Building a Startup with NoSQL: Polyglot Persistence for Time Series, Caching, and Logging
Sebastian Cohnen – Building a Startup with NoSQL At StormForger we use several NoSQL systems to handle all kinds of different data. We have a lot of time series data based on the fact, that we do load testing and performance analysis of HTTP-based infrastructure and services. For time series data, we use InfluxDB. We also use several Redis instances for caching and storing structured data, that needs to be fast on read and write access. Lately we also started to integrate ArangoDB into our architecture, which is a perfect fit for storing and working with our complex test case definition data structures. In this talk I’d like to present how we build our startup on the foundation provided by several NoSQL databases, how we came to choose those systems and how we use them.
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Building a Startup with NoSQL: Polyglot Persistence for Time Series, Caching, and Logging
- 1. Building a Startup with NoSQL Sebastian Cohnen, @tisba stormforger.com, @StormForgerApp NoSQL Matters Barcelona 2014
- 2. About me • Sebastian Cohnen, M. Sc. • Developer & Founder from Cologne, Germany • (Web-) Architectures, Performance & Scalability • Founder of StormForger.com (services around load testing HTTP-based systems)
- 3. Startups • agile & open minded • small teams, pragmatic • solve new problems
- 4. NoSQL? "A NoSQL (often interpreted as Not Only SQL) database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases." "Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability." https://en.wikipedia.org/wiki/NoSQL
- 5. NoSQL • Ease of use (development & operations) • Very special problem
- 6. StormForger
- 7. StormForger • load and performance testing of HTTP APIs • SaaS product • plan, configure and execute tests • analyze and compare results
- 8. We are a startup!
- 9. Our Needs • handle highly structured, complex data • ingest, process and query time series data • caching of structured data • centralized logging and log analysis
- 10. How to find the perfect solution?
- 11. There isn't one :-/
- 12. Polyglot Persistence (NoSQL) Technology Problem
- 13. …but be careful • What are the downsides and tradeoffs? • Availability of tooling? • How mature is the technology? • What about (community or commercial) support?
- 14. Back to StormForger's needs…
- 15. Time Series Data • SQL-ish, declarative query language • easy to get started with • powerful background/stream processing
- 16. Caching • very, very fast • primitive functions to work with data structures • awesome community
- 17. Logging • ELK (Elastic Search + Logstash + Kibana) • Centralized, aggregated logging • Great analysis and search features
- 18. Structured Data • Not really tackled yet • For now: serialize and store as TEXT in RDMS
- 19. Structured Data (2) • we already evaluated some solutions • great query language (AQL) • support for JOINS & Graphs
- 20. Conclusion • focus on ease of use aspects • be pragmatic and think agile • pick the right tool for the job
- 21. And if you think you found the right solution… …there are always alternatives…
- 22. What about using a SQL DB for NoSQL?
- 23. PostgreSQL • hstore: Key/Value • json & jsonb: JSON data • just use your existing tools • query via SQL, indexable, …
- 25. Thanks! Questions? Sebastian Cohnen (@tisba) https://stormforger.com