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Logging
Elasticsearch
Elasticsearch is widely used for full-text search, analytics, and centralized logging. In DevOps it is often part of the ELK stack for collecting, indexing, and querying logs.
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Key Concepts
Indexes and shards define how data is stored and distributed.
Strong full-text search capabilities are a major strength.
Frequently used for centralized logging and analytics.
Common Interview Questions
What is Elasticsearch used for?
Why is Elasticsearch common in logging stacks?
What is an index in Elasticsearch?
Use Case
Use Elasticsearch when you need powerful indexing and search over large datasets such as logs.
Common Use Cases
Building centralized logging systems.
Searching large volumes of application logs.
Running analytics over text-heavy operational data.
Pros
Powerful indexing and search capabilities.
Good for large-scale log and text analytics.
Flexible query and aggregation support.
Cons
Operationally heavy compared with lighter alternatives.
Resource usage can be significant.
Requires careful tuning and cluster management.