VictoriaMetrics VS TimescaleDB

VictoriaMetrics CTO here.

The referred library is the official OpenTelemetry package for reading metrics in Go language [1] - more details are available at [2].

Note that we at VictoriaMetrics like the idea of unified observability standard like OpenTelemetry. The issue is in the current otel implementation. It is too bloated and very inefficient. This contradicts to our experience with observability cases, which need very optimized format for metrics' transfer in order to reduce costs on CPU and network traffic needed to transfer and process these metrics.

VictoriaMetrics continues investing in OpenTelemetry by providing integration docs [3] and improving the existing functionality for otel metrics' ingestion [4].

[1] https://github.com/open-telemetry/opentelemetry-proto-go

[2] https://github.com/VictoriaMetrics/VictoriaMetrics/pull/2570...

[3] https://docs.victoriametrics.com/guides/getting-started-with...

[4] https://github.com/VictoriaMetrics/VictoriaMetrics/issues/60...

Victoria Metrics

VictoriaMetrics is primarily a time-series database designed for efficiently storing and querying time-series data. It is often used as a back-end data store for time-series data generated by monitoring systems like Prometheus. VictoriaMetrics excels at handling large volumes of time-series data, offering efficient storage and query capabilities.

Not sure I follow since there are very competitive tools written in Go such as https://victoriametrics.com for an example in this space.

Did you try VictoriaMetrics [1] and vmagent [2]? It is a single self-contained binary without external dependencies. It requires relatively low amounts of CPU, RAM, disk space and disk IO, and it runs on ARM.

[1] https://github.com/VictoriaMetrics/VictoriaMetrics/

[2] https://docs.victoriametrics.com/vmagent.html

https://github.com/VictoriaMetrics/VictoriaMetrics#cardinali...

If I understanding correctly, it deal with high cardinality by dropping data, the operators need to monitor for this and adjust their data to lower the cardinality.

VictoriaMetrics

Don't forget PostgreSQL extensions. For something like a chat log, TimescaleDB (https://www.timescale.com/) can be surprisingly efficient. It will handle partitioning for you, with additional features like data reordering, compression, and retention policies.

TimescaleDB{:target="_blank"}: an extension of PostgreSQL that adds time-series capabilities to the relational database model. It provides scalability and performance optimizations for handling large volumes of time-stamped data while maintaining the flexibility of a relational database.

What about getting in touch with commercial organisations that have products/services based on PostgreSQL? For example Timescale, EDB, and Citus Data, or really any hosting provider that offers a managed PostgreSQL service.

Friends don't let their friends choose Mysql :)

A super long time ago (decades) when I was using Oracle regularly I had to make a decision on which way to go. Although Mysql then had the mindshare I thought that Postgres was more similar to Oracle, more standards compliant, and more of a real enterprise type of DB. The rumor was also that Postgres was heavier than MySQL. Too many horror stories of lost data (MyIsam), bad transactions (MyIsam lacks transaction integrity), and the number of Mysql gotchas being a really long list influenced me.

In time I actually found out that I had underestimated one of the most important attributes of Postgres that was a huge strength over Mysql: the power of community. Because Postgres has a really superb community that can be found on Libera Chat and elsewhere, and they are very willing to help out, I think Postgres has a huge advantage over Mysql. RhodiumToad [Andrew Gierth] https://github.com/RhodiumToad & davidfetter [David Fetter] https://www.linkedin.com/in/davidfetter are incredibly helpful folks.

I don't know that Postgres' licensing made a huge difference or not but my perception is that there are a ton of 3rd party products based on Postgres but customized to specific DB needs because of the more liberalness of the PG license which is MIT/BSD derived https://www.postgresql.org/about/licence/

Some of the PG based 3rd party DBs:

Enterprise DB https://www.enterprisedb.com/ - general purpose PG with some variants

Greenplum https://greenplum.org/ - Data warehousing

Crunchydata https://www.crunchydata.com/products/hardened-postgres - high security Postgres for regulated environments

Citus https://www.citusdata.com - Distributed DB & Columnar

Timescale https://www.timescale.com/

Why Choose PG today?

If you want better ACID: Postgres

If you want more compliant SQL: Postgres

If you want more customizability to a variety of use-cases: Postgres using a variant

If you want the flexibility of using NOSQL at times: Postgres

If you want more product knowledge reusability for other backend products: Postgres

TimescaleDB is Postgres with extensions to automatically partition tables for fast processing of time series data.

Have you heard of timescale? https://www.timescale.com/ Seems similar to ocient but specifically for time series data.

Slowly I understood that instead of a vanilla PostgreSQL database I need to use to use Timescale which is based on PostgreSQL. I am sure others would have come to this conclusion much faster than I did.

Good question... haha I haven't really considered it. I'm no too versed in this domain and so the whole project will be a learning experience. One of the things is that it will include time-series harvest data. I was searching around for ways to implement this and found solutions like TimescaleDB and InfluxDB. Seems like also there are just some plugins that can sit on top of PostgreSQL.