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
Building an AI-Powered Quantitative Trading System with Hermes Agent and IBKR How I set up a multi-signal ETF trading bot that runs on autopilot — and the 7 things that broke along the way I wanted a system that watches the market 24/7, analyzes technical indicators across multiple ETFs, and executes trades automatically. No manual chart-checking. No emotional decisions. Just cold, calculated si
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
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
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
Most trading fee calculators show you two numbers: the dollar amount and the percentage of notional. Both are correct. Neither is useful. Here's the problem. Say you're trading Bitcoin perpetuals on Bybit. Taker fee is 0.055% each side. You buy $10,000 notional. Entry fee: $5.50 Exit fee: $5.50 Round trip: $11.00 Does that matter? Impossible to say without knowing one more number: how much are you
Automating Hermitage to see how transactions differ in MySQL and MariaDB