Dataiku pig - hive - cascading

Pig Hive Cascading Hadoop In Practice

}  Devoxx 2013 }  Florian Douetteau

Florian Douetteau <florian.douetteau@dataiku.com>

}  CEO at Dataiku }  Freelance at Criteo (Online Ads) }  CTO at IsCool Ent. (#1 French Social Gamer) }  VP R&D Exalead (Search Engine Technology)

About me

4/14/13 Dataiku Training – Hadoop for Data Science 2

}  Hadoop and Context (->0:03) }  Pig, Hive, Cascading, … (->0:06) }  How they work (->0:09) }  Comparing the tools (->0:25) }  Wrap’up and question (->0:)

Agenda

Dataiku - Pig, Hive and Cascading

CHOOSE TECHNOLOGY

Hadoop Ceph

Sphere Cassandra Spark

Scikit-Learn

Mahout WEKA

MLBase LibSVM

SAS RapidMiner

SPSS Panda

QlickView Tableau SpotFire HTML5/D3

InfiniDB Vertica

GreenPlum Impala Netezza

Elastic Search SOLR

MongoDB Riak

Membase

Pig Cascading

Talend

Machine Learning !Mystery Land!

Scalability Central!NoSQL-Slavia!

SQL Colunnar Republic!

Vizualization County! Data Clean Wasteland!

Statistician Old !House!

How do I (pre)process data? Implicit User Data (Views, Searches…)

Content Data (Title, Categories, Price, …)

Explicit User Data (Click, Buy, …)

User Information (Location, Graph…)

Transformation Matrix

Transformation Predictor

Per User Stats

Per Content Stats

User Similarity

Rank Predictor

Content Similarity

A/B Test Data

Predictor Runtime

Online User Information

}  Analyse Raw Logs (Trackers, Web Logs)

}  Extract IP, Page, … }  Detect and remove

robots }  Build Statistics ◦  Number of page view, per

produt ◦  Best Referers ◦  Traffic Analysis ◦  Funnel ◦  SEO Analysis ◦  …

Typical Use Case 1 Web Analytics Processing

}  Extract Query Logs }  Perform query

normalization }  Compute Ngrams }  Compute Search

“Sessions” }  Compute Log-

Likehood Ratio for ngrams across sesions

Typical Use Case 2 Mining Search Logs for Synonyms

}  Compute User – Product Association Matrix

}  Compute different similarities ratio (Ochiai, Cosine, …)

}  Filter out bad predictions

}  For each user, select best recommendable products

Typical Use Case 3 Product Recommender

}  Hadoop and Context }  Pig, Hive, Cascading, … }  How they work }  Comparing the tools

Agenda

}  Yahoo Research in 2006 }  Inspired from Sawzall, a Google Paper

from 2003 }  2007 as an Apache Project

}  Initial motivation ◦  Search Log Analytics: how long is the

average user session ? how many links does a user click ? on before leaving a website ? how do click patterns vary in the course of a day/week/month ? …

Pig History

words = LOAD '/training/hadoop-wordcount/output‘ USING PigStorage(‘\t’)

AS (word:chararray, count:int); sorted_words = ORDER words BY count DESC; first_words = LIMIT sorted_words 10; DUMP first_words;

}  Developed by Facebook in January 2007 }  Open source in August 2008 }  Initial Motivation ◦  Provide a SQL like abstraction to perform

statistics on status updates

Hive History

create external table wordcounts ( word string, count int ) row format delimited fields terminated by '\t' location '/training/hadoop-wordcount/output'; select * from wordcounts order by count desc limit 10; select SUM(count) from wordcounts where word like ‘th%’;

}  Authored by Chris Wensel 2008 }  Associated Projects ◦  Cascalog : Cascading in Closure ◦  Scalding : Cascading in Scala (Twitter

in 2012) ◦  Lingual ( to be released soon): SQL

layer on top of cascading

Cascading History

Agenda

MapReduce Simplicity is a complexity

4/14/13 Dataiku - Innovation Services 14

Pig & HiveMapping to Mapreduce jobs

* VAT excluded

events = LOAD ‘/events’ USING PigStorage(‘\t’) AS (type:chararray, user:chararray, price:int, timestamp:int);

events_filtered = FILTER events BY type;

by_user = GROUP events_filtered BY user;

price_by_user = FOREACH by_user GENERATE type, SUM(price) AS total_price, MAX(timestamp) as max_ts;

high_pbu = FILTER price_by_user BY total_price > 1000;

Job 1 : Mapper Job 1 : Reducer1 LOAD FILTER GROUP FOREACH FILTER Shuffle and

sort by user

Pig & HiveMapping to Mapreduce jobs

events = LOAD ‘/events’ USING PigStorage(‘\t’) AS (type:chararray, user:chararray, price:int, timestamp:int);

events_filtered = FILTER events BY type;

by_user = GROUP events_filtered BY user;

price_by_user = FOREACH by_user GENERATE type, SUM(price) AS total_price, MAX(timestamp) as max_ts;

high_pbu = FILTER price_by_user BY total_price > 1000;

recent_high = ORDER high_pbu BY max_ts DESC;

STORE recent_high INTO ‘/output’;

Job 1: Mapper Job 1 :Reducer LOAD FILTER GROUP FOREACH FILTER Shuffle and

sort by user

Job 2: Mapper Job 2: Reducer LOAD

(from tmp) STORE Shuffle and sort by max_ts

Pig How does it work

Data Execution Plan compiled into 10 map reduce jobs executed in parallel (or not)

84 TResolution = LOAD '$PREFIX/dwh_dim_external_tracking_resolution/dt=$DAY' USING PigStorage('\u0001'); 85 TResolution = FOREACH TResolution GENERATE $0 AS SKResolutionId, $1 as ResolutionId; 86 87 88 TSiteMap = LOAD '$PREFIX/dwh_dim_sitemapnode/dt=$DAY' USING PigStorage('\u0001'); 89 TSiteMap = FOREACH TSiteMap GENERATE $0 AS SKSimteMapNodeId, $2 as SiteMapNodeId; 90 91 92 TCustomer = LOAD '$PREFIX/customer_relation/dt=$DAY' USING PigStorage('\u0001') 93 as (SKCustomerId:chararray, 94 CustomerId:chararray); 95 96 F1 = FOREACH F1 GENERATE *, (date_time IS NOT NULL ? CustomFormatToISO(date_time, 'yyyy-MM-dd HH:mm:ss' 97 98 F2 = FOREACH F1 GENERATE *, 99 CONCAT(CONCAT(CONCAT(CONCAT(visid_high,'-'), visid_low), '-'), visit_num) as VisitId, 100 (referrer matches '.*cdiscount.com.*' OR referrer matches 'cdscdn.com' ? NULL :referrer ) as Referrer, 101 (iso IS NOT NULL ? ISODaysBetween(iso, '1899-12-31T00:00:00') : NULL) 102 AS SkDateId, 103 (iso IS NOT NULL ? ISOSecondsBetween(iso, ISOToDay(iso)) : NULL) 104 AS SkTimeId, 105 ((event_list is not null and event_list matches '.*\\b202\\b.*') ? 'Y' : 'N') as is_202,

106 ((event_list is not null and event_list matches '.*\\b10\\b.*') ? 'Y' : 'N') as is_10, 107 ((event_list is not null and event_list matches '.*\\b12\\b.*') ? 'Y' : 'N') as is_12, 108 ((event_list is not null and event_list matches '.*\\b13\\b.*') ? 'Y' : 'N') as is_13, 109 ((event_list is not null and event_list matches '.*\\b14\\b.*') ? 'Y' : 'N') as is_14, 110 ((event_list is not null and event_list matches '.*\\b11\\b.*') ? 'Y' : 'N') as is_11, 111 ((event_list is not null and event_list matches '.*\\b1\\b.*') ? 'Y' : 'N') as is_1, 112 REGEX_EXTRACT(pagename, 'F-(.*)\\:.*', 1) AS ProductReferenceId, 113 NULL AS OriginFile; 114 115 SET DEFAULT_PARALLEL 24; 116 117 F3 = JOIN F2 BY post_search_engine LEFT, TSearchEngine BY SearchEngineId USING 'replicated' PARALLEL 20 ; 118 F3 = FOREACH F3 GENERATE *, (SKSearchEngineId IS NULL ? '-1' : SKSearchEngineId) as SKSearchEngineId; 119 --F3 = FOREACH F2 GENERATE *, NULL AS SKSearchEngineId, NULL AS SearchEngineId; 120 121 F4 = JOIN F3 BY browser LEFT, TBrowser BY BrowserId USING 'replicated' PARALLEL 20; 122 F4 = FOREACH F4 GENERATE *, (SKBrowserId IS NULL ? '-1' : SKBrowserId) as SKBrowserId; 123 124 --F4 = FOREACH F3 GENERATE *, NULL AS SKBrowserId, NULL AS BrowserId; 125 126 127 F5 = JOIN F4 BY os LEFT, TOperatingSystem BY OperatingSystemId USING 'replicated' PARALLEL 20; 128 F5 = FOREACH F5 GENERATE *, (SKOperatingSystemId IS NULL ? '-1' : SKOperatingSystemId) as SKOperatingSystemId; 129 130 --F5 = FOREACH F4 GENERATE *, NULL AS SKOperatingSystemId, NULL AS OperatingSystemId; 131 132

133 F6 = JOIN F5 BY resolution LEFT, TResolution BY ResolutionId USING 'replicated' PARALLEL 20; 134 F6 = FOREACH F6 GENERATE *, (SKResolutionId IS NULL ? '-1' : SKResolutionId) as SKResolutionId; 135 136 --F6 = FOREACH F5 GENERATE *, NULL AS SKResolutionId, NULL AS ResolutionId; 137 138 F7 = JOIN F6 BY post_evar4 LEFT, TSiteMap BY SiteMapNodeId USING 'replicated' PARALLEL 20; 139 F7 = FOREACH F7 GENERATE *, (SKSimteMapNodeId IS NULL ? '-1' : SKSimteMapNodeId) as SKSimteMapNodeId; 140 141 --F7 = FOREACH F6 GENERATE *, NULL AS SKSimteMapNodeId, NULL AS SiteMapNodeId; 142 143 144 SPLIT F7 INTO WITHOUT_CUSTOMER IF post_evar30 IS NULL, WITH_CUSTOMER IF post_evar30 IS NOT NULL; 145 146 F8 = JOIN WITH_CUSTOMER BY post_evar30 LEFT, TCustomer BY CustomerId USING 'skewed' PARALLEL 20; 147 WITHOUT_CUSTOMER = FOREACH WITHOUT_CUSTOMER GENERATE *, NULL as SKCustomerId, NULL as CustomerId; 148 149 --F8_UNION = FOREACH F7 GENERATE *, NULL as SKCustomerId, NULL as CustomerId; 150 F8_UNION = UNION F8, WITHOUT_CUSTOMER; 151 --DESCRIBE F8; 152 --DESCRIBE WITHOUT_CUSTOMER; 153 --DESCRIBE F8_UNION; 154 155 F9 = FOREACH F8_UNION GENERATE 156 visid_high, 157 visid_low, 158 VisitId,

159 post_evar30, 160 SKCustomerId, 161 visit_num, 162 SkDateId, 163 SkTimeId, 164 post_evar16, 165 post_evar52, 166 visit_page_num, 167 is_202, 168 is_10, 169 is_12,

CascadingFrom Code To Jobs

Reducer 2 Mappers output

Reducer 1

Hive JoinsHow to join with MapReduce ?

tbl_idx uid name

1 1 Dupont

1 2 Durand

tbl_idx uid type

2 1 Type1

2 1 Type2

2 2 Type1

Shuffle by uid Sort by (uid, tbl_idx)

Uid Tbl_idx Name Type

1 1 Dupont

1 2 Type1

1 2 Type2

Uid Tbl_idx Name Type

2 1 Durand

2 2 Type1

Uid Name Type

1 Dupont Type1

1 Dupont Type2

Uid Name Type

2 Durand Type1

Agenda

}  Philosophy ◦  Procedural Vs Declarative ◦  Data Model and Schema

}  Productivity ◦  Headachability ◦  Checkpointing ◦  Testing and environment

}  Integration ◦  Partitioning ◦  Formats Integration ◦  External Code Integration

}  Performance and optimization

Comparing without Comparable

}  Transformation as a sequence of operations

}  Transformation as a set of formulas

Procedural Vs Declarative

insert into ValuableClicksPerDMA select dma, count(*) from geoinfo join (

select name, ipaddr from users join clicks on (users.name = clicks.user)

where value > 0; ) using ipaddr

group by dma;

Users = load 'users' as (name, age, ipaddr); Clicks = load 'clicks' as (user, url, value); ValuableClicks = filter Clicks by value > 0; UserClicks = join Users by name, ValuableClicks by user; Geoinfo = load 'geoinfo' as (ipaddr, dma); UserGeo = join UserClicks by ipaddr, Geoinfo by ipaddr; ByDMA = group UserGeo by dma; ValuableClicksPerDMA = foreach ByDMA generate group, COUNT(UserGeo); store ValuableClicksPerDMA into 'ValuableClicksPerDMA';

}  All three Extend basic data model with extended data types ◦  array-like [ event1, event2, event3] ◦  map-like { type1:value1, type2:value2, …}

}  Different approach ◦  Resilient Schema ◦  Static Typing ◦  No Static Typing

Data type and Model Rationale

HiveData Type and Schema

4/14/13 24

Simple type Details TINYINT, SMALLINT, INT, BIGINT 1, 2, 4 and 8 bytes FLOAT, DOUBLE 4 and 8 bytes BOOLEAN

STRING Arbitrary-length, replaces VARCHAR TIMESTAMP

Complex type Details ARRAY Array of typed items (0-indexed) MAP Associative map STRUCT Complex class-like objects

Dataiku Training – Hadoop for Data Science

CREATE TABLE visit ( user_name STRING, user_id INT, user_details STRUCT<age:INT, zipcode:INT>

rel = LOAD '/folder/path/' USING PigStorage(‘\t’) AS (col:type, col:type, col:type);

Data types and Schema Pig

4/14/13 25

Simple type Details int, long, float, double

32 and 64 bits, signed

chararray A string

bytearray An array of … bytes boolean A boolean

Complex type Details tuple a tuple is an ordered fieldname:value map bag a bag is a set of tuples

}  Support for Any Java Types, provided they can be serialized in Hadoop

}  No support for Typing

Data Type and Schema Cascading

Simple type Details Int, Long, Float, Double

32 and 64 bits, signed

String A string

byte[] An array of … bytes Boolean A boolean

Complex type Details Object Object must be « Hadoop serializable »

Style Summary

Style Typing Data Model Metadata store

Pig Procedural Static + Dynamic

scalar + tuple+ bag

(fully recursive)

No (HCatalog)

Hive Declarative Static + Dynamic,

enforced at execution

scalar+ list + map

Integrated

Cascading Procedural Weak scalar+ java objects

}  Productivity ◦  Headachability ◦  Checkpointing ◦  Testing, error management and environment

}  Integration ◦  Partitioning ◦  Formats Integration ◦  External Code Integration

}  Does debugging the tool lead to bad headaches ?

Headachility Motivation

}  Out Of Memory Error (Reducer)

}  Exception in Building / Extended Functions (handling of null)

}  Null vs “”

}  Nested Foreach and scoping

}  Date Management (pig 0.10)

}  Field implicit ordering

HeadachesPig

A Pig Error

}  Out of Memory Errors in Reducers

}  Few Debugging Options

}  Null / “”

}  No builtin “first”

HeadachesHive

}  Weak Typing Errors (comparing Int and String … )

}  Illegal Operation Sequence (Group after group …)

}  Field Implicit Ordering

HeadachesCascading

}  How to perform unit tests ? }  How to have different versions of the same script

(parameter) ?

TestingMotivation

}  System Variables }  Comment to test }  No Meta Programming }  pig –x local to execute on local files

TestingPig

}  Junit Tests are possible }  Ability to use code to actually comment out some

variables

Testing / Environment Cascading

}  Lots of iteration while developing on Hadoop }  Sometime jobs fail }  Sometimes need to restart from the start …

Checkpointing Motivation

Page User Correlation Output Filtering Parse Logs Per Page Stats

FIX and relaunch

}  STORE Command to manually store files

PigManual Checkpointing

// COMMENT Beginning of script and relaunch

}  Ability to re-run a flow automatically from the last saved checkpoint

Cascading Automated Checkpointing

addCheckpoint(…)

}  Check each file intermediate timestamp }  Execute only if more recent

Cascading Topological Scheduler

Productivity Summary

Headaches Checkpointing/Replay

Testing / Metaprogrammation

Pig Lots Manual Save Difficult

Hive Few, but without

debugging options

None (That’s SQL) None (That’s SQL)

Cascading Weak Typing Complexity

Checkpointing Partial Updates

Possible

}  Integration ◦  Formats Integration ◦  Partitioning ◦  External Code Integration

}  Ability to integrate different file formats ◦  Text Delimited ◦  Sequence File (Binary Hadoop format) ◦  Avro, Thrift ..

}  Ability to integrate with external data sources or sink ( MongoDB, ElasticSearch, Database. …)

Formats IntegrationMotivation

Format Size on Disk (GB) HIVE Processing time (24 cores)

Text File, uncompressed 18.7 1m32s

1 Text File, Gzipped 3.89 6m23s (no parallelization)

JSON compressed 7.89 2m42s

multiple text file gzipped 4.02 43s

Sequence File, Block, Gzip 5.32 1m18s

Text File, LZO Indexed 7.03 1m22s

Format impact on size and performance

}  Hive: Serde (Serialize-Deserializer) }  Pig : Storage }  Cascading: Tap

Format Integration

}  No support for “UPDATE” patterns, any increment is performed by adding or deleting a partition

}  Common partition schemas on Hadoop ◦  By Date /apache_logs/dt=2013-01-23 ◦  By Data center /apache_logs/dc=redbus01/… ◦  By Country ◦  … ◦  Or any combination of the above

PartitionsMotivation

Hive PartitioningPartitioned tables

4/14/13 46

CREATE TABLE event ( user_id INT, type STRING, message STRING)

PARTITIONED BY (day STRING, server_id STRING);

Disk structure /hive/event/day=2013-01-27/server_id=s1/file0 /hive/event/day=2013-01-27/server_id=s1/file1 /hive/event/day=2013-01-27/server_id=s2/file0 /hive/event/day=2013-01-27/server_id=s2/file1 … /hive/event/day=2013-01-28/server_id=s2/file0 /hive/event/day=2013-01-28/server_id=s2/file1

INSERT OVERWRITE TABLE event PARTITION(ds='2013-‐01-‐27', server_id=‘s1’) SELECT * FROM event_tmp;

}  No Direct support for partition }  Support for “Glob” Tap, to build read from files

using patterns

}  è You can code your own custom or virtual

partition schemes

Cascading Partition

External Code IntegrationSimple UDF

Pig Hive

Cascading

Hive Complex UDF (Aggregators)

Cascading Direct Code Evaluation

IntegrationSummary

Partition/Incremental Updates

External Code Format Integration

Pig No Direct Support

Simple Doable and rich community

Hive Fully integrated, SQL Like

Very simple, but complex dev setup

Doable and existing

community Cascading With Coding Complex UDFS

but regular, and Java Expression

embeddable

Doable and growing

commuinty

}  Integration ◦  Formats Integration ◦  Partitioning ◦  External Code Integration

}  Several Common Map Reduce Optimization Patterns ◦  Combiners ◦  MapJoin ◦  Job Fusion ◦  Job Parallelism ◦  Reducer Parallelism

}  Different support per framework ◦  Fully Automatic ◦  Pragma / Directives / Options ◦  Coding style / Code to write

Optimization

SELECT date, COUNT(*) FROM product GROUP BY date

CombinerPerform Partial Aggregate at Mapper Stage

Map Reduce 2012-‐02-‐14 4354

2012-‐02-‐15 21we2

2012-‐02-‐14 qa334

2012-‐02-‐15 23aq2

2012-‐02-‐14 20

2012-‐02-‐15 35

2012-‐02-‐16 1

2012-‐02-‐14 4354

2012-‐02-‐15 21we2

2012-‐02-‐14 qa334

2012-‐02-‐15 23aq2

SELECT date, COUNT(*) FROM product GROUP BY date

CombinerPerform Partial Aggregate at Mapper Stage

Map Reduce 2012-‐02-‐14 4354

2012-‐02-‐15 21we2

2012-‐02-‐14 qa334

2012-‐02-‐15 23aq2

2012-‐02-‐14 12 2012-‐02-‐15 23 2012-‐02-‐16 1

2012-‐02-‐14 8 2012-‐02-‐15 12

2012-‐02-‐14 20

2012-‐02-‐15 35

2012-‐02-‐16 1

Reduced network bandwith. Better parallelism

Join OptimizationMap Join

set hive.auto.convert.join = true; Hive

Cascading

( no aggregation support after HashJoin)

}  Critical for performance

}  Estimated per the size of input file ◦  Hive

�  divide size per hive.exec.reducers.bytes.per.reducer (default 1GB) ◦  Pig

�  divide size pig.exec.reducers.bytes.per.reducer (default 1GB)

Number of Reducers

Combiner Optimization

Join Optimization

Number of reducers optimization

Pig Automatic Option Estimate or DIY Cascading DIY HashJoin DIY Hive Partial

DIY Automatic (Map Join)

Estimate or DIY

Performance & Optimization Summary

}  Hadoop and Context (->0:03) }  Pig, Hive, Cascading, … (->0:06) }  How they work (->0:09) }  Comparing the tools (->0:25) }  Wrap’up and question (->0:30)

Agenda

}  Want to keep close to SQL ? ◦  Hive

}  Want to write large flows ? ◦  Pig

}  Want to integrate in large scale programming projects ◦  Cascading (cascalog / scalding)

Dataiku pig - hive - cascading

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