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Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Optimizing Elasticsearch involves using filters, caching, and measuring performance.

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

Redis is a fast in-memory data store used for caching and low-latency applications.

Elasticsearch rescoring improves search result rankings using a two-stage process.

Elasticsearch distributes search queries across shards to find and retrieve results quickly.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

Caching helps applications respond faster by serving data from memory instead of hitting the database.

The number of Elasticsearch nodes depends on storage, workload, and performance needs.

API pagination helps manage large datasets by breaking them into smaller parts.

Elasticsearch uses shards for data distribution and replicas for redundancy.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

Connection pooling reuses database connections but can cause issues if misconfigured.

Replicas keep your system available, while snapshots allow for data recovery.

Distributed caching spreads data across multiple servers for better performance and reliability.

Backpressure helps manage message flow in Kafka during traffic spikes.

Elasticsearch has different node roles that help manage data and scale clusters effectively.

Choosing the right distributed lock depends on your system's needs and existing tools.

Primary shards partition data, while replica shards duplicate it for redundancy.

Choose Elasticsearch for complex text searches, but use databases for structured queries.

A document in Elasticsearch isn't searchable right after it's saved due to the refresh process.

The USE Method helps find and analyze infrastructure bottlenecks in software systems.