Introduction to Data Analysis with Hadoop and Hive

Jonathan Seidman

ChicagoDB

February 21 | 2011

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://www.meetup.com/Chicago-area-Hadoop-User-Group-CHUG/)

•  Recovering Java developer •  jseidman@orbitz.com •  @jseidman •  @OrbitzTalent

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Why Hadoop and Hive?

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.

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Utterly redonkulous amounts of money

$ per managed TB

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Utterly redonkulous amounts of money

More reasonable amounts of money $ per managed TB

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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.

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Hadoop brings our cost per TB down to $1500 (or even less)

Hadoop Distributed File System

HDFS provides economical, reliable, fault tolerant and scalable storage of very large datasets across machines in a cluster.

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Some Hadoop “Clichés” (Which are still true…)

Hadoop places no constraints on how data is processed.

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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.

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The Problem with MapReduce

•  package org.myorg;

•  2.

•  3. import java.io.IOException;

•  4. import java.util.*;

•  5.

•  6. import org.apache.hadoop.fs.Path;

•  7. import org.apache.hadoop.conf.*;

•  8. import org.apache.hadoop.io.*;

•  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 += values.next().get();

•  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. }

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Hive Overview

Hive 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.

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Hive provides the basis for a new data analysis infrastructure.

We currently run Hive 0.6.0 with Cloudera CDH2 (Hadoop 0.20.1)

Hive Architecture (Simplified)

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Hive Overview – Comparison to Traditional DBMS Systems

Although 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.

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Hive - Data Model

Tables – analogous to tables in a standard RDBMS.

Partitions and buckets – Allow Hive to prune data during query processing.

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Not Yet, But Soon

Multiple databases

Views

Indexes

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Hive – Data Types

Supports primitive types such as int, double, and string.

Also supports complex types such as structs, maps (key/value tuples), and arrays (indexable lists).

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Extensible Storage Model

Row formats determine how records are stored.

Row format is defined by a SerDe (Serializer-Deserializer).

Container format is determined by the file format.

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Hive – Hive Query Language

HiveQL – 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).

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Hive – User Defined Functions

HiveQL 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.

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Hive – User Defined Functions

Example UDF – Find hotel’s position in an impression list:

package com.orbitz.hive;!

import org.apache.hadoop.hive.ql.exec.UDF;!

import org.apache.hadoop.io.Text;!

/**!

* returns hotel_id's 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;!

}!

}!

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Hive – User Defined Functions

hive> 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 !

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Hive MapReduce

Allows analysis not possible through standard HiveQL queries.

Can be implemented in any language.

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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 "%s\t%s" % (fields[0], fields[1])

hive> ADD FILE /home/jseidman/parse_impressions.py; hive> FROM     >   hotel_searches             > SELECT     >   TRANSFORM(impressions)                   > USING     >   'parse_impressions.py'                    > AS     >   hotel, pos;

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Processing Web Analytics Logs

Hive 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.

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Processing Flow – Step 1

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Processing Flow – Step 2

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Processing Flow – Step 3

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Processing Flow – Step 4

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Processing Flow – Step 5

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Processing Flow – Step 6

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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;!

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Exporting Data from Hive Tables

hive> INSERT OVERWRITE LOCAL DIRECTORY !

> '/tmp/searches.dat' !

> SELECT * FROM hotel_searches; !

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Analyzing Prepared Data

Example - 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);!

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Analyzing Prepared Data

Example - 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!

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Hive vs. Pig

Both 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.

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Hive vs. HBase

HBase 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.

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Hive – Lessons Learned

Job 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.

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References

•  Hadoop project: http://hadoop.apache.org/ •  Hive project: http://hadoop.apache.org/hive/ •  Hive – A Petabyte Scale Data Warehouse Using Hadoop:

http://i.stanford.edu/~ragho/hive-icde2010.pdf •  Hadoop The Definitive Guide, Second Edition, Tom White, O’Reilly

Press, 2011 •  Hive Evolution, John Sichi, November 2010: http://

www.slideshare.net/jsichi/hive-evolution-apachecon-2010

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