HBase: Just the Basics
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HBase is a NoSQL datastore built on Hadoop's HDFS designed for handling large amounts of big data, enabling high throughput for random reads and writes. Its architecture includes components like the HBase master, regionservers, and ZooKeeper to manage data organization in tables and column families. Schema design in HBase requires a data-centric approach, focusing on access patterns and optimal row key construction.
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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 =
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HBase: Just the Basics
- 1. 1 HBase: Just the Basics Jesse Anderson – Curriculum Developer and Instructor v1
- 2. 2 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
- 3. 3 Why Use HBase? ©2014 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? ©2014 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. 5 HBase Architecture How it works
- 6. 6 Meet the Daemons ©2014 Cloudera, Inc. All rights reserved.6 • HBase Master • RegionServer • ZooKeeper • HDFS • NameNode/Standby NameNode • DataNode
- 7. 7 Daemon Locations ©2014 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 ©2014 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 ©2014 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 ©2014 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 ©2014 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 ©2014 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 ©2014 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 ©2014 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 ©2014 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 ©2014 Cloudera, Inc. All rights reserved.17 1 2 3 4 Put p = new Put(Bytes.toBytes(ROW_KEY_BYTES); p.add(COLFAM_BYTES, COLDESC_BYTES, Bytes.toBytes("value")); table.put(p);
- 18. 18 HBase Schema Design How to design
- 19. 19 No SQL Means No SQL ©2014 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 ©2014 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
- 22. 22 Schema Design ©2014 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 ©2014 Cloudera, Inc. All rights reserved. Jesse Anderson @jessetanderson