HBASEHBASE

THE SCALABLE DATA STORETHE SCALABLE DATA STORE

Sampath RachakondaSampath Rachakonda

Agenda

Evolution of HBASE Overview Data Model Architecture Hbase and Zookeper

Evolution of HBASE

File-Systems Tapes → Linear Access or Sequential Access. Disc → Random Access

Seek Time Transfer Rate

DBMS RDBMS Now NOSQL

Hadoop It comprises mainly two

things HDFS and MapReduce.

HDFS is scalable, fault tolerant, and high performance DFS can run on commodity hardware.

Map-Reduce is software framework for distributed computation.

Master/Slave Architecture

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Limitations Batch processing Sequential Data

look-up Not intended for

real time querying No Support for

Random Access

NOSQL

Massive Data Volumes Schema Evolution

As it is almost impossible for fixed Schema for web scale database.

With NOSQL Schema changes can be gradually introduced into systems.

Extreme Query Load Bottleneck is Joins

Why HBASE ?

Column-Oriented Stores Distributed – Designed to serve large tables Horizontally Scalable High Performance & Availability Storage System The base goal of HBASE is Billions of Rows,

Millions of Columns and Thousand of versions Supports random real time CRUD operations unlike

HDFS

Who uses Hbase ?

Facebook Adobe Twitter Yahoo Meetup Netflix Many More..

When to use HBASE ?

Good for large amounts of data 100's of millions or billions of rows Have to have enough hardware Large Amounts of

client requests Single Random Selects and range scans by key Great for variable schema Analytical

HBASE Data Model

Data is stored in Tables Tables contain rows

Rows are referenced by Unique key Key is array of bytes anything can be a key.

Rows made of columns are grouped in column families

Data is stored in cells and identified by row x column-family x column

Tables are sorted by the row key in lexicographical order.

HBASE Families Rows are grouped as families

Labeled as “Family:column” Example: “user:name”

Different features are applied to families Stored together – HFile/StoreFile Compression

Table Schema defines its Column Families Each family can consist of any number of columns

and Versions Column exists when inserted, NULLS are free. Columns with family are sorted and stored together. Except table names everything is byte[]

HBASE Timestamps

Cells Values are versioned and 3 versions are kept by default.

Versions are stored in decreasing time-stamp order. Reads the latest first – which will be our current

value. Value will be

Value = Table + RowKey + Family + Column + TimeStamp

Index will be always unique

HBASE Cells Example

Example of how values are storedRow Key Time stamp Name Family Address Family

first_name last_name number address

row1 t1 Bob Smith

t5 10 First Lane

t10 30 Other Lane

t15 7 Last Street

row2 t20 Mary Tompson

t22 77 One Street

t30 Thompson

HBASE Architecture

Table is made up of regions Region is a range of rows sorted together

Dynamically splits as they become too big and merge when they are too small

Master Server is responsible for managing Hbase cluster (i.e.., Region Servers)

Hbase stores its data into HDFS which makes to rely it on high tolerant and high availability and fault tolerance features.

Zookeper is used for distributed coordination.

HBASE Architecture

As Follows:

HBASE Regions Region is a range of keys start key to end key

exclusive Initially there will be one region as addition of data

exceed the configured maximum (256 MB default) the region will be split

No of regions per server varies from 10 to 10000 as per hardware per region server.

Splitting data into regions help us in different ways: Fast Recovery when a region fails Load Balancing when a server overloaded Splitting is fast

HBASE Data Storage When data is added it will be written on to WAL

(Write Ahead Log) and also in memory (Memstore) When the data exceeds maximum value then it is

flushed out of WAL to HFile RegionServer still serves read-writes during the

flush operations, writing values to WAL & Memstore.

Hfile is nothing much than a Key-Value map. As HDFS doesn't support updates to an existing file

therefore HFiles are immutable. Delete Marker is saved to indicate whether record is

available or removed.

HBASE Data Storage(Contd.) Periodic Data Computations are performed to

control no of Hfiles and to keep cluster balanced Minor Complication:

Smaller Hfiles are merged into larger Hfiles Fast as data is already sorted Delete Markers are not applied

Major Complication: Scanning for all the entries and apply deletes as

necessary Merge all Hfiles of a region into a single file lies

within a column family

HBASE Master

Manages Regions and their locations Assigns Regions Balances workload Recovers if any region server is unavailable Uses Zookeeper for distributed coordination

service Clients directly communicate with Region Servers Performs Schema Management and changes

Adding/Removing tables and Column Families

HBASE and Zookeeper HBASE uses zookeeper for region assignments Zookeeper is a centralized server for maintaining

configuration information, Naming, Providing distributed synchronization, and providing group service.

File like API, performs operations on directories and files (Znodes)

Clients connect with a session to zookeeper Session is maintained via Heart-Beat Clients listening for updates will be notified of the

deleted nodes and new nodes.

HBASE and Zookeeper(Contd.)

Each region server creates a Ephemeral Node. Master monitors these nodes to discover available

region servers and for server failures. Use Zookeeper to make sure that only one master is

registered HBASE cannot exist in distributed without

Zookeeper.

HBASE Access

Hbase Shell Native JAVA API

Fastest and very capable options. Avro Server

Requires running Avro Server. Hbql

SQL like syntax for HBASE