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Structured Interview Pack
High-Throughput SQL Database Optimization & Indexing
Master B-Trees, composite index selectivity, EXPLAIN ANALYZE interpretation, and connection pool sizing to eliminate database bottlenecks.
Hardware disk and memory bus limits are real. More database connections do not yield more throughput; past the hardware saturation point, additional connections cause context-switch thrashing and latency degradation.
The optimal database connection pool size formula is: Pool Size = (Core Count * 2) + Effective Spindle/Disk Count.
- Understand how B-Tree indexes execute seeks vs full sequential scans
- Design high-cardinality composite indexes respecting the leftmost prefix rule
- Analyze EXPLAIN (ANALYZE, BUFFERS) query execution plans
- Size database connection pools using HikariCP hardware saturation formulas
1. B-Tree Internals: What an Index Actually Looks Like
Free Chapter PreviewA B-Tree index is a balanced, multi-way search tree where leaf pages store ordered key values pointing directly to physical row pointers (TIDs/ctid in PostgreSQL).
Why Index Scans Can Be Slower than Seq Scans:
If a query matches 30% of a table, PostgreSQL will deliberately choose a Sequential Scan over an Index Scan. Why? Random I/O disk seeks through an index leaf tree can be significantly slower than streaming contiguous disk blocks in one sequential read.
2. The Leftmost Prefix Rule & Composite Indexes
In this section, we examine the production failure mechanics, internal socket buffers, and kernel-level trace diagnostics...
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