Why Apache Flink Beats Spark and Storm in Stream Processing

Introduction

With the rise of big data, many processing products appear. This article investigates Apache Flink, a distributed open‑source data‑processing framework that treats streams as first‑class citizens, and compares it with Spark and Storm from a streaming perspective.

Implementation Approaches of Stream Frameworks

Two main approaches exist: Native Streaming , where data is processed immediately as it arrives (e.g., Storm and Flink), and Micro‑batch , where the stream is divided into small time‑based batches (e.g., Spark and Storm Trident).

Key Comparison Metrics

The comparison focuses on functionality, fault tolerance, throughput and latency.

Functionality

Event time versus processing time, watermark, lateness, and window operations are examined. Flink updates watermarks per record, supports flexible window assigners, and can accept late data within a configurable lateness window. Spark’s watermark is derived from the maximum timestamp of the previous batch, leading to higher latency, while Storm relies on an ack mechanism that is heavier.

SQL API

Flink’s streaming SQL is still limited (supports selection, projection, union, tumble) and will add window aggregation in version 1.3, but still lacks distinct and top‑N. Spark Structured Streaming already supports a richer set of SQL features.

Kafka Source Integration

Flink works with Kafka 0.8, 0.9 and 0.10, whereas Spark Structured Streaming only supports Kafka 0.10 and newer.

Interoperation with Static Data

Spark shares a common RDD abstraction for batch and streaming, enabling seamless interaction. Flink’s DataSet and DataStream are independent, so direct interoperation is not possible.

Fault Tolerance

Spark relies on checkpointing per micro‑batch; Storm uses an ack mechanism that guarantees at‑least‑once delivery but incurs high overhead; Flink employs the Chandy‑Lamport asynchronous distributed snapshot algorithm, inserting lightweight barriers that trigger non‑blocking checkpoints and provide exactly‑once semantics.

Throughput & Latency

Spark achieves the highest throughput due to micro‑batch processing but suffers second‑level latency. Storm offers the lowest latency (tens of milliseconds) but its throughput drops dramatically when ack is enabled; Storm Trident’s throughput is moderate. Flink combines native streaming with lightweight checkpointing, delivering high throughput and latency in the sub‑second range (hundreds of milliseconds). Benchmark images illustrate Flink’s throughput being 3.5× that of Storm and its latency staying below 30 ms for most records.

Conclusion

Overall, Flink is a well‑designed framework offering strong functionality, lightweight fault tolerance, high throughput and low latency, though its SQL support and overall maturity still lag behind Spark and Storm. Ongoing community efforts are expected to close these gaps.