Inside Jataka's Sub-Second Profiler
Architecture Deep-Dive
CTOs and Lead Architects often ask: "How does Jataka profile Salesforce transactions without access to production data?" This is the right question. The answer reveals an architecture designed for security, isolation, and sub-second profiling speed.
The Challenge
Salesforce Governor Limits are enforced at runtime. You can't predict them from static code because they depend on:
- Data volumes (how many records trigger your code)
- Execution context (trigger recursion, flow chaining)
- Concurrent operations (lock contention, sharing recalculation)
- User behavior (batch sizes, UI interactions)
To catch limit breaches before production, we need to execute code in an environment that mimics production data volumes—without ever touching actual production data.
The Architecture
Jataka's profiler operates in five distinct layers:
1. PR Integration
GitHub/GitLab webhook triggers profiler on every PR. No manual intervention required.
2. Sandbox Connection
Instant OAuth connection to your existing Integration/Staging Sandbox. No slow provisioning—uses your existing data volumes.
3. Transaction Execution
Apex code executed via REST/Tooling API. Real user scenarios simulated with your actual data volumes.
4. Real-Time Telemetry
Sforce-Limit-Info HTTP headers + Debug Log parsing. No injected Apex—pure external observation.
5. Breach Detection
CUMULATIVE_LIMIT_USAGE parsing with line-level attribution. Exact code location of limit breach.
Isolation & Security
Instead of slow scratch org provisioning, Jataka connects instantly to your existing Integration or Staging Sandbox via OAuth. This provides:
- Real data volumes — Profile against actual record counts in your sandbox
- No data copying — We never read or store your actual records
- Instant setup — OAuth connection in milliseconds, not minutes
- Existing metadata — No redeployment needed, your sandbox is ready
Security guarantee: Jataka only reads limit headers and debug logs. We never query your actual records. Your data stays in your Salesforce org—we only observe the telemetry that Salesforce already exposes.
Profiling With Your Data Volumes
Your Integration/Staging sandbox already has realistic data volumes. We profile your transactions against those actual record counts—no synthetic data needed:
- Actual record counts — If your sandbox has 10,000 Accounts, we test against 10,000 Accounts
- Real relationships — Parent-child ratios match your actual org structure
- Data skew detection — We identify skewed parent records from your actual data model
- No seeding delay — Your data is already there, profiling starts instantly
This is why Jataka can profile in milliseconds instead of minutes. We don't provision environments—we connect to yours.
Real-Time Telemetry via Headers & Logs
Jataka doesn't rely on injected Apex code. We fire the transaction via the REST/Tooling API and capture telemetry through two mechanisms:
// Salesforce returns limit info in every API response Sforce-Limit-Info: api-usage=5250/15000; per-app-api-usage=42/500 // We parse this instantly after each transaction // No code injection needed—pure external observation
// Raw debug log excerpt: 11:23:45.123 (123456789) CUMULATIVE_LIMIT_USAGE 11:23:45.123 LIMIT_USAGE_FOR_NS Number of SOQL queries: 87 out of 100 Number of query rows: 4823 out of 50000 Number of DML statements: 142 out of 150 <-- APPROACHING LIMIT Maximum CPU time: 8234ms out of 10000ms Maximum heap size: 4123456 out of 6000000 // We parse this block and attribute to exact line numbers
This approach gives us line-level attribution—we can tell you exactly which line of code triggered the 142nd DML statement. No sampling, no approximation, just the actual limit consumption captured from Salesforce's own telemetry.
Performance Characteristics
Average profiling time
<500ms
Sandbox connection
Instant (OAuth)
Header capture latency
<50ms
Debug log parsing
~200ms
The entire profiling pipeline—from PR webhook to breach report—completes in under 2 minutes for most transactions. Your developers get feedback before they context-switch.
What This Means for Your Team
Jataka's profiler gives you the confidence that your code will survive production data volumes— without ever exposing production data. It's the missing piece between static analysis (which catches syntax errors) and production incidents (which catch limit breaches too late).
The bottom line: If you're only running static analysis, you're catching 30% of the problems. Runtime profiling catches the other 70%—the ones that cause 2:00 AM production incidents.
See the profiler in action
Book a Demo
Watch Jataka profile a real transaction and catch a limit breach in real-time.