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Data Analysis with Hadoop and Hive, ChicagoDB 2/21/2011


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Slides from presentation on using Hadoop and Hive as a new data analysis platform. Presented at the ChicagoDB user group on February 21st, 2011.

Slides from presentation on using Hadoop and Hive as a new data analysis platform. Presented at the ChicagoDB user group on February 21st, 2011.

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  • 1. Introduction to Data Analysis with Hadoop and Hive Jonathan Seidman ChicagoDB February 21 | 2011
  • 2. About Me•  Lead Engineer on Business Intelligence/Data Infrastructure team at Orbitz, former member of Machine Learning team •  Co-organizer/founder of Chicago Hadoop User Group (http:// •  Recovering Java developer • •  @jseidman •  @OrbitzTalent page 2
  • 3. Why Hadoop and Hive? page 3
  • 4. Some Hadoop “Clichés” (Which are still true…)Hadoop allows you to store and process data that was previously impractical because of cost, technical issues, etc. page 4
  • 5. Utterly redonkulous amounts of money $ per managed TB page 5
  • 6. Utterly redonkulous amounts of money More reasonable amounts of money $ per managed TB page 6
  • 7. Adding data to our data warehouse also requires a lengthy plan/implement/deploy cycle. Because of the expense and time our data teams need to be very judicious about which data gets added. This means that potentially valuable data may not be saved. page 7
  • 8. Hadoop brings our cost per TB down to $1500 (or even less) page 8
  • 9. Hadoop Distributed File SystemHDFS provides economical, reliable, fault tolerant and scalable storage of very large datasets across machines in a cluster. page 9
  • 10. Some Hadoop “Clichés” (Which are still true…) Hadoop places no constraints on how data is processed. page 10
  • 11. Some Hadoop “Clichés” (Which are still true…) Hadoop makes it relatively easy to efficiently process all the data stored in HDFS. MapReduce is a programming model for efficient distributed processing. Designed to reliably perform computations on large volumes of data in parallel. MapRedue Removes much of the burden of writing distributed computations. page 11
  • 12. The Problem with MapReduce•  package org.myorg;•  2.•  3. import;•  4. import java.util.*;•  5.•  6. import org.apache.hadoop.fs.Path;•  7. import org.apache.hadoop.conf.*;•  8. import*;•  9. import org.apache.hadoop.mapred.*;•  10. import org.apache.hadoop.util.*;•  11.•  12. public class WordCount {•  13.•  14. public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {•  15. private final static IntWritable one = new IntWritable(1);•  16. private Text word = new Text();•  17.•  18. public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {•  19. String line = value.toString();•  20. StringTokenizer tokenizer = new StringTokenizer(line);•  21. while (tokenizer.hasMoreTokens()) {•  22. word.set(tokenizer.nextToken());•  23. output.collect(word, one);•  24. }•  25. }•  26. }•  27.•  28. public static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {•  29. public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {•  30. int sum = 0;•  31. while (values.hasNext()) {•  32. sum +=;•  33. }•  34. output.collect(key, new IntWritable(sum));•  35. }•  36. }•  37.•  38. public static void main(String[] args) throws Exception {•  39. JobConf conf = new JobConf(WordCount.class);•  40. conf.setJobName("wordcount");•  41.•  42. conf.setOutputKeyClass(Text.class);•  43. conf.setOutputValueClass(IntWritable.class);•  44.•  45. conf.setMapperClass(Map.class);•  46. conf.setCombinerClass(Reduce.class);•  47. conf.setReducerClass(Reduce.class);•  48.•  49. conf.setInputFormat(TextInputFormat.class);•  50. conf.setOutputFormat(TextOutputFormat.class);•  51.•  52. FileInputFormat.setInputPaths(conf, new Path(args[0]));•  53. FileOutputFormat.setOutputPath(conf, new Path(args[1]));•  54.•  55. JobClient.runJob(conf);•  57. }•  58. } page 12
  • 13. Hive OverviewHive is an open-source data warehousing solution built on top of Hadoop which allows for easy data summarization, ad-hoc querying and analysis of large datasets stored in Hadoop. Developed at Facebook to provide a structured data model over Hadoop data. Simplifies Hadoop data analysis – users can use a familiar SQL model rather than writing low level custom code. Hive queries are compiled into Hadoop MapReduce jobs. Designed for scalability, not low latency. page 13
  • 14. Hive provides the basis for a new data analysis infrastructure. We currently run Hive 0.6.0 with Cloudera CDH2 (Hadoop 0.20.1) page 14
  • 15. Hive Architecture (Simplified) page 15
  • 16. Hive Overview – Comparison to Traditional DBMS SystemsAlthough Hive uses a model familiar to database users, it does not support a full relational model and only supports a subset of SQL. Schema on read vs. schema on write What Hadoop/Hive offers is highly scalable and fault-tolerant processing of very large data sets. Hive However is moving more and more towards being a parallel DBMS. page 16
  • 17. Hive - Data ModelTables – analogous to tables in a standard RDBMS. Partitions and buckets – Allow Hive to prune data during query processing. page 17
  • 18. Not Yet, But SoonMultiple databases Views Indexes page 18
  • 19. Hive – Data TypesSupports primitive types such as int, double, and string. Also supports complex types such as structs, maps (key/ value tuples), and arrays (indexable lists). page 19
  • 20. Extensible Storage ModelRow formats determine how records are stored. Row format is defined by a SerDe (Serializer-Deserializer). Container format is determined by the file format. page 20
  • 21. Hive – Hive Query LanguageHiveQL – Supports basic SQL-like operations such as select, join, aggregate, union, sub-queries, etc. HiveQL queries are compiled into MapReduce processes. Supports embedding custom MapReduce scripts. Built in support for standard relational, arithmetic, and boolean operators. Supports aggregate functions, including statistical functions (avg, standard deviation, covariance, percentiles). page 21
  • 22. Hive – User Defined FunctionsHiveQL is extensible through user defined functions implemented in Java. Also supports aggregation functions. Provides table functions when more than one value needs to be returned. page 22
  • 23. Hive – User Defined FunctionsExample UDF – Find hotel’s position in an impression list:package com.orbitz.hive;!import org.apache.hadoop.hive.ql.exec.UDF;!import;!/**! * returns hotel_ids position given a hotel_id and impression list! */!public final class GetPos extends UDF {! public Text evaluate(final Text hotel_id, final Text impressions) {! if (hotel_id == null || impressions == null)! return null;! String[] hotels = impressions.toString().split(";");! String position;! String id = hotel_id.toString();! int begin=0, end=0;! for (int i=0; i<hotels.length; i++) {! begin = hotels[i].indexOf(",");! end = hotels[i].lastIndexOf(",");! position = hotels[i].substring(begin+1,end);! if (id.equals(hotels[i].substring(0,begin)))! return new Text(position);! }! return null;! }!}! page 23
  • 24. Hive – User Defined Functionshive> add jar path-to-jar/pos.jar; !hive> create temporary function getpos as com.orbitz.hive.GetPos;!hive> select getpos(‘1’, ‘1,3,100.00;2,1,100.00’);!…!hive> 3 ! page 24
  • 25. Hive MapReduceAllows analysis not possible through standard HiveQL queries. Can be implemented in any language. page 25
  • 26. Hive MapReduce•  #!/usr/bin/python
 import sys
 for line in sys.stdin:
         line = line.replace(;, |)
         impressions = line.split(|)
         for impression in impressions:
                 fields = "".join(impression).split(,)
                 print "%st%s" % (fields[0], fields[1])
 hive> ADD FILE /home/jseidman/;
 hive> FROM
     >   hotel_searches         
     > SELECT
     >   TRANSFORM(impressions)               
     > USING
     > AS
     >   hotel, pos;
 page 26
  • 27. Processing Web Analytics LogsHive provides the infrastructure to support analysis of web analytics logs stored in Hadoop Used to support analysis for machine learning tasks, cache optimization, keyword performance, etc. page 27
  • 28. Processing Flow – Step 1 page 28
  • 29. Processing Flow – Step 2 page 29
  • 30. Processing Flow – Step 3 page 30
  • 31. Processing Flow – Step 4 page 31
  • 32. Processing Flow – Step 5 page 32
  • 33. Processing Flow – Step 6 page 33
  • 34. Importing Prepared Data to Hive$HIVE_HOME/bin/hive -e "LOAD DATA INPATH ! ’/output/part-00000 OVERWRITE INTO! TABLE hotel_searches PARTITION(dt=$YEAR-$MONTH-$DAY)"!CREATE TABLE hotel_searches( ! session_id STRING, host STRING, visitors_ip STRING, search_date STRING, search_time STRING, dept_date STRING, ret_date STRING, destination STRING, location_id STRING, number_of_guests INT, number_of_rooms INT, ! impressions STRING)!PARTITIONED BY (dt STRING)!ROW FORMAT DELIMITED!FIELDS TERMINATED BY t’!STORED AS TEXTFILE;! page 34
  • 35. Exporting Data from Hive Tableshive> INSERT OVERWRITE LOCAL DIRECTORY ! > /tmp/searches.dat ! > SELECT * FROM hotel_searches; ! page 35
  • 36. Analyzing Prepared DataExample - Find the Position of Each Booked Hotel in Search Results: CREATE TABLE positions(! session_id STRING,! booked_hotel_id STRING,! position INT);! INSERT OVERWRITE TABLE
 positions! SELECT
 h.session_id, h.booked_hotel_id, i.position! FROM
 hotel_impressions i JOIN hotel_bookings h! ON
 (h.booked_hotel_id = i.hotel_id and h.session_id = i.session_id);! page 36
  • 37. Analyzing Prepared DataExample - Aggregate Booking Position by Location by Day: CREATE TABLE position_aggregate_by_day(! location_id STRING,!   booking_date STRING,!   position INT,!   pcount INT);! INSERT OVERWRITE TABLE! position_aggregate_by_day! SELECT! h.location_id, h.booking_date, i.position, count(1)! FROM! hotel_bookings h JOIN hotel_impressions i! ON! (i.hotel_id = h.booked_hotel_id and i.session_id = h.session_id)! GROUP BY! h.location_id, h.booking_date, i.position! page 37
  • 38. Hive vs. PigBoth are declarative languages, but Hive is SQL-like, Pig is a scripting language. Explicit schema vs. implicit schema. Hive metadata can be accessed by external tools. page 38
  • 39. Hive vs. HBaseHBase is a column-based key value store as opposed to an SQL model. HBase offers lower latency and random access to data. Hive/HBase integration was recently released, allowing Hive queries to be executed over HBase tables. page 39
  • 40. Hive – Lessons LearnedJob scheduling – Default Hadoop scheduling is FIFO. Consider using something like the fair scheduler. Multi-user Hive – Default install is single user. Multi-user installs require an external relational store. set mapred.reduce.tasks is your friend. Migrating Hive between clusters is not fun. Documentation is still a little sparse. page 40
  • 41. References•  Hadoop project: •  Hive project: •  Hive – A Petabyte Scale Data Warehouse Using Hadoop: •  Hadoop The Definitive Guide, Second Edition, Tom White, O’Reilly Press, 2011 •  Hive Evolution, John Sichi, November 2010: http:// page 41