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Resolve job running errors

Last Updated: Apr 11, 2018

TLogParseException type conversion exception

Exception cause: Generally, when using ARMS for the first time, users split sample data with the intelligent splitting function of ARMS. The number fields in the sample are converted to LongKey type by the splitter. However, some of real data is of the String type. As a result, TLogParseException is thrown.

Solution:

  1. Go to the monitoring job editing page.
  2. On the Log Cleansing page, select Custom Splitting.
  3. Based on the error message, modify the fields with the TLogParseException error. For example, if a String field cannot be converted to the Long field, open keys and replace StringKey with LongKey.

FlowControlException dimension throttling exception

Exception cause: If a dataset has a large number of dimensions, the computing and storage resources are greatly consumed. When resources are limited, ARMS restricts the number of dataset dimensions to ensure that most monitoring jobs can run as expected. A dataset can contain up to 1000 dimensions currently. If the number of dimensions exceeds this threshold, ARMS throws the FlowControlException.

Solution:

  1. Choose Custom Monitoring > Datasets and search for the dataset with the exception based on the dataset ID displayed in the error message.
  2. View the dimension settings and check if the listed dimensions are the needed ones. If they are not the ones to be focused on, modify the dataset and set dimension configuration to None.
  3. Return to the monitoring job page, click Pause, and click Resume. The new configuration takes effect.

If you confirm that the dimensions set are the ones to be focused on, click Contact Us on the homepage of the ARMS console to apply for a VIP membership. Independent computing and storage resources will be activated for you to solve the throttling exception.

ExpressionRuntimeException expression exception

Exception cause: usually the real data does not match the configured splitter.

Solution:

  1. Pause the monitoring job and go to the monitoring job editing page.
  2. Go to the Log Cleansing page.
  3. Paste the abnormal log in the text box under the log capturing result.
  4. Gradually reduce the splitter complexity, click Log Splitting Preview to check if the splitting result is as expected, and finally identify what causes the exception.
  5. Adjust the splitter or modify the output data.

JSONException JSON splitting exception

Exception cause: It’s usually the abnormal JSON data that prevents the JSON splitter from working.

Solution: Based on the error message, change the log to the JSON format that meets the splitting rules.

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