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MongoDB uses sharding to distribute data across multiple servers for better scalability.

MongoDB uses replica sets for data redundancy and sharding for scalability.

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

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

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

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

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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.

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

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.

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.

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.

Dynamo-style databases are designed for high availability and fault tolerance in distributed systems.

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

Elasticsearch ranks search results using BM25 based on relevance scoring.

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

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

Cache stampede occurs when many requests hit the database due to a cache miss.

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.

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

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

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.

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

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

Choosing the right labels in time-series data is crucial to avoid inefficiencies.

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.

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

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

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

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.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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

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

Kafka consumers can pause and resume message processing to handle backpressure effectively.

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

Kafka lag recovery time is how fast consumers can process backlogged messages.