Postgresql Import Integration

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The Data Connector for PostgreSQL enables you to directly import data from your PostgreSQL database to Treasure Data. For PostgreSQL data export, see PostgreSQL Export Integration.

Prerequisites

• Basic knowledge of Treasure Data
• Basic knowledge of PostgreSQL
• A PostgreSQL instance running remotely, for example, on RDS.

Static IP Address of Treasure Data Integration

If your security policy requires IP whitelisting, you must add Treasure Data's IP addresses to your allowlist to ensure a successful connection.

Please find the complete list of static IP addresses, organized by region, at the following link:
https://api-docs.treasuredata.com/en/overview/ip-addresses-integrations-result-workers/

Import from PostgreSQL using TD Console

Create Authentication

When you configure a data connection, you provide authentication to access the integration. In Treasure Data, you configure the authentication and then specify the source information.

1. Open TD Console.
2. Navigate to Integrations Hub >  Catalog
3. Search for and select PostgreSQL. Select Create.

4. The following dialog opens.

5. Enter the required credentials and set the parameters. Select Continue.

6. Type a name for your connection.
7. Select Done.

Create a Source

After creating the authenticated connection, you are automatically taken to Authentications.

1. Search for the connection you created.
2. Select New Source. The Create Source dialog opens.

Connection

1. Type a name for your Source in the Data Transfer field**.**

2. Click Next.

Source Table

1. Edit the following parameters

Data Settings

1. Select Next. The Data Settings page opens.
2. Optionally, edit the data settings or skip this page of the dialog.

Data Preview

You can see a preview of your data before running the import by selecting Generate Preview. Data preview is optional and you can safely skip to the next page of the dialog if you choose to.

1. Select Next. The Data Preview page opens.
2. If you want to preview your data, select Generate Preview.
3. Verify the data.

Data Placement

For data placement, select the target database and table where you want your data placed and indicate how often the import should run.

1. Select Next. Under Storage, you will create a new or select an existing database and create a new or select an existing table for where you want to place the imported data.

2. Select a Database > Select an existing or Create New Database.

3. Optionally, type a database name.

4. Select a Table> Select an existing or Create New Table.

5. Optionally, type a table name.

6. Choose the method for importing the data.

   • Append (default)-Data import results are appended to the table. If the table does not exist, it will be created.
   • Always Replace-Replaces the entire content of an existing table with the result output of the query. If the table does not exist, a new table is created.
   • Replace on New Data-Only replace the entire content of an existing table with the result output when there is new data.

7. Select the Timestamp-based Partition Key column. If you want to set a different partition key seed than the default key, you can specify the long or timestamp column as the partitioning time. As a default time column, it uses upload_time with the add_time filter.

8. Select the Timezone for your data storage.

9. Under Schedule, you can choose when and how often you want to run this query.

Run once

1. Select Off.
2. Select Scheduling Timezone.
3. Select Create & Run Now.

Repeat Regularly

1. Select On.
2. Select the Schedule. The UI provides these four options: @hourly, @daily and @monthly or custom cron.
3. You can also select Delay Transfer and add a delay of execution time.
4. Select Scheduling Timezone.
5. Select Create & Run Now.

After your transfer has run, you can see the results of your transfer in Data Workbench > Databases.

Further Information

• List of Options for PostgreSQL Data Connector

Import from PostgreSQL via CLI (Toolbelt)

Install ‘td’ Command

Install the newest Treasure Data Toolbelt.

Create Seed Config File (seed.yml)

Configure seed.yml with your PostgreSQL access information:

in:  type: postgresql  host: postgresql_host_name  port: 5432  ssl: true  ssl_version: TLS  user: test_user  password: test_password  driver_version: 42.7.x  database: test_database  table: test_table  select: "*"  default_column_options:    TIMESTAMP: {type: string, timestamp_format: '%Y-%m-%d', timezone: '+0900'}    BIGINT: {type: string}     mode: replace

This example imports all records inside the table. You can have more detailed control with additional parameters.

For more details on available out modes or on available ssl_version, see the Appendix.

Guess Fields (Generate load.yml)

Use td connector:guess. This command automatically reads the target data and intelligently guesses the data format.

$ td connector:guess seed.yml -o load.yml

If you open load.yml, you will see guessed file format definitions, including, in some cases, file formats, encodings, column names, and types.

Optional: Preview Data to be Imported

You can preview data to be imported using the command td connector:preview.

td connector:preview load.yml

Execute Load Job

Submit the load job. The job may take a couple of hours to run, depending on the data size. You must specify the database and table where your data is stored.

It is recommended to specify --the time-column option because Treasure Data’s storage is partitioned by time (see also data partitioning). If the option is not given, the data connector chooses the first long or timestamp column as the partitioning time. The type of the column specified by --time-column must be either long or timestamp type.

If your data doesn’t have a time column, you may add it using the add_time filter option. For more details, see the add_time filter plugin.

$ td connector:issue load.yml --database td_sample_db --table td_sample_table

The connector:issue command assumes that you have already created a database(td_sample_db) and a table(td_sample_table). If the database or the table do not exist in TD, the connector:issue command fails. In this case, create the database and table manually or use --auto-create-tableoption with td connector:issuecommand to auto create the database and table:

$ td connector:issue load.yml --database td_sample_db --table td_sample_table --time-column created_at --auto-create-table

You can assign the Time Format column to the "Partitioning Key" by the "--time-column" option.

Import from PostgreSQL using Workflow

For sample workflows showing how to import data from your PostgreSQL, view Treasure Boxes.

Incremental Load

You can load records incrementally by specifying columns from your table to the incremental_columns parameter. Optionally, you may specify some initial values to the last_record parameter.

in:
  type: postgresql
  host: postgresql_host_name
  port: 5432
  user: test_user
  password: test_password
  database: test_database
  table: test_table
  incremental: true
  incremental_columns: [id, created_at]
  last_record: [10000, '2014-02-16T13:01:06.000000Z']
out:
  mode: append
  exec: {}

CREATE INDEX embulk_incremental_loading_index ON test_table (id, created_at);

The connector automatically creates the query and sort value.

-- when last_record wasn't given
SELECT * FROM(
    ...original query is here
)
ORDER BY id, created_at

-- when last_record was given
SELECT * FROM(
    ...original query is here
)
WHERE id > 10000 OR (id = 10000 AND created_at > '2014-02-16T13:01:06.000000Z')
ORDER BY id, created_at

The connector automatically generates last_record and uses it at the next scheduled execution.

in:
  type: postgresql
  ...
out:
  ...

Config Diff
---
in:
  last_record:
  - 20000
  - '2015-06-16T16:32:14.000000Z'

The query option is unavailable when you set incremental: true. Only strings, integers, timestamp, and timestamptz(timestamp with timezone) are supported as incremental_columns.

Load Array Column

PostgreSQL’s array type is retrieved as string type.

Load hstore Column

PostgreSQL’s hstore type is retrieved as string type when the data connector reads it at first. Therefore, if you want to use hstore type as json type, you need to specify config_options and explicitly convert the type to json type.

For example, v_hstore is hstore type in PostgreSQL:

in:
  type: postgresql
  host: xxx
  ...
  table: my_tbl
  select: "*"
  column_options:
    v_hstore: {type: json} # explicit type conversion: string type to json type
out:
  ...

SSL Version

You can use specific SSL version that your PostgreSQL server is using with ssl_version option.

in:
  type: postgresql
  ...
  ssl: true
  ssl_version: TLSv1.1

Supported values are as follows.

• TLS
• TLSv1.1
• TLSv1.2
• TLSv1.3

Default Column Options

You can use a specific default format of the SQL data type. In the example below, TIMESTAMP is converted to string with the format %Y-%m-%d and with the time zone +0900 The SQL type should be in upper case such as TIMESTAMP, BIGINT

in:
  type: postgresql
  ...
   default_column_options:
    TIMESTAMP: {type: string, timestamp_format: '%Y-%m-%d', timezone: '+0900'}
    BIGINT: {type: string}