Spark Streaming processes real-time data by breaking it into small, manageable batches and using Spark’s core engine for parallel computation. Instead of handling each record individually, it groups incoming data into micro-batches (e.g., every 1-10 seconds) using a Discretized Stream (DStream) abstraction. A DStream is a sequence of Resilient Distributed Datasets (RDDs), where each RDD represents a chunk of data collected during a specific time interval. For example, if you set a batch interval of 2 seconds, Spark Streaming will create an RDD every 2 seconds containing all data received in that window. This approach balances latency and throughput, enabling near-real-time processing without overwhelming the system.
Once data is divided into batches, Spark applies transformations (e.g., map, filter, reduceByKey) and actions (e.g., saving results to a database) in parallel across the cluster. For instance, if you’re counting hashtags from a Twitter stream, each batch might filter tweets, extract hashtags, and aggregate counts per tag. Spark Streaming also supports stateful operations like updateStateByKey, which maintains aggregated results across multiple batches (e.g., tracking hourly trends). Fault tolerance is achieved through RDD lineage: if a node fails, lost data partitions are recomputed using the original data source or checkpoints.
Spark Streaming integrates with data sources like Kafka, Flume, or TCP sockets and outputs results to databases, dashboards, or file systems. For example, you might ingest sensor data from Kafka, compute rolling averages over 5-minute windows, and store results in Cassandra. Developers configure a StreamingContext to define batch intervals and job scheduling. While newer APIs like Structured Streaming offer improved semantics, classic Spark Streaming remains widely used for its simplicity and compatibility with existing Spark code. Performance tuning involves adjusting batch intervals, parallelism, and memory allocation to balance latency and resource usage.