NoSQL Tel Aviv Meetup #2: Who Is Afraid of Graphs?
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This document discusses graph databases and their advantages over relational databases. It begins with a brief history of graph databases, which have been used for over 400 years. It then discusses how graph databases use nodes, edges, and properties to store data in a graph structure. The document outlines some common use cases for graph databases and then discusses some popular graph database systems like Neo4j. It also covers topics like querying graph databases, using different query languages, and challenges of scaling graph databases.
![CYPHER (NEO4J)
a b
FRIEND
(a) –[:FRIEND]-> (b)](https://image.slidesharecdn.com/whosafraidofgraphs-151118083702-lva1-app6891-151118104344-lva1-app6892/85/NoSQL-Tel-Aviv-Meetup-2-Who-Is-Afraid-of-Graphs-11-320.jpg)
NoSQL Tel Aviv Meetup #2: Who Is Afraid of Graphs?
- 1. D AV I D O S T R O V S K Y WHO’S AFRAID OF GRAPHS?
- 2. A LITTLE HISTORY NoSQL 10 years Relational 40 years Graphs 400 years
- 3. T H E S E V E N B R I D G E S O F K O N I G S B E R G P R O B L E M LEONARD EULER
- 4. Use Nodes, Edges and Properties to store data. GRAPH DATABASES
- 5. USE CASES FOR GRAPH DATABASES
- 7. DATABASES VS FRAMEWORKS Databases • Real-time queries • Smaller datasets • Standard NoSQL features (scaling, HA, etc.) Frameworks • Offline/batch • Larger datasets • Relies on big data platform (usually Hadoop)
- 9. g.v(1).outE('friend').inV.name // Starting with vertex 1 // find outgoing edges ‘friend’, // follow to the next vertex, // and return the property ‘name’.
- 11. CYPHER (NEO4J) a b FRIEND (a) –[:FRIEND]-> (b)
- 13. C L U S T E R I N G A R C H I T E C T U R E NEO4J
- 14. TO T H E R E S C U E POLYGLOT PERSISTENCE
Editor's Notes
- #4: Devise a route through the city that only crosses each bridge once. Paper published in 1736 – regarded as the first paper on Graph Theory. Konigsberg, Prussia – which is Kaliningrad, Russia today.
- #5: Important to note that a graph database has: Native graph storage – the engine is built to handle graph data Native graph processing capability, including index-free adjacency to facilitate traversals
- #6: Social – of course Recommendation systems (a logical extension from the social graph, or stand-alone – find all customers who bought a book that X customers liked., then find all books similar to that one, etc.) Managing interconnected datasets: Networks, Organization Hierarchies, ACL, in-game economy, etc. Geo-location and routing (think Waze or network routing.) Use-cases for migrating from RDBMS: Problems with JOIN performance Continually evolving dataset or open-ended business requirements The domain is naturally designed for graph representation
- #7: For comparison – MongoDB has a score of 304.61, Cassandra 132.92
- #8: Frameworks: Giraph – apache project, used by Facebook to power it’s graph search and process trillions of connections. GraphX – Integrated with Apache Spark, has a library of build in algorithms and ETL functionality. Doesn’t perform as well as Giraph. Franus (from the same team as Titan) GraphLab – open source graph toolkit.