6.2. BigQuery Connector#
The BigQuery connector allows querying the data stored in BigQuery. This can be used to join data between different systems like BigQuery and Hive. The connector uses the BigQuery Storage API to read the data from the tables.
Beta Disclaimer#
The BigQuery Storage API and this connector are in Beta and are subject to change.
Changes may include, but are not limited to:
- Type conversion
- Partitioning
- Parameters
BigQuery Storage API#
The Storage API streams data in parallel directly from BigQuery via gRPC without using Google Cloud Storage as an intermediary. It has a number of advantages over using the previous export-based read flow that should generally lead to better read performance:
Direct Streaming
It does not leave any temporary files in Google Cloud Storage. Rows are read directly from BigQuery servers using an Avro wire format.
Column Filtering
The new API allows column filtering to only read the data you are interested in. Backed by a columnar datastore, it can efficiently stream data without reading all columns.
Dynamic Sharding
The API rebalances records between readers until they all complete. This means that all Map phases will finish nearly concurrently. See this blog article on how dynamic sharding is similarly used in Google Cloud Dataflow.
Requirements#
Enable the BigQuery Storage API#
Follow these instructions.
Authentication#
On GCE/Dataproc the authentication is taken from the machine’s role.
Outside GCE/Dataproc you have 3 options:
- Use a service account JSON key and
GOOGLE_APPLICATION_CREDENTIALSas described here. - Set
bigquery.credentials-keyin the catalog properties file. It should contain the contents of the JSON file, encoded using base64. - Set
bigquery.credentials-filein the catalog properties file. It should point to the location of the JSON file.
Configuration#
To configure the BigQuery connector, create a catalog properties file in
etc/catalog named, for example, bigquery.properties, to mount the
BigQuery connector as the bigquery catalog. Create the file with the
following contents, replacing the connection properties as appropriate for
your setup:
connector.name=bigquery
bigquery.project-id=<your Google Cloud Platform project id>
Multiple GCP Projects#
The BigQuery connector can only access a single GCP project.Thus, if you have
data in multiple GCP projects, You need to create several catalogs, each
pointingto a different GCP project. For example, if you have two GCP projects,
one for the sales and one for analytics, you can create two properties files in
etc/catalog named sales.properties and analytics.properties, both
having connector.name=bigquery but with different project-id. This will
create the two catalogs, sales and analytics respectively.
Configuring Partitioning#
By default the connector creates one partition per 400MB in the table being
read (before filtering). This should roughly correspond to the maximum number
of readers supported by the BigQuery Storage API. This can be configured
explicitly with the bigquery.parallelism property. BigQuery may limit the
number of partitions based on server constraints.
Reading From Views#
The connector has a preliminary support for reading from BigQuery views. Please note there are a few caveats:
- BigQuery views are not materialized by default, which means that the connector needs to materialize them before it can read them. This process affects the read performance.
- The materialization process can also incur additional costs to your BigQuery bill.
- By default, the materialized views are created in the same project and
dataset. Those can be configured by the optional
bigquery.view-materialization-projectandbigquery.view-materialization-datasetproperties, respectively. The service account must have write permission to the project and the dataset in order to materialize the view. - Reading from views is disabled by default. In order to enable it, set the
bigquery.views-enabledconfiguration property totrue.
Configuration Properties#
All configuration properties are optional.
| Property | Description | Default |
|---|---|---|
bigquery.project-id |
The Google Cloud Project ID where the data reside | Taken from the service account |
bigquery.parent-project-id |
The project ID Google Cloud Project to bill for the export | Taken from the service account |
bigquery.parallelism |
The number of partitions to split the data into | The number of executors |
bigquery.views-enabled |
Enables the connector to read from views and not only tables. Please read this section before enabling this feature. | false |
bigquery.view-materialization-project |
The project where the materialized view is going to be created | The view’s project |
bigquery.view-materialization-dataset |
The dataset where the materialized view is going to be created | The view’s dataset |
bigquery.max-read-rows-retries |
The number of retries in case of retryable server issues | 3 |
bigquery.credentials-key |
The base64 encoded credentials key | None. See authentication |
bigquery.credentials-file |
The path to the JSON credentials file | None. See authentication |
Data Types#
With a few exceptions, all BigQuery types are mapped directly to their Presto counterparts. Here are all the mappings:
| BigQuery | Presto | Notes |
|---|---|---|
BOOLEAN |
BOOLEAN |
|
BYTES |
VARBINARY |
|
DATE |
DATE |
|
DATETIME |
TIMESTAMP |
|
FLOAT |
DOUBLE |
|
GEOGRAPHY |
VARCHAR |
In Well-known text (WKT) format |
INTEGER |
BIGINT |
|
NUMERIC |
DECIMAL(38,9) |
|
RECORD |
ROW |
|
STRING |
VARCHAR |
|
TIME |
TIME_WITH_TIME_ZONE |
Time zone is UTC |
TIMESTAMP |
TIMESTAMP_WITH_TIME_ZONE |
Time zone is UTC |
FAQ#
What is the Pricing for the Storage API?#
See the BigQuery pricing documentation.