Timescale Newsletter Roundup: November 2020 Edition
Get a breakdown of what's new from Team Timescale, from the much-anticipated TimescaleDB 2.0 release to our tips and tools for wrangling time-series data, speeding up database performance, and beyond 🚀.
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
⭐️[Bonus Announcement]: TimescaleDB vs. Amazon Timestream: 6000x higher inserts, 5-175x faster queries, 150x-220x cheaper >>
While this didn't quite make it into our last November newsletter, the work behind it took place in November, so we're giving it special mention here.
We ran Amazon Timestream through the open-source Time Series Benchmarking Suite, and as the title suggests, the results were pretty shocking: even after attempting 10+ different configurations, TimescaleDB dramatically outperformed Amazon Timestream in every area. Read the full post for detailed benchmark results, get 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 and open-source business sustainability.
- 💻 Get Time Series Benchmarking Suite code (GitHub)
[Product Announcement #1]: TimescaleDB 2.0 RC - multi-node, petabyte-scale, 100% free relational database for time-series - has arrived >>
This release is a huge milestone for us, the TimescaleDB community, and the industry as a whole: TimescaleDB is now a multi-node, petabyte-scale relational database for time-series – and completely free. In addition to multi-node, we’ve added new functionality and enhanced core features to give users more control and flexibility.
- 🚀 Read our announcement blog post to learn what's new, our journey to 2.0, and why we believe relational databases are the past and future of software development.
- 🎓 Watch our All Things TimescaleDB 2.0 Youtube playlist (5 videos) to get an overview of all new features, then dive into feature-specific videos, demos, and tips.
- 🐤 See this Twitter thread from Mike, Timescale CTO, for a quick - and emoji-packed - breakdown.
- 🙏 Biggest thank you to the Timescale Engineering team and to our countless beta testers for your feedback and support.
[Product Update #2]: Introducing dynamic scaling on Timescale Cloud >>
We just shipped dynamic scaling capabilities on Timescale Cloud, allowing you to resize your database compute and storage on demand. Result: more flexibility, better cost control, same great cloud-native hosted TimescaleDB instance.
- 👉 Explore Timescale Cloud - 100% free for 30 days
- 💬 Share feedback on Slack (#timescale-cloud channel)
New technical content, videos & tutorials
[PostgreSQL Pro Tips]: 5 essential PostgreSQL functions for monitoring & analytics >>
We love PostgreSQL, but it’s not always easy to write efficient, useful queries for DevOps scenarios. In this 45 min. session, @avthars demos his favorite queries for real-time monitoring and historical reporting, including TimescaleDB-specific functions for complex time-series analysis.
[Grafana Guide]: Tackle Grafana nuances with advanced tips and workarounds >>
Learn @avthars go-to Grafana workarounds for common scenarios, complete with demos, resources, and beyond. You’ll see how - and why - to enable timeshifting, autoswitch aggregations in a single graph, and alert on templated queries.
New #remote-friendly events & community
[Office Hours with Mike]: Join our Community Q & A sessions >>
If you haven’t joined our monthly sessions yet, 2021's your chance! Office Hours are always different - with topics ranging from best ways to integrate with 3rd party tools to musings on open-source technology - and always chock-full of expert advice, community projects, and fun.
- ✅ RSVP for Tues, January 5th - we love to see returning and fresh faces.
[Community Spotlight]: We predicted our TimescaleDB performance would get us to 3B Queries: here’s what really happened >>
Shoutout to our friends and long-time community members @DNSFilter for sharing how they've scaled their infrastructure over the last 24 months, why they moved to bare metal to support massive increase in users (and 6B+ requests per day!), and more.
Watch @avthars Open Source Summit EU session to start building your own flexible, 100% open source, 100% free observability stack. You’ll cover the pros and cons of various approaches, get tips from production deployments, and see how to deploy your own stack in <5 mins.
- 💡 Visit Avthar's blog for background on the session and bonus resources.
- 🐤 See Twitter thread for talk highlights and key takeaways.
[Community Article #1]: Switching from InfluxDB to TimescaleDB >>
In this 💯 writeup, our friends at AgriConnect detail how they use TimescaleDB to power their IoT platform for agriculture, as well as their experiences with time-series databases and their evaluation criteria.
[Community Article #2]: TimescaleDB and Django >>
Our friends from Protohaus Makerspace use TimescaleDB and Django to handle massive streams of data from hydroponic gardens. In this how-to, they share how to integrate TimescaleDB with a Django app in a few simple steps, complete with code samples 🎉.
TimescaleDB tips, reading list & etc.
[TimescaleDB Tip #1]: Use real-time aggregates for faster queries on raw (aka the latest) data >>
In this <7 minute how-to video, @avthars breaks down how real-time aggregates give you the best of both worlds: the speed of continuous aggregates and the ability to query your not-yet-materialized raw data. You’ll get demos, benchmarks, and resources to get started quicksmart ✅.
- 📑 Read our engineering blog post for more details and step-by-step examples.
[TimescaleDB Tip #2]: Explore sample apps, integrations, and more>>
From analyzing cryptocurrency trends and real-time bus locations to measuring environmental changes with Raspberry Pi, this repo has a variety of apps, clients, and integrations to inspire your time-series analysis.
- 🧭 Want more examples? Choose your own adventure with 20+ tutorials
[TimescaleDB Tip #3]: Speed up batch inserts with parallel-copy >>
Use this handy tool to speed up inserts and data migrations for large time-series workloads (100K+ row CSVs). Our goal: you spend more time analyzing and querying your data, not executing single COPY commands 🙌.
[Reading List]: Get answers to 25+ frequently asked questions >>
We’ve rounded up common questions from community members into one handy FAQ to help you find answers quickly. Topics range from how TimescaleDB scales to how we compare to other databases and details about how core features work.
[Team Timescale Fun]: From competing in asynchronous challenges to showing off our kitchen prowess and culinary skills, we're finding fun ways to stay connected, especially as we onboard new teammates across the world.
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,