Is system lag affects autoscaling?
Google does not really expose the specifics of its autoscaling algorithm. Generally, though, it is based on CPU utilization, throughput and backlog. Since you're using Pub/Sub, backlog in by itself should be based on the number of unacknowledged messages. Still, the rate at which these are being consumed (i.e. the throughput at the Pub/Sub read stage) is also taken into account. Now, throughput as a whole relates to the rate at which each stage processes input bytes. As for CPU utilization, if the aforementioned don't "run smoothly", 60% usage is already too high. So, system lag at some stage could be interpreted as the throughput of that stage and therefore should affect autoscaling. Then again, these two should not always be conflated. If for example a worker gets stuck due to a hot key, system lag is high but there's no autoscaling, as the work is not parallelizable. So, all in all, it depends.
If so, is there any solutions or ways to debugging this problem?
The most important tools you have at hand are the execution graph, stackdriver logging and stackdriver monitoring. From monitoring, you should consider jvm, compute and dataflow metrics. gcloud dataflow jobs describe can also be useful, mostly to see how steps are fused and, by extension, see which steps are run in the same worker, like so:
gcloud dataflow jobs describe --full $JOB_ID --format json | jq '.pipelineDescription.executionPipelineStage[] | {"stage_id": .id, "stage_name": .name, "fused_steps": .componentTransform }'
Stackdriver monitoring exposes all three of the main autoscaling components.
Now, how you're going to take advantage of the above obviously depends on the problem. In your case, at first glance I'd say that, if you can work without autoscaling and 40 workers, you should normally expect that you can do the same with autoscaling when you've set maxNumWorkers to 40. Then again, the number of messages alone does not say the full story, their size/content also matters. I think you should start by analyzing your graph, check which step has the highest lag, see what's the input/output ratio and check for messages with severity>=WARNING in your logs. If you shared any of those here maybe we could spot something more specific.
