Timescale Newsletter Roundup: October 2020 Edition

Timescale Newsletter Roundup: October 2020 Edition

Get a cornucopia of resources to help you do more with your data - with a special focus on all things open source ✨ -  and some of our favorite new content from TimescaleDB community members.

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 Product Update]: TimescaleDB 2.0 RC - multi-node, petabyte-scale, 100% free relational database for time-series - has arrived >>

This news didn't quite make it into our biweekly newsletter cadence, but it's definitely a noteworthy October happening; 2.0 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 it's free.

Check out our explainer video series for a whirlwind tour of what's new in TimescaleDB 2.0

[Product Update]: Introducing Promscale: an open-source analytical platform for Prometheus metrics >>

We just announced Promscale, a new open-source platform built to scale and augment Prometheus for analytics, combining the power of PromQL and SQL with a rock-solid long-term data store. Ask any question, analyze recent & historical data, assess issues in real-time, forecast future trends, and more 🚀.

  • 🔥 See our blog post to learn more about Promscale, how it originated (3.5+ years of community feedback!), how it works, and ways to get started.
  • ⚙ Go to our GitHub README for various installation options - we recommend tobs, our CLI tool.
  • 🙋 Have feedback or questions? Let us know on Slack (#Prometheus channel).
Watch this 15 min. demo video to learn how Promscale works, plus ways to query your data and answer questions about your systems (using PromQL, SQL, and Grafana).

New technical content, videos & tutorials

[PostgreSQL Pro Tips]: Save time with PostgreSQL Cheatsheet >>

We’ve rounded up essential psql commands in one easy-to-navigate place, so you spend more time querying your data, not trying to remember that command that always escapes you. Click, copy, done ✅.

[PostgreSQL Pro Tips]: Get 10+ PostgreSQL functions for advanced analytics >>

Use this to-the-point reference documentation to run complex queries on your time-series data, from calculating deltas with window functions and finding anomalies in your monitoring metrics to generating histograms.

New #remote-friendly events & community

[Office Hours with Mike]: Join our next monthly Q & A and time-series watercooler session >>

Fun fact: Mike - our CTO - is also a computer science professor at Princeton, so it’s only fitting that he hosts our Office Hours. Each month’s session is different, with topics ranging from TimescaleDB-specific to all things database optimization, favorite tools, and distributed computing.

  • ✅  RSVP for Nov. 10 - everyone’s welcome, whether you have a question or just want to talk time-series, PostgreSQL, and open source.

[Virtual Session]: Observability Solutions w/ Open-Source Software: Lessons from the Field (demos and recommendations) >>

Catch @avthars Open Source Summit EU recording to learn how to build a flexible observability stack with 100% open-source (aka free!) tools. You’ll get a breakdown of available open-source components, hear considerations and best practices from real engineers, and see how to get started in <5 mins.

 Tweet with screenshot of "Let's build" session slides and call-to-action to visit the tobs GitHub repo to build your own
Check out @avthars Twitter thread to learn more – and visit tobs GitHub repo to get started

[Community Spotlight #1]: How FlightAware fuels flight prediction models for global travelers with TimescaleDB and Grafana >>

Our friends @flightaware - the world’s largest flight tracking platform - share how they built a monitoring system that allows them to predict flight arrival and departure times for 75K+ flights a day. The team breaks down how FlightAware works - and ways to get involved - and shares example Grafana dashboards + SQL queries, pro tips, and more.

  • 🙏 to Caroline, FlightAware Sr. Engineer & Predict Team Lead, for sharing your story!

[Community Spotlight #2]: How WsprDaemon combines TimescaleDB and Grafana to measure and analyze radio transmissions >>

Learn how the WsprDaemon team uses SQL, TimescaleDB, and Grafana to bring radio transmission data and analysis to developers everywhere. Rob & Gywn share example queries, Grafana dashboards, and why they switched from InfluxDB (hint: high-cardinality).

[Community Article]: GTM Stack: IoT Data Analytics at the Edge >>

We love this piece from Gary Stafford, which takes you through building an open-source IoT analytics stack with Grafana, TimescaleDB, and Mosquitto (plus why this is the ideal setup).

[Meetup Replay]: Paris Time-Series Meetup: Intro to TimescaleDB (demos and best practices) >>

Timescale Developer Advocate Avthar shares TimescaleDB fundamentals, including how hypertables and chunking works, then dives into 5 pros and cons – and ways to work around “cons,” like using native compression to reduce storage overhead.

TimescaleDB tips, reading list & etc.

[TimescaleDB Tip #1]: Speed up your Grafana dashboards with UNION ALL >>

If Grafana is slow to load your dashboards with fine-grained, non-aggregated data, you’re not alone. Use this short guide to see how to apply PostgreSQL UNION ALL to speed up your visualizations - saving you time and CPU resources 🔥.

-- Use Daily aggregate for intervals greater than 14 days
SELECT day as time, ride_count, 'daily' AS metric
FROM rides_daily
WHERE  $__timeTo()::timestamp - $__timeFrom()::timestamp > '14 days'::interval AND  $__timeFilter(day)

-- Use hourly aggregate for intervals between 3 and 14 days
SELECT hour, ride_count, 'hourly' AS metricFROM rides_hourly
WHERE $__timeTo()::timestamp - $__timeFrom()::timestamp BETWEEN '3 days'::interval AND '14 days'::interval AND  $__timeFilter(hour)

-- Use raw data (minute intervals) intervals between 0 and 3 days
SELECT bucket, ride_count, '10min' AS metric
FROM rides_10mins
WHERE $__timeTo()::timestamp - $__timeFrom()::timestamp < '3 days'::interval AND  $__timeFilter(bucket)
See our how-to blog post for more sample queries and pro tips

[TimescaleDB Tip #2]: Set up streaming replication to protect against failovers, outages and unforeseen issues >>

Check out this step-by-step tutorial to configure PostgreSQL streaming replication on your TimescaleDB instances before anything goes wrong. You’ll get guidance for synchronous and asynchronous replication and viewing diagnostics, as well as a few example scenarios.

[Reading List]: When Boring is Awesome: Building a scalable time-series database on PostgreSQL >>

Go back to the beginning with our first-ever blog, where we introduced the TimescaleDB beta to the world. We've come a long way in 3 years, but "boring" is even more awesome than ever — especially when it’s your database.

[Reading List]: Time-series data: Why (and how) to use a relational database instead of NoSQL >>

In this old-but-great post, we detail how traditional databases handle time-series data, why neither option is quite right, and how TimescaleDB takes a different approach. The result: a relational database with minimized memory usage, robust index support, and scale (including 15x+ INSERT rate improvements 🎉).

[Reading List]: 5 reasons why relational databases > NoSQL for IoT scenarios >>

The above blog applies to all scenarios, and, in this one, our product team focuses on why relational databases reign supreme for IoT scenarios specifically, from eliminating data silos - all of your data in one place! - to reliability and flexibility.

[Time-series Fun]: Explore time-series analysis with IoT & DevOps sample datasets >>

We've created some sample datasets and example queries to get you up and running. Each scenario includes various database sizes, time intervals, and partition field values 🎉.

[Team Timescale Fun]: Last, but certainly not least, Timescale People Manager Mel continues to bring her A+ game to all things remote team bonding.

Screenshot of Zoom meeting with song lyrics on screen
🎁 The prize for our most recent async Slack challenge: an original composition featuring the winners, performed via ukulele at our weekly All Hands 🎼
Screenshot of designed image from Embroker site
TimescaleDB made @Embroker’s Top 200 Startup List (#49) 🥳 We’re honored to be featured along so many amazing companies - check out the full list.

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).

Happy building!

The open-source relational database for time-series and analytics.
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