Hey there! Let's break down why your Flink test is running significantly slower than Hazelcast Jet for this modified WordCount task. Based on the details you shared, here are some key areas to investigate:

1. Sink Parallelism Mismatch #

Looking at your setups: Hazelcast Jet uses a sink with localParallelism=1, while Flink's sink runs with parallelism 8. Even though both sinks are "do-nothing", Flink's parallel sink introduces extra thread coordination and scheduling overhead that Jet avoids. Try setting Flink's sink parallelism to 1 to align with Jet's configuration—this could cut down on unnecessary overhead.

2. Flink's Heavier Startup Overhead #

Flink has a more heavyweight distributed architecture (with JobManager-TaskManager coordination) compared to Hazelcast Jet, which tends to have faster startup times. The "Job Runtime" you're seeing for Flink includes the full lifecycle: job submission, TaskManager resource allocation, and initialization—all of which add to the total time.

To get a clearer picture, check Flink's web UI or detailed logs to isolate the actual data processing time (from when the first record is processed to the last) versus the total job runtime. Jet's duration might be measuring just the processing phase, not the full startup/shutdown cycle.

3. Overly Aggressive Memory Configuration #

Your Mac has 16GB of total RAM, but you've allocated 12288m (12GB) to Flink's TaskManager. This leaves very little memory for the OS and other processes, which can trigger disk swapping—a major performance killer. Try reducing the TaskManager memory to a more reasonable value, like 4096m or 6144m, to avoid memory contention.

Also, Flink's memory model is complex (heap, off-heap, managed memory). Double-check that you're not over-allocating managed memory if you're not using it (e.g., for batch processing or state backend storage).

4. Operator Chaining & Fusion Differences #

Jet's DAG shows that flat-map and filter are fused into a single operator, which reduces thread-to-thread data transfer overhead. Flink enables operator chaining by default, but verify that your code isn't explicitly disabling it (e.g., via disableChaining() on operators).

Additionally, check if your custom source in Flink is optimized—sometimes custom sources can have unexpected bottlenecks compared to framework-provided ones. Ensure the source is reading the file efficiently and emitting records without unnecessary delays.

5. Network Buffer & Batching Tuning #

Flink's default network buffer settings might not be optimal for your high-throughput scenario (since each word is emitted 1000 times, you're dealing with a massive number of records). Adjusting buffer parameters like taskmanager.network.buffer.number or taskmanager.network.buffer.size can reduce frequent buffer flushes and improve data transfer between operators.

For example, increasing the buffer size can help batch more records before sending, cutting down on the number of network calls between TaskManagers.

6. JVM GC Tuning #

Hazelcast Jet might use more optimized default GC settings for your workload, while Flink's default GC (ParallelGC) could be causing longer pauses with your large record volume. Try switching Flink's TaskManager to use G1GC by adding these options to flink-conf.yaml:

env.java.opts.taskmanager: "-XX:+UseG1GC -XX:MaxGCPauseMillis=200"

This can help reduce garbage collection pauses that add to the total runtime.

Next Steps to Validate #

• Isolate Bottlenecks: Run partial pipelines (e.g., just source → flatmap, then add aggregation, then sink) to see which step is adding the most latency.
• Use Flink's Metrics: Check the Flink Web UI for metrics like operator throughput, processing time, and backpressure—this will pinpoint exactly where the slowdown is happening.
• Verify Input Consistency: Double-check that both frameworks are processing exactly the same input (same file, same word-splitting logic, same 1000x emission per word) to eliminate any variables in the test setup.

Hope these pointers help you narrow down the issue!

内容的提问来源于stack exchange，提问作者Kuawiiii