Comparing Apache Spark and Apache Flink: Origins, Architecture, and Processing Models

Origins of Big Data Processing Engines

Hadoop and other MapReduce‑based systems were created to meet data‑processing needs that traditional databases could not handle, and since Google's 2004 MapReduce paper, Hadoop‑like ecosystems have become industry standards for big data.

Developing a custom data‑processing system still presents many challenges, often requiring far more investment than anticipated to extract value from data.

The following sections describe the most common of these issues, providing context for the ongoing competition between Spark and Flink.

Steep Learning Curve

Newcomers to big data are overwhelmed by the sheer number of technologies required; a typical Lambda architecture already involves at least four to five subsystems for batch and stream processing, not counting alternatives such as real‑time queries, interactive analytics, or machine learning.

Consequently, organizations must evaluate and integrate many tools, leading to a massive information‑digestion burden for stakeholders.

Inefficient Development and Operations

The multitude of systems, each with its own tooling and language, limits development efficiency. Data must be transferred between systems, incurring additional development and operational costs, while data consistency remains hard to guarantee.

In many organizations, over half of development effort is spent on data movement between systems.

Operational Complexity and Data‑Quality Issues

Each system requires its own operation and maintenance, raising runtime costs and increasing the likelihood of failures. Ensuring data quality is difficult, and when problems arise, tracing and fixing them is challenging.

Human factors also play a role: different departments may be responsible for supporting various subsystems, often with misaligned goals and priorities.

A Solution Emerges

Given these problems, Spark’s popularity is understandable. Since its 2014 rise, Spark not only outperformed Hadoop MapReduce but also offered a unified engine supporting batch, stream, interactive queries, and machine learning, making the transition to Spark relatively easy for many developers.

Flink, on the other hand, entered the scene to provide a more convenient solution for real‑time stream processing.

The following sections compare the two frameworks from a technical perspective.

Processing Engines in Spark and Flink

This section discusses the architectural characteristics, strengths, and limitations of Spark and Flink, focusing on their data models, processing models, state handling, and programming APIs.

Data Model and Processing Model

Spark uses the Resilient Distributed Dataset (RDD) model, an abstraction over files that enables fault recovery and can be implemented as shared memory or fully virtualized. Transformations (e.g., map, filter, join) generate new RDDs, forming a directed acyclic graph (DAG) with narrow and wide dependencies.

Flink’s fundamental data model is a continuous data stream of events. Operators applied to the stream produce new streams, and the overall model mirrors Spark’s DAG, with vertices analogous to Spark stages.

Flink’s stream execution can forward processed events to the next operator immediately, eliminating extra latency, whereas Spark’s micro‑batch model processes an entire batch before downstream stages begin.

Flink also employs asynchronous checkpoints for state recovery, reducing I/O latency and improving performance.

Data Processing Scenarios

Spark supports batch, real‑time stream, interactive queries, machine learning, and graph computation, leveraging in‑memory RDDs for low‑latency processing.

Flink treats bounded streams as batch jobs, allowing the same logic to run on both bounded and unbounded data, and also provides libraries for machine learning and graph processing.

Stateful Processing

Flink introduces managed state to support stateful stream processing, which is essential for aggregations and other operations that depend on prior events. This built‑in state handling offers better performance and consistency guarantees compared to user‑managed state in Spark’s earlier streaming versions.

Programming Model

Spark originally offered an RDD‑based API, later adding higher‑level DataFrame and Dataset APIs, as well as Spark SQL, Structured Streaming, and MLlib, making development easier and more expressive.

Flink’s API follows a similar trajectory, with core stream operators comparable to Spark’s, and it remains ahead in stream‑specific features such as watermarks, windows, and triggers.

Key Takeaways

Both Spark and Flink are general‑purpose compute engines capable of massive scale data processing. Their main difference lies in how they handle stream processing: Spark originally used micro‑batches, while Flink provides native continuous streaming with managed state. Recent Spark releases (e.g., Structured Streaming and experimental continuous processing) narrow the gap, but the evolution of both engines continues.

(Original author: Wang Haitao)

This article is part of Alibaba’s Flink series.