Hadoop and big data training

Hadoop and Big Data Training
Lessons learned
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What’s Cloudera?
 Leading company in the NoSQL and cloud computing space
 Most popular Hadoop distribution
 Ex-es from Google, Facebook, Oracle and other leading tech
companies
 Sample Bn$ companies client list:
eBay,JPMorganChase,Experian,Groupon,MorganStanley,Nokia
,Orbitz,NationalCancerInstitute,RIM,TheWaltDisney Company
 Consulting and training services
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Why this training?
 MongoDB is great for OLTP
 Not an OLAP DB, not really aspiring to become one
 Big Data coming in, need for more advanced analysis
processes
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Intended audience
 Software engineers and friends 
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 The Apache Hadoop software library is a framework that
allows for the distributed processing of large data sets across
clusters of computers using simple programming models
 Modules:
 HadoopCommon
 Hadoop Distributed File System (HDFS™)
 HadoopYARN
 HadoopMapReduce
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What’s Hadoop?

How does it fit in our Big Goal?
 MongoDB for OLTP
 RDBMS (MySQL) for config data
 Hadoop for OLAP
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What’s Map Reduce?
 MapReduce is a programming model for processing large data
sets, and the name of an implementation of the model by
Google. MapReduce is typically used to do distributed
computing on clusters of computers. © Wiki
 Practically?
 Can perform computations in a distributed fashion
 Highly scalable
 Inherently highly available
 By design fault tolerant
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Bindings
 Native Java
 any language, even scripting ones, using Streaming
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MapReduce framework vs. MapReduce functionality
 Several NoSQL technologies provide MR functionality
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MR functionality
 Compromise….
 i.e. MongoDB
 CouchDB select * from foo; ;;
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MapReduce V1 vsMapReduce V2
 MR V1 can not scale past 4k nodes per cluster
 More important to our goals, MR V1 is monolithic
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MR V2 YARN
 Pluggable implementations on top of Hadoop
 Whole new set of problems can be solved:
 Graph processing
 MPI
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MR V1 daemons
 client
 NameNode (HDFS)
 JobTracker
 DataNode(HDFS) + TaskTracker
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MR V2 daemons
 Client
 Resource manager/Application manager
 NodeManager
 Application Master (resource containers)
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Data Locality in Hadoop
 First replica placed in client node (or random if off cluster
client)
 Second off-rack
 Third in same rack as second but different node
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HDFS - Architecture
 Hot
 Very large files
 Streaming data access (seek time ~<1% transfer time)
 Commodity hardware (no iphones…)
 Not
 Low-latency data access
 Lots of small files
 Multiple writers, arbitrary file modification
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HDFS – NameNode
 Namenode Master
 Filesystem tree
 Metadata for all files and directories
 Namespace image and edit log
 Secondary Namenode
 Not a backup node!
 Periodically merges edit log into namespace image
 Could take 30 mins to come back online
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HDFS HA - NameNode
 2.x Hadoop brings in HDFS HA
 Active-standby config for NameNodes
 Gotchas:
 Shared storage for edit log
 Datanodes send block reports to both NameNodes
 NameNode needs to be transparent to clients
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HDFS - Read
 Client requests file from namenode (for first 10 blocks)
 Namenode returns addresses of datanodes
 Client contacts directly datanodes
 Blocks are read in order
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HDFS - Write
 RPC initial call to create the file
 Permissions/file exists checks in NameNode etc
 As we write data, data queue in client which asks the
NameNode for datanode to store data
 List of datanodes form a pipeline
 ack queue to verify all replicas have been written
 Close file
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Job Configuration
 setInputFormatClass
 setOutputFormatClass
 setMapperClass
 setReducerClass
 Set(Map)OutputKeyClass
 set(Map)OutputValueClass
 setNumReduceTasks
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Job Configuration
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);
OR job.submit();
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Job Configuration
 Simple to invoke:
 bin/hadoop jar WordCountinputPathoutputPath
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Mapper – Life cycle
 Mapper inputs <K1,V1> outputs <K2,V2>
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Shuffle and Sort
 All same keys are guaranteed to end up in the same reducer,
sorted by key
 Mapper output <K2,V2><‘the’,1>, <‘the’,2>, <‘cat’,1>
 Reducer input <K2,[V2]><‘cat’,*1+>, <‘the’,*1,2+>
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Reducer – Life cycle
 Reducer inputs <K2, [V2]> outputs <K3, V3>
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Hadoop interfaces and classes
 >=0.23 new API favoring abstract classes
 <0.23 old API with interfaces
 Packages mapred.* OLD API, mapreduce.* NEW API
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Speculative execution
 At least one minute into a mapper or reducer, the Jobtracker
will decide based on the progress of a task
 Threshold of each task progress compared to
avgprogress(configurable)
 Relaunch task in different NameNode and have them race..
 Sometimes not wanted
 Cluster utilization
 Non idempotent partial output (OutputCollector)
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Input Output Formats
 InputFormat<K,V> ->FileInputFormat<K,V> ->TextInputFormat,
KeyValueTextInputFormat, SequenceFileInputFormat
 Default TextInputFormat key=byte offset, value=line
 KeyValueTextInputFormat (key t value)
 Binary splittable format
 Corresponding Output formats
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Compression
 The billion files problem
 300B/file * 10^9 files  300G RAM
 Big Data storage
 Solutions:
 Containers
 Compression
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Containers
 HAR (splittable)
 Sequence Files, RC files, Avro files (splittable, compressable)
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Compression codecs
 LZO, LZ4, snappy codecs are best VFM in compression speed
 Bzip2 offers native splitting but can be slow
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Long story short
 Compression + sequence files
 Compression that supports splitting
 Split file into chunks in application layer with chunk size
aligned to HDFS block size
 Don’t bother
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Partitioner
 Default is HashPartitioner
 Why implement our own partitioner?
 Sample case: Total ordering
 1 reducer
 Multiple reducers?
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Partitioner
 TotalOrderPartitioner
 Sample input to determine number of reducers for maximum
performance
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Hadoop Ecosystem
 Pig
 Apache Pig is a platform for analyzing large data sets. Pig's
language, Pig Latin, lets you specify a sequence of data
transformations such as merging data sets, filtering them, and
applying functions to records or groups of records.
 Procedural language, lazy evaluated, pipeline split support
 Closer to developers (or relational algebra aficionados) than
not
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Hadoop Ecosystem
 Hive
 Access to hadoop clusters for non developers
 Data analysts, data scientists, statisticians, SDMs etc
 Subset of SQL-92 plus Hive extensions
 Insert overwrite, no update or delete
 No transactions
 No indexes, parallel scanning
 “Near” real time
 Only equality joins
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Hadoop Ecosystem
 Mahout
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Mean Shift clustering
Dirichlet process clustering
Latent Dirichlet Allocation
Singular value decomposition
Parallel Frequent Pattern mining
Complementary Naive Bayes classifier
Random forest decision tree based classifier
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Hadoop ecosystem
 Algorithmic categories:
 Classification
 Clustering
 Pattern mining
 Regression
 Dimension reduction
 Recommendation engines
 Vector similarity
…
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Reporting Services
 Pentaho, Microstrategy, Jasper all can hook up to a hadoop
cluster
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References
 Hadoop the definite guide 3rd edition
 apache.hadoop.org
 Hadoop in practice
 Cloudera Custom training slides
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Hadoop and big data training

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