Jobs
Jobs represent individual executions of workflows and knowledge processing tasks in Vectense Platform. They provide comprehensive tracking, monitoring, and debugging capabilities for all automated processes.
Overview
Jobs are created whenever:
- Workflows Execute: Triggered by schedules, webhooks, or manual runs
- Knowledge Processing: Indexing documents or refreshing knowledge bases
- AI Model Operations: Processing requests and generating responses
- Integration Tasks: External system communications and data transfers
Job Lifecycle
- Creation: Job is queued when a trigger activates
- Preparation: System allocates resources and prepares execution
- Execution: Workflow steps or processing tasks run sequentially
- Completion: Job finishes with success or failure status
- Cleanup: Resources are released and results are stored
Job Types
Workflow Jobs
Executions of automated workflows:
- Trigger Source: Shows what initiated the workflow
- Step Progress: Track progress through workflow steps
- Data Flow: Monitor data passing between steps
- Performance Metrics: Execution time and resource usage
Knowledge Jobs
Processing tasks for knowledge bases:
- Indexing Jobs: Processing new documents and content
- Refresh Jobs: Updating existing knowledge with changes
- Optimization Jobs: Maintaining and optimizing search indexes
- Cleanup Jobs: Removing outdated or duplicate content
System Jobs
Internal platform maintenance tasks:
- Backup Operations: Data backup and archival tasks
- Maintenance Tasks: System optimization and cleanup
- Update Processes: Platform and component updates
- Health Checks: System monitoring and validation
Job Status
Execution States
Queued
- Job is waiting to start execution
- System is allocating necessary resources
- May be delayed due to resource constraints
Preparing
- System is setting up execution environment
- Loading required models and data
- Initializing integrations and connections
Running
- Job is actively executing workflow steps
- Progress can be monitored in real-time
- Resources are actively being consumed
Completed
- Job finished successfully
- All steps executed without errors
- Results are available for review
Failed
- Job encountered an error during execution
- Execution stopped at the point of failure
- Error details are available for debugging
Progress Tracking
- Step Completion: Track which steps have finished
- Current Activity: See what the job is currently doing
- Time Estimates: Projected completion times
- Resource Usage: Current CPU, memory, and AI token consumption
Job Monitoring
Real-Time Monitoring
Active Jobs Dashboard
- View all currently running jobs
- Monitor progress and resource usage
- Cancel jobs if necessary
- Real-time status updates
Performance Metrics
- Execution Time: How long jobs take to complete
- Resource Usage: CPU, memory, and storage consumption
- Success Rates: Percentage of jobs completing successfully
- Error Frequency: Common failure patterns and causes
Historical Analysis
Job History
- Complete record of all job executions
- Filter by date range, status, or job type
- Search by workflow name or trigger source
- Export data for further analysis
Trend Analysis
- Performance trends over time
- Resource usage patterns
- Success rate improvements or degradations
- Capacity planning insights
Job Details and Debugging
Execution Logs
Comprehensive logging for every job:
- Step-by-Step Logs: Detailed execution trace
- Error Messages: Complete error information and stack traces
- Performance Data: Timing and resource usage for each step
- Debug Information: Internal system details for troubleshooting
Data Inspection
Input Data
- View data that triggered the job
- Inspect webhook payloads or file content
- Understand what initiated the execution
Intermediate Results
- See data flowing between workflow steps
- Inspect AI model inputs and outputs
- Validate data transformations
Final Outputs
- Review job results and generated content
- Verify expected outcomes
- Check output quality and format
Error Analysis
Error Details
- Complete error messages and context
- Stack traces for technical debugging
- Suggested solutions and remediation steps
- Links to relevant documentation
Failure Patterns
- Common causes of job failures
- Trends in error types and frequencies
- Recommendations for workflow improvements
- Proactive failure prevention
Performance Optimization
Execution Efficiency
Workflow Optimization
- Identify slow or resource-intensive steps
- Optimize AI model usage and prompts
- Improve data flow and processing efficiency
- Reduce unnecessary operations
Resource Management
- Monitor CPU and memory usage patterns
- Optimize concurrent job execution
- Balance workload across available resources
- Plan capacity for peak usage periods
Cost Management
AI Model Costs
- Track token usage and associated costs
- Identify expensive operations and optimization opportunities
- Monitor trends in AI usage and spending
- Set up cost alerts and budgets
Infrastructure Costs
- Monitor compute resource usage
- Optimize job scheduling and resource allocation
- Identify opportunities for cost reduction
- Plan for scaling and growth
Job Management
Manual Operations
Manual Job Execution
- Run workflows manually for testing
- Test with specific input data
- Debug workflow configurations
- Validate changes before deployment
Job Control
- Cancel running jobs when necessary
- Restart failed jobs with corrections
- Pause and resume long-running operations
- Prioritize critical jobs
Automation
Retry Logic
- Automatic retry of failed jobs
- Configurable retry limits and delays
- Intelligent retry based on error types
- Exponential backoff for transient failures
Cleanup Automation
- Automatic cleanup of old job data
- Configurable retention policies
- Archive important job results
- Optimize storage usage
Alerts and Notifications
Real-Time Alerts
Failure Notifications
- Immediate alerts for job failures
- Email, Slack, or webhook notifications
- Escalation for critical workflow failures
- Custom alert rules and conditions
Performance Alerts
- Alerts for slow or resource-intensive jobs
- Threshold-based monitoring
- Proactive performance issue detection
- Capacity planning warnings
Reporting
Regular Reports
- Daily, weekly, or monthly job summaries
- Performance and reliability reports
- Cost analysis and optimization recommendations
- Trend analysis and insights
Custom Dashboards
- Create custom views for specific needs
- Monitor key metrics and KPIs
- Share dashboards with team members
- Export data for external analysis
Security and Compliance
Job Data Security
- Encryption: Job data encrypted at rest and in transit
- Access Control: Role-based access to job information
- Audit Logging: Complete tracking of job access and modifications
- Data Retention: Configurable policies for job data storage
Compliance Monitoring
- Audit Trails: Complete records for compliance reporting
- Data Lineage: Track data flow through job executions
- Privacy Controls: Respect data privacy and confidentiality
- Regulatory Compliance: Meet industry-specific requirements
Troubleshooting
Common Issues
Jobs Not Starting
- Check trigger configurations
- Verify resource availability
- Review user permissions
- Validate workflow configurations
Jobs Failing
- Review error logs and messages
- Check integration configurations
- Validate input data formats
- Test with simplified scenarios
Performance Issues
- Monitor resource usage patterns
- Identify bottlenecks in workflow steps
- Optimize AI model usage
- Review concurrent job limits
Getting Help
- Documentation: Reference specific integration and workflow guides
- Error Codes: Look up specific error codes and solutions
- Community: Ask questions in user forums
- Support: Contact technical support for complex issues
Best Practices
Monitoring Strategy
- Proactive Monitoring: Set up alerts before issues occur
- Regular Reviews: Periodically review job performance
- Trend Analysis: Look for patterns and improvement opportunities
- Capacity Planning: Plan for growth and peak usage
Optimization
- Continuous Improvement: Regularly optimize workflows and processes
- Performance Baseline: Establish performance baselines and targets
- Cost Optimization: Monitor and optimize resource usage costs
- Quality Assurance: Ensure job outputs meet quality standards
Maintenance
- Regular Cleanup: Clean up old job data and logs
- Archive Strategy: Archive important historical data
- System Health: Monitor overall system health and performance
- Update Management: Keep systems updated and optimized
Jobs provide comprehensive visibility into all automation processes in Vectense Platform. Use this monitoring and debugging capability to ensure your workflows run smoothly and efficiently.