Micro-batching in data streaming refers to a processing technique where incoming data is collected and grouped into small batches before being processed in bulk. This approach allows systems to handle streams of data more efficiently by working with small sets of records at a time rather than processing each piece of data individually as it arrives. By aggregating data, micro-batching can lead to improved performance and reduced latency compared to traditional batch processing, where large volumes of data are processed at once.
One common example of micro-batching can be found in frameworks like Apache Spark or Apache Flink. For instance, in Spark Streaming, incoming data from sources like Kafka can be automatically buffered for a specified time interval—often milliseconds to a few seconds. Once this interval elapses, Spark processes the batched data as a single job. This batching allows the system to optimize resource usage, as operations on multiple pieces of data can be executed simultaneously, making better use of compute resources and reducing wait times.
However, micro-batching also has trade-offs. Depending on the batch size and processing interval, it can introduce a small delay in data availability. For real-time applications where every millisecond counts, this could be a concern. Developers must find a balance between latency and throughput. For example, a financial trading application might prefer smaller batch sizes to ensure timely execution, while data analytics platforms could allow for larger batches to improve efficiency. Ultimately, the choice of micro-batching configuration will depend on the specific needs of the application and the volume of incoming data.