BigQuery connector#
The BigQuery connector lets you query 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 data from tables. See the BigQuery Storage API for more information.
SEP includes additional enterprise features that are built on top of the existing Trino connector functionality. For more information on key feature differences between Trino and SEP, see the connectors feature matrix.
Requirements#
To connect to BigQuery, you need:
To enable the BigQuery Storage Read API.
Network access from your SEP coordinator and workers to the Google Cloud API service endpoint. This endpoint uses HTTPS, or port 443.
To configure BigQuery so the SEP coordinator and workers have permissions in BigQuery.
A valid Starburst Enterprise license.
To set up authentication. Authentication options vary, depending on whether or not you are using Dataproc/Google Compute Engine (GCE). On Dataproc/GCE authentication is based on the machine’s role. If using Outside Dataproc/GCE you have the following options to choose from:
Use a service account JSON key and
GOOGLE_APPLICATION_CREDENTIALS
as described in the Google Cloud authentication getting started guide.Set
bigquery.credentials-key
in the catalog properties file. It should contain the contents of the JSON file, encoded using base64.Set
bigquery.credentials-file
in 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 that
specifies the BigQuery connector by setting the connector.name
to
bigquery
.
For example, to access a database as example
, create the file
etc/catalog/example.properties
. Replace the connection properties as
appropriate for your setup:
connector.name=bigquery
bigquery.project-id=<your Google Cloud Platform project ID>
Property name |
Description |
---|---|
|
Allow access to other projects in BigQuery by including the project ID with the schema name in queries: SELECT *
FROM catalog."project-id.dataset".table
The default is |
|
Frequency of checks for project names as duration.
The default is |
Multiple GCP projects#
The BigQuery connector can only access a single GCP project. If you have data in multiple GCP projects, you need to create several catalogs that each point to a different GCP project.
For example, if you have two GCP projects, one called sales
and one called
analytics
, create two properties files in etc/catalog
named
sales.properties
and analytics.properties
. They both need to include the
connector.name=bigquery
property but with different project-id
.
Reading from views#
The connector supports reading from BigQuery views.
By default, reading from BigQuery views is disabled.
To enable reading from views, set the bigquery.views-enabled
configuration
property to true
.
BigQuery views are not materialized by default. In addition, materialized views are created in the same project and dataset. This means that the connector needs to materialize them before it can read them which affects read performance. The materialization process can also incur additional costs to your BigQuery bill.
The optional configuration properties are
bigquery.view-materialization-project
and
bigquery.view-materialization-dataset
. The service account must have write
permission to the project and dataset in order to materialize the view.
Arrow serialization support#
This connector supports using Apache Arrow as the serialization format when reading from BigQuery. Using Apache Arrow serialization is enabled by default. You need to add the following property to the SEP JVM config.
--add-opens=java.base/java.nio=ALL-UNNAMED
Configuration properties#
Property name |
Description |
Default |
---|---|---|
|
The Google Cloud Project ID where the data resides. |
Taken from the service account |
|
The project ID Google Cloud Project to bill for the export. |
Taken from the service account |
|
Enables the connector to read from views and not only tables. Please read this section before enabling this feature. |
|
|
Expire duration for the materialized view. |
|
|
The project where the materialized view is going to be created. See also, Reading from views. |
The view’s project |
|
The dataset where the materialized view is going to be created. See also, Reading from views. |
The view’s project |
|
Use REST API to access views instead of Storage API. BigQuery |
|
|
Use filter conditions when materializing views. |
|
|
Duration for which the materialization of a view will be cached and reused.
Set to |
|
|
Duration for which metadata retrieved from BigQuery is cached and reused.
Set to |
|
|
The number of retries in case of retryable server issues. |
|
|
The base64 encoded credentials key. |
None. See the requirements section |
|
The path to the JSON credentials file. |
None. See the requirements section |
|
Match dataset and table names case-insensitively. |
|
|
Duration for which case insensitive schema and table
names are cached. Set to |
|
|
Enable query results cache. |
|
|
Enable using Apache Arrow serialization when reading data from BigQuery. Please read this section before using this feature. |
|
|
Use a proxy for communication with BigQuery. |
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|
Proxy URI to use, if connecting through a proxy. |
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Proxy user name to use, if connecting through a proxy. |
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Proxy password to use, if connecting through a proxy. |
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|
Keystore containing client certificates to present to proxy, if connecting through a proxy. Only required if proxy uses mutual TLS. |
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|
Password of the keystore specified by |
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|
Truststore containing certificates of the proxy server, if connecting through a proxy. |
|
|
Password of the truststore specified by
|
Type mapping#
Because Trino and BigQuery each support types that the other does not, this connector modifies some types when reading or writing data. Data types may not map the same way in both directions between Trino and the data source. Refer to the following sections for type mapping in each direction.
BigQuery type to Trino type mapping#
The connector maps BigQuery types to the corresponding Trino types following this table:
BigQuery type |
Trino type |
Notes |
---|---|---|
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The default precision and scale of |
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Precision greater than 38 is not supported. The default precision and scale
of |
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Time zone is UTC. |
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In Well-known text (WKT) format. |
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No other types are supported.
Trino type to BigQuery type mapping#
The connector maps Trino types to the corresponding BigQuery types following this table:
Trino type |
BigQuery type |
Notes |
---|---|---|
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The default precision and scale of |
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No other types are supported.
SQL support#
The connector provides read and write access to data and metadata in the BigQuery. In addition to the globally available and read operation statements, the connector supports the following features:
SQL DELETE#
If a WHERE
clause is specified, the DELETE
operation only works if the
predicate in the clause can be fully pushed down to the data source.
ALTER TABLE EXECUTE#
This connector supports the following commands for use with ALTER TABLE EXECUTE:
collect_statistics#
The collect_statistics
command is used with
Managed statistics to collect statistics for a table
and its columns.
The following statement collects statistics for the example_table
table
and all of its columns:
ALTER TABLE example_table EXECUTE collect_statistics;
Collecting statistics for all columns in a table may be unnecessarily
performance-intensive, especially for wide tables. To only collect statistics
for a subset of columns, you can include the columns
parameter with an
array of column names. For example:
ALTER TABLE example_table
EXECUTE collect_statistics(columns => ARRAY['customer','line_item']);
System tables#
For each Trino table that maps to a BigQuery view, there exists a system table
which exposes the corresponding BigQuery view definition. For example, if you
have a view named example_view
, run the following query to see the SQL that
defines the view:
SELECT * example_view$view_definition
Special columns#
In addition to the defined columns, the BigQuery connector exposes partition information in a number of hidden columns:
$partition_date
: Equivalent to_PARTITIONDATE
pseudo-column in BigQuery$partition_time
: Equivalent to_PARTITIONTIME
pseudo-column in BigQuery
You can use these columns in your SQL statements like any other column. They can be selected directly or used in conditional statements.
Example
Inspect the partition date and time for each record:
SELECT *, "$partition_date", "$partition_time"
FROM example.web.page_views;
Retrieve all records stored in the partition _PARTITIONDATE = '2022-04-07'
:
SELECT *
FROM example.web.page_views
WHERE "$partition_date" = date '2022-04-07';
Note
Two special partitions __NULL__
and __UNPARTITIONED__
are not supported.
Fault-tolerant execution support#
The connector supports Fault-tolerant execution of query processing. Read and write operations are both supported with any retry policy.
Table functions#
The connector provides specific table functions to access BigQuery.
query(varchar) -> table
#
The query
function lets you query the underlying BigQuery database directly.
It requires syntax native to BigQuery because the full query is pushed down and
processed by BigQuery. This can be useful for accessing native features which
are not implemented in SEP or for improving query performance in situations
where running a query natively may be faster.
The native query passed to the underlying data source is required to return a table as a result set. Only the data source performs validation or security checks for these queries using its own configuration. Trino does not perform these tasks. Only use passthrough queries to read data.
For example, query the example
catalog and group and concatenate all
employee IDs by manager ID:
SELECT
*
FROM
TABLE(
example.system.query(
query => 'SELECT
manager_id, STRING_AGG(employee_id)
FROM
company.employees
GROUP BY
manager_id'
)
);
Note
The query engine does not preserve the order of the results of this
function. If the passed query contains an ORDER BY
clause, the
function result may not be ordered as expected.
Performance#
The connector includes a number of performance features, detailed in the following sections.
Table statistics#
BigQuery does not expose table statistics. Therefore, the connector cannot use cost based optimizations to improve query processing performance based on the actual data in the data source.
Managed statistics#
The connector supports Managed statistics which lets SEP collect and store table and column statistics that can then be used for performance optimizations in query planning.
Statistics must be collected manually using the built-in collect_statistics
command, see ALTER TABLE EXECUTE for details and examples.
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 and should improve 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 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.
For information about BigQuery pricing, see the BigQuery pricing documentation.
Security#
The connector includes a number of security-related features, detailed in the following sections.
OAuth 2.0 token pass-through#
The BigQuery connector supports OAuth 2.0 token pass-through when SEP is configured to use Google Cloud Platform as an OAuth 2.0 identity provider.
To enable OAuth 2.0 passthrough, set the following catalog configuration property:
bigquery.authentication.type=TOKEN_PASS_THROUGH