1 analyzing twitter data with hadoop gwen shapira, software engineer @gwenshap ©2012 cloudera, inc

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Analyzing Twitter Data with HadoopGwen Shapira, Software Engineer@Gwenshap

©2012 Cloudera, Inc.

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©2014 Cloudera, Inc. All rights reserved.

I have15 years of experience in

moving data around

©2014 Cloudera, Inc. All rights reserved.

• Oracle ACE Director• Member of Oak Table• Blogger• Presenter – Hotsos, IOUG, OOW, OSCON• NoCOUG board• Contributor to Apache Oozie, Sqoop, Kafka• Author – Hadoop Application Architectures

In my spare time…

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Analyzing Twitter Data with Hadoop

BUILDING AN HADOOP APPLICATION


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Hive Level Architecture


Data Source HDFSFlume

Hive +Oozie

Impala / Oracle

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AN EXAMPLE USE CASE


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Analyzing Twitter

• Social media popular with marketing teams• Twitter is an effective tool for promotion• Which twitter user gets the most retweets?• Who is influential in our industry?• Which topics are trending?


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HOW DO WE ANSWER THESE QUESTIONS?


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Techniques

• Bring Data with Flume• Complex data

• Deeply nested• Variable schema

• Clean, Standardize, Partition, etc• SQL

• Filtering• Aggregation• Sorting

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FLUME

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Flume Agent design

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In our case…

• Twitter source• Pulls JSON format files from twitter

• Memory Channel• HDFS Sink – directory per hour

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What is JSON?


{ "retweeted_status": { "contributors": null, "text": "#Crowdsourcing – drivers already generate traffic data for your smartphone to suggest alternative routes when a road is clogged. #bigdata", "retweeted": false, "entities": { "hashtags": [ { "text": "Crowdsourcing", "indices": [0, 14] }, { "text": "bigdata", "indices": [129,137] } ], "user_mentions": [] } }}

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But Wait! There’s More!

• Many sources – directory, files, log4j, net, JMS• Interceptors – process data in flight• Selectors – choose which sink• Many channels – Memory, file• Many sinks – HDFS, Hbase, Solr

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High Level Pipeline Architecture

Web App Flume Avro Client








Flume Agent

Flume Agent

Flume Agent

Flume Agent

HDFS

SparkStreaming HBase

Report App

Fan-in Pattern

Multi Agents for Failover and rolling restarts

SparkStreaming data is sub set of whole events

ML Map/Reduce Jobs

Batch Report Updates

Pull Near Real Time Results

Query With Hbase API Or Impala

Client providing, multi-threading, compression, encryption, and batching

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TwitterAgent.sources = TwitterTwitterAgent.channels = MemChannelTwitterAgent.sinks = HDFS

TwitterAgent.sources.Twitter.type = com.cloudera.flume.source.TwitterSourceTwitterAgent.sources.Twitter.channels = MemChannelTwitterAgent.sources.Twitter.consumerKey = TwitterAgent.sources.Twitter.consumerSecret = TwitterAgent.sources.Twitter.accessToken = TwitterAgent.sources.Twitter.accessTokenSecret = TwitterAgent.sources.Twitter.keywords = hadoop, big data, flume, sqoop, oracle, oow

TwitterAgent.sinks.HDFS.channel = MemChannelTwitterAgent.sinks.HDFS.type = hdfsTwitterAgent.sinks.HDFS.hdfs.path = hdfs://quickstart :8020/user/flume/tweets/%Y/%m/%d/%H/TwitterAgent.sinks.HDFS.serializer = text

TwitterAgent.channels.MemChannel.type = memory

Configuration

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FLUME DEMO


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HIVE


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What is Hive?

• Created at Facebook• HiveQL

• SQL like interface• Hive interpreter

converts HiveQL to MapReduce code

• Returns results to the client


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Hive Details

• Metastore contains table definitions• Stored in a relational database• Basically a data dictionary

• SerDes parse data • and converts to table/column structure• SerDe:

• CSV, XML, JSON, Avro, Parquet, OCR files• Or write your own (We created one for CopyBook)

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Complex Data


SELECT t.retweet_screen_name, sum(retweets) AS total_retweets, count(*) AS tweet_countFROM (SELECT retweeted_status.user.screen_name AS retweet_screen_name, retweeted_status.text, max(retweeted_status.retweet_count) AS retweets FROM tweets GROUP BY

retweeted_status.user.screen_name, retweeted_status.text) tGROUP BY t.retweet_screen_nameORDER BY total_retweets DESCLIMIT 10;

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HIVE DEMO


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IT’S A TRAP


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Not a Database


RDBMS Hive Impala

LanguageGenerally >= SQL-92

Subset of SQL-92 plus Hive specific extensions

Subset of SQL-92

Update Capabilities

INSERT, UPDATE, DELETE

Bulk INSERT, UPDATE, DELETE

Insert, truncate

Transactions Yes Yes No

Latency Sub-second Minutes Sub-second

Indexes Yes Yes No

Data size Few Terabytes Petabytes Lots of Terabytes

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DATA FORMATS

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I don’t like our data

• Lots of small files• JSON – requires parsing• Can’t compress• Sensitive to changes

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I’d rather use Avro

• Few large files containing records• Schema in file• Schema evolution• Can compress• Well supported in Hadoop• Clients in other languages

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Lets convert

• Create table AVRO_TWEETS• Insert into Avro_tweets

select …. From tweets

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IMPALA ASIDE


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Cloudera ImpalaReal-Time Query for Data Stored in Hadoop.

FAMILIAR Supports Hive SQL

FAST 4-30X faster than Hive over MapReduce

Uses existing drivers, integrates with existing metastore, works with leading BI toolsINTEGRATED

100% OPEN SOURCE Flexible, cost-effective, no lock-in

EASY TO USE Deploy & operate withCloudera Enterprise RTQ

FLEXIBLE Supports multiple storage engines & file formats


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Benefits of Cloudera ImpalaReal-Time Query for Data Stored in Hadoop

SPEED TO INSIGHT

COST SAVINGS

FULL FIDELITY ANALYSIS

DISCOVERABILITY

• Real-time queries run directly on source data• No ETL delays• No jumping between data silos

• No double storage with EDW/RDBMS• Unlock analysis on more data• No need to create and maintain complex ETL between systems• No need to preplan schemas

• All data available for interactive queries• No loss of fidelity from fixed data schemas

• Single metadata store from origination through analysis• No need to hunt through multiple data silos


Cloudera Impala Details

35 ©2012 Cloudera, Inc.

HDFS DN

Query Exec Engine

Query Coordinator

Query Planner

HBase

ODBC

SQL App

HDFS DN

Query Exec Engine

Query Coordinator

Query Planner

HBaseHDFS DN

Query Exec Engine

Query Coordinator

Query Planner

HBase

Fully MPP Distributed

Local Direct Reads

State Store

HDFS NNHive Metastore YARN

Common Hive SQL and interface

Unified metadata and scheduler

Low-latency scheduler and cache(low-impact failures)

LOAD DATA TO ORACLE

Oracle Connectors for Hadoop

• Oracle Loader for Hadoop

• Oracle SQL Connector for Hadoop

• BigData SQL

Oracle Loader for Hadoop

• Load data from Hadoop into Oracle• Map-Reduce job inside Hadoop• Converts data types, partitions and sorts• Direct path loads• Reduces CPU utilization on database • Supports Avro and compression

Oracle SQL Connector for Hadoop

• Run a Java app• Creates an external table• Runs MapReduce when external table is queries• Can use Hive Metastore for schema• Optimized for parallel queries• Supports Avro and compression

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Big Data SQL

• Also external table• Can also use Hive metastore for schema• But …. NO MapReduce• Instead – an agent will do SMART SCANS

• Bloom filters• Storage indexes• Filters

• Supports any Hadoop data format

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PUTTING IT ALL TOGETHER


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Hive Level Architecture


Data Source HDFSFlume

Hive +Oozie

Impala / Oracle

What next?

• Download Hadoop!• CDH available at www.cloudera.com• Cloudera provides pre-loaded VMs

• https://ccp.cloudera.com/display/SUPPORT/Cloudera+Manager+Free+Edition+Demo+VM

• Clone the source repo• https://github.com/cloudera/cdh-twitter-example

http://www.cloudera.com/

https://ccp.cloudera.com/display/SUPPORT/Cloudera+Manager+Free+Edition+Demo+VM

https://ccp.cloudera.com/display/SUPPORT/Cloudera+Manager+Free+Edition+Demo+VM

https://github.com/cloudera/cdh-twitter-example







1 analyzing twitter data with hadoop gwen shapira, software engineer @gwenshap ©2012 cloudera, inc

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