Скачать или смотреть BigQuery: Lookup Array of IDs and Join Data Using SQL

Discover how to replace record arrays with corresponding region names in BigQuery using SQL for data transformation.
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Transforming Data in BigQuery: Joining Records with Territory Names

When working with data in Google BigQuery, you may come across situations where you want to merge information from multiple tables. A common task is to join records containing arrays of IDs with their corresponding names from another reference table. In this guide, we will explore how to take region_ids from a Products table and replace them with the actual region names from a Regions table using SQL.

The Problem Statement

Consider the following data structure:

Products Table

nameregion_idsshoec32, a43, x53hatc32, f42Schema:

name: STRING

region_ids: RECORD (REPEATED)

region_ids.value: STRING

Regions Table

_idnamec32londona43manchesterx53bristolf42liverpoolSchema:

_id: STRING

name: STRING

Your goal is to take the region_ids from the Products table and output a new table that lists the corresponding region names, resulting in a structure like this:

_idnameregion_namesd22shoelondon, manchester, bristolt64hatlondon, liverpoolNow, let’s explore how to achieve this in BigQuery SQL.

The Solution

Using BigQuery Standard SQL, you can effectively join these tables and transform your data. Here’s a step-by-step guide on how to accomplish this:

SQL Query

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Query

SELECT Statement: This part of the query selects the _id, name, and creates a new field region_names by aggregating region names into a comma-separated string.

FROM Clause: The query is selecting data from the Products table.

UNNEST Function: This function is used to flatten the array of region_ids. WITH OFFSET is included so that the original order of the region_ids is preserved.

JOIN Operation: A LEFT JOIN is performed with the Regions table based on the condition that the value from region_ids matches the _id in Regions.

GROUP BY Clause: This groups the results based on the _id and name so that we can apply the aggregate function.

Sample Data Execution

To test this logic, we can use the sample data provided in your question as shown below:

[[See Video to Reveal this Text or Code Snippet]]

The result of running this query will give you:

_idnameregion_namesd22shoelondon, manchester, bristolt64hatlondon, liverpoolAdditional Options

If you need the region_names as an array instead of a string, simply replace STRING_AGG(r.name, ', ' ORDER BY OFFSET) with ARRAY_AGG(r.name ORDER BY OFFSET) in the query. This will return region_names as an array of names.

Conclusion

In this guide, we successfully demonstrated how to join a table with an array of IDs with another table to convert those IDs to meaningful names in BigQuery. By breaking down the SQL query, we've made it simpler to grasp the process involved. Whether you're new to BigQuery or looking to refine your SQL skills, understanding how to perform this type of join is invaluable for data transformation.

By applying the concepts outlined here, you'll be better equipped to handle complex data relationships in your own projects, transforming IDs into actionable insights!