We’re building advanced analytics dashboards for our ERP system that heavily uses OSS for document storage. The built-in OSS monitoring in CloudMonitor provides basic metrics (requests, bandwidth, storage size), but we need more detailed insights for our business reporting - things like document upload success rates by department, average processing time for different document types, storage usage trends by business unit, and cost allocation per project. We’re considering implementing custom metrics in CloudMonitor but wondering if it’s worth the development effort versus just using the built-in OSS metrics with external analytics tools. What’s the best approach for flexible ERP analytics and reporting when you need metrics beyond what OSS provides out of the box?
The SLS approach is interesting, but wouldn’t that require parsing and processing large volumes of OSS access logs? Our system generates millions of document operations per day. I’m concerned about the cost and complexity of analyzing that much log data in real-time. With custom metrics, we could aggregate the data at the application level before sending to CloudMonitor, which seems more efficient. How do others handle high-volume scenarios? Also, we need the metrics integrated into our existing CloudMonitor dashboards where we monitor all our cloud resources.
You’re raising valid concerns about volume and cost. Here’s the reality: both approaches have trade-offs. Custom metrics in CloudMonitor are cost-effective for aggregated, pre-computed metrics (like success rate, average processing time). SLS is better for detailed, queryable logs but costs more at high volumes. For your ERP analytics needs, I’d suggest a hybrid approach: use custom metrics for real-time monitoring and alerting, and SLS for detailed historical analysis and business reporting. This gives you the best of both worlds without overwhelming costs.
This is an incredibly comprehensive analysis - exactly what we needed to make an informed decision. We’re moving forward with the hybrid approach: CloudMonitor custom metrics for real-time monitoring and alerting, with OSS access logs in SLS for detailed monthly business intelligence reports.
Key takeaways from this discussion:
1. Built-in OSS Monitoring Limitations: The standard CloudMonitor metrics for OSS are infrastructure-focused (storage size, bandwidth, request count) and lack business context. They’re useful for operational monitoring but insufficient for ERP analytics that require insights by department, project, document type, etc.
2. Custom Metrics Advantages:
- Business context: Can group metrics by any dimension that matters to your business
- Cost efficiency: Aggregating at application level before pushing to CloudMonitor keeps costs low
- Real-time alerting: Immediate notifications when KPIs violate thresholds
- Integration: Unified dashboards with other cloud resource monitoring
- Flexibility: Can evolve metrics as business needs change
3. When Each Approach Makes Sense:
- Custom Metrics: Real-time monitoring, alerting, aggregated KPIs, cost-effective for high-level insights
- SLS Logs: Detailed forensics, ad-hoc queries, compliance audits, historical trend analysis
- Hybrid: Best of both worlds - custom metrics for operations, SLS for deep analytics
4. Implementation Insights: The key is instrumenting your application to capture business metadata at the point of operation. Aggregate per minute before pushing to CloudMonitor to control costs. Use dimensions (department, project, document type) to enable flexible slicing and dicing of metrics.
Our Implementation Plan:
- Week 1-2: Instrument ERP application to collect document operation metrics with business context
- Week 3: Implement aggregation layer and CloudMonitor API integration
- Week 4: Build real-time dashboards in CloudMonitor for operations team
- Week 5: Enable OSS access logging to SLS for detailed audit trail
- Week 6: Create monthly business intelligence reports combining custom metrics and SLS data
Expected Outcomes:
- Real-time visibility into document upload success rates, processing times, and storage growth
- Cost allocation per department/project for accurate billing
- Proactive alerting on performance degradation or failures
- Detailed audit trail for compliance and forensic analysis
- Business intelligence dashboards for executive reporting
The consensus is clear: for advanced ERP analytics and reporting flexibility, custom metrics in CloudMonitor are essential. The built-in OSS monitoring provides the foundation, but custom metrics add the business context needed for meaningful insights. Thanks everyone for the detailed cost analysis, architecture recommendations, and implementation guidance!
Based on implementing this for a high-volume ERP system, here’s my detailed recommendation for advanced OSS monitoring and ERP analytics:
CloudMonitor Custom Metrics vs Built-in OSS Monitoring - Use Case Analysis:
Built-in OSS Metrics (What You Get Out of the Box): CloudMonitor provides these standard OSS metrics automatically:
- Storage size by bucket and storage class
- Request count (GET, PUT, POST, DELETE)
- Bandwidth (inbound/outbound)
- Request latency (average, P99)
- HTTP status codes (2xx, 4xx, 5xx)
- Metered storage (billable bytes)
These are great for infrastructure monitoring but lack business context. You can’t answer questions like “Which department uploaded the most documents?” or “What’s the success rate for invoice processing?”
When to Use Custom Metrics:
Custom metrics in CloudMonitor are ideal when you need:
- Business-level aggregations: Metrics grouped by department, project, document type, user role
- Application-specific metrics: Upload success rates, processing times, validation failures
- Real-time alerting: Immediate notifications when metrics exceed thresholds
- Cost allocation: Track storage and bandwidth usage per business unit
- Integration with existing monitoring: Unified dashboards with your other cloud resources
Our Implementation for ERP Analytics:
We built a metrics collection layer in our Java ERP application that pushes custom metrics to CloudMonitor:
Metric Categories:
-
Document Upload Metrics (by department/document type):
- upload_success_rate: Percentage of successful uploads
- upload_latency_avg: Average time to complete upload
- upload_size_distribution: Document sizes by category
- upload_count_by_dept: Number of uploads per department
-
Storage Usage Metrics (for cost allocation):
- storage_by_project: Total bytes stored per project
- storage_by_business_unit: Storage breakdown for billing
- storage_growth_rate: Daily/weekly storage increase
- object_count_by_category: Number of documents per type
-
Processing Metrics (application-level):
- document_processing_time: Time from upload to availability
- validation_failure_rate: Percentage of documents failing validation
- thumbnail_generation_time: Processing time for image documents
- ocr_processing_time: Time for text extraction from PDFs
-
Access Pattern Metrics (for optimization):
- document_retrieval_latency: Time to fetch documents
- cache_hit_rate: Percentage served from cache vs OSS
- concurrent_access_count: Number of simultaneous users
- popular_document_access: Most frequently accessed documents
Implementation Approach:
We use an event-driven architecture where document operations trigger metric updates:
// Pseudocode - Metric Collection Pattern:
1. Document upload initiated by user
2. Application logs operation metadata (user, dept, doc type, timestamp)
3. Upload to OSS with error handling
4. Record outcome (success/failure, latency, size)
5. Aggregate metrics per minute at application level
6. Push aggregated metrics to CloudMonitor every 60 seconds
7. CloudMonitor stores metrics for alerting and dashboards
This aggregation at the application level is crucial for cost efficiency. Instead of pushing millions of individual events, we aggregate per minute and push summary statistics.
Custom Metrics vs SLS Logs - When to Use Each:
Use CloudMonitor Custom Metrics for:
- Real-time monitoring dashboards (refresh every 1-5 minutes)
- Alerting on threshold violations (success rate drops, latency spikes)
- Aggregated metrics that don’t require detailed drill-down
- Cost-effective monitoring of high-level KPIs
- Integration with existing CloudMonitor infrastructure monitoring
Use SLS (Log Service) for:
- Detailed forensic analysis (“Show me all failed uploads for Project X on March 15”)
- Ad-hoc business intelligence queries
- Compliance audit trails (detailed access logs)
- Long-term historical analysis (trends over months/years)
- Complex correlations across multiple data sources
Recommended Hybrid Architecture for Your ERP System:
Layer 1: Application Metrics Collection
- Instrument your ERP application to capture document operations
- Include business context (department, project, user, document type)
- Aggregate metrics per minute before pushing to CloudMonitor
- Cost: Minimal - just application logic
Layer 2: CloudMonitor Custom Metrics
- Push aggregated metrics every 60 seconds
- Create dashboards for real-time monitoring
- Set up alerts for critical thresholds (success rate < 95%, latency > 2s)
- Estimated cost: ¥800-1200/month for 100 metric types
Layer 3: OSS Access Logs to SLS (Optional)
- Enable OSS access logging to SLS for detailed audit trail
- Use for monthly business intelligence reports
- Query on-demand rather than continuous processing
- Estimated cost: ¥25-50/month (storage only, minimal querying)
Layer 4: Data Warehouse Integration (For Advanced Analytics)
- Export daily summaries from CloudMonitor to MaxCompute or AnalyticDB
- Build comprehensive business intelligence dashboards
- Combine OSS metrics with ERP business data
- Cost: Depends on data volume and query frequency
Practical Dashboard Examples:
Real-time Operations Dashboard (CloudMonitor):
- Document upload success rate (last 15 minutes)
- Average upload latency by document type
- Active concurrent users
- Storage growth rate (bytes/hour)
- Alert indicators for threshold violations
Business Analytics Dashboard (SLS/BI Tool):
- Monthly document uploads by department
- Cost allocation per project (storage + bandwidth)
- Document type distribution over time
- User activity patterns (peak hours, seasonal trends)
- Compliance metrics (retention policy adherence)
ERP Analytics Flexibility:
The key to flexible analytics is capturing the right metadata at the application level. When you push custom metrics to CloudMonitor, include dimensions that matter to your business:
- Department/Business Unit (for cost allocation)
- Project/Customer (for billing and reporting)
- Document Type (invoice, contract, PO, etc.)
- User Role (for access pattern analysis)
- Processing Stage (uploaded, validated, processed, archived)
These dimensions allow you to slice and dice the metrics in CloudMonitor dashboards without needing to query raw logs.
Performance and Scalability:
For high-volume scenarios (millions of operations/day):
- Aggregate at application level (1-minute windows)
- Use asynchronous metric pushing (don’t block user operations)
- Batch multiple metrics in single API calls to CloudMonitor
- Cache frequently calculated metrics in Redis
- Use CloudMonitor’s metric statistics (Sum, Average, Max, Min) rather than storing individual data points
Conclusion:
For your ERP system’s advanced analytics needs, implement CloudMonitor custom metrics for real-time monitoring and alerting, supplemented with OSS access logs in SLS for detailed historical analysis when needed. This hybrid approach provides:
- Cost efficiency (custom metrics are cheaper than continuous log processing)
- Real-time visibility (CloudMonitor dashboards update every minute)
- Business context (metrics grouped by department, project, document type)
- Flexibility (can drill down into SLS logs when needed)
- Integration (unified monitoring with your existing cloud resources)
The built-in OSS metrics are valuable for infrastructure monitoring, but custom metrics are essential for business-level analytics and reporting in an ERP context. The development effort to instrument your application is well worth the insights you’ll gain and the flexibility for future analytics needs.