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```services: taskmanager: ... deploy: ...
# troubleshooting
d
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services:
  taskmanager:
    ...
    deploy:
      resources:
        limits:
          cpus: '2'
          memory: 2G
        reservations:
          cpus: '1'
          memory: 1G
ensure that worker nodes Taskmanagers have enough resources in terms of memory and cpus.
a
Hi @D. Draco O'Brien. Is this an alternative to the following?
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taskmanager:
    ...
    command: taskmanager
    environment:
      ...
      - |
        FLINK_PROPERTIES=
        jobmanager.rpc.address: jobmanager
        taskmanager.numberOfTaskSlots: 11
        taskmanager.memory.process.size: 4096m
        taskmanager.memory.jvm-metaspace.size: 256m
d
I think both can be defined and are complementary but should be consistent. What you see below sets max and guaranteed min.
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services:
  taskmanager:
    ...
    deploy:
      resources:
        limits:
          cpus: '2' # Maximum CPU cores the container can use
          memory: 2G # Maximum memory the container can consume
        reservations:
          cpus: '1' # Guaranteed minimum CPU cores for the container
          memory: 1G # Guaranteed minimum memory for the container
Your setting allows up to ten tasks to run concurrently. Lets think about the number of resources needed to support this
To determine the host resources necessary to support your Flink setup and the number of tasks it could theoretically support, let’s break down the configurations you’ve mentioned: Your TaskManager is configured to handle up to 11 concurrent tasks.
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taskmanager.memory.process.size: 4096m
allocates 4 GB of memory for the Flink process. This includes memory for data processing as well as JVM overhead, including the heap, direct memory, and metaspace.
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taskmanager.memory.jvm-metaspace.size: 256m
if you set a reservation of 1 CPU core and a limit of 2 CPU cores for the TaskManager. Thus, to support this configuration, your host machine should have at least 1 CPU core available for the TaskManager to function properly, with headroom for up to 2 cores for peak performance. The Flink process size is set to 4 GB (4096m). Considering you’ve also reserved 256 MB for JVM metaspace, the total memory requirement is already included in the taskmanager.memory.process.size. Therefore, you need to ensure that the host has at least 4 GB of memory free for this TaskManager, plus additional memory for the operating system, other services, and potential fluctuations. Note that Docker’s memory reservation (reservations: 1G) suggests a guaranteed minimum, but Flink’s effective operation relies on the full 4 GB being available.
I think you at need least 6GB available of memory on your host to support the memory requirements and probably 1-2 available CPU
a
Hi @D. Draco O'Brien. Thanks for all the information. Please can I ask, what does the Flink taskmanager do by default if these configurations are missing? Does it not scale up automatically based on available resources on the server? Or is there a requirement for specified values?
d
That's a good question. By default, if resource configurations are not provided to the Flink TaskManager, it will operate with a more basic approach. Concerning CPU Utillization, Flink does not automatically scale the number of cores it uses based on what's available on the host by default. Without explicit configuration, it will utilize whatever resources Docker or the underlying OS assigns to it, which can lead it to under utilize or overload of resources, depending on the defaults and other applications running on the same host.
Flink manages its memory usage into different parts like JVM heap, direct memory, and off-heap memory for its operations. If taskmanager.memory.process.size is not set, Flink will calculate the heap size based on a fraction of the total available Java memory (taskmanager.heap.size defaults to fractions of the total memory), and other memory areas like direct memory and network buffers which each have their own defaults. This automatic calculation might not optimize for the specific workload or environment and can result in suboptimal resource allocation or out-of-memory errors. So it's generally better to specify a configuration based on your available resources and expectations.
If taskmanager.numberOfTaskSlots is not defined, Flink defaults to a certain number of slots which I think is usually just 1, and might not fully utilize the available resources for highly parallel jobs. Automatic scaling based on available resources is generally handled at the cluster level e.g., with Kubernetes autoscaling, rather than being a part of Flink itself.
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