Apache Spark Overview part2 (20161117)

아파치 스파크 소개Part 2

2016.11.17

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Contents

• Spark Streaming

• Spark GraphX

• Spark ML

Spark Streaming

Spark Streaming

• Spark Streaming is an extension of the core API that scalable, high-throughput, fault-tolerant stream processing of live data streams

• Spark’s built-in machine learning algorithms and graph processing algorithms can be applied to data streams

Spark Streaming Motivation

Limitations of Streaming of Hadoop

• MR is not suitable for streaming

• Apache storm needs learning new API’s and new paradigm

• No way to combine batch results from MR with apache storm streams

• Maintaining two runtimes are always hard

How does Spark Streaming work?

• Chop up data streams into batches of few secs

• Spark treats each batch of data as RDDs and processes them using RDD operations

• Processed results are pushed out in batches

https://stanford.edu/~rezab/sparkclass/slides/td_streaming.pdf


Discretized streams(DStream)

• Basic abstraction provided by Spark Streaming

• Represents a continuous stream of data• Input data stream received from source

• The processed data stream generated by transforming the input stream

• Represented by a continuous series of RDDs

• Each RDD in a DStream contains data from a certain interval

DStream Transformation

• Any operation applied on a DStream translates to opeartions on the underlying RDDs.

val conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")val ssc = new StreamingContext(conf, Seconds(1))

// Create a DStream that will connect to hostname:port, like localhost:9999val lines = ssc.socketTextStream("localhost", 9999)

// Split each line into wordsval words = lines.flatMap(_.split(" "))

Transformations on DStreams

Transformation Meaning

map(func) Return a new DStream by passing each element of the source DStream through a function func.

flatMap(func) Similar to map, but each input item can be mapped to 0 or more output items.

filter(func) Return a new DStream by selecting only the records of the source DStream on which funcreturns true.

repartition(numPartitions) Changes the level of parallelism in this DStream by creating more or fewer partitions.

union(otherStream) Return a new DStream that contains the union of the elements in the source DStream and other DStream.

count() Return a new DStream of single-element RDDs by counting the number of elements in each RDD of the source DStream.

reduce(func) Return a new DStream of single-element RDDs by aggregating the elements in each RDD of the source DStream using a function func (which takes two arguments and returns one). The function should be associative so that it can be computed in parallel.

Transformations on DStreams


countByValue() When called on a DStream of elements of type K, return a new DStream of (K, Long) pairs where the value of each key is its frequency in each RDD of the source DStream.

reduceByKey(func, [numTasks])

When called on a DStream of (K, V) pairs, return a new DStream of (K, V) pairs where the values for each key are aggregated using the given reduce function. Note: By default, this uses Spark's default number of parallel tasks (2 for local mode, and in cluster mode the number is determined by the config property spark.default.parallelism) to do the grouping. You can pass an optional numTasks argument to set a different number of tasks.

Join(otherStream, [numTasks])

When called on two DStreams of (K, V) and (K, W) pairs, return a new DStream of (K, (V, W)) pairs with all pairs of elements for each key.

Cogroup(otherStream, [numTasks])

When called on a DStream of (K, V) and (K, W) pairs, return a new DStream of (K, Seq[V], Seq[W]) tuples.

transform(func) Return a new DStream by applying a RDD-to-RDD function to every RDD of the source DStream. This can be used to do arbitrary RDD operations on the DStream.

updateStateByKey(func) Return a new "state" DStream where the state for each key is updated by applying the given function on the previous state of the key and the new values for the key. This can be used to maintain arbitrary state data for each key.

Input DStreams and Receivers

Spark Streaming provides two categories of built-in streaming sources

• Basic Sources: Sources directly available in the StreamingContext API. • Eg) file systems, socket connections, and Akka actors.

• Advanced Sources: Kafka, Flume, Kinesis, Twitter, etc. are available through extra utility classes.

Receivers – Advanced Sources

Source Artifact

Kafka spark-streaming-kafka_2.10

Flume spark-streaming-flume_2.10

Kinesis spark-streaming-kinesis-asl_2.10 [Amazon Software License]

Twitter spark-streaming-twitter_2.10

ZeroMQ spark-streaming-zeromq_2.10

MQTT spark-streaming-mqtt_2.10

https://spark.apache.org/docs/1.6.2/streaming-programming-guide.html

https://spark.apache.org/docs/1.6.2/streaming-programming-guide.html

Example: Word Count with Kafka

object WordCount {def main(args: Array[String]) {

val context = new StreamingContext(conf, Seconds(1))

val lines = KafkaUtils.createStream(context,...)val words = lines.flatMap(_.split(“ "))

val wordCounts = words.map( x => (x, 1)).reduceByKey(_ + _)

wordCounts.print()context.start()context.awaitTermination()

}}

Window Operations

• Transformations over a sliding window of data

• parameters• window length: The duration of the window (3 in the figure).

• sliding interval: The interval at which the window operation is performed (2 in the figure).

// Reduce last 30 seconds of data, every 10 secondsval windowedWordCounts = pairs.reduceByKeyAndWindow((a:Int,b:Int) => (a + b), Seconds(30), Seconds(10))

Window Operations


window(windowLength, slideInterval) Return a new DStream which is computed based on windowed batches of the source DStream.

countByWindow(windowLength, slideInterval)

Return a sliding window count of elements in the stream.

reduceByWindow(func, windowLength, slideInterval)

Return a new single-element stream, created by aggregating elements in the stream over a sliding interval using func. The function should be associative so that it can be computed correctly in parallel.

reduceByKeyAndWindow(func, windowLength, slideInterval, [numTasks])

When called on a DStream of (K, V) pairs, returns a new DStream of (K, V) pairs where the values for each key are aggregated using the given reduce function func over batches in a sliding window.

reduceByKeyAndWindow(func, invFunc, windowLength, slideInterval, [numTasks])

A more efficient version of the above reduceByKeyAndWindow() where the reduce value of each window is calculated incrementally using the reduce values of the previous window.

countByValueAndWindow(windowLength,slideInterval, [numTasks])

When called on a DStream of (K, V) pairs, returns a new DStream of (K, Long) pairs where the value of each key is its frequency within a sliding window.

Join Operations

• Stream-stream joins• Streams can be easily joined with other streams

• Support leftOuterJoin, rightOuterJoin, fullOutherJoin

• Stream-dataset joins• dynamically change the dataset you want to join against.

Output Operations on DStreams

Output operations allow Dstream’s data to be pushed out to external systems like a database or a file systems.

Output Operation Meaning

print() Prints the first ten elements of every batch of data in a DStream on the driver node running the streaming application. This is useful for development and debugging.

saveAsTextFiles(prefix, [suffix]) Save this DStream's contents as text files. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]".

saveAsObjectFiles(prefix, [suffix]) Save this DStream's contents as SequenceFiles of serialized Java objects. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]".

saveAsHadoopFiles(prefix, [suffix]) Save this DStream's contents as Hadoop files. The file name at each batch interval is generated based on prefix and suffix: "prefix-TIME_IN_MS[.suffix]".

foreachRDD(func) The most generic output operator that applies a function, func, to each RDD generated from the stream. This function should push the data in each RDD to an external system, such as saving the RDD to files, or writing it over the network to a database.

Dstreams + RDDs = Power

• Combine live data streams with historical data• Generate historical data models with Spark, etc

• Use data models to process live data stream

• Combine streaming with Mllib, GraphX algos• Offline learning, online prediction

• Online learning and prediction

• Combine streaming with DataFrame and SQL operations



DataFrame and SQL Operations

Combine streaming with DataFrame and SQL operations

/** DataFrame operations inside your streaming program */

val words: DStream[String] = ... words.foreachRDD { rdd =>

// Get the singleton instance of SQLContextval sqlContext = SQLContext.getOrCreate(rdd.sparkContext)import sqlContext.implicits._

// Convert RDD[String] to DataFrameval wordsDataFrame = rdd.toDF("word")

// Register as tablewordsDataFrame.registerTempTable("words")

// Do word count on DataFrame using SQL and print itval wordCountsDataFrame = sqlContext.sql("select word, count(*) as total from words group by word")wordCountsDataFrame.show()

}

Advantage of an Unified Stack

• Explore data interactively to identify problems

• Use same code in Spark for processing large logs

• Use similar code in Spark Streaming for realtimeprocessing



Performance

• Can processes 60M records/sec(6 GB/sec) on 100 nodes at sub-second latency



Spark GraphXGraph Analytics in Spark

Spark GraphX

• Spark API for graphs and graph-parallel computation• Flexibility: seamlessly work with both graphs and collections• Speed: Comparable performance to the fastest specialized graph

processing systems• Algorithms: PageRank, Connected Components, Triangle count etc.

• Resilient Distributed Property Graph(RDPG, extends RDD)• Directed multigraph (-> parallels edges)• Properties attached to each vertex and edge

• Common graph operations (subgraph computation, joining vertices, …)

• Growing collection of graph algorithms

http://www.slideshare.net/PetrZapletal1/spark-concepts-spark-sql-graphx-streaming


Timeline

http://www.slideshare.net/SparkSummit/finding-graph-isomorphisms-in-graphx-and-graphframes


Graph 101

• A graph is a mathematical representation of linked data

• It’s defined in term of its Vertices and Edges, G(V,E)

Edge

Vertex

Social Networks

https://stanford.edu/~rezab/nips2014workshop/slides/ankur.pdf


Web Graphs



User-Item Graphs



Page Rank – Identifying Leaders

http://www.slideshare.net/SessionsEvents/joey-gonzalez-graph-lab-m-lconf-2013


Triangle Counting – Finding Community

Count triangles passing through each vertex:

Measures “cohensiveness” of local community



Collaborative Filtering – Recommending Product



The Graph–Parallel Pattern

Computation depends only on the neighbors

https://amplab.cs.berkeley.edu/wp-content/uploads/2014/02/graphx.pdf


Many Graph-Parallel Algorithms

Collaborative FilteringAlternating Least SquaresStochastic Gradient DescentTensor Factorization

Structured PredictionLoopy Belief PropagationMax-Product Linear ProgramsGibbs Sampling

Semi-supervised MLGraph SSLCoEM

Community DetectionTriangle-CountingK-core DecompositionK-Truss

Graph AnalyticsPageRankPersonalized PageRankShortest PathGraph Coloring

ClassificationNeural Networks



GraphX Motivation

Modern Analytics

http://www.slideshare.net/ankurdave/graphx-ampcamp5http://ampcamp.berkeley.edu/big-data-mini-course/graph-analytics-with-graphx.html

http://www.slideshare.net/ankurdave/graphx-ampcamp5

http://ampcamp.berkeley.edu/big-data-mini-course/graph-analytics-with-graphx.html

Tables




Graphs




Structure of Computation

https://stanford.edu/~rezab/sparkclass/slides/ankur_graphx.pdf


Modern Analytics Problems

• Difficult to Program and Use• Users must Learn, Deploy, and Manage multiple systems

• Inefficient• Extensive data movement and duplication across



Solution: The GraphX Unified Approach



Tables and Graphs are composable "views of the same physical data

Each view has its own operators that exploit the semantics of the view

to achieve efficient execution

The Property Graph: Graph as a Table

http://spark.apache.org/docs/1.6.2/graphx-programming-guide.html


Creating a Graph (Scala)



Graph Operations (Scala)



Graph Operations (Scala)

PageRank Triangle Count Connected Components



Triplets Join Vertices and Edges

• The triplets operator joins vertices and edges:

• The mrTriplets operator sums adjacent triplets

SELECT src.Id, dst.Id, src.attr, e.attr, dst.attrFROM edges AS e JOIN vertices AS src, vertices AS dstON e.srcId = src.Id AND e.dstId = dst.Id

SELECT t.dstId, reduceUDF( mapUDF(t) ) AS sumFROM triplets AS t GROUPBY t.dstId

https://spark.apache.org/docs/1.6.2/graphx-programming-guide.htmlhttps://stanford.edu/~rezab/sparkclass/slides/ankur_graphx.pdf

https://spark.apache.org/docs/1.6.2/graphx-programming-guide.html


Map Reduce Triplets



Example: Oldest Follower



Distributed Graphs as Tables(RDDs)



Graph System Optimizations

PageRank Benchmark

Spark ML

Spark MLlib

• MLlib is Spark’s machine learning (ML) library to make practical machine learning scalable and easy.

• Shipped with Spark since 2013.09 (0.8.1)

• two packages• spark.mllib: contains the original API built on top of RDDs

• spark.ml: provides higher-level API built on top of DataFrames for constructing ML pipelines

MLlib’s Mission

•MLlib‘s mission is to make practical machine learning easy and scalable.• Easy to build machine learning applications• Capable of learning from large-scale datasets• Easy to integrate into existing workflows

http://www.slideshare.net/databricks/combining-machine-learning-frameworks-with-apache-spark


spark.mllib (w/RDD)

Data Types - Local Vector

• A local vector has integer-typed and 0-based indices and double-typed values, stored on a single machine.

• Two types of vectors

• Dense vectors: double array, [1.0, 0.0, 3.0]

• Sparse vectors: indices and values, (3, [0, 2], [1.0, 3.0])

• Vectors: local vectors를 위한 utility(local vector를 생성)

• Two types of libraries

• Breeze: 초기버전에 포함, library for numerical processing for scala

• Jblas: 최신버전, library for linear algebra for java

import org.apache.spark.mllib.linalg.{Vector, Vectors}

// Create a dense vector (1.0, 0.0, 3.0).val dv: Vector = Vectors.dense(1.0, 0.0, 3.0)

// Create a sparse vector (1.0, 0.0, 3.0) by specifying its indices and values corresponding to nonzero entries.val sv1: Vector = Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0))

// Create a sparse vector (1.0, 0.0, 3.0) by specifying its nonzero entries.val sv2: Vector = Vectors.sparse(3, Seq((0, 1.0), (2, 3.0)))

http://www.scalanlp.org/

http://jblas.org/

Data Types – Labeled Point

• A local vector, either dense or sparse, associated with a label/response

• Types of labeled point

• Regression and classification: Use a double

• Binary classification: either 0(negative) or 1(positive).

• Multiclass classification: class indices starting from zero: 0, 1, 2, …

• Sparse data

• Support LIBSVM format: default format used by LIBSVM and LIBLINEAR

• Indices are one-based and in ascending order.

• After loading, the feature indices are converted to zero-based

label index1:value1 index2:value2 ...

// Create a labeled point with a positive label and a dense feature vector.val pos = LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0))

// Create a labeled point with a negative label and a sparse feature vector. corresponding to nonzero entries.val neg = LabeledPoint(0.0, Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0)))

val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")

http://www.csie.ntu.edu.tw/~cjlin/libsvm/

http://www.csie.ntu.edu.tw/~cjlin/liblinear/

Data Types – Local Matrix

• Local Matrix: single machine에 저장되는 matrix, size는 Int 타입• dense matrices: (row, column) 값(int) + 1차원 배열(double)

• Matrix size: (3,2)

• Value: 1차원 배열로 저장, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0)

• sparse matrices: CSC(Compressed Sparse Column) format• Matrix size(3,2)

• col_ptr:Array(0,3,6)

• row_ind: Array(0,1,2,0,1,2)

• val: Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0)1.0 2.03.0 4.05.0 6.0

9.0 0.00.0 8.00.0 6.0

1.0 0.0 2.00.0 0.0 3.04.0 5.0 6.0

• Matrix size: (3,3)• col_ptr:Array(0,2,3,6)• row_ind:Array(0,2,2,0,1,2)• val:Array(1.0,4.0,5.0,2.0,3.0,6.0)

http://www.scipy-lectures.org/advanced/scipy_sparse/csc_matrix.htmlhttps://spark.apache.org/docs/1.6.2/api/scala/index.html#org.apache.spark.mllib.linalg.SparseMatrix

http://www.scipy-lectures.org/advanced/scipy_sparse/csc_matrix.html

https://spark.apache.org/docs/1.6.2/api/scala/index.html#org.apache.spark.mllib.linalg.SparseMatrix

Data Types – Distributed Matrix

1개 이상의 RDD들로 분산으로 저장되는 Matrix

Row Matrix- row-oriented distributed matrix- RDD[Vector]를 rows로 정의- QR decomposition

Indexed Row Matrix: - row index + Row Matrix- RDD[IndexedRow]를 rows로 정의- case class IndexedRow(index: Long, vector: Vector)

Data Types – Distributed Matrix

Coordinate Matrix• RDD[MatrixEntry]를 entries로 정의

• MatrixEntry(i: Long, j: Long, value: Double)

Block Matrix• RDD[MatrixBlock]를 entries로 정의

• MatrixBlock = ((bri:Long, bci:Long), sub-matrix:Matrix)

• add, multiply지원

• block size: 1024*1024 default

Basic Statistics

• Summary statistics: colum단위 통계, colStats()

• Correlations: series간의 상관관계를 제공, 2*RDD[Double] or RDD[Vector]

Basic Statistics

• Stratified sampling

• Hypothesis testing• chi-squared tests: vector, matrix, laveledPoint를 인자로 받을 수 있음

• Kolmogorov-Smirnov test: 1-sample, 2-sided Kolmogorov-Smirnov test

Basic Statistics

• Random data generation• Useful for Randomized algorithms, prototyping, and performance testing

• Given distribution: Uniform, standard normal, poisson

• Kernel density estimation• Technique useful for visualizing empirical probability distributions

MLlib Algorithms

http://www.slideshare.net/databricks/dataframes-and-pipelines


Example: K-means

https://stanford.edu/~rezab/sparkclass/slides/reza_mllib.pdf


Example: PCA(Principal component analysis)



Example: Collaborative filtering

https://spark.apache.org/docs/1.6.1/mllib-collaborative-filtering.htmlhttps://stanford.edu/~rezab/sparkclass/slides/reza_mllib.pdf

https://spark.apache.org/docs/1.6.1/mllib-collaborative-filtering.html


MLlib + SQL

http://www.slideshare.net/Hadoop_Summit/th-210p212menghttps://gist.github.com/freeman-lab/9672685https://stanford.edu/~rezab/sparkclass/slides/reza_mllib.pdf

http://www.slideshare.net/Hadoop_Summit/th-210p212meng

https://gist.github.com/freeman-lab/9672685


MLlib + Streaming





MLlib + GraphX





spark.ml(w/DataFrame) – ML Pipelines

ML Workflows are complex


* Evan Sparks. “ML Pipelines.”amplab.cs.berkeley.edu/ml-‐pipelines

Image classification pipeline*

Specify pipeline Inspect & debugRe‐run on new dataTune parameters


Data scientist’s wish list:

• Run original code on a production environment

• Use distributed data sources

• Use familiar APIs and libraries

• Distribute ML workload piece by piece• Only distribute as needed

• Easily switch between local & distributed settings

Example: Text Classification

• Goal: Given a text document, predict its topic.

Subject: Re: Lexan Polish?

Suggest McQuires #1 plastic

polish. It will help somewhat

but nothing will remove deep

scratches without making it

worse than it already is.

McQuires will do something...

Features Label

1: about science0: not about science

Dataset: “20 Newsgroups”From UCI KDD Archive



Training & Testing

Training Testing / Production

http://www.slideshare.net/databricks/practical-machine-learning-pipelines-with-mllib


Example ML Workflow

Training

Load data

Extract features

Train model

Evaluate

labels + plain text

labels + feature vectors

labels + predictions

Create many RDDsval lables:RDD[Double] =

data.map(_.label)val features:RDD[Vector]val predictions:RDD[Double]

Explicitly unzip & zip RDDsval lables:RDD[Double] =

data.map(_.label)labels.zip(predictions).map {if (_._1 == _._2) …

}

Pain Point



Example ML Workflow

Training

Load data

Extract features

Train model

Evaluate

labels + plain text




Write as a script Not modular Difficult to re-use workflow

Pain Point


Example ML Workflow

Training Testing/Production

Load data

Extract features

Train model

Evaluate

labels + plain text



Load new data

Extract features

Predict using model

Act on predictions

plain text

feature vectors

predictions

Almostidenticalworkflow



Example ML Workflow

Training

Load data

Extract features

Train model

Evaluate

labels + plain text



Parameter tuning Key part of ML Involves training many models

For different splits of the data For different sets of parameters

Pain Point



Main Concepts

• DataFrame: The ML Dataset(= Table in Spark SQL)

• Abstractions• Transformer: An algorithm that transforms one DataFrame into

another

• Estimator: An algorithm that can be fit on a DataFrame to produce a Transformer

• Pipeline: Chains multiple transformers and estimators to produce a ML workflow

• Evaluator: compute metrics from predictions

• Parameter: Common API for specifying parameters

Load data

Extract features

Train model

Evaluate

DataFrame: Load Data

• Training

Load data

Extract features

Train model

Evaluate

labels + plain text





Abstraction: Transformer

• Training

Extract features

Train model

Evaluate



def transform(DataFrame): DataFrame


label: Doubletext: String

label: Doubletext: Stringfeatures: Vector


Abstraction: Estimator

• Training

Extract features

Train model

Evaluate



def fit(DataFrame): Model

LogisticRegression Model



label: Doubletext: Stringfeatures: Vectorprediction: Double


Abstraction: Evaluator

• Training

Extract features

Train model

Evaluate



def evaluate(DataFrame): Double

Metric:accuracyAUCMSE…




Abstraction: Model

• Testing/ProductionModel is a type of Transformer

def transform(DataFrame): DataFrame


Extract features

Predict using model

Act on predictions

text: Stringfeatures: Vector

text: Stringfeatures: Vectorprediction: Double


(Recall) Abstraction: Estimator

• Training

Extract features

Train model

Evaluate

def fit(DataFrame): Model

LogisticRegression Model




Load data


Abstraction: Pipeline

• Training

Extract features

Train model

Evaluate

PipelineModel



Load data

Pipeline is a type of Estimatordef fit(DataFrame): Model


Abstraction: PipelineModel

• Testing/Production

Evaluate



text: Stringfeatures: Vectorprediction: Double

Load data

PipelineModel is a type of Transformerdef transform(DataFrame): DataFrame

Extract features

Predict using model


Abstractions: Summary

Load data

Extract features

Train model

Evaluate

Load data

Extract features

Predict using model

Act on predictions

Training Testing/Production

DataFrame

Transformer

Estimator

Evaluator



Parameter Tuning

Given:• Estimator

• Parameter grid

• Evaluator

Find best parameters

CrossValidator

Hashing TF

Logistic Regression

BinaryClassificationEvaluator

TokenizerhashingTF.numFeatures{100, 1000, 10000}

lr.regParam{0.01, 0.1, 0.5}



References

Papers

• Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters : http://people.csail.mit.edu/matei/papers/2012/hotcloud_spark_streaming.pdf

• GraphX: Unifying Data-Parallel and Graph-Parallel Analytics : https://amplab.cs.berkeley.edu/wp-content/uploads/2014/02/graphx.pdf

• MLlib: Machine Learning in Apache Spark : http://www.jmlr.org/papers/volume17/15-237/15-237.pdf

Stanford 자료

• Distributed Computing with Spark: https://stanford.edu/~rezab/sparkclass/slides/reza_introtalk.pdf

• Spark Streaming: https://stanford.edu/~rezab/sparkclass/slides/td_streaming.pdf

• GraphX: Unified Graph Analytics on Spark: https://stanford.edu/~rezab/sparkclass/slides/ankur_graphx.pdf

• GraphX: Graph Analytics in Spark: https://stanford.edu/~rezab/nips2014workshop/slides/ankur.pdf

• MLlib and Distributing the Singular Value Decomposition: https://stanford.edu/~rezab/sparkclass/slides/reza_mllib.pdf

http://people.csail.mit.edu/matei/papers/2012/hotcloud_spark_streaming.pdf


http://www.jmlr.org/papers/volume17/15-237/15-237.pdf

https://stanford.edu/~rezab/sparkclass/slides/reza_introtalk.pdf





References - Spark Streaming

• Spark Streaming Programming Guide: http://spark.apache.org/docs/1.6.2/streaming-programming-guide.html

• INTRODUCTION TO SPARK STREAMING: http://hortonworks.com/hadoop-tutorial/introduction-spark-streaming/

• Introduction to Spark Streaming: http://www.slideshare.net/datamantra/introduction-to-spark-streaming

• Building Robust, Adaptive Streaming Apps with Spark Streaming: http://www.slideshare.net/databricks/building-robust-adaptive-streaming-apps-with-spark-streaming

• Apache storm vs. Spark Streaming: http://www.slideshare.net/ptgoetz/apache-storm-vs-spark-streaming

• Four Things to Know About Reliable Spark Streaming with Typesafe and Databricks: http://www.slideshare.net/Typesafe_Inc/four-things-to-know-about-reliable-spark-streaming-with-typesafe-and-databricks

• Spark streaming , Spark SQL: http://www.slideshare.net/jerryjung7/spark-streaming-spark-sql

• Structuring Spark: DataFrames, Datasets, and Streaming: http://www.slideshare.net/databricks/structuring-spark-dataframes-datasets-and-streaming

• Taking Spark Streaming to the Next Level with Datasets and DataFrames: http://www.slideshare.net/databricks/taking-spark-streaming-to-the-next-level-with-datasets-and-dataframes

• Integrating Kafka and Spark Streaming: Code Examples and State of the Game: http://www.michael-noll.com/blog/2014/10/01/kafka-spark-streaming-integration-example-tutorial/

• Spark Streaming with Kafka & HBase Example: http://henning.kropponline.de/2015/04/26/spark-streaming-with-kafka-hbase-example/

• Real-Time Streaming Data Pipelines with Apache APIs: Kafka, Spark Streaming, and HBase: https://www.mapr.com/blog/real-time-streaming-data-pipelines-apache-apis-kafka-spark-streaming-and-hbase

• A Deep Dive into Structured Streaming: Apache Spark Meetup at Bloomberg 2016: http://www.slideshare.net/databricks/a-deep-dive-into-structured-streaming-apache-spark-meetup-at-bloomberg-2016

http://spark.apache.org/docs/1.6.2/streaming-programming-guide.html

http://hortonworks.com/hadoop-tutorial/introduction-spark-streaming/

http://www.slideshare.net/datamantra/introduction-to-spark-streaming

http://www.slideshare.net/databricks/building-robust-adaptive-streaming-apps-with-spark-streaming

http://www.slideshare.net/ptgoetz/apache-storm-vs-spark-streaming

http://www.slideshare.net/Typesafe_Inc/four-things-to-know-about-reliable-spark-streaming-with-typesafe-and-databricks

http://www.slideshare.net/jerryjung7/spark-streaming-spark-sql

http://www.slideshare.net/databricks/structuring-spark-dataframes-datasets-and-streaming

http://www.slideshare.net/databricks/taking-spark-streaming-to-the-next-level-with-datasets-and-dataframes

http://www.michael-noll.com/blog/2014/10/01/kafka-spark-streaming-integration-example-tutorial/

http://henning.kropponline.de/2015/04/26/spark-streaming-with-kafka-hbase-example/

https://www.mapr.com/blog/real-time-streaming-data-pipelines-apache-apis-kafka-spark-streaming-and-hbase

http://www.slideshare.net/databricks/a-deep-dive-into-structured-streaming-apache-spark-meetup-at-bloomberg-2016

References - Spark GraphX

• GraphX: http://ampcamp.berkeley.edu/big-data-mini-course/graph-analytics-with-graphx.html

• GraphX: Graph Analytics in Apache Spark (AMPCamp 5, 2014-11-20): http://www.slideshare.net/ankurdave/graphx-ampcamp5

• Machine Learning and GraphX: http://www.slideshare.net/noootsab/machine-learning-and-graphx

• Finding Graph Isomorphisms In GraphX And GraphFrames: http://www.slideshare.net/SparkSummit/finding-graph-isomorphisms-in-graphx-and-graphframes

• 스사모 테크톡 - GraphX: http://www.slideshare.net/sangwookimme/graphx

• GraphFrames: DataFrame-based graphs for Apache® Spark™: http://www.slideshare.net/databricks/graphframes-dataframebased-graphs-for-apache-spark

• Graphs are everywhere! Distributed graph computing with Spark GraphX: http://www.slideshare.net/andreaiacono/graphs-are-everywhere-distributed-graph-computing-with-spark-graphx

• GraphX and Pregel - Apache Spark: http://www.slideshare.net/AshutoshTrivedi3/graphx-and-pregel-apache-spark

• Joey gonzalez, graph lab, m lconf 2013: http://www.slideshare.net/SessionsEvents/joey-gonzalez-graph-lab-m-lconf-2013

• Spark Concepts - Spark SQL, Graphx, Streaming: http://www.slideshare.net/PetrZapletal1/spark-concepts-spark-sql-graphx-streaming

• Using spark for timeseries graph analytics: http://www.slideshare.net/SigmoidHR/using-spark-for-timeseries-graph-analytics

• An excursion into Graph Analytics with Apache Spark GraphX: http://www.slideshare.net/ksankar/an-excursion-into-graph-analytics-with-apache-spark-graphx

• Exploring Titan and Spark GraphX for Analyzing Time-Varying Electrical Networks: http://www.slideshare.net/HadoopSummit/exploring-titan-and-spark-graphx-for-analyzing-timevarying-electrical-networks



http://www.slideshare.net/noootsab/machine-learning-and-graphx


http://www.slideshare.net/sangwookimme/graphx

http://www.slideshare.net/databricks/graphframes-dataframebased-graphs-for-apache-spark

http://www.slideshare.net/andreaiacono/graphs-are-everywhere-distributed-graph-computing-with-spark-graphx

http://www.slideshare.net/AshutoshTrivedi3/graphx-and-pregel-apache-spark



http://www.slideshare.net/SigmoidHR/using-spark-for-timeseries-graph-analytics

http://www.slideshare.net/ksankar/an-excursion-into-graph-analytics-with-apache-spark-graphx

http://www.slideshare.net/HadoopSummit/exploring-titan-and-spark-graphx-for-analyzing-timevarying-electrical-networks

References - Spark Mllib

• Apache® Spark™ MLlib: From Quick Start to Scikit-Learn: http://www.slideshare.net/databricks/spark-mllib-from-quick-start-to-scikitlearn

• Introduction to Spark ML: http://www.slideshare.net/hkarau/introduction-to-spark-ml

• Apache Spark Machine Learning- MapR: http://www.slideshare.net/caroljmcdonald/spark-machine-learning

• Distributed ML in Apache Spark: http://www.slideshare.net/databricks/distributed-ml-in-apache-spark

• Practical Machine Learning Pipelines with MLlib: http://www.slideshare.net/databricks/practical-machine-learning-pipelines-with-mllib

• ML Pipelines: A New High-Level API for MLlib: https://databricks.com/blog/2015/01/07/ml-pipelines-a-new-high-level-api-for-mllib.html

• CTR Prediction using Spark Machine Learning Pipelines: http://www.slideshare.net/ManishaSule/ctr-prediction-using-spark-machine-learning-pipelines

• Combining Machine Learning Frameworks with Apache Spark: http://www.slideshare.net/databricks/combining-machine-learning-frameworks-with-apache-spark

• Machine learning pipeline with spark ml: http://www.slideshare.net/datamantra/machine-learning-pipeline-with-spark-ml

• Introduction to Spark ML: http://www.slideshare.net/dbtsai/m-llib-sf-machine-learning

• Introduction to ML with Apache Spark MLlib: http://www.slideshare.net/tmatyashovsky/introduction-to-ml-with-apache-spark-mllib

• MLlib: Spark's Machine Learning Library: http://www.slideshare.net/jeykottalam/mllib

• Large Scale Machine Learning with Apache Spark: http://www.slideshare.net/cloudera/large-scale-machine-learning-with-apache-spark

• Spark DataFrames and ML Pipelines: http://www.slideshare.net/databricks/dataframes-and-pipelines

• Why you should use Spark for machine learning: http://www.infoworld.com/article/3031690/analytics/why-you-should-use-spark-for-machine-learning.html

• Neural Networks, Spark MLlib, Deep Learning: http://www.slideshare.net/AsimJalis/neural-networks-spark-mllib-deep-learning

• Apache Spark MLlib 2.0 Preview: Data Science and Production: http://www.slideshare.net/databricks/apache-spark-mllib-20-preview-data-science-and-production

http://www.slideshare.net/databricks/spark-mllib-from-quick-start-to-scikitlearn

http://www.slideshare.net/hkarau/introduction-to-spark-ml

http://www.slideshare.net/caroljmcdonald/spark-machine-learning

http://www.slideshare.net/databricks/distributed-ml-in-apache-spark


https://databricks.com/blog/2015/01/07/ml-pipelines-a-new-high-level-api-for-mllib.html

http://www.slideshare.net/ManishaSule/ctr-prediction-using-spark-machine-learning-pipelines


http://www.slideshare.net/datamantra/machine-learning-pipeline-with-spark-ml

http://www.slideshare.net/dbtsai/m-llib-sf-machine-learning

http://www.slideshare.net/tmatyashovsky/introduction-to-ml-with-apache-spark-mllib

http://www.slideshare.net/jeykottalam/mllib

http://www.slideshare.net/cloudera/large-scale-machine-learning-with-apache-spark


http://www.infoworld.com/article/3031690/analytics/why-you-should-use-spark-for-machine-learning.html

http://www.slideshare.net/AsimJalis/neural-networks-spark-mllib-deep-learning

http://www.slideshare.net/databricks/apache-spark-mllib-20-preview-data-science-and-production

Appendix

구글 빅데이터 관련 기술

기술 연도 내용

GFS 2003 Google File System: A Distributed Storage

MapReduce 2004 Simplified Data Processing on Large Clusters

Sawzall 2005 Interpreting the Data: Parallel Analysis with Sawzall

Chubby 2006 The Chubby Lock Service for Loosely-Coupled Distributed Systems

BigTable 2006 A Distributed Storage System for Structured Data

Paxos 2007 Paxos Made Live - An Engineering Perspective

Colossus 2009 GFS II

Percolator 2010 Large-scale Incremental Processing Using Distributed Transactions and Notifications

Pregel 2010 A System for Large-Scale Graph Processing

Dremel 2010 Interactive Analysis of Web-Scale Datasets

Tenzing 2011 A SQL Implementation On The MapReduce Framework

Megastore 2011 Providing Scalable, Highly Available Storage for Interactive Services

Spanner 2012 Google's Globally-Distributed Database

F1 2012 The Fault-Tolerant Distributed RDBMS Supporting Google's Ad Business

Spark Streaming 2.0

• Structured Streaming• High-level streaming API built on Spark SQL engine

• Runs the same queries on DataFrames

• Event time, windowing, sessions, sources & sinks

• Unifies streaming, interactive and batch queries• Aggregate data in a stream, then serve using JDBC

• Change queries at runtime

• Build and apply ML models

Spark Streaming vs. Storm

Reliability Models

구분 Core Storm Storm Trident Spark Streaming

At Most Once Yes Yes No

At Least Once Yes Yes No*

Once and Only Once(Exactly Once)

No Yes Yes*



Programing Model

Core Storm Storm Trident Spark Streaming

Stream Primitive TupleTuple, Tuple Batch, Partition

Dstream

Stream Sources Spouts Spouts, Trident Spouts HDFS, Network

Computation/Transformation

Bolts

Filters,Functions,Aggregations,Joins

Transformation,Window Operations

Stateful OperationNo(roll your own)

Yes Yes

Output/Persistence

Bolts State, MapState foreachRDD

2014, http://www.slideshare.net/ptgoetz/apache-storm-vs-spark-streaming


Performance

• Storm capped at 10k msgs/sec/node?

• Spark Streaming 40x faster than Storm?

System Performance

Storm(Twitter) 10,000 records/s/node

Spark Streaming 400,000 records/s/node

Apache S4 7,000 records/s/node

Other Commercial Systems 100,000 records/s/node

2014, http://www.slideshare.net/ptgoetz/apache-storm-vs-spark-streaming


Features in Spark Streamingthat Help Prevent Data Loss

Spark Streaming Application: Receive data



Spark Streaming Application: Process Data



Fault Tolerance and Reliability

What if an executor fails?



What if an driver fails?

Recovering Driver w/ DStream Checkpointing

Recovering data with Write Ahead Logs

RDD checkpointing

• Stateful stream processing can lead to long RDD lineages

• Long lineage = bad for fault-tolerance, too much recomputation

• RDD checkpointing saves RDD data to the fault-tolerant storage to limit lineage and recomputation

•More: http://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing

http://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing

Fault-Tolerance Semantics



GraphX

Connected vs. Strongly connected vs. Complete graphs

• Connected is usually associated with undirected graphs (two way edges): there is a path between every two nodes.

• Strongly connected is usually associated with directed graphs (one way edges): there is a route between every two nodes.

• Complete graphs are undirected graphs where there is an edge between every pair of nodes.

http://mathoverflow.net/questions/6833/difference-between-connected-vs-strongly-connected-vs-complete-graphs

Connected Graph Strongly Connected Graph Completed Graph

http://mathoverflow.net/questions/6833/difference-between-connected-vs-strongly-connected-vs-complete-graphs

Pregel - 쾨니히스베르크 다리건너기 문제(Königsberg's bridge problem)

• 독일에는 프레겔(Pregel) 강이 지나는 쾨니히스베르크(Königsberg)라는 도시가 있다. 18세기경 쾨니히스베르크에는 강에 있는 두 개의 섬을 연결하는 일곱 개의 다리가 있었는데, 그 곳 사람들은 그 다리를 건너 산보하기를즐겼다.

• '각각의 다리들을 정확히 한 번씩만 지나 모든 다리를 건너갈 수 있을까?‘

• 오일러가 솔루션이 없음을 증명 (위상수학)

http://www.slideshare.net/payberah/pregel-43904273https://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg[네이버 지식백과] 쾨니히스베르크 다리건너기 문제 [Königsberg's bridge problem] (수학백과, 2015.5, 대한수학회)

http://www.slideshare.net/payberah/pregel-43904273

https://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg

http://terms.naver.com/entry.nhn?docId=3338204

https://en.wikipedia.org/wiki/File:Konigsberg_bridges.png

https://en.wikipedia.org/wiki/File:Konigsberg_bridges.png

https://en.wikipedia.org/wiki/File:7_bridges.svg

https://en.wikipedia.org/wiki/File:7_bridges.svg

https://en.wikipedia.org/wiki/File:K%C3%B6nigsberg_graph.svg

https://en.wikipedia.org/wiki/File:K%C3%B6nigsberg_graph.svg

Bulk Synchronous Parallel(BSP)

• BSP abstract computer is a bridging model for designing parallel algorithms

• developed by Leslie Valiant of Harvard University during the 1980s

• Model consist of• Concurrent computation: every participating processor

may perform local computations. The computations occur asynchronously of all the others but may overlap with communication.

• Communication: The processes exchange data between themselves to facilitate remote data storage capabilities.

• Barrier synchronisation: When a process reaches this point (the barrier), it waits until all other processes have reached the same barrier.

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

https://en.wikipedia.org/wiki/Barrier_(computer_science)

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

Pregel

• A System for Large-Scale Graph Processing: Parallel programming model

• Inspired by bulk synchronous parallel (BSP) model

• Vertex-Centrix Program• Think as Vertex

• Each vertex computes individually its value: in parallel

• Each vertex can see its local context, and updates its value accodingly



Pregel – Execution Model

• Applications run in sequence of iterations: supersteps

• During a superstep, user-dened functions for each vertex is invoked(method Compute()): in parallel

• A vertex in superstep S can:• reads messages sent to it in superstep S-1.• sends messages to other vertices: receiving at superstep S+1.• modies its state.

• Vertices communicate directly with one another by sending messages.




• Superstep 0: all vertices are in the active state.

• A vertex deactivates itself by voting to halt: no further work to do.

• A halted vertex can be active if it receives a message.

• The whole algorithm terminates when:• All vertices are simultaneously inactive.

• There are no messages in transit.




• Aggregation: a mechanism for global communication, monitoring, and data.

• Runs after each superstep.

• Each vertex can provide a value to an aggregator in superstep S.

• The system combines those values and the resulting value is made available to all vertices in superstep S + 1.

• A number of predened aggregators, e.g., min, max, sum.

• Aggregation operators should be commutative and associative.



Pregel - Example: Max Value

i_val := val

for each message mif m > val then val := m

if i_val == val thenvote_to_halt

elsefor each neighbor v

send_message(v, val)



Graph Processing vs. Graph Database



Features & Performance



Page Rank – Identifying Leaders



Page Rank

http://www.slideshare.net/itsmedv91/google-search-linear-algebra-ppt

http://www.slideshare.net/itsmedv91/google-search-linear-algebra-ppt

GraphX - Caching for Iterative mrTriplets

GraphX - mrTriplets Execution

Spark ML

MLlib - Classification and Regression

Problem Type Supported Methods

Binary Classification linear SVMs, logistic regression, decision trees, random forests, gradient-boosted trees, naive Bayes

Multiclass Classification logistic regression, decision trees, random forests, naive Bayes

Regression linear least squares, Lasso, ridge regression, decision trees, random forests, gradient-boosted trees, isotonic regression

MLlib - Collaborative Filtering

• Collaborative Filtering: aim to fill in the missing entries of a user-item association matrix

• Mllib• Support model-based collaborative filtering

• Use ALS(alternating least squares) algorithm to learn latent factors.• numBlocks: the number of blocks used to parallelize computation (set to -1 to auto-configure).

• rank: the number of latent factors in the model.

• iterations: the number of iterations to run.

• lambda: specifies the regularization parameter in ALS.

• implicitPrefs: specifies whether to use the explicit feedback ALS variant or one adapted for implicit feedback data.

• alpha: a parameter applicable to the implicit feedback variant of ALS that governs the baseline confidence in preference observations.

• Explicit vs. implicit feedback • Implicit feedback (e.g. views, clicks, purchases, likes, shares etc)을 지원함

• Scaling of the regularization parameter• Scale the regularization parameter(lambda)

• ALS-WR(alternating least squares with weighted-λ regularization)

MLlib - Clustering

• K-means

• Gaussian mixture

• Power iteration clustering

• Latent Dirichlet allocation(LDA)

• Bisecting k-means

• Streaming k-means

MLlib - Dimensionality Reduction

• Singular value decomposition (SVD)

• Principal component analysis(PCA)

MLlib - Feature Extraction and Transformation• TF-IDF

• Word2Vec

• StandardScaler

• Normalizer

• ChiSqSelector

• ElementwiseProduct

• PCA

MLlib - Frequest Pattern Mining

• FP-growth

• Association Rules

• PrefixSpan

MLlib - Evaluation Metrics

• Classification model evaluation• Binary classification

• Threshold tuning

• Multiclass classification• Label based metrics

• Multilabel classification

• Ranking systems

• Regression model evaluation

MLlib - Optimization

• Mathematical description• Gradient descent

• Stochastic gradient descent(SGC)

• Update schemes for distributed SGD

• Limited-memory BGFS(L-BFGS)

• Choosing and Optimization Method

• Implementation in Mllib• Gradient descent and stochastic gradient descent

• L-BFGS

Apache Spark Overview part2 (20161117)

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Transcript of Apache Spark Overview part2 (20161117)