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Graph Studio in Oracle Autonomous Database
Transcript of Graph Studio in Oracle Autonomous Database
Hans Viehmann, Product Manager at OracleHans.Viehmann@oracle,com @SpatialHannes https://www.linkedin.com/in/hansviehmann/
Jayant Sharma, Senior Director Product Management at Oracle
[email protected] https://www.linkedin.com/in/jayant62/1
Graph Studio in Oracle Autonomous Database
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Graph Studio in Autonomous DatabaseAnalytics and Data Oracle User Community – TechCast
Hans Viehmann & Jayant SharmaProduct Management – Spatial and GraphMarch 18, 2021
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The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation.
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Typical example: Social Networks• People as vertices, connection as edges
Graph analysis can automatically identify important entities• People with many connections• People connected to people who in turn have many
connections• People who are indirectly connected to a given
person – "Six degrees of Kevin Bacon" problem• Communities of people which are more closely
connected• People connected to two or more communities• Outliers who are only connected to one person
Graphs help modeling relationships between entities
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Graph databases explicitly store
relationships to enable fast traversal
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Smarter With Gartner, Gartner Top 10 Data and Analytics Trends for 2021, February 22, 2021
What is a graph?• Data model representing entities as vertices
and relationships as edges• Optionally including attributes
What are typical graphs?• Social networks• LinkedIn, Facebook, Twitter, ...
• Physical networks, supplier networks, ...• Dependency graphs• Part hierarchies, data lineage, org charts, ...
• Workflows• Knowledge graphs• Associating related entities, eg. in public safety
Graph data model
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Easy data modeling• All you need is pen and paper
Flexible data model• No predefined schema, easily extensible• Particularly useful for sparse data
Insight from graphical representation• Intuitive visualization
Enable new kinds of analysis• Graphs store rather than compute relationships so
traversals (friend-of-a-friend, is there a path) are quick• Additional features for Machine Learning
Why are graphs so popular?
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Enabling use of graph algorithms, eg. for• Influencer analysis
• Customer 360, targeted marketing, ...• Community detection• Product recommendation• Anomaly detection
• Fraud preventionSupporting graph pattern queries, eg.• Circular relationships
• Money laundering, fraud rings, ...• Dependency analysis
• Supplier relationships, workflows, ...
Extending Autonomous Database with Graph Analysis
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Algorithm
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Query processing• Search for surrounding nodes• Traverse property paths• Pattern matching• Extract sub-graphs
Executing graph algorithms• Rank importance of nodes • Detect components and clusters• Evaluate structure of communities• Shortest paths
Two types of processing in graph analytics
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Install and configure analytics environment• OCI marketplace image• Graph server and client kit on-premises
Create graph model• Map relational data to graph model• Load native graph file formats
Perform graph analysis• JShell client, Python client, or Notebook• Built-in graph visualization tool
Operate and maintain environment• Graph analytics engine• Notebook and interpreters
Our offering so far: Graph Server and Client
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Self-service graph database and analytics environment• Feature of the autonomous database• Self-driving, self-securing,
self-repairing
Comes with comprehensive tooling• Graph modeling tool to map relational
data to graphs• Browser-based notebooks for interactive
analysis and collaboration• Integrated graph visualization
Uses the proven capabilities of the Graph Analytics Engine
Graph Studio in Autonomous Database (Limited Availability)
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Graph Studio covers the entire workflowExtending the Autonomous Database with graph analytics
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Build and model
Collect and prepare
See and detect
Learn and share
Deploy and scale
Most customers start with relational model• Prepare data using SQL
Graph modeler suggests mapping
• Entities become vertices• Primary key–foreign key relationships
become edges• N:m relationships turn into direct connections
Iteratively modify initial graph to fit needs
• Change properties to vertices or vice versa• Convert edges into vertices• Extend graph with additional data
Use UI or PGQL DDL source code for editing• Graphical representation of graph model
Build and model
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Graph model is persisted in tables in ADB• One table for vertices, one for edges• Modify data using regular SQL• Bulk load capability• Merge with additional data using SQL
Query Playground tool to inspect graph• Using PGQL on data in database tables
Load graph data into in-memory engine• Execute graph algorithms• Perform PGQL pattern matching queries
Collect and prepare
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In-memory Graph Server
Edge table Vertex table1
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Property graph schemaDatabase tables
Execute graph algorithms• Community detection, influencer analysis• Ranking and walking• Structure evaluation• Path finding• Link prediction
Perform query processing using PGQL• Search for surrounding nodes• Traverse property paths• Pattern matching• Extract sub-graphs
E.g., Personalized page rank:Natural relative distance (or closeness) with respect to the vertices from the starting set
See and detect
Starting set of vertices
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PGQL MATCH clause describes graph pattern
Find common ancestors of Mario and Luigi
Pattern matching–PGQL queries
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SELECT p.nameFROM MATCH (m:Person)-[:likes]->(p:Person)WHERE m.name = 'Mario'
PATH has_parent AS () -[:has_father|has_mother]-> (:Person)SELECT ancestor.nameFROM MATCH (p1:Person) -/:has_parent+/-> (ancestor)
, MATCH (p2:Person) -/:has_parent+/-> (ancestor)WHERE p1.name = 'Mario’AND p2.name = 'Luigi'
Graph analytics: 60+ parallelized, in-memory algorithms out-of-the-box
Detecting components and communitiesStrongly Connected Components,Weakly Connected Components,Label Propagation,Conductance Minimization,Infomap
Ranking and walkingPageRank, Personalized PageRank,Degree Centrality, Closeness Centrality,Vertex Betweenness Centrality,Eigenvector Centrality, HITS, SALSA,Random Walk with Restart
Evaluating structuresAdamic-Adar Index, Conductance,Cycle Detection, Degree Distribution, Eccentricity, K-Core, LCC, Modularity, Reachability Topological Ordering,Triangle Counting
Path-findingShortest Path (Bellman-Ford, Dijkstra, Bidirectional Dijkstra), Fattest Path,Compute Distance Index,Enumerate Simple Paths,Fast Path Finding, Hop Distance
Link prediction and othersWTF (Who to follow)Minimum Spanning-Tree,Matrix Factorization
Machine learningDeepWalk *Supervised GraphWise *
Pg2Vec *
*on-premises, not yet in Graph Studio
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Multi-purpose notebook for collaboration• Documentation and visualization• Interactive analysis
Notebook interpreters• Markdown for documentation• Java for processing and graph algorithms• PGQL for pattern matching queries
Visualization• Interactive graph visualization• Tables, charts, ...
Learn and share
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Fully automated environment• Native feature of autonomous database• Complete system deployed in minutes• Only need to enable graphs for users• Integrated tooling
In-memory analytics engine• Memory allocated automatically upon load• De-allocated when not used• Pre-allocation possible
Deploy and scale
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“Oracle’s graph database easily achieved scalability while managing massive amounts of user entity information and their keys.”
Tatsuro KamoshidaCTO and Director, AMENIDY, Inc.
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“With Graph Studio in Oracle Autonomous Database, you don't need to be a graph expert to explore the power of graphs.”
Gianni CeresaGianni Ceresa, Managing Director, DATAlysisOracle ACE Director
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“Graph Studio’s point-and-click, low-code user interface enables us to cast traditional relational tables as a graph, allowing us to capitalize on Oracle's comprehensive set of graph algorithms.”
Dan VlamisPresident, Vlamis Software Solutions
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Graph databases are powerful tools, complementing relational databases• Especially strong for analysis of graph topology and connectedness
Graph analytics offer new insight
• Especially relationships, dependencies and behavioral patterns• Providing additional feature(s) for machine learning
Graph Studio in Autonomous Database offers end-to-end tooling• Graph Studio to model, manage and query graphs• Notebook UI for interactive visualization and analysis
• Scalable, parallel in-memory processing with 60+ pre-built graph algorithms• PGQL as pattern matching query language
Built on the capabilities of the Autonomous Database• Secure and scalable graph storage • Self-driving, self-securing, self-repairing
Summary
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TechCast Days – Spring Session• May 11 – 13• Interactive format
Planned workshops on May 12• Spatial Studio Hands-on• Graph Studio Hands-on• Running on Autonomous Database
Why join the workshops?• Test-drive the latest software• Learn from experts• Network with peers
Learn more
https://analyticsanddatasummit.org/techcastdays/
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Oracle Graph technologies landing page on oracle.com• https://www.oracle.com/database/graphOracle LiveLabs• Search for "Graph“: https://bit.ly/golivelabsBlog: Examples, tips and tricks• http://bit.ly/OracleGraphBlogAskTOM Series• https://asktom.oracle.com/pls/apex/asktom.search?office=3084Social Media• Twitter: @SpatialHannes, @JeanIhm, @ryotaymnk, @AnnamalaiMelli• LinkedIn: Oracle Spatial and Graph Group• YouTube: youtube.com/c/OracleSpatialandGraph
Helpful links
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Key takeaway for today ...
“Whenever you‘re analyzing relationships, think graphs!”