promscale
timescale-analytics
Our great sponsors
promscale | timescale-analytics | |
---|---|---|
18 | 8 | |
1,330 | 336 | |
- | 4.5% | |
0.0 | 6.0 | |
29 days ago | 4 days ago | |
Go | Rust | |
Apache License 2.0 | 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.
promscale
-
Promscale Deprecation
Now that Promscale has been deprecated, what are the other ideal means of self-hosted long term Prometheus storage?
-
What do you use when you have to store high cardinality metrics?
Oh wow, I browsed the project just a few weeks ago, didn't see it then. I see the deprecation is recent (https://github.com/timescale/promscale/issues/1836)
- Promscale Has Been Discontinued
-
Show HN: SigNoz – open-source alternative to DataDog, NewRelic
They say:
> if you want to have a seamless experience between metrics and traces, then current experience of stitching together Prometheus & Jaeger is not great.
But I wonder if using Promscale https://github.com/timescale/promscale would make Prometheus & Jaeger not such a big problem as SigNoz imply.
Promscale readme:
> Promscale is a unified metric and trace observability backend for Prometheus, Jaeger and OpenTelemetry built on PostgreSQL and TimescaleDB.
Either way, SigNoz seems interesting indeed. And am glad to see that SigNoz supports OpenTelemetry.
-
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
-
Tools for Querying Logs with SQL
Promscale is a connector for Prometheus, one of the leading open-source monitoring solutions. Promscale is developed by Timescale, a time series database with full compatibility to Postgres. Since logs are time series events, Timescale developed Promscale to ingest events from Prometheus and make them available in SQL. You can install Promscale in numerous ways.
- New release Promscale
-
Can Apache Druid replace Thanos? Can they complement themself?
In case it helps, Promscale (from Timescale) offers long-term storage for Prometheus data and supports both PromQL and SQL queries. Here's the project page: https://www.timescale.com/promscale/ and the repo is here https://github.com/timescale/promscale It also support OpenTelemetry tracing if that's of interest.
-
Benchmarking: TimescaleDB vs. ClickHouse
At first, let's give the definition of `time series`. This is a series of (timestamp, value) pairs ordered by timestamp. The `value` may contain arbitrary data - a floating-point value, a text, a json, a data structure with many columns, etc. Each time series is uniquely identified by its name plus an optional set of {label="value"} labels. For example, temperature{city="London",country="UK"} or log_stream{host="foobar",datacenter="abc",app="nginx"}.
ClickHouse is perfectly optimized for storing and querying of such time series, including metrics. That's true that ClickHouse isn't optimized for handling millions of tiny inserts per second. It prefers infrequent batches with big number of rows per each batch. But this isn't the real problem in practice, because:
1) ClickHouse provides Buffer table engine for frequent inserts.
2) It is easy to create a special proxy app or library for data buffering before sending it to ClickHouse.
TimescaleDB provides Promscale [1] - a service, which allows using TimescaleDB as a storage backend for Prometheus. Unfortunately, it doesn't show outstanding performance comparing to Prometheus itself and to other remote storage solutions for Prometheus. Promscale requires more disk space, disk IO, CPU and RAM according to production tests [2], [3].
[1] https://github.com/timescale/promscale
[2] https://abiosgaming.com/press/high-cardinality-aggregations/
[3] https://valyala.medium.com/promscale-vs-victoriametrics-reso...
Full disclosure: I'm CTO at VictoriaMetrics - competing solution for TimescaleDB. VictoriaMetrics is built on top of architecture ideas from ClickHouse.
-
Zabbix anything I should know?
Promscale + TimescaleDB
timescale-analytics
-
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
-
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)
-
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.)
-
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
-
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?
thanos - Highly available Prometheus setup with long term storage capabilities. A CNCF Incubating project.
orioledb - OrioleDB – building a modern cloud-native storage engine (... and solving some PostgreSQL wicked problems)  🇺🇦
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
kube-thanos - Kubernetes specific configuration for deploying Thanos.
Telegraf - The plugin-driven server agent for collecting & reporting metrics.
prometheus - The Prometheus monitoring system and time series database.
pgx - Build Postgres Extensions with Rust! [Moved to: https://github.com/tcdi/pgrx]
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
t-digest - A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means
tsbs - Time Series Benchmark Suite, a tool for comparing and evaluating databases for time series data