Pig i Hive: Szybkie wprowadzenie

Radosław Stankiewicz Bartłomiej Tartanus

Agenda

Wstęp -> 2 słowa o Map Reduce -> Pig -> Hive

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Wprowadzenie

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V O LUME5

Variety

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A|123|10$ B|555|20$ Y|333|15$

{ 'typ'='A', 'id'=123, 'kwota'='10$'

}

Velocity

OLAP

Real Time

Batch

Streaming Interactive analytics

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Value

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Klasyfikacja problemu• Baza danych ulic Warszawy, Dane w formacie JSON,

optymalizacja odbioru śmieci jednego z usługodawców.

• Zdarzenia z bazy transakcyjnej i kart kredytowych w celu lepszego wykrywania fraudów

• System wyszukujący dobre oferty samochodów z wielu serwisów - web crawling, parsowanie danych, analiza trendów cen samochodów

• Centralne repozytorium skanów umów, TB danych, codziennie przybywa kilkaset nowych dokumentów

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Geneza

• za dużo danych

• pady serwerów

• wolne relacyjne bazy danych

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Architektura

13 źródło: Hortonworks

Ekosystem Hadoop

14 źródło: Hortonworks

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HDFS - Namenode, Datanode

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● User Commands o dfs o fsck

● Administration Commands o datanode o dfsadmin o namenode

dfs: appendToFile cat chgrp chmod chown copyFromLocal copyToLocal count cp du dus expunge get getfacl getfattr getmerge ls lsr mkdir moveFromLocal moveToLocal mv put rm rmr setfacl setfattr setrep stat tail test text touchz

hdfs dfs -put localfile1 localfile2 /user/tmp/hadoopdir hdfs dfs -getmerge /user/hadoop/output/ localfile

komendy

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Architektura YARN

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Map Reduce Framework

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Map Reduce Framework

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M

M

M

M

R

R

R

R

R

Mapper

#!/usr/bin/env python import sys for line in sys.stdin: words = line.strip().split() for word in words: print '%s\t%s' % (word, 1)

line = “Ala ma kota”

Ala 1 ma 1 kota 1

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Reducer#!/usr/bin/env python import sys current_word = None current_count = 0 word = None for line in sys.stdin: line = line.strip() word, count = line.split('\t', 1) count = int(count) if current_word == word: current_count += count else: if current_word: print '%s,%s' % (current_word, current_count) current_count = count current_word = word if current_word == word: print '%s,%s' % (current_word, current_count)

ala 1 ala 1 bela 1 dela 1

ala,2 bela,1 dela,1

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Uruchomienie streaming

cat input.txt | ./mapper.py | sort | ./reducer.py

bin/yarn jar [..]/hadoop-*streaming*.jar \ -file mapper.py -mapper ./mapper.py -file reducer.py -reducer ./reducer.py \-input /tmp/wordcount/input -output /tmp/wordcount/output

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Map Reduce w Java(input) <k1, v1> -> map -> <k2, v2> -> combine -> <k2, v2> -> reduce -> <k3, v3> (output) 1) Mapper 2) Reducer 3) run public class WordCount extends Configured implements Tool { public static class TokenizerMapper{...} public static class IntSumReducer{...} public int run(...){...}

}

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Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>

public static class TokenizerMapper extends Mapper<LongWritable, Text, Text, IntWritable>{ private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } } public void setup(...) {...} public void cleanup(...) {...} public void run(...) {...} }

value = “Ala ma kota”

Ala,1 ma,1 kota,1

Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT>

public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } public void setup(...) {...} public void cleanup(...) {...} public void run(...) {...} }

kota,(1,1,1,1)

kota,4

Mainpublic int run(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } public static void main(String[] args) throws Exception { int res = ToolRunner.run(new Configuration(), new WordCount(),args); System.exit(res); }

yarn jar wc.jar WordCount /tmp/wordcount/input /tmp/wordcount/output

Wprowadzenie do przetwarzania danych na

przykładzie Pig

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Architektura Pig

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Czy warto?

Top 5 stron odwiedzanych przez użytkowników mających 18 lat

import java.io.IOException; import java.util.ArrayList; import java.util.Iterator; import java.util.List; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.io.Writable; import org.apache.hadoop.io.WritableComparable; import org.apache.hadoop.mapred.FileInputFormat; import org.apache.hadoop.mapred.FileOutputFormat; import org.apache.hadoop.mapred.JobConf; import org.apache.hadoop.mapred.KeyValueTextInputFormat; import org.apache.hadoop.mapred.Mapper; import org.apache.hadoop.mapred.MapReduceBase; import org.apache.hadoop.mapred.OutputCollector; import org.apache.hadoop.mapred.RecordReader; import org.apache.hadoop.mapred.Reducer; import org.apache.hadoop.mapred.Reporter; import org.apache.hadoop.mapred.SequenceFileInputFormat; import org.apache.hadoop.mapred.SequenceFileOutputFormat; import org.apache.hadoop.mapred.TextInputFormat; import org.apache.hadoop.mapred.jobcontrol.Job; import org.apache.hadoop.mapred.jobcontrol.JobControl; import org.apache.hadoop.mapred.lib.IdentityMapper; public class MRExample { public static class LoadPages extends MapReduceBase implements Mapper<LongWritable, Text, Text, Text> { public void map(LongWritable k, Text val, OutputCollector<Text, Text> oc, Reporter reporter) throws IOException { // Pull the key out String line = val.toString(); int firstComma = line.indexOf(','); String key = line.substring(0, firstComma); String value = line.substring(firstComma + 1); Text outKey = new Text(key); // Prepend an index to the value so we know which file // it came from. Text outVal = new Text("1" + value); oc.collect(outKey, outVal); } }

public static class LoadAndFilterUsers extends MapReduceBase implements Mapper<LongWritable, Text, Text, Text> { public void map(LongWritable k, Text val, OutputCollector<Text, Text> oc, Reporter reporter) throws IOException { // Pull the key out String line = val.toString(); int firstComma = line.indexOf(','); String value = line.substring(firstComma + 1); int age = Integer.parseInt(value); if (age < 18 || age > 25) return; String key = line.substring(0, firstComma); Text outKey = new Text(key); // Prepend an index to the value so we know which file // it came from. Text outVal = new Text("2" + value); oc.collect(outKey, outVal); } } public static class Join extends MapReduceBase implements Reducer<Text, Text, Text, Text> { public void reduce(Text key, Iterator<Text> iter, OutputCollector<Text, Text> oc, Reporter reporter) throws IOException { // For each value, figure out which file it's from and store it // accordingly. List<String> first = new ArrayList<String>(); List<String> second = new ArrayList<String>(); while (iter.hasNext()) { Text t = iter.next(); String value = t.toString(); if (value.charAt(0) == '1') first.add(value.substring(1)); else second.add(value.substring(1)); reporter.setStatus("OK"); } // Do the cross product and collect the values for (String s1 : first) { for (String s2 : second) { String outval = key + "," + s1 + "," + s2; oc.collect(null, new Text(outval)); reporter.setStatus("OK"); } } } }

public static class LoadJoined extends MapReduceBase implements Mapper<Text, Text, Text, LongWritable> { public void map( Text k, Text val, OutputCollector<Text, LongWritable> oc, Reporter reporter) throws IOException { // Find the url String line = val.toString(); int firstComma = line.indexOf(','); int secondComma = line.indexOf(',', firstComma); String key = line.substring(firstComma, secondComma); // drop the rest of the record, I don't need it anymore, // just pass a 1 for the combiner/reducer to sum instead. Text outKey = new Text(key); oc.collect(outKey, new LongWritable(1L)); } } public static class ReduceUrls extends MapReduceBase implements Reducer<Text, LongWritable, WritableComparable, Writable> { public void reduce( Text key, Iterator<LongWritable> iter, OutputCollector<WritableComparable, Writable> oc, Reporter reporter) throws IOException { // Add up all the values we see long sum = 0; while (iter.hasNext()) { sum += iter.next().get(); reporter.setStatus("OK"); } oc.collect(key, new LongWritable(sum)); } }

public static class LoadClicks extends MapReduceBase implements Mapper<WritableComparable, Writable, LongWritable, Text> { public void map( WritableComparable key, Writable val, OutputCollector<LongWritable, Text> oc, Reporter reporter) throws IOException { oc.collect((LongWritable)val, (Text)key); } } public static class LimitClicks extends MapReduceBase implements Reducer<LongWritable, Text, LongWritable, Text> { int count = 0; public void reduce( LongWritable key, Iterator<Text> iter, OutputCollector<LongWritable, Text> oc, Reporter reporter) throws IOException { // Only output the first 100 records while (count < 100 && iter.hasNext()) { oc.collect(key, iter.next()); count++; } } }

public static void main(String[] args) throws IOException { JobConf lp = new JobConf(MRExample.class); lp.setJobName("Load Pages"); lp.setInputFormat(TextInputFormat.class); lp.setOutputKeyClass(Text.class); lp.setOutputValueClass(Text.class); lp.setMapperClass(LoadPages.class); FileInputFormat.addInputPath(lp, new Path("/user/gates/pages")); FileOutputFormat.setOutputPath(lp, new Path("/user/gates/tmp/indexed_pages")); lp.setNumReduceTasks(0); Job loadPages = new Job(lp); JobConf lfu = new JobConf(MRExample.class); lfu.setJobName("Load and Filter Users"); lfu.setInputFormat(TextInputFormat.class); lfu.setOutputKeyClass(Text.class); lfu.setOutputValueClass(Text.class); lfu.setMapperClass(LoadAndFilterUsers.class); FileInputFormat.addInputPath(lfu, new Path("/user/gates/users")); FileOutputFormat.setOutputPath(lfu, new Path("/user/gates/tmp/filtered_users")); lfu.setNumReduceTasks(0); Job loadUsers = new Job(lfu); JobConf join = new JobConf(MRExample.class); join.setJobName("Join Users and Pages"); join.setInputFormat(KeyValueTextInputFormat.class); join.setOutputKeyClass(Text.class); join.setOutputValueClass(Text.class); join.setMapperClass(IdentityMapper.class); join.setReducerClass(Join.class); FileInputFormat.addInputPath(join, new Path("/user/gates/tmp/indexed_pages")); FileInputFormat.addInputPath(join, new Path("/user/gates/tmp/filtered_users")); FileOutputFormat.setOutputPath(join, new Path("/user/gates/tmp/joined")); join.setNumReduceTasks(50); Job joinJob = new Job(join); joinJob.addDependingJob(loadPages); joinJob.addDependingJob(loadUsers); JobConf group = new JobConf(MRExample.class); group.setJobName("Group URLs"); group.setInputFormat(KeyValueTextInputFormat.class); group.setOutputKeyClass(Text.class); group.setOutputValueClass(LongWritable.class); group.setOutputFormat(SequenceFileOutputFormat.class); group.setMapperClass(LoadJoined.class); group.setCombinerClass(ReduceUrls.class); group.setReducerClass(ReduceUrls.class); FileInputFormat.addInputPath(group, new Path("/user/gates/tmp/joined")); FileOutputFormat.setOutputPath(group, new Path("/user/gates/tmp/grouped")); group.setNumReduceTasks(50); Job groupJob = new Job(group);

groupJob.addDependingJob(joinJob); JobConf top100 = new JobConf(MRExample.class); top100.setJobName("Top 100 sites"); top100.setInputFormat(SequenceFileInputFormat.class); top100.setOutputKeyClass(LongWritable.class); top100.setOutputValueClass(Text.class); top100.setOutputFormat(SequenceFileOutputFormat.class); top100.setMapperClass(LoadClicks.class); top100.setCombinerClass(LimitClicks.class); top100.setReducerClass(LimitClicks.class); FileInputFormat.addInputPath(top100, new Path("/user/gates/tmp/grouped")); FileOutputFormat.setOutputPath(top100, new Path("/user/gates/top100sitesforusers18to25")); top100.setNumReduceTasks(1); Job limit = new Job(top100); limit.addDependingJob(groupJob); JobControl jc = new JobControl("Find top 100 sites for users 18 to 25"); jc.addJob(loadPages); jc.addJob(loadUsers); jc.addJob(joinJob); jc.addJob(groupJob); jc.addJob(limit); jc.run(); } }

Users = load ‘users’ as (name, age); Fltrd = filter Users by age >= 18 and age <= 25; Pages = load ‘pages’ as (user, url); Jnd = join Fltrd by name, Pages by user; Grpd = group Jnd by url; Smmd = foreach Grpd generate group, COUNT(Jnd) as clicks; Srtd = order Smmd by clicks desc; Top5 = limit Srtd 5; store Top5 into ‘top5sites’;

Architektura Pig

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Tryb Pracy

Interaktywny lub Wsadowy

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Tryb Pracy

Lokalny lub Rozproszony

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Tryb Pracy

Map Reduce lub Tez

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Typy danych

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int long float double

chararray datetime boolean

bytearray biginteger bigdecimal

Złożone typy

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tuple bag map

Podstawy Pig Latin - wielkość liter

• A = LOAD 'data' USING PigStorage() AS (f1:int, f2:int, f3:int);B = GROUP A BY f1;C = FOREACH B GENERATE COUNT ($0);DUMP C;

• Nazwy zmiennych A, B, and C (tzw. aliasy) są case sensitive.• Wielkość liter jest też istotna dla:

• nazwy pól f1, f2, i f3• nazwy zmiennych A, B, C• nazwy funkcji PigStorage, COUNT

• Z wyjątkiem: LOAD, USING, AS, GROUP, BY, FOREACH, GENERATE, oraz DUMP

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assert, and, any, all, arrange, as, asc, AVG, bag, BinStorage, by, bytearray, BIGINTEGER, BIGDECIMAL, cache, CASE, cat, cd, chararray, cogroup, CONCAT, copyFromLocal, copyToLocal, COUNT, cp, cross, datetime, %declare, %default, define, dense, desc, describe, DIFF, distinct, double, du, dump, e, E, eval, exec, explain, f, F, filter, flatten, float, foreach, full, generate, group, help, if, illustrate, import, inner, input, int, into, is, join, kill, l, L, left, limit, load, long, ls, map, matches, MAX, MIN, mkdir, mv, not, null, onschema, or, order, outer, output, parallel, pig, PigDump, PigStorage, pwd, quit, register, returns, right, rm, rmf, rollup, run, sample, set, ship, SIZE, split, stderr, stdin, stdout, store, stream, SUM, TextLoader, TOKENIZE, through, tuple, union, using, void

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Słowa kluczowe

Pierwsze kroki

data = LOAD 'input' AS (query:CHARARRAY);

A = LOAD 'data' USING PigStorage('\t') AS (f1:int, f2:int, f3:int);

STORE A INTO '/tmp/result' USING PigStorage(';')

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Pierwsze kroki

SAMPLEDESCRIBE

DUMPEXPLAIN

ILLUSTRATE

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Kolejne kroki - operacje na danych

A = LOAD 'student' USING PigStorage() AS (name:chararray, age:int, semestre:int, scholarship:float);

B = FILTER A BY age > 20;

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Kolejne kroki - operacje na danych

A = LOAD 'student' USING PigStorage() AS (name:chararray, age:int, semestre:int, scholarship:float);

B = FILTER A BY age > 20; C = LIMIT B 5;

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Kolejne kroki - operacje na danych

A = LOAD 'student' USING PigStorage() AS (name:chararray, age:int, semestre:int, scholarship:float);

B = FILTER A BY age > 20; C = LIMIT B 5;

D = FOREACH C GENERATE name, scholarship*semestre as funds

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Kolejne kroki - operacje na danych

A = LOAD 'student' USING PigStorage() AS (name:chararray, age:int, semestre:int, scholarship:float);

E = GROUP A by age

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Kolejne kroki - operacje na danych

A = LOAD 'student' USING PigStorage() AS (name:chararray, age:int, semestre:int, scholarship:float);

E = GROUP A by age F = FOREACH E GENERATE group as age, AVG(A.scholarship)

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Wydajność

Tez, Projekcje, Filtrowanie, Join

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Warsztat adres serwera ssh: 52.5.251.228

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https://www.notehub.org/veuk1

Wprowadzenie do analizy danych na przykładzie

Hive

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Architektura

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Unikalne cechy Hive

Zapytania SQL na plikach płaskich, np. CSV

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Unikalne cechy Hive

Znaczne przyspieszenie analizy - nie potrzeba pisać Map Reduce Optymalizacja, wykonywanie części operacji w pamięci zamiast MR

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Unikalne cechy Hive

Nieograniczone formy integracji - MongoDB, Elastic Search, HBase

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Unikalne cechy Hive

Integracja narzędzi BI oraz DWH z Hive poprzez JDBC

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Hive CLITryb Interaktywny

hive Tryb Wsadowy:

hive -e ‘select foo from bar’ hive -f ‘/path/to/my/script.q’ hive -f ‘hdfs://namenode:port/path/to/my/script.q’

więcej opcji: hive --help

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Typy danychINT, TINYINT, SMALLINT, BIGINTBOOLEANDECIMALFLOAT, DOUBLESTRINGBINARYTIMESTAMPARRAY, MAP, STRUCT, UNIONDATECHARVARCHAR

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Składnia zapytańSELECT, INSERT, UPDATE

GROUP BY

UNION

LEFT, RIGHT, FULL INNER, FULL OUTER JOIN

OVER, RANK

(NOT) IN, HAVING

(NOT) EXISTS

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Data Definition Language

• CREATE DATABASE/SCHEMA, TABLE, VIEW, FUNCTION, INDEX • DROP DATABASE/SCHEMA, TABLE, VIEW, INDEX • TRUNCATE TABLE • ALTER DATABASE/SCHEMA, TABLE, VIEW • MSCK REPAIR TABLE (or ALTER TABLE RECOVER PARTITIONS) • SHOW DATABASES/SCHEMAS, TABLES, TBLPROPERTIES, PARTITIONS, FUNCTIONS, INDEX[ES], COLUMNS, CREATE TABLE

• DESCRIBE DATABASE/SCHEMA, table_name, view_name

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Tabele

CREATE TABLE page_view(viewTime INT, userid BIGINT, page_url STRING, referrer_url STRING, ip STRING COMMENT 'IP Address of the User') COMMENT 'This is the page view table' PARTITIONED BY(dt STRING, country STRING) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\001' STORED AS TEXTFILE;

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Pierwsze kroki w Hive

CREATE TABLE tablename1 (foo INT, bar STRING) PARTITIONED BY (ds STRING); LOAD DATA [LOCAL] INPATH 'filepath' [OVERWRITE] INTO TABLE tablename1;

INSERT INTO TABLE tablename1 PARTITION (ds='2014') select_statement1 FROM from_statement;

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Inne formaty plików? SerDe

127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326 "http://www.example.com/

start.html" "Mozilla/4.08 [en] (Win98; I ;Nav)"

CREATE TABLE apachelog (

host STRING, identity STRING, user STRING, time STRING, request STRING, status STRING,

size STRING, referer STRING, agent STRING)

ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe'

WITH SERDEPROPERTIES (

"input.regex" = "([^]*) ([^]*) ([^]*) (-|\\[^\\]*\\]) ([^ \"]*|\"[^\"]*\") (-|[0-9]*) (-|[0-9]*)(?:

([^ \"]*|\".*\") ([^ \"]*|\".*\"))?"

)

STORED AS TEXTFILE;

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Inne formaty plików? SerDe

CREATE TABLE table (

foo STRING, bar STRING)

STORED AS TEXTFILE; ← lub SEQUENCEFILE, ORC, AVRO lub PARQUET

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Zalety, wady, porównanieHive Pig

deklaratywny proceduralny

tabele tymczasowe pipeline

polegamy na optymalizatorze bardziej ingerujemy w implementacje

UDF, Transform UDF, streaming

sterowniki sql data pipeline splits

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Stinger

http://hortonworks.com/labs/stinger/67

Tips & Trickshive.vectorized.execution.enabled=true

ORC

hive.execution.engine=tez

John Lund Stone Getty Images68

Warsztat

źródło:HikingArtist69

https://www.notehub.org/bjnwl

Warsztat 2https://www.notehub.org/daxzs

Now what?72

Chcesz wiedzieć więcej?Szkolenia pozwalają na indywidualną pracę z każdym uczestnikiem

• pracujemy w grupach 4-8 osobowych

• program może być dostosowany do oczekiwań grupy

• rozwiązujemy i odpowiadamy na indywidualne pytania uczestników

• mamy dużo więcej czasu :)

Szkolenie dedykowane dla Ciebie

Jesteś architektem lub team leaderem?

• na przekrojowym szkoleniu 5-dniowym omawiamy i ćwiczymy cały ekosystem Hadoopa

• na szkoleniu dedykowanym dla architektów dyskutujemy o projektowaniu systemów BigData

Jesteś analitykiem?

• na dedykowanym szkoleniu przećwiczysz w szczegółach Pig i Hive i rozwiążesz przykładowe problemy analityczne

Szkolenie dedykowane dla Ciebie

Jesteś programistą?

• szkolenie 3-dniowe pozwala w szczegółach zapoznać się z programowaniem zaawansowanych aspektów MapReduce w Javie i programowaniem w podejściu strumieniowym

Interesuje Cię całość zagadnienia BigData?

• Przetwarzanie Big Data z użyciem Apache Spark

• Bazy danych NoSQL - Cassandra

• Bazy danych NoSQL - MongoDB

źródła

• HikingArtist.com - rysunki

• hortonworks.com - architektura HDP

• apache.org - grafiki Pig, Hive, Hadoop