Voxxed days thessaloniki 21/10/2016 - Streaming Engines for Big Data

Streaming Engines for Big DataSpark Streaming: a case study

Stavros KontopoulosSenior Software Engineer @ Lightbend, M.Sc.

21st October 2016, Thessaloniki

#VoxxedDaysThessaloniki

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Who Am I?

Fast Data Team Engineer @ Lightbend

OSS contributor (Apache Spark on Mesos) https://github.com/skonto


https://github.com/skonto

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● A bit of history...● Streaming Engines for Big Data

○ Key concepts - Design Considerations○ Modern analysis of infinite streams○ Streaming Engines Examples○ Which one to use?

● Spark Streaming A Case Study○ DStream API○ Structured Streaming


Who likes history?

#VoxxedDaysThessaloniki4

Why Streaming?

5#VoxxedDaysThessaloniki

Big Data - The story● One decade ago people started looking to the problem of how to process

massive data sets (Velocity, Variety, Volume).

● The Apache Hadoop project appeared at that time and became the golden solution for batch processing running on commodity hardware. Later became an ecosystem of several other projects: Pig, Hive, HBase etc.

present

GFS paper

2003

Mapreduce Paper

2004

Hadoop project, 0.1.0 release

2006 2009

Hadoop sorts 1 Petabyte

Spark on Yarn by Clouder, Yarn in production

2010

Hadoop 2.4, 2.5, 2.6 releases

2014

HBase, Pig, Hive graduate

2013 2015

Hadoop 2.7release


Big Data - The story

X

Y

Z

MAP

MAP

SHUFFLEMAP

MAP-REDUCE

A

B

A

REDUCE

REDUCE

Q

W


Big Data - The story

Hadoop pros/cons

● Batch jobs usually take hours if not days to complete, in many applications that is not acceptable anymore.

● Traditionally focus is on throughput than latency. Frameworks like Hadoop were designed with that in mind.

● Accuracy is the best you can get.


Big Data - The story● Giuseppe DeCandia et al., ”Dynamo: amazon's highly available key-value

store.” changed the DataBase world in 2007.

● NoSQL Databases along with general system like Hadoop solve problems cannot be solved with traditional RDBMs.

● Technology facts: Cheap memory, SSDs, HDDs are the new tape, more cpus over more powerful cpus.


Big Data - The story● Disruptive companies need to utilize ML and latest information to come up

with smart decisions sooner.

● And so we need streaming in the enterprise… We no longer talk about Big Data only, its Fast Data first.

Searching Recommendations Real-time financial activities Fraud Detection


Big Data - The storyOpsClarity Report Summary:

● 92% plan to increase their investment in stream processing applications in the next year

● 79% plan to reduce or eliminate investment in batch processing● 32% use real time analysis to power core customer-facing applications● 44% agreed that it is tedious to correlate issues across the pipeline● 68% identified lack of experience and underlying complexity of new data

frameworks as their barrier to adoption

http://info.opsclarity.com/2016-fast-data-streaming-applications-report.html


http://info.opsclarity.com/2016-fast-data-streaming-applications-report.html


Key Concepts

Streams ● A Stream is flow of data. The flow consists of ephemeral data elements

flowing from a source to a sink.● Streams become useful when a set of operations/transformations are applied

on them.● Can be infinite or finite in size. This translates to the notions of bounded/

unbounded data.


Stream Processing

Stream Processing: processing done on an (un)bounded data stream. Not all data are available.

Source Sink

Processing


Stream Processing

Multiple Streams Source1

Sink

Processing

Source 2


Stream ProcessingProcessing can be…

● Stream management: connect, iterate...● Data manipulation: map, flatmap…● Input/Output

Graph as the abstraction for defining how all the pieces are put together and how data flows between them. Some systems use a DAG.


Map ReduceCount

Distinct DFS

DBDFS

Stream Processing - Parallelism

Source Sink


map

map17

partitioner

Stream Processing - Execution ModelMap your graph to an execution plan and run it.

Execution Model Abstractions: Job, Task etc.

Actors: JobManager, TaskManager.

Where TaskManager and Tasks run? Threads, nodes etc…

Important: code runs close to the data… Serialize and send over the network the task code along with any dependencies, communicate back the results to the application...


Stream vs Batch ProcessingBatch processing is processing done on finite data set with all data available.

Two types of engines: batch and streaming engines which can actually be used for both types of processing!


Streaming ApplicationsUser code that materializes streams and applies stream processing.

...

...


Streaming Engines for Big DataStreaming Engines allows to building streaming applications:

Streaming Engines for Big data provide in addition:

● A rich ecosystem built around them for example connectors for common sources, outputs to different sinks etc.

● Fault tolerance, scalability (cluster management support), management of strugglers

● ML, Graph, CEP, processing capabilities

+ API Streaming App


Streaming Engines for Big DataA big data system at minimum needs:

● A data processing framework eg. a streaming engine.● A Distributed File System.


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Designing A Streaming Engine

Design Considerations of A Streaming Engine

● Strong consistency. If a machine fails how my results are affected?○ Exactly once processing.○ Checkpointing

● Appropriate semantics for integrating time. Late data?● API (Language Support, DAG, SQL Support etc)



● Execution Model - integration with cluster manager(s)● Elasticity - Dynamic allocation● Performance: Throughput vs Latency● Libraries for CEP, Graph, ML, SQL based processing



● Deployment modes: local vs cluster mode● Streaming vs Batch mode, Code looks the same?● Logging ● Local state management● Support for session state



● Backpressure● Off Heap Management● Caching● Security● UI● CLI env for interactive sessions


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State of the Art Stream Analysis

Analyzing Infinite Data Streams

● Recent advances in Streaming are a result of the pioneer work:

○ MillWheel: Fault-Tolerant Stream Processing at Internet Scale, VLDB 2013.

○ The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing, Proceedings of the VLDB Endowment, vol. 8 (2015), pp. 1792-1803


Analyzing Infinite Data Streams● Two cases for processing:

○ Single event processing: event transformation, trigger an alarm on an error event

○ Event aggregations: summary statistics, group-by, join and similar queries. For example compute the average temperature for the last 5 minutes from a sensor data stream.


Analyzing Infinite Data Streams● Event aggregation introduces the concept of windowing wrt the notion of time

selected:○ Event time (the time that events happen): Important for most use cases where context and

correctness matter at the same time. Example: billing applications, anomaly detection.

○ Processing time (the time they are observed during processing): Use cases where I only care about what I process in a window. Example: accumulated clicks on a page per second.

○ System Arrival or Ingestion time (the time that events arrived at the streaming system).

● Ideally event time = Processing time. Reality is: there is skew.


Time in Modern Data Stream Analysis Windows come in different flavors:

● Tumbling windows discretize a stream into non-overlapping windows.○ Eg. report all distinct users every 10 seconds

● Sliding Windows: slide over the stream of data.○ Eg. report all distinct users for the last 10 minutes every 1 minute.


Analyzing Infinite Data Streams

● Watermarks: indicates that no elements with a timestamp older or equal to

the watermark timestamp should arrive for the specific window of data.

○ Allows us to mark late data. Late data can either be added to the window or discarded.

● Triggers: decide when the window is evaluated or purged.○ Allows complex logic for window processing


Analyzing Infinite Data Streams● Apache Beam is the open source successor of Google’s DataFlow

● It is becoming the standard api streaming. Provides the advanced semantics needed for the current needs in streaming applications.


Streaming Engines for Big Data OSS

● Apache Flink● Apache Spark Streaming● Apache Storm● Apache Samza● Apache Apex● Apache Kafka Streams (Confluent Platform)● Akka Streams/Gearpump● Apache Beam

Cloud:

● Amazon Kinesis● Google Dataflow 35


Streaming Engines for Big Data - Pick oneMany criteria: use case at hand, existing infrastructure, performance, customer support, cloud vendor, features

Recommend to first to look at:

● Apache Flink for low latency and advanced semantics● Apache Spark for its maturity and rich set of functionality: ML, SQL, GraphX● Apache Kafka Streams for simple data transformations from and back to

Kafka topics


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Apache Spark 2.0

Spark in a NutshellApache Spark: A memory optimized distributed computing framework.

Supports caching of data in memory for speeding computations.


Spark in a Nutshell - RDDsRepresents a bounded dataset as an RDD (Resilient Distributed Dataset).

An RDD can be seen as an immutable distributed collection.

Two types of operations can be applied on an RDD: transformations like map and actions like collect.

Transformations are lazy while actions trigger computation on the cluster.

Operations like groupBy cause shuffle of data across the network.


Spark in a Nutshell - Deployment Mode


Spark in a Nutshell - Basic Components



Spark Batch Sample Word Count

https://github.com/skonto/talks/tree/master/voxxed-days-thess-2016

Spark in a nutshell - Key FeaturesDynamic Allocation

Memory management (Project Tungsten + off heap operations)

Cluster managers: Yarn, StandAlone, Mesos

Scala, Python, Java, R

Micro-batch engine

SQL API, ML library, GraphX

Monitoring UI43


Spark Streaming

Two flavors of Streaming:

● DStream API Spark 1.X -> mature API

● Structured Streaming (Alpha), Spark 2.0 -> Don’t go to production yet

“Based on Spark SQL. User does not need to reason about streaming end to end”


Spark Streaming DStream API Discretizes the stream based on batchDuration (batch interval) which is configured once.

Provides exactly one semantics with KafkaDirect for DStream or with WAL enabled for reliable receivers/drivers plus checkpointing for driver context recovery.

Many transformations and actions you get on a RDD you can get them on DStream as well.


Spark Structured Streaming ● Integrates with DF and Dataset API (Spark SQL) for structured queries● Allows for end-to-end exactly once for specific sources/sinks (HDFS/S3)

○ Requires replayable sources and idempotent sinks

● Input is sent to a query and output of the query is written to a sink.

Two types of output implemented:

● Complete Mode - The entire updated Result Table will be written to the external storage. It is up to the storage connector to decide how to handle writing of the entire table.

● Append Mode - Only the new rows appended in the Result Table since the last trigger will be written to the external storage. This is applicable only on the queries where existing rows in the Result Table are not expected to change.

○


Spark Structured Streaming - Not Yet Implemented● More Sources/Sinks● Watermarks● Late data management● State Sessions



DStream API Example

reportMax rdd.map(data => data.toInt).max()



reportMax rdd.map(data => data.toInt).max()

DStream API ExampleCheckPointing

get or create the streaming context

All streaming code goes here


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Spark SQL - Batch


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Structured Streaming

mean code same as batch

readStream instead of read

writeStream instead of write

Session creation is the same as with batch case


Thank You!

Questions?


References1. http://data-artisans.com/batch-is-a-special-case-of-streaming/2. http://www.slideshare.net/rolandkuhn/reactive-streams3. https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-1014. https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-1025. http://www.slideshare.net/FlinkForward/flink-case-study-capital-one6. http://flink.apache.org/poweredby.html7. https://en.wikipedia.org/wiki/Apache_Hadoop8. http://data-artisans.com/how-apache-flink-enables-new-streaming-applications-part-1/9. http://data-artisans.com/batch-is-a-special-case-of-streaming/

10. https://databricks.com/blog/2015/01/15/improved-driver-fault-tolerance-and-zero-data-loss-in-spark-streaming.html

11. Ellen Friedman & Kostas Tzoumas, Introduction to Apache Flink, Oreilly 201612. http://spark.apache.org/docs/latest/sql-programming-guide.html13. https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html


http://data-artisans.com/batch-is-a-special-case-of-streaming/


http://www.slideshare.net/rolandkuhn/reactive-streams

http://www.slideshare.net/rolandkuhn/reactive-streams

https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-101




http://www.slideshare.net/FlinkForward/flink-case-study-capital-one

http://www.slideshare.net/FlinkForward/flink-case-study-capital-one

http://flink.apache.org/poweredby.html

http://flink.apache.org/poweredby.html

https://en.wikipedia.org/wiki/Apache_Hadoop

https://en.wikipedia.org/wiki/Apache_Hadoop

http://data-artisans.com/how-apache-flink-enables-new-streaming-applications-part-1/

http://data-artisans.com/how-apache-flink-enables-new-streaming-applications-part-1/



https://databricks.com/blog/2015/01/15/improved-driver-fault-tolerance-and-zero-data-loss-in-spark-streaming.html



http://spark.apache.org/docs/latest/sql-programming-guide.html

http://spark.apache.org/docs/latest/sql-programming-guide.html

https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html

https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html