Caching Strategies
Caching stores the results of expensive operations — database queries, external API calls, computed aggregations — so subsequent requests for the same result can be served from the cache without repeating the underlying operation. Cache design decisions include cache placement, eviction policy, invalidation strategy, and consistency requirements.
Cache-Aside
In the cache-aside pattern, the application manages cache population and invalidation explicitly. On a cache miss, the application fetches from the source of truth and populates the cache. On data update, the application invalidates or updates the cached entry. This pattern provides the application with direct control over caching behavior but requires consistent cache management logic wherever data is read or written.
Write-Through and Write-Behind
Write-through caching updates the cache and the underlying data store synchronously on writes, ensuring the cache always reflects current data. Write-behind (write-back) caching updates the cache immediately and the data store asynchronously, reducing write latency at the cost of a window where cache and data store may be inconsistent.
Cache Invalidation
Cache invalidation — ensuring stale data is removed from the cache when the underlying data changes — is one of the more complex aspects of cache design. Time-to-live (TTL) based expiration provides simple invalidation at the cost of serving stale data for up to the TTL duration. Event-driven invalidation triggers cache removal when specific data changes are detected, providing more immediate consistency at the cost of invalidation logic complexity and the need for reliable change event delivery.
Database Query Optimization
Query optimization reduces the compute and I/O cost of database operations by improving query structure, index coverage, and data access patterns.
Index Design
Index design requires understanding the query patterns that the index must support. Covering indexes — indexes that include all columns referenced by a query — eliminate the need for a separate data row lookup after the index scan, reducing I/O for read-heavy queries. Composite index column order matters: columns used in equality predicates should appear before columns used in range predicates for the index to be used efficiently.
N+1 Query Problem
The N+1 query pattern occurs when an application executes one query to retrieve a list of N items, then N additional queries to retrieve related data for each item. Depending on N and query latency, this pattern can generate hundreds or thousands of queries for a single logical request. Resolutions include eager loading (fetching related data in the initial query using joins or subqueries), batched loading (grouping the N lookups into a single parameterized query), or moving the aggregation to a purpose-built read model.
Query Plan Analysis
Database query planners generate execution plans that describe how a query will be executed. Query plan analysis — reviewing the planned execution to identify full table scans, unindexed joins, or high row-count intermediate results — is the primary tool for identifying optimization opportunities without guessing. Most relational databases expose query plans through an EXPLAIN or EXPLAIN ANALYZE command.
Asynchronous Processing
Asynchronous processing defers work that does not need to complete before the response is returned to the caller. Moving non-critical work out of the synchronous request path reduces response latency and decouples the processing capacity for background work from the response time requirements of interactive requests.
Task Queues
Task queues accept work items submitted by producers and deliver them to consumer workers for processing. Queue depth provides a buffer that absorbs temporary production spikes without requiring immediate processing capacity expansion. Consumer concurrency can be scaled independently from producer capacity. The primary design consideration is the acceptable delay between task submission and task completion for the specific work type.
Event-Driven Patterns
Event-driven processing decouples the service that produces a state change from the services that need to react to it. Producers publish events to a broker; consumers subscribe to event types and process them independently. This pattern scales consumer processing horizontally without affecting producers and allows new consumers to be added without modifying the event producer.
Connection Efficiency
HTTP/2 multiplexes multiple requests over a single TCP connection, eliminating the per-request connection overhead of HTTP/1.1 and the head-of-line blocking that affects HTTP/1.1 pipelining. For services that make many small requests to a small set of upstream services, HTTP/2 connection reuse provides measurable latency and resource improvements. Compression (gzip, Brotli) reduces the bandwidth required for text-based response payloads, which affects latency on constrained network links.
CDN and Edge Delivery
Content delivery networks serve cacheable content from geographically distributed points of presence, reducing the latency for users whose requests would otherwise traverse long network paths to origin servers. CDN effectiveness depends on the cacheability of the served content and the cache hit rate achievable with the configured TTLs and cache keys. Dynamic, personalized content is typically not CDN-cacheable without edge logic (edge functions) that can construct or modify responses at the CDN layer.
Performance Profiling Approaches
Performance profiling identifies where a system is spending time or consuming resources. Profiling should be driven by observed performance characteristics — specific latency percentiles, specific request types, specific time periods — rather than applied broadly to all code paths.
Continuous profiling systems collect profiling data from production workloads at low overhead, providing a baseline against which regressions can be detected without requiring reproduction of production conditions in staging environments. The primary operational consideration for continuous profiling is the overhead introduced by the profiling agent, which should be measured and verified to be acceptable before enabling it on production workloads.