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

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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

Filters check conditions without affecting relevance, while queries do.

Elasticsearch index templates automatically apply settings to new indices based on patterns.

You can update Elasticsearch indices without downtime using alias switching.

Elasticsearch fuzzy search allows finding results even with typos in queries.

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

Elasticsearch aliases allow zero-downtime switching between indices for applications.

Elasticsearch ILM automates data management through defined lifecycle phases.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

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

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Elasticsearch mapping defines how fields are indexed and searched, with dynamic and explicit options.

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

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

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

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.

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

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

An inverted index helps search engines quickly find documents containing specific terms.

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

Search engines collect, index, retrieve, and rank documents to provide relevant results.

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