SlideShare a Scribd company logo

Introduction to TitanDB

•
•
Knoldus Inc.
Knoldus Inc.

This document introduces TitanDB, a scalable graph database, and Apache TinkerPop, an open-source graph computing framework. It defines what a graph database is, the need for graph databases and TitanDB. It describes key features of TitanDB like support for various storage backends and integration with tools like Spark and Giraph. It also summarizes the CAP theorem, TitanDB architecture, its acquisition by DataStax, and what Apache TinkerPop is and why it is needed when dealing with complex graph databases.

1 of 14
Download to read offline
Introduction to TitanDB Bharat Singh Software Consultant Knoldus Software LLP.
Agenda ● Graph Database ● What is Graph Database ● Need for Graph Database ● Titan DB ● Why Titan DB ● CAP theorem ● Architecture overview ● Future of TitanDB ● Apache TinkerPop ● What is Apache TinkerPop ● Need for Apache TinkerPop
What is Graph Database ● A database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data. ● Most graph databases are NoSQL in nature ● Store data in a key-value store or document-oriented database. ● Store relationships between values as first class citizens.
Need for Graph Database ● Data is more connected : Being shared across multiple applications on the web ● It is easier to query data stored in a graph structure where nodes are highly connected ● It removes the need to perform multiple join operations between adjacent neighbours ● It allows the use of many algorithms that helps in optimization ● Allows visualization of data and infer hidden relationships or derive predictions from data.
Why Titan DB ● Support for very large graphs. Titan graphs scale with the number of machines in the cluster. ● Support for ACID properties and eventual consistency. ● Support for very many concurrent transactions and operational graph processing. ● Titan’s transactional capacity scales with the number of machines in the cluster and answers complex traversal queries on huge graphs in milliseconds. ● Vertex-centric indices provide vertex-level querying to solve infamous super node problem. ● Provides an optimized disk representation to allow for efficient use of storage and speed of access. ● Open source with the liberal Apache 2 license.
Features of Titan DB ● Support for various storage backends: – Apache Cassandra – Apache HBase – Oracle BerkeleyDB ● Support for global graph data analytics, reporting, and ETL through integration with big data platforms: – Apache Spark – Apache Giraph – Apache Hadoop ● Support for geo, numeric range, and full-text search via: – ElasticSearch – Solr – Lucene ● Native integration with the TinkerPop graph stack: – Gremlin graph query language – Gremlin graph server – Gremlin applications
CAP Theorem ● CAP Theorem – C=Consistency – A=Availability – P=Partitionability ● HBase favours consistency – At expense of yield – i.e. non completed requests ● Cassandra favours availability – At expense of harvest – i.e. completeness of answer ● Berkeley DB is non distributed
Architecture overview of Titan DB
Future of TitanDB ● Aurelius is the startup behind Titan, an open source graph database ● DataStax, the company that delivers Apache Cassandra™ to the enterprise have now acquired Aurelius on Feb 3rd, 2015 ● The Aurelius team will join DataStax to build DataStax Enterprise (DSE) Graph, adding graph database capabilities into DSE alongside Apache Cassandra
What is Apache TinkerPop ● A Graph processing system, currently under Apache incubation ● Has Tinkerpop3 Structure API ● Graph, Element, Property ● Has Tinkerpop3 Process API ● TraversalSource, GraphComputer ● Gremlin query language ● A scripting language for graph traversal and mutation ● REST API
Need for Apache TinkerPop Dealing with such complex databases, requires a well-implemented API by the vendor. But using a vendor specific API, makes migrating to another database impossible. The solution is provided by Apache Tinkerpop
Introduction to TitanDB
References •https://en.wikipedia.org/wiki/Graph_database •http://thinkaurelius.github.io/titan/ •http://tinkerpop.apache.org/docs/3.2.0-incubatin g/reference/ •http://www.datastax.com/2015/02/datastax-acqui res-aurelius-the-experts-behind-titandb
Thank You

More Related Content

Introduction to TitanDB

  • 1. Introduction to TitanDB Bharat Singh Software Consultant Knoldus Software LLP.
  • 2. Agenda ● Graph Database ● What is Graph Database ● Need for Graph Database ● Titan DB ● Why Titan DB ● CAP theorem ● Architecture overview ● Future of TitanDB ● Apache TinkerPop ● What is Apache TinkerPop ● Need for Apache TinkerPop
  • 3. What is Graph Database ● A database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data. ● Most graph databases are NoSQL in nature ● Store data in a key-value store or document-oriented database. ● Store relationships between values as first class citizens.
  • 4. Need for Graph Database ● Data is more connected : Being shared across multiple applications on the web ● It is easier to query data stored in a graph structure where nodes are highly connected ● It removes the need to perform multiple join operations between adjacent neighbours ● It allows the use of many algorithms that helps in optimization ● Allows visualization of data and infer hidden relationships or derive predictions from data.
  • 5. Why Titan DB ● Support for very large graphs. Titan graphs scale with the number of machines in the cluster. ● Support for ACID properties and eventual consistency. ● Support for very many concurrent transactions and operational graph processing. ● Titan’s transactional capacity scales with the number of machines in the cluster and answers complex traversal queries on huge graphs in milliseconds. ● Vertex-centric indices provide vertex-level querying to solve infamous super node problem. ● Provides an optimized disk representation to allow for efficient use of storage and speed of access. ● Open source with the liberal Apache 2 license.
  • 6. Features of Titan DB ● Support for various storage backends: – Apache Cassandra – Apache HBase – Oracle BerkeleyDB ● Support for global graph data analytics, reporting, and ETL through integration with big data platforms: – Apache Spark – Apache Giraph – Apache Hadoop ● Support for geo, numeric range, and full-text search via: – ElasticSearch – Solr – Lucene ● Native integration with the TinkerPop graph stack: – Gremlin graph query language – Gremlin graph server – Gremlin applications
  • 7. CAP Theorem ● CAP Theorem – C=Consistency – A=Availability – P=Partitionability ● HBase favours consistency – At expense of yield – i.e. non completed requests ● Cassandra favours availability – At expense of harvest – i.e. completeness of answer ● Berkeley DB is non distributed
  • 9. Future of TitanDB ● Aurelius is the startup behind Titan, an open source graph database ● DataStax, the company that delivers Apache Cassandra™ to the enterprise have now acquired Aurelius on Feb 3rd, 2015 ● The Aurelius team will join DataStax to build DataStax Enterprise (DSE) Graph, adding graph database capabilities into DSE alongside Apache Cassandra
  • 10. What is Apache TinkerPop ● A Graph processing system, currently under Apache incubation ● Has Tinkerpop3 Structure API ● Graph, Element, Property ● Has Tinkerpop3 Process API ● TraversalSource, GraphComputer ● Gremlin query language ● A scripting language for graph traversal and mutation ● REST API
  • 11. Need for Apache TinkerPop Dealing with such complex databases, requires a well-implemented API by the vendor. But using a vendor specific API, makes migrating to another database impossible. The solution is provided by Apache Tinkerpop