Updating a bunch of records based on a whole different table is one of those things that seems like it should be a one-liner. It is, technically. But if you've ever run a "simple" script on a Friday afternoon only to realize you just nullified 400,000 rows of production data because your join logic was slightly off, you know it's a nightmare. Honestly, the syntax for an update from another table varies so much between SQL Server, MySQL, and PostgreSQL that it’s almost impressive how inconsistent it is.
We’ve all been there. You have a Products table and a SupplierUpdates table. You need to pull the new prices from one into the other. Simple, right? But the moment you start typing UPDATE, the stakes get high. You aren't just adding data; you are overwriting what's already there. There is no "undo" button in a standard SQL console once that transaction commits.
Why the Syntax is a Mess
The biggest headache is that the SQL standard (ISO/IEC 9075) doesn't actually define a super clear way to do a join inside an update. It’s a bit of a "choose your own adventure" situation for database engine developers.
In SQL Server (T-SQL), you’re usually looking at an UPDATE FROM structure. It feels natural to most developers because it mirrors how you write a SELECT statement with a join. You basically point at the table you want to change, and then tell the engine where the source data is hiding.
PostgreSQL does things differently. It uses an UPDATE...SET...FROM pattern. If you try to use the SQL Server syntax in Postgres, it’ll just stare at you with a syntax error. Or worse, it might do something you didn't intend.
MySQL is the odd one out. It prefers a multi-table update syntax where the UPDATE keyword is followed immediately by the joined tables. It’s actually quite readable once you get used to it, but it’s a total departure from the others.
SQL Server: The JOIN Method
When you're working in SQL Server, the most common way to handle an update from another table is using a JOIN right in the update block. It looks something like this:
$$UPDATE\ p$$
$$SET\ p.Price\ =\ s.NewPrice$$
$$FROM\ Products\ AS\ p$$
$$INNER\ JOIN\ SupplierUpdates\ AS\ s\ ON\ p.ProductID\ =\ s.ProductID$$
Notice the alias p. You have to be careful here. If you use the table name directly in the SET clause instead of the alias defined in the FROM clause, SQL Server can get confused about which instance of the table you're talking about. It’s a classic foot-gun.
One thing people often overlook is what happens when the source table has duplicate keys. If SupplierUpdates has two different prices for the same ProductID, SQL Server doesn't throw an error. It just picks one. Which one? Usually the one it finds first based on the physical order of data or the index. That is terrifying for data integrity. You should always ensure your source data is unique or use an aggregation (like MAX or AVG) in a subquery before you even think about updating.
The PostgreSQL "FROM" Clause
Postgres is a bit more rigid, which is actually a good thing for your data. In Postgres, the syntax for an update from another table looks like this:
$$UPDATE\ products$$
$$SET\ price\ =\ supplier_updates.new_price$$
$$FROM\ supplier_updates$$
$$WHERE\ products.product_id\ =\ supplier_updates.product_id$$
Wait. Did you see that? You don't repeat the target table in the FROM clause. If you do UPDATE products ... FROM products, supplier_updates, you are actually performing a self-join that creates a massive cartesian product. I’ve seen developers lock up entire databases for hours because they accidentally joined a table to itself during an update.
It’s also worth noting that Postgres doesn't allow aliases in the UPDATE clause quite the same way SQL Server does. You update the table name, then join the others in the FROM section. It’s a subtle shift, but it’s where most people trip up when switching between environments.
MySQL’s Multi-Table Approach
MySQL is actually pretty elegant here. You basically write a join like you would for a SELECT, but you swap the SELECT for UPDATE.
$$UPDATE\ products,\ supplier_updates$$
$$SET\ products.price\ =\ supplier_updates.new_price$$
$$WHERE\ products.product_id\ =\ supplier_updates.product_id$$
Or, more modernly:
$$UPDATE\ products$$
$$JOIN\ supplier_updates\ ON\ products.product_id\ =\ supplier_updates.product_id$$
$$SET\ products.price\ =\ supplier_updates.new_price$$
It’s flexible. It’s easy to read. But again, the danger of multiple matches remains. If your join isn't a 1-to-1 relationship, you’re gambling with your data.
The "Safe" Way: Using CTEs
If you want to stay sane and keep your job, Common Table Expressions (CTEs) are your best friend. Instead of writing a complex join inside the update, you define your "clean" dataset first. This works beautifully in SQL Server and PostgreSQL.
Think about it this way: you create a temporary "view" of exactly what you want the update to look like. Then you run the update against that view.
$$WITH\ CleanedData\ AS\ ($$
$$\ \ \ \ SELECT\ ProductID,\ MIN(NewPrice)\ as\ SafePrice$$
$$\ \ \ \ FROM\ SupplierUpdates$$
$$\ \ \ \ GROUP\ BY\ ProductID$$
$$)$$
$$UPDATE\ p$$
$$SET\ p.Price\ =\ c.SafePrice$$
$$FROM\ Products\ p$$
$$JOIN\ CleanedData\ c\ ON\ p.ProductID\ =\ c.ProductID$$
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This approach forces you to handle the "duplicate record" problem before the update even touches your main table. It makes the code much easier to debug. You can run the SELECT part of the CTE first to see exactly what’s going to happen.
Common Pitfalls and Why They Happen
Most "update gone wrong" stories come down to a few specific mistakes.
First: The Missing WHERE Clause. It sounds like a joke, but it’s real. If you’re using a subquery to update a column and the subquery doesn't return a value for a specific row, some databases will just set that row to NULL. Suddenly, half your catalog has no price.
Second: The Correlation Error. This happens when you think you’re filtering the update, but you’re actually just filtering the join. The update still runs on the whole table, but the values it pulls in are null or incorrect.
Third: Locks and Blocking. An update from another table is often a heavy operation. If you're updating 10 million rows, you’re going to lock that table. In a high-traffic environment, this is a death sentence for your application. The "site is down" calls start coming in 30 seconds later.
Better Performance Strategies
If you’re dealing with massive datasets, don’t just run a giant update. It’s a bad move.
- Batching: Update in chunks of 5,000 or 10,000 rows. Use a loop. It’s slower overall, but it keeps the transaction log small and prevents the database from locking up for everyone else.
- Indexing: Ensure the columns you are joining on (the IDs) are indexed in both tables. If they aren't, the database has to do a full table scan for every single row it updates. It’s the difference between an update taking 2 seconds or 2 hours.
- Temporary Tables: Sometimes it’s faster to select the data you need into a temp table, index that temp table, and then run the update from there. This is especially true if the source table is complex or slow.
The "Safety Net" Protocol
Before you hit execute on any update from another table, do these three things.
Run a SELECT first. Take your update query, change the UPDATE and SET parts into a SELECT *, and look at the results. Are the columns lining up? Is the row count what you expected? If you expected to update 50 rows and the select returns 5,000, stop.
Use Transactions. Start with BEGIN TRANSACTION. Run your update. Check the results with a SELECT. If everything looks good, run COMMIT. If you see zeros where there should be numbers, run ROLLBACK immediately. It’s your only real safety net.
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Check the execution plan. If you're on SQL Server Management Studio or using EXPLAIN in Postgres/MySQL, look at what the engine is actually doing. If you see a "Nested Loop" on two giant tables, your database is about to cry.
Moving Forward with Confidence
Handling an update from another table isn't just about knowing the syntax. It’s about defensive programming. Databases are incredibly powerful, but they are also incredibly literal. They will do exactly what you tell them to do, even if what you told them to do is delete your company's revenue for the last quarter.
Start small. Use CTEs whenever possible to keep your logic clean. Always, always verify your join logic with a SELECT before committing.
Next time you need to sync data between tables, don't just copy-paste a snippet from a forum. Think about the specific dialect you’re using and the volume of data you’re moving.
Actionable Steps:
- Identify the specific SQL flavor you are using (T-SQL, MySQL, or PostgreSQL) as the syntax differs significantly.
- Create a backup or a staging copy of your target table before performing bulk updates.
- Write your join logic as a
SELECTstatement first to verify that the rows being matched are correct. - Implement batching for updates exceeding 100,000 rows to avoid transaction log overflows and long-term table locks.
- Wrap your update in a transaction block (
BEGIN/ROLLBACK/COMMIT) to allow for an immediate revert if the affected row count is unexpected.