Timescale Newsletter Roundup: January 2022
Welcome to the first newsletter roundup of 2022! 🥳 We hope you had a safe and happy holiday season! The Timescale Team had a wonderful time – our company Slack was inundated with recipes 🥞, movie and book recommendations, and lots of skiing 🏔.
We’re back full of energy for the new year, with many product launches and exciting announcements on the horizon.
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
Promscale 0.8 🚀 – A new version of Promcale was released in January. This release includes new features that make trace data management easier, giving users the ability to configure the retention period for tracing data in Promscale and deleting all their tracing data easier. This release also comes with improved CLI flags, new extension packages for Linux, additional instrumentation, and the ability to configure the <default_chunk_interval> in Promscale.
- See the release notes for the complete list of changes.
- Install it using tobs, our CLI tool (you will get a full open-source observability stack directly installed and configured into your Kubernetes cluster).
New Promscale docs – Right before the holidays, we gave some 💛 to our Promscale documentation, adding more general information about Promscale and tobs, and instructions on how to ingest, query, and visualize data with Promscale.
New install docs – We recently updated our “Install TimescaleDB” instructions. You will now find information on how to install TimescaleDB in a self-hosted manner, through pre-built containers, and pre-built cloud images as well as new instructions on how to sign up for Timescale Cloud and Manage Service for TimescaleDB, our two hosted options.
- 💡 New to TimescaleDB? Review our getting started guide.
New technical content, videos & tutorials
time_bucket_gapfill() functions – Watch Timescale team members Miranda Auhl and David Kohn dive into the
time_bucket_gapfill functions and explore what it is, how to use them, and how it works!
Analyzing Wordle data from Twitter using PostgreSQL and TimescaleDB – In this live stream recording, Timescale Developer Advocate Ryan Booz and Timescale Engineer David Kohn play around with Wordle data taken from Twitter.
Visualize time-series data with TimescaleDB and Apache Superset – Did you know you can use TimescaleDB as a backend for Apache Superset? Learn how to create fast dashboards using TimescaleDB continuous aggregates and how to use TimescaleDB functions with/inside Superset (e.g.,
time_bucket, first/last, other hyperfunctions).
New #remote-friendly events & community
Virtual – Timescale virtual monthly Office Hours (Feb 1)
If you haven’t joined our monthly sessions yet, now’s your chance! Office Hours are always different, topics ranging from best ways to integrate with third-party tools to musings on open-source technology. No matter what, they are always chock-full of expert advice, community projects... and fun!
💬 If you can’t join but have a question, reach out to our engineering team on Slack!
Webinar – Postgres Conference Webinar Series (Feb 1)
Timescale Developer Advocate Ryan Booz will be giving a virtual talk on how to best use TimescaleDB for the most demanding time-series workloads, demoing features like native compression, continuous aggregates, specialized analytics functions, query planner enhancements - in essence, all the TimescaleDB goodness!
Virtual – DoKC Talks: What more can I learn from my OpenTelemetry traces? (Feb 1)
We join our friends from Data on Kubernetes Community to talk about distributed tracing! In this meetup, Promscale Engineer John Pruitt will show you how to use SQL queries to build awesome Grafana dashboards 🔥 for your traces.
Virtual – FOSDEM ‘22 (Feb 5-6)
Hear from Timescale team members at FOSDEM ‘22. Here’s more information on their sessions:
- How to create (lots!) of sample time-series data with PostgreSQL generate_series() by Ryan Booz
- Automatically refresh materialized views in PostgreSQL Tactics to make refreshing a painless process by Attila Toth
- Transforming and ingesting complex JSON data with Python Transform and insert complex JSONs into a relational database - without Pandas by Attila Toth
- What More Can I Learn From My OpenTelemetry Traces? by John Pruitt
TimescaleDB tips, reading list & more
How to store and analyze NFT data in a relational database – In this post, we share the technical insights we gained from designing and building the Timescale NFT Starter Kit. If you are interested in tracking and analyzing NFT transactional data, this post is for you!
A different and (often) better way to downsample your Prometheus metrics – A better option for downsampling Prometheus metrics, enabling developers to do accurate and flexible trend analysis on those metrics over long periods of time with high performance and reduced storage costs.
Generating sample time-series data three-part series – Learn how to quickly create recurring time-series data for charting and testing PostgreSQL and TimescaleDB functions.
Part 1: How to create (lots!) of sample time-series data with PostgreSQL generate_series()
Part 2: Generating more realistic sample time-series data with PostgreSQL
Part 3: How to shape sample data with PostgreSQL
generate_series() and SQL
How to run SQL commands in a PostgreSQL Docker container? – This article will explain how to run your arbitrary SQL commands against a PostgreSQL database running in a Docker container on Windows.
Building a cryptocurrency site with Svelte, Python, and TimescaleDB – Great engineering blog post from Trading Strategy - Enabling non-custodial trading across Uniswap Labs compatible exchanges (Ethereum, Binance, Polygon) powered by TimescaleDB for its real-time APIs.
Collecting system information from a bunch of Kubernetes clusters – Pavel Golovin, Software Engineer at Flant, shares his journey to get an overall state of K8s clusters started from a simple Bash script. Today, Flant makes Python-based data analytics using data from Promscale connected to Grafana Agent.
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).