Lecture on Hadoop Technology MCA Engg.ppt

UT DALLAS Erik Jonsson School of Engineering & Computer Science FEARLESS engineering Cloud Tools Overview

UT DALLAS Erik Jonsson School of Engineering & Computer Science FEARLESS engineering Hadoop

FEARLESS engineering Outline • Hadoop - Basics • HDFS – Goals – Architecture – Other functions • MapReduce – Basics – Word Count Example – Handy tools – Finding shortest path example • Related Apache sub-projects (Pig, HBase,Hive)

FEARLESS engineering Hadoop - Why ? • Need to process huge datasets on large clusters of computers • Very expensive to build reliability into each application • Nodes fail every day – Failure is expected, rather than exceptional – The number of nodes in a cluster is not constant • Need a common infrastructure – Efficient, reliable, easy to use – Open Source, Apache Licence

FEARLESS engineering Who uses Hadoop? • Amazon/A9 • Facebook • Google • New York Times • Veoh • Yahoo! • …. many more

FEARLESS engineering Commodity Hardware • Typically in 2 level architecture – Nodes are commodity PCs – 30-40 nodes/rack – Uplink from rack is 3-4 gigabit – Rack-internal is 1 gigabit Aggregation switch Rack switch

UT DALLAS Erik Jonsson School of Engineering & Computer Science FEARLESS engineering Hadoop Distributed File System (HDFS) Original Slides by Dhruba Borthakur Apache Hadoop Project Management Committee

FEARLESS engineering Goals of HDFS • Very Large Distributed File System – 10K nodes, 100 million files, 10PB • Assumes Commodity Hardware – Files are replicated to handle hardware failure – Detect failures and recover from them • Optimized for Batch Processing – Data locations exposed so that computations can move to where data resides – Provides very high aggregate bandwidth

FEARLESS engineering Distributed File System • Single Namespace for entire cluster • Data Coherency – Write-once-read-many access model – Client can only append to existing files • Files are broken up into blocks – Typically 64MB block size – Each block replicated on multiple DataNodes • Intelligent Client – Client can find location of blocks – Client accesses data directly from DataNode

FEARLESS engineering HDFS Architecture

FEARLESS engineering Functions of a NameNode • Manages File System Namespace – Maps a file name to a set of blocks – Maps a block to the DataNodes where it resides • Cluster Configuration Management • Replication Engine for Blocks

FEARLESS engineering NameNode Metadata • Metadata in Memory – The entire metadata is in main memory – No demand paging of metadata • Types of metadata – List of files – List of Blocks for each file – List of DataNodes for each block – File attributes, e.g. creation time, replication factor • A Transaction Log – Records file creations, file deletions etc

FEARLESS engineering DataNode • A Block Server – Stores data in the local file system (e.g. ext3) – Stores metadata of a block (e.g. CRC) – Serves data and metadata to Clients • Block Report – Periodically sends a report of all existing blocks to the NameNode • Facilitates Pipelining of Data – Forwards data to other specified DataNodes

FEARLESS engineering Block Placement • Current Strategy – One replica on local node – Second replica on a remote rack – Third replica on same remote rack – Additional replicas are randomly placed • Clients read from nearest replicas • Would like to make this policy pluggable

FEARLESS engineering Heartbeats • DataNodes send hearbeat to the NameNode – Once every 3 seconds • NameNode uses heartbeats to detect DataNode failure

FEARLESS engineering Replication Engine • NameNode detects DataNode failures – Chooses new DataNodes for new replicas – Balances disk usage – Balances communication traffic to DataNodes

FEARLESS engineering Data Correctness • Use Checksums to validate data – Use CRC32 • File Creation – Client computes checksum per 512 bytes – DataNode stores the checksum • File access – Client retrieves the data and checksum from DataNode – If Validation fails, Client tries other replicas

FEARLESS engineering NameNode Failure • A single point of failure • Transaction Log stored in multiple directories – A directory on the local file system – A directory on a remote file system (NFS/CIFS) • Need to develop a real HA solution

FEARLESS engineering Data Pieplining • Client retrieves a list of DataNodes on which to place replicas of a block • Client writes block to the first DataNode • The first DataNode forwards the data to the next node in the Pipeline • When all replicas are written, the Client moves on to write the next block in file

FEARLESS engineering Rebalancer • Goal: % disk full on DataNodes should be similar – Usually run when new DataNodes are added – Cluster is online when Rebalancer is active – Rebalancer is throttled to avoid network congestion – Command line tool

FEARLESS engineering Secondary NameNode • Copies FsImage and Transaction Log from Namenode to a temporary directory • Merges FSImage and Transaction Log into a new FSImage in temporary directory • Uploads new FSImage to the NameNode – Transaction Log on NameNode is purged

FEARLESS engineering User Interface • Commads for HDFS User: – hadoop dfs -mkdir /foodir – hadoop dfs -cat /foodir/myfile.txt – hadoop dfs -rm /foodir/myfile.txt • Commands for HDFS Administrator – hadoop dfsadmin -report – hadoop dfsadmin -decommision datanodename • Web Interface – http://host:port/dfshealth.jsp

UT DALLAS Erik Jonsson School of Engineering & Computer Science FEARLESS engineering MapReduce Original Slides by Owen O’Malley (Yahoo!) & Christophe Bisciglia, Aaron Kimball & Sierra Michells-Slettvet

FEARLESS engineering MapReduce - Dataflow

FEARLESS engineering MapReduce - Features • Fine grained Map and Reduce tasks – Improved load balancing – Faster recovery from failed tasks • Automatic re-execution on failure – In a large cluster, some nodes are always slow or flaky – Framework re-executes failed tasks • Locality optimizations – With large data, bandwidth to data is a problem – Map-Reduce + HDFS is a very effective solution – Map-Reduce queries HDFS for locations of input data – Map tasks are scheduled close to the inputs when possible

FEARLESS engineering Word Count Example • Mapper – Input: value: lines of text of input – Output: key: word, value: 1 • Reducer – Input: key: word, value: set of counts – Output: key: word, value: sum • Launching program – Defines this job – Submits job to cluster

FEARLESS engineering Word Count Dataflow

FEARLESS engineering Word Count Mapper public static class Map extends MapReduceBase implements Mapper<LongWritable,Text,Text,IntWritable> { private static final IntWritable one = new IntWritable(1); private Text word = new Text(); public static void map(LongWritable key, Text value, OutputCollector<Text,IntWritable> output, Reporter reporter) throws IOException { String line = value.toString(); StringTokenizer = new StringTokenizer(line); while(tokenizer.hasNext()) { word.set(tokenizer.nextToken()); output.collect(word,one); } } }

FEARLESS engineering Word Count Reducer public static class Reduce extends MapReduceBase implements Reducer<Text,IntWritable,Text,IntWritable> { public static void map(Text key, Iterator<IntWritable> values, OutputCollector<Text,IntWritable> output, Reporter reporter) throws IOException { int sum = 0; while(values.hasNext()) { sum += values.next().get(); } output.collect(key, new IntWritable(sum)); } }

FEARLESS engineering Word Count Example • Jobs are controlled by configuring JobConfs • JobConfs are maps from attribute names to string values • The framework defines attributes to control how the job is executed – conf.set(“mapred.job.name”, “MyApp”); • Applications can add arbitrary values to the JobConf – conf.set(“my.string”, “foo”); – conf.set(“my.integer”, 12); • JobConf is available to all tasks

FEARLESS engineering Putting it all together • Create a launching program for your application • The launching program configures: – The Mapper and Reducer to use – The output key and value types (input types are inferred from the InputFormat) – The locations for your input and output • The launching program then submits the job and typically waits for it to complete

FEARLESS engineering Putting it all together JobConf conf = new JobConf(WordCount.class); conf.setJobName(“wordcount”); conf.setOutputKeyClass(Text.class); conf.setOutputValueClass(IntWritable.class); conf.setMapperClass(Map.class); conf.setCombinerClass(Reduce.class); conf.setReducer(Reduce.class); conf.setInputFormat(TextInputFormat.class); Conf.setOutputFormat(TextOutputFormat.class); FileInputFormat.setInputPaths(conf, new Path(args[0])); FileOutputFormat.setOutputPath(conf, new Path(args[1])); JobClient.runJob(conf);

FEARLESS engineering Input and Output Formats • A Map/Reduce may specify how it’s input is to be read by specifying an InputFormat to be used • A Map/Reduce may specify how it’s output is to be written by specifying an OutputFormat to be used • These default to TextInputFormat and TextOutputFormat, which process line-based text data • Another common choice is SequenceFileInputFormat and SequenceFileOutputFormat for binary data • These are file-based, but they are not required to be

FEARLESS engineering How many Maps and Reduces • Maps – Usually as many as the number of HDFS blocks being processed, this is the default – Else the number of maps can be specified as a hint – The number of maps can also be controlled by specifying the minimum split size – The actual sizes of the map inputs are computed by: • max(min(block_size,data/#maps), min_split_size • Reduces – Unless the amount of data being processed is small • 0.95*num_nodes*mapred.tasktracker.tasks.maximum

FEARLESS engineering Some handy tools • Partitioners • Combiners • Compression • Counters • Speculation • Zero Reduces • Distributed File Cache • Tool

FEARLESS engineering Partitioners • Partitioners are application code that define how keys are assigned to reduces • Default partitioning spreads keys evenly, but randomly – Uses key.hashCode() % num_reduces • Custom partitioning is often required, for example, to produce a total order in the output – Should implement Partitioner interface – Set by calling conf.setPartitionerClass(MyPart.class) – To get a total order, sample the map output keys and pick values to divide the keys into roughly equal buckets and use that in your partitioner

FEARLESS engineering Combiners • When maps produce many repeated keys – It is often useful to do a local aggregation following the map – Done by specifying a Combiner – Goal is to decrease size of the transient data – Combiners have the same interface as Reduces, and often are the same class – Combiners must not side effects, because they run an intermdiate number of times – In WordCount, conf.setCombinerClass(Reduce.class);

FEARLESS engineering Compression • Compressing the outputs and intermediate data will often yield huge performance gains – Can be specified via a configuration file or set programmatically – Set mapred.output.compress to true to compress job output – Set mapred.compress.map.output to true to compress map outputs • Compression Types (mapred(.map)?.output.compression.type) – “block” - Group of keys and values are compressed together – “record” - Each value is compressed individually – Block compression is almost always best • Compression Codecs (mapred(.map)?.output.compression.codec) – Default (zlib) - slower, but more compression – LZO - faster, but less compression

FEARLESS engineering Counters • Often Map/Reduce applications have countable events • For example, framework counts records in to and out of Mapper and Reducer • To define user counters: static enum Counter {EVENT1, EVENT2}; reporter.incrCounter(Counter.EVENT1, 1); • Define nice names in a MyClass_Counter.properties file CounterGroupName=MyCounters EVENT1.name=Event 1 EVENT2.name=Event 2

FEARLESS engineering Speculative execution • The framework can run multiple instances of slow tasks – Output from instance that finishes first is used – Controlled by the configuration variable mapred.speculative.execution – Can dramatically bring in long tails on jobs

FEARLESS engineering Zero Reduces • Frequently, we only need to run a filter on the input data – No sorting or shuffling required by the job – Set the number of reduces to 0 – Output from maps will go directly to OutputFormat and disk

FEARLESS engineering Distributed File Cache • Sometimes need read-only copies of data on the local computer – Downloading 1GB of data for each Mapper is expensive • Define list of files you need to download in JobConf • Files are downloaded once per computer • Add to launching program: DistributedCache.addCacheFile(new URI(“hdfs://nn:8020/foo”), conf); • Add to task: Path[] files = DistributedCache.getLocalCacheFiles(conf);

FEARLESS engineering Tool • Handle “standard” Hadoop command line options – -conf file - load a configuration file named file – -D prop=value - define a single configuration property prop • Class looks like: public class MyApp extends Configured implements Tool { public static void main(String[] args) throws Exception { System.exit(ToolRunner.run(new Configuration(), new MyApp(), args)); } public int run(String[] args) throws Exception { …. getConf() …. } }

FEARLESS engineering Finding the Shortest Path • A common graph search application is finding the shortest path from a start node to one or more target nodes • Commonly done on a single machine with Dijkstra’s Algorithm • Can we use BFS to find the shortest path via MapReduce?

FEARLESS engineering Finding the Shortest Path: Intuition • We can define the solution to this problem inductively – DistanceTo(startNode) = 0 – For all nodes n directly reachable from startNode, DistanceTo(n) = 1 – For all nodes n reachable from some other set of nodes S, DistanceTo(n) = 1 + min(DistanceTo(m), m  S)

FEARLESS engineering From Intuition to Algorithm • A map task receives a node n as a key, and (D, points-to) as its value – D is the distance to the node from the start – points-to is a list of nodes reachable from n  p  points-to, emit (p, D+1) • Reduces task gathers possible distances to a given p and selects the minimum one

FEARLESS engineering What This Gives Us • This MapReduce task can advance the known frontier by one hop • To perform the whole BFS, a non-MapReduce component then feeds the output of this step back into the MapReduce task for another iteration – Problem: Where’d the points-to list go? – Solution: Mapper emits (n, points-to) as well

FEARLESS engineering Blow-up and Termination • This algorithm starts from one node • Subsequent iterations include many more nodes of the graph as the frontier advances • Does this ever terminate? – Yes! Eventually, routes between nodes will stop being discovered and no better distances will be found. When distance is the same, we stop – Mapper should emit (n,D) to ensure that “current distance” is carried into the reducer

UT DALLAS Erik Jonsson School of Engineering & Computer Science FEARLESS engineering Hadoop Subprojects

FEARLESS engineering Hadoop Related Subprojects • Pig – High-level language for data analysis • HBase – Table storage for semi-structured data • Zookeeper – Coordinating distributed applications • Hive – SQL-like Query language and Metastore • Mahout – Machine learning

UT DALLAS Erik Jonsson School of Engineering & Computer Science FEARLESS engineering Pig Original Slides by Matei Zaharia UC Berkeley RAD Lab

FEARLESS engineering Pig • Started at Yahoo! Research • Now runs about 30% of Yahoo!’s jobs • Features – Expresses sequences of MapReduce jobs – Data model: nested “bags” of items – Provides relational (SQL) operators (JOIN, GROUP BY, etc.) – Easy to plug in Java functions

FEARLESS engineering An Example Problem • Suppose you have user data in a file, website data in another, and you need to find the top 5 most visited pages by users aged 18-25 Load Users Load Pages Filter by age Join on name Group on url Count clicks Order by clicks Take top 5

FEARLESS engineering In MapReduce

FEARLESS engineering In Pig Latin Users = load ‘users’ as (name, age); Filtered = filter Users by age >= 18 and age <= 25; Pages = load ‘pages’ as (user, url); Joined = join Filtered by name, Pages by user; Grouped = group Joined by url; Summed = foreach Grouped generate group, count(Joined) as clicks; Sorted = order Summed by clicks desc; Top5 = limit Sorted 5; store Top5 into ‘top5sites’;

FEARLESS engineering Ease of Translation Load Users Load Pages Filter by age Join on name Group on url Count clicks Order by clicks Take top 5 Users = load … Fltrd = filter … Pages = load … Joined = join … Grouped = group … Summed = … count()… Sorted = order … Top5 = limit …

FEARLESS engineering Ease of Translation Load Users Load Pages Filter by age Join on name Group on url Count clicks Order by clicks Take top 5 Users = load … Fltrd = filter … Pages = load … Joined = join … Grouped = group … Summed = … count()… Sorted = order … Top5 = limit … Job 1 Job 2 Job 3

UT DALLAS Erik Jonsson School of Engineering & Computer Science FEARLESS engineering HBase Original Slides by Tom White Lexeme Ltd.

FEARLESS engineering HBase - What? • Modeled on Google’s Bigtable • Row/column store • Billions of rows/millions on columns • Column-oriented - nulls are free • Untyped - stores byte[]

FEARLESS engineering HBase - Data Model Row Timestamp Column family: animal: Column family repairs: animal:type animal:size repairs:cost enclosure1 t2 zebra 1000 EUR t1 lion big enclosure2 … … … …

FEARLESS engineering HBase - Data Storage Column family animal: (enclosure1, t2, animal:type) zebra (enclosure1, t1, animal:size) big (enclosure1, t1, animal:type) lion Column family repairs: (enclosure1, t1, repairs:cost) 1000 EUR

FEARLESS engineering HBase - Code HTable table = … Text row = new Text(“enclosure1”); Text col1 = new Text(“animal:type”); Text col2 = new Text(“animal:size”); BatchUpdate update = new BatchUpdate(row); update.put(col1, “lion”.getBytes(“UTF-8”)); update.put(col2, “big”.getBytes(“UTF-8)); table.commit(update); update = new BatchUpdate(row); update.put(col1, “zebra”.getBytes(“UTF-8”)); table.commit(update);

FEARLESS engineering HBase - Querying • Retrieve a cell Cell = table.getRow(“enclosure1”).getColumn(“animal:type”).getValue(); • Retrieve a row RowResult = table.getRow( “enclosure1” ); • Scan through a range of rows Scanner s = table.getScanner( new String[] { “animal:type” } );

UT DALLAS Erik Jonsson School of Engineering & Computer Science FEARLESS engineering Hive Original Slides by Matei Zaharia UC Berkeley RAD Lab

FEARLESS engineering Hive • Developed at Facebook • Used for majority of Facebook jobs • “Relational database” built on Hadoop – Maintains list of table schemas – SQL-like query language (HiveQL) – Can call Hadoop Streaming scripts from HiveQL – Supports table partitioning, clustering, complex data types, some optimizations

FEARLESS engineering Creating a Hive Table • Partitioning breaks table into separate files for each (dt, country) pair Ex: /hive/page_view/dt=2008-06-08,country=USA /hive/page_view/dt=2008-06-08,country=CA CREATE TABLE page_views(viewTime INT, userid BIGINT, page_url STRING, referrer_url STRING, ip STRING COMMENT 'User IP address') COMMENT 'This is the page view table' PARTITIONED BY(dt STRING, country STRING) STORED AS SEQUENCEFILE;

FEARLESS engineering A Simple Query SELECT page_views.* FROM page_views WHERE page_views.date >= '2008-03-01' AND page_views.date <= '2008-03-31' AND page_views.referrer_url like '%xyz.com'; • Hive only reads partition 2008-03-01,* instead of scanning entire table • Find all page views coming from xyz.com on March 31st :

FEARLESS engineering Aggregation and Joins • Count users who visited each page by gender: • Sample output: SELECT pv.page_url, u.gender, COUNT(DISTINCT u.id) FROM page_views pv JOIN user u ON (pv.userid = u.id) GROUP BY pv.page_url, u.gender WHERE pv.date = '2008-03-03';

FEARLESS engineering Using a Hadoop Streaming Mapper Script SELECT TRANSFORM(page_views.userid, page_views.date) USING 'map_script.py' AS dt, uid CLUSTER BY dt FROM page_views;

UT DALLAS Erik Jonsson School of Engineering & Computer Science FEARLESS engineering Storm Original Slides by Nathan Marz Twitter

FEARLESS engineering Storm • Developed by BackType which was acquired by Twitter • Lots of tools for data (i.e. batch) processing – Hadoop, Pig, HBase, Hive, … • None of them are realtime systems which is becoming a real requirement for businesses • Storm provides realtime computation – Scalable – Guarantees no data loss – Extremely robust and fault-tolerant – Programming language agnostic

FEARLESS engineering Before Storm

FEARLESS engineering Before Storm – Adding a worker Deploy Reconfigure/Redeploy

FEARLESS engineering Problems • Scaling is painful • Poor fault-tolerance • Coding is tedious

FEARLESS engineering What we want • Guaranteed data processing • Horizontal scalability • Fault-tolerance • No intermediate message brokers! • Higher level abstraction than message passing • “Just works” !!

FEARLESS engineering Storm Cluster Master node (similar to Hadoop JobTracker) Used for cluster coordination Run worker processes

FEARLESS engineering Concepts • Streams • Spouts • Bolts • Topologies

FEARLESS engineering Streams Tuple Tuple Tuple Tuple Tuple Tuple Tuple Unbounded sequence of tuples

FEARLESS engineering Spouts Source of streams

FEARLESS engineering Bolts Processes input streams and produces new streams: Can implement functions such as filters, aggregation, join, etc

FEARLESS engineering Topology Network of spouts and bolts

FEARLESS engineering Topology Spouts and bolts execute as many tasks across the cluster

FEARLESS engineering Stream Grouping When a tuple is emitted which task does it go to?

FEARLESS engineering Stream Grouping • Shuffle grouping: pick a random task • Fields grouping: consistent hashing on a subset of tuple fields • All grouping: send to all tasks • Global grouping: pick task with lowest id

Lecture on Hadoop Technology MCA Engg.ppt

More Related Content

Similar to Lecture on Hadoop Technology MCA Engg.ppt

More from gayatridwahane

Recently uploaded

Lecture on Hadoop Technology MCA Engg.ppt