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HBase: Just the BasicsJesse Anderson – Curriculum Developer and Instructor

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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 ArchitectureHow 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 APIHow 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(Bytes.toBytes(ROW_KEY_BYTES);

p.add(COLFAM_BYTES, COLDESC_BYTES,

Bytes.toBytes("value"));

table.put(p);

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HBase Schema DesignHow 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

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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

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