[solved] SQL query to join tables with duplicate rows and getting unique rows
In the realm of data management and analysis within SQL databases like Google BigQuery, a common challenge is joining multiple tables that contain duplicate rows, aiming to achieve a unique dataset. This challenge is pivotal when handling complex datasets involving historical changes and discrepancies across different tables. This guide explores the intricacies of employing SQL queries for such purposes, highlighting the use of LEFT JOIN and DISTINCT within the STRING_AGG function to ensure all rows from a primary table are included in the result, thereby achieving uniqueness.
Understanding the Challenge
Effectively joining tables in relational databases is crucial for in-depth data analysis. The complexity rises when dealing with duplicate rows or uneven datasets. Consider a retail database scenario involving an item
table, an item_change_history
table for logging changes over time, and a mic_item_discrepancy
table for tracking discrepancies in item data across stores.
The primary objective is compiling a singular view that captures the current state of each item, encompassing its change history and any discrepancies, yet maintaining data integrity amidst duplicates.
Utilizing LEFT JOIN for Comprehensive Merges
LEFT JOIN plays a crucial role under such circumstances. It combines rows from two or more tables based on a related column, ensuring all rows from the left table (item) are included in the result, even without matching rows in the right table(s). This ensures a complete dataset, including items without change history or discrepancies.
SELECT
item.id, ich.id, ich.existing_stores, COALESCE(mid.missing_stores, '') AS missing_stores
FROM
`projectID.datasetID.item` item
LEFT JOIN
(
SELECT id, existing_stores, MAX(createdTime) AS latest_createdTime
FROM `projectID.datasetID.item_change_history`
WHERE DATE(createdTime) = CURRENT_DATE()
GROUP BY id, existing_stores
) ich ON item.id = ich.id
LEFT JOIN
(
SELECT ID, STRING_AGG(DISTINCT MISSING_STORE, ',') AS missing_stores
FROM `projectID.datasetID.mic_item_discrepancy`
GROUP BY ID
) mid ON item.id = mid.ID;
Achieving Uniqueness with DISTINCT and STRING_AGG
The DISTINCT keyword within STRING_AGG effectively compiles unique, comma-separated values from duplicate data. This approach ensures each store is listed only once per item in scenarios of multiple discrepancies, enhancing data uniqueness.
The Power of COALESCE
Handling NULL values, a common challenge in data analysis, is adeptly managed by COALESCE, allowing for the specification of a default value when results are NULL. The usage of COALESCE(mid.missing_stores, '') AS missing_stores
guarantees the return of a blank string rather than a NULL value for items without recorded discrepancies.
Real-World Application and Benefits
Businesses, particularly in retail, can leverage such SQL queries on platforms like Google BigQuery for maintaining a holistic inventory view. This fosters informed decisions, efficient inventory management, and enhanced customer satisfaction through accurate item data.
Moreover, analytics tools like Flowpoint.ai emerge as vital for teams seeking to identify and address technical errors in data management processes. Flowpoint aids in pinpointing technical errors impacting website conversion rates and directly provides recommendations for remediation, harnessing features like behaviour analytics and AI-generated advice.
Get a Free AI Website Audit
Automatically identify UX and content issues affecting your conversion rates with Flowpoint's comprehensive AI-driven website audit.
Conclusion
Mastering SQL functionalities such as LEFT JOIN, DISTINCT within STRING_AGG, and COALESCE equips analysts with an efficient means to manage complex data joining scenarios, ensuring data integrity and uniqueness. As vast datasets continue to be navigated, such SQL expertise becomes indispensable for delivering insightful and actionable analytics.