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What is Batch Simulation?

A Batch Simulation runs a policy version against a large set of historical execution data, then compares the results against a baseline version. Think of it as a regression test suite — before deploying a new version, you can see exactly how it would have performed on past traffic.
Batch Simulation vs Dry Run: A Dry Run tests one input at a time for quick iteration. A batch simulation tests hundreds or thousands of inputs at once and produces aggregate metrics (match rate, metric deltas, per-rule statistics). Use dry runs during development; use batch simulation before deployment.

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What You Get

MetricDescription
Match RatePercentage of inputs where at least one rule matched
Per-Rule StatsHow many times each rule matched and its metric contribution
Metric DeltaDifference in aggregate output between baseline and candidate
Policy ImpactMatched count delta, match rate delta, metric value delta

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Running a Simulation

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Console

Navigate to a policy group → Simulation tab → select candidate version, baseline version, date range → click Start.

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CLI

lexq analytics simulation start --json '{
  "policyVersionId": "<candidate-version-id>",
  "dataset": {
    "type": "HISTORICAL",
    "source": "EXECUTION_LOGS",
    "from": "2025-01-01",
    "to": "2025-01-31"
  },
  "options": {
    "baselinePolicyVersionId": "<baseline-version-id>",
    "includeRuleStats": true,
    "maxRecords": 10000,
    "metricConfig": {
      "targetVariable": "discount_amount",
      "aggregationType": "SUM"
    }
  }
}'
Simulations run asynchronously. Poll until status is COMPLETED or FAILED: 
lexq analytics simulation status --id <simulationId>

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Dataset Types

TypeSourceDescription
HISTORICALEXECUTION_LOGSRe-run past execution inputs from the specified date range
MANUALREQUEST_BODYProvide inputs directly in the request body
UPLOADEDS3_BUCKETUse a pre-uploaded CSV or JSON dataset from S3

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File Upload Dataset

Upload a CSV or JSON file to use as simulation input. This is useful when you have custom test data that doesn’t come from execution history.

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Supported Formats

First row must be a header with fact keys. Each subsequent row is one test record.
payment_amount,customer_tier,is_first_purchase
150000,VIP,true
50000,REGULAR,false
80000,VIP,false
Type inference: Numbers, booleans (true/false), and strings are automatically detected. Empty values become null. Quoted fields with commas are supported.
Max file size: 10 MB

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Download a Template

Don’t know the expected columns? Download a template pre-filled with example values based on the version’s required facts:
In the New Simulation dialog, select File Upload (CSV / JSON) as dataset type. Click CSV or JSON under “Download template.”

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Upload & Run Simulation

  1. Go to Batch Simulations → New Simulation
  2. Select policy group and target version
  3. Set Dataset Type to File Upload (CSV / JSON)
  4. Drag & drop your file or click to select
  5. Wait for the Uploaded badge
  6. Click Start Simulation

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Managing Simulations

lexq analytics simulation list
lexq analytics simulation list --status COMPLETED
lexq analytics simulation cancel --id <simulationId>
lexq analytics simulation export --id <simulationId> --format json
lexq analytics simulation export --id <simulationId> --format csv

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Best Practices

  1. Always set a baseline. Without a baseline version, you only see absolute numbers — no deltas.
  2. Use a meaningful date range. At least 7 days of data for statistically significant results.
  3. Check maxRecords. Start with 5,000–10,000 for quick validation.
  4. Run simulation before every production deployment.
Batch simulation re-executes rules against historical input facts — it does not replay external side effects (webhooks, notifications). Integration calls are always mocked during simulation.

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Next Steps

Dry Run

Quick single-input validation during development.

A/B Testing

Compare versions with live production traffic.