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Using group by on two fields and count in SQL

sql
best-practices
group-by
count
Alex KataevbyAlex Kataev·Nov 30, 2024
TLDR

For a quick solution, GROUP BY with two columns and utilize COUNT() to note the occurrences:

SELECT Col1, Col2, COUNT(*) AS Total FROM Table GROUP BY Col1, Col2;

If your columns share names with SQL keywords or functions, employ aliases for unique identifiers:

SELECT myGroup AS GroupName, mySubGroup AS SubGroupName, COUNT(*) AS GroupCount FROM myTable GROUP BY myGroup, mySubGroup;

Test your SQL queries in their specific database environments for accurate counts. Be wary of case sensitivity, and avoid using reserved SQL keywords for column names to bypass naming conflicts.

GROUP BY and COUNT: Best practices and potential pitfalls

Keyword and case sensitivity issues

Be mindful of your column names in SQL —keywords or case-sensitive names can generate errors:

  • Aliases offer a workaround for keyword usage in column names.
  • Running a trial of your query in the intended database can help spot issues with case sensitivity.

Demystifying COUNT()

COUNT() can be a tricky function, but these pointers may help:

  • COUNT(*) counts all rows, including those with NULL values.
  • To count only non-NULL values in a column, use COUNT(column_name).
  • To maintain consistency, ensure the columns in your SELECT statement match those used in GROUP BY.

Mastering GROUP BY: Optimization and techniques

Unlocking the power of subqueries

Subqueries can bring considerable power to complex aggregations:

-- Subqueries: befriending aggregation one layer at a time! SELECT friend_pair, MAX(times_met) FROM ( SELECT concat(friend1, ' & ', friend2) AS friend_pair, COUNT(*) AS times_met FROM photo_album GROUP BY friend1, friend2 ) AS subquery GROUP BY friend_pair;

Scalability with indexes

Creating indexes on GROUP BY columns can swift up performance:

  • Place the index order in line with the GROUP BY order.
  • Use composite indexes for multiple GROUP BY columns.

Advanced techniques: OLAP functions

Modern SQL databases support OLAP functions that allow for an additional layer of analysis:

-- OLAP functions: because SQL has its ups and downs! SELECT Col1, Col2, COUNT(*) OVER(PARTITION BY Col1, Col2) AS RunningTotal FROM Table;

This query performs a similar function to GROUP BY while allowing for more flexibility within the same query.