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Benchmarking Real-time Processing Engines: A Latency and Throughput Analysis of Apache Flink vs. Spark Structured Streaming on Amazon EMR

Apache Flink

A Latency and Throughput Analysis of Apache Flink vs. Spark Structured Streaming on Amazon EMR

  • Use Case: Many industries (finance, IoT, e-commerce, healthcare) require real-time data processing for fraud detection, sensor monitoring, clickstream analysis, and anomaly detection. Choosing the right streaming engine—Apache Flink vs. Spark Structured Streaming—can significantly impact latency, throughput, and cost efficiency.

Objective

  • To benchmark Flink and Spark Structured Streaming on AWS EMR for real-time stream ingestion and analytics.

    Measure latency, throughput, and scalability under varying workloads.

    Provide performance-cost trade-off insights to guide architecture decisions.

Project Description

  • This project sets up a real-time data streaming pipeline using AWS services:
  • Data Ingestion: Simulated real-time streams (IoT sensors, clickstreams) pushed into Amazon Kinesis Data Streams or Kafka on MSK.

Processing Engines

  • Flink on EMR for event-driven, low-latency processing.
  • Spark Structured Streaming on EMR for micro-batch stream processing.

Benchmarking Metrics

  • Latency (event-to-result delay).

    Throughput (records/sec).

    Resource utilization (CPU, memory).

    Cost (EMR usage, data transfer).

    Storage & Analytics: Results stored in Amazon S3, queried via Athena/Redshift.

    Visualization: Use Amazon QuickSight to compare cost-performance between Flink and Spark.
  • AWS Services & Technologies :
    AWS Service Role
    Amazon Kinesis Data Streams / Amazon MSK (Kafka) Real-time data ingestion layer.
    Amazon EMR (with Flink & Spark) Cluster-based execution of Flink and Spark Structured Streaming jobs.
    Amazon S3 Storage of raw streaming data and processed benchmark results.
    Amazon CloudWatch Monitoring cluster performance, job latency, and throughput metrics.
    AWS Lambda Automating job orchestration and stream injection.
    Amazon Athena Query benchmark logs stored in S3.
    Amazon QuickSight Visualization of latency vs. throughput vs. cost comparisons.
    Amazon Redshift (optional) Deeper analytical queries on processed streaming data.