Batch Indexing
Batch indexing is a data processing technique where multiple documents or data entries are indexed in bulk rather than individually, typically used in search engines, databases, and data pipelines to improve efficiency and performance. It involves collecting a set of data changes and applying them as a single operation to update an index, reducing overhead from frequent small updates. This approach is common in systems like Elasticsearch, Apache Solr, and relational databases to optimize indexing throughput and resource usage.
Developers should use batch indexing when dealing with large-scale data ingestion, such as in log processing, ETL (Extract, Transform, Load) pipelines, or search engine updates, to minimize latency and improve scalability by reducing the number of index update operations. It is particularly useful in scenarios where data arrives in batches (e.g., nightly data imports, streaming aggregations) or when indexing performance is critical, as it can significantly cut down on network round-trips and indexing overhead compared to real-time indexing.