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Elasticsearch aggregations help analyze data by grouping, calculating, and estimating values.

Elasticsearch aggregations help summarize data using metrics for calculations and buckets for grouping.

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

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

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

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

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

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

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

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

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

Kafka's `seek()` lets you replay only specific messages, saving time.

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

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

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

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

FAANG engineers use high-level design principles to create scalable systems.

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

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

Kafka uses state stores to remember previous events in stateful stream processing.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

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

Eager rebalancing revokes all assignments, while cooperative allows incremental changes.