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![Cypher Query Example
Make two nodes
CREATE (:person {id: 1, name: “Kisung Kim”, birthday: 1980-01-05});
CREATE (:company {id: 1, name: “Bitnine Global”});
Make a relationship between the two nodes
MATCH (p:person {id: 1}), (c:company {id:1})
CREATE (p)-[:workFor {title: “CTO”, since: 2014}]->(c);
Kisung Kim Bitnine Global
workFor](https://image.slidesharecdn.com/implementinggraphdatabasebased-onpostgresql-170305223138/75/AgensGraph-a-Multi-model-Graph-Database-based-on-PostgreSql-7-2048.jpg)
![Cypher Query Example
Querying
MATCH (p:person {name: “Kisung Kim”})-[:workFor]->(c:company)
RETURN (p), (c)
No Table Definitions and No Joins
Query with variable length relationships
MATCH (p:person {name: “Kisung Kim”})-[:knows*..3]->(f:person)
RETURN (f)
Kisung Kim ?
workFor
Kisung Kim ?
knows
?
knows
?
knows](https://image.slidesharecdn.com/implementinggraphdatabasebased-onpostgresql-170305223138/75/AgensGraph-a-Multi-model-Graph-Database-based-on-PostgreSql-8-2048.jpg)
![So, What We Want to Make is
• Hybrid database engine with graph and relational model
• Cypher query processing on PostgreSQL
• Online transactional graph database
• Disk-based persistent graph storage
( ) -[:processes]->(Cypher)](https://image.slidesharecdn.com/implementinggraphdatabasebased-onpostgresql-170305223138/75/AgensGraph-a-Multi-model-Graph-Database-based-on-PostgreSql-11-2048.jpg)
![Cypher Query Example
Make two nodes
CREATE (:person {id: 1, name: “Kisung Kim”, birthday: 1980-01-05});
CREATE (:company {id: 1, name: “Bitnine Global”});
Make a relationship between the two nodes
MATCH (p:person {id: 1}), (c:company {id:1})
CREATE (p)-[:workFor {title: “CTO”, since: 2014}]->(c);
Kisung Kim Bitnine Global
workFor](https://crownmelresort.com/image.slidesharecdn.com/implementinggraphdatabasebased-onpostgresql-170305223138/75/AgensGraph-a-Multi-model-Graph-Database-based-on-PostgreSql-7-2048.jpg)
![Cypher Query Example
Querying
MATCH (p:person {name: “Kisung Kim”})-[:workFor]->(c:company)
RETURN (p), (c)
No Table Definitions and No Joins
Query with variable length relationships
MATCH (p:person {name: “Kisung Kim”})-[:knows*..3]->(f:person)
RETURN (f)
Kisung Kim ?
workFor
Kisung Kim ?
knows
?
knows
?
knows](https://crownmelresort.com/image.slidesharecdn.com/implementinggraphdatabasebased-onpostgresql-170305223138/75/AgensGraph-a-Multi-model-Graph-Database-based-on-PostgreSql-8-2048.jpg)
![So, What We Want to Make is
• Hybrid database engine with graph and relational model
• Cypher query processing on PostgreSQL
• Online transactional graph database
• Disk-based persistent graph storage
( ) -[:processes]->(Cypher)](https://crownmelresort.com/image.slidesharecdn.com/implementinggraphdatabasebased-onpostgresql-170305223138/75/AgensGraph-a-Multi-model-Graph-Database-based-on-PostgreSql-11-2048.jpg)
Uploaded byKisung Kim
AgensGraph: a Multi-model Graph Database based on PostgreSql
AgensGraph is a multi-model graph database built on PostgreSQL. It stores graph data as vertices and edges indexed in B-trees for efficient traversal. While the current version supports Cypher queries and integrated processing of graph and SQL, future work includes distributed graph support using Postgres-XL, specialized storage and indexes for graph queries, and integration with big data systems.
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AgensGraph: a Multi-model Graph Database based on PostgreSql
- 1. AgensGraph: a Multi-ModelGraph Database based-on PostgreSQL Kisung Kim (kskim@bitnine.net) Bitnine R&D Center 2017-1-14
- 2. Who am I •Ph.D Kisung Kim -Chief Technology Officer of Bitnine Global Inc. • Researched query optimization for graph-structured data during doctorate degree • Developed a distributed relational database engine in TmaxSoft • Lead the development of a new graph database, AgensGraph in Bitnine Global
- 3. What is GraphDatabase? Images from http://www.slideshare.net/debanjanmahata/an-introduction-to-nosql-graph-databases-and-neo4j
- 4. What is GraphDatabase? • Relationship is the first-class citizen in the graph database • Make your data connected in the graph database Relational Database Graph Database Entity Row Node (Vertex) Relationship Row Relationship (Edge)
- 5. What is theGraph Database? • Handle data in different view • Data model similar to entity-relationship model • Gartner says it represents a radical change in how data is organized and processed
- 6. Cypher Query Language •Declarative query language for the property graph model • Inspired by SQL and SPARQL – Designed to be human-readable query language • Developed by Neo technology Inc. since 2011 • Current version is 3.0 • OpenCypher.org (http://opencypher.org) – Participate in developing the query language
- 7. Cypher Query Example Maketwo nodes CREATE (:person {id: 1, name: “Kisung Kim”, birthday: 1980-01-05}); CREATE (:company {id: 1, name: “Bitnine Global”}); Make a relationship between the two nodes MATCH (p:person {id: 1}), (c:company {id:1}) CREATE (p)-[:workFor {title: “CTO”, since: 2014}]->(c); Kisung Kim Bitnine Global workFor
- 8. Cypher Query Example Querying MATCH(p:person {name: “Kisung Kim”})-[:workFor]->(c:company) RETURN (p), (c) No Table Definitions and No Joins Query with variable length relationships MATCH (p:person {name: “Kisung Kim”})-[:knows*..3]->(f:person) RETURN (f) Kisung Kim ? workFor Kisung Kim ? knows ? knows ? knows
- 9. GraphDB to PostgreSQLCase • From Hipolabs http://engineering.hipolabs.com/graphdb-to-postgresql/
- 10. Graph Database andHybrid Database Magic Quadrant for Operational Database Management Systems, Gartner, 2016
- 11. So, What WeWant to Make is • Hybrid database engine with graph and relational model • Cypher query processing on PostgreSQL • Online transactional graph database • Disk-based persistent graph storage ( ) -[:processes]->(Cypher)
- 12. Why We ChoosePostgreSQL? • Fully-featured enterprise-ready open source database • Graph processing actually uses relational algebra – Graph is serialized as tables in disk – Every graph traversal step is in principle a join (from LDBC documentation) • It is important to optimize the joins speed up join processing – PostgreSQL has an excellent query optimizer • And…. Abundant eco-system of PostgreSQL
- 13. Challenges • How tostore graph data – Efficient structure for graph pattern matching – At the same time, efficient for transaction processing • How to process graph queries – Processing complex graph pattern matching: variable length path, shortest path – Mismatches between graph data model & relational data model – Graph query optimization
- 14. Graph Storage • Graphdata is stored in disk as decomposed into vertexes and edges • When processing graph pattern matching, it is essential to find adjacent vertexes or edges efficiently – Given a start vertex, find end vertexes – Given an end vertex, find start vertexes v1
- 15. Two Graph Databases SolutionCompany Latest Version Features Neo Technology 3.1 Most famous graph database, Cypher O(1) access using fixed-size array Datastax - Distributed graph system based on Cassandra Titan
- 16. Graph Storage -Neo4j •Fixed-size array for nodes and relationships • Relationships for a node is organized as a doubly-linked list • Index-free adjacency • O(1) access for adjacent edges: follow the pointer From Graph Databases 2nd ed. O’Reilly, 2015
- 17. Graph Storage –Titan (DSE Graph) • Titan stores graphs in adjacency list format • Each edge is stored twice • Vertex and edge list are stored in backend storage like HBase Cassandra or BerkeleyDB From http://s3.thinkaurelius.com/docs/titan/1.0.0/data-model.html
- 18. Graph Storage -AgensGraph •Fixed-size array is hard to implement in PostgreSQL – Tuples are moved when updated • Titan’s big row approach is also inadequate • We chose B-tree index for graph traversal Graph Vertex Edge Vertex ID Properties Edge ID PropertiesStart Vertex ID End Vertex ID B-tree Vertex ID B-tree (Start, End) B-tree (End, Start)
- 19. Index Problems • CurrentB-tree has several disadvantages for our workload – Composite index is preferable but the size increases – There exists a lot of duplicate keys (vertex ID)on start_ID or end_ID – Property updates incur insertions into B-trees • We are developing a new index having bucket structure (like GIN index), in-direct index and supports for index-only scan for the graph traversals
- 20. Graph Storage -AgensGraph •Vertexes and edges are grouped into labels • Labels are organized as a label hierarchy • We use PostgreSQL’s table hierarchy feature Vertex ID Properties ag_vertex Vertex ID Properties Person Vertex ID Properties Message Vertex ID Properties Comment Vertex ID Properties Post
- 21. Current Status • AgensGraphv0.9 (https://github.com/bitnine-oss/agens-graph or http://bitnine.net/downloads/) – Graph data model and DDL on PostgreSQL 9.6 – Cypher query processing (70% of OpenCypher spec.) – Integrated query processing (Cypher + SQL) – Client library (JDBC, ODBC, Python) – Monitoring and development using Tadpole DB-hub
- 22. Tadpole for AgensGraph • Tadpole DB Hub is open-source project for managing unified infrastructure (https://github.com/hangum/TadpoleForDBTools) • Support various databases including (PostgreSQL and Agens Graph) • Features of Tadpole for Agens Graph – Monitoring Agens Graph server – Cypher query browser and graph visualization
- 23.
- 24. Future Roadmap • Distributedgraph database – Plan to exploit Postgres-XL • Specialized storage and index for graph traversals • Dictionary compression for JSONB (ZSON) • Graph query optimization using graph statistics • Integration with big data systems – HDFS Storage – Graph analysis using GraphX
- 25. Join Us • AgensGraphis an open-source project https://github.com/bitnine-oss/agens- graph • We also wish to contribute PostgreSQL community • Graph database meetup in Silicon Valley – http://www.meetup.com/Graph-Database-in-Silicon-Valley/
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