Database optimisation is the process of improving database performance, reducing resource consumption, and increasing scalability through design improvements, query optimisation, and infrastructure choices. Well-optimised databases support higher transaction volumes, enable faster queries, and cost less to operate. Poor database performance frequently becomes the application bottleneck as systems scale.
Performance Bottlenecks
Database performance issues typically stem from:
Slow queries - Queries taking excessive time to execute.
Missing indexes - Queries scanning entire tables rather than using indexes.
Inefficient query plans - Databases executing queries suboptimally.
Database locks - Excessive locking preventing concurrent access.
I/O bottlenecks - Disk I/O becoming the limiting factor.
Memory pressure - Insufficient memory forcing disk access.
Identifying bottlenecks through monitoring is the first step to optimisation.
Indexing Strategy
Indexes dramatically improve query performance:
How indexes work - Indexes create sorted data structures enabling fast lookups without scanning entire tables.
Primary keys - Automatically indexed, enabling fast lookup by primary key.
Foreign keys - Often indexed to improve join performance.
Covering indexes - Indexes containing all columns needed for a query, avoiding table access.
Index trade-offs - Indexes speed reads but slow writes and consume storage. Balance is necessary.
Index monitoring - Regularly reviewing index usage, removing unused indexes, adding missing indexes.
Proper indexing typically provides the greatest performance improvement.
Query Optimisation
Well-written queries execute efficiently:
Avoid SELECT * - Selecting only needed columns reduces data transfer and improves performance.
Join order - Databases often reorder joins. Understanding join costs enables writing efficient queries.
WHERE clauses - Filtering early reduces data processed.
GROUP BY and aggregates - Understanding how databases aggregate data enables efficient aggregations.
Subqueries vs. joins - Choosing efficient approaches for complex queries.
Query plans - Examining query execution plans reveals how databases execute queries.
Database Monitoring
Identifying bottlenecks requires monitoring:
Query performance - Monitoring slow query logs identifies problematic queries.
Resource utilisation - CPU, memory, disk I/O metrics reveal resource bottlenecks.
Connection count - Connection count approaching limits indicates scaling issues.
Lock contention - Understanding where locks occur reveals concurrency issues.
Cache hit rates - Low cache hit rates indicate memory pressure.
Regular monitoring enables proactive optimisation.
Denormalisation and Schema Design
Schema design affects performance:
Normalisation benefits - Reduces data redundancy and improves data integrity.
Normalisation costs - Normalised schemas require joins, which can be slow.
Denormalisation - Storing redundant data improves query performance at cost of update complexity.
Selective denormalisation - Denormalising heavily-queried data whilst maintaining normalisation elsewhere.
Schema evolution - Changing schemas as understanding improves.
Caching Strategies
Caching dramatically reduces database load:
Application caching - Caching query results in application memory.
Cache invalidation - Ensuring caches remain valid. Cache invalidation is notoriously difficult.
Distributed caches - Redis, Memcached provide shared caching across servers.
Query result caching - Some databases cache query results.
Cache-aside pattern - Applications check cache before querying databases.
Proper caching can reduce database load by 90 per cent or more.
Read Scaling
Distributing read load:
Read replicas - Databases replicate to read-only replicas.
Read distribution - Routing reads to replicas, writes to primary.
Replication lag - Replicas lag behind primary. Applications must handle eventual consistency.
Consistency guarantees - Choosing strong consistency (slower) vs. eventual consistency (faster).
Write Scaling
Scaling writes is more complex:
Sharding - Partitioning data across databases by key (user ID, region).
Replication - Replicating writes to multiple databases, making writes slower but enabling read scaling.
Async writes - Writing to queues asynchronously, then processing into database.
Write optimisation - Batching writes, using bulk insert operations.
Write scaling is fundamentally more complex than read scaling.
Cloud Database Services
Managed databases handle some optimisation:
RDS (AWS) - Managed PostgreSQL, MySQL, Oracle, SQL Server.
DynamoDB - Managed NoSQL with automatic scaling.
Spanner (Google) - Globally distributed, ACID-compliant database.
Cosmos DB (Azure) - Globally distributed, multi-model database.
Managed services handle replication, backups, and some scaling automatically.
Connection Pooling
Managing database connections:
Connection overhead - Opening connections is expensive.
Connection pooling - Maintaining pool of open connections, reusing them.
Pool sizing - Too many connections exhaust database, too few create bottlenecks.
Connection timeout - Managing stale connections.
Proper connection pooling dramatically improves performance.
Partitioning and Sharding
Distributing data for scale:
Sharding key - Choosing column to partition by (user ID, region).
Shard assignment - Determining which data goes to which shard.
Cross-shard queries - Querying across shards is expensive.
Rebalancing - Redistributing data when shards become unbalanced.
Sharding enables write scaling but introduces complexity.
Database Optimisation at PixelForce
PixelForce optimises database performance through proper schema design, effective indexing, and query optimisation. For PostgreSQL (our preferred database), we leverage advanced features like JSON operators, window functions, and full-text search. Our experience with high-scale applications ensures databases perform efficiently.
Monitoring Tools
Database monitoring tools:
Query performance insights - Cloud provider tools revealing query performance.
Custom monitoring - Application-level monitoring revealing database pressure.
Logging and analysis - Slow query logs identifying problematic queries.
Profiling - Understanding where time is spent in query execution.
Backup and Recovery
Optimisation includes efficient backups:
Backup strategies - Full backups, incremental backups, or WAL-based replication.
Recovery objectives - RPO (Recovery Point Objective) and RTO (Recovery Time Objective) guide backup strategy.
Backup testing - Regularly testing recovery ensures backups are valid.
Storage efficiency - Compression and deduplication reduce backup storage.
Conclusion
Database optimisation improves performance and enables scaling through indexing, query optimisation, caching, and thoughtful schema design. Well-optimised databases support high throughput, maintain fast response times, and cost less to operate. Regular monitoring and proactive optimisation prevent databases from becoming application bottlenecks.