Cloudera presentation on Apache Spark Project

1. CONFIDENTIAL - RESTRICTED
Introduction to Spark
SDBigData Meetup #6
January 14th 2015
Maxime Dumas
Systems Engineer, Cloudera

2. Thirty Seconds About Max
• Systems Engineer
• aka Sales Engineer
• SoCal, AZ, NV
• former coder of PHP
• teaches meditation + yoga
• avid cyclist
• from Montreal, Canada
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3. What Does Cloudera Do?
• product
• distribution of Hadoop components, Apache licensed
• enterprise tooling
• support
• training
• services (aka consulting)
• community
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Quick and dirty, for context.
The Apache Hadoop Ecosystem

5. ©2014 Cloudera, Inc. All rights
reserved.
• Scalability
• Simply scales just by adding nodes
• Local processing to avoid network bottlenecks
• Efficiency
• Cost efficiency (<$1k/TB) on commodity hardware
• Unified storage, metadata, security (no duplication or
synchronization)
• Flexibility
• All kinds of data (blobs, documents, records, etc)
• In all forms (structured, semi-structured, unstructured)
• Store anything then later analyze what you need
Why Hadoop?

6. Why “Ecosystem?”
• In the beginning, just Hadoop
• HDFS
• MapReduce
• Today, dozens of interrelated components
• I/O
• Processing
• Specialty Applications
• Configuration
• Workflow
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7. HDFS
• Distributed, highly fault-tolerant filesystem
• Optimized for large streaming access to data
• Based on Google File System
• http://research.google.com/archive/gfs.html
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8. Lots of Commodity Machines
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Image:Yahoo! Hadoop cluster [ OSCON ’07 ]

9. MapReduce (MR)
• Programming paradigm
• Batch oriented, not realtime
• Works well with distributed computing
• Lots of Java, but other languages supported
• Based on Google’s paper
• http://research.google.com/archive/mapreduce.html
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10. Apache Hive
• Abstraction of Hadoop’s Java API
• HiveQL “compiles” down to MR
• a “SQL-like” language
• Eases analysis using MapReduce
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11. CDH: the App Store for Hadoop
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Integration
Storage
Resource Management
Metadata
NoSQL
DBMS
…
Analytic
MPP
DBMS
Search
Engine
In-
Memory
Batch
Processing
System
Management
Data
Management
Support
Security
Machine
Learning
MapReduce

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Introduction to Apache Spark
Credits:
• Ben White
• Todd Lipcon
• Ted Malaska
• Jairam Ranganathan
• Jayant Shekhar
• Sandy Ryza

13. Can we improve on MR?
• Problems with MR:
• Very low-level: requires a lot of code to do simple
things
• Very constrained: everything must be described as
“map” and “reduce”. Powerful but sometimes
difficult to think in these terms.
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14. Can we improve on MR?
• Two approaches to improve on MapReduce:
1. Special purpose systems to solve one problem domain
well.
• Giraph / Graphlab (graph processing)
• Storm (stream processing)
• Impala (real-time SQL)
2. Generalize the capabilities of MapReduce to
provide a richer foundation to solve problems.
• Tez, MPI, Hama/Pregel (BSP), Dryad (arbitrary DAGs)
Both are viable strategies depending on the problem!
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15. What is Apache Spark?
Spark is a general purpose computational framework
Retains the advantages of MapReduce:
• Linear scalability
• Fault-tolerance
• Data Locality based computations
…but offers so much more:
• Leverages distributed memory for better performance
• Supports iterative algorithms that are not feasible in MR
• Improved developer experience
• Full Directed Graph expressions for data parallel computations
• Comes with libraries for machine learning, graph analysis, etc.
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16. What is Apache Spark?
Run programs up to 100x faster than Hadoop
MapReduce in memory, or 10x faster on disk.
One of the largest open source projects in big data:
• 170+ developers contributing
• 30+ companies contributing
• 400+ discussions per month on the mailing list
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17. Popular project
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18. Getting started with Spark
• Java API
• Interactive shells:
• Scala (spark-shell)
• Python (pyspark)
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19. Execution modes
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20. Execution modes
• Standalone Mode
• Dedicated master and worker daemons
• YARN Client Mode
• Launches a YARN application with the
driver program running locally
• YARN Cluster Mode
• Launches a YARN application with the
driver program running in the YARN
ApplicationMaster
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Dynamic resource
management
between Spark,
MR, Impala…
Dedicated Spark
runtime with static
resource limits

21. Spark Concepts
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22. RDD – Resilient Distributed Dataset
• Collections of objects partitioned across a cluster
• Stored in RAM or on Disk
• You can control persistence and partitioning
• Created by:
• Distributing local collection objects
• Transformation of data in storage
• Transformation of RDDs
• Automatically rebuilt on failure (resilient)
• Contains lineage to compute from storage
• Lazy materialization
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23. RDD transformations
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24. Operations on RDDs
Transformations lazily transform a RDD
to a new RDD
• map
• flatMap
• filter
• sample
• join
• sort
• reduceByKey
• …
Actions run computation to return a
value
• collect
• reduce(func)
• foreach(func)
• count
• first, take(n)
• saveAs
• …
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25. Fault Tolerance
• RDDs contain lineage.
• Lineage – source location and list of transformations
• Lost partitions can be re-computed from source data
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msgs = textFile.filter(lambda s: s.startsWith(“ERROR”))
.map(lambda s: s.split(“t”)[2])
HDFS File Filtered RDD Mapped RDD
filter
(func = startsWith(…))
map
(func = split(...))

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Examples

27. Word Count in MapReduce
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package org.myorg;
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class WordCount {
public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value, Context context) throws IOException,
InterruptedException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
context.write(word, one);
}
}
}
public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
context.write(key, new IntWritable(sum));
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = new Job(conf, "wordcount");
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.waitForCompletion(true);
}
}

28. Word Count in Spark
sc.textFile(“words”)
.flatMap(line => line.split(" "))
.map(word=>(word,1))
.reduceByKey(_+_).collect()
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29. Logistic Regression
• Read two sets of points
• Looks for a plane W that separates them
• Perform gradient descent:
• Start with random W
• On each iteration, sum a function of W over the data
• Move W in a direction that improves it
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30. Intuition
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31. Logistic Regression
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32. Logistic Regression Performance
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33. 33
Spark and Hadoop:
a Framework within a Framework

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35. 35
Integration
Storage
Resource Management
Metadata
HBase …Impala Solr Spark
Map
Reduce
System
Management
Data
Management
Support
Security

36. Spark Streaming
• Takes the concept of RDDs and extends it to DStreams
• Fault-tolerant like RDDs
• Transformable like RDDs
• Adds new “rolling window” operations
• Rolling averages, etc.
• But keeps everything else!
• Regular Spark code works in Spark Streaming
• Can still access HDFS data, etc.
• Example use cases:
• “On-the-fly” ETL as data is ingested into Hadoop/HDFS.
• Detecting anomalous behavior and triggering alerts.
• Continuous reporting of summary metrics for incoming data.
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37. Micro-batching for on the fly ETL
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38. What about SQL?
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http://databricks.com/blog/2014/07/01/shark-spark-sql-hive-on-spark-and-the-future-of-sql-on-spark.html
http://blog.cloudera.com/blog/2014/07/apache-hive-on-apache-spark-motivations-and-design-principles/

39. Fault Recovery Recap
• RDDs store dependency graph
• Because RDDs are deterministic:
Missing RDDs are rebuilt in parallel on other nodes
• Stateful RDDs can have infinite lineage
• Periodic checkpoints to disk clears lineage
• Faster recovery times
• Better handling of stragglers vs row-by-row streaming
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40. Why Spark?
• Flexible like MapReduce
• High performance
• Machine learning,
iterative algorithms
• Interactive data
explorations
• Concise, easy API for
developer productivity
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41. 41
Demo Time!
• Log file Analysis
• Machine Learning
• Spark Streaming

42. What’s Next?
• Download Hadoop!
• CDH available at www.cloudera.com
• Try it online: Cloudera Live
• Cloudera provides pre-loaded VMs
• http://tiny.cloudera.com/quickstartvm
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43. 43
Preferably related to the talk… or not.
Questions?

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Thank You!
Maxime Dumas
mdumas@cloudera.com
We’re hiring.

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