TimescaleDB: Fast And Scalable Timeseries with Ajay Kulkarni and Mike Freedman - Episode 18
Data Engineering Podcast
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Summary
As communications between machines become more commonplace the need to store the generated data in a time-oriented manner increases. The market for timeseries data stores has many contenders, but they are not all built to solve the same problems or to scale in the same manner. In this episode the founders of TimescaleDB, Ajay Kulkarni and Mike Freedman, discuss how Timescale was started, the problems that it solves, and how it works under the covers. They also explain how you can start using it in your infrastructure and their plans for the future.
Preamble
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- Your host is Tobias Macey and today I’m interviewing Ajay Kulkarni and Mike Freedman about Timescale DB, a scalable timeseries database built on top of PostGreSQL
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by explaining what Timescale is and how the project got started?
- The landscape of time series databases is extensive and oftentimes difficult to navigate. How do you view your position in that market and what makes Timescale stand out from the other options?
- In your blog post that explains the design decisions for how Timescale is implemented you call out the fact that the inserted data is largely append only which simplifies the index management. How does Timescale handle out of order timestamps, such as from infrequently connected sensors or mobile devices?
- How is Timescale implemented and how has the internal architecture evolved since you first started working on it?
- What impact has the 10.0 release of PostGreSQL had on the design of the project?
- Is timescale compatible with systems such as Amazon RDS or Google Cloud SQL?
- For someone who wants to start using Timescale what is involved in deploying and maintaining it?
- What are the axes for scaling Timescale and what are the points where that scalability breaks down?
- Are you aware of anyone who has deployed it on top of Citus for scaling horizontally across instances?
- What has been the most challenging aspect of building and marketing Timescale?
- When is Timescale the wrong tool to use for time series data?
- One of the use cases that you call out on your website is for systems metrics and monitoring. How does Timescale fit into that ecosystem and can it be used along with tools such as Graphite or Prometheus?
- What are some of the most interesting uses of Timescale that you have seen?
- Which came first, Timescale the business or Timescale the database, and what is your strategy for ensuring that the open source project and the company around it both maintain their health?
- What features or improvements do you have planned for future releases of Timescale?
Contact Info
- Ajay
- @acoustik on Twitter
- Timescale Blog
- Mike
- Website
- @michaelfreedman on Twitter
- Timescale Blog
- Timescale
- Website
- @timescaledb on Twitter
- GitHub
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Timescale
- PostGreSQL
- Citus
- Timescale Design Blog Post
- MIT
- NYU
- Stanford
- SDN
- Princeton
- Machine Data
- Timeseries Data
- List of Timeseries Databases
- NoSQL
- Online Transaction Processing (OLTP)
- Object Relational Mapper (ORM)
- Grafana
- Tableau
- Kafka
- When Boring Is Awesome
- PostGreSQL
- RDS
- Google Cloud SQL
- Azure DB
- Docker
- Continuous Aggregates
- Streaming Replication
- PGPool II
- Kubernetes
- Docker Swarm
- Citus Data
- Database Indexing
- B-Tree Index
- GIN Index
- GIST Index
- STE Energy
- Redis
- Graphite
- Prometheus
- pg_prometheus
- OpenMetrics Standard Proposal
- Timescale Parallel Copy
- Hadoop
- PostGIS
- KDB+
- DevOps
- Internet of Things
- MongoDB
- Elastic
- DataBricks
- Apache Spark
- Confluent
- New Enterprise Associates
- MapD
- Benchmark Ventures
- Hortonworks
- 2σ Ventures
- CockroachDB
- Cloudflare
- EMC
- Timescale Blog: Why SQL is beating NoSQL, and what this means for the future of data
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