Data Stream Processing - Concepts and Frameworks

Data Stream Processing – Concepts and FrameworksMatthias Niehoff

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AGENDA

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Typical Problems

Basic Ideas

Streaming Frameworks

Current Innovations

Recommendations

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Basic IdeasData Stream Processing – Why and what is it?

Batch Layer

Speed Layer

Current Situation of Dealing with (Big) Data

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IoT Sensor DataIndustrial Machines,

Consumer Electronic,

Agriculture

Click StreamsOnline Shops, Self Service

Portals, Comparison Portals

MonitoringSystem Health, Traffic

between Systems,

Resource Utilization

Online GamingGamer Interactions, Reward

Systems, Custom Content

& Experiences

Automotive IndustryVehicle Tracking, Predictive

Maintenance , Routing

Information

Financial TransactionsFraud Detection, Trade

Monitoring and

Management

Sources for streaming data can not only be found in the frequently mentioned IoT area. In many other industries incur streaming data. Strictly speaking, any data can be viewed as a stream. Some of the most popular use cases and examples are:

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Sources for Streaming Data

Distributed Stream Processing

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Endless & Continuous Data

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Speed & Realtime

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Distributed & Scalable

First step – Microbatching

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Source

Processing

Sink

Microbatches

Native Streaming

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Source

Processing

Sink

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Typical Problemsand the way frameworks tackle them

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Time

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Order

Event time vs processing time

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event

processing

1 2 3 4 5 6 7 8 9t in minutes

Windowing - Slicing data into chunks

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Tumbling Window Sliding Window Session Window

Time Trigger Count Trigger Content Trigger

Tumbling & Sliding Windows

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4 5 3 6 1 5 9 2 8 6 7 2

4 5 3 6 1 5 9 2 8 6 7 2

18 17 23

tumbling windows

sum

4 5 3 6 1 5 9 2 8 6 7 2

18 17 23 sum

4 5 3 6 1 5 9 2 8 6 7 2

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sliding windows

Session Window

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time

user 1

user 2

?logout

delayed event

Session Window

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time

user 1

user 2

logout

delayed event

The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing

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[...] stop trying to groom unbounded datasets into finite pools of information that eventually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted [...]

http://www.vldb.org/pvldb/vol8/p1792-Akidau.pdf

•Part 1 of „When will the result be calculated?“

•Watermark of all received data

•A watermark of 10:00 means „It is assumed that all data until 10:00 now arrived“

• fix watermark

• heuristic watermark

•A window will be materialized/processed when watermark equals end of window

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Watermarks

Watermarks

22event time

proc

essi

ng ti

me

3

6

4,5

Trigger

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ContentEvent Time Processing Time Count Composite

•Part 2 of „When will the result be calculated?“

•Triggers an (additional) materialization of the window

•Example

• every 10 minutes (in processing time)

• & when the watermark reached the end of the window

• & with each delayed event

• but only for additional 15 minutes in processing time (allowed lateness)

AccumulatorsJoining the individual (triggered) results

•every result on its own (discarding)

•Results based on each other (accumulating)

•Results based on each other & correction of the old result (accumulating & retracting)

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Accumulators

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Discarding Accumulating Accumulating & Retracting

(5,2) 7 7 7

(8,3) 11 18 18, -7

(4) 4 22 22, -18

Last value 4 22 22

Total sum 22 47 22

5 2 | 8 3 | 4

Watermarks, Trigger, Accumulators

vgl. https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-102

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input.apply(Window.into(FixedWindows.of(Duration.standardMinutes(60))).triggering(AtWatermark().withEarlyFirings(AtPeriod(Duration.standardMinutes(10))).withLateFirings(AtCount(1)))).withAllowedLateness(Duration.standardMinutes(15))) .discardingFiredPanes()).apply(Sum.integersPerKey());

Stream ~ Table Model •Aggregating a stream over time yields a table

•Changes to a table over time yields a stream

•Table will be updated by every entry in the stream

•Every new entry triggers a computation

•Retention period for late events (c.f. allowed lateness)

•Stream/Table ⊆ Dataflow

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(key1, value1) key1 value1 1

key1 value3 2

key2 value2 1

key1 value1 1

key2 value2 1(key2, value2)

(key1, value3)

key valueupdate count

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Stateful Processing

State & Window Processing•Non trivial applications mostly need some kind of (temporal) persistent state

• i.e aggregations over a longer time, counter, slowly refreshing metadata

• held in memory, can be stored on disk

• interesting: partitioning, rescaling, node failure?

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state

operation

State Implementations•State is most of the time partitioned

• Distributed over multiple nodes

• Number of nodes might change

•State must be fault-tolerant

•State access must be fast

•Storage backend

• native/own-build: i.e. in Spark Streaming

• existing tools: RocksDB in Kafka Streams

• pluggable: Flink, amongst others also RocksDB

•Carbone et. al. (2017), State Management in Flink, http://www.vldb.org/pvldb/vol10/p1718-carbone.pdf

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Data Lookup

Lookup Additional Data

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Results

Queue Processing

Metadata

Lookup - Remote Read

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Queue Metadata

Node 2

Node 1

cccc

cccc

cccc

cccc

Lookup - Local Read

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Queue Metadata

Node 2

Node 1

cccc

cccc

cccc

cc cc

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Deployment & Runtime Environment

Runtime Environment - Cluster vs. Library

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YARN

Framework Dependent

•UI

•REST APIs

•Metrics

Scheduler Monitoring

Own Logging

•Technical

•Business

Java „Classics"

•JMX

•Profiler

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Monitoring

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Delivery Guarantees

Guarantees

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at-most-once at-least-once exactly-once

Record Acknowledgement

Micro Batching

Snapshots/Checkpoints

Changelogs

Guarantees

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at-most-once at-least-once exactly-once

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Streaming FrameworksHelping you implement your solution

Tyler Akidau

“ ... an execution engine designed for unbounded data sets, and nothing more”

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T. Akidau et. al (2015): The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing - http://www.vldb.org/pvldb/vol8/p1792-Akidau.pdf

Apache Spark•Open Source (2010) & Apache project (2013)

•Unified Batch & Stream Processing

•Wide distribution, especially in Hadoop environments

•Batch: RDD as base, DataFrames and DataSets as optimization

•Streaming: DStream & Structured Streaming

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Apache Spark Streaming•Microbatching

•Similiar, partly unified, programming model as with batch processing

•State and window operations

•Missing support for event time

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Apache Spark Structured Streaming•DataSets/DataFrames for streaming processing

•DataStream as an ever-growing table

•Unified API

•Limited support for event time operations

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valds=sparkSession.read.json("someFile.json")

ds.write.json("otherFile.json")

valds=sparkSession.readStream.format("kafka").option("...","...").load

ds.writeStream.outputMode("complete").format("console").start()

Apache Flink•Started as research project in 2010 (Stratosphere), Apache project since 2014

•Low latency streaming and high throughput batch processing

•Streaming first

•Flexible state and window handling

•Rich support for event time handling

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Apache Kafka Streams API•Only a library, no runtime environment

•Requires Kafka cluster ( >= 0.10)

•Uses Kafka consumer technologies for

• Ordering

• Partitioning

• Scaling

•Source & sink: Kafka topics only

•Kafka Connect for sources & sinks

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Current developmentsThe latest promises and features

Queryable State•Known as

• Queryable state (Flink)

• Interactive Queries (Kafka Streams)

•Still low level

• Data lifecycle

• (De)Serialization

• Partitioned state discovery

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state

operation

query interface

Streaming SQL•Use SQL to query Streaming Data

• time varying relations i.e. [12:00, 12:00)

• query on multiple points in time

•Standard ANSI SQL + some extensions

• SELECT TABLE, SELECT STREAM

• WINDOWS

• TRIGGERS

•Supported by

• Flink

• Kafka Streams (KSQL)

•https://s.apache.org/streaming-sql-strata-nyc

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CREATE TABLE possible_fraud AS SELECT card_number, count(*) FROM authorization_attempts WINDOW TUMBLING (SIZE 5 SECONDS) GROUP BY card_number HAVING count(*) > 3;

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RecommendationsOr at least some hints when choosing a framework

Spark Streaming might be an option if•Spark is already used for batch processing

•Hadoop, and therefore YARN, is used

•A huge community is important

•Scala is not a problem

•Latency is not an important criteria *

•Event time handling is not needed *

•* those change in Structured Streaming

• event time support

• reduce microbatching overhead

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Flink is good for...•flexible event time processing

• watermarks

• trigger

• accumulator

•connectivity to the most important peripheral systems

•low latency stream processing

•excellent state handling

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And finally Kafka Streams, for...•you want an easy deployment

•you already have a scheduler/micro service platform

•low latency and high throughput

•event time support

•a lightweight start in streaming

•if you already use Kafka

•if you are fine with making Kafka your central backbone

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Comparison

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Engine Microbatching Native Nativ

Programmingmodel Declarative Declarative Declarativ

Guarantees Exactly-Once Exactly-Once Exactly-Once

Event time Handling No/Yes* Yes Yes

State Storage Own Pluggable RocksDB + Topic

Community & Ecosystem Big Medium Big

Deployment Cluster Cluster Library

MonitoringUI, REST API, Dropwizard

MetricsUI, Metrics (JMX, Ganglia),

Rest APIKafka Tools, Confluent Control Center, JMX

A word on•Apache Beam

• High-level API for different streaming runner, i.e Google Cloud Dataflow, Flink and Spark Streaming

•Google Cloud Data Flow

• Cloud Streaming by Google

•Apex

• YARN based with a static topology which can be changed at runtime

•Flume

• Logfile Shipping, especially into HDFS

•Storm/Heron

• Streaming pioneer by Twitter, Heron as a successor with the same API

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Take aways•Streaming is not easy

• (Event) Time

• State

• Deployment

• Correctness

•Different concepts and implementations

•Be aware of

• Monitoring

• „Overkill“

•Ongoing research and development

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