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Alter a streaming job

This document explains how to modify the logic in streaming pipelines within RisingWave. Understanding these mechanisms is essential for effectively managing your data processing workflows.

Alter a table or source

To add or drop columns from a table or source, simply use the ALTER TABLE or ALTER SOURCE command. For example:

ALTER TABLE customers ADD COLUMN birth_date;

ALTER SOURCE test_source ADD COLUMN birth_date;

The new column will be NULL for existing records.

Alter a materialized view

To alter a materialized view, you need to create a new materialized view and drop the existing one.

For example, suppose we want to add a new column to the materialized view mv1:

CREATE MATERIALIZED VIEW mv1 AS
SELECT
customer_id,
SUM(total_price) AS sales_amount,
FROM test_source
GROUP BY customer_id;

Here we create a new materialized view mv1_new with the new column sales_count:

CREATE MATERIALIZED VIEW mv1_new AS
SELECT
customer_id,
SUM(total_price) AS sales_amount,
COUNT(*) AS sales_count -- The new column
FROM test_source
GROUP BY customer_id;

After the new materialized view is created, we can drop the old materialized view mv1 and rename mv1_new to mv1:

DROP MATERIALIZED VIEW mv1;
ALTER MATERIALIZED VIEW mv1_new RENAME TO mv1;

Alter a sink

To alter a sink, you will need to create a new sink and drop the old sink. Please check the example from the last section.

If your sink is created based on a materialized view using the CREATE SINK ... FROM ... statement, you have the option to specify without_backfill = true to exclude existing data.

CREATE SINK ... FROM ... WITH (without_backfill = true).

Why is it not possible to modify a streaming job in place?

Streaming systems like RisingWave need to maintain internal state for streaming operators, such as joins and aggregations. Generally, modifying a materialized view would require consistent changes to the internal state accordingly, which is not always feasible. Let’s see an example.

Given a table adult_users that tracks the number of users aged ≥ 18.

CREATE MATERIALIZED VIEW adult_users AS
SELECT
COUNT(*) as user_count
FROM users
WHERE age >= 18;

It was discovered later that the legal definition for adulthood should be set at ≥16. Initially, one might consider modifying the filter condition from age >= 18 to age >= 16 as a straightforward solution. However, this is not feasible in stream processing since records with ages between 16 and 18 have already been filtered out. Therefore, the only option to restore the missing data is to recompute the entire stream from the beginning.

Therefore, we recommend persistently storing the source data in a long-term storage solution, such as a RisingWave table. This allows for the recomputation of the materialized view when altering the logic becomes necessary.

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