Spark vs Flink: What you should know and what to choose

Spark vs Flink: what are they?

Data processing frameworks are the backbone of modern analytics. Apache Spark and Apache Flink are two dominant players in this area. Spark has been the go-to in big data ecosystems for years, known for its robust batch processing and broad ecosystem (SQL, ML, graph processing — you name it). Flink, on the other hand, is streaming-first: built for millisecond latencies, complex event handling, and real-time workloads like fraud detection or IoT. Both Spark and Flink are meant for both batch and streaming workloads. But the order in which they were built — Spark (batch-first) vs. Flink (streaming-first) — defines how engineers look at them today.

Here’s how you can understand Spark vs. Flink better: Flink, Swiss Army knife — versatile, broad, good at many jobs, not always perfect at one. Flink: a specialist surgeon — precise, built for one complex job (real-time), excellent when you need.

Here’s how Spark works

Apache Spark is a distributed analytics engine built to handle large-scale data processing, combining both batch and streaming workloads. At its heart lies a layered architecture with distinct but cooperating components:

• Driver Program (SparkContext / SparkSession): This is the brain of a Spark application. It coordinates execution, converts your high-level code into a graph of tasks, requests resources, and tracks progress. It maintains the connection to the cluster and orchestrates work.

• Cluster Manager / Resource Manager: Spark does not manage hardware itself. Instead, it plugs into systems like YARN, Kubernetes, Mesos, or its own standalone cluster manager. This component hands out resources—CPU, memory, slots—for applications.

• Executors: These are worker processes launched on cluster nodes. Executors run the tasks assigned by the driver and store intermediate or cached data in memory or on local disk. They’re the ones doing the heavy lifting.

• Core + Libraries: Spark Core provides the core execution engine (task scheduling, I/O, memory management, fault tolerance).

• It also has libraries such as Spark SQL, MLlib (machine learning), GraphX, and Spark streaming / structured streaming.

Spark is often misunderstood as real-time processing tool. Spark isn’t real-time; it processes in micro-batches. With structured streaming, one might feel it’s near-real-time but expect latency.

Apache Flink: How does it work

Apache Flink is a distributed stream-processing engine built for event-at-a-time real-time workloads, although it handles batch data too (treating bounded data as a special case of streams). At its core is a master-worker architecture with robust fault tolerance and state management.

Key components of Apache Flink include:

• JobManager

JobManager is the brain of the Flink cluster, which schedules tasks, manages checkpoints, handles recovery on failures, and orchestrates job execution. In high-availability setups, there are standby Job managers to avoid single points of failure.

• TaskManagers

These are the workers that execute the sub-tasks of a job. They hold the state, manage operator execution, exchange data between operators, and communicate heartbeats to the Job manager. Each Task manager has “task slots” which define how many subtasks it can run concurrently.

• Checkpointing / state management

Flink is built for stateful streaming. It regularly takes snapshots (checkpoints) of operator state and stream positions to durable storage, enabling fault tolerance (If a failure occurs, the job can resume from the last checkpoint with minimal data loss).

• APIs / Runtime Model

1 - DataStream API — for unbounded streaming data; supports transformations, event-time processing, windowing, etc. 2 - DataSet API — for batch workloads (bounded data). 3 - Table / SQL API — a higher-level abstraction (relational style) that works over streams and batches.

• Operator Chaining & Task Slots

To optimize performance, Flink can chain multiple operators together into a single task — reducing overhead from serialization, buffering, and thread handoffs. Tasks run in slots that isolate memory usage.

• Time Semantics and Event Time Processing

Flink supports event-time, processing-time, and ingestion-time semantics. With event-time, Flink can handle out-of-order data and late arrivals, which is crucial in many real-time analytics scenarios.

So Flink is real time, and Spark isn’t. Does that mean Flink is faster? Flink can be lower latency, but performance depends on workload, tuning, and infrastructure. Spark can outperform Flink for many batch-heavy jobs.

Limitations of Apache Spark vs Flink

What to choose: Spark or Flink?

Choosing between Spark and Flink really comes down to what kind of problems you’re solving.

You can choose Apache Spark, if you want:

-> a general-purpose engine that can handle almost everything — batch processing, SQL queries, ML pipelines, and even streaming extensions. Here, Spark is the safer bet. It has the libraries, the community, and the maturity most teams want.

You can choose Flink, if you want:

But if your world is all about real-time—think fraud detection, IoT sensors, live monitoring, or any case where milliseconds matter — Flink is built for that. Its event-driven architecture and powerful state management make it better for mission-critical streaming pipelines.