Performance - Caching And Indexing


Implementing caching and indexing strategies is crucial for improving the performance of our applications.

Here's how we incorporate these techniques in our tech stack:

  • Caching: Content Delivery Networks (CDNs): For static assets like images, CSS, and JavaScript, we leverage CDNs to cache and distribute content to users from geographically distributed edge servers. This reduces latency and accelerates content delivery.:
  • Page Caching: We employ page caching mechanisms to store rendered HTML pages, reducing the load on application servers and improving response times for frequently accessed pages.:
  • Object Caching: Object caching systems like Redis or Memcached are used to cache frequently accessed data, reducing the need to repeatedly query the database or compute results.
  • Query Caching: We cache database query results for frequently executed read queries, minimizing the overhead of redundant database queries.
  • HTTP Caching Headers: We utilize HTTP caching headers, such as "Cache-Control" and "ETag" to instruct web browsers and proxies to cache static content and make conditional requests, reducing network and server load.
  • Caching Invalidation: We implement cache invalidation mechanisms to ensure that cached data remains up-to-date. When data changes, caches are updated or invalidated accordingly.
  • Indexing: Database Indexing: We create and maintain appropriate indexes on database tables to speed up data retrieval for frequently used columns in WHERE clauses and JOIN conditions.
  • Full-Text Indexing: For text-based searches, we use full-text indexing features provided by databases or dedicated search engines like Elasticsearch for efficient text-based searching.
  • Compound Indexes: Compound indexes are employed when queries involve multiple columns. These indexes optimize multi-column search conditions.
  • Explain and Query Optimization: : We use database query optimizers to understand query execution plans, identify slow queries, and apply appropriate indexing strategies to improve performance.
  • Index Maintenance: Regular index maintenance tasks, such as reindexing and statistics updates, are carried out to ensure that indexes remain efficient and up-to-date.
  • Use of In-Memory Databases: In-memory databases, such as Redis, are employed for specific use cases where extremely fast data access is required.
  • Hybrid Approaches: In some cases, we employ a combination of caching and indexing to maximize performance
  • Cache-aside: Data is initially retrieved from the cache, and if not found, a database query is executed, and the result is stored in the cache for future access.
  • Database Read Replicas: We use read replicas of the database for read-heavy workloads, reducing the load on the primary database and improving query performance.
  • Content Preloading: We proactively preload frequently accessed content and data into caches during off-peak hours to ensure rapid access during peak traffic.
  • Monitoring and Evolving: We monitor the performance of caches and indexes, constantly evolving our strategies to adapt to changing traffic patterns and data access requirements. Performance testing and profiling are conducted to identify and address bottlenecks.

By implementing these caching and indexing techniques, we ensure that our applications are highly responsive, capable of handling high loads, and delivering a smooth user experience, even in the face of demanding workloads.