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What’s in it for you? What is HBase? 1

What’s in it for you? What is HBase? 1 HBase Use Case 2

What’s in it for you? What is HBase? Applications of HBase 1 HBase Use Case 2 3

What’s in it for you? What is HBase? Applications of HBase HBase vs RDBMS 1 HBase Use Case 2 3 4

What’s in it for you? What is HBase? Applications of HBase HBase Storage HBase vs RDBMS 1 HBase Use Case 2 3 45

What’s in it for you? What is HBase? Applications of HBase HBase Storage HBase Architectural Components HBase vs RDBMS 1 HBase Use Case 2 3 45 6

What’s in it for you? What is HBase? Applications of HBase HBase Storage HBase Architectural Components Demo on HBase HBase vs RDBMS 1 HBase Use Case 2 3 45 6 7

Introduction to HBaseIntroduction to HBase

Introduction to HBase This data could be easily stored in a Relational Database (RDMS) Structured data Back in the days, data used to be less and was mostly structured

Introduction to HBase Then, Internet evolved and huge volumes of structured and semi- structured data got generated Storing and processing this data on RDBMS became a major problem Semi-structured data

Introduction to HBase Apache HBASE was the solution for this Semi-structured data SolutionThen, Internet evolved and huge volumes of structured and semi- structured data got generated

Introduction to HBaseHBase History

HBase History 1 Google released the paper on BigTable Nov 2006

HBase History 1 2 Google released the paper on BigTable HBase prototype was created as a Hadoop contribution Nov 2006 Feb 2007

HBase History 1 2 3 Google released the paper on BigTable HBase prototype was created as a Hadoop contribution First usable HBase along with Hadoop 0.15.0 was released Nov 2006 Feb 2007 Oct 2007

HBase History 1 2 3 4 Google released the paper on BigTable HBase prototype was created as a Hadoop contribution First usable HBase along with Hadoop 0.15.0 was released HBase became the subproject of Hadoop Nov 2006 Feb 2007 Oct 2007 Jan 2008

HBase History 1 2 3 4 5 Google released the paper on BigTable HBase prototype was created as a Hadoop contribution First usable HBase along with Hadoop 0.15.0 was released HBase became the subproject of Hadoop HBase 0.81.1, 0.19.0 and 0.20.0 was released between Oct 2008 – Sep 2009 Nov 2006 Feb 2007 Oct 2007 Jan 2008 Oct 2008 – Sep 2009

HBase History 1 2 3 4 5 6 Google released the paper on BigTable HBase prototype was created as a Hadoop contribution First usable HBase along with Hadoop 0.15.0 was released HBase became the subproject of Hadoop HBase 0.81.1, 0.19.0 and 0.20.0 was released between Oct 2008 – Sep 2009 HBase became Apache top-level project Nov 2006 Feb 2007 Oct 2007 Jan 2008 Oct 2008 – Sep 2009 May 2010

Introduction to HBaseWhat is HBase?

What is HBase? HBase is a column oriented database management system derived from Google’s NoSQL database BigTable that runs on top of HDFS

What is HBase? HBase is a column oriented database management system derived from Google’s NoSQL database BigTable that runs on top of HDFS Open source project that is horizontally scalable1

What is HBase? Open source project that is horizontally scalable1 2 HBase is a column oriented database management system derived from Google’s NoSQL database BigTable that runs on top of HDFS NoSQL database written in JAVA which performs faster querying

What is HBase? Open source project that is horizontally scalable NoSQL database written in JAVA which performs faster querying Well suited for sparse data sets (can contain missing or NA values) 1 2 3 HBase is a column oriented database management system derived from Google’s NoSQL database BigTable that runs on top of HDFS

Introduction to HBaseCompanies using HBase

Introduction to HBaseHBase Use Case

HBase Use Case Telecommunication company that provides mobile voice and multimedia services across China

HBase Use Case Telecommunication company that provides mobile voice and multimedia services across China Generated billions of Call Detail Records (CDR)

HBase Use Case Telecommunication company that provides mobile voice and multimedia services across China Traditional database systems were unable to scale up to the vast volumes of data and provide a cost-effective solution Generated billions of Call Detail Records (CDR)

HBase Use Case Telecommunication company that provides mobile voice and multimedia services across China Generated billions of Call Detail Records (CDR) Traditional database systems were unable to scale up to the vast volumes of data and provide a cost-effective solution

HBase Use Case Telecommunication company that provides mobile voice and multimedia services across China Storing and real-time analysis of billions of call records was a major problem Generated billions of Call Detail Records (CDR)

HBase Use Case Telecommunication company that provides mobile voice and multimedia services across China HBase stores billions of rows of detailed call records Solution Generated billions of Call Detail Records (CDR)

HBase Use Case Telecommunication company that provides mobile voice and multimedia services across China HBase performs fast processing of records using SQL queries Generates billions of Call Detail Records (CDR)

Introduction to HBaseApplications of HBase

Applications of HBase Medical HBase is used for storing genome sequences Storing disease history of people or an area

Applications of HBase Medical E-Commerce HBase is used for storing genome sequences Storing disease history of people or an area HBase is used for storing logs about customer search history Performs analytics and target advertisement for better business insights

Applications of HBase Medical E-Commerce Sports HBase is used for storing genome sequences Storing disease history of people or an area HBase is used for storing logs about customer search history Performs analytics and target advertisement for better business insights HBase stores match details and history of each match Uses this data for better prediction

Introduction to HBaseHBase vs RDBMS

HBase vs RDBMS Does not have a fixed schema (schema-less). Defines only column families Has a fixed schema which describes the structure of the tables HBase RDBMS

HBase vs RDBMS Does not have a fixed schema (schema-less). Defines only column families Has a fixed schema which describes the structure of the tables Works well with structured and semi-structured data Works well with structured data HBase RDBMS

HBase vs RDBMS Does not have a fixed schema (schema-less). Defines only column families Has a fixed schema which describes the structure of the tables Works well with structured and semi-structured data Works well with structured data RDBMS can store only normalized data HBase RDBMS It can have de-normalized data (can contain missing or NA values)

HBase vs RDBMS Does not have a fixed schema (schema-less). Defines only column families Has a fixed schema which describes the structure of the tables Works well with structured and semi-structured data Works well with structured data It can have de-normalized data (can contain missing or NA values) RDBMS can store only normalized data Built for wide tables that can be scaled horizontally Built for thin tables that is hard to scale HBase RDBMS

Introduction to HBaseFeatures of HBase

Features of HBase Scalable Data can be scaled across various nodes as it is stored in HDFS

Features of HBase Scalable Data can be scaled across various nodes as it is stored in HDFS Automatic failure support Write Ahead Log across clusters which provides automatic support against failure

Features of HBase Scalable Data can be scaled across various nodes as it is stored in HDFS Consistent read and write HBase provides consistent read and write of data Automatic failure support Write Ahead Log across clusters which provides automatic support against failure

Features of HBase Scalable Data can be scaled across various nodes as it is stored in HDFS Consistent read and write HBase provides consistent read and write of data JAVA API for client access Provides easy to use JAVA API for clients Automatic failure support Write Ahead Log across clusters which provides automatic support against failure

Features of HBase Scalable Data can be scaled across various nodes as it is stored in HDFS Consistent read and write HBase provides consistent read and write of data JAVA API for client access Provides easy to use JAVA API for clients Automatic failure support Write Ahead Log across clusters which provides automatic support against failure Block cache and bloom filters Supports block cache and bloom filters for high volume query optimization

Introduction to HBaseHBase Storage

HBase column oriented storage Column Family 1 Column Family 2 Column Family 3 Rowid Col 1 Col 2 Row 1 Row 2 Row 3 Col 3 Col 3Col 1 Col 2 Col 3Col 1 Col 2 Row Key Column Family Column Qualifiers Cells

HBase column oriented storage Personal data Professional dataRowid name 1 2 3 Row Key Column Family Column Qualifiers Cells city age salaryempid Angela Dwayne David Chicago Boston Seattle 31 35 29 Data Analyst Web Developer Big Data Architect $70,000 $65,000 $55,000 designation

Introduction to HBaseHBase Architecture

HBase Architectural Components Region Server HLog MemStore StoreFile StoreFile HFile HFile StoreRegion Region Server HLog MemStore StoreFile StoreFile HFile HFile StoreRegion Region Server HLog MemStore StoreFile StoreFile HFile HFile StoreRegion HDFS HMaster HBase Master assigns regions and load balancing ZooKeeper is used for monitoring Region server serves data for read and write

HBase Architectural Components - Regions Key col col xxx val val xxx val val Key col col xxx val val xxx val val Key col col xxx val Val xxx val Val Key col col xxx val val xxx val val Region 1 Region 2 Region 3 Region 4 ……... ….…. startKey endKey endKey Client HBase tables are divided horizontally by row key range into “Regions” A region contains all rows in the table between the region’s start key and end key Regions are assigned to the nodes in the cluster, called “Region Servers” These servers serve data for read and write startKey get Region Server 1 Region Server 2

HBase Architectural Components - HMaster Key col col xxx val val xxx val val Key col col xxx val val xxx val val Key col col xxx val Val xxx val Val Key col col xxx val val xxx val val Region 1 Region 2 Region 3 Region 4 ……... ….…. ClientRegion assignment, Data Definition Language operation (create, delete) are handled by HMaster Assigning and re-assigning regions for recovery or load balancing and monitoring all servers Region Server 1 Region Server 2 HMaster create, delete, update table

HBase Architectural Components - HMaster Key col col xxx val val xxx val val Key col col xxx val val xxx val val Key col col xxx val Val xxx val Val Key col col xxx val val xxx val val Region 1 Region 2 Region 3 Region 4 ……... ….…. ClientRegion assignment, Data Definition Language operation (create, delete) are handled by HMaster Assigning and re-assigning regions for recovery or load balancing and monitoring all servers Region Server 1 Region Server 2 HMaster create, delete, update table Monitors region servers

HBase Architectural Components - HMaster Key col col xxx val val xxx val val Key col col xxx val val xxx val val Key col col xxx val Val xxx val Val Key col col xxx val val xxx val val Region 1 Region 2 Region 3 Region 4 ……... ….…. ClientRegion assignment, Data Definition Language operation (create, delete) are handled by HMaster Assigning and re-assigning regions for recovery or load balancing and monitoring all servers Region Server 1 Region Server 2 HMaster create, delete, update table Monitors region servers Assigns regions to region servers HBase has a distributed environment where HMaster alone is not sufficient to manage everything. Hence, ZooKeeper was introduced Assigns regions to region servers

Inactive HMaster HBase Architectural Components - ZooKeeper Key col col xxx val val xxx val val Key col col xxx val val xxx val val Key col col xxx val Val xxx val Val Key col col xxx val val xxx val val Region 1 Region 2 Region 3 Region 4 ……... ….…. ZooKeeper is a distributed coordination service to maintain server state in the cluster Zookeeper maintains which servers are alive and available, and provides server failure notification Region Server 1 Region Server 2 Active HMaster ZooKeeper Active HMaster sends a heartbeat signal to ZooKeeper indicating that its active

Inactive HMaster HBase Architectural Components - ZooKeeper Key col col xxx val val xxx val val Key col col xxx val val xxx val val Key col col xxx val Val xxx val Val Key col col xxx val val xxx val val Region 1 Region 2 Region 3 Region 4 ……... ….…. ZooKeeper is a distributed coordination service to maintain server state in the cluster Zookeeper maintains which servers are alive and available, and provides server failure notification Region Server 1 Region Server 2 Active HMaster heartbeat Region servers send their status to ZooKeeper indicating they are ready for read and write operation ZooKeeper

Inactive HMaster HBase Architectural Components - ZooKeeper Key col col xxx val val xxx val val Key col col xxx val val xxx val val Key col col xxx val Val xxx val Val Key col col xxx val val xxx val val Region 1 Region 2 Region 3 Region 4 ……... ….…. ZooKeeper is a distributed coordination service to maintain server state in the cluster Zookeeper maintains which servers are alive and available, and provides server failure notification Region Server 1 Region Server 2 Active HMaster heartbeat Inactive server acts as a backup. If the active HMaster fails, it will come to rescue ZooKeeper

How the components work together? Key col col xxx val val xxx val val Key col col xxx val val xxx val val Key col col xxx val Val xxx val Val Key col col xxx val val xxx val val Region 1 Region 2 Region 3 Region 4 ……... ….…. Region Server 1 Region Server 2 HMaster ZooKeeper 1 master is active • Active HMaster selection • Region Server session Active HMaster and Region Servers connect with a session to ZooKeeper

How the components work together? Key col col xxx val val xxx val val Key col col xxx val val xxx val val Key col col xxx val Val xxx val Val Key col col xxx val val xxx val val Region 1 Region 2 Region 3 Region 4 ……... ….…. Region Server 1 Region Server 2 HMaster heartbeat 1 master is active • Active HMaster selection • Region Server session Active HMaster and Region Servers connect with a session to ZooKeeper ZooKeeper

How the components work together? Key col col xxx val val xxx val val Key col col xxx val val xxx val val Key col col xxx val Val xxx val Val Key col col xxx val val xxx val val Region 1 Region 2 Region 3 Region 4 ……... ….…. Region Server 1 Region Server 2 HMaster heartbeat 1 master is active Ephemeral node Ephemeral node • Active HMaster selection • Region Server session ZooKeeper maintains ephemeral nodes for active sessions via heartbeats to indicate that region servers are up and running ZooKeeper

Introduction to HBaseHBase Read or Write

HBase Read or Write ZooKeeper .META location is stored in ZooKeeper There is a special HBase Catalog table called the META table, which holds the location of the regions in the cluster Here is what happens the first time a client reads or writes data to HBase Client Region Server Region Server DataNode DataNode The client gets the Region Server that hosts the META table from ZooKeeper Request for Region Server

HBase Read or Write ZooKeeper .META location is stored in ZooKeeper There is a special HBase Catalog table called the META table, which holds the location of the regions in the cluster Here is what happens the first time a client reads or writes data to HBase Client Region Server Region Server DataNode DataNode Meta table location The client gets the Region Server that hosts the META table from ZooKeeper Request for Region Server

HBase Read or Write ZooKeeper There is a special HBase Catalog table called the META table, which holds the location of the regions in the cluster Here is what happens the first time a client reads or writes data to HBase Client Meta Cache Region Server Region Server DataNode DataNode The client will query the .META server to get the region server corresponding to the row key it wants to access The client caches this information along with the META table location Meta table location Request for Region Server Get region server for row key from meta table .META location is stored in ZooKeeper

HBase Read or Write ZooKeeper There is a special HBase Catalog table called the META table, which holds the location of the regions in the cluster Here is what happens the first time a client reads or writes data to HBase Client Region Server Region Server DataNode DataNode Put row Meta Cache It will get the Row from the corresponding Region Server Get region server for row key from meta table .META location is stored in ZooKeeper Meta table location Request for Region Server Get row

Introduction to HBaseHBase Meta Table

HBase Meta Table Meta Table Row key value table, key, region region server Key col col xxx val val xxx val val Key col col xxx val val xxx val val Region 1 Region 2 Region Server Key col col xxx val val xxx val val Key col col xxx val val xxx val val Region 3 Region 4 Region Server Special HBase catalog table that maintains a list of all the Region Servers in the HBase storage system META table is used to find the Region for a given Table key

Introduction to HBaseHBase Write Mechanism

HBase Write Mechanism WAL Region Server Region MemStore MemStore HFile HFile HDFS DataNodeClient 1 When client issues a put request, it will write the data to the write-ahead log (WAL)1 Write Ahead Log (WAL) is a file used to store new data that is yet to be put on permanent storage. It is used for recovery is the case of failure.

HBase Write Mechanism WAL Region Server Region MemStore MemStore HFile HFile HDFS DataNodeClient 1 2 Once data is written to the WAL, it is then copied to the MemStore2 MemStore is the write cache that stores new data that has not yet been written to disk. There is one MemStore per column family per region.

HBase Write Mechanism WAL Region Server Region MemStore MemStore HFile HFile HDFS DataNodeClient 1 3 ACK 2 Once the data is placed in MemStore, the client then receives the acknowledgment3

HBase Write Mechanism WAL Region Server Region MemStore MemStore HFile HFile HDFS DataNodeClient 1 3 ACK 2 4 4 When the MemStore reaches the threshold, it dumps or commits the data into a HFile4 Hfiles store the rows of data as sorted KeyValue on disk

Introduction to HBaseDemo on HBase

HBase Tutorial For Beginners | HBase Architecture | HBase Tutorial | Hadoop Tutorial | Simplilearn

HBase Tutorial For Beginners | HBase Architecture | HBase Tutorial | Hadoop Tutorial | Simplilearn

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