Distributed SQL Deep Dive: A 5-Part Series

Scaling a database is as much about physics as it is about software. When you hit the limits of a single-node system, the rules of the game change. Consistency becomes expensive, the network becomes your bottleneck, and your mental models must evolve.

I’ve spent the last several months documenting the operational realities of running HTAP (Hybrid Transactional/Analytical Processing) and distributed SQL systems in production.

This 5-part series is designed to take you from a curious senior engineer to someone who can predict exactly where a distributed query will fail.

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Part 1: SingleStore vs PostgreSQL: When Distributed SQL Actually Wins

The most important question isn't which database is faster—it’s where your performance curve bends. This post provides a decision framework for when to stick with Postgres and when it's time to move to a distributed system.

Part 2: How SingleStore Handles Real-Time Analytics at Scale

A deep dive into HTAP architecture. Learn how a system can handle massive write ingestion and complex analytical queries simultaneously without the "fairy dust" of marketing claims.

Part 3: How Distributed SQL Execution Engines Really Work

Explain how a distributed SQL execution engine actually works in practice. We break down query planning, data movement (shuffling), and why joins and aggregations sometimes explode in cost.

Part 4: Why HTAP Systems Fail Quietly (And How to Notice Early)

The "brutally honest" deep dive. We explore why distributed systems don't fail with a bang, but with a sideways drift in latency. Learn to spot memory pressure and silent contention before they become outages.

Part 5: Why 'Indexes Everywhere' Destroys Database Performance

A warning about reflexive indexing. Learn how indexing "pitfalls" in distributed systems silently destroy write throughput and memory locality.

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Who is this for?

This series is written for senior backend engineers, data engineers, and SREs. I assume you already know SQL and basic database concepts, but haven't yet had to manage the complexity of distributed query execution at scale.

Why read this?

The goal isn't to sell you a specific database. The goal is to provide operational intuition. By the end of this series, you should be able to look at a query plan and identify exactly which node is going to run out of memory first.

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Ready to start? Jump into Part 1: SingleStore vs PostgreSQL.