Timescale Newsletter Roundup: December 2020 Edition

Explore exciting Timescale Forge feature drops (TimescaleDB 2.0, DIY multi-node & more 🔥), awesome resources from community members, and tools, tips, and advice for doing more with your time-series data.

We’re always releasing new features, creating new documentation and tutorials, and hosting virtual sessions to help developers do amazing things with their data. And, to make it easy for our community members to discover and get the resources they need to power their projects, teams, or business with analytics, we round up our favorite new pieces in our biweekly newsletter.

We’re on a mission to teach the world about time-series data, supporting and growing communities around the world.

And, sharing educational resources as broadly as possible is one way to do just that :).

Here’s a snapshot of the content we shared with our readers this month (subscribe to get updates straight to your inbox).

Product Updates & Announcements

[Product Update]: TimescaleDB 2.0, DIY multi-node & and new parameter config features come to Timescale Forge >>

Exciting news from the Timescale Forge team: TimescaleDB RC 3 and multi-node are now available for new Timescale Forge services ✨. In addition, we just shipped a handy feature that makes it easy to view and customize your database parameters, verify they’re set correctly, and make tweaks to improve performance – directly from the Timescale Forge UI.

📐 See multi-node on Timescale Forge docs for detailed instructions, considerations, and more.
💻 Watch Creating multi-node TimescaleDB clusters for a quick overview and step-by-step demo.
👉 Share feedback in #timescale-forge Slack channel.

GIF showing how to find and change parameters from the Timescale Forge UI — 🔥 Custom db parameters are automatically available in your Timescale Forge console

[NEW Benchmarks]: TimescaleDB vs. Amazon Timestream: 6000x higher inserts, 5-175x faster queries, 150x-220x cheaper >>

Our results surprised us, but even after attempting 10+ different configurations, TimescaleDB dramatically outperformed Amazon Timestream in every area. Get detailed benchmark results, configuration details to run your own analysis, and learn how our approach to licensing and software development gives us an advantage.

👉 See Ryan’s Twitter thread for an at-a-glance summary, complete with 💯 graphs.
🔖 Check out Hacker News discussion (100+ comments!)
🏅 Read Ajay’s Twitter thread for more on Cloud Protection Licenses & building sustainable open-source businesses.
💻 Get Time Series Benchmarking Suite code (GitHub)

New technical content, videos & tutorials

[PostgreSQL Pro Tips]: 5 ways to speed up PostgreSQL INSERT performance >>

@avthars shows you why ingest (insert) rate is so critical to time-series scenarios and factors that impact performance. You’ll get step-by-step demos, best practices, and resources to apply our favorite techniques to your projects.

🐘 Check out our Postgres INSERTS blog post for even more optimization tips

New #remote-friendly events & community

[Community Spotlight]: How we’ve come to prefer managing high frequency time-series data in database >>

Our friends at Flowtale Analytics share their at-a-glance breakdown of TimescaleDB - including how and why they use it for clients' projects, common use cases, performance, and best practices for IoT scenarios.

⚖️ See our comparison overview for key database evaluation criteria and how TimescaleDB stacks up to alternatives.

[Community Project Spotlight]: tsdbperf - async Rust tool to evaluate TimescaleDB ingest performance >>

In this project and 💯 README, you’ll get a quick overview of how to ingest simulated time-series data into a Rust app, run queries, report performance, and more.

👏 to Vince for sharing with community members and Rust developers everywhere.

[Office Hours]: Join us for community Q & A and time-series watercooler >>

New year, new projects and questions, same great monthly session with Timescale engineers and community members. Join to ask questions about TimescaleDB, get advice about optimizing your queries, or simply to chat all things databases.

🗓 RSVP for an upcoming session.

TimescaleDB tips, reading list & more

[Time-series Fun]: What the heck is time-series data and why do I need a time-series database? >>

We - shoutout to @ryanbooz - just refreshed this classic post with updated charts and trend data, examples of time-series hiding in plain sight, ways to analyze time-series data, and more.

🍿 More inclined to videos? Get a whirlwind tour of all things time-series in < 35 mins: what it is, how to analyze it to solve problems, and 3 common queries you can use immediately.
✨ Whether you’re new to time-series or a seasoned DBA brushing up on the basics, we think (hope!) you’ll find it valuable.

[Reading List - Prometheus Community]: Update - PromQL compatibility across vendors >>

@PromLabs just released updated PromQL test compatibility results – and we’re excited to share that Promscale, our new open-source analytics platform and long-term data store for Prometheus metrics, passed 100% tests for the 2nd time 🙌.

🧵 Check out @PromLabs Twitter announcement and join in on the conversation.
📈 Read our engineering blog for more about Promscale’s origin, design principles, and ways to get started.
🔎 Fun fact: Promscale supports PromQL and SQL for even more complex analysis.

[Reading List]: Rethinking the database materialized view as an index >>

In this classic post, we examine why materialized views and indexes are treated so differently, despite doing the same thing: allowing users to get results faster. From there, we detail how - and why - we’ve designed TimescaleDB to deliver always-current, always-correct materialized views (✅ making them work like indexes).

[Reading List]: Your time-series questions, answered >>

Get our answers to common questions from community members, including advice and resources for optimizing queries, effective indexing, and partitioning data.

[Listen List - Community]: Scaling Postgres Podcast Episode #143 >>

If you 💛 PostgreSQL, check out @CrestonJamison’s weekly podcast to learn about new resources, news, and more. Big thanks to Creston for giving our Amazon Timestream vs. TimescaleDB benchmarks a shoutout on Episode #143.

[TimescaleDB Tip #1]: Learn when (and why) to use narrow and wide tables >>

We use a sample IoT scenario to illustrate data model performance tradeoffs and considerations, so you can choose the one that works best for you. You’ll also get a few ways to use JOINS to easily query your time-series and relational metadata.

[TimescaleDB Tip #2]: Benchmark time-series workloads in production >>

Learn how to use the Time Series Benchmarking Suite to generate and load realistic sample data, test read/write performance, and evaluate query execution speed. We also include time-series queries and how to interpret results to help you choose the right database for your needs.

[Team Timescale Fun]: We ended December - and 2020 - with quite a few things to celebrate, from making CRN's Hottest Database Startups of 2020 list to a slew of launches (check out our Release Notes to follow along with the latest releases and what’s coming next), complete with fresh new Eon designs ✨ .

“Ready for Launch” Eon cheers us on as we hit merge and ship updates (courtesy of our amazing design team 👏)

Wrapping Up

And, that concludes this month’s newsletter roundup. We’ll continue to release new content, events, and more - posting monthly updates for everyone.

If you’d like to get updates as soon as they’re available, subscribe to our newsletter (2x monthly emails, prepared with 💛 and no fluff or jargon, promise).