Sink data from RisingWave to Snowflake

This guide describes how to sink data from RisingWave to Snowflake using the Snowflake sink connector in RisingWave.

Snowflake is a cloud-based data warehousing platform that allows for scalable and efficient data storage and analysis. For more information about Snowflake, see Snowflake official website.

Sinking from RisingWave to Snowflake utilizes Snowpipe for data loading. Initially, data is staged in a user-managed S3 bucket in JSON format, and then loaded into the Snowflake table via Snowpipe. For more information, see Overview of the Snowpipe REST endpoints to load data.

Premium Edition Feature

This feature is only available in the premium edition of RisingWave. The premium edition offers additional advanced features and capabilities beyond the free and community editions. If you have any questions about upgrading to the premium edition, please contact our sales team at sales@risingwave-labs.com.

Public Preview

This feature is in the public preview stage, meaning it's nearing the final product but is not yet fully stable. If you encounter any issues or have feedback, please contact us through our Slack channel. Your input is valuable in helping us improve the feature. For more information, see our Public preview feature list.

Prerequisite

• Ensure you have an S3 bucket that RisingWave can connect to.

• Ensure you have an upstream materialized view or source in RisingWave that you can sink data from.

• Ensure the S3 user account has WRITE permission.

• Ensure that Snowflake and S3 are set up in the same manner as described in the Automating Snowpipe for Amazon S3, as RisingWave is only responsible for writing data to S3.

note

RisingWave will not be responsible for deleting data already imported by S3. You can manually set the lifecycle configuration of your S3 bucket to clear out unnecessary data. See Lifecycle configuration and Delete staged files for more details.

Syntax

Use the following syntax to create a sink in RisingWave:

CREATE SINK [ IF NOT EXISTS ] sink_name
[FROM sink_from | AS select_query]
WITH (
   connector='snowflake',
   connector_parameter = 'value', ...
);

Parameter

All parameters are required unless specified otherwise.

Parameter	Description
s3.bucket_name	The S3 bucket where intermediate sink files will be stored.
s3.path	Optional. The S3 path to be specified. If specified, the actual file location would be <s3_bucket>://<s3_path>/<rw_auto_gen_file_name>. Otherwise, it would be <s3_bucket>://<rw_auto_gen_file_name>.
s3.credentials.access	S3 access credentials.
s3.credentials.secret	S3 secret credentials.
s3.region_name	The S3 region, e.g., us-east-2.
force_append_only	Optional. If true, forces the sink to be append-only, even if it cannot be.

Data type mapping

The following table shows the corresponding data types between RisingWave and Snowflake. For details on native RisingWave data types, see Overview of data types.

RisingWave type	Snowflake type
SMALLINT	SMALLINT
INTEGER	INTEGER
BIGINT	BIGINT
REAL	FLOAT4
DECIMAL	DECIMAL
DOUBLE	FLOAT8
BYTEA	BINARY
VARCHAR	VARCHAR
BOOLEAN	BOOLEAN
DATE	DATE
TIME	TIME
TIMESTAMP	TIMESTAMP
TIMESTAMPTZ	TIMESTAMP_TZ
INTERVAL	Unsupported
ARRAY	ARRAY
JSONB	VARIANT (You need to convert JSONB to VARIANT using parse_json.)

Example

Here is an example on how you can sink data from RisingWave to Snowflake.

Set up S3

Set up an external S3 bucket and ensure you have the corresponding credentials. Both Snowflake stage and RisingWave sink creation require these credentials:

• snowflake.s3_bucket: URL in Snowflake stage.
• snowflake.aws_access_key_id: AWS_KEY_ID in Snowflake stage.
• snowflake.aws_secret_access_key: AWS_SECRET_KEY in Snowflake stage.

Set up Snowflake

Next, you need to set up a table, a stage, and a pipe. Additionally, make sure to open the SQS queue in S3.

To complete the setup, follow the instructions in Automating Snowpipe for Amazon S3.

Sink data with append-only

Now you can start sinking data. Launch your RisingWave cluster and execute the following SQL queries to create source and sink. See the examples below:

Create source

CREATE SOURCE s1_source (id int,name varchar)
WITH (
	connector ='datagen',
	fields.id.kind ='sequence',
	fields.id.start ='1',
	fields.id.end ='10000',
	fields.name.kind ='random',
	fields.name.length ='10',
	datagen.rows.per.second ='200'
 ) FORMAT PLAIN ENCODE JSON;

Create sink

CREATE SINK snowflake_sink FROM ss_mv WITH (
    connector = 'snowflake',
    type = 'append-only',
    s3.bucket_name = 'EXAMPLE_S3_BUCKET',
    s3.credentials.access = 'EXAMPLE_AWS_ACCESS',
    s3.credentials.secret = 'EXAMPLE_AWS_SECRET',
    s3.region_name = 'EXAMPLE_REGION',
    s3.path = 'EXAMPLE_S3_PATH',
    force_append_only = 'true'
);

Sink data with upsert

Snowflake tables don't support direct updates. To handle this, RisingWave imports incremental logs and modification order for each data piece into Snowflake. You can then merge these as you read, or create dynamic tables to read the upsert results. See How dynamic tables work for more details.

Below are some examples for your reference.

Create source in RisingWave

CREATE SOURCE s1_source (id int,name varchar)
WITH (
    connector ='datagen',
    fields.id.kind ='sequence',
    fields.id.start ='1',
    fields.id.end ='10000',
    fields.name.kind ='random',
    fields.name.length ='10',
    datagen.rows.per.second ='200'
 ) FORMAT PLAIN ENCODE JSON;

Create materialized view in RisingWave

CREATE MATERIALIZED VIEW ss_mv AS
WITH sub AS changelog FROM user_behaviors
SELECT
    user_id,
    target_id,
    event_timestamp AT TIME ZONE 'America/Indiana/Indianapolis' as event_timestamp,
    changelog_op AS __op,
    _changelog_row_id::bigint AS __row_id
FROM
    sub;

Note that RisingWave uses changelog to transform streaming data into incremental logs. In the example above, changelog_op represents the type of modification (Insert/Update/Delete), while _changelog_row_id indicates the order of the modification. For more information, see AS CHANGELOG.

Create sink in RisingWave

CREATE SINK snowflake_sink FROM ss_mv WITH (
   connector = 'snowflake',
   type = 'append-only',
   s3.bucket_name = 'EXAMPLE_S3_BUCKET',
   s3.credentials.access = 'EXAMPLE_AWS_ACCESS',
   s3.credentials.secret = 'EXAMPLE_AWS_SECRET',
   s3.region_name = 'EXAMPLE_REGION',
   s3.path = 'EXAMPLE_S3_PATH',
);

Create warehouse

CREATE WAREHOUSE test_warehouse;

Create dynamic table in Snowflake

CREATE OR REPLACE DYNAMIC TABLE user_behaviors
TARGET_LAG = '1 minute'
WAREHOUSE = test_warehouse
AS SELECT *
   FROM (
       SELECT *, ROW_NUMBER() OVER (PARTITION BY {primary_key} ORDER BY __row_id DESC) AS dedupe_id
       FROM t3
   ) AS subquery
WHERE dedupe_id = 1 AND (__op = 1 or __op = 3);