Practical guides to AI, Python, Git, SQL, Web/API, SAP/ERP and mathematical models, with explanations, code examples, sources and limitations.
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Compare RESTRICT, CASCADE and SET NULL using a small isolated example, then check the actual constraint and dependent rows before changing real data.
Use ON CONFLICT to atomically insert or update rows based on unique constraints. Specify the exact conflict target, handle duplicate input rows, and understand that excluded values replace, not accumulate.
Distinguish missing and empty settings, parse ports and booleans explicitly, and report configuration errors without exposing secrets.
A technical guide explaining why syntactically correct JSON from Large Language Models (LLMs) is insufficient for production systems and how JSON Schema provides the necessary structural and type guarantees.
Understand the differences between offset-based and cursor-based pagination and when to use each, especially when dealing with frequently changing datasets.
Find the expensive step and distinguish estimates from measurements.
Learn how to parse JSON data in Python from both string literals and files, and how to effectively handle potential decoding errors using the built-in json module.
Understand IS NULL, three-valued logic and the difference between COUNT(*) and COUNT(column).
Transactions in PostgreSQL allow grouping SQL operations into atomic blocks using the commands BEGIN, COMMIT, and ROLLBACK. This ensures data integrity: either all changes are applied or none at all. The mechanism is based on ACID principles - atomicity, consistency, isolation, and durability.
A detailed technical breakdown of the differences between WHERE and HAVING clauses, focusing on execution order, aggregate function compatibility, and performance optimization strategies.