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

Time-based partitioning organizes time-series data into smaller, efficient segments for better querying.

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

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.

Downsampling and aggregation reduce data volume while preserving essential trends.

Elasticsearch offers two pagination methods: from & size for shallow pages and search_after for deep pages.

Sharding splits databases into smaller parts to improve performance and scalability.

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

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

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

Fetch limits control data size from Kafka, while poll records limit processed records.

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

Time-series databases are designed to store and query time-stamped metrics efficiently.

B-Trees help databases find data quickly without scanning every row.

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.

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

Elasticsearch's function_score combines text relevance with business signals for better rankings.

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

The choice between SQL and NoSQL databases depends on your application's specific needs.

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

Backpressure prevents system overload by managing message processing rates in Kafka.

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

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.

Older offsets can overwrite newer ones in Kafka if not handled properly.

Latency, throughput, and response time are key metrics in system performance.