Announcing google-cloud-bigquery Version 1.17.0: Query Results to DataFrame 31x Faster with Apache Arrow


Upgrade to the latest google-cloud-bigquery and google-cloud-bigquery-storage packages to download query results to a DataFrame 4.5 times faster compared to the same method with version 1.16.0. If you aren't using the BigQuery Storage API yet, use it to download your query results 15 times faster compared to the BigQuery API. (31 times, if you don't mind using the default Arrow to pandas conversion.)

This speed-up also works with the pandas-gbq library. Update, the google-cloud-bigquery and google-cloud-bigquery-storage packages and install pyarrow, and set the use_bqstorage_api parameter to True.

Code samples

To use the faster method to download large results, use the BigQuery Storage API from your Python programs or notebooks.

Before you begin

Using pandas-gbq

import pandas_gbq

sql = "SELECT * FROM `bigquery-public-data.irs_990.irs_990_2012`"

# Use the BigQuery Storage API to download results more quickly.
df = pandas_gbq.read_gbq(sql, use_bqstorage_api=True)

Using the BigQuery client library

import google.auth
from import bigquery
from import bigquery_storage_v1beta1

# Create a BigQuery client and a BigQuery Storage API client with the same
# credentials to avoid authenticating twice.
credentials, project_id = google.auth.default(
client = bigquery.Client(credentials=credentials, project=project_id)
bqstorage_client = bigquery_storage_v1beta1.BigQueryStorageClient(
sql = "SELECT * FROM `bigquery-public-data.irs_990.irs_990_2012`"

# Use a BigQuery Storage API client to download results more quickly.
df = client.query(sql).to_dataframe(bqstorage_client=bqstorage_client)

What's next

New features

In addition to faster performance, google-cloud-bigquery package version 1.17.0 adds a RowIterator.to_arrow() method to download a table or query results as a pyarrow.Table object.

Arrow provides a cross-language standard for in-memory, column-oriented data with a rich set of data types. It is fast to create a pandas DataFrame from an Arrow Table with the to_pandas() method. With the fletcher library, the Arrow Table can be used directly as the backing data structure of a pandas extension array.

Better performance

We tested the performance of downloading BigQuery table data to pandas DataFrame and Arrow Tables by sampling the bigquery-public-data.new_york_taxi_trips.tlc_green_trips_* tables.

Sample Rate

Table Size



~130 MB



~266 MB



~533 MB



~1 GB


We then timed how long it took to download these data to a pandas DataFrame from a Google Compute Engine n1-standard-8 (8 vCPUs, 30 GB memory) machine. We used the following methods:

~130 MB 108.5 seconds 32.3 seconds 7.1 seconds 3.4 seconds
~266 MB 215.3 64.5 13.9 7.2
~533 MB 447.0 130.1 30.0 13.8
~1 GB 890.7 264.1 58.9 27.7

The speedup is quite stable across data sizes. Using the BigQuery Storage API with the Avro data format is about a 3.5x speedup over the BigQuery tabledata.list API. It's about a 15x speedup to use the to_dataframe() method with the Arrow data format, and a 31x speedup to use the to_arrow() method, followed by to_pandas() with the BigQuery Storage API.

~130 MB 1x speed-up 3.4x speed-up 15.3x speed-up 31.5x speed-up
~266 MB 1x 3.3x 15.4x 30.1x
~533 MB 1x 3.4x 14.9x 32.4x
~1 GB 1x 3.4x 15.1x 32.2x

In conclusion, the fastest way to get a pandas DataFrame from BigQuery is to call RowIterator.to_arrow(bqstorage_client=bqstorage_client).to_pandas(). The reason for this difference is that to_dataframe() converts each message into a DataFrame and then concatenates them into a single DataFrame at the end. Whereas to_arrow() converts each message to a RecordBatch and creates a Table at the end. The difference in time is likely because pandas.concat(dfs) can make copies, where as pyarrow.Table.from_batches() doesn't make any copies. Also, pyarrow.Table.to_pandas() is often zero-copy.


This BigQuery Storage API does not have a free tier, and is not included in the BigQuery Sandbox. Because of these limitations, you are required to have a billing account to use this API. See the BigQuery Storage API pricing page for details.

The BigQuery Storage API does not yet read from small anonymous query results tables.

There are restrictions on the ability to reorder projected columns and the complexity of row filter predicates. Currently, filtering support when serializing data using Apache Avro is more mature than when using Apache Arrow.