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Database indexing speeds up data retrieval by allowing quick lookups.

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

Composite indexes speed up database queries that filter or sort by multiple columns.

Partition pruning helps databases skip unnecessary data scans, speeding up queries.

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

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

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

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

Filters check conditions without affecting relevance, while queries do.

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

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

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

Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

BM25 and field boosting help Elasticsearch rank search results based on relevance.

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

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

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

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

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