Load PostgreSQL data into BigQuery

You can load data from PostgreSQL to BigQuery by using the BigQuery Data Transfer Service for PostgreSQL connector. The connector supports PostgreSQL instances hosted in your on-premises environment, Cloud SQL, and other public cloud providers such as Amazon Web Services (AWS) and Microsoft Azure. With the BigQuery Data Transfer Service, you can schedule recurring transfer jobs that add your latest data from PostgreSQL to BigQuery.

Limitations

PostgreSQL data transfers are subject to following limitations:

  • The maximum number of simultaneous transfer runs to a single PostgreSQL database is determined by the maximum number of concurrent connections supported by the PostgreSQL database. The number of concurrent transfer jobs should be limited to a value less than the maximum number of concurrent connections supported by the PostgreSQL database.

  • A single transfer configuration can only support one data transfer run at a given time. When a second data transfer is scheduled to run before the first transfer is completed, then only the first data transfer completes while any other data transfers that overlap with the first transfer are skipped.

    To avoid skipped transfers within a single transfer configuration, we recommend that you increase the duration of time between large data transfers by configuring the repeat frequency.

  • During a data transfer, the PostgreSQL connector identifies indexed and partitioned key columns to transfer your data in parallel batches. For this reason, we recommend that you specify primary key columns or use indexed columns in your table to improve the performance and reduce the error rate in your data transfers. Consider the following:

    • If you have indexed or primary key constraints, only the following column types are supported for creating parallel batches:
      • INTEGER
      • TINYINT
      • SMALLINT
      • FLOAT
      • REAL
      • DOUBLE
      • NUMERIC
      • BIGINT
      • DECIMAL
      • DATE
    • PostgreSQL data transfers that don't use primary key or indexed columns can't support more than 2,000,000 records per table.

Incremental transfer limitations

Incremental PostgreSQL transfers are subject to the following limitations:

  • You can only choose TIMESTAMP columns as watermark columns.
  • Incremental ingestion is only supported for assets with valid watermark columns.
  • Values in a watermark column must be monotonically increasing.
  • Incremental transfers cannot sync delete operations in the source table.
  • A single transfer configuration can only support either incremental or full ingestion.
  • You cannot update objects in the asset list after the first incremental ingestion run.
  • You cannot change the write mode in a transfer configuration after the first incremental ingestion run.
  • You cannot change the watermark column or the primary key after the first incremental ingestion run.
  • The destination BigQuery table is clustered using the provided primary key and is subject to clustered table limitations.
  • When you update an existing transfer configuration to the incremental ingestion mode for the first time, the first data transfer after that update transfers all available data from your data source. Any subsequent incremental data transfers will transfer only the new and updated rows from your data source.
  • We recommend that you create indexes on the watermark column. This connector uses watermark columns for filters in incremental transfers, so indexing these columns can improve performance.

Data ingestion options

The following sections provide information about the data ingestion options when you set up a PostgreSQL data transfer.

TLS configuration

The PostgreSQL connector supports the configuration for transport level security (TLS) to encrypt your data transfers into BigQuery. The PostgreSQL connector supports the following TLS configurations:

  • The Encrypt data, and verify CA and hostname mode. This mode performs a full validation of the server using TLS over the TCPS protocol. It encrypts all data in transit and verifies that the database server's certificate is signed by a trusted certificate authority (CA). This mode also checks that the hostname you're connecting to exactly matches the Common Name (CN) or a Subject Alternative Name (SAN) on the server's certificate. This mode prevents attackers from using a valid certificate for a different domain to impersonate your database server.

    If your hostname does not match the certificate CN or SAN, the connection fails. You must configure a DNS resolution to match the certificate or use a different security mode. Use this mode for the most secure option to prevent person-in-the-middle (PITM) attacks.

  • The Encrypt data, and verify CA only mode. This mode encrypts all data using TLS over the TCPS protocol and verifies that the server's certificate is signed by a CA that the client trusts. However, this mode does not verify the server's hostname. This mode successfully connects as long as the certificate is valid and issued by a trusted CA, regardless of whether the hostname in the certificate matches the hostname you are connecting to.

    Use this mode if you want to ensure that you are connecting to a server whose certificate is signed by a trusted CA, but the hostname is not verifiable or you don't have control over the hostname configuration.

  • The Encryption only mode. This mode encrypts all data transferred between the client and the server. It does not perform any certificate or hostname validation.

    This mode provides some level of security by protecting data in transit, but it can be vulnerable to PITM attacks.

    Use this mode if you need to ensure all data is encrypted but can't or don't want to verify the server's identity. We recommend using this mode when working with private VPCs.

  • The No encryption or verification mode. This mode does not encrypt any data and does not perform any certificate or hostname verification. All data is sent as plain text.

    We don't recommend using this mode in an environment where sensitive data is handled. We only recommend using this mode for testing purposes on an isolated network where security is not a concern.

Trusted Server Certificate (PEM)

If you are using either the Encrypt data, and verify CA and hostname mode or the Encrypt data, and verify CA mode, then you can also provide one or more PEM-encoded certificates. These certificates are required in some scenarios where the BigQuery Data Transfer Service needs to verify the identity of your database server during the TLS connection:

  • If you are using a certificate signed by a private CA within your organization or a self-signed certificate, you must provide the full certificate chain or the single self-signed certificate. This is required for certificates issued by internal CAs of managed cloud provider services, such as the Amazon Relational Database Service (RDS).
  • If your database server certificate is signed by a public CA (for example, Let's Encrypt, DigiCert, or GlobalSign), you don't need to provide a certificate. The root certificates for these public CAs are pre-installed and trusted by the BigQuery Data Transfer Service.

You can specify PEM-encoded certificates in the Trusted PEM Certificate field in the transfer configuration, with the following requirements:

  • The certificate must be a valid PEM-encoded certificate chain.
  • The certificate must be entirely correct. Any missing certificates in the chain or incorrect content causes the TLS connection to fail.
  • For a single certificate, you can provide a single, self-signed certificate from the database server.
  • For a full certificate chain issued by a private CA, you must provide the full chain of trust. This includes the certificate from the database server and any intermediate and root CA certificates.

Full or incremental transfers

You can specify how data is loaded into BigQuery by selecting either the Full or Incremental write preference in the transfer configuration when you set up a PostgreSQL transfer. Incremental transfers are supported in Preview.

You can configure a full data transfer to transfer all data from your PostgreSQL datasets with each data transfer.

Alternatively, you can configure an incremental data transfer (Preview) to only transfer data that was changed since the last data transfer, instead of loading the entire dataset with each data transfer. If you have configured an incremental data transfer, then you must specify either the append or upsert write modes to define how data is written to BigQuery during an incremental data transfer. The following sections describe the available write modes.

Append write mode

The append write mode only inserts new rows to your destination table. This option strictly appends transferred data without checking for existing records, so this mode can potentially cause data duplication in the destination table.

When you select the append mode, you must select a watermark column. A watermark column is required for the PostgreSQL connector to track changes in the source table.

For PostgreSQL transfers, we recommend selecting a column that is only updated when the record was created, and that won't change with subsequent updates—for example, the CREATED_AT column.

Upsert write mode

The upsert write mode either updates a row or inserts a new row in your destination table by checking for a primary key. You can specify a primary key to let the PostgreSQL connector determine what changes are needed to keep your destination table up to date with your source table. If the specified primary key is present in the destination BigQuery table during a data transfer, then the PostgreSQL connector updates that row with new data from the source table. If a primary key is not present during a data transfer, then the PostgreSQL connector inserts a new row.

When you select the upsert mode, you must select a watermark column and a primary key:

  • A watermark column is required for the PostgreSQL connector to track changes in the source table.

    Select a watermark column that updates every time a row is modified. We recommend columns similar to the UPDATED_AT or LAST_MODIFIED column.

  • The primary key can be one or more columns on your table that are required for the PostgreSQL connector to determine if it needs to insert or update a row.

    Select columns that contain non-null values that are unique across all rows of the table. We recommend columns that include system-generated identifiers, unique reference codes (for example, auto-incrementing IDs), or immutable time-based sequence IDs.

    To prevent potential data loss or data corruption, the primary key columns that you select must have unique values. If you have doubts about the uniqueness of your chosen primary key column, then we recommend that you use the append write mode instead.

Incremental ingestion behavior

When you make changes to the table schema in your data source, incremental data transfers from those tables are reflected in BigQuery in the following ways:

Changes to data source Incremental ingestion behavior
Adding a new column A new column is added to the destination BigQuery table. Any previous records for this column will have null values.
Deleting a column The deleted column remains in the destination BigQuery table. New entries to this deleted column are populated with null values.
Changing the data type in a column The connector only supports data type conversions that are supported by the ALTER COLUMN DDL statement. Any other data type conversions causes the data transfer to fail.

If you encounter any issues, we recommend creating a new transfer configuration.
Renaming a column The original column remains in the destination BigQuery table as is, while a new column is added to the destination table with the updated name.

Before you begin

Required roles

If you intend to set up transfer run notifications for Pub/Sub, ensure that you have the pubsub.topics.setIamPolicy Identity and Access Management (IAM) permission. Pub/Sub permissions are not required if you only set up email notifications. For more information, see BigQuery Data Transfer Service run notifications.

To get the permissions that you need to create a BigQuery Data Transfer Service data transfer, ask your administrator to grant you the BigQuery Admin (roles/bigquery.admin) IAM role on your project. For more information about granting roles, see Manage access to projects, folders, and organizations.

This predefined role contains the permissions required to create a BigQuery Data Transfer Service data transfer. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to create a BigQuery Data Transfer Service data transfer:

  • BigQuery Data Transfer Service permissions:
    • bigquery.transfers.update
    • bigquery.transfers.get
  • BigQuery permissions:
    • bigquery.datasets.get
    • bigquery.datasets.getIamPolicy
    • bigquery.datasets.update
    • bigquery.datasets.setIamPolicy
    • bigquery.jobs.create

You might also be able to get these permissions with custom roles or other predefined roles.

For more information, see Grant bigquery.admin access.

Network connections

If a public IP address is not available for the PostgreSQL database connection, you must set up a network attachment.

For detailed instructions on the required network setup, refer to the following documents:

Set up a PostgreSQL data transfer

Add PostgreSQL data into BigQuery by setting up a transfer configuration using one of the following options:

Console

  1. Go to the Data transfers page.

    Go to Data transfers

  2. Click Create transfer.

  3. In the Source type section, for Source, select PostgreSQL.

  4. In the Data source details section, do the following:

    • For Network attachment, select an existing network attachment or click Create Network Attachment. For more information, see the Network connections section of this document.
    • For Host, enter the hostname or IP address of the PostgreSQL database server.
    • For Port number, enter the port number for the PostgreSQL database server.
    • For Database name, enter the name of the PostgreSQL database.
    • For Username, enter the username of the PostgreSQL user initiating the PostgreSQL database connection.
    • For Password, enter the password of the PostgreSQL user initiating the PostgreSQL database connection.
    • For TLS Mode, select an option from the menu. For more information about TLS modes, see TLS configuration.
    • For Trusted PEM Certificate, enter the public certificate of the certificate authority (CA) that issued the TLS certificate of the database server. For more information, see Trusted Server Certificate (PEM).
    • For Enable legacy mapping, select true (default) to use the legacy data type mapping. Select false to use the updated data type mapping. For more information about the data type mapping updates, see March 16, 2027. database server. For more information, see Trusted Server Certificate (PEM).
    • For Ingestion type, select Full or Incremental.

    • For PostgreSQL objects to transfer, click Browse.

      Select any objects to be transferred to the BigQuery destination dataset. You can also manually enter any objects to include in the data transfer in this field.

      • If you have selected Append as your incremental write mode, you must select a column as the watermark column.
      • If you have selected Upsert as your incremental write mode, you must select a column as the watermark column, and then select one or more columns as the primary key.

  5. In the Transfer config name section, for Display name, enter a name for the transfer. The transfer name can be any value that lets you identify the transfer if you need to modify it later.

  6. In the Schedule options section, do the following:

    • Select a repeat frequency. If you select the Hours, Days (default), Weeks, or Months option, you must also specify a frequency. You can also select the Custom option to create a more specific repeat frequency. If you select the On-demand option, this data transfer only runs when you manually trigger the transfer.
    • If applicable, select either the Start now or Start at a set time option and provide a start date and run time.

  7. In the Destination settings section, for Dataset, select the dataset that you created to store your data, or click Create new dataset and create one to use as the destination dataset.

  8. Optional: In the Notification options section, do the following:

    • To enable email notifications, click the Email notifications toggle to the on position. When you enable this option, the transfer administrator receives an email notification when a transfer run fails.
    • To configure Pub/Sub run notifications for your transfer, click the Pub/Sub notifications toggle to the on position. You can select your topic name or click Create a topic to create one.

  9. Click Save.

bq

Enter the bq mk command and supply the transfer creation flag --transfer_config:

bq mk
    --transfer_config
    --project_id=PROJECT_ID
    --data_source=DATA_SOURCE
    --display_name=DISPLAY_NAME
    --target_dataset=DATASET
    --params='PARAMETERS'

Replace the following:

  • PROJECT_ID (optional): your Google Cloud project ID. If the --project_id flag isn't supplied to specify a particular project, the default project is used.
  • DATA_SOURCE: the data source, which is postgresql.
  • DISPLAY_NAME: the display name for the data transfer configuration. The transfer name can be any value that lets you identify the transfer if you need to modify it later.
  • DATASET: the target dataset for the data transfer configuration.

  • PARAMETERS: the parameters for the created transfer configuration in JSON format. For example: --params='{"param":"param_value"}'. The following are the parameters for a PostgreSQL transfer:

    • connector.networkAttachment (optional): the name of the network attachment to connect to the PostgreSQL database.
    • connector.database: the name of the PostgreSQL database.
    • connector.endpoint.host: the hostname or IP address of the database.
    • connector.endpoint.port: the port number of the database.
    • connector.authentication.username: the username of the database user.
    • connector.authentication.password: the password of the database user.
    • connector.tls.mode: specify a TLS configuration to use with this transfer:
      • ENCRYPT_VERIFY_CA_AND_HOST to encrypt data, and verify CA and hostname
      • ENCRYPT_VERIFY_CA to encrypt data, and verify CA only
      • ENCRYPT_VERIFY_NONE for data encryption only
      • DISABLE for no encryption or verification
    • connector.tls.trustedServerCertificate: (optional) provide one or more PEM-encoded certificates. Required only if connector.tls.mode is ENCRYPT_VERIFY_CA_AND_HOST or ENCRYPT_VERIFY_CA.
    • ingestionType: specify either FULL or INCREMENTAL. Incremental transfers are supported in Preview. For more information, see Full or incremental transfers.
    • writeMode: specify either WRITE_MODE_APPEND or WRITE_MODE_UPSERT.
    • watermarkColumns: specify columns in your table as watermark columns. This field is required for incremental transfers.
    • primaryKeys: specify columns in your table as primary keys. This field is required for incremental transfers.
    • assets: a list of the names of the PostgreSQL tables to be transferred from the PostgreSQL database as part of the transfer.

For example, the following command creates a PostgreSQL transfer called My Transfer:

bq mk
    --transfer_config
    --target_dataset=mydataset
    --data_source=postgresql
    --display_name='My Transfer'
    --params='{"assets":["DB1/PUBLIC/DEPARTMENT","DB1/PUBLIC/EMPLOYEES"],
        "connector.authentication.username": "User1",
        "connector.authentication.password":"ABC12345",
        "connector.database":"DB1",
        "connector.endpoint.host":"192.168.0.1",
        "connector.endpoint.port":5432,
        "ingestionType":"incremental",
        "writeMode":"WRITE_MODE_APPEND",
        "watermarkColumns":["createdAt","createdAt"],
        "primaryKeys":[['dep_id'], ['report_by','report_title']],
        "connector.tls.mode": "ENCRYPT_VERIFY_CA_AND_HOST",
        "connector.tls.trustedServerCertificate": "PEM-encoded certificate"}'

When you specify multiple assets during an incremental transfer, the values of the watermarkColumns and primaryKeys fields correspond to the position of values in the assets field. In the following example, dep_id corresponds to the table DB1/USER1/DEPARTMENT, while report_by and report_title corresponds to the table DB1/USER1/EMPLOYEES.

      "primaryKeys":[['dep_id'], ['report_by','report_title']],
      "assets":["DB1/USER1/DEPARTMENT","DB1/USER1/EMPLOYEES"],

API

Use the projects.locations.transferConfigs.create method and supply an instance of the TransferConfig resource.

When you save the transfer configuration, the PostgreSQL connector automatically triggers a transfer run according to your schedule option. With every transfer run, the PostgreSQL connector transfers all available data from PostgreSQL into BigQuery.

To manually run a data transfer outside of your regular schedule, you can start a backfill run.

Data type mapping

The following table maps PostgreSQL data types to the corresponding BigQuery data types.

PostgreSQL data type BigQuery data type Updated BigQuery data type
array STRING
bigint INTEGER
bigserial INTEGER
bit(n) STRING
bit varying(n) STRING
boolean BOOLEAN
box STRING
bytea BYTES
character STRING
character varying STRING
cidr STRING
circle STRING
circularstring STRING
compoundcurve STRING
curvepolygon STRING
date DATE
double precision FLOAT
enum STRING
geometrycollection STRING
inet STRING
integer INTEGER
interval STRING
json STRING JSON
jsonb STRING JSON
line STRING
linestring STRING
lseg STRING
macaddr STRING
macaddr8 STRING
money STRING
multicurve STRING
multilinestring STRING
multipoint STRING
multipolygon STRING
multisurface STRING
numeric(precision, scale)/decimal(precision, scale) NUMERIC
path STRING
point STRING
polygon STRING
polyhedralsurface STRING
range STRING
real FLOAT
serial INTEGER
smallint INTEGER
smallserial INTEGER
text STRING
time [ (p) ] [ without timezone ] TIMESTAMP
time [ (p) ] with time zone TIMESTAMP
tin STRING
timestamp [ (p) ] [ without timezone ] TIMESTAMP DATETIME
timestamp [ (p) ] with time zone TIMESTAMP
triangle STRING
tsquery STRING
tsvector STRING
uuid STRING
xml STRING

Troubleshoot

If you are having issues setting up your data transfer, see PostgreSQL transfer issues.

Pricing

For pricing information about PostgreSQL transfers, see Data Transfer Service pricing.

What's next