PLC4X Scraper运行异常求助：多台PLC数据采集触发报错

Let's break down the possible causes and actionable fixes for your issue—since you're hitting a consistent crash when the message count hits ~65526 regardless of PLC type or PLC4X version, we can narrow this down systematically.

1. First, Audit Your Kafka Producer Setup #

Looking at your code, the asynchronous Kafka send operation is a prime suspect here. If your producer isn't configured to handle the scraper's throughput, you could be hitting internal queue limits or resource leaks:

• Reuse your Kafka Producer instance: Never create a new Producer inside the result handler lambda. A single producer instance should be reused across all messages—spinning up one per message will flood your system with connections and exhaust resources quickly.
• Tweak Kafka Producer configs:
  • Increase queue.buffering.max.messages (default is 100,000; if your scraper pushes messages faster than Kafka can consume, this buffer will fill up and trigger failures)
  • Allocate more memory with buffer.memory to accommodate pending messages
  • Temporarily set max.in.flight.requests.per.connection to 1 to rule out concurrency issues in message sending
• Test without Kafka: Temporarily remove the Kafka send logic and just log results. If the crash stops, you know the problem lies in your Kafka integration rather than PLC4X itself.

2. Investigate PLC4X Resource Leaks #

Even with PooledPlcDriverManager, hidden resource leaks or internal limits in the scraper could be causing the crash:

• Enable PLC4X debug logging: Set the log level for org.apache.plc4x to DEBUG. Watch for warnings about unclosed connections, queue overflow, or task timeouts as you approach the 65526 message mark—this will reveal if the scraper is leaking resources.
• Simplify your test setup: Try scraping just one PLC (either S7 or Modbus) instead of 20. If you still hit the same crash at the same message count, the issue isn't tied to the number of PLCs—it's a general scraper or JVM resource problem.
• Validate your scraper config: Ensure your trigger schedule doesn't overlap tasks. If your scrape interval is shorter than the time it takes to process a batch, tasks will pile up, consuming more memory and connections over time.

3. Check JVM/System Resource Limits #

The number 65526 is extremely close to the default maximum number of open file handles (65535) on most Unix-like systems—this is a common bottleneck for network-heavy applications:

• Verify file handle limits: Run ulimit -n to see your current limit. If it's set to 65535, increasing it (e.g., to 655350) might resolve the crash.
• Monitor JVM memory: Add JVM flags like -XX:+HeapDumpOnOutOfMemoryError to capture a heap dump if the crash is due to OOM. Use tools like MAT to analyze where memory is being held (e.g., unclosed connections, pending Kafka messages).
• Track thread growth: Use jconsole or jstack to check if the number of threads is growing unbounded—this could indicate a thread leak in either PLC4X or your Kafka callback logic.

4. Critical Missing Info: The Exception Stack Trace #

You mentioned an exception is triggered, but didn't share the actual error message or stack trace. This is the single most important detail to pinpoint the issue. Is it an IOException, OutOfMemoryError, a PLC4X-specific PlcConnectionException, or something else? Sharing this will immediately narrow down the root cause.

5. Try the Latest Stable PLC4X Version #

You tested 0.8.0-SNAPSHOT, but make sure you're using the latest official stable release (as of now, 0.10.0 is the latest). Snapshot builds can have unresolved bugs, and newer stable versions often include fixes for resource leaks and edge-case crashes.

内容的提问来源于stack exchange，提问作者David Zamora