Introduction to Stream Processing

10. Example Use Case
Truck-2
Truck-1
Truck-3
truck_
position
detect_danger
ous_driving
Truck
Driver
jdbc-source
join_dangerous_driv
ing_driver
dangerous_dri
ving_driver
Count By Event Type
Window (1m, 30s)
count_by_event
_type

11. Spark Streaming vs. Kafka Streams
- Overview

12. Spark (Structured) Streaming
Spark Streaming
• 1st generation
• one of the first APIs to enable stream
processing using high-level functional
operators like map and reduce
• Like RDD API the DStreams API is based
on
relatively low-level operations on
Java/Python objects
• Used by many organizations in production
Spark Structured Streaming
• 2nd generation
• Structured API through DataFrames /
Datasets rather than RDDs
• Easier code reuse between batch and
streaming
• marked production ready in Spark 2.2.0
• Support for Java, Scala, Python, R and SQL
• Focus of this talk

13. Apache Spark Streaming as part of Spark Stack
Spark (Structured) Streaming
Resilient Distributed Dataset (RDD)
Spark
Standalone
MESOS /
Kubernetes
YARN HDFS S3
RDBMS &
NoSQL
Kafka
Libraries
Low Level API
Cluster Resource Managers Data Sources / Data Sinks
Advanced Analytics Libraries & Ecosystem
Data Frame
Structured API
Datasets SQL
Distributed Variables

14. Kafka Streams – part of Kafka Core
• Designed as a simple and lightweight
library in Apache Kafka
• no external dependencies on systems
other than Apache Kafka
• Part of open source Apache Kafka,
introduced in 0.10+
• Leverages Kafka as its internal
messaging layer
• Support for Java and SQL (KSQL)

15. Spark Structured Streaming vs.
Kafka Streams – in Action

16. Infrastructure
• Runs as part of a full Spark stack
• Cluster can be either Spark
Standalone, YARN-based or
container-based
• Many cloud options
• Just a Java library
• Runs anyware Java runs: Web
Container, Java Application, Container-
based …

17. Main Abstractions
Dataset/Data Frame API
• DataFrames and Datasets can represent
static, bounded data, as well as streaming,
unbounded data
• Use readStream() instead of read()
Transformation & Actions
• Almost all transformations from Spark
bounded data processing (Batch) are also
usable for streaming
Input Sources and Sinks
Triggers
• triggers define when data is output
• As soon as last group is finished
• Fixed interval between micro-batches
• One-time micro-batch
Output Mode
• Define how data is output
• Append – only add new records to
output
• Update – update changed records in
place
• Complete – rewrite full output

18. Main Abstractions
Topologyval schema = new StructType()
.add(...)
val inputDf = spark
.readStream
.format(...)
.option(...)
.load()
val filteredDf = inputDf.where(...)
val query = filteredDf
.writeStream
.format(...)
.option(...)
.start()
I
F
O

19. Main Abstractions
Stream Processing Application
• program that uses Kafka Streams library
Topology
• logic that needs to be performed by stream
processing
• functional DSL or low-level Processor API
Stream Processor
• a node in the processor topology
KStream
• Abstraction of a stream of records
• Interpreted as events
KTable
• Abstraction of a change log stream
• Interpreted as update of same record (by
key)
GlobalKTable
• Like KTable, but not partitioned => all data
is available on all parallel application
instances

20. Main Abstractions
Topologypublic static void main(String[] args) {
Properties streamsConfiguration = new Properties();
streamsConfiguration.put(...);
final StreamsBuilder builder = new StreamsBuilder();
KStream<..,..> stream = builder.stream(...);
KStream<..,..> filtered = stream.filter(…)
filtered.to(...)
KafkaStreams streams = new KafkaStreams(
builder.build(),streamsConfiguration);
streams.start();
}
I
F
O

21. Streaming Data Sources
• File Source
• Reads files as a stream of data
• Supports text, csv, json, orc parquet
• Files must be atomically placed
• Kafka Source
• Reads from Kafka Topic
• Supports Kafka broker > 0.10.x
• Socket Source (for testing)
• Reads UTF8 text from socket
connection
• Rate Source (for testing)
• Generate data at specified number of
rows per second
val rawDf = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "broker-1:9092")
.option("subscribe", "truck_position")
.load()

22. Streaming Data Sources
"Kafka only"
KStream from Topic
KTable from Topic
Use Kafka Connect s reading
other data sources into Kafka
first
KStream<String, TruckPosition> positions =
builder.stream("truck_position"
, Consumed.with(Serdes.String()
, truckPositionSerde));
KTable<String, Driver> driver =
builder.table("trucking_driver"
, Consumed.with(Serdes.String()
, driverSerde)
, Materialized.as("driver-store"));

23. Streaming Sinks
• File Sink – stores output to a directory
• Kafka Sink – publishes to Kafka
• Foreach Sink - Runs arbitrary computation on the records in the output
• Console Sink – for debugging, prints output to console
• Memory Sink – for debugging, stores output in-memory table
val query = jsonTruckPlusDriverDf
.selectExpr("to_json(struct(*)) AS value")
.writeStream
.format("kafka")
.option("kafka.bootstrap.servers", "broker-1:9092")
.option("topic","dangerous_driving ")
.option("checkpointLocation", "/tmp")
.start()

24. Streaming Sinks
"Kafka only"
For testing only:
Use Kafka Connect for
writing out to other targets
KStream<String, TruckPosition> posDriver = ..
posDriver.to("dangerous_driving"
,Produced.with(Serdes.String()
, truckPositionDriverSerde));
KStream<String, TruckPosition> posDriver = ..
// print to system output
posDriver.print(Printed.toSysOut())
// shortcut for
posDriver.foreach((key,value) ->
System.out.println(key + "=" + value))

25. Processing Model: Event-at-a-time vs. Micro Batch
Introduction to Stream Processing
Micro-Batch Processing
• Splits incoming stream in
small batches
• Higher latency
• Fault tolerance easier
Event-at-a-time Processing
• Events processed as they arrive
• low-latency
• fault tolerance expensive

26. Stateless Operations – Selection & Projection
Most common operations on
DataFrame/Dataset are supported for
streaming as well
select, filter, map, flatMap, …
KStream and KTable interfaces support
variety of transformation operations
filter, filterNot, map, mapValues,
flatMap, flatMapValues, branch,
selectKey, groupByKey …
val filteredDf =
truckPosDf.where(
"eventType !='Normal'")
KStream<> filtered =
positions.filter((key,value) ->
!value.eventType.equals("Normal")
)

27. Stateful Operations – Aggregations
Held in distributed memory with option to
spill to disk (fault tolerant through
checkpointing to Hadoop-like FS)
Output modes: Complete, Append,
Update
count, sum, mapGroupsWithState,
flatMapGroupsWithState, reduce ...
Require state store which can be in-
memory, RocksDB or custom impl (fault
tolerant through Kafka topics)
Result of Aggregation is a KTable
count, sum, avg, reduce, aggregate
...
val c = source
.withWatermark("timestamp"
, "10 minutes")
.groupBy()
.count()
KTable<..> c = stream
.groupByKey(..)
.count(...);

28. Stateful Operations – Time Abstraction
Clock
Event Time
Processing Time
Ingestion Time
1 2 3 4 5
adapted from Matthias Niehoff (Codecentric)

29. Stateful Operations – Time Abstraction
Event Time
• New with Spark Structured Streaming
• Extracted from the message (payload)
Ingestion Time
• for sources which capture ingestion time
Processing Time
• “Old” Spark Streaming only supported
processing time
• generate the timestamp upon processing
Event Time
• Point in time when event occurred
• Extracted from the message (payload or
header)
Ingestion Time
• Point in time when event is stored in Kafka
(sent in message header)
Processing Time
• Point in time when event happens to be
processed by stream processing
applicationdf.withColumn("processingTime"
,current_timestamp())
.option("includeTimestamp", true)

30. Stateful Operations - Windowing
streams are unbounded
need some meaningful time frames to do
computations (i.e. aggregations)
Computations over events done using
windows of data
Windows are tracked per unique key
Fixed Window Sliding Window Session Window
Time
Stream of Data Window of Data

31. Stateful Operations - Windowing
Support for Tumbling & Hopping
(Sliding) Time Windows
Handling Late Data with
Watermarking
val c = source
.withWatermark("timestamp"
, "10 minutes")
.groupBy(window($"eventTime"
, "1 minutes"
, "30 seconds")
, $"word")
.count()
Data older than watermark
not expected / get discarded
event time
Trailing gap
of 10 mins
max event time
watermark
12:20
12:10
12:25
Trailing gap
of 10 mins
processing time

32. Stateful Operations - Windowing
Support for Tumbling & Hopping Windows
Support for Session Windows
Handling Late Data with Data
Retention (optional)
KTable<..> c = stream
.groupByKey(...)
.windowedBy(
SessionWindows
.with(5 * 60 * 1000)
).count();
KTable<..> c = stream
.groupByKey(..)
.windowedBy(
TimeWindows.of(60 * 1000)
.advanceBy(30 * 1000)
.until(10 * 60 * 1000)
).count(...);
Data older than watermark
not expected / get discarded
event time
Trailing gap
of 10 mins
max event time
Data Retention
12:20
12:10
12:25
Trailing gap
of 10 mins
processing time

33. Stateful Operations - Joins
Introduction to Stream Processing
Challenges of joining streams
1. Data streams need to be aligned as they
come because they have different timestamps
2. since streams are never-ending, the joins
must be limited; otherwise join will never end
3. join needs to produce results continuously as
there is no end to the data
Stream to Static (Table) Join
Stream to Stream Join (one window join)
Stream to Stream Join (two window join)
Stream-to-
Static Join
Stream-to-
Stream
Join
Stream-to-
Stream
Join
Time
Time
Time

34. Stateful Operations - Joins
Stream-to-Static and Stream-to-Stream
(since 2.3) Joins on Dataset/DataFrame
Watermarking helps Spark to know for
how long to retain data
• Optional for Inner Joins
• Mandatory for Outer Joins
val jsonTruckPlusDriverDf =
jsonFilteredDf.join(driverDf
, Seq("driverId")
, "left")
Source: Spark Documentation

35. Supports following joins
• KStream-to-KStream
• KTable-to-KTable
• KStream-to-KTable
• KStream-to-GlobalKTable
• KTable-to-GlobalKTable
Stateful Operations - Joins
KStream<String, TruckPositionDriver> joined =
filteredRekeyed.leftJoin(driver
, (left,right) -> new TruckPositionDriver(left
, StringUtils.defaultIfEmpty(right.first_name,"")
, StringUtils.defaultIfEmpty(right.last_name,""))
, Joined.with(Serdes.String()
, truckPositionSerde
, driverSerde));
Source: Confluent Documentation

36. There is more ….
• Streaming Deduplication
• Run-Once Trigger / fixed Interval
Micro-Batching
• Continuous Trigger with fixed
checkpoint interval (experimental in
2.3)
• Streaming Machine Learning
• REPL
• Queryable State
• Processor API
• Exactly Once Processing
• Microservices with Kafka Streams
• Automatic Scale-up / Scale-Down
• Stand-by replica of local state
• Streaming SQL

37. There is more … Streaming SQL with KSQL
• Enables stream processing with
zero coding required
• The simplest way to process
(structured) streams of data in real-
time
• Powered by Kafka Streams
• KSQL server with REST API
• Spark SQL also offers SQL on
streaming data, but not as a “first-
class citizen”
ksql> CREATE STREAM truck_position_s
(timestamp BIGINT,
truckId BIGINT,
driverId BIGINT,
routeId BIGINT,
eventType VARCHAR,
latitude DOUBLE,
longitude DOUBLE)
WITH (kafka_topic='truck_position',
value_format='JSON');
ksql> SELECT * FROM truck_position_s;
1506922133306 | "truck/13/position0 | 2017-10-
02T07:28:53 | 31 | 13 | 371182829 | Memphis to
Little Rock | Normal | 41.76 | -89.6 | -
2084263951914664106
ksql> SELECT * FROM truck_position_s
WHERE eventType != 'Normal';

38. Summary

39. Spark Structured Streaming vs. Kafka Streams
• Runs on top of a Spark cluster
• Reuse your investments into Spark
(knowledge and maybe code)
• A HDFS like file system needs to be
available
• Higher latency due to micro-batching
• Multi-Language support: Java, Python,
Scala, R
• Supports ad-hoc, notebook-style
development/environment
• Available as a Java library
• Can be the implementation choice of a
microservice
• Can only work with Kafka for both input and
output
• low latency due to continuous processing
• Currently only supports Java, Scala support
available soon
• KSQL abstraction provides SQL on top of
Kafka Streams

40. Comparison
Kafka Streams Spark Streaming Spark Structured Streaming
Language Options Java (KIP for Scala), KSQL Scala, Java, Python, R, SQL Scala, Java, Python, R, SQL
Processing Model Continuous Streaming Micro-Batching Micro-Batching
Core Abstraction KStream / KTable DStream (RDD) Data Frame / Dataset
Programming Model Declarative/Imperative Declarative Declarative
Time Support Event / Ingestion / Processing Processing Event / Ingestion/ Processing
State Support Memory / RocksDB + Kafka Memory / Disk Memory / Disk
Time Window Support Fixed, Sliding, Session Fixed, Sliding Fixed, Sliding
Join Stream-Static, Stream-Stream Stream-Static Stream-Static, Stream-Stream (2.3)
Event Pattern detection No No No
Query Language Support KSQL No Spark SQL (limited)
Queryable State Interactive Queries No No
Scalability & Reliability Yes Yes Yes
Guarantees At Least Once/Exactly Once At Least Once/Exactly Once (partial) At Least Once/Exactly Once (partial)
Latency Sub-second seconds seconds
Deployment Java Library Cluster (with HDFS like FS) Cluster (with HDFS like FS)

41. Technology on its own won't help you.
You need to know how to use it properly.