Apache HBase is a technology that turns everything in Hadoop infrastructure upside down. An elephant cannot become an antelope, but yet it is possible to do a group dance on its back.

1. HBase
DANCES ON THE
ELEPHANT BACK
Roman Nikitchenko, 13.08.2014

2. 2www.vitech.com.ua
Agenda
Integration with
Hadoop, crazy
ideas, magic.
Architecture, data
model, features.
Motivation and
place for HBase
in NoSQL world
HBASE: WHO
AND WHY?
HBASE as is
AROUND HBASE

3. 3www.vitech.com.ua
Is hadoop good for data?
… so
attractive
● Hadoop is open source
framework for big
data. Both distributed
storage and
processing.
● Hadoop is reliable and
fault tolerant with no
rely on hardware for
these properties.
● Hadoop has unique
horisontal scalability.
Currently — from
single computer up to
thousands of cluster
nodes.

4. 4www.vitech.com.ua
Hadoop: classical picture
Hadoop
historical
top view
● HDFS serves as file
system layer
● MapReduce originally
served as distributed
processing framework.
● Native client API is
Java but there are lot
of alternatives.
● But where is SQL
server here?

5. 5www.vitech.com.ua
HBase motivation
● Designed for throughput, not
for latency.
● HDFS blocks are expected to be
large. There is issue with lot of
small files.
● Write once, read many times
ideology.
● MapReduce is not so flexible so
any database built on top of it.
● How about realtime?
So Hadoop is...

6. 6www.vitech.com.ua
HBase motivation
BUT WE OFTEN
NEED...
LATENCY, SPEED and all
Hadoop properties.

7. 7www.vitech.com.ua
So HBASE is for this.
● Open source Google BigTable implementation
with appropriate infrastructure place.
● Realtime, low latency, linear scalability.
● Distributed, reliable and fault tolerant.
● Natural integration with other Hadoop
components.
● No any SQL, secondary indexes out of the box.
● Limited ACID guarantees.
● Really good for massive scans.

8. 8www.vitech.com.ua
Google Bigtable / Hadoop architecture and HBase
High layer applications
MapReduce (Hadoop
MapReduce)
YARN (resource management)
Distributed file system (Google FS, HDFS).

9. 9www.vitech.com.ua
HBASE facts and trends
2006 2007 2008 2009 2010 … 2014 … future
2008, HBase goes OLTP (online transaction
processing). 0.20 is first performance release
2010, HBase becomes
Apache top-level project
HBase 0.92 is considered
production ready release
November 2010, Facebook
elected HBase to implement
new messaging platform
2007, First code is
released as part of
Hadoop 0.15. Focus is on
offline, crawl data storage
2006, Google BigTable
paper is published. HBase
development starts

10. 10www.vitech.com.ua
HBase data paths on conceptual level
Analytics, long running jobs Realtime operations
Adapters
(Hive)
MapReduce API HBase API
Adapters
(Impala)
MapReduce (Hadoop
MapReduce)
YARN (resource management)
Distributed file system (Google FS, HDFS)
● HBase can be used both for long running analytics and real time
low latency operations.
● Third party adapters are possible if you need fast track. Some
functionality and performance drawbacks are the price you pay.

11. 11www.vitech.com.ua
Loose data structure
Book: title, author,
pages, price
Ball: color, size,
material, price
Toy car: color, type,
radio control, price
Kind Price Title Author Pages Color Size Material Type Radio
control
Book + + + +
Ball + + + +
Toy car + + + +
● Data looks like tables with large number of columns.
● Columns set can vary from row to row.
● No table modification is needed to add column to row.
Book #1: Kind, Price, Title, Author, Pages
Ball #1: Kind, Price, Color, Size, Material
Toy car #1: Price, Color, Type +Radio control
Book #2: Kind, Price, Title, Author

12. 12www.vitech.com.ua
Table
Logical data structure
Region
Region
Row
Key Family #1 Family #2 ...
Column Column ... ...
...
...
...
Data is
placed in
tables.
Tables are split
into regions
based on row
key ranges.
Columns are
grouped into
families.Every table row
is identified by
unique row key.
Every row
consists of
columns.

13. 13www.vitech.com.ua
Table
Region
Data storage structure
Region
Row
Key Family #1 Family #2 ...
Column Column ... ...
...
● Data is stored in HFile.
● Families are stored on
disk in separate files.
● Row keys are
indexed in memory.
● Column includes key,
qualifier, value and timestamp.
● No column limit.
● Storage is block based (default 64K).
HFile: family #1
Row key Column Value TS
... ... ... ...
... ... ... ...
HFile: family #2
Row key Column Value TS
... ... ... ...
... ... ... ...
● Delete is just another
marker record.
● Periodic compaction is
required.

14. 14www.vitech.com.ua
Architecture
● Zookeeper coordinates distributed elements and
is primary contact point for client.
● Master server keeps metadata and manages
data distribution over Region servers.
● Region servers manage data table regions but
actual data storage service including replication
is on HDFS data nodes. Clients directly
communicate with region server for data.
DATA
META
Rack
DN DN
RS RS
Rack
DN DN
RS RS
Rack
DN DN
RS RS
NameNode
Client
HMaster
Zookeeper

15. 15www.vitech.com.ua
CRUD: Put and Delete
● Writes are logged and cached in memory.
● Main thing to remember: lower layer is
WRITE ONLY filesystem (HDFS). So both
PUT and DELETE path is identical.
● Both PUT and DELETE requests are per
row key. No row key range for DELETE.
● DELETE is just another marker added.
● Actual DELETE is performed during
compactions.
● Don't forget we can have several families.

16. 16www.vitech.com.ua
CRUD: Put and Delete, write path
● Actual write is to region server. Master is not involved.
● All requests are coming to WAL (write ahead log) to
provide recovery.
● Region server keeps MemStore as temporary storage.
● Only when needed write is flushed to disk (into HFile).

17. 17www.vitech.com.ua
CRUD: Get and Scan
● Get operation is simple data request by row key.
● Scan operation is performed based on row key
range which could involve several table regions.
● Both Get and Scan can include client filters —
expressions that are processed on server side
and can seriously limit results so traffic.
● Both Scan and Get operations can be performed
on several column families.
● Get operation is implemented through Scan.

18. 18www.vitech.com.ua
DATA
META
Integration with MapReduce
● HBase provides number of classes for native
MapReduce integration. Main point is data locality.
● TableInputFormat allows massive MapReduce table
processing (maps table with one region per mapper).
● HBase classes like Result (Get / Scan result) or Put (Put
request) can be passed between MapReduce job stages.
● We have moderate experience of making things here
even better.
DataNode
NameNodeJobTracker TaskTracker
RegionServerHMaster Ofen single node
so data is local

19. 19www.vitech.com.ua
Coprocessors: Key points
● Coprocessors is feature that allows to extend
HBase without product code modification.
● RegionObserver can attach code to operations
on region level.
● Similar functionality exists for HMaster.
● Endpoints is the way to provide functionality
equal to stored procedure.
● Together coprocessor infrastructure can bring
realtime distributed processing framework
(lightweight MapReduce).

20. 20www.vitech.com.ua
Request
Coprocessors: Region observer
Client
Table
Region observer Region observer
Result
Region Region
RegionServer RegionServer
Region observer
works like hook on
region operations. Region observer Region observerRegion observer Region observer
Region observers
can be stacked.

21. 21www.vitech.com.ua
RegionServer RegionServer
Coprocessors: Endpoints
Request (RPC)
Client Table
Region Region
Direct communication
via separate protocol.
Response
Endpoint Endpoint
Your commands
can have effect on
table regions.

22. 22www.vitech.com.ua
Secondary indexes
● HBase has no support for secondary
indexes out-of-the-box.
● Coprocessor (RegionObserver) is used to
track Put and Delete operations and
update index table.
● Scan operations with index column filter
are intercepted and processed based on
index table content.
Table
Client
Index
table
Region
observerPut / Delete Index update
Scan with filter
Region
Index search

23. 23www.vitech.com.ua
Bulk load
● There is ability to load data in table MUCH FASTER.
● HFile is generated with required data.
● It is preferable to generate one HFile per table
region. MapReduce can be used.
● Prepared HFile is merged with table storage on
maximum speed.
Data
importers
HFile generator
HFile generator
HFile generator
Table region
Table region
Table region
Mappers Reducers
HFile
HFile
HFile

24. 24www.vitech.com.ua
HDFS
Replication and search integration
WAL, Regions
Data update
Client
User just puts (or
deletes) data.
Search responses
Lily HBase
NRT indexer
Replication can be
set up to column
family level.
REPLICATION
HBase
cluster
Translates data
changes into SOLR
index updates.
SOLR cloud
Search requests (HTTP)
Apache
Zookeeper does
all coordination
Finally provides
search
Serves low level
file system.

25. 25www.vitech.com.ua
HUG benefits for members
USER GROUP MEMBERSHIP
Just enter ‘ug367’ in
the Promotional Code
box when you check
out at manning.com.
To get this discount, please
shop on www.oreilly.com
and quote reference DSUG.

26. 26www.vitech.com.ua
Future meetups
http://hug-lviv.blogspot.com
hug.lviv@gmail.com
We and O’Reilly
encourage you to
host future meetups,
speech on them and
participate in group
activities.

27. 27www.vitech.com.ua
Questions and discussion
Any
questions?