tsbs
timescale-analytics
Our great sponsors
tsbs | timescale-analytics | |
---|---|---|
76 | 8 | |
1,216 | 336 | |
2.2% | 4.5% | |
1.9 | 6.0 | |
about 1 month ago | 6 days ago | |
Go | Rust | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tsbs
- tsbs: NEW Data - star count:1149.0
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Fuzz Testing Is the Best Thing to Happen to Our Application Tests
1. correctness: from small units tests to relatively complex integrations tests. they typically populate a test database and query it via various interfaces, such as REST or the Postgres protocol. we use Azure Pipelines to execute them - testing in MacoOS, Linux (both Intel and ARM) and Windows.
2. performance: we tend to use the TSBS project for most of our performance testing and profiling. fun fact: we actually had to patch it as the vanilla TSBS was a bottleneck in some tests. Sadly, the PR with the improvements is still not merged: https://github.com/timescale/tsbs/pull/186
- tsbs: NEW Data - star count:1058.0
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MongoDB Time Series Benchmark and Review
As usual, we use the industry standard Time Series Benchmark Suite (TSBS) as the benchmark tool. Unfortunately, TSBS upstream does not support MongoDB time series collections.
timescale-analytics
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Timescale raises $110M Series C
Hi! So the team is over 100 at this point, but engineering effort is spread across multiple products at this point.
The core timescaledb repo [0] has 10-15 primary engineers (although we are aggressively hiring for database internal engineers), with a few others working on DB hyperfunctions and our function pipelining [1] in a separate extension [2]. I think generally the set of folks who contribute to low-level database internals in C is just smaller than other type of projects.
We also have our promscale product [3], which is our observability backend powered by SQL & TimescaleDB.
And then there is Timescale Cloud, which is obviously a large engineering effort (most of which does not happen in public repos).
And we are hiring. Fully remote & global.
https://www.timescale.com/careers
[0] https://github.com/timescale/timescaledb
[1] https://www.timescale.com/blog/function-pipelines-building-f...
[2] https://github.com/timescale/timescaledb-toolkit
[3] https://github.com/timescale/promscale ; https://github.com/timescale/tobs
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Function pipelines: Building functional programming into PostgreSQL
(NB: Post author here)
This is in the TimescaleDB Toolkit extension [1] which is licensed under our community license for now and it's not available on DO. It is available on our cloud service fully managed. You can also install it and run it for free yourself.
[1]: https://github.com/timescale/timescaledb-toolkit
- How percentile approximation works (and why it's more useful than averages)
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How PostgreSQL aggregation works and how it inspired our hyperfunctions’ design
Absolutely! We're actually developing a lot of that: https://github.com/timescale/timescaledb-toolkit/tree/main/d...
A number of the things you're looking for we've done experimentally and we'll be stabilizing over the next few releases. So we'd love some feedback while we're still able to futz with the API without making breaking changes.
But the two you're asking about are, I think, going to be covered by hyperloglog (we just reimplemented the internals with HLL++) and stats_agg family of functions, which have both 1D (which will give you avg, stddev, variance, etc) and 2D (co-variance, slope, intercept, x-intercept etc as well as all the 1D functions).
Would also love issues if you think we're missing other stuff, going to be generalizing this and want to make it useful for folks.
(NB: Post author here.)
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Postgres downsampling performance
If you know that you're going to be doing downsampling at the hourly level then a continuous aggregate on the hour is probably a good idea. We're also building some functions to make some of the continuous aggregate stuff for these sorts of cases easier/more accurate in more cases, especially if you need things like exact averages when you don't have the same number of points in an hour and want to re-aggregate on top of the continuous agg. See: https://github.com/timescale/timescale-analytics/pull/141/files
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TimescaleDB Raises $40M
Fair point about adaptive chunking. You sound like a long-term user!
There is always a trade-off between getting features to users quickly to experiment and incrementally improve, versus doing it always very conservatively.
When we launched adaptive chunking (introduced in 0.11, deprecated in 1.2), we explicitly marked it as beta and default off, to hopefully reflect that. [1]
The approach we are now taking with Timescale Analytics [2] is to have an explicit distinction between experimental features (which will be part of a distinct"experimental" schema in the database, and must be expressly turned on with appropriate warnings) and stable features. Hopefully this can help find a good balance between stability and velocity, but feedback welcome!
[1] https://github.com/timescale/timescaledb/releases/tag/0.11.0
[2] https://github.com/timescale/timescale-analytics/tree/main/e...
What are some alternatives?
QuestDB - An open source time-series database for fast ingest and SQL queries
orioledb - OrioleDB – building a modern cloud-native storage engine (... and solving some PostgreSQL wicked problems)  🇺🇦
cql-proxy - A client-side CQL proxy/sidecar.
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
Telegraf - The plugin-driven server agent for collecting & reporting metrics.
promscale - [DEPRECATED] Promscale is a unified metric and trace observability backend for Prometheus, Jaeger and OpenTelemetry built on PostgreSQL and TimescaleDB.
dbt-clickhouse - The Clickhouse plugin for dbt (data build tool)
pgx - Build Postgres Extensions with Rust! [Moved to: https://github.com/tcdi/pgrx]
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
t-digest - A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means