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

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

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

Elasticsearch ILM automates data management through defined lifecycle phases.

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

You can update Elasticsearch indices without downtime using alias switching.

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

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

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

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

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

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

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

Kafka consumer offsets help track message processing to avoid loss and duplication.

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

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

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

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

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

Transactions ensure database operations are reliable using ACID properties.

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

REST API design focuses on creating clean, efficient, and scalable web services.

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

Database per Service ensures each microservice owns its data, reducing dependencies.

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

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

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

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

Choosing the right boundaries for microservices is crucial for effective architecture.

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

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