SPACE

Appearance

Investigating errors ​

SPACE AI can help investigate errors and unexpected behavior across IBM Planning Analytics and SPACE. Describe what went wrong, what you expected to happen, and approximately when the issue occurred. The assistant can use available model information, logs, and other SPACE-controlled tools to gather related evidence and help identify the likely cause.

Providing details such as the affected connection, cube, process, job, error message, or approximate time can help narrow the investigation, but you do not need to determine which log or SPACE module should be searched first.

Investigating TM1 issues ​

For issues involving a connected IBM Planning Analytics environment, SPACE AI can combine model information and logic with available TM1 logs.

Depending on the issue, relevant evidence may include:

  • Server log - Investigate TM1 errors, warnings, process failures, and other server activity.
  • Transaction log - Trace data changes by cube, user, elements, and time.
  • Audit log - Review relevant configuration, security, and administrative events.

For direct access to these logs in SPACE, see Logs.

The assistant can also inspect related rules, feeders, TurboIntegrator processes, and model dependencies when understanding the error requires model context. See Models and logic.

Investigating SPACE issues ​

For errors involving SPACE itself, the assistant can search available SPACE application logs using relevant messages, keywords, and time ranges.

An investigation can begin with the error shown to the user or a description of what happened. When related log entries contain a trace ID, SPACE AI can use that identifier to find additional entries associated with the same request and follow the activity across the application.

For example:

  • I received this error in the AI Assistant about 20 minutes ago. What happened?
  • Why did this SPACE operation fail?
  • Search the logs for this error and investigate the related trace.

Providing an approximate time is especially useful for log investigations because it narrows the search to the relevant activity.

Jobs and Python execution ​

The SPACE Journal records activity that is particularly useful when investigating SPACE jobs and Python execution. For these issues, the assistant can use Journal information to identify the relevant execution and then investigate related details.

For example:

  • Why did this scheduled job fail?
  • Investigate the error from this Python execution.

The Journal is not a complete application log for every SPACE action. For general SPACE application errors, the assistant can use SPACE application logs instead.

See Journal and Jobs for the underlying SPACE functionality.

Troubleshooting often requires more than one source of information. SPACE AI can move between available evidence as the investigation develops.

For example, a failed TurboIntegrator process may lead from an error or log entry to the process code, related model objects, and configuration. A SPACE application error may lead from an initial log message to a trace ID and related log entries.

This allows the investigation to start with the symptom rather than requiring the user to know in advance where the cause is located.

Reviewing the result ​

SPACE AI can summarize the evidence it finds, explain the likely cause, and suggest appropriate next steps. When the available information does not establish a clear cause, the assistant may identify what is known, explain remaining uncertainty, and suggest additional information to check.

Review important conclusions before making production changes. For model changes or development work resulting from an investigation, use the normal review and testing practices for your environment.