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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

Based on @karthikeyann's work on this PR rapidsai/cudf#9767 I'm wondering if it makes sense to consider removing the defaults for the stream parameters in various detail functions. It is pretty surprising how often these are getting missed.

The most common case seems to be in factory functions and various ::create functions. Maybe just do it for those?

Edited request

Can polars allow mixing string and type selection using col(["x", pl.Int64])

See suggestion here.

Initial request

Any thoughts on an .include() method that is the opposite of .exclude()?

That way these would both work:

df.select(col('x').include(pl.Int64))

df.se

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

Is your feature request related to a problem or challenge? Please describe what you are trying to do.

#1514 points out a secondary goal for the python library to expose the datafusion and arrow-rust versions.
I think Spark has a good implementation of this by binding the spark version to SparkContext in the jvm code, then exposing it to the pyspark API. pyspark's version itself is hard-coded.

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

An Operator that both filters and maps.

Akin to Rust's own FilterMap but on a Stream rather than Iterator.

let strings = ["1", "two", "NaN", "four", "5"];
let mut app = Application::default()
  .iterator(strings, |conf| {
     conf.set_arcon_time(ArconTime::Process);
  })
  .filter_map(|s| s.parse().ok())
  .b