promscale
tsbs
Our great sponsors
promscale | tsbs | |
---|---|---|
18 | 76 | |
1,330 | 1,213 | |
- | 2.0% | |
0.0 | 1.9 | |
26 days ago | about 1 month ago | |
Go | Go | |
Apache License 2.0 | MIT License |
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 β 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
tsbs
- tsbs: NEW Data - star count:1149.0
-
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
-
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.
What are some alternatives?
thanos - Highly available Prometheus setup with long term storage capabilities. A CNCF Incubating project.
QuestDB - An open source time-series database for fast ingest and SQL queries
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
cql-proxy - A client-side CQL proxy/sidecar.
kube-thanos - Kubernetes specific configuration for deploying Thanos.
prometheus - The Prometheus monitoring system and time series database.
orioledb - OrioleDB β building a modern cloud-native storage engine (... and solving some PostgreSQL wicked problems) Β πΊπ¦
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
dbt-clickhouse - The Clickhouse plugin for dbt (data build tool)
Telegraf - The plugin-driven server agent for collecting & reporting metrics.
Elasticsearch - Free and Open, Distributed, RESTful Search Engine