The MapReduce Jobs Interface in C3 AI Studio
The Jobs > MapReduce page in C3 AI Studio provides visibility into active and recent MapReduce jobs within an application. MapReduce jobs process large datasets in parallel. The C3 Agentic AI Platform splits the data into subsets, runs them on different nodes, and combines the results. The platform manages distribution, redundancy, and fault tolerance. You define the logic for map and reduce functions.
MapReduce jobs are typically started through Data Integration workflows or by creating a custom MapReduce Type in application code. For more information, see The Data Integration Interface in C3 AI Studio and Run MapReduce Jobs.
The MapReduce page consists of the following sections:
Page header
The page header displays summary statistics for recent jobs:
- Initial: Number of jobs currently starting.
- Running: Number of jobs actively processing.
- Completed jobs: Count and chart of jobs completed in the last 7 days.
- Failed jobs: Count and chart of jobs that failed in the last 7 days.
These roll-ups provide a quick overview of job activity and reliability.
Filter panel
The filter panel allows you to refine the list of jobs. You can filter by:
- Job type
- Job status
- Initiating user
- Execution time range (start and end dates)
This helps you isolate specific runs when monitoring or troubleshooting.
MapReduce jobs list
The jobs list displays recent MapReduce runs in descending order of start time. Each row includes:
- ID: A unique identifier. Select the ID to view job details.
- Status: Current state, such as Running or Completed.
- Job type: The job definition that initiated the MapReduce run.
- Started by: The user or process that initiated the job.
- Start time: The time the job began.
- Elapsed time: How long the job has run, or the total duration if completed.
Use this list to monitor in-progress jobs and identify failed runs. For more details on Jobs, see Create Long-Running Jobs.