Trade-offs: When to Choose Spark vs. Alternatives

The Core Trade-off: Spark sacrifices ultra low latency for high throughput, generality, and fault tolerance. Understanding when these trade-offs align with your needs versus when alternatives are better is critical for system design decisions. Spark vs. Stream Processors: Spark operates in micro batches with JVM based executors that have substantial startup and scheduling overhead. For streaming workloads requiring sub 50 millisecond latencies consistently, Apache Flink or managed streaming services with record at a time processing are better choices. Flink can achieve p99 latencies in the tens of milliseconds for simple operations. You accept more complex state management and a more specialized model in exchange for that latency. Choose Spark when your streaming SLA allows 2 to 5 second latency and you want unified batch and streaming semantics. Choose Flink when you need sub 100 millisecond p99 latency and can invest in specialized streaming infrastructure. Spark vs. MPP Warehouses: Compared to dedicated MPP data warehouses like Snowflake or BigQuery, Spark provides flexibility in language support, arbitrary code execution including machine learning, and custom user defined functions. However, warehouses are faster for pure SQL analytics over structured data, especially star schemas, because their storage and execution engines are tightly integrated and optimized. Spark jobs with complex joins and UDFs might run with p99 latencies in tens of seconds where a tuned warehouse query finishes in a few seconds. You accept that trade for expressiveness, open formats like Parquet and Iceberg, and tight integration with ML ecosystems like scikit learn and TensorFlow. Choose Spark when you need flexible compute for ETL, ML pipelines, or custom transformations beyond SQL. Choose a warehouse when your workload is primarily SQL analytics on structured data with star schemas and you prioritize query speed. RDDs vs. DataFrames: Within Spark itself, there's a trade-off. RDDs give full control and strong typing at the expense of losing optimizer benefits. DataFrames allow Catalyst and Tungsten to reorder operations, prune columns, push filters, and generate optimized bytecode. Modern Spark code almost always uses DataFrames unless you need specific low level control that the optimizer cannot provide.