Category: Indexing

Stephen Planck explains the value (and limitations) of filtered indexes:

When a table holds millions of rows yet most queries touch only a small, well-defined subset, a traditional non-clustered index feels like using a searchlight to find something sitting under a desk lamp: the index still stores an entry for every row even though the workload rarely needs most of them. SQL Server’s answer is the filtered index—introduced in SQL Server 2008 and still under-used today—allowing you to index just the rows that match a predicate you supply in a WHERE clause.

I always thought I would get more value out of filtered indexes. Instead, they’re very useful for specific circumstances, but not the all-purpose performance dynamos you’d want them to be, particularly because of the limitations.

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Lukas Vileikis explains how hash indexes work in MySQL:

In MySQL, hash indexes are indexes that are used in queries that use the equality operators like = or <=> (which is the MySQL NULL safe equality operator, equivalent to the SQL Standard IS NOT DISTINCT FROM). Hash indexes are not used in other situations, so they can be useful for things like random or generated PRIMARY KEY values where you are looking up single rows and not needing to order rows or searching for a range of values.

Just like in SQL Server hash indexes only work for in-memory tables, and some of the mechanisms look very similar.

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Kevin Hill goes back to basics:

“What Is an Index?”
I get this question a lot, especially from developers and sysadmins who’ve been handed a SQL Server and told, “Keep it running fast.” No pressure.

Given that the percentage of people in this industry who have actually seen a phone book continually decreases, it’s for the best that Kevin didn’t use that worn-out example.

If you’re new to indexing in SQL Server, this is a good way to jump-start your knowledge. Then, you spend a while learning the exceptions to the rules and the foibles in existing systems.

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Hans-Jürgen Schönig builds some indexes on vector data:

In the previous post we have imported a fairly large data set containing Wikipedia data, which we downloaded using pgai. However, importing all this data is not enough because we also need to keep an eye on efficiency. Therefore, it is important to understand that indexing is the key to success.

Click through for an overview of what options are available for indexing vectors in PostgreSQL, as well as the trade-offs and disk space requirements.

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Lukas Fittl compares and contrasts:

When it comes to optimizing query performance, indexing is one of the most powerful tools available to database engineers. Both PostgreSQL and Microsoft SQL Server (or Azure SQL) use B-Tree indexes as their default indexing structure, but the way each system implements, maintains, and uses those indexes varies in subtle but important ways.

In this blog post, we explore key areas where PostgreSQL and SQL Server diverge: how their B-Tree indexes implementations behave under the hood and how they store and access data on disk. We’ll also benchmark the impact of deduplication of values on index size in each database system.

I love this kind of post because you hear that SQL Server has indexes and PostgreSQL has indexes (or Oracle has indexes or whatever), and thus, all of your index building knowledge in one applies to the other…right?

One thing that changes the article a bit is that the author doesn’t use page-level compression on indexes in SQL Server. I’d expect the results to change a fair amount, even if the SQL Server non-clustered indexes still ended up larger in the end than PostgreSQL indexes.

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