Document databases handle streaming data by leveraging their flexible schema design and optimized write operations. Unlike relational databases, document stores like MongoDB or Couchbase can ingest unstructured or semi-structured data in real time without requiring predefined schemas. This flexibility is critical for streaming use cases, where data formats may evolve or vary between sources. For example, IoT sensor data might include different fields based on device types, and a document database can store each payload as a separate JSON document without schema modifications. Additionally, many document databases are designed for high write throughput, using techniques like write-ahead logging and in-memory caching to handle rapid data ingestion.
To manage continuous data streams, document databases often provide features like time-to-live (TTL) indexes and bulk write operations. TTL indexes automatically remove outdated data (e.g., expiring temporary sensor readings after 24 hours), which helps maintain performance in high-volume scenarios. Bulk writes allow applications to batch incoming records and insert them in chunks, reducing overhead from frequent individual writes. For instance, a real-time logging system might buffer application logs for 5 seconds before inserting 1,000 documents at once into MongoDB. Some document databases also support change streams or triggers, enabling downstream processing (like analytics) to react immediately to new data without polling.
Use cases for document databases with streaming data often involve scenarios requiring both scale and flexibility. A retail app might stream user clickstream events to track navigation patterns, storing each interaction as a document with varying fields like timestamp, product ID, and session duration. In financial services, document databases can ingest market data feeds, where each tick includes price, volume, and instrument ID, while accommodating new fields as trading protocols evolve. Tools like Firebase Firestore or Amazon DocumentDB further simplify streaming integrations by offering native SDKs for pub/sub systems or serverless platforms, allowing developers to pipe data directly from event sources like Apache Kafka or AWS Kinesis into the database with minimal code.