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  "description": "Benchmarks show near-identical ML performance: Spark for batch pipelines, Flink for low-latency streaming; minimal metric gaps.",
  "path": "/spark-mllib-vs-flink-ml-benchmark-results/",
  "publishedAt": "2026-04-20T03:20:45.000Z",
  "site": "https://stackrundown.com",
  "tags": [
    "Apache Spark MLlib",
    "Apache Flink ML",
    "ECBDL14",
    "Hadoop",
    "CIFAR-10",
    "TensorFlow",
    "Kasdi Merbah University",
    "RocksDB",
    "Databricks",
    "PySpark",
    "Gemini 3.1 vs Sonnet 4.6: Performance & Cost Guide",
    "Best AI Tools for Real-Time Capacity Planning",
    "Best AI Tools for Payment Fraud Detection 2026",
    "How AI Powers Real-Time Decision Optimization Systems"
  ],
  "textContent": "When comparing **Apache Spark MLlib** and **Apache Flink ML** , the choice ultimately depends on your machine learning workload. Both frameworks excel in specific areas, and their performance is nearly identical for batch tasks. Here's what you need to know:\n\n  * **Spark MLlib** : Best for batch processing and historical data analysis. It uses in-memory computation and micro-batching, making it ideal for large-scale datasets stored in data lakes or warehouses.\n  * **Flink ML** : Designed for real-time, event-driven applications. Its streaming-first architecture enables continuous model training and inference with lower latency.\n\n\n\n### Key Benchmark Findings:\n\n  * Training Time: Spark (4,006.4 seconds) vs. Flink (4,003.2 seconds)\n  * Accuracy: Spark (74.7%) vs. Flink (74.9%)\n  * Inference Throughput: Spark (8.4 images/sec) vs. Flink (8.2 images/sec)\n  * Memory Usage: Both frameworks used 30.2% of available memory.\n\n\n\n### Quick Comparison\n\n**Metric** | **Spark MLlib** | **Flink ML**\n---|---|---\n**Training Time** | 4,006.4 seconds | 4,003.2 seconds\n**Accuracy** | 74.7% | 74.9%\n**Inference Throughput** | 8.4 images/sec | 8.2 images/sec\n**Processing Model** | Batch / Micro-batch | Native Streaming\n\n**Choose Spark MLlib** if your focus is on batch-oriented tasks and established pipelines. **Choose Flink ML** if you need low-latency streaming and continuous learning capabilities.\n\nSpark MLlib vs Flink ML Performance Benchmark Comparison\n\n## How We Tested These Frameworks\n\n### Test Environment and Setup\n\nTo evaluate performance, we relied on two datasets. First, the **ECBDL14 dataset** from the GECCO-2014 conference, a binary classification problem with a massive **32 million instances** and **631 features**. Since the dataset had class imbalances, we applied Random OverSampling (ROS), doubling the dataset size to **65 million instances** for a fairer comparison.\n\nOur hardware setup featured a **10-node cluster** : nine computing nodes and one master node. Each node was equipped with dual Intel Xeon CPU E5-2630 v3 processors (8 cores, 2.40 GHz each), **128 GB of RAM** , and dual 2TB hard drives. Altogether, the cluster provided **1.15 TB of total memory**. The software environment included Hadoop 2.6.0, Spark 1.6.0, and Flink 1.0.3.\n\nFor deep learning tests, we turned to the **CIFAR-10 dataset** , which contains 60,000 32x32 color images, and integrated TensorFlow to simulate practical applications. The ECBDL14 dataset was tested at five sampling rates - 10%, 30%, 50%, 75%, and 100% - to assess scalability under different loads.\n\nThis setup ensured a solid foundation for comparing performance across various frameworks.\n\n### Metrics We Measured\n\nWith the environment in place, we focused on four key performance metrics:\n\n  * **Learning runtime** (measured in seconds)\n  * **Scalability** with increasing data volumes\n  * **Inference throughput** (images processed per second)\n  * **Model accuracy** (percentage of correct predictions)\n\n\n\nThese metrics are critical for assessing infrastructure costs, real-time responsiveness, and the overall effectiveness of the models.\n\nThe algorithms we tested included both traditional and modern machine learning methods. **Support Vector Machines (SVM)** and **Linear Regression** served as baselines since they are natively implemented in all frameworks. Additionally, we evaluated **Distributed Information Theoretic Feature Selection (DITFS)** , a custom greedy algorithm, to see how each framework handled iterative processes and data persistence. For deep learning, TensorFlow's integration was used to measure training efficiency and inference speed, consuming **30.2%** of the available memory during testing.\n\n###### sbb-itb-fd683fe\n\n## How to Use FlinkML and MLLib for ML Model Training and Retraining!\n\n## Spark MLlib Benchmark Results\n\nSpark MLlib showcased impressive batch processing capabilities, thanks to its design tailored for batch execution and micro-batch streaming. This makes it a solid choice for traditional machine learning tasks that involve processing data in sizable chunks.\n\n### Performance Numbers\n\nUsing a controlled test setup, researchers recorded notable performance metrics for Spark MLlib.\n\nIn June 2025, Messaoud Mezati and Ines Aouria from Kasdi Merbah University conducted a benchmarking study with the CIFAR-10 dataset. Their findings showed that Spark MLlib completed training in 4,006.4 seconds with an accuracy of 74.7%. While this accuracy was slightly below Flink ML's 74.9%, Spark MLlib outperformed in inference throughput, processing 8.4 images per second compared to Flink ML's 8.2 images per second. This throughput advantage highlights Spark’s strength in processing efficiency.\n\n> \"Inference throughput is slightly higher for Spark MLlib (8.4 images/sec) compared to Flink-ML (8.2 images/sec), suggesting that Spark's batch execution provides a slight advantage in processing efficiency.\" - Messaoud Mezati and Ines Aouria, Researchers, Kasdi Merbah University\n\nMemory usage for Spark MLlib stood at 30.2%, identical to Flink ML. This parity suggests that the resource consumption is largely driven by TensorFlow operations rather than the frameworks themselves. These results underscore Spark MLlib's ability to handle large-scale, batch-centric machine learning tasks effectively, aligning with its design for historical data processing.\n\nSpark’s Project Tungsten further boosts performance by bypassing the standard JVM heap, minimizing garbage collection delays, and leveraging CPU cache more efficiently. Additionally, Resilient Distributed Datasets (RDDs) store intermediate results in memory, optimizing iterative algorithms. Combined with Catalyst and Tungsten, this architecture enables Spark to process data up to 100 times faster in-memory compared to traditional disk-based systems.\n\n## Flink ML Benchmark Results\n\nFlink ML demonstrated impressive benchmark performance, completing its training in 4,003.2 seconds. This result highlights its capability for handling low-latency, streaming tasks effectively.\n\n### Performance Numbers\n\nHere’s a closer look at the benchmark metrics that showcase Flink ML's performance.\n\nIn June 2025, researchers Messaoud Mezati and Ines Aouria revealed that Flink ML completed training in 4,003.2 seconds, narrowly edging out Spark MLlib's 4,006.4 seconds. The results also showed Flink ML achieving a slightly higher accuracy of 74.9%, compared to Spark MLlib's 74.7%. These findings suggest that both frameworks deliver nearly identical batch training results when integrated with TensorFlow.\n\n> \"Accuracy results show that Flink-ML (74.9%) slightly outperforms Spark MLlib (74.7%), suggesting that continuous learning in Flink-ML may contribute to better generalization.\" - Messaoud Mezati and Ines Aouria, Kasdi Merbah University\n\nThe comparison extended beyond training time and accuracy. Inference throughput for Flink ML stood at 8.2 images per second, just behind Spark MLlib's 8.4 images per second. Both frameworks consumed 30.2% of memory during deep learning benchmarks, indicating that TensorFlow operations, rather than the frameworks themselves, primarily influenced memory usage. Flink’s memory model, which separates the JVM heap from off-heap memory (including Managed Memory for the RocksDB state backend), plays a crucial role in supporting stateful streaming and large-scale training tasks.\n\n## Side-by-Side Comparison\n\nResearchers Messaoud Mezati and Ines Aouria from Kasdi Merbah University conducted benchmarks in June 2025, showing that both frameworks deliver nearly identical performance. Differences in training time, accuracy, and resource usage were minimal. This suggests that **choosing between the two frameworks largely depends on the specific requirements of your processing model rather than raw performance metrics**. The table below outlines the key comparisons.\n\nMetric | Spark MLlib | Flink ML\n---|---|---\n**Training Time** | 4,006.4 seconds | 4,003.2 seconds\n**Accuracy** | 74.7% | 74.9%\n**Inference Throughput** | 8.4 images/sec | 8.2 images/sec\n**Memory Usage** | 30.2% | 30.2%\n**Processing Model** | Batch / Micro-batch | Native Streaming / Event-driven\n**Iterative Computation** | Acyclic graph plans | Cyclic data flows\n\nThis comparison highlights the frameworks' strengths in different scenarios. Spark MLlib shows a slight edge in throughput with 8.4 images per second, making it well-suited for batch processing tasks. On the other hand, Flink ML achieves marginally higher accuracy at 74.9%, which may stem from its ability to handle continuous learning in native streaming setups. Ultimately, your decision should align with whether batch efficiency or streaming precision is more critical to your project.\n\n## Which Framework Should You Choose?\n\n### Matching the Framework to Your Needs\n\nThe 2025 benchmarking results reveal that Spark MLlib and Flink ML deliver nearly identical performance metrics. This means the decision comes down to aligning the framework with the specific requirements of your application rather than focusing solely on speed.\n\n**Opt for Spark MLlib if** your workload is predominantly batch-oriented (around 70% or more). It’s ideal for those who need well-established machine learning pipelines with broad algorithm support or are already working within the Databricks or Hadoop ecosystem. Spark also benefits from a vast community of over 2,100 contributors, which translates to better third-party library options and seamless Python integration through PySpark. For startups, Spark is often a safer bet due to the availability of skilled developers and detailed documentation.\n\n**Opt for Flink ML if** your application demands sub-second latency. Flink ML processes each record as it arrives, achieving millisecond-level latency compared to Spark's micro-batching, which typically introduces delays ranging from hundreds of milliseconds to a few seconds. Flink ML is also well-suited for handling large, long-lived states in complex event processing, thanks to its RocksDB integration and savepoint features.\n\n### Benchmark Results at a Glance\n\nThe benchmarks showed both frameworks performing almost identically, with Spark MLlib having a slight edge in inference throughput, while Flink ML demonstrated marginally higher accuracy. These minor differences highlight that the choice should be guided by your workload's nature. For teams building machine learning infrastructure, the decision boils down to whether you favor batch-oriented efficiency and a mature ecosystem (Spark MLlib) or prioritize low-latency streaming and event-driven capabilities (Flink ML).\n\n## FAQs\n\n### What workload is best for Spark MLlib vs. Flink ML?\n\nSpark MLlib is designed for **batch processing** , making it a great choice for offline machine learning tasks. For example, it's well-suited for training models using historical datasets. While it does offer micro-batch streaming capabilities, its strength lies in handling large-scale batch workflows.\n\nOn the other hand, Flink ML shines in **real-time, event-driven machine learning** scenarios. It supports low-latency streaming and continuous learning, making it ideal for applications like real-time analytics, fraud detection, or processing IoT data. Your choice between the two should depend on whether your focus is on batch processing or real-time performance.\n\n### Why are the benchmark results so close between Spark and Flink?\n\nWhen it comes to large-scale machine learning tasks, **Spark MLlib** and **Flink ML** deliver nearly identical performance. The difference in training times is minimal - just a few seconds - and they both demonstrate similar levels of accuracy and throughput. This parity stems from continuous improvements in areas like streaming, iterative computations, and resource management, which have effectively reduced the performance gap between the two frameworks.\n\n### What should I change to run this benchmark on my own cluster?\n\nTo test the benchmark on your cluster, make sure you're using the same versions of **Spark** and **Flink** as outlined in the study. Configure **Spark MLlib** for in-memory processing and **Flink ML** for distributed processing. It's crucial to allocate enough memory and CPU resources for the tasks.\n\nFine-tune settings such as **executor memory** , **parallelism** , and **network configurations** to match or even enhance the results. Stick to established best practices when deploying Spark and Flink in distributed setups to ensure reliable and consistent performance.\n\n## Related Blog Posts\n\n  * Gemini 3.1 vs Sonnet 4.6: Performance & Cost Guide\n  * Best AI Tools for Real-Time Capacity Planning\n  * Best AI Tools for Payment Fraud Detection 2026\n  * How AI Powers Real-Time Decision Optimization Systems\n\n",
  "title": "Spark MLlib vs. Flink ML: Benchmark Results",
  "updatedAt": "2026-04-20T03:51:44.607Z"
}