Data merge purge3/24/2023 ![]() The patterns seen so far in this post is not an exhaustive list. WHEN MATCHED AND T.description = 'Furniture' THEN STRUCT('ABC123456' AS sku, 5 AS quantity, 100 AS price), In the below example, MERGE operation INSERTs the row when there are new rows in source that are not found in target and UPDATEs the row when there are matching rows from both source and target tables. Let’s look at MERGE operation with INSERT else UPDATE pattern using subqueries. MERGE operation can be used with source as subqueries, joins, nested and repeated structures. The following picture illustrates MERGE operation on the source and target tables with the corresponding actions - INSERT, UPDATE and DELETE: When there is more than one row matched, the operation errors out. The MERGE operation must match at most one source row for each target row. Then depending on the match status - MATCHED, NOT MATCHED BY TARGET and NOT MATCHED BY SOURCE - corresponding action is taken. THEN update_statement | delete_statement Ī MERGE operation performs JOIN between the target and the source based on merge_condition. UPDATE with WHERE clause: Use WHERE clause in the UPDATE statement to identify specific rows that need to be modified and use SET clause to update specific columns. To update all rows in the table, use WHERE true.įollowing are few patterns to update rows in a BigQuery table: Similar to DELETE statement, each UPDATE statement must include the WHERE clause followed by a condition. UPDATE statement allows you to modify existing rows in a table. ![]() ![]() The subquery can query other tables or perform JOINs with other tables. When using a DELETE statement, you must use WHERE clause followed by a condition.ĭELETE FROM `` WHERE true ĭELETE with WHERE clause: This approach uses WHERE clause to identify the specific rows to be deleted.ĭELETE FROM `` WHERE price = 0 ĭELETE with subqueries: This approach uses a subquery to identify the rows to be deleted. JOIN `` AS productĭELETE statement allows you to delete rows from a table. ON customer.customer_id = txn.customer_id SELECT txn.id, txn.ts, txn.customer_id, product_sku AS sku, product.quantity, product.price INSERT INTO `` (id, ts, customer_id, sku, quantity, price) Best practices for using DML in BigQueryįollowing tables will be used in the examples in this post:.Considerations when using DML, including topics like quotas and pricing.Use cases and syntax of common DML statements.This means you can apply changes to data in a table more frequently and keep your data warehouse up to date with the changes in data sources. DML in BigQuery supports data manipulation at an arbitrarily large number of rows in a table in a single job and supports an unlimited number of DML statements on a table. Let’s get started! Data Manipulation in BigQueryīigQuery has supported Data Manipulation Language (DML) functionality since 2016 for standard SQL, which enables you to insert, update, and delete rows and columns in your BigQuery datasets. In this blog post, we will show you how to run data manipulation statements in BigQuery to add, modify and delete data stored in BigQuery. In the previous posts of BigQuery Explained, we reviewed how to ingest data into BigQuery and query the datasets.
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