A Fine Slice Of SQL Server
Greg Low continues a series on vector data in SQL Server
:In this article, I’ll explain how to implement vector search in SQL Server using the
VECTOR_DISTANCEandVECTOR_SEARCHfunctions. You’ll learn how vector indexes work, when to use them, and what the key trade-offs are — including the current read-only table limitation. I’ll also cover the latest syntax changes in Azure SQL Database, where that limitation has already been lifted.
Click through to learn more.
Leave a CommentQiaosheng Liu makes an announcement:
We started pg_ducklake in January, and today we are releasing v1.0, the first production-ready version. Three things define this release:
- Full DuckLake workflow. v1.0 brings most of DuckLake’s feature set to PostgreSQL SQL: DML, schema evolution, time travel, partitioning, sorted tables, the variant type, ACID transactions, and automatic maintenance. Every table stays open to any DuckDB client.
- Production-grade. pg_ducklake is tuned to live inside an OLTP database: stable under continuous load, disciplined with memory, and offering the fastest ingestion path for DuckLake.
- Standalone and compatible. pg_ducklake started as a fork of pg_duckdb. v1.0 no longer forks or depends on it. It is a self-contained extension, built on a reusable kernel we extracted called
libpgduckdb, and it stays compatible with pg_duckdb.
Read on to learn more about what’s in this production release.
Leave a CommentPaul Randal explains what a log record is:
The simplest definition of a log record: it describes a single change to a database. A single operation in the database may cause multiple changes, but each change will usually have its own log record to describe it. An example of this is updating a column in a single row—it will do the following:
Read on to see what it will do, what it looks like, and what kinds of log records exist.
Leave a CommentLouis Davidson lays out the facts:
Which brings me to the point. There is a myth that goes around that you need to place filters in your SQL statements as early in the statement as possible. Most of this is due to the wild misunderstanding of how a query is executed (versus how your query is processed, which I covered last week.) The actual issue here is that the concept of filtering early is actually true, but certainly not in the way it has been taught.
SQL is a fourth-generation language and implementations approach it. With fourth-generation languages, the actual query you write is not the thing that runs, and there is an entire process to interpret what you wrote and execute operations that meet the intent of your query in the most efficient manner.
Now, this is where someone chimes in and gives all of the circumstances in which T-SQL (or pick your variant) fails to live up to its fourth-generation heritage, such as particularly complex queries, nested views with multiple joins, you using mechanisms that force a specific plan, etc. This is because real life is messy, as Louis shows in some of the examples.
So what’s the point of the first paragraph, then? Because I never miss an opportunity to talk about language generations.
Leave a CommentNikola Ilic digs into a feature:
I hear you, I hear you: Nikola, that’s what deployment rules in Fabric Deployment Pipelines are for, isn’t it? Well, partly. But there’s a Fabric item built specifically to put an end to this whole genre of pain, and it’s the variable library. This article is the long version: what it is, how it’s wired together under the hood, who can actually consume it, when you should reach for it, when you absolutely shouldn’t, how it compares to the other parameterization features in Fabric, and a real telco demo to make it all concrete.
Click through for a deep dive into how it works.
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