Apache Spark Overview part2 (20161117)

아파치 스파크 소개
Part 2
2016.11.17
민형기

Contents
• Spark Streaming
• Spark GraphX
• Spark ML

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:9999
val lines = ssc.socketTextStream("localhost", 9999)
// Split each line into words
val 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 func
returns 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

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 seconds
val 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 SQLContext
val sqlContext = SQLContext.getOrCreate(rdd.sparkContext)
import sqlContext.implicits._
// Convert RDD[String] to DataFrame
val wordsDataFrame = rdd.toDF("word")
// Register as table
wordsDataFrame.registerTempTable("words")
// Do word count on DataFrame using SQL and print it
val 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 realtime
processing

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

Spark GraphX
Graph 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 Filtering
Alternating Least Squares
Stochastic Gradient Descent
Tensor Factorization
Structured Prediction
Loopy Belief Propagation
Max-Product Linear Programs
Gibbs Sampling
Semi-supervised ML
Graph SSL
CoEM
Community Detection
Triangle-Counting
K-core Decomposition
K-Truss
Graph Analytics
PageRank
Personalized PageRank
Shortest Path
Graph Coloring
Classification
Neural Networks
https://amplab.cs.berkeley.edu/wp-content/uploads/2014/02/graphx.pdf

Modern Analytics
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.attr
FROM edges AS e JOIN vertices AS src, vertices AS dst
ON e.srcId = src.Id AND e.dstId = dst.Id
SELECT t.dstId, reduceUDF( mapUDF(t) ) AS sum
FROM triplets AS t GROUPBY t.dstId
https://spark.apache.org/docs/1.6.2/graphx-programming-guide.html

Map Reduce Triplets

Example: Oldest Follower

Distributed Graphs as Tables(RDDs)

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

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)))

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")

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 2.0
3.0 4.0
5.0 6.0
9.0 0.0
0.0 8.0
0.0 6.0
1.0 0.0 2.0
0.0 0.0 3.0
4.0 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.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.html

MLlib + SQL
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 & debug
Re‐run on new data
Tune 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 science
0: 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 RDDs
val lables:RDD[Double] =
data.map(_.label)
val features:RDD[Vector]
val predictions:RDD[Double]
Explicitly unzip & zip RDDs
val 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
Almost
identical
workflow

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: Double
text: String
label: Double
text: String
features: Vector

Abstraction: Estimator
• Training
Extract features
Train model
Evaluate
def fit(DataFrame): Model
LogisticRegression Model
label: Double
text: String
features: Vector
label: Double
text: String
features: Vector
prediction: Double

Abstraction: Evaluator
• Training
Extract features
Train model
Evaluate
def evaluate(DataFrame): Double
Metric:
accuracy
AUC
MSE
…
label: Double
text: String
features: Vector
prediction: Double

Abstraction: Model
• Testing/Production
Model is a type of Transformer
Extract features
Predict using model
Act on predictions
text: String
features: Vector
text: String
features: Vector
prediction: Double

(Recall) Abstraction: Estimator
• Training
Extract features
Train model
Evaluate
LogisticRegression Model
label: Double
text: String
features: Vector
label: Double
text: String
features: Vector
prediction: Double
Load data

Abstraction: Pipeline
• Training
Extract features
Train model
Evaluate
PipelineModel
label: Double
text: String
Load data
Pipeline is a type of Estimator

Abstraction: PipelineModel
• Testing/Production
Evaluate
label: Double
text: String
text: String
features: Vector
prediction: Double
Load data
PipelineModel is a type of Transformer
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
BinaryClassification
Evaluator
Tokenizer
hashingTF.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

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

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

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

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

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

Programing Model
Core Storm Storm Trident Spark Streaming
Stream Primitive Tuple
Tuple, Tuple Batch,
Partition
Dstream
Stream Sources Spouts Spouts, Trident Spouts HDFS, Network
Computation/
Transformation
Bolts
Filters,
Functions,
Aggregations,
Joins
Transformation,
Window Operations
Stateful Operation
No
(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 Streaming
that Help Prevent Data Loss

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

Spark Streaming Application: Process Data

Fault Tolerance and Reliability

What if an executor 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

Fault-Tolerance Semantics

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

Pregel - 쾨니히스베르크 다리건너기 문제
(Königsberg's bridge problem)
• 독일에는 프레겔(Pregel) 강이 지나는 쾨니히스베르크(Königsberg)라는 도
시가 있다. 18세기경 쾨니히스베르크에는 강에 있는 두 개의 섬을 연결하
는 일곱 개의 다리가 있었는데, 그 곳 사람들은 그 다리를 건너 산보하기를
즐겼다.
• '각각의 다리들을 정확히 한 번씩만 지나 모든 다리를 건너갈 수 있을까?‘
• 오일러가 솔루션이 없음을 증명 (위상수학)
http://www.slideshare.net/payberah/pregel-43904273
https://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg
[네이버 지식백과] 쾨니히스베르크 다리건너기 문제 [Königsberg's bridge problem] (수학백과, 2015.5, 대한수학회)

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

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 m
if m > val then val := m
if i_val == val then
vote_to_halt
else
for 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

GraphX - Caching for Iterative mrTriplets

MLlib - Classification and Regression
Problem Type Supported Methods
Binary Classification linear SVMs, logistic regression, decision trees, random forests, gradient-b
oosted 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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Apache Spark Overview part2 (20161117)