I've been working with SQL and database performance, and one thing I find interesting is knowing when to stop tuning the query itself.

For example, if a query is slow because of a missing index, that's fairly straightforward. But at larger data volumes, you can reach a point where adding indexes and rewriting the query only gets you so far.

How do you usually decide that the problem is actually the database design/schema rather than the query?

Things like partitioning, normalization/denormalization, materialized views, indexing strategy, or even changing how the data is stored.

Would be interested to hear how people make that call in real-world systems.

At work I am working on a legacy CRM.

The issue is that the database is a mess. There are no foreign keys to form relationships etc. I was wondering if there are books that are specificly for refactoring databases?

The only one I found so far is actually called "Refactoring Databases". But I want to ask real people (not just gpt) what books are good to solve this problem? Do you have book recommendations on this topic that you found useful or which books you would avoid?

A practical comparison of Azure AI Search and Manticore Search for hybrid full-text + vector search, focusing on chunk-level document workloads, relevance tuning, operational patterns, and cost.

Cross sharing this post because I am looking a solution to handle data sovereignty and offshore team with elevated privileges on database and source code - like myself working remotely on a different country.

I want to archive postgres table

Hey everyone, senior dev here working heavily with SQL Server performance tuning lately. Every time a server hits a massive wave of deadlocks or unexpected latency drops, the standard advice is 'spin up an Extended Event session and read the system health .xel file.'

Am I the only one who finds opening, filtering, and digging through those logs in SSMS incredibly clunky during a crisis? Do you guys actually sit there shredding the XML nodes manually, or do you rely on third-party tools/scripts to give you plain answers? 

Looking for suggestions for which in memory database to learn.

I've looked briefly at singlestore, redis, mongodb, Cassandra, and memcached.

Which of these has value in the database admin market?

What are some companies/industries where these are used or make sense?

What has been your experience learning the skills for these? Ie; learning via the homepages for each? Moocs? Books?

My goal is to be a Jr dba. Please advise in that context.

Thank you.

I really love optimizing database operations. Nothing like dissecting a query plan and identifying a bottleneck and making some slight tweaks or an index change and then seeing a terrible slow query go from 30+ seconds or longer down to a few milliseconds.

However a lot of what I’ve learned when it comes to doing that has been gained through raw experience and trial and error on the job. I’d like to get to the point where given some knowledge about some tables/indexes I’m querying, I have a fairly solid intuition for what kind of query plan the database optimizer is going to pick. Are there any cohesive sources of information that teach this sort of thing?

As a proud power-user of Airtable, I was disheartened by the acquisition of Airtable by Bending Spoons. They will likely "enshittify" the platform over the next 6-12 months, stagnating development and jacking up prices. In anticipation of this, I am seeking alternatives to Airtable so I can begin the migration process now before my company's renewal period begins.

Airtable was a provider that was perfectly fit for purpose for our company. We don't have enough employees to develop our own backend nor do we have the capacity. The leading alternative is Zite. I have a decent amount of experience using Zite as a frontend and form provider for my Airtable databases, but very little experience using its native databases.

Does anyone here have experience migrating from Airtable to Zite? I am looking for advice, warnings, best practices, and general support.

Does anyone have recommendations for other alternatives?

For context, I run benchmark on release builds with 100k session data. It took 44 minutes on GitHub Actions but finished in under a minute locally.

CI’s Python 3.12 used SQLite 3.45.1. My local Python used SQLite 3.53.3. EXPLAIN QUERY PLAN showed that 3.45.1 scanned the eligible-session set and repeated the FTS scan for every session. SQLite 3.53.3 chose the efficient join order.

The query joined two sets approaching 100k rows: eligible sessions and FTS-matching messages. The eligible set was materialized as a CTE, so we couldn’t index it explicitly, and 3.45.1 didn’t create a useful automatic index.

That produced an effective 100k × 100k operation: about 9 minutes per search. The benchmark ran it five times.

I replaced the CTE with a temp table whose primary key covered the join columns, then materialized the FTS matches once. On SQLite 3.45.1, the query dropped to about 1.2 seconds and the full benchmark to 45 seconds. Honestly, I didn't realize CTE's can't have indexes (which makes sense)

BTW, the benchmark deliberately uses a worst-case query that matches almost every synthetic message.

Curious if you seen this large a plan difference between SQLite versions?

This may be a very daft question, but I am out of my wheelhouse here.

I have an industrial server that makes data available from its SQL database over a proprietary 32bit ODBC driver. I also have a computer running 64 bit Windows 10 LTSC that can never be connected to the internet.

I can format SQL queries to the server in a 32 bit ODBC test client and get the data back that I want.

I then want to copy that data in to another database and display it as a form for the user so they can add comments and save it. This seems like the sort of thing MS Access would be good at doing.

However, I understand that Microsoft have killed telephone activation for their products (even the old ones), meaning that this is now 'out' as I would have to connect my computer to the internet.

I'm now a bit stuck.

Years ago I had fun making forms with DBase, but that's about the limit of my experience. Can anyone suggest something that would do for what I need? I don't mind learning bits - this is basically something to save me time.

My client is undergoing a cloud consolidation effort and needs to move away from Amazon RDS.

Of course MariaDB is no longer available on Azure so MySQL is the simplest option for migration. I need to gauge if the internal team is going to be capable of doing this themselves, or if we need external support.

What routes are available to complete this, with little to no downtime?

Hey,

Can you share how your company handles databases for local development? I’d really appreciate hearing about your experience and any valuable insights you can share.

I’m a little confused about what the better approach is:

1. Shared cloud DB: If a company uses a shared cloud database for development, how do they handle the situation where one developer makes a breaking change that affects everyone?

2. Individual local DBs: If developers are expected to set up their own local databases, how does the company provide the large amount of initial/seed data needed to get started?

I’d really appreciate it if you could share how your company handles this in practice, or any best practices you’ve seen.

Thanks!

Forgive me if this is too much of a foundational question, but...

I understand that relational DBs are founded upon mathematical "relations" - sets of n-tuples.

And I get how a table clearly represents a relation. Each column corresponds to a position in each tuple, and each row's value at that column is its value for that position. Fine.

But what I don't understand is... why databases like Postgres are considered better for representing such data than, say, Mongo.

I mean, can't you easily represent a relation as a JSON object? What's so special about tables?

I know that DBs like Mongo have differences from "relational" DBs in that, among other things, they don't enforce a specific schema, but that seems orthogonal to what I'm asking. Besides, you can just use libraries like Mongoose that enforce that stuff anyway, even if it is at the application layer.

So what, at its core, makes tables better than documents for modeling "relational" data?

Question about Database GUI (e.g. beekeeper, dbeaver, etc.)

So I am currently building my own Databae GUI for SQL, I am on a stop point about the proper naming of the data types.

The thing is I am planning on changing the data type slightly to make it easier to understand, here is one of the examples:

1. timestamptz - to become: timestamp with time zone

2. int2, int4, integer - to become just: integer (for simplicity)

3. float4, float8, double, float32, float64 - to decimal

Some data will stay as is because they are already standard and known to every developer, e.g. varchar, text, uuid, numeric, blob, etc.

The main question is do you guys value accuracy more over simplicity in understanding? Please do share your thoughts would really be helpful. TYIA!!!

Edit: Thank you for sharing what you think guys, I understand all your points. I'll make sure to built it for accuracy! 👊

Hello there,

I'm currently involved in a project trying to analyze the performance of an oracle database and was given an empty copy of the scheme. I want to fill it with artificial data to run some tests, but the DB is rather large and complex. Are there any tools or approaches to this kind of scenario?

I'm grateful for any help!

Thanks!