Restlet: Building a multi-tenant API PaaS with DataStax Enterprise Search
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Starting from the persistence needs of an API PaaS, we'll explain how we selected Cassandra and, finally, DSE Search, the main challenges we faced both in term of development and operations, and the solutions we have implemented.
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Restlet: Building a multi-tenant API PaaS with DataStax Enterprise Search
- 1. Building a multi-tenant API PaaS with DataStax Enterprise Search Presented by Jerome Louvel, Chief Geek & Guillaume Blondeau, Technical Detective
- 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
- 5. © Restlet 2015 x © Restlet 2015 Restlet API Platform • Includes 3 components ○ open source foundation ○ cloud platform ○ web IDE • Power the entire API lifecycle ○ design ○ deployment ○ management
- 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 ?
- 34. Thank you