1. HBase and Bigtable Storage Xiaoming Gao Judy Qiu Hui Li

2. Outline HBase and Bigtable Storage HBase Uses Cases Load CSV file to Hbase table with MapReduce Demo Search Engine System with MapReduce Technologies (Hadoop/HDFS/HBase/Pig)

3. HBase Introduction HBase is an open source, distributed, sorted map modeled after Google’s BigTable HBase is built on Hadoop: – Fault tolerance – Scalability – Batch processing with MapReduce HBase uses HDFS for storage

4. HBase Cluster Architecture Tables split into regions and served by region servers Regions vertically divided by column families into “stores” Stores saved as files on HDFS

5. Data Model: A Big Sorted Map A Big Sorted Map Not a relational database, no sql, Tables consist of rows, each of which has a primary key (row key) Each row has any number of columns: sortedMap

6. HBase VS. RDBMS RDBMSHBase Data layoutRow-orientedColumn-family-oriented IndexesOn row and columnsOn row Hardware requirementLarge arrays of fast and expensive disks Designed for commodity hardware Max data sizeTBs~1PB Read/write throughput1000s queries/secondMillions of queries/second Query languageSQL (Join, Group)Get/Put/ Easy of useRelational data modeling, easy to learn A sorted Map, significant learning curve, communities and tools are increasing

7. When to Use HBase Dataset Scale – Indexing huge amount of web pages in internet or genome data – Need data mining large social media data sets Read/Write Scale – reads/writes are distributed as tables are distributed across nodes – Writes are extremely fast and require no index updates Batch Analysis – Massive and convoluted SQL queries can be executed in parallel via MapReduce jobs

8. Use Cases: Facebook Analytics – Real-time counters of URLs shared, preferred links Twitter – 25 TB of message every month Mozilla – Store crashes report, 2.5 million per day.

9. Programming with HBase 1.HBase shell – Scan, List, Create 2.Native Java API – Get(byte[] row, byte[] column, long ts, int version) 3.Non-Java Clients – Thrift server (Ruby, C++, Php) – REST server 4.HBase MapReduce API – TableInput/TableOuputFormat for MapReduce 5.High Level Interface – Pig, Hive

10. Hands-on HBase MapReduce Programming HBase MapReduce API import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.hbase.HBaseConfiguration; import org.apache.hadoop.hbase.client.Result; import org.apache.hadoop.hbase.client.Scan; import org.apache.hadoop.hbase.io.ImmutableBytesWritable; import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil; import org.apache.hadoop.hbase.mapreduce.TableMapper; import org.apache.hadoop.hbase.mapreduce.TableReducer; import org.apache.hadoop.hbase.util.Bytes; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.util.GenericOptionsParser;

11. Hands-on: load CSV file into HBase table with MapReduce Main entry point of program public static void main(String[] args) throws Exception { Configuration conf = HBaseConfiguration.create(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if(otherArgs.length != 2) { System.err.println("Wrong number of arguments: " + otherArgs.length); System.err.println("Usage: "); System.exit(-1); }//end if Job job = configureJob(conf, otherArgs); System.exit(job.waitForCompletion(true) ? 0 : 1); }//main

12. Hands-on: load CSV file into HBase table with MapReduce Configure HBase MapReduce job public static Job configureJob(Configuration conf, String [] args) throws IOException { Path inputPath = new Path(args[0]); String tableName = args[1]; Job job = new Job(conf, NAME + "_" + tableName); job.setJarByClass(Uploader.class); FileInputFormat.setInputPaths(job, inputPath); job.setInputFormatClass(SequenceFileInputFormat.class); job.setMapperClass(Uploader.class); TableMapReduceUtil.initTableReducerJob(tableName, null, job); job.setNumReduceTasks(0); return job; }//public static Job configure

13. Hands-on: load CSV file into HBase table with MapReduce The map function public void map(LongWritable key, Text line, Context context) throws IOException { // Input is a CSV file Each map() is a single line, where the key is the line number // Each line is comma-delimited; row,family,qualifier,value String [] values = line.toString().split(","); if(values.length != 4) { return; } byte [] row = Bytes.toBytes(values[0]); byte [] family = Bytes.toBytes(values[1]); byte [] qualifier = Bytes.toBytes(values[2]); byte [] value = Bytes.toBytes(values[3]); Put put = new Put(row); put.add(family, qualifier, value); try { context.write(new ImmutableBytesWritable(row), put); } catch (InterruptedException e) { e.printStackTrace(); } if(++count % checkpoint == 0) { context.setStatus("Emitting Put " + count); } } }

14. Hands-on: load CSV file into HBase table with MapReduce Steps to run program 1.Create hbase table with specified data schema 2.Compile the program with Ant 3.Run the program /bin/hadoop org.apache.hadoop.hbase.mapreduce.CSV2HBase input.csv “test” 4.Check inserted records in Hbase table

15. Extension: write output to HBase table public static Job configureJob(Configuration conf, String [] args) throws IOException { conf.set(TableInputFormat.SCAN, TableMapReduceUtil.convertScanToString(new Scan())); conf.set(TableInputFormat.INPUT_TABLE, tableName); conf.set("index.tablename", tableName); conf.set("index.familyname", columnFamily); String[] fields = new String[args.length - 2]; for(int i = 0; i < fields.length; i++) { fields[i] = args[i + 2]; } conf.setStrings("index.fields", fields); conf.set("index.familyname", "attributes"); Job job = new Job(conf, tableName); job.setJarByClass(IndexBuilder.class); job.setMapperClass(Map.class); job.setNumReduceTasks(0); job.setInputFormatClass(TableInputFormat.class); job.setOutputFormatClass(MultiTableOutputFormat.class); return job; }

16. Extension: write output to HBase table public static class Map extends Mapper { private byte[] family; private HashMap indexes; protected void map(ImmutableBytesWritable rowKey, Result result, Context context) throws IOException, InterruptedException { for(java.util.Map.Entry index : indexes.entrySet()) { byte[] qualifier = index.getKey(); ImmutableBytesWritable tableName = index.getValue(); byte[] value = result.getValue(family, qualifier); if (value != null) { Put put = new Put(value); put.add(INDEX_COLUMN, INDEX_QUALIFIER, rowKey.get()); context.write(tableName, put); }//if }//for }//map

17. Big Data Challenge Mega 10^6 Giga 10^9 Tera 10^12 Peta 10^15

18. Search Engine System with MapReduce Technologies 1.Search Engine System for Summer School 2.To give an example of how to use MapReduce technologies to solve big data challenge. 3.Using Hadoop/HDFS/HBase/Pig 4.Indexed 656K web pages (540MB in size) selected from Clueweb09 data set. 5.Calculate ranking values for 2 million web sites.

19. Architecture for SESSS Web UI Apache Server on Salsa Portal PHP script Hive/Pig script Thrift client HBase Thrift server HBase Tables 1. inverted index table 2. page rank table Hadoop Cluster on FutureGrid Ranking System Pig script Inverted Indexing System Apache Lucene

20. Demo Search Engine System for Summer School build-index-demo.exebuild-index-demo.exe (build index with HBase) pagerank-demo.exepagerank-demo.exe (compute page rank with Pig) http://salsahpc.indiana.edu/sesss/index.php

21. High Level Language: Pig Latin Hui Li Judy Qiu Some material adapted from slides by Adam Kawa the 3 rd meeting of WHUG June 21, 2012

22. What is Pig Framework for analyzing large un-structured and semi- structured data on top of Hadoop. – Pig Engine Parses, compiles Pig Latin scripts into MapReduce jobs run on top of Hadoop. – Pig Latin is simple but powerful data flow language similar to scripting languages.

23. Motivation of Using Pig Faster development – Fewer lines of code (Writing map reduce like writing SQL queries) – Re-use the code (Pig library, Piggy bank) One test: Find the top 5 words with most high frequency – 10 lines of Pig Latin V.S 200 lines in Java – 15 minutes in Pig Latin V.S 4 hours in Java

24. Word Count using MapReduce

25. Word Count using Pig Lines=LOAD ‘input/hadoop.log’ AS (line: chararray); Words = FOREACH Lines GENERATE FLATTEN(TOKENIZE(line)) AS word; Groups = GROUP Words BY word; Counts = FOREACH Groups GENERATE group, COUNT(Words); Results = ORDER Words BY Counts DESC; Top5 = LIMIT Results 5; STORE Top5 INTO /output/top5words;

26. Pig Tutorial Basic Pig knowledge: (Word Count) – Pig Data Types – Pig Operations – How to run Pig Scripts Advanced Pig features: (Kmeans Clustering) – Embedding Pig within Python – User Defined Function

27. Pig Data Types Concepts: fields, tuples, bags, relations, – A Field is a piece of data – A Tuple is an ordered set of fields – A Bag is a collection of tuples – A Relation is a bag Simple Types – Int, long, float, double, boolean,nul, chararray, bytearry, Complex types – Tuple  Row in Database ( 0002576169, Tome, 21, “Male”) – Data Bag  Table or View in Database {(0002576169, Tome, 21, “Male”), (0002576170, Mike, 20, “Male”), (0002576171 Lucy, 20, “Female”)…. }

28. Pig Operations Loading data – LOAD loads input data – Lines=LOAD ‘input/access.log’ AS (line: chararray); Projection – FOREACH … GENERTE … (similar to SELECT) – takes a set of expressions and applies them to every record. Grouping – GROUP collects together records with the same key Dump/Store – Dump displays results to screen, Store save results to file system Aggregation – AVG, COUNT, COUNT_STAR, MAX, MIN, SUM

29. How to run Pig Latin scripts Local mode – Local host and local file system is used – Neither Hadoop nor HDFS is required – Useful for prototyping and debugging MapReduce mode – Run on a Hadoop cluster and HDFS Batch mode - run a script directly – Pig –x local my_pig_script.pig – Pig –x mapreduce my_pig_script.pig Interactive mode use the Pig shell to run script – Grunt> Lines = LOAD ‘/input/input.txt’ AS (line:chararray); – Grunt> Unique = DISTINCT Lines; – Grunt> DUMP Unique;

30. Hands-on: Word Count using Pig Latin 1.cd pigtutorial/pig-hands-on/ 2.tar –xf pig-wordcount.tar 3.cd pig-wordcount 4.pig –x local 5.grunt> Lines=LOAD ‘input.txt’ AS (line: chararray); 6.grunt>Words = FOREACH Lines GENERATE FLATTEN(TOKENIZE(line)) AS word; 7.grunt>Groups = GROUP Words BY word; 8.grunt>counts = FOREACH Groups GENERATE group, COUNT(Words); 9.grunt>DUMP counts;

31. Sample: Kmeans using Pig Latin A method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. Assignment step: Assign each observation to the cluster with the closest mean Update step: Calculate the new means to be the centroid of the observations in the cluster. Reference: http://en.wikipedia.org/wiki/K-means_clustering

32. Kmeans Using Pig Latin PC = Pig.compile("""register udf.jar DEFINE find_centroid FindCentroid('$centroids'); raw = load 'student.txt' as (name:chararray, age:int, gpa:double); centroided = foreach raw generate gpa, find_centroid(gpa) as centroid; grouped = group centroided by centroid; result = Foreach grouped Generate group, AVG(centroided.gpa); store result into 'output'; """)

33. Kmeans Using Pig Latin while iter_num<MAX_ITERATION: PCB = PC.bind({'centroids':initial_centroids}) results = PCB.runSingle() iter = results.result("result").iterator() centroids = [None] * v distance_move = 0.0 # get new centroid of this iteration, calculate the moving distance with last iteration for i in range(v): tuple = iter.next() centroids[i] = float(str(tuple.get(1))) distance_move = distance_move + fabs(last_centroids[i]-centroids[i]) distance_move = distance_move / v; if distance_move<tolerance: converged = True break ……

34. Embedding Python scripts with Pig Statements Pig does not support flow control statement: if/else, while loop, for loop, etc. Pig embedding API can leverage all language features provided by Python including control flow: – Loop and exit criteria – Similar to the database embedding API – Easier parameter passing JavaScript is available as well The framework is extensible. Any JVM implementation of a language could be integrated

35. User Defined Function What is UDF – Way to do an operation on a field or fields – Called from within a pig script – Currently all done in Java Why use UDF – You need to do more than grouping or filtering – Actually filtering is a UDF – Maybe more comfortable in Java land than in SQL/Pig Latin P = Pig.compile("""register udf.jar DEFINE find_centroid FindCentroid('$centroids');

36. Hands-on Run Pig Latin Kmeans 1.export PIG_CLASSPATH= /opt/pig/lib/jython-2.5.0.jar 2.Hadoop dfs –copyFromLocal input.txt./input.txt 3.pig –x mapreduce kmeans.py 4.pig—x local kmeans.py

37. Hands-on Run Pig Latin Kmeans 2012-07-14 14:51:24,636 [main] INFO org.apache.pig.scripting.BoundScript - Query to run: register udf.jar DEFINE find_centroid FindCentroid('0.0:1.0:2.0:3.0'); raw = load 'student.txt' as (name:chararray, age:int, gpa:double); centroided = foreach raw generate gpa, find_centroid(gpa) as centroid; grouped = group centroided by centroid; result = foreach grouped generate group, AVG(centroided.gpa); store result into 'output'; Input(s): Successfully read 10000 records (219190 bytes) from: "hdfs://iw-ubuntu/user/developer/student.txt" Output(s): Successfully stored 4 records (134 bytes) in: "hdfs://iw-ubuntu/user/developer/output“ last centroids: [0.371927835052,1.22406743491,2.24162171881,3.40173705722]

38. References: 1.http://pig.apache.org (Pig official site)http://pig.apache.org 2.http://en.wikipedia.org/wiki/K-means_clusteringhttp://en.wikipedia.org/wiki/K-means_clustering 3.Docs http://pig.apache.org/docs/r0.9.0http://pig.apache.org/docs/r0.9.0 4.Papers: http://wiki.apache.org/pig/PigTalksPapershttp://wiki.apache.org/pig/PigTalksPapers 5.http://en.wikipedia.org/wiki/Pig_Latinhttp://en.wikipedia.org/wiki/Pig_Latin 6.Slides by Adam Kawa the 3 rd meeting of WHUG June 21, 2012 Questions?

39. Acknowledgement