20170126 big data processing

Big Data Processing
January 2017

• Data Architect at unbelievable machine Company
• Software Engineering Background
• Jack of all Trades who also dives into Business topics,
Systems Engineering and Data Science.
• Big Data since 2011
• Cross-Industry: From Automotive to Transportation
• Other Activities
• Trainer: Hortonworks Apache Hadoop Certified Trainer
• Author: Articles and book projects
• Lector: Big Data at FH Technikum and FH Wiener Neustadt
Stefan Papp

Agenda
• Big Data Processing
• Evolution in Processing Big Data
• Data Processing Patterns
• Components of a Data Processing Engine
• Apache Spark
• Concept
• Ecosystem
• Apache Flink
• Concept
• Ecosystem

Big Data Processing Engines on a Hadoop 2.x Reference(!) Stack
HADOOP 2.x STACK
HDFS
(redundant, reliable storage)
YARN
(cluster resource management)
Batch
MapReduce
Direct
Java
Search
Solr
API
Engine
Data
Operating
System
File
System
Batch & Interactive
Tez
Script
Pig
SQL
Hive
Cascading
Java
Real-Time
Slider
NoSQL
HBase
Stream
Storm
RDD & PACT
Spark, Flink
Machine
Learning
SparkML
Other
Application
Graph
Giraph
Applications

Big Data Roots – Content Processing for Search Engines

IO Read Challenge: Read 500 GB Data (as a Reference)
• Assumption
• Shared nothing, plain read
• You can read 256 MB in 1.9 seconds
• Single Node
• Total Blocks in 500 GB = 1954 Blocks
• 1954 * 1,9 / 3600 = approx. 1 hour sequential read.
• A 40 node cluster with 8 HDs on each node
• 320 HDs -> 6 to 7 blocks on each disk
• 7 blocks * 1,9 = 13,3 seconds total read time

Parallelism and Concurrency are Complex
a = 1
a = a + async_add(1)
a = a * async_mul(2)

Data Flow Engine to Abstract Data Processing
• Provide a programming interface
• Express jobs as graphs of high-level operators
• System picks how to split each operator into task
• and where to run each task
• Solve topics such as
• Concurrency
• Fault recovery

MapReduce: Divide and Conquer
Map
Map
Map
Reduce
Reduce
Input Output
Read Read Read ReadWrite Write Write Write
Iter. Iter. Iter. Iter.
Iteration: Map Reduce

Evolution of Data Processing
2004
2007
2010 2010

Data Processing
Patterns
Classification and Processing Patterns

Batch Processing
14
Source
Source
Storage Layer
Periodic ingestion
Batch Processor
Periodic analysis job
Consumer
Job scheduler
SQL
Import
File
Import

Stream processor / Kappa Architecture
15
Source
Source
Consumer
Forward events
immediately to
pub/sub bus
Stream
Processor
Process at event time &
update serving layer
Messaging
System

Hybrid / Lambda processing
16
Storage Layer
Batch job(s) for
analysis
Serving
layer
Job scheduler
Stream Processor
Messaging
System
Consumer
Source
Source
Source

Technology Mapping
Messaging
System
Storage
Layer
Processing
Serving
layer
Job scheduler

Data Processing
Engines
Essential Components

General Purpose Data Processing Engines
Processing Engine (with API)
Abstraction
Engines
SQL/Query
Lang.engine
Real-time
Processing
Machine
Learning
Graph
Processing
<interface to> Storage Layer
Apache Spark
Pig
SparkSQL,
SparkR
Spark
Streaming
MLLib
H20
GrpahX
Cloud, Hadoop, Local Env…..
Apache Flink
HadoopMR,
Cascading
TabeAPI
CEP
FlinkML
Gelly
Cloud, Hadoop, Local Env…..

Features of Data Processing Engines
• Processing Mode: Batch, Streaming, Hybrid
• Category: DC/SEP/ESP/CEP
• Delivery guarantees: at least once/exactly once
• State management: distributed snapshots/checkpoints
• Out of ordering processing: y/n
• Windowing: time-based, count-based
• Latency: low or medium

A Unified engine across data workloads and platforms

Diffentiation to Map Reduce
• MapReduce was designed to process shared nothing data
• Processing with data sharing:
• complex, multi-pass analytics (e.g. ML, graph)
• interactive ad-hoc queries
• real-time stream processing
• Improvements for coding:
• Less boilerplate code, richer API
• Support of various programming languages

Two Key Innovation of Spark
24
Execution optimization
via DAGs
Distributed data containers
(RDDs) to avoid Serialization.
Query
Input
Query
Query

Start REPL locally, delegate execution via cluster manager
Execute on REPL
Ø ./bin/spark-shell --master local
Ø ./bin/pyspark --master yarn-
client
Execute as application
Ø ./bin/spark-submit
--class
org.apache.spark.examples.SparkPi
--master spark://207.184.161.138:7077
--executor-memory 20G
--total-executor-cores 100
/path/to/examples.jar
1000
Execute within Application

Components of a spark application
Driver program
• SparkContext/SparkSession as Hook to Execution
Environment
• Java, Scala, Python or R Code (REPL or App)
• Creates a DAG of Jobs and
Cluster manager
• grants executors to a Spark application
• Included: Standalone, Yarn, Mesos or local
• Custom made: e.g. Cassandra
• Distributes Jobs to executors
Executors
• Worker processes that execute tasks and store
data
Resource Manager (default port 4040)
•Supervise execution

Spark in Scala and Python
// Scala:
val distFile = sc.textFile("README.md")
distFile.map(l => l.split(" ")).collect()
distFile.flatMap(l => l.split(" ")).collect()
29
// Python:
distFile = sc.textFile("README.md")
distFile.map(lambda x: x.split(' ')).collect()
distFile.flatMap(lambda x: x.split(' ')).collect()
// Java 7:
JavaRDD<String> distFile = sc.textFile("README.md");
JavaRDD<String> words = distFile.flatMap(
new FlatMapFunction<String, String>() {
public Iterable<String> call(String line) {
return Arrays.asList(line.split(" "));
}});
// Java 8:
JavaRDD<String> distFile = sc.textFile("README.md");
JavaRDD<String> words =
distFile.flatMap(line -> Arrays.asList(line.split("
")));

History of Spark API Containers

SparkSession / SparkContext – the standard way to create container
• SparkSession (starting from 2.0) as Hook to the Data,
• SparkContext still available (can be created via SparkSession.sparkContext())
• Use SparkSession to create DataSets
• Use SparkContext to create RDD
• A session object knows about the execution environment
• Can be used to load data into a container

Operations on Collections: Transformations and Actions
val lines = sc.textFile("hdfs:///data/shakespeare/input") // Transformation
val lineLengths = lines.map(s => s.length) // Transformation
val totalLength = lineLengths.reduce((a, b) => a + b) // Action
Transformation:
• Create a new distributed data set from Source or from other data set
• Transformations are stacked until execution (Lazy Loading)
Actions:
• Trigger an Execution
• Create the most optimal execution path

Common Transformations
val rdd = sc.parallelize(Array("This is a line of text", "And so is this"))
map - apply a function while preserving structure
rdd.map( line => line.split("s+") )
→ Array(Array(This, is, a, line, of, text), Array(And, so, is, this))
flatMap - apply a function while flattening structure
rdd.flatMap( line => line.split("s+") )
→ Array(This, is, a, line, of, text, And, so, is, this)
filter - discard elements from an RDD which don’t match a condition
rdd.filter( line => line.contains("so") )
→ Array(And so is this)
reduceByKey - apply a function to each value for each key
pair_rdd.reduceByKey( (v1, v2) => v1 + v2 )
→ Array((this,2), (is,2), (line,1), (so,1), (text,1), (a,1), (of,1), (and,1))

Common Spark Actions
collect - gather results from nodes and return
first - return the first element of the RDD
take(N) - return the first N elements of the RDD
saveAsTextFile - write the RDD as a text file
saveAsSequenceFile - write the RDD as a SequenceFile
count - count elements in the RDD
countByKey - count elements in the RDD by key
foreach - process each element of an RDD
(e.g., rdd.collect.foreach(println) )

WordCount in Scala
val text = sc.textFile(source_file)
words = text.flatMap( line => line.split("W+") )
val kv = words.map( word => (word.toLowerCase(), 1) )
val totals = kv.reduceByKey( (v1, v2) => v1 + v2 )
totals.saveAsTextFile(output)

BDAS – Berkeley Data Analytics Stack

How to use SQL on Spark
• Spark SQL: Component direct on the Berkeley ecosystem
• Hive on Spark: Use Spark as execution engine for hive
• BlinkDB: Approximate SQL Engine

Spark SQL
Spark SQL uses DataFrames (Typed Data Containers) for SQL
Hive:
c = HiveContext(sc)
rows = c.sql(“select * from titanic”)
rows.filter(rows[‘age’] > 25).show()
JSON:
c.read.format(‘json’).load(’file:///root/tweets.json”).registerTe
mpTable(“tweets”)
c.sql(“select text, user.name from tweets”)
39
28.01.17

BlinkDB
• An approximate query engine for running interactive SQL queries.
• allows to trade-off query accuracy for response time,
• enabling interactive queries over massive data by running queries on data samples and
presenting results annotated with meaningful error bars.

Streaming
Spark Streaming and Structured Streaming

Spark Streaming
Streaming on RDDs
Structured Streaming
Streaming on DataFrames

Machine Learning and Graph
Analytics
SparkML, Spark MLLib, GraphX, GraphFrames,

Typical Use Cases
Classification and regression
• Linear support vector machine
• Logistic regression
• Linear least squares, Lasso, ridge regression
• Decision tree
• Naive Bayes
Collaborative filtering
• Alternating least squares
Clustering
• K-means
Dimensionality reduction
• Singular value decomposition
• Principal component analysis
Optimization
• Stochastic gradient descent
• Limited-memory BFGS
http://spark.apache.org/docs/latest/mllib-guide.html

MLLIb and H2O
• DataBricks-ML Libraries: inspired by the sci-kit learn library.
• MLLIB works with RDDs
• ML works with DataFrames
• H2O- library: Library build by the company H2O.
• H2O can be integrated with Spark with the 'Sparkling Water' connector.

Graph Analytics
Graph Engine that analyzes tabular data
• Nodes: People and things (nouns/keys)
• Edges: relationships between nodes
Algorithms
PageRank
Connected components
Label propagation
SVD++
Strongly connected components
Triangle count
One Framework per Container-API
•GraphX is designed for RDDs,
•GraphFrames for DataFrames

Survival of the “Fastest“
Apache Flink
0 4,000,000 8,000,000 12,000,000 16,000,000
Storm
Flink
Flink (10 GigE)
Throughput: msgs/sec
10 GigE end-to-end
15m msgs/sec

49
Streaming: continuous processing on
data that is continuously produced
Sources
Message
Broker
Stream processor
collect publish/subscribe analyse serve&store

Apache Flink Ecosystem
50
Gelly
Table
ML
SAMOA
DataSet (Java/Scala)DataStream (Java / Scala)
HadoopM/R
LocalClusterYARN
ApacheBeam
ApacheBeam
Table
Cascading
Streaming dataflow
runtime
StormAPI
Zeppelin
CEP

Expressive APIs
51
case class Word (word: String, frequency: Int)
val lines: DataStream[String] = env.fromSocketStream(...)
lines.flatMap {line => line.split(" ")
.map(word => Word(word,1))}
.window(Time.of(5,SECONDS)).every(Time.of(1,SECONDS))
.groupBy("word").sum("frequency")
.print()
val lines: DataSet[String] = env.readTextFile(...)
lines.flatMap {line => line.split(" ")
.map(word => Word(word,1))}
.groupBy("word").sum("frequency")
.print()
DataSet API (batch):
DataStream API (streaming):

Flink Engine – Core Design Principles
1. Execute everything as streams
2. Allow some iterative (cyclic) dataflows
3. Allow some (mutable) state
4. Operate on managed memory

Memory Management
further reading: https://flink.apache.org/news/2015/05/11/Juggling-with-Bits-and-Bytes.html

54
Streaming
Source
Streaming
Source
Streaming
Source
Consumer
Forward events
immediately to pub/sub bus
Stream Processor
Process at event time &
update serving layer
Message
Broker
Low latency
High throughput
Windowing / Out
of order events
State handling
Fault tolerance
and correctness

Windowing
Apache Flink
Low latency
High throughput
State handling
Windowing
Fault tolerance
and correctness

Building windows from a stream
“Number of visitors in the last 5 minutes per country”
56
source
Kafka topic
Stream processor
// create stream from Kafka source
DataStream<LogEvent> stream = env.addSource(new KafkaConsumer());
// group by country
DataStream<LogEvent> keyedStream = stream.keyBy(“country“);
// window of size 5 minutes
keyedStream.timeWindow(Time.minutes(5))
// do operations per window
.apply(new CountPerWindowFunction());

Building windows: Execution
57
Kafka
Source
Window
Operator
S
S
S
W
W
W
group by
country
// window of size 5 minutes
keyedStream.timeWindow(Time.minutes(5));
Job plan Parallel execution on the cluster
Time

Streaming: Windows
58
Time
Aggregates on streams
are scoped by windows
Time-driven Data-driven
e.g. last X minutes e.g. last X records

Window types in Flink
Tumbling windows
Sliding windows
Custom windows with window assigners, triggers and evictors
59
Further reading: http://flink.apache.org/news/2015/12/04/Introducing-windows.html

1977 1980 1983 1999 2002 2005 2015
Processing Time
Episode
IV
Episode
V
Episode
VI
Episode
I
Episode
II
Episode
III
Episode
VII
Event Time
Event Time vs. Processing Time
60

State Handling
Apache Flink
Low latency
High throughput
State handling
Windowing
Fault tolerance
and correctness

Batch vs. Continuous
62
• No state across batches
• Fault tolerance within a job
• Re-processing starts empty
Batch Jobs
Continuous
Programs
• Continuous state across time
• Fault tolerance guards state
• Reprocessing starts stateful

Streaming: Savepoints
63
Savepoint A Savepoint B
Globally consistent point-in-time snapshot
of the streaming application

Re-processing data (continuous)
• Draw savepoints at times that you will want to start new jobs from (daily, hourly, …)
• Reprocess by starting a new job from a savepoint
• Defines start position in stream (for example Kafka offsets)
• Initializes pending state (like partial sessions)
64
Savepoint
Run new streaming
program from savepoint

Stream processor: Flink
Managed state in Flink
• Flink automatically backups and restores state
• State can be larger than the available memory
• State back ends: (embedded) RocksDB, Heap memory
65
Operator with windows
(large state)
State backend(local)
Distributed File
System
Periodic backup /
recovery
Source Kafka

Fault Tolerance
Apache Flink
Low latency
High throughput
State handlingWindowing 7
Fault tolerance
and correctness

Fault tolerance in streaming
• How do we ensure the results are always correct?
• Failures should not lead to data loss or incorrect results
67
Source
Kafka
topic
Stream processor

Fault tolerance in streaming
• At least once: ensure all events are transmitted
• May lead to duplicates
• At most once: ensure that a known state of data is transmitted
• May lead to data loss
• Exactly once: ensure that operators do not perform duplicate updates to their state
• Flink achieves exactly once with Distributed Snapshots

Low Latency
Apache Flink
Low latency
High throughput
State handlingWindowing
Fault tolerance
and correctness

Yahoo! Benchmark
• Count ad impressions grouped by campaign
• Compute aggregates over a 10 second window
• Emit window aggregates to Redis every second for query
70
Full Yahoo! article: https://yahooeng.tumblr.com/post/135321837876/benchmarking-
streaming-computation-engines-at
“Storm […] and Flink […] show sub-second latencies at relatively high
throughputs with Storm having the lowest 99th percentile latency.
Spark streaming 1.5.1 supports high throughputs, but at a relatively higher
latency.”
(Quote from the blog post’s executive summary)

Windowing with state in Redis
• Original use case did not use Flink’s windowing implementation.
• Data Artisans implemented the use case with Flink windowing.
71
KafkaConsumer
map()
filter()
group
Flink event
time windows
realtime queries

Results after rewrite
72
0 750,000 1,500,000 2,250,000 3,000,000 3,750,000
Storm
Flink
400k msgs/sec

Can we even go further?
73
KafkaConsumer
map()
filter()
group
Flink event
time windows
Network link to Kafka
cluster is bottleneck!
(1GigE)
Data Generator
map()
filter()
group
Flink event
time windows
Solution: Move data
generator into job (10
GigE)

Results without network bottleneck
74
0 4,000,000 8,000,000 12,000,000 16,000,000
Storm
Flink
Flink (10 GigE)
10 GigE end-to-end
15m msgs/sec
400k msgs/sec
3m msgs/sec

Survival of the Fastest – Flink Performance
• throughput of 15 million messages/second on 10 machines
• 35x higher throughput compared to Storm (80x compared to Yahoo’s runs)
• exactly once guarantees
• Read the full report: http://data-artisans.com/extending-the-yahoo-streaming-benchmark/

The unbelievable Machine Company GmbH
Museumsplatz 1/10/13
1070 Wien
Contact:
Stefan Papp, Data Architect
stefan.papp@unbelievable-machine.com
Tel. +43 - 1 - 361 99 77 - 215
Mobile: +43 664 2614367

20170126 big data processing

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