About the Working with Data category

Discussions about crud operations, aggregation, indexes, performance, data modelling, schema validation, change streams, and transactions.

CRUD Operations

Create or insert operations add new documents to a collection.
Read operations retrieve documents from a collection.
Update operations modify existing documents in a collection.
Delete operations remove documents from a collection.

Indexes & Performance

Indexes support the efficient execution of queries in MongoDB. Without indexes, MongoDB must perform a collection scan, i.e. scan every document in a collection, to select those documents that match the query statement. If an appropriate index exists for a query, MongoDB can use the index to limit the number of documents it must inspect.

Aggregation

Aggregation operations process multiple documents and return computed results. You can use aggregation operations to group values from multiple documents together, perform operations on the grouped data to return a single result or analyze data changes over time.

Data modeling & Schema design

The key challenge in data modeling is balancing the needs of the application, the performance characteristics of the database engine, and the data retrieval patterns. When designing data models, always consider the application usage of the data (i.e. queries, updates, and processing of the data) as well as the inherent structure of the data itself.

Change streams

Change streams allow applications to access real-time data changes without the complexity and risk of tailing the oplog. Applications can use change streams to subscribe to all data changes on a single collection, a database, or an entire deployment, and immediately react to them. Because change streams use the aggregation framework, applications can also filter for specific changes or transform the notifications at will.

Transactions

In MongoDB, an operation on a single document is atomic. Because you can use embedded documents and arrays to capture relationships between data in a single document structure instead of normalizing across multiple documents and collections, this single-document atomicity obviates the need for multi-document transactions for many practical use cases.

For situations that require atomicity of reads and writes to multiple documents (in a single or multiple collections), MongoDB supports multi-document transactions. With distributed transactions, transactions can be used across multiple operations, collections, databases, documents, and shards.

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