PostgreSQL Query Rewriting Techniques The previous articles in this series covered performance problems you fix by adding indexes, restructuring joins, or tuning memory. This one is about the queries where the plan is "fine" — every node is doing something reasonable — but the query itself is asking the wrong question, producing unnecessarily large intermediate results or forcing the planner dow
A common problem with a familiar shape: a process can dial outbound to the internet, but nothing on the internet can dial it back. Your dev server on a laptop. A service in a private VPC. A homelab app behind your router. A container in a pod with no ingress. Same shape every time — outbound works, inbound doesn't. rift is a small Go binary I built to solve that. Run it as a server on a VPS you ow
SQL is widely known for data querying and manipulation but systems do grow; data becomes larger; processes become repetitive and operations become sensitive. SQL has some features which enables it to be considered a fully fledged programming language. Some of the features which I discuss in this article are procedures, functions and transactions. Each of these concepts serve distinct purposes. Sto
Hi 👋, In this post we shall explore Bedrock's structured KB with this architecture: Upload CSVs to S3 > SNS Queue > Crawl data with Glue > Query with Redshift > Bedrock KB > Query with LLM. Let's do some of this with code. Let's get started. Clone the repo and switch to the project directory. git clone [email protected]:networkandcode/networkandcode.github.io.git cd structured-kb-demo/ Do a uv sync
Posted by the RagLeap team — building RagLeap, a private-server AI business platform When we started building RagLeap, the easiest path was obvious: spin up an API, connect to OpenAI, store everything in a managed cloud database, and ship fast. The Problem Nobody Talks About You upload your documents, customer data, order history It works. But ask yourself: where is your data right now? What Our U
Subqueries vs. CTEs in SQL: A Practical Guide to Writing Cleaner, Smarter Queries Whether you're just getting comfortable with SQL or leveling up your data skills, two tools will come up again and again when working with complex queries: subqueries and Common Table Expressions (CTEs). They solve similar problems — breaking a complex query into manageable pieces — but they do it in different ways
I like servers. Not in a "let me spend Saturday hand-tuning nginx" way. More in a "this $6 VPS is sitting right here and could probably run half my side projects" way. The weird part is that deploying to one still feels more complicated than it should. For a lot of small and medium web apps, the app itself is not the hard part. The annoying part is everything around it: building the app getting it
In a previous post, I explored Codd's connection trap in PostgreSQL and MongoDB — the classic pitfall where joining two independent many-to-many relationships through a shared attribute produces spurious combinations that look like facts but aren't. The example followed Codd's 1970 suppliers–parts–projects model: we know which suppliers supply which parts, and which projects use which parts, but j