tidyquery VS VictoriaMetrics

tidyquery

> SQL is a perfect language for analytics.

Slightly off topic, but I strongly agree with this statement and wonder why the languages used for a lot of data science work (R, Python) don't have such a strong focus on SQL.

It might just be my brain, but SQL makes so much logical sense as a query language and, with small variances, is used to directly query so many databases.

In R, why learn the data.tables (OK, speed) or dplyr paradigms, when SQL can be easily applied directly to dataframes? There are libraries to support this like sqldf[1], tidyquery[2] and duckdf[3] (author). And I'm sure the situation is similar in Python.

This is not a post against great libraries like data.table and dplyr, which I do use from time to time. It's more of a question about why SQL is not more popular as the query language de jour for data science.

[1] https://cran.r-project.org/web/packages/sqldf/index.html

[2] https://github.com/ianmcook/tidyquery

[3] https://github.com/phillc73/duckdf

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

You shouldn't unless you want to use the new open source standard for telemetry. You won't benefit from simplicity or performance improvements. It would be quite the opposite. You can check what is the actual cost of open telemetry adoption here [0]

But if you ever decide to go this path - VictoriaMetrics supports OpenTelemetry protocol for metrics [1]

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

[1] https://docs.victoriametrics.com/Single-server-VictoriaMetri...