Elasticsearch DSL Queries - Mql Typing CST Test
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Elasticsearch DSL Queries — Mql Code
JSON-based query DSL examples in Elasticsearch.
{
"query": {
"bool": {
"must": [
{ "match": {title: "database" } },
{ "range": { "year": { "gte": 2020 } } }
]
}
}
}Mql Language Guide
MongoDB Query Language (MQL) is a rich, expressive, and flexible query language used to interact with MongoDB, a document-oriented NoSQL database. MQL enables data retrieval, manipulation, aggregation, and management of JSON-like documents.
Primary Use Cases
- ▸CRUD operations (Create, Read, Update, Delete)
- ▸Complex querying with filters
- ▸Aggregation and reporting
- ▸Indexing for performance optimization
- ▸Data modeling for NoSQL document storage
- ▸ETL and analytics pipelines
Notable Features
- ▸Flexible JSON-style document queries
- ▸Aggregation pipelines for data transformations
- ▸Index support for performance
- ▸Support for geospatial and text search queries
- ▸Atomic operations on single documents
- ▸Integration with drivers for many programming languages
Origin & Creator
Created by MongoDB Inc. as part of the MongoDB ecosystem, MQL has evolved since MongoDB's initial release in 2009 to support modern NoSQL document database operations.
Industrial Note
MQL dominates applications requiring flexible schema design, real-time analytics, and scalable document storage. Its aggregation framework allows for complex data transformations, making it essential for modern web apps, microservices, and big data analytics pipelines.