Database performance is often the primary bottleneck in API-driven applications. Whether you're serving a mobile app, powering a microservices architecture, or exposing enterprise data through REST APIs, slow queries translate directly to poor user experience, increased infrastructure costs, and system scalability challenges. This guide explores proven query optimization strategies that development teams can implement to dramatically improve API performance.
When APIs interact with databases, every millisecond counts. A query that takes 500ms instead of 50ms doesn't just delay one request—it cascades through your entire system. Slow queries consume database connections longer, increase memory usage, strain connection pools, and ultimately limit how many concurrent users your API can handle.
Modern API gateways like DreamFactory, which automatically generate REST APIs from databases, make it trivially easy to expose database tables through standardized endpoints. However, the ease of API generation doesn't eliminate the need for query optimization. In fact, auto-generated APIs often benefit most from optimization strategies because they handle diverse query patterns from multiple consumers.
Before optimizing, you need to understand where time is actually spent. Database queries go through several stages:
The most common bottlenecks occur during data retrieval (missing indexes) and processing (inefficient joins or sorts on large datasets).
EXPLAIN ANALYZE to see actual execution times and row countsEXPLAIN for query plansexplain() method reveals index usage and execution statisticsEXPLAIN PLAN and Oracle Enterprise Manager provide detailed insightsIndexes are the single most impactful optimization for read-heavy APIs. An index allows the database to locate rows without scanning entire tables.
Best practices:
WHERE clauses, JOIN conditions, and ORDER BY statementsExample scenario:
-- Slow query without index
SELECT * FROM orders WHERE customer_id = 12345 AND status = 'pending';
-- Create composite index
CREATE INDEX idx_orders_customer_status ON orders(customer_id, status);
With DreamFactory's database services, you maintain full control over your schema and indexes. The platform generates APIs that automatically benefit from your indexing strategy without requiring code changes.
APIs often return more data than clients actually need. Over-fetching wastes bandwidth, increases serialization time, and slows response times.
Strategies:
DreamFactory implementation:
DreamFactory's auto-generated APIs support powerful query parameters out of the box:
?fields=id,name,email - Return only specified columns?filter=status='active' - Server-side filtering using SQL-like syntax?limit=50&offset=100 - Efficient paginationThis allows frontend developers to optimize their own queries without backend changes.
Join operations are resource-intensive, especially when multiple tables or large datasets are involved.
Optimization approaches:
INNER JOIN is faster than LEFT JOIN when referencing integrity guarantees existMongoDB optimization:
MongoDB doesn't natively support joins efficiently. When using MongoDB APIs:
$lookup sparingly (it's slower than embedded data)The fastest query is the one you don't run. Strategic caching can reduce database load by 70-90% for read-heavy APIs.
Caching layers:
DreamFactory's caching capabilities:
DreamFactory provides built-in caching services that support Redis, Memcached, and local storage. You can configure cache TTL (time-to-live) per endpoint:
// Example: DreamFactory post-process script to invalidate cache
if (event.request.method === 'POST' || event.request.method === 'PUT') {
platform.api.delete('cache/products');
}
Returning thousands of rows in a single API call is almost never necessary and severely impacts performance.
Implementation strategies:
COUNT(*) can be expensiveDreamFactory pagination:
GET /api/v2/db/_table/products?limit=50&offset=0
DreamFactory automatically supports both limit/offset and cursor-based pagination, with metadata returned in response headers.
The N+1 problem occurs when an API retrieves a list of N records, then makes N additional queries to fetch related data for each record.
Example of N+1:
1. SELECT * FROM orders LIMIT 10; -- 1 query
2. SELECT * FROM customers WHERE id = ?; -- 10 queries (one per order)
Solution approaches:
WHERE id IN (...)DreamFactory's relationship handling allows you to define table relationships that automatically fetch related data efficiently:
GET /api/v2/db/_table/orders?related=customer,items
This single API call generates optimized queries with proper joins rather than N+1 queries.
Each database platform has unique optimization opportunities:
PostgreSQL:
WHERE clausesSQL Server:
WITH (NOLOCK) for read-heavy scenarios)MySQL:
JOIN buffer sizes for complex queriesMongoDB:
$match early, $sort on indexed fields)Database connections are expensive to establish. Connection pooling reuses existing connections, dramatically reducing overhead.
Best practices:
DreamFactory manages connection pooling automatically for all database services, with configurable pool sizes and timeout settings per service. This ensures efficient resource usage without manual connection management in your application code.
For complex reports or data exports that take seconds or minutes, synchronous API calls create poor user experience and risk timeouts.
Alternative approaches:
DreamFactory's server-side scripting supports asynchronous patterns using queuing services, allowing you to offload long-running queries to background workers while immediately returning a response to the client.
For large result sets, network transfer time often exceeds query execution time. Compression reduces payload size by 70-85%.
Implementation:
DreamFactory supports HTTP compression out of the box, automatically compressing responses when clients send appropriate Accept-Encoding headers.
Query optimization isn't a one-time task. As data grows and usage patterns change, new bottlenecks emerge.
Ongoing practices:
DreamFactory integrates with monitoring solutions like Grafana, Elasticsearch, and Logstash for comprehensive API observability. You can track query performance, identify slow endpoints, and correlate database metrics with API response times.
DreamFactory excels in scenarios where query optimization and rapid API development must coexist:
Automatic optimization features:
Developer productivity benefits:
Enterprise-grade performance:
For organizations that need to expose database access through APIs while maintaining strict performance requirements, DreamFactory provides the right balance of automation and control. You get instant API generation without sacrificing the ability to optimize queries, manage caching, and monitor performance at a granular level.
Start with indexing. Add indexes to columns used in WHERE clauses, JOIN conditions, and ORDER BY statements. This single change often delivers 10-100x performance improvements. Use your database's EXPLAIN command to identify missing indexes on slow queries.
No. Cache stable reference data (product catalogs, categories) with longer TTLs (hours/days), and user-specific or frequently changing data with shorter TTLs (seconds/minutes). Skip caching for write operations or real-time data where freshness is critical. Aim for 70-90% cache hit rates on read-heavy endpoints.
Monitor your database query count per API request. If fetching 10 records triggers 11+ queries (1 main query + 10 related queries), you have an N+1 problem. Use database query logs or APM tools to detect this pattern, then fix it with joins, batch lookups, or relationship pre-loading.