BigQuery: to_arrow() method similar to to_dataframe()

[cpcloud]

Current there's a to_dataframe() method that returns a pandas DataFrame from a query. DataFrames don't efficiently support array and struct values, but pyarrow provides efficient support for them:

In [11]: import pyarrow as pa

In [13]: a = pa.array(
    ...:     [
    ...:         {'a': [1, 2, 3]}
    ...:     ],
    ...:     pa.struct([pa.field('a', pa.list_(pa.int64()))])
    ...: )

In [14]: a
Out[14]: 
<pyarrow.lib.StructArray object at 0x7f55f8764b88>
[
  {'a': [1, 2, 3]}
]

A to_arrow() method will make it easier to efficiently support more complex types going forward in downstream libraries like ibis.

[max-sixty]

I realize out of scope for this library, but it would awesome to have this as an export rather than JSON -> python -> arrow.
Or, even better, be able to request smaller datasets in arrow over HTTP, rather than needing to go through GCS

quasi-ref googleapis/python-bigquery-pandas#133

[tswast]

Fixed in #8609

To be released in google-cloud-bigquery 1.17.0 (#8663).

[tswast]

FYI: results.to_arrow(bqstorage_client=bqstorage_client).to_pandas() is currently the fastest way to get a pandas DataFrame from your query results. About 4 seconds from results to DataFrame for a 125 MB table.

[tswast]

Looks like I added the bqstorage_client arg to RowIterator, but I missed adding it to QueryResults. It can still be used by calling QueryResults.results().to_arrow(bqstorage_client=bqstorage_client), but we should add it to QueryJob for consistency.

[tseaver]

@tswast Did you mean to reopen here?

[tswast]

@tseaver Yep. I missed one parameter update in the PR that added this feature.