11. 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
11
12. Geneza
• za dużo danych
• pady serwerów
• wolne relacyjne bazy danych
12
19. ● 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
19
20. HDFS - uprawnienia
• prawie POSIX
• Users, Groups
• chmod, chgrp, chown
• ACL
• getfacl, setfacl
• można wyłączyć kontrolę uprawnień
• dodatkowo:
• Apache Knox
• Apache Ranger
24. Mapper
#!/usr/bin/env python
import sys
for line in sys.stdin:
words = line.strip().split()
for word in words:
print '%st%s' % (word, 1)
line = “Ala ma kota”
Ala 1
ma 1
kota 1
24
25. 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
25
27. 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(...){...}
}
27
28. Mapper<KEYIN,VALUEIN,KEY
OUT,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
29. Reducer<KEYIN,VALUEIN,KEY
OUT,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
30. Main
public 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
31. Co dalej?
• Map Reduce w Javie
• Testowanie MRUnit
• Joins
• Avro
• Custom Key, Value
• Złączanie wielu zadań
• Custom Input, Output
31
35. Czy warto?
Top 5 stron odwiedzanych przez
użytkowników mających 18 lat
36. 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");
}
}
}
37. 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++;
}
}
}
38. 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();
}
}
39. 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’;
46. 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
46
48. 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(';')
48
50. 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;
50
51. 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;
51
52. 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
52
53. Kolejne kroki - operacje na
danych
A = LOAD 'student' USING PigStorage() AS (name:chararray, age:int, semestre:int,
scholarship:float);
E = GROUP A by age
53
54. 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)
54
61. Unikalne cechy Hive
Znaczne przyspieszenie analizy - nie potrzeba pisać Map Reduce
Optymalizacja, wykonywanie części operacji w pamięci zamiast MR
61
64. Hive CLI
Tryb 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
64
65. Typy danych
INT, TINYINT, SMALLINT, BIGINT
BOOLEAN
DECIMAL
FLOAT, DOUBLE
STRING
BINARY
TIMESTAMP
ARRAY, MAP, STRUCT, UNION
DATE
CHAR
VARCHAR
65
66. Składnia zapytań
SELECT, INSERT, UPDATE
GROUP BY
UNION
LEFT, RIGHT, FULL INNER, FULL OUTER JOIN
OVER, RANK
(NOT) IN, HAVING
(NOT) EXISTS
66
67. 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
67
68. 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;
68
69. 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;
69
70. 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;
70
71. Inne formaty plików? SerDe
CREATE TABLE table (
foo STRING, bar STRING)
STORED AS TEXTFILE; ← lub SEQUENCEFILE, ORC, AVRO lub PARQUET
71
72. Zalety, wady, porównanie
Hive Pig
deklaratywny proceduralny
tabele tymczasowe pipeline
polegamy na optymalizatorze
bardziej ingerujemy w
implementacje
UDF, Transform UDF, streaming
sterowniki sql data pipeline splits
72
79. 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 :)
80. Szkolenie dedykowane dla Ciebie
Interesuje Cię tematyka warsztatu?
Zapoznaj się z programami szkoleń:
Projektowanie rozwiązań Big Data z wykorzystaniem Apache
Hadoop & Family
Analiza danych tekstowych i języka naturalnego
Przetwarzanie Big Data z użyciem Apache Spark
Podstawy uczenia maszynowego w języku Python
