DataStax: Datastax Enterprise - The Multi-Model Platform
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DataStax Enterprise (DSE) is a multi-model data platform that supports various data models through a unified backend, primarily aimed at enhancing OLTP and OLAP workloads. It features a highly scalable graph database that allows for the efficient management of complex relationships within data, making it ideal for applications in IoT, customer data analytics, and network management. The platform integrates seamlessly with Apache Cassandra and supports real-time analytics and query capabilities through the Gremlin graph query language.
![Recommendation Query – RDBMS vs. Graph
©2015 DataStax
SELECT TOP (5) [t14].[ProductName]
FROM (SELECT COUNT(*) AS [value],
[t13].[ProductName]
FROM [customers] AS [t0]
CROSS APPLY (SELECT [t9].[ProductName]
FROM [orders] AS [t1]
CROSS JOIN [order details] AS [t2]
INNER JOIN [products] AS [t3]
ON [t3].[ProductID] = [t2].[ProductID]
CROSS JOIN [order details] AS [t4]
INNER JOIN [orders] AS [t5]
ON [t5].[OrderID] = [t4].[OrderID]
LEFT JOIN [customers] AS [t6]
ON [t6].[CustomerID] = [t5].[CustomerID]
CROSS JOIN ([orders] AS [t7]
CROSS JOIN [order details] AS [t8]
INNER JOIN [products] AS [t9]
ON [t9].[ProductID] = [t8].[ProductID])
WHERE NOT EXISTS(SELECT NULL AS [EMPTY]
FROM [orders] AS [t10]
CROSS JOIN [order details] AS [t11]
INNER JOIN [products] AS [t12]
ON [t12].[ProductID] = [t11].[ProductID]
WHERE [t9].[ProductID] = [t12].[ProductID]
AND [t10].[CustomerID] = [t0].[CustomerID]
AND [t11].[OrderID] = [t10].[OrderID])
AND [t6].[CustomerID] <> [t0].[CustomerID]
AND [t1].[CustomerID] = [t0].[CustomerID]
AND [t2].[OrderID] = [t1].[OrderID]
AND [t4].[ProductID] = [t3].[ProductID]
AND [t7].[CustomerID] = [t6].[CustomerID]
AND [t8].[OrderID] = [t7].[OrderID]) AS [t13]
WHERE [t0].[CustomerID] = N'ALFKI'
GROUP BY [t13].[ProductName]) AS [t14]
ORDER BY [t14].[value] DESC
g.V('customerId','ALFKI').as('customer')
.out('ordered').out('contains').out('is').as('products')
.in('is').in('contains').in('ordered').except('customer')
.out('ordered').out('contains').out('is').except('products')
.groupCount().cap().orderMap(T.decr)[0..<5].productName
VS.](https://image.slidesharecdn.com/datastaxsummit2015multimodel-151003214953-lva1-app6891/85/DataStax-Datastax-Enterprise-The-Multi-Model-Platform-20-320.jpg)
DataStax: Datastax Enterprise - The Multi-Model Platform
- 1. DataStax Enterprise: The Multi-Model Data Platform
- 2. 1 Multi-Model Defined 2 The evolution of Models 3 Graph Databases Overview 4 The DataStax Approach to Graph Databases 5 DataStax in the Open Source Graph Community 6 Conclusion 2© 2015. All Rights Reserved.
- 3. Multi Model Defined © 2015. All Rights Reserved. 3 Most database management systems are organized around a single data model that determines how data can be organized, stored, and manipulated. In contrast, a multi- model database is designed to support multiple data models against a single, integrated backend. Source - https://en.wikipedia.org/wiki/Multi-model_database
- 4. The evolution of Models © 2015. All Rights Reserved. 4 Application Application Application (OLTP and OLAP) Data Access Abstraction RDBMS / SQL Polyglot Persistence Multi Model
- 5. DSE Multi Model © 2015. All Rights Reserved. 5 Multiple (polygot) Models exposed via cohesive interaction mechanisms, (APIs) for OLTP and OLAP workloads, (mixed workload) with a unified persistence layer, (Apache Cassandra) providing GR, always on characteristics within a TCO efficient data base platform for a variety of use cases Cassandra
- 6. ©2015 DataStax Confidential. Do not distribute without consent. 6 DataStax Enterprise – Wide Row / MVs C1 C2 MV1c1 MV1c2 Agg1c1 Agg2c1
- 7. ©2015 DataStax Confidential. Do not distribute without consent. 7 DataStax Enterprise - JSON Inserting JSON data is easy Reading JSON data is easy Finding JSON errors is easy
- 8. Introduction to Graph Databases © 2015. All Rights Reserved. 8
- 9. What is a Graph Database? ©2015 DataStax Confidential. Do not distribute without consent. • Store, manage and query highly connected data • Data stored as nodes (vertices), edges and properties to represent a domain model • By explicitly embedding relationships in the data model, you store a more logical business model using the natural data access language Gremlin • Think of a graph as a pre-joined database
- 10. Choose the Right Model to Fit your Business Needs © 2015 DataStax, All Rights Reserved. Company Confidential 10 DSE Wide Row - Build and maintain models using CQL’s DDL features - Super-fast CRUD at scale with CQL DML features - Good option for denormalized schemas with high-throughput requirements - Perfect fit for IoT applications that require consuming enormous amounts of data with specific data retrieval requirements: product catalogs, high-volume messaging systems, collecting and storing sensor data DSE Graph - Flexible schema that is easy to modify and maintain with Gremlin - Clearly maps business semantics to a logical model for easy maintenance and understanding - Ideal model for highly-connected data models - Perfect fit for social-engagement models, recommendation engines and IT network / device management - Update and query the graph in real-time with easy-to-learn open-source Gremlin language - Good option for transitioning from slow RDBMS 3NF models with lots of JOINs
- 11. What is DataStax Enterprise (DSE) Graph? ©2015 DataStax • Highly scalable graph database for modern web, mobile, and IoT applications that need to manage highly connected and heterogeneous data • Built-in support for real-time search, and analytic graph queries via tight integration with the DSE platform • A property graph model native inside the DataStax product, engineered specifically for Cassandra • Store & find relationships in data fast and easy on huge graphs
- 12. DSE Graph: Built-in Scalability ©2015 DataStax • Scale out Graph vs. Scale up only • Graph partitioning built on Cassandra’s scale-out architecture • Graph index structures integrated into Cassandra • Domain model maps more naturally to data model, allowing for greater understanding between business and IT • Traverse millions of relationships in a short period of time, faster than modeling the data in RDBMS • Flexible data model that can be easily adapted to business changes
- 13. DSE Graph: Integrated Search, Analytics and Ops ©2015 DataStax • Real-time traversals over complex-structured graph data • Integrates with DSE Search to mix search with traversal queries • Integrates with DSE Analytics and Spark to support OLAP and breadth-first graph traversals • Iterative graph analytics like PageRank or other centrality measures • Reporting and aggregates over graph data • Integrated with DataStax OpsCenter
- 14. Graph Database Use Cases © 2015. All Rights Reserved. 14
- 15. Additional Graph Use Cases 360 Degree View of Your Customer • Collect massive amounts of data point about your customer • Data collected from social networks, web analytics, digital ads, mobile devices, CRM • Bring heterogeneous customer data together into DSE Graph • Uncover buying patterns and customer behaviors • Graph becomes a master data hub for customer data • Use the graph customer hub to build better products for your target customers • Keep the customers you already have with customer intelligence Customer 360 View Social Store Sensors Email CRM Mobile Weblogs
- 16. Additional Graph Use Cases IT Network and Device Management • Allows IT to monitor, manage and protect corporate networks and devices (laptops, iPads, mobile phones, etc.) • Requires understanding of network topologies and relationships between devices, interfaces, equipment, people, services … • A traditional RDBMS would require expensive query-time joins • A graph model intrinsically knows how to traverse the topology because the relationships are already stored • This makes for quick & easy recognition of problems, root cause analysis and event correlation
- 17. DataStax in the Graph Open Source Community TinkerPop / Gremlin © 2015. All Rights Reserved. 17
- 18. DataStax Role in TinkerPop ©2015 DataStax • DataStax utilizes the TinkerPop framework for the DSE Graph product • DataStax will contribute to the TinkerPop community and is heavily invested in the success of the Gremlin language • DataStax will provide resource guides, documentation, samples and training on building and querying graphs with Gremlin, using DSE Graph as the graph engine
- 19. Gremlin The open-source standard graph query language • DataStax contributes and supports the Apache TinkerPop community, along with the Gremlin Graph query language • g.V.hasLabel('person').as('a').out('knows').as('b').select('a','b').by('age') .by('age') "for all people in the graph, give me the ages of the people on each end of a friendship relationship“ • g.V.has('name','marko').out('knows').out('mother').outE('worksFor'). has('time',between(2001,2002)).inV.name “what are the names of the places that marko's friends' mothers worked for from 2001 to 2002” Deep traversal == multiple levels of query-time joins in RDBMS
- 20. Recommendation Query – RDBMS vs. Graph ©2015 DataStax SELECT TOP (5) [t14].[ProductName] FROM (SELECT COUNT(*) AS [value], [t13].[ProductName] FROM [customers] AS [t0] CROSS APPLY (SELECT [t9].[ProductName] FROM [orders] AS [t1] CROSS JOIN [order details] AS [t2] INNER JOIN [products] AS [t3] ON [t3].[ProductID] = [t2].[ProductID] CROSS JOIN [order details] AS [t4] INNER JOIN [orders] AS [t5] ON [t5].[OrderID] = [t4].[OrderID] LEFT JOIN [customers] AS [t6] ON [t6].[CustomerID] = [t5].[CustomerID] CROSS JOIN ([orders] AS [t7] CROSS JOIN [order details] AS [t8] INNER JOIN [products] AS [t9] ON [t9].[ProductID] = [t8].[ProductID]) WHERE NOT EXISTS(SELECT NULL AS [EMPTY] FROM [orders] AS [t10] CROSS JOIN [order details] AS [t11] INNER JOIN [products] AS [t12] ON [t12].[ProductID] = [t11].[ProductID] WHERE [t9].[ProductID] = [t12].[ProductID] AND [t10].[CustomerID] = [t0].[CustomerID] AND [t11].[OrderID] = [t10].[OrderID]) AND [t6].[CustomerID] <> [t0].[CustomerID] AND [t1].[CustomerID] = [t0].[CustomerID] AND [t2].[OrderID] = [t1].[OrderID] AND [t4].[ProductID] = [t3].[ProductID] AND [t7].[CustomerID] = [t6].[CustomerID] AND [t8].[OrderID] = [t7].[OrderID]) AS [t13] WHERE [t0].[CustomerID] = N'ALFKI' GROUP BY [t13].[ProductName]) AS [t14] ORDER BY [t14].[value] DESC g.V('customerId','ALFKI').as('customer') .out('ordered').out('contains').out('is').as('products') .in('is').in('contains').in('ordered').except('customer') .out('ordered').out('contains').out('is').except('products') .groupCount().cap().orderMap(T.decr)[0..<5].productName VS.
- 21. Thank you