duckdf VS loki

Quite a while ago, when duckdb was just a duckling, I wrote an R package that supported direct manipulation of R dataframes using SQL.[1] duckdb was the engine for this.

The approach was never as fast as data.table but did approach the speed of dplyr for more complex queries.

Life had other things in store for me and I haven’t touched this library for a while now.

At the time there was no Julia connector for duckdb, but now that there is, I’d like to try this approach in that language.

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

Yeah, I agree sqldf is quite slow. Fair point.

As you've seen, duckdb registers an "R data frame as a virtual table." I'm not sure what they mean by "yet" either.

Of course it is possible to write an R dataframe to an on-disk duckdb table, if that's what you want to do.

There are some simple benchmarks on the bottom of the duckdf README[1]. Essentially I found for basic SQL SELECT queries, dplyr is quicker, but for much more complex queries, the duckdf/duckdb combination performs better.

If you really want speed of course, just use data.table.

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

That's a really good point that I'd not really thought about. I'd never really considered the difference between calling just functions versus macros.

Thinking about Query.jl and DataFramesMeta.jl, and I am for sure not an expert in either, I can't specifically speak to your `head` example, but other base functions can be combined with macros. For example, see the LINQ examples from DataFramesMeta.jl[1] where `mean` is being used. Or again the LINQ style examples in Query.jl[2], where `descending` is used in the first example, or `length` later in the Grouping examples.

Is that the kind of thing you meant?

For whatever reason, with the way my brain is wired, the LINQ style of query just works for me. I have never directly used LINQ, but do have some SQL experience. In fact, I wrote some dinky little wrapper functions[3] around duckdb[4] so I could directly query R dataframes and datatables with SQL using that backend, rather than sqldf[5].

[1] https://juliadata.github.io/DataFramesMeta.jl/stable/#@linq-...

[2] https://www.queryverse.org/Query.jl/stable/linqquerycommands...

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

[4] https://duckdb.org/

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

I also needed to make a small patch to Promtail to make this work: https://github.com/grafana/loki/pull/10256

We don't pull logs, we forward logs to a centralized logging service.

Over the past few months, I've been spending a lot of time creating dashboards on Grafana using Loki for MyUnisoft (the company I work for).

For log systems you generally don't migrate data. Logs lose value over time. What you want to do is to go ahead and start ingesting data into the new system (OpenObserve in this case) and slowly, the data in the old system will become stale and then you can retire it. However if you need to export logs anyhow, there is no straightforward way in loki to do this. You could run a script to query loki and export it to a file. If found this thread with a sample script - https://github.com/grafana/loki/issues/409

That snap is woefully out of date. The upstream repo was recently updated to 2.8.2, but the snap stable channel has 2.4.1 from 18 months ago. https://github.com/grafana/loki/releases/tag/v2.8.2

Loki

I installed promtail a few weeks back and I ran into this bug, that has been outstanding for months: https://github.com/grafana/loki/issues/8663 (e.g. a fix had been written but had not been released):

Due to a buffering issue, Loki would exit in case of configuration error without printing any error message or anything at all

There is definitely something weird about how the project is run.