Apache Flink vs. Spark Structured Streaming: Real-Time Data Pipelines at Scale

1. Core Architecture: Native Streaming vs. Micro-Batching

Selecting between Apache Flink and Spark Structured Streaming hinges on whether your workload demands true continuous event streaming or if small batch windows are sufficient:

• Apache Flink (Native Event-Driven): Every incoming event from a broker triggers pipeline execution immediately. Tasks stay active indefinitely on TaskManagers, passing records through memory channels without write-ahead barriers.
• Spark Structured Streaming (Micro-Batch Engine): Accumulates events into discrete chunks over a trigger interval (e.g., 200ms). While Spark offers a Continuous Processing Mode (CPM) with ~1ms latency, it remains limited in SQL operator support and fault-tolerant joins.

To maintain high ingestion rates in either engine, ensure your upstream broker cluster is tuned properly. See our step-by-step setup in the Apache Kafka KRaft Deployment Guide .

2. Benchmark: Latency, Throughput & Resource Overhead

We benchmarked both engines processing a 500,000 events/sec stream with complex session-window aggregations across an 8-node Kubernetes cluster:

3. Stateful Processing, Checkpointing & RocksDB

For long-running pipelines maintaining session state, user histories, or sliding windows, state architecture determines system stability:

Flink's Embedded RocksDB StateBackend offloads memory off-heap, allowing state sizes to exceed available RAM by paging directly to NVMe SSDs. When combined with incremental checkpointing, Flink only syncs modified SST files to Amazon S3 or Google Cloud Storage.

In contrast, Spark requires dedicated heap tuning to prevent memory pressure during large window shuffles. You can review optimal executor settings in our Spark Memory Tuning Playbook .

4. PyFlink vs. PySpark Structured Streaming Code Comparison

PyFlink Windowed Aggregation (Event-Time):

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.window import TumblingEventTimeWindows
from pyflink.common.time import Time
from pyflink.common.watermark_strategy import WatermarkStrategy

env = StreamExecutionEnvironment.get_execution_environment()
env.enable_checkpointing(10000) # 10s async checkpoint

stream = env.from_source(kafka_source, WatermarkStrategy.for_monotonous_timestamps(), "KafkaEvents")
aggregated = stream.key_by(lambda x: x['user_id']) \
                   .window(TumblingEventTimeWindows.of(Time.seconds(60))) \
                   .reduce(lambda a, b: {'user_id': a['user_id'], 'count': a['count'] + b['count']})
aggregated.sink_to(iceberg_sink)

PySpark Structured Streaming Equivalent:

from pyspark.sql import SparkSession
from pyspark.sql.functions import from_json, col, window

spark = SparkSession.builder.appName("StreamingSink").getOrCreate()

df = spark.readStream.format("kafka") \
    .option("kafka.bootstrap.servers", "kafka:9092") \
    .option("subscribe", "telemetry-events").load()

processed = df.select(from_json(col("value").cast("string"), schema).alias("data")) \
    .select("data.*") \
    .withWatermark("event_time", "10 minutes") \
    .groupBy(window(col("event_time"), "1 minute"), col("user_id")) \
    .count()

query = processed.writeStream \
    .format("delta") \
    .outputMode("append") \
    .option("checkpointLocation", "s3://checkpoints/telemetry/") \
    .start("s3://lakehouse/telemetry/")

When writing to modern storage formats, Delta Lake and Apache Iceberg handle streaming commits differently. Explore those differences in our Delta Lake vs. Iceberg Benchmark .

5. Architecture Decision Matrix: Which One to Choose?

Frequently Asked Questions