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of 39What is ElasticSearch and what are its main use cases?
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ElasticSearch is a distributed, RESTful search and analytics engine built on Apache Lucene. It's designed for horizontal scalability, reliability, and real-time search capabilities.
Main use cases:
- Full-text search: Website search, document search, e-commerce product search
- Log analytics: Application logs, system logs, security logs (ELK stack)
- Real-time analytics: Business metrics, application performance monitoring
- Data aggregation: Complex data analysis and reporting
- Geospatial analysis: Location-based search and analytics
ElasticSearch stores data as JSON documents and provides a powerful Query DSL for searching and analyzing data.
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Explain the basic architecture of ElasticSearch.
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ElasticSearch follows a distributed architecture with these key components:
Node: A single running instance of ElasticSearch
Cluster: A collection of nodes that work together
Index: A collection of documents with similar characteristics (like a database)
Document: Basic unit of information stored as JSON
Shard: A subset of an index's data, distributed across nodes
Replica: A copy of a shard for redundancy and increased search performance
Cluster
├── Node 1
│ ├── Index A (Primary Shard 0)
│ └── Index B (Replica Shard 1)
├── Node 2
│ ├── Index A (Primary Shard 1)
│ └── Index B (Primary Shard 0)
└── Node 3
├── Index A (Replica Shard 0)
└── Index B (Replica Shard 0)
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What is the difference between ElasticSearch and traditional relational databases?
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| ElasticSearch | Relational Database |
|---|---|
| Document-oriented (JSON) | Table-based (rows/columns) |
| Schema-flexible | Fixed schema |
| Horizontal scaling | Vertical scaling (primarily) |
| Near real-time search | ACID transactions |
| Denormalized data | Normalized data with JOINs |
| RESTful API | SQL queries |
| Optimized for read-heavy workloads | Balanced read/write operations |
ElasticSearch excels at search, analytics, and handling unstructured data, while traditional databases are better for transactional operations requiring ACID properties.
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How do you perform CRUD operations on documents?
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Create/Index a document:
PUT /users/_doc/1
{
"name": "John Doe",
"email": "john@example.com",
"age": 30
}
Read/Get a document:
GET /users/_doc/1
Update a document:
POST /users/_update/1
{
"doc": {
"age": 31
}
}
Delete a document:
DELETE /users/_doc/1
Bulk operations:
POST /_bulk
{"index": {"_index": "users", "_id": "1"}}
{"name": "Alice", "age": 25}
{"index": {"_index": "users", "_id": "2"}}
{"name": "Bob", "age": 30}
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What is the difference between index and create operations?
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Index Operation (PUT /index/_doc/id):
- Creates a new document or completely replaces existing one
- Overwrites the entire document if it exists
- Always succeeds (creates or replaces)
Create Operation (PUT /index/_create/id):
- Only creates a new document
- Fails if document with same ID already exists
- Returns 409 Conflict error for existing documents
# Index - will replace if exists
PUT /users/_doc/1
{"name": "John", "age": 30}
# Create - will fail if ID 1 exists
PUT /users/_create/2
{"name": "Jane", "age": 25}
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Explain the Query DSL and its main components.
What is the difference between match and term queries?
What is mapping in ElasticSearch and why is it important?
What are the main field data types in ElasticSearch?
What is the difference between text and keyword field types?
What is the analyze API and how is it used?
What is the difference between Elasticsearch and OpenSearch?
What are the different types of nodes in ElasticSearch?
Explain sharding in ElasticSearch. What are primary and replica shards?
How does ElasticSearch achieve high availability?
Explain the document versioning in ElasticSearch.
What are the different refresh options in ElasticSearch?
How does scoring work in ElasticSearch?
Explain different types of search in ElasticSearch.
What are compound queries in ElasticSearch?
Explain dynamic mapping in ElasticSearch.
What are analyzers and how do they work?
How would you create a custom analyzer?
What are aggregations in ElasticSearch? Explain different types.
How do you perform date histogram aggregations?
Explain nested aggregations with an example.
How do you monitor ElasticSearch cluster health and performance?
How do you secure an ElasticSearch cluster?
What is Index Lifecycle Management (ILM)?
What are aliases in ElasticSearch and how are they used?
Explain the concept of index templates.
What are Elasticsearch snapshots and how do you implement backup strategies?
How do you implement search suggestions and autocomplete in Elasticsearch?
What is split-brain problem and how does ElasticSearch prevent it?
What are the best practices for ElasticSearch performance optimization?
What is the difference between index and search performance tuning?
Explain hot-warm-cold architecture in ElasticSearch.
What is the difference between ElasticSearch and Solr?
How do you handle ElasticSearch cluster scaling?
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Elasticsearch cheatsheet
Elasticsearch Interview Cheat Sheet
- Summary01
- 1. Core Concepts02
- 2. Architecture03
- 3. Basic Operations (CRUD)04
- 4. Search & Query DSL05
- 5. Aggregations06
- 6. Mapping & Index Management07
- 7. Performance Optimization08
- 8. Cluster Management09
- 9. Advanced Features10
- 10. Use Cases & Design Patterns11
- 11. Security Features12
- + 2 more inside
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