Cassandra Summit 2015 - Building a multi-tenant API PaaS with DataStax Enterprise Search
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Lessons learned by Restlet when deploying DataStax Enterprise search with APISpark. Presentation by Jerome Louvel and Guillaume Blondeau at the Cassandra Summit 2015. Includes 7 challenges and solutions when deploying DataStax.
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Cassandra Summit 2015 - Building a multi-tenant API PaaS with DataStax Enterprise Search
- 2. 1. Introduction 2. Persistence needs of an API PaaS 3. Selecting DataStax Enterprise Search 4. Main challenges and solutions 5. Conclusion 6. Q&A Agenda
- 3. Introduction
- 4. ● Jérôme Louvel ○ founder & CTO of Restlet, Web API platform vendor ○ created Restlet Framework, first REST framework in 2004 ○ contributor to “RESTful Web Services” (O’Reilly, 2007) ○ member of the JAX-RS 1.0 expert group (2007 - 2009) ○ co-author of “Restlet in Action” (Manning, 2012) ○ InfoQ editor covering Web APIs since 2014 ● Guillaume Blondeau ○ DevOps engineer at Restlet ○ working on APISpark cloud platform ○ Cassandra Administrator certified by DataStax About the Speakers
- 6. ● Key features ○ visual creation & deployment of data APIs ○ operation of APIs & their local data sources ○ management of any API ● Benefits ○ accessible via web browser, no technical expertise required ○ companies of any size can become API providers ○ get started for free, then pay when the API generates traffic About APISpark
- 7. Persistence Needs of an API PaaS
- 8. High Availability of APIs and their Data Stores
- 9. Low Latency for Users Across the Globe Rugby World Cup Data
- 10. High Scalability & Elasticity ● For API traffic ○ concurrent calls ○ workload types ○ peaks handling ● For data storage ○ number of stores ○ volume of data ... ... ... ...
- 11. ● Filtering on properties ● Pagination ● Sorting Rich Query Capabilities
- 12. High Multi-tenant Density ● Balance between ○ data isolation ○ low cost ● Many customers & projects ○ sharing persistence infrastructure ○ isolated data stores ● Many users & groups ○ personal data ○ shared group data
- 14. Step 1: Prototyping with AWS NoSQL ● Started with SimpleDB ○ zero ops, highly available & low latency ○ mono-region & limited query capabilities ● Upgraded to DynamoDB ○ better scalability & predictability ○ not really for multi-tenant use cases (soft limits) ○ not very elastic (provisioned throughput) ● Other limitations ○ unable to develop and test locally (MySQL mode) ○ strong AWS lock-in
- 15. Step 2: Moving to Apache Cassandra ● For APISpark beta version ○ increasing multi-tenancy needs ○ increasing cost concerns ● Benefits ○ fully open source & free (vendor support) ○ on-premise deployments possible ○ proven scalability on AWS (Netflix) ○ richer query capabilities ○ natively multi-region
- 16. Step 3: Upgrading to DataStax Enterprise ● For APISpark GA ○ DataStax certified stack ○ production support ● Improved capabilities ○ much richer query capabilities with Solr integration ○ administration console ○ command line tooling ○ comprehensive documentation ● Still open source foundation ○ limited vendor lock-in ○ mature open source components
- 17. Current Persistence Design Entity Store Entity Property Primary Key
- 18. 7 Main Challenges & Solutions DataStax Enterprise Search 4.6.7 (Cassandra 2.0.14, Solr 4.6.0)
- 19. ● Using Ec2MultiRegionSnitch ● 1 Entity Store = 1 Keyspace ○ Each keyspace can set its own replication policy I. Deploying Across Multiple Regions
- 20. ● 1 Entity Store = 1 Keyspace ○ Data isolated in File System and Memory ● Complementary benefit ○ ACL per keyspace II. Isolating Customer Data & Keeping Cost Low Keyspace Table
- 21. Composite property List property III. Supporting Complex Data Models
- 22. IV. Dealing with Dynamic Schema Changes (1/3) ALTER TABLE DROP ALTER TABLE ADD
- 23. IV. Dealing with Dynamic Schema Changes (2/3) User Action on Entity Store Action performed in DB Create Entity CQL: “CREATE TABLE <tableName>” + Solr Core creation Delete Entity CQL: “DROP TABLE <tableName>” Create Property CQL: “ALTER TABLE ADD <columnName> <type>” + Solr Core schema update Delete Property CQL: “ALTER TABLE DROP <columnName>” + Solr Core schema update Add Property in composite Java: Alter JSON for all rows Delete Property in composite Java: Alter JSON for all rows
- 24. ● Advantages ○ flexibility compared to RDBMS ■ no lock ○ available actions ■ add / drop / rename column ■ change type of column ● Limitations ○ schema deployment can take time ○ in some edge cases can’t recreate columns IV. Dealing with Dynamic Schema Changes (3/3)
- 25. V. High Multi-tenant Density (1/2) Schema deployment time with growing # of tables
- 26. ● Challenge ○ large number of C* tables & Solr cores ○ memory usage (ex: 1 C* table takes more than 1MB of heap) ● Solutions ○ adjust JVM memory settings ○ need to create additional clusters ○ deprovision unused Entity Stores V. High Multi-tenant Density (2/2)
- 27. VI. Query Capabilities 1/2 Search queries Upsert / Delete / “Get by id” queries
- 28. ● Filtering on a property ● Pagination ● Sorting VI. Query Capabilities 2/2 Solr Queries
- 29. VII. Analytics (1/2) Provide analytics about API calls
- 30. VII. Analytics (2/2) used for latest API calls issue with wide rows (heavily used APIs) 1 table per report use of C* counters
- 31. Conclusion
- 32. ● Special use case of DataStax Enterprise ○ not a lot of shared knowledge about it ○ great support from DataStax ○ DSE is a good fit despite some challenges ● Looking forward to DSE 4.8 ! ○ User Defined Types with Solr indexing ○ live indexing of C* data into Solr ○ improved overall performance Conclusion
- 33. Questions ?