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Month: June 2026

Bloom Filters with Valkey

Published 2026-06-09 by Kevin Feasel

Jay Miller checks for a record:

A bloom filter is a small, probabilistic data structure designed to answer one question: “Have I seen this item before?” It provides two potential answers: Absolutely Not, and Probably.

You may think that 100% Yes or No would be better but here’s the thing, probably is really fast and you’re really concerned about the Absolutely Not’s taking up unnecessary connections.

The article speaks to Aiven’s implementation of a Bloom filter in Valkey, but does get into some neat details on bloom filters in general. And if you want to go further down that route, Paul White explains how SQL Server uses Bloom filters.

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Testing SQL Code in Python

Published 2026-06-09 by Kevin Feasel

Jamal Hansen writes some tests:

I once had a query that ran fine for months. Then someone added a column to the source table and a SELECT * downstream started returning unexpected data. The query didn’t error. It just silently gave wrong results. A test would have caught it immediately.

Schema changes break queries silently. Refactoring a CTE can shift results in ways you don’t notice. New data patterns expose assumptions you didn’t know you made. SQL deserves the same testing discipline as the rest of your code, and Python makes it straightforward.

PyTest, the library Jamal uses here, is one of my favorites for this kind of work. You can build up tests without a lot of ceremony and it’s pretty easy to deal with for most use cases.

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The Pain of Moving Indexes between Filegroups

Published 2026-06-09 by Kevin Feasel

Erik Darling explains a process:

At some point you’re going to want to move some indexes to a new filegroup. Maybe you’re separating data across storage, maybe you’re cleaning up after someone who put everything on PRIMARY and walked away, maybe you’ve got your reasons and they’re none of my business.

Whatever the cause, you’d think this would be a solved problem in a database that’s been around since the Clinton administration.

It is not.

Some days, I’m convinced that the only way to win is not to play at all. Erik explaining how to migrate LOB data across filegroups fits that bill perfectly.

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A Primer on Apache Iceberg

Published 2026-06-08 by Kevin Feasel

Walt Riberio lays out an explanation:

Data lakes had a reputation problem. The promise was compelling: dump all your data into cheap object storage—S3, GCS, Azure Blob—and query it whenever you need. The reality was a mess of stale partitions, schema drift, and silent data corruption caused by unsafe concurrent writes. Engineers knew the risks and worked around them rather than fixing them.

Apache Iceberg was built to fix that. And it’s catching on fast—even in the Kafka® world Iceberg is a hot topic. If you’re running ClickHouse® or building a pipeline that feeds into it, Iceberg is quickly becoming hard to ignore if you care about accuracy at scale. It’s becoming the connective tissue of the modern data stack.

Click through to learn more about the product and where it potentially fits in an organization.

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The Ulam Prime Spiral

Published 2026-06-08 by Kevin Feasel

Tomaz Kastrun re-creates a classic:

Stanislaw Ulam, Los Alamos, 1963 was bored in a meeting and he started dooddling integers in a spiral and circled the primes. Diagonal lines appeared. He later showed it to Martin Gardner, to Ulam surprise, Gardner published his findings in Scientific American. We are still confused to this day.

Click through for a demonstration of this in action.

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What pg_stat_statements Cannot Do

Published 2026-06-08 by Kevin Feasel

Radim Marek enumerates a list:

Part one made the core case: pg_stat_statements counts, it doesn’t record. It walked through how the queryid jumble fragments one logical query into many rows, how the first-seen text freezes your per-request tags, and how the averages bury the p99 that actually pages you. All of that was about data the extension has and distorts.

This part is about the rest: the entries it silently throws away, the query text that can vanish all at once, the plans and replicas it never records, and the knobs that bite. It ends where part one started, with the question the whole investigation was really about: is this the query store Postgres is missing, or just the floor you’d build one on?

Click through for those limitations.

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The Limits of Parallelism with Fabric Dataflows Gen2

Published 2026-06-08 by Kevin Feasel

Chris Webb notes that parallelism does not mean “free performance improvements”:

To finish off my series of posts on concurrent evaluation in Fabric Dataflows Gen2 (see part 1 and part 2) I decided to do some more realistic tests to see how much parallelism I could get. To do this I uploaded 244 identical Excel files containing almost 542000 rows of data each to a SharePoint document library. Excel files are probably the worst-performing file format for dataflows (see here for some tests that show this), while SharePoint is probably the worst-performing place to store data for a dataflow and also has a reputation for throttling applications that make too many requests.

Click through for Chris’s test results.

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Optional Parameter Plan Optimization

Published 2026-06-08 by Kevin Feasel

Hugo Kornelis concludes a mini-series:

It’s time to finish the triptych on bad parameter sniffing, and how Microsoft tries (and fails) to fix this for you.

After first talking about bad parameter sniffing in general, I used my last video to explain Parameter Sensitive Plan Optimization, the feature that Microsoft released in SQL Server 2022 as an attempt to fix one of the three root causes for bad parameter sniffing. If you want to see why I consider that a failed attempt, go watch that video.

In SQL Server 2025, Microsoft then added Optional Parameter Plan Optimization (OPPO). Their attempt to fix the second of those three root causes. Put where PSPO in most cases simply doesn’t do anything, or does do something, but not enough, OPPO does too much. It fixes bad parameter sniffing due to optional parameters in demos that are carefully curated to showcase the feature, but it does more bad than good in more realistic scenarios.

Read on for Hugo’s overview, as well as a video.

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Vector Search with Oracle against Iceberg Tables

Published 2026-06-05 by Kevin Feasel

Brendan Tierney performs a search:

In my previous blog posts I’ve explored how to use Iceberg Tables and how to integrate these in with your Data Lake. Additionally, I showed how to setup your Oracle Data Lake (Database) to access the data stored in Iceberg Tables stored in OCI Object Storage. To access this Iceberg Table data from the Oracle Database we created an External Table. This allows us to query the Iceberg Table data as if it was internal to the database. With all new releases there is continuous improvement in the features and to make them easier to use. One such new feature (as of 23.26.1) is the ability to read vector data types from an External Table. This new feature is called or referred to as ‘Vectors on Ice’.

With Oracle Database External Tables now supporting vector embedding stored in Iceberg Tables, means you can generate vector embeddings with your preferred embedding model (external to Oracle using your faviourite tool/library), store them in Iceberg Tables in cloud object storage (OCI Object Storage, AWS S3, etc.), and run semantic search from Oracle AI Database, accessing vector data stored within the database and externally with the minimum of data movement and with similar SQL queries.

Click through to see how.

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