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HBase: Just the Basics
Jesse Anderson – Curriculum Developer and Instructor
v2
2
What Is HBase?
Š2014 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
3
Why Use HBase?
Š2014 Cloudera, Inc. All rights reserved.3
• 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
4
When to Consider Not Using HBase?
Š2014 Cloudera, Inc. All rights reserved.4
• 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
5
HBase Architecture
How it works
6
Meet the Daemons
Š2014 Cloudera, Inc. All rights reserved.6
• HBase Master
• RegionServer
• ZooKeeper
• HDFS
• NameNode/Standby NameNode
• DataNode
7
Daemon Locations
Š2014 Cloudera, Inc. All rights reserved.7
NameNode
DataNodeDataNode
Standby
NameNode
DataNode
RegionServer
Master
RegionServerRegionServer
ZooKeeper ZooKeeper ZooKeeper
Master Master
DataNodeDataNode DataNode
RegionServerRegionServerRegionServer
Master Nodes
Slave Nodes
8
Tables and Column Families
Š2014 Cloudera, Inc. All rights reserved.8
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
9
Rows and Columns
Š2014 Cloudera, Inc. All rights reserved.9
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
10
Regions
Š2014 Cloudera, Inc. All rights reserved.10
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
RegionServerRegionServer
ZooKeeper ZooKeeper ZooKeeper
Master Master
DataNodeDataNode DataNode
RegionServerRegionServerRegionServer
Row key Column Family “contactinfo”
mrossi fname: Mario lname: Rossi
zstevens fname: Zack lname: Stevens
Regions are served by
RegionServers
11
Client
Write Path
Š2014 Cloudera, Inc. All rights reserved.11
NameNode
DataNodeDataNode
Standby
NameNode
DataNode
RegionServer
Master
RegionServerRegionServer
ZooKeeper ZooKeeper ZooKeeper
Master Master
DataNodeDataNode DataNode
RegionServerRegionServerRegionServer
1. Which
RegionServer is
serving the Region?
2. Write to
RegionServer
12
Client
Read Path
Š2014 Cloudera, Inc. All rights reserved.12
NameNode
DataNodeDataNode
Standby
NameNode
DataNode
RegionServer
Master
RegionServerRegionServer
ZooKeeper ZooKeeper ZooKeeper
Master Master
DataNodeDataNode DataNode
RegionServerRegionServerRegionServer
1. Which
RegionServer is
serving the Region?
2. Read from
RegionServer
13
HBase API
How to access the data
14
No SQL Means No SQL
Š2014 Cloudera, Inc. All rights reserved.14
• 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
15
Types of Access
Š2014 Cloudera, Inc. All rights reserved.15
• 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
16
Gets
Š2014 Cloudera, Inc. All rights reserved.16
1
2
3
4
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);
17
Puts
Š2014 Cloudera, Inc. All rights reserved.17
1
2
3
4
Put p = new Put(ROW_KEY_BYTES);
p.add(COLFAM_BYTES, COLDESC_BYTES,
Bytes.toBytes("value"));
table.put(p);
18
HBase Schema Design
How to design
19
No SQL Means No SQL
Š2014 Cloudera, Inc. All rights reserved.19
• 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
20
Treating HBase like a traditional RDBMS will lead
to abject failure!
Captain Picard
21
Row Keys
Š2014 Cloudera, Inc. All rights reserved.21
• 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>
22
Schema Design
Š2014 Cloudera, Inc. All rights reserved.22
• Schema design does not start in an ERD
• Access pattern must be known and ascertained
• Denormalize to improve performance
• Fewer, bigger tables
23 Š2014 Cloudera, Inc. All rights reserved.
Jesse Anderson
@jessetanderson

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HBaseCon 2014-Just the Basics

  • 1. 1 HBase: Just the Basics Jesse Anderson – Curriculum Developer and Instructor v2
  • 2. 2 What Is HBase? Š2014 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
  • 3. 3 Why Use HBase? Š2014 Cloudera, Inc. All rights reserved.3 • 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
  • 4. 4 When to Consider Not Using HBase? Š2014 Cloudera, Inc. All rights reserved.4 • 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
  • 6. 6 Meet the Daemons Š2014 Cloudera, Inc. All rights reserved.6 • HBase Master • RegionServer • ZooKeeper • HDFS • NameNode/Standby NameNode • DataNode
  • 7. 7 Daemon Locations Š2014 Cloudera, Inc. All rights reserved.7 NameNode DataNodeDataNode Standby NameNode DataNode RegionServer Master RegionServerRegionServer ZooKeeper ZooKeeper ZooKeeper Master Master DataNodeDataNode DataNode RegionServerRegionServerRegionServer Master Nodes Slave Nodes
  • 8. 8 Tables and Column Families Š2014 Cloudera, Inc. All rights reserved.8 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
  • 9. 9 Rows and Columns Š2014 Cloudera, Inc. All rights reserved.9 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
  • 10. 10 Regions Š2014 Cloudera, Inc. All rights reserved.10 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 RegionServerRegionServer ZooKeeper ZooKeeper ZooKeeper Master Master DataNodeDataNode DataNode RegionServerRegionServerRegionServer Row key Column Family “contactinfo” mrossi fname: Mario lname: Rossi zstevens fname: Zack lname: Stevens Regions are served by RegionServers
  • 11. 11 Client Write Path Š2014 Cloudera, Inc. All rights reserved.11 NameNode DataNodeDataNode Standby NameNode DataNode RegionServer Master RegionServerRegionServer ZooKeeper ZooKeeper ZooKeeper Master Master DataNodeDataNode DataNode RegionServerRegionServerRegionServer 1. Which RegionServer is serving the Region? 2. Write to RegionServer
  • 12. 12 Client Read Path Š2014 Cloudera, Inc. All rights reserved.12 NameNode DataNodeDataNode Standby NameNode DataNode RegionServer Master RegionServerRegionServer ZooKeeper ZooKeeper ZooKeeper Master Master DataNodeDataNode DataNode RegionServerRegionServerRegionServer 1. Which RegionServer is serving the Region? 2. Read from RegionServer
  • 13. 13 HBase API How to access the data
  • 14. 14 No SQL Means No SQL Š2014 Cloudera, Inc. All rights reserved.14 • 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
  • 15. 15 Types of Access Š2014 Cloudera, Inc. All rights reserved.15 • 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
  • 16. 16 Gets Š2014 Cloudera, Inc. All rights reserved.16 1 2 3 4 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);
  • 17. 17 Puts Š2014 Cloudera, Inc. All rights reserved.17 1 2 3 4 Put p = new Put(ROW_KEY_BYTES); p.add(COLFAM_BYTES, COLDESC_BYTES, Bytes.toBytes("value")); table.put(p);
  • 19. 19 No SQL Means No SQL Š2014 Cloudera, Inc. All rights reserved.19 • 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
  • 20. 20 Treating HBase like a traditional RDBMS will lead to abject failure! Captain Picard
  • 21. 21 Row Keys Š2014 Cloudera, Inc. All rights reserved.21 • 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>
  • 22. 22 Schema Design Š2014 Cloudera, Inc. All rights reserved.22 • Schema design does not start in an ERD • Access pattern must be known and ascertained • Denormalize to improve performance • Fewer, bigger tables
  • 23. 23 Š2014 Cloudera, Inc. All rights reserved. Jesse Anderson @jessetanderson