Understanding Data Types (Numeric, String/Varchar, Date/Time, Boolean, JSON) · Flow Cadax | FlowCadax

Beginner/Introduction to Databases & SQL

Lesson 04

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Understanding Data Types (Numeric, String/Varchar, Date/Time, Boolean, JSON)

Understanding Data Types (Numeric, String/Varchar, Date/Time, Boolean, JSON): Deep dive into storage footprint and performance: INT vs BIGINT, CHAR vs VARCHAR vs NVARCHAR, DATETIME2, BIT, and JSON.

Legacy / Unoptimized Pattern

Unindexed full table scans, non-SARGable wrappers, cursors, missing transaction boundaries, and unhandled deadlocks.

Enterprise Relational Standard

Covering B-Tree index seeks, 3NF schema integrity, deterministic ACID transactions, and sub-millisecond execution plans.

Deep Dive

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Core Architectural Foundation & Operational Principles

SQL data types specify the kind of value a column holds, dictating physical byte storage, validation constraints, and indexing behavior.

Sub-topic 1

Unicode storage trade-offs: VARCHAR (1 byte/char) vs NVARCHAR (2 bytes/char) and memory grant overhead

Sub-topic 2

Precision and scale: DECIMAL(18,2) vs FLOAT rounding bugs and modern DATETIME2 vs legacy DATETIME

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Visual Execution Architecture & Storage Pipeline

The relational and storage engine execution flow for Understanding Data Types (Numeric, String/Varchar, Date/Time, Boolean, JSON):

SQL Execution Plan & Relational Visualizer

Schema / Query Flow

ENTERPRISE SQL PIPELINE (Understanding Data Types (Numeri):\n┌────────────────────────────────────────────────────────────────────────┐\n│ Relational Engine: Query Parser -> Algebrizer -> Cost-Based Optimizer  │\n│  • SARGable Predicates  • Execution Plan Cache  • Cardinality Estimates │\n└───────────────────────────────────┬────────────────────────────────────┘\n                                    ▼\n┌────────────────────────────────────────────────────────────────────────┐\n│ Storage Engine: Buffer Pool Caching & B-Tree Page Traversal            │\n│  ┌───────────────────────────────────┐  ┌────────────────────────────┐ │\n│  │ Index Seeks & Covering In-Memory  │  │ Page Locks & Concurrency   │ │\n│  │ 8KB Data Pages / Extents          │  │ MVCC / Snapshot Isolation  │ │\n│  └───────────────────────────────────┘  └────────────────────────────┘ │\n└───────────────────────────────────┬────────────────────────────────────┘\n                                    ▼\n┌────────────────────────────────────────────────────────────────────────┐\n│ Transaction Durability & Persistence: Write-Ahead Logging (WAL)       │\n│  • Atomic COMMIT / ROLLBACK  • LDF Log Flush  • Zero Data Loss         │\n└────────────────────────────────────────────────────────────────────────┘

Swipe horizontally for full architecture⟷

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Under The Hood: How the Database Executes This

The storage engine allocates fixed bytes (INT=4 bytes) or variable bytes with row offset arrays (VARCHAR length + 2 bytes overhead).

Senior Automation Architect Field Insight

“Use INT/BIGINT for keys, DECIMAL for financial figures, NVARCHAR only when international Unicode characters are required, and DATETIME2.”

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Enterprise Production Scenario

Designing a high-volume financial ledger requiring DECIMAL(19,4) to guarantee zero precision loss across currency conversions.

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Critical Pitfalls & What to Avoid

Common developer mistake: Using FLOAT or REAL for monetary values, causing floating-point rounding discrepancies during balance reconciliation.

Pitfall

Using FLOAT or REAL for monetary values, causing floating-point rounding discrepancies during balance reconciliation.

Enterprise Standard

Use INT/BIGINT for keys, DECIMAL for financial figures, NVARCHAR only when international Unicode characters are required, and DATETIME2.

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Senior Technical Interview Angle

Why should financial applications avoid FLOAT and REAL, and how does NVARCHAR impact index sizing compared to VARCHAR?

Senior Automation Architect Field Insight

“Hands-on Exercise: Create a table comparing storage of identical numbers across FLOAT, INT, and DECIMAL(10,2), and observe mathematical precision behavior.”

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