performance.md

SQLY Performance Optimization

📖 Introduction

Optimizing SQLY queries ensures fast execution, efficient resource usage, and scalability. SQLY provides indexing, caching, parallel execution, and query profiling tools.

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⚡ Indexing for Faster Queries

Indexes improve query performance by reducing lookup time.

✅ Example 1: Create an Index on a Frequently Queried Column

performance:
  indexing:
    table: orders
    columns: [customer_id]
    type: btree

This creates a B-tree index on customer_id in the orders table for faster lookups.

✅ Example 2: Use a Full-Text Index for Faster Searches

performance:
  indexing:
    table: articles
    columns: [content]
    type: full_text

This enables optimized full-text searches on the content column.

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🗄️ Query Caching

SQLY caches query results to improve response time for repeated queries.

✅ Example 3: Enable Query Caching

performance:
  caching:
    enabled: true
    expiration: 10m

This caches query results for 10 minutes to reduce redundant computation.

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🚀 Parallel Query Execution

SQLY supports parallel execution for large datasets.

✅ Example 4: Enable Parallel Query Execution

performance:
  parallel_execution:
    enabled: true
    max_threads: 8

This allows queries to run on up to 8 parallel threads for faster execution.

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📊 Query Profiling

Profiling helps identify slow queries and optimize execution plans.

✅ Example 5: Profile a Query

performance:
  profiling:
    query:
      select: [customer_id, total_spent]
      from: purchases
      where:
        total_spent: "> 1000"

This analyzes execution time and resource usage for the query.

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📌 Summary

• Indexing speeds up lookups and searches.
• Query caching reduces repeated computation.
• Parallel execution improves performance on large datasets.
• Query profiling helps optimize slow queries.