batch

batch runs parallel analysis across all JSON files in a directory. It produces aggregated statistics, per-file summaries, and batch-level observations — processing hundreds or thousands of files in seconds via Rayon-based parallelism.

Where single-document commands analyze one file, batch analyzes the population.

Usage

vajra batch <directory> [flags]

Arguments:

Flags:

What It Does

1. Discovers files. Scans the directory for all supported files (.json, .yaml, .csv, .ndjson, etc.).

2. Parallel analysis. Each file is analyzed independently using Rayon’s work-stealing thread pool. On an 8-core machine, 8 files are analyzed simultaneously.

3. Per-file statistics. For each file: node count, path count, depth, fingerprint, anomaly count.

4. Aggregated statistics. Across the entire batch: merged frequency distributions, merged DDSketch quantiles, population-level entropy, cross-file type stability.

5. Batch-level observations. Structural families (via clustering), population anomalies, files that deviate from the batch norm.

Example: Text Output

vajra batch ./claims/

=== Batch Analysis ===
Directory: ./claims/
Files processed: 247
Total nodes: 208,729
Processing time: 1.4s (148,378 nodes/s)

=== Per-File Summary ===
  FILE                  NODES  PATHS  DEPTH  ANOMALIES  FINGERPRINT
  claim_001.json          847     23      6          0  a1b2c3d4...
  claim_002.json          891     23      6          0  a1b2c3d4...
  claim_003.json          723     23      6          1  a1b2c3d4...
  claim_048.json         1102     27      7          0  e5f6a7b8...
  claim_199.json          412     18      5          3  c9d0e1f2...
  ... (242 more files)

=== Structural Families ===
  Family 1: 198 files (80.2%) — 23 paths, signature a1b2c3d4...
  Family 2:  41 files (16.6%) — 27 paths, signature e5f6a7b8...
  Family 3:   8 files ( 3.2%) — 18 paths, signature c9d0e1f2...

=== Aggregated Statistics ===
  $.claims[*].service_lines[*].charge_amount
    Population median: 285.00
    Population MAD: 195.00
    Population p95: 1,420.00
    Cross-file consistency: high (coefficient of variation = 0.12)

  $.claims[*].service_lines[*].status
    Population entropy: 1.45 bits
    Dominant value: "adjudicated" (72.3%)
    Cardinality: 5 values across batch

=== Batch-Level Anomalies ===
  claim_199.json: structural outlier (Jaccard distance 0.31 from dominant family)
  claim_201.json: structural outlier (Jaccard distance 0.28 from dominant family)
  claim_834.json: contains numeric outlier (charge_amount = 47,250.00, z_MAD = 6.3)

Example: JSON Output

vajra batch ./claims/ --format json

{
  "directory": "./claims/",
  "files_processed": 247,
  "total_nodes": 208729,
  "processing_time_ms": 1400,
  "per_file": [
    {
      "file": "claim_001.json",
      "nodes": 847,
      "paths": 23,
      "depth": 6,
      "anomaly_count": 0,
      "fingerprint": "a1b2c3d4..."
    }
  ],
  "structural_families": [
    {
      "id": 0,
      "count": 198,
      "percentage": 80.2,
      "distinct_paths": 23,
      "signature": "a1b2c3d4..."
    },
    {
      "id": 1,
      "count": 41,
      "percentage": 16.6,
      "distinct_paths": 27,
      "signature": "e5f6a7b8..."
    },
    {
      "id": 2,
      "count": 8,
      "percentage": 3.2,
      "distinct_paths": 18,
      "signature": "c9d0e1f2..."
    }
  ],
  "aggregated_stats": {
    "$.claims[*].service_lines[*].charge_amount": {
      "population_median": 285.0,
      "population_mad": 195.0,
      "population_p95": 1420.0
    }
  },
  "batch_anomalies": [
    {
      "file": "claim_199.json",
      "type": "structural_outlier",
      "jaccard_distance": 0.31
    },
    {
      "file": "claim_834.json",
      "type": "numeric_outlier",
      "path": "$.claims[*].service_lines[*].charge_amount",
      "value": 47250.0,
      "z_mad": 6.3
    }
  ]
}

Parallelism and Performance

Batch uses Rayon’s work-stealing thread pool. The number of threads defaults to the number of CPU cores.

Performance targets:

DDSketch instances are computed per-file and merged globally with no accuracy loss — this is the key property that makes parallel batch processing exact rather than approximate.

When to Use It

• Daily batch monitoring. Run batch on each day’s incoming data. Track structural families, anomaly counts, and distribution shifts over time.
• Pre-processing audit. Before feeding a batch to a downstream system, run batch to verify structural consistency and flag outliers.
• Population baselines. Establish population-level statistics (median charge amount, expected null rates, typical structural signature) that individual-file analysis can compare against.
• Quick directory survey. “What is in this folder?” — batch answers in seconds.

Pairs Well With

• cluster — batch includes lightweight clustering; cluster provides detailed similarity analysis
• anomalies — batch flags files with anomalies; drill into specific files for details
• drift — compare today’s batch aggregates to yesterday’s for population-level drift
• essence — run essence on specific files that batch identified as notable