rapids

Describe the bug
cudf.pivot doesn't understand "values" when it's column name instead of list and the column name includes numeric

Steps/Code to reproduce bug

import cudf
df = cudf.DataFrame([
    ['A', 'a', 0, 0, 0],
    ['A', 'b', 1, 1, 1],
    ['A', 'c', 2, 2, 2],
    ['B', 'a', 0, 0, 0],
    ['B', 'b', 1, 1, 1],
    ['B', 'c', 2, 2, 2],
    ['C', 'a', 0, 0, 0],
    [

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

This task could live in cuDF but I am putting it here since it is Spark related. We can add another issue in cuDF if we want to proceed.

This issue: NVIDIA/spark-rapids#4669 brought to light a shutdown race between JVM's shutdown hooks and Spark's orderly ShutdownHookManager (also triggered by the JVM). Since the JVM doesn't guarantee the order of execution, the `Memor

I'm submitting a

• [x ] bug report.

Current Behaviour:

After #249
Trying to run tests with pytest tests/rdf_tests/test_rdf_basic.py -k test_rdf_runner -s, you get a report file with all the tests run.
Some tests return errors, for example:

{
    "Basic - Term 7": {
        "input": "basic/data-4.ttl",
        "query": "basic/term-7.rq",
        "error": "Expected {Sele

It would be nice to be able to set the initial_pool_size with a string like "500mb" or "2gb" as opposed to integer sizes like 500000000. We could vendor the code Dask uses to accomplish this:

https://github.com/dask/dask/blob/31af7f7040643c447a72c87a8f12457094ec15ff/dask/utils.py#L1171

In trying to write tests for #189, I'm finding very difficult to add columns to existing tests, as in some cases like the all_types table, the table is defined in a separate file than the tests and multiple tests try to write to the same table.

Additionally, our test suite doesn't prove that the data that are uploaded are the same as the data downloaded for all types.

We should consider m

As seen with gumdropsteve/turbo-telegram@3e2f3b3, the data for the first half of 2016 can be downloaded & preprocessed just like that of 2015. Is there any other data in the effective range? I.e. is pre-2015 data recorded the same?

If so, let's add it.