HBaseCon 2014-Just the Basics

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HBase: Just the Basics
Jesse Anderson – Curriculum Developer and Instructor
v2

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What Is HBase?
©2014 Cloudera, Inc. All rights reserved.2
• NoSQL datastore built on top of HDFS (Hadoop)
• An Apache Top Level Project
• Handles the various manifestations of Big Data
• Based on Google’s BigTable paper

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Why Use HBase?
• Storing large amounts of data (TB/PB)
• High throughput for a large number of requests
• Storing unstructured or variable column data
• Big Data with random read and writes

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When to Consider Not Using HBase?
• Only use with Big Data problems
• Read straight through files
• Write all at once or append new files
• Not random reads or writes
• Access patterns of the data are ill-defined

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HBase Architecture
How it works

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Meet the Daemons
• HBase Master
• RegionServer
• ZooKeeper
• HDFS
• NameNode/Standby NameNode
• DataNode

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Daemon Locations
NameNode
DataNodeDataNode
Standby
NameNode
DataNode
RegionServer
Master
RegionServerRegionServer
ZooKeeper ZooKeeper ZooKeeper
Master Master
DataNodeDataNode DataNode
RegionServerRegionServerRegionServer
Master Nodes
Slave Nodes

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Tables and Column Families
Column Family “contactinfo” Column Family “profilephoto”
Tables are broken into groupings called Column Families.
Group data frequently
accessed together and
compress it Group photos with different settings

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Rows and Columns
Row key Column Family “contactinfo” Column Family “profilephoto”
adupont fname: Andre lname: Dupont
jsmith fname: John lname: Smith image: <smith.jpg>
mrossi fname: Mario lname: Rossi image: <mario.jpg>
Row keys identify a row
No storage penalty for unused columns
Each Column Family can have many columns

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Regions
Row key Column Family “contactinfo”
adupont fname: Andre lname: Dupont
jsmith fname: John lname: Smith
A table is broken into regions
NameNode
DataNodeDataNode
Standby
NameNode
DataNode
RegionServer
Master
Master Master
Row key Column Family “contactinfo”
mrossi fname: Mario lname: Rossi
zstevens fname: Zack lname: Stevens
Regions are served by
RegionServers

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Client
Write Path
NameNode
DataNodeDataNode
Standby
NameNode
DataNode
RegionServer
Master
Master Master
1. Which
RegionServer is
serving the Region?
2. Write to
RegionServer

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Client
Read Path
NameNode
DataNodeDataNode
Standby
NameNode
DataNode
RegionServer
Master
Master Master
1. Which
RegionServer is
serving the Region?
2. Read from
RegionServer

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HBase API
How to access the data

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No SQL Means No SQL
• Data is not accessed over SQL
• You must:
• Create your own connections
• Keep track of the type of data in a column
• Give each row a key
• Access a row by its key

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Types of Access
• Gets
• Gets a row’s data based on the row key
• Puts
• Upserts a row with data based on the row key
• Scans
• Finds all matching rows based on the row key
• Scan logic can be increased by using filters

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Gets
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Get g = new Get(ROW_KEY_BYTES);
Result r= table.get(g);
byte[] byteArray =
r.getValue(COLFAM_BYTS,COLDESC_BYTS);
String columnValue =
Bytes.toString(byteArray);

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Puts
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Put p = new Put(ROW_KEY_BYTES);
p.add(COLFAM_BYTES, COLDESC_BYTES,
Bytes.toBytes("value"));
table.put(p);

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HBase Schema Design
How to design

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No SQL Means No SQL
• Designing schemas for HBase requires an in-depth
knowledge
• Schema Design is ‘data-centric’ not ‘relationship-
centric’
• You design around how data is accessed
• Row keys are engineered

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Treating HBase like a traditional RDBMS will lead
to abject failure!
Captain Picard

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Row Keys
• A row key is more than the glue between two tables
• Engineering time is spent just on constructing a row
key
• Contents of a row key vary by access pattern
• Often made up of several pieces of data:
<group_id><email>

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Schema Design
• Schema design does not start in an ERD
• Access pattern must be known and ascertained
• Denormalize to improve performance
• Fewer, bigger tables

HBaseCon 2014-Just the Basics

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