Apache Flink vs Apache Spark Streaming

No two environments are alike. Within the same organisation, two projects running on the same infrastructure can have entirely different operational realities. That is why choosing a stream processing framework based on what worked for someone else in a similar industry, similar domain, or even a similar technical setup rarely holds up. The differences surface when work moves from staging to production, and by then, the cost of switching is real.

Apache Spark and Apache Flink see this more than any other pair of technologies in the data engineering space. Both are capable, both are production-grade, and both are frequently evaluated for the same workload. The organisations that get the decision right tend to understand one thing before they start building: the two engines process data fundamentally differently, and that difference matters at scale in ways that no staging environment will show you.

This guide covers the full decision: how each engine processes data at the architectural level, how state management works inside each runtime, how event time and watermarks behave with working code in Java and Python, latency benchmarks, and production case studies from real Ksolves engagements. By the end, the choice between Flink and Spark should be a clear engineering decision, not a preference.

TL;DR

• Flink processes each event the instant it arrives. Spark batches events into micro-windows before processing. That single difference determines every latency and state management tradeoff in this guide.
• Choose Flink if your pipeline must react in under 100ms: fraud detection, live pricing, real-time alerting, or any workload where a delayed response has a direct business cost.
• Choose Spark if your team works in PySpark or SQL, needs unified batch and streaming from one platform, or is building on Delta Lake, Iceberg, or MLlib.
• Many production platforms run both: Flink for real-time streaming and Spark for batch ETL, connected via Kafka or shared storage.

What Is Apache Spark? A Brief Overview with Example

Apache Spark is a distributed data processing engine built for large-scale computation. It breaks work into chunks and processes each chunk as a mini batch job, whether that work is last month’s sales data or a live stream of transactions arriving right now.

Think of it as a factory floor that processes orders in shifts. Every 500 milliseconds, the shift starts, the engine picks up everything that arrived since the last run, processes it, and produces output. An event that arrives at millisecond zero of a new shift will wait until that shift runs. Nothing ships until the shift ends.

This model makes Spark exceptionally good at high-volume batch and ETL workloads. A healthcare company processing 2TB of patient records nightly, joining streaming vitals data with historical records, and running ML model training on the same cluster is in exactly the right place with Spark. The latency requirement is seconds, the team writes PySpark, and the ecosystem around Delta Lake and Airflow handles everything the job needs.

What Is Apache Flink? A Brief Overview with Example

Apache Flink is a stream processing engine built around a different premise. Every event is processed the instant it arrives. There are no shifts, no batch windows, no waiting for other events to accumulate before computation begins. Each event moves through a pipeline of stateful operators immediately and produces a result on its own.

Think of it as a conveyor belt with a specialist at every station. The moment an item lands on the belt, it moves. Each station does its work and passes it forward. Nothing waits for the next item. Nothing groups with the previous one.

A payments platform processing 50,000 transactions per second needs this model. At that scale, a 500ms batch window means thousands of transactions are in flight before the first fraud check runs. A real-time data pipeline on Flink processes each transaction the instant it arrives and fires a fraud alert before the bank authorization completes. No batch interval tuning gets Spark to that point because the floor is built into the architecture.

Apache Flink vs Apache Spark: How Each Engine Processes Streaming Data

Flink processes each event one-by-one through stateful operators, async checkpoints, and a sink. Spark accumulates a micro-batch first, runs the Catalyst query plan, then executes via the RDD batch engine. Flink p99 latency: 10-500ms. Spark p99 latency: 500ms-2s.

How Apache Spark Structured Streaming Works: Micro-Batching

Spark treats streaming as a batch problem, which it solves very quickly. Incoming data gets sliced into small intervals, typically 100ms to a few seconds, and each slice is processed as a batch job. This is called micro-batching, and it is the architecture behind Spark Structured Streaming.

If your batch interval is 500ms, no result can appear in fewer than 500ms. An event that arrives at millisecond zero still waits for the window to close before any computation begins. That is a hard latency floor built into how Spark processes streams.

How Apache Flink Stream Processing Works: True Event Processing

Flink processes each event the instant it arrives. There is no batch boundary, no buffer to fill, no window to wait for. Each event moves through a pipeline of stateful operators immediately, and results are emitted as each event is processed. End-to-end latencies in the tens of milliseconds are achievable in default configurations.

Flink vs Spark Latency: What the Numbers Look like

Each Flink event produces an output immediately. Spark delays output until the batch window closes, 500ms minimum, regardless of when events arrived.

Latency p99 on a log scale. Shorter bar = lower latency. Each gridline = 10x increase. Flink is consistently lower across all configurations.

Spark’s throughput ceiling is often higher in raw volume scenarios. Batching reduces per-event scheduling overhead, so when a one-second delay is acceptable, Spark can be the more efficient choice.

Window types in Apache Flink vs Apache Spark

Window operations group events over time before aggregating them. The two engines support different window types, and this gap matters for certain workloads.

Flink supports four window types: tumbling windows (fixed-size, non-overlapping), sliding windows (fixed-size, overlapping), session windows (gap-based, where the window closes after a period of inactivity), and global windows (all events in one window, user-defined trigger required). Session windows are the practically important ones here. They are ideal for user session analytics where the gap between events defines the session boundary, not a fixed time interval. Spark does not support session windows natively.

Spark Structured Streaming supports tumbling and sliding windows via the window() function. For most aggregation workloads, this is sufficient. For session-based analytics, teams using Spark either approximate with a fixed sliding window or implement custom state logic using mapGroupsWithState.

The same payment tracking pipeline in Flink and Spark

The code below implements the same merchant spend tracker in both engines. The structural difference between event-driven and micro-batch processing is visible in the trigger line.

Flink: each event triggers immediately (Java)

StreamExecutionEnvironment env =

    StreamExecutionEnvironment.getExecutionEnvironment();

 

DataStream<PaymentEvent> payments = env

    .fromSource(

        KafkaSource.<PaymentEvent>builder()

            .setBootstrapServers(“kafka:9092”)

            .setTopics(“payments.raw”)

            .setValueOnlyDeserializer(new PaymentEventSchema())

            .build(),

        WatermarkStrategy.forBoundedOutOfOrderness(

            Duration.ofSeconds(5)),

        “payment-source”);

 

payments

    .keyBy(PaymentEvent::getMerchantId)

    .process(new MerchantSpendTracker())

    .sinkTo(KafkaSink.<AlertEvent>builder()

        .setBootstrapServers(“kafka:9092”)

        .setRecordSerializer(

            KafkaRecordSerializationSchema.builder()

                .setTopic(“merchant.alerts”)

                .setValueSerializationSchema(

                    new AlertEventSchema())

                .build())

        .build());

 

env.execute(“Payment Spend Tracker”);

Spark: events accumulate before processing (Python)

payments = (

    spark.readStream

    .format(“kafka”)

    .option(“kafka.bootstrap.servers”, “kafka:9092”)

    .option(“subscribe”, “payments.raw”)

    .load()

    .select(

        from_json(

            col(“value”).cast(“string”),

            payment_schema

        ).alias(“data”))

    .select(“data.*”)

)

 

merchant_totals = (

    payments

    .withWatermark(“event_time”, “10 seconds”)

    .groupBy(“merchant_id”)

    .agg(sum(“amount”).alias(“total_spend”))

)

 

merchant_totals.writeStream \

    .trigger(processingTime=”500 milliseconds”) \

    .format(“kafka”) \

    .option(“kafka.bootstrap.servers”, “kafka:9092”) \

    .option(“topic”, “merchant.alerts”) \

    .start()

The .trigger(processingTime=”500 milliseconds”) line is where the latency floor is set. No result appears until that window closes, regardless of when individual events arrived. In Flink, there is no equivalent line because processing is continuous.

Apache Flink vs Apache Spark State Management: Built-In vs External

Flink: state lives inside the operator, zero network hops, output emitted immediately. Spark: Every event requires two network round-trips to an external store before output.

How Spark Streaming Handles Stateful Logic

Spark’s approach to complex stateful logic typically relies on an external store such as Redis or HBase. Your code reads the state across a network boundary, updates it, and writes it back on every event. That is two network crossings per event, plus an external system to provision, monitor, and recover.

Spark 3.x introduced applyInPandasWithState and mapGroupsWithState for native in-job state without Redis for simpler patterns. For complex stateful logic at scale, the developer owns state lifetime, eviction, and serialisation entirely.

How Flink handles stateful stream processing

Flink keeps state co-located with the operator. There is no network hop, no external dependency, and no additional system to operate. Flink automatically backs up state via asynchronous checkpoints and scales to hundreds of gigabytes using RocksDB.

Three state backends are available:

• HashMapStateBackend: fully in-memory, fastest reads, suitable for smaller state volumes
• EmbeddedRocksDBStateBackend: spills to disk, handles terabytes of state, slower reads
• FsStateBackend: deprecated in recent Flink versions

If your team is already managing Redis externally to handle Spark’s stateful gaps, that is the clearest signal that Flink’s built-in state management is worth evaluating.

Login attempt tracker: state in Flink vs Spark

The same security pattern implemented in both engines shows how state ownership changes between the runtime and the developer.

Flink: state lives inside the operator (Java)

public class LoginAttemptTracker

    extends KeyedProcessFunction<

        String, LoginEvent, SecurityAlert> {

 

    private ValueState<Integer> failedAttempts;

    private ValueState<Long>    firstFailureTimestamp;

 

    @Override

    public void open(Configuration cfg) {

        failedAttempts = getRuntimeContext().getState(

            new ValueStateDescriptor<>(

                “failed-count”, Integer.class));

        firstFailureTimestamp = getRuntimeContext().getState(

            new ValueStateDescriptor<>(

                “first-failure-ts”, Long.class));

    }

 

    @Override

    public void processElement(

            LoginEvent event, Context ctx,

            Collector<SecurityAlert> out) throws Exception {

        if (event.isSuccess()) {

            failedAttempts.clear();

            firstFailureTimestamp.clear();

            return;

        }

        int count = failedAttempts.value() == null

            ? 1 : failedAttempts.value() + 1;

        failedAttempts.update(count);

        if (count == 1)

            firstFailureTimestamp.update(event.getTimestamp());

        if (count >= 5) {

            out.collect(new SecurityAlert(

                event.getUserId(),

                “Five failures since “

                    + firstFailureTimestamp.value(),

                SecurityAlert.Severity.HIGH));

            failedAttempts.clear();

            firstFailureTimestamp.clear();

        }

    }

}

Spark: developer-managed state via applyInPandasWithState (Python)

def track_login_attempts(user_id, events, state: GroupState):

    current = (state.get if state.exists

               else {“count”: 0, “first_ts”: None})

    alerts = []

    for event in sorted(events, key=lambda e: e.timestamp):

        if event.success:

            current = {“count”: 0, “first_ts”: None}

            continue

        current[“count”] += 1

        if current[“first_ts”] is None:

            current[“first_ts”] = event.timestamp

        if current[“count”] >= 5:

            alerts.append(SecurityAlert(

                user_id=user_id,

                message=(

                    f”Five failures since “

                    f”{current[‘first_ts’]}”),

                severity=”HIGH”))

            current = {“count”: 0, “first_ts”: None}

    state.update(current)

    return iter(alerts)

 

login_stream \

    .groupBy(“user_id”) \

    .applyInPandasWithState(

        track_login_attempts,

        output_schema, state_schema,

        “Update”, GroupStateTimeout.NoTimeout)

applyInPandasWithState works, but state lifetime, eviction, and serialisation are entirely the developer’s responsibility. For complex patterns tracking millions of entities at scale, most teams still reach for Redis externally and carry the network overhead.

Fault Tolerance And Exactly-Once Semantics: How Each Engine Recovers From Failure

Both Flink and Spark support exactly-once semantics, but the recovery mechanism is different, and that difference affects both checkpoint overhead and recovery time.

Flink uses distributed snapshots based on the Chandy-Lamport algorithm. At configurable intervals, Flink takes a consistent snapshot of all operator state and writes it to durable storage. On failure, the job restarts from the last successful checkpoint and replays any events that arrived after it. Checkpoint intervals are tunable: tighter intervals mean faster recovery but higher write overhead.

Spark uses write-ahead logs combined with lineage-based recovery. On failure, Spark replays the RDD lineage from the last known good state. This is effective for batch-style recovery but adds overhead on every micro-batch and can make recovery slower for large state volumes.

For pipelines where recovery time directly affects business outcome, such as a fraud detection system that must resume scoring within seconds of a node failure, Flink’s checkpoint-based model gives more predictable recovery behaviour.

Event Time vs Processing Time in Apache Flink and Spark

What the distinction means for your pipeline

Processing time is when an event arrives at your cluster. Event time is when the event actually occurred. These diverge by seconds, minutes, or hours, depending on network delays, mobile reconnects, or edge devices batching locally before sending.

A payment made at 14:00:00 might arrive at your cluster at 14:00:45. If your pipeline windows on processing time, that payment lands in a different five-minute window than where it belongs. For revenue reporting, fraud detection, or any pipeline where correctness over time matters, processing-time windows produce wrong results.

Apache Flink event-time processing and watermarks

Flink was designed around event time from the start. Watermarks signal progress through event time and trigger window computation. Late-arriving events can be routed to a side output for audit rather than dropped, so your pipeline knows what it missed and can act on it.

On-time events (teal) are included in the window. Allowed-late events (amber) still make it in. Events too far behind the watermark (coral) are routed to a side output for audit rather than silently dropped.

How Apache Spark Streaming handles late data

Spark supports watermarking, but any data older than the threshold is dropped with no built-in side output path. If late event auditing matters to your pipeline, the routing logic needs to be built at the application layer.

Five-minute revenue windows: Flink side output vs Spark drop

Flink: late events routed to audit sink (Java)

DataStream<Purchase> purchases = paymentStream

    .assignTimestampsAndWatermarks(

        WatermarkStrategy

            .<Purchase>forBoundedOutOfOrderness(

                Duration.ofSeconds(10))

            .withTimestampAssigner(

                (p, ts) -> p.getPurchaseTimestamp()));

 

purchases

    .keyBy(Purchase::getRegion)

    .window(TumblingEventTimeWindows.of(Time.minutes(5)))

    .allowedLateness(Time.seconds(30))

    .sideOutputLateData(lateEventTag)

    .aggregate(

        new RegionalRevenueAggregator(),

        new RegionalRevenueWindowFunction())

    .print();

 

// Late events go to a separate audit sink

DataStream<Purchase> lateEvents =

    purchases.getSideOutput(lateEventTag);

lateEvents.addSink(new LateEventAuditSink());

Spark: late data dropped at watermark threshold (Python)

purchases = (

    raw_stream

    .select(

        col(“region”),

        col(“amount”).cast(“double”),

        to_timestamp(

            col(“purchase_ts”),

            “yyyy-MM-dd HH:mm:ss”

        ).alias(“event_time”))

    .withWatermark(“event_time”, “10 seconds”)

)

 

regional_revenue = (

    purchases

    .groupBy(

        window(col(“event_time”), “5 minutes”),

        col(“region”))

    .agg(sum(“amount”).alias(“total_revenue”))

)

 

regional_revenue.writeStream \

    .outputMode(“update”) \

    .format(“console”) \

    .start()

Spark drops all data older than the watermark threshold with no built-in routing for late events. Any auditing of what was dropped requires additional application-layer logic.

When to Use Apache Flink: Use Cases, Real-World Example, and Decision Criteria

Apache Flink use cases in production

Flink is the right engine when the cost of a delayed response is measurable in money or operational risk. These are the workloads where it consistently gets deployed:

• Real-time fraud detection: flagging suspicious transactions before authorization completes, where a 30-minute batch cycle allows dozens of fraudulent transactions to clear first
• Live pricing and real-time bidding: updating prices or bids within milliseconds of a market signal, where Spark’s micro-batch floor makes sub-100ms responses structurally impossible
• IoT anomaly detection: identifying equipment failures or sensor threshold breaches the instant data arrives from edge devices
• Security threat monitoring with CEP: detecting attack patterns across event sequences using FlinkCEP, such as five failed logins within 30 seconds or a velocity burst across multiple accounts
• Kafka-native event pipelines: where every millisecond of additional latency has downstream cost, and Flink’s native Kafka connector processes each message as it arrives

Real-world Apache Flink example: fraud detection for a FinTech platform

A high-growth FinTech platform was running batch-based fraud detection on 15 to 30-minute cycles. By the time an alert fired, the transaction had already cleared, and that is when they approached Ksolves for Apache Flink consulting. The detection pipeline was rebuilt on Apache Flink 1.20 and Kafka 4.3, routing each authorization event through a stateful CEP engine the instant it arrived off the Kafka topic, scoring it asynchronously against XGBoost and Isolation Forest models, and firing alerts before the authorization response left the platform. Detection latency dropped from 30 minutes to under 500 milliseconds.

Read the full Ksolves case study: Real-Time Fraud Detection for a FinTech Platform Using Apache Flink

Is Apache Flink the right engine for your workload?

• Your pipeline must produce a result in under 100ms from the moment an event arrives
• Your application maintains complex stateful logic per entity, such as fraud counters, session trackers, or behavioral profiles, that needs to survive failures without an external store
• You need to detect patterns across event sequences using CEP, such as five matching events within a time window or ordered event chains
• Your data flow is Kafka in and Kafka out, and latency accumulates with every additional processing hop
• You need precise event-time semantics with auditable routing of late-arriving data
• Your team is prepared to invest in Flink’s runtime: state backends, checkpoint strategies, watermark configuration, and operational observability before going to production

When to Use Apache Spark Streaming: Use Cases, Real-World Example, and Decision Criteria

Apache Spark Streaming use cases in production

Spark earns its place in workloads where volume, ecosystem integration, and platform consistency matter more than millisecond precision. These are the scenarios where it consistently gets chosen:

• Large-scale batch ETL alongside streaming: processing terabytes of historical data and live streams from the same codebase, cluster, and deployment pipeline
• Data lakehouse ingestion on Delta Lake or Iceberg: writing structured, versioned data with ACID guarantees, schema evolution, and time travel built in
• ML feature engineering and real-time inference: building and serving models as part of the streaming pipeline using MLlib, without routing through an external batch layer
• SQL analytics on streaming data: analysts querying live data in PySpark or Spark SQL without switching platforms or learning a new programming model
• Regulated industry data platforms: healthcare, finance, and government workloads requiring HIPAA or equivalent compliance, where Spark’s mature managed-service ecosystem reduces operational risk

Real-world Apache Spark example: HIPAA-compliant data platform for a US healthcare enterprise

A US healthcare enterprise needed a HIPAA-compliant big data processing platform to handle 2TB of sensitive patient and operational data, with two fully isolated environments and a seven-week delivery deadline. The platform was built on Apache Spark and Apache Airflow on Proxmox, with HIPAA controls, data encryption, and access management configured from infrastructure up, and Airflow handling all pipeline scheduling across both environments. Both production and development environments were handed over fully verified within seven weeks.

Read the full Ksolves case study: HIPAA-Compliant Apache Spark and Airflow Cluster for a US Healthcare Enterprise

Is Apache Spark the right engine for your workload?

• Your team works primarily in PySpark or Spark SQL, and streaming needs to run on the same platform as batch, with shared tooling and no context switching
• You are building on the lakehouse stack: Delta Lake, Apache Iceberg, or Databricks Unity Catalog
• Batch ETL and streaming share the same cluster, codebase, and deployment pipeline
• ML model inference or retraining is part of your pipeline, and you want it tightly integrated with the processing layer
• Your deployment targets a managed cloud service with years of operational tooling: Databricks, AWS Glue, or Azure HDInsight
• Your latency requirement is measured in seconds, and throughput at scale is the primary constraint

Built for lakehouse ingestion, unified batch and streaming, and ML pipelines at scale. If this is your workload, our Apache Spark consulting team has built it in regulated, high-volume environments. 

Talk to Ksolves

Apache Flink vs Apache Spark: Ecosystem and Integration Comparison

Apache Flink vs Apache Spark: Full Feature Comparison

Operational Complexity: Running Apache Flink vs Apache Spark in Production

Spark’s operational model is familiar to most data engineering teams

The Spark UI is mature, configuration is well-documented, and most production problems have documented solutions in the community. For teams new to stream processing, that familiarity reduces ramp-up time significantly.

Flink requires investment before it runs cleanly in production

Distributed snapshots, watermark strategies, state backends, and checkpoint intervals are concepts most engineers encounter for the first time on Flink. Debugging a stuck watermark or checkpoint timeout requires knowledge of runtime internals that Spark operators can largely avoid. For workloads where millisecond latency is a hard requirement, that investment is worth making. For everything else, Spark gets you there faster.

Conclusion

Choosing between Apache Flink and Apache Spark is a workload decision. Flink is the right engine when latency is measured in milliseconds, state needs to live inside the runtime, and event-time precision is non-negotiable. Spark is the right engine when the team works in PySpark or SQL, batch and streaming need to share a platform, and the ecosystem around Delta Lake, Airflow, and managed cloud services is where the work happens.

Many mature data platforms run both Flink for real-time streaming and Spark for batch ETL, connected via Kafka or shared storage. That is a deliberate architectural choice, and often the most honest answer to the question this guide started with. If you need a stream processing consulting partner to help make or validate that decision, the Ksolves Big Data team has built both in production.

How Ksolves Builds Production Pipelines with Apache Flink and Apache Spark

Our Big Data consulting work with both engines in production across fraud detection, real-time alerting, healthcare data platforms, and high-throughput decision systems. We choose the engine based on what the workload actually requires, and we have the production case studies to show what that looks like in practice.

Our Apache Flink engineering practice

Topology and state backend selection, checkpoint strategy, CEP pattern development, Kafka-Flink integration with exactly-once guarantees, Kubernetes deployment using the native Flink operator, and Prometheus/Grafana observability for job health and checkpoint lag.

Our Apache Spark engineering practice

Structured Streaming alongside batch jobs, Delta Lake and Iceberg integration, PySpark engineering with CI/CD, and performance optimisation across shuffle bottlenecks, executor memory pressure, and partition skew.