Download as PDF, PPTX
![A
peek
at
the
Avro
schema
$ hive -e "DESCRIBE EXTENDED events"!
...!
{!
"type" : "record",!
"name" : "Event",!
"namespace" : "com.example",!
"fields" : [!
{ "name" : "id", "type" : "long" },!
{ "name" : "timestamp", "type" : "long" },!
{ "name" : "source", "type" : "string" }!
]!
}!
20](https://image.slidesharecdn.com/buildinghadoopdataapplicationswithkite-140625073911-phpapp02/85/Building-Hadoop-Data-Applications-with-Kite-20-320.jpg)
![Applica;ons
• [Batch]
Analyze
an
archive
of
songs1
• [Interac;ve
SQL]
Ad
hoc
queries
on
recommenda;ons
from
social
media
applica;ons2
• [Search]
Searching
email
traffic
in
near-‐real;me3
• [ML]
Detec;ng
fraudulent
transac;ons
using
clustering4
43
[1]
hMp://blog.cloudera.com/blog/2012/08/process-‐a-‐million-‐songs-‐with-‐apache-‐pig/
[2]
hMp://blog.cloudera.com/blog/2014/01/how-‐wajam-‐answers-‐business-‐ques;ons-‐faster-‐with-‐hadoop/
[3]
hMp://blog.cloudera.com/blog/2013/09/email-‐indexing-‐using-‐cloudera-‐search/
[4]
hMp://blog.cloudera.com/blog/2013/03/cloudera_ml_data_science_tools/](https://image.slidesharecdn.com/buildinghadoopdataapplicationswithkite-140625073911-phpapp02/85/Building-Hadoop-Data-Applications-with-Kite-43-320.jpg)
![Morphlines
Example
46
morphlines
:
[
{
id
:
morphline1
importCommands
:
["com.cloudera.**",
"org.apache.solr.**"]
commands
:
[
{
readLine
{}
}
{
grok
{
dic;onaryFiles
:
[/tmp/grok-‐dic;onaries]
expressions
:
{
message
:
"""<%{POSINT:syslog_pri}>%{SYSLOGTIMESTAMP:syslog_;mestamp}
%
{SYSLOGHOST:syslog_hostname}
%{DATA:syslog_program}(?:[%{POSINT:syslog_pid}])?:
%
{GREEDYDATA:syslog_message}"""
}
}
}
{
loadSolr
{}
}
]
}
]
Example Input
<164>Feb 4 10:46:14 syslog sshd[607]: listening on 0.0.0.0 port 22
Output Record
syslog_pri:164
syslog_timestamp:Feb 4 10:46:14
syslog_hostname:syslog
syslog_program:sshd
syslog_pid:607
syslog_message:listening on 0.0.0.0 port 22.](https://image.slidesharecdn.com/buildinghadoopdataapplicationswithkite-140625073911-phpapp02/85/Building-Hadoop-Data-Applications-with-Kite-46-320.jpg)
Uploaded byhuguk
Building Hadoop Data Applications with Kite
The document discusses the development of Hadoop data applications using the Kite SDK, highlighting its features, components, and example use cases. It outlines the advantages of Kite for simplifying data input into Hadoop with flexible schema models and modular storage options. The presentation also covers various Apache Hadoop tools and their integration with Kite, providing an overview of batch processing, interactive SQL querying, and data management techniques.
More Related Content
Similar to Building Hadoop Data Applications with Kite
Building Hadoop Data Applications with Kite
- 1. 11 Headline Goes Here Speaker Name or Subhead Goes Here Building Hadoop Data Applica;ons with Kite Tom White @tom_e_white Hadoop Users Group UK, London 17 June 2014
- 2. About me • Engineer at Cloudera working on Core Hadoop and Kite • Apache Hadoop CommiMer, PMC Member, Apache Member • Author of “Hadoop: The Defini;ve Guide” 2
- 3. Hadoop 0.1 %cat bigdata.txt | hadoop fs -put - in! % hadoop MyJob in out! % hadoop fs -get out! 3
- 4. Characteris;cs • Batch applica;ons only • Low-‐level coding • File format • Serializa;on • Par;;oning scheme 4
- 5.
- 6. Common Data, Many Tools # tools >> # file formats >> # file systems 6
- 7. Glossary • Apache Avro – cross-‐language data serializa;on library • Apache Parquet (incuba;ng) – column-‐oriented storage format for nested data • Apache Hive – data warehouse (SQL and metastore) • Apache Flume – streaming log capture and delivery system • Apache Oozie – workflow scheduler system • Apache Crunch – Java API for wri;ng data pipelines • Impala – interac;ve SQL on Hadoop 7
- 8. Outline • A Typical Applica;on • Kite SDK • An Example • Advanced Kite 8
- 9. A typical applica;on (zoom 100:1) 9
- 10. A typical applica;on (zoom 10:1) 10
- 11. A typical pipeline (zoom 5:1) 11
- 12.
- 13. Kite Codifies Best Prac;ce as APIs, Tools, Docs and Examples 13
- 14. Kite • A client-‐side library for wri;ng Hadoop Data Applica;ons • First release was in April 2013 as CDK • 0.14.1 last month • Open source, Apache 2 license, kitesdk.org • Modular • Data module (HDFS, Flume, Crunch, Hive, HBase) • Morphlines transforma;on module • Maven plugin 14
- 15.
- 16. Kite Data Module • Dataset – a collec;on of en;;es • DatasetRepository – physical storage loca;on for datasets • DatasetDescriptor – holds dataset metadata (schema, format) • DatasetWriter – write en;;es to a dataset in a stream • DatasetReader – read en;;es from a dataset • hMp://kitesdk.org/docs/current/apidocs/index.html 16
- 17. 1. Define the Event En;ty public class Event {! private long id;! private long timestamp;! private String source;! // getters and setters! }! 17
- 18. 2. Create the Events Dataset DatasetRepository repo = DatasetRepositories.open("repo:hive");! DatasetDescriptor descriptor =! new DatasetDescriptor.Builder()! .schema(Event.class).build();! repo.create("events", descriptor);! 18
- 19. (2. or with the Maven plugin) $ mvn kite:create-dataset ! -Dkite.repositoryUri='repo:hive' ! -Dkite.datasetName=events ! -Dkite.avroSchemaReflectClass=com.example.Event! 19
- 20. A peek at the Avro schema $ hive -e "DESCRIBE EXTENDED events"! ...! {! "type" : "record",! "name" : "Event",! "namespace" : "com.example",! "fields" : [! { "name" : "id", "type" : "long" },! { "name" : "timestamp", "type" : "long" },! { "name" : "source", "type" : "string" }! ]! }! 20
- 21. 3. Write Events Logger logger = Logger.getLogger(...);! Event event = new Event();! event.setId(id);! event.setTimestamp(System.currentTimeMillis());! event.setSource(source);! logger.info(event);! 21
- 22. Log4j configura;on log4j.appender.flume= org.kitesdk.data.flume.Log4jAppender! log4j.appender.flume.Hostname = localhost! log4j.appender.flume.Port = 41415! log4j.appender.flume.DatasetRepositoryUri = repo:hive! log4j.appender.flume.DatasetName = events! 22
- 23. The resul;ng file layout /user! /hive! /warehouse! /events! /FlumeData.1375659013795! /FlumeData.1375659013796! 23 Avro files
- 24. 4. Generate Summaries with Crunch PCollection<Event> events = read(asSource(repo.load("events"), Event.class));! PCollection<Summary> summaries = events! .by(new GetTimeBucket(), // minute of day, source! Avros.pairs(Avros.longs(), Avros.strings()))! .groupByKey()! .parallelDo(new MakeSummary(),! Avros.reflects(Summary.class));! write(summaries, asTarget(repo.load("summaries"))!24
- 25. … and run using Maven $ mvn kite:create-dataset -Dkite.datasetName=summaries ...! <plugin>! <groupId>org.kitesdk</groupId>! <artifactId>kite-maven-plugin</artifactId>! <configuration>! <toolClass>com.example.GenerateSummaries</toolClass>! </configuration>! </plugin>! $ mvn kite:run-tool! 25
- 26. 5. Query with Impala $ impala-shell -q ’DESCRIBE events'! +-----------+--------+-------------------+! | name | type | comment |! +-----------+--------+-------------------+! | id | bigint | from deserializer |! | timestamp | bigint | from deserializer |! | source | string | from deserializer |! +-----------+--------+-------------------+! 26
- 27. … Ad Hoc Queries $ impala-shell -q 'SELECT source, COUNT(1) AS cnt FROM events GROUP BY source'! +--------------------------------------+-----+! | source | cnt |! +--------------------------------------+-----+! | 018dc1b6-e6b0-489e-bce3-115917e00632 | 38 |! | bc80040e-09d1-4ad2-8bd8-82afd1b8431a | 85 |! +--------------------------------------+-----+! Returned 2 row(s) in 0.56s! 27
- 28.
- 29. Unified Storage Interface • Dataset – streaming access, HDFS storage • RandomAccessDataset – random access, HBase storage • Par;;onStrategy defines how to map an en;ty to par;;ons in HDFS or row keys in HBase 29
- 30. Filesystem Par;;ons PartitionStrategyp = new PartitionStrategy.Builder()! .year("timestamp")! .month("timestamp")! .day("timestamp").build();! /user/hive/warehouse/events! /year=2014/month=02/day=08! /FlumeData.1375659013795! /FlumeData.1375659013796! 30
- 31. HBase Keys: Defined in Avro {! "name": "username",! "type": "string",! "mapping": { "type": "key", "value": "0" }! },! {! "name": "favoriteColor",! "type": "string",! "mapping": { "type": "column", "value": "meta:fc" }! }! 31
- 32. Random Access Dataset: Crea;on RandomAccessDatasetRepository repo = DatasetRepositories.openRandomAccess(! "repo:hbase:localhost");! RandomAccessDataset<User> users = repo.load("users");! users.put(new User("bill", "green"));! users.put(new User("alice", "blue"));! 32
- 33. Random Access Dataset: Retrieval Key key = new Key.Builder(users)! .add("username", "bill").build();! User bill = users.get(key);! 33
- 34. Views View<User> view= users.from("username", "bill");! DatasetReader<User> reader = view.newReader();! reader.open();! for (User user : reader) {! System.out.println(user);! }! reader.close();! 34
- 35. Parallel Processing • Goal is for Hadoop processing frameworks to “just work” • Support Formats, Par;;ons, Views • Na;ve Kite components, e.g. DatasetOutputFormat for MR 35 HDFS Dataset HBase Dataset Crunch Yes Yes MapReduce Yes Yes Hive Yes Planned Impala Yes Planned
- 36. Schema Evolu;on publicclass Event {! private long id;! private long timestamp;! private String source;! @Nullable private String ipAddress;! }! $ mvn kite:update-dataset ! -Dkite.datasetName=events ! -Dkite.avroSchemaReflectClass=com.example.Event! 36
- 37. Searchable Datasets • Use Flume Solr Sink (in addi;on to HDFS Sink) • Morphlines library to define fields to index • SolrCloud runs on cluster from indexes in HDFS • Future support in Kite to index selected fields automa;cally 37
- 38.
- 39. Kite makes it easy to get data into Hadoop with a flexible schema model that is storage agnos;c in a format that can be processed with a wide range of Hadoop tools 39
- 40. Genng Started With Kite • Examples at github.com/kite-‐sdk/kite-‐examples • Working with streaming and random-‐access datasets • Logging events to datasets from a webapp • Running a periodic job • Migra;ng data from CSV to a Kite dataset • Conver;ng an Avro dataset to a Parquet dataset • Wri;ng and configuring Morphlines • Using Morphlines to write JSON records to a dataset 40
- 41. Ques;ons? kitesdk.org @tom_e_white tom@cloudera.com 41
- 42.
- 43. Applica;ons • [Batch] Analyze an archive of songs1 • [Interac;ve SQL] Ad hoc queries on recommenda;ons from social media applica;ons2 • [Search] Searching email traffic in near-‐real;me3 • [ML] Detec;ng fraudulent transac;ons using clustering4 43 [1] hMp://blog.cloudera.com/blog/2012/08/process-‐a-‐million-‐songs-‐with-‐apache-‐pig/ [2] hMp://blog.cloudera.com/blog/2014/01/how-‐wajam-‐answers-‐business-‐ques;ons-‐faster-‐with-‐hadoop/ [3] hMp://blog.cloudera.com/blog/2013/09/email-‐indexing-‐using-‐cloudera-‐search/ [4] hMp://blog.cloudera.com/blog/2013/03/cloudera_ml_data_science_tools/
- 44. … or use JDBC Class.forName("org.apache.hive.jdbc.HiveDriver");! Connection connection = DriverManager.getConnection(! "jdbc:hive2://localhost:21050/;auth=noSasl");! Statement statement = connection.createStatement();! ResultSet resultSet = statement.executeQuery(! "SELECT * FROM summaries");! 44
- 45. Apps • App – a packaged Java program that runs on a Hadoop cluster • cdk:package-‐app – create a package on the local filesystem • like an exploded WAR • Oozie format • cdk:deploy-‐app – copy packaged app to HDFS • cdk:run-‐app – execute the app • Workflow app – runs once • Coordinator app – runs other apps (like cron) 45
- 46. Morphlines Example 46 morphlines : [ { id : morphline1 importCommands : ["com.cloudera.**", "org.apache.solr.**"] commands : [ { readLine {} } { grok { dic;onaryFiles : [/tmp/grok-‐dic;onaries] expressions : { message : """<%{POSINT:syslog_pri}>%{SYSLOGTIMESTAMP:syslog_;mestamp} % {SYSLOGHOST:syslog_hostname} %{DATA:syslog_program}(?:[%{POSINT:syslog_pid}])?: % {GREEDYDATA:syslog_message}""" } } } { loadSolr {} } ] } ] Example Input <164>Feb 4 10:46:14 syslog sshd[607]: listening on 0.0.0.0 port 22 Output Record syslog_pri:164 syslog_timestamp:Feb 4 10:46:14 syslog_hostname:syslog syslog_program:sshd syslog_pid:607 syslog_message:listening on 0.0.0.0 port 22.