Hello,
Considering your amazing efficiency on pandas, numpy, and more, it would seem to make sense for your module to work with even bigger data, such as Audio (for example .mp3 and .wav). This is something that would help a lot considering the nature audio (ie. where one of the lowest and most common sampling rates is still 44,100 samples/sec). For a use case, I would consider vaex.open('Hu

Is this a BUG REPORT or FEATURE REQUEST?:

/kind feature

What happened:

Automatically set GOMAXPROCS to match Linux container CPU quota, xref https://github.com/uber-go/automaxprocs

This is to track implementation of the ML-Features: https://spark.apache.org/docs/latest/ml-features

Bucketizer has been implemented in dotnet/spark#378 but there are more features that should be implemented.

• Feature Extractors
  • TF-IDF
  • Word2Vec (dotnet/spark#491)
  • CountVectorizer (https://github.com/dotnet/spark/p

Currently, we need specified column names to make sql work.

But it is easy to add support to:

1. select *
2. select count(*)