Seed a Database with Flock

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Prerequisites

• Pidgeon CLI installed (dotnet tool install --global Pidgeon.CLI --version 0.1.0-beta.2), plus the Flock desktop app or Pro CLI (the flock command is not in the free community build)
• A database with an existing schema (PostgreSQL, SQL Server, or MySQL)
• Database credentials with read/write access

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Step 1: Connect to your database

pidgeon flock connect \
  --provider postgres \
  --connection "Host=localhost;Port=5432;Database=ehr_dev;Username=dev;Password=${DB_PASSWORD}"

• Tables classified as patient, encounter, clinical, financial, or reference
• Foreign key relationships mapped
• Column types and constraints detected

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Step 2: Learn patterns from existing data (optional)

pidgeon flock learn --sample 500

• Column value distributions (age ranges, sex ratios)
• Referential patterns (which diagnosis codes appear together)
• Temporal patterns (encounter durations, admission-to-discharge intervals)

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Step 3: Generate a synthetic population

pidgeon flock generate \
  --count 1000 \
  --format sql \
  --state TX \
  --seed 42 \
  --output ./seed-data/

• Demographics: age, sex, race, and geography distributions matching US Census data
• Correlated conditions: realistic disease correlations (diabetes with hypertension, obesity with sleep apnea)
• Temporal coherence: admissions before discharges, lab orders before results
• Family linkage: household structures and family relationships

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Step 4: Preview with dry-run

pidgeon flock seed \
  --target postgres \
  --connection "Host=localhost;Port=5432;Database=ehr_dev;Username=dev;Password=${DB_PASSWORD}" \
  --from ./seed-data/ \
  --dry-run

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Step 5: Seed the database

pidgeon flock seed \
  --target postgres \
  --connection "Host=localhost;Port=5432;Database=ehr_dev;Username=dev;Password=${DB_PASSWORD}" \
  --from ./seed-data/

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Step 6: Verify the results

psql "$DATABASE_URL" -c "SELECT count(*) FROM patients;"

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Alternative output formats

pidgeon flock generate --count 1000 --format sql --output ./seed-data/

pidgeon flock generate --count 1000 --format csv --output ./seed-data/

pidgeon flock generate --count 1000 --format hl7 --output ./seed-data/

pidgeon flock generate --count 1000 --format fhir --output ./seed-data/

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Clean up synthetic data

pidgeon flock cleanup \
  --provider postgres \
  --connection "Host=localhost;Port=5432;Database=ehr_dev;Username=dev;Password=${DB_PASSWORD}"

curl -X DELETE "http://localhost:5102/api/flock/seed/cleanup?connectionString=Host%3Dlocalhost%3BDatabase%3Dehr_dev&provider=postgres"

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Next steps

• Explore output formats for HL7 streams and FHIR bundles
• Generate test messages from seeded population data
• Monitor interfaces processing your synthetic data