arrow

Feature Request

Many locales have the bare minimum when it comes to test cases. While I understand it can be tedious and repetitive to write out test case

We now have native ODBC support upstream. This has to be exposed in polars similarly to existing IO readers and writers.

Is your feature request related to a problem? Please describe.
When working on #10770 , @ttnghia pointed out groupby::hash is the only use case of unflatten_nested_columns (see rapidsai/cudf#10770 (comment)).

Describe the solution you'd like
We should remove this function once #10770 is merged.

Is your feature request related to a problem or challenge? Please describe what you are trying to do.
DataFusion mostly follows postgres when it comes to compatibility. However, it also supports some usability features from mysql, notably SHOW TABLES;

It would be nice to also get basic schema information (column names, types) about a table using DESCRIBE <TABLE>

**Describe the

We no longer need to control the number of concurrent kernels, since now we control the number of concurrent tasks

Note sure if it could be interesting but:

When registering a table:

addr: 0.0.0.0:8084
tables:
  - name: "example"
    uri: "/data/"
    option:
      format: "parquet"
      use_memory_table: false

add in options:
glob

pattern: "file_typev1*.parquet"

or regexp

pattern: "\wfile_type\wv1\w*.parquet"

It would allow selecting in uri's with different exte

It would be helpful to have Fletchgen output warnings for unused metadata fields that start with fletcher_. For example, (this happened to me) when someone adds fletchgen_epc to Schema metadata instead of Field metadata.

Problem description

Reading a dataset with eager's read functionality raises a ValueError when providing columns.

Example code (ideally copy-pastable)

import pandas as pd

from tempfile import TemporaryDirectory
from functools import partial
from storefact import get_store_from_url

from kartothek.io.eager import store_dataframes_as_dataset, read_dataset_as_data

Motivation:

1. Improved compile times (at least by 2x compared to arrow-rs).
2. Faster Parquet impl
3. Projects are migrating to arrow2 (including Datafusion and Polars)