Built for Real Workloads

-- Clinical team: PostgreSQL protocol
SELECT p.name, p.dob, v.vitals
FROM patients p
JOIN visits v ON v.patient_id = p.id
WHERE p.id = 'PT-7842'
  AND v.visit_date > NOW() - INTERVAL '30 days'
ORDER BY v.visit_date DESC;

// Patient Portal: MongoDB protocol
db.patients.findOne({
  _id: "PT-7842"
}, {
  name: 1,
  allergies: 1,
  medications: 1,
  insurance: 1,
  emergencyContacts: 1
});

# Real-time bed tracking: Redis protocol
import redis
r = redis.Redis(host="synergy.hospital.internal")

# Check bed availability in ICU
available = r.scard("beds:icu:available")
r.smove("beds:icu:available",
        "beds:icu:occupied", "ICU-12")
r.hset("bed:ICU-12", mapping={
    "patient": "PT-7842",
    "admitted": "2026-02-10T08:30:00Z"
})

// Care team relationships: Graph protocol
MATCH (p:Patient {id: "PT-7842"})
      -[:ASSIGNED_TO]->(t:CareTeam)
      -[:INCLUDES]->(d:Doctor)
RETURN d.name, d.specialty, t.name
ORDER BY d.specialty;

// User Service — PostgreSQL protocol
import { Pool } from "pg";

const pool = new Pool({
  host: "synergy.internal",  // was: pg-primary.internal
  port: 5432,
  database: "ecommerce",
  user: "user_svc",
  password: process.env.DB_PASS,
});

// All existing queries work unchanged
const user = await pool.query(
  "SELECT * FROM users WHERE id = $1", [userId]
);

// Product Catalog — MongoDB protocol
import { MongoClient } from "mongodb";

const client = new MongoClient(
  "mongodb://synergy.internal:27017" // was: mongo-rs.internal
);

const db = client.db("ecommerce");

// All existing queries work unchanged
const product = await db
  .collection("products")
  .findOne({ sku: "WDG-4401" });

// Operational write — row-optimized storage
await db.query(`
  INSERT INTO shipments
    (id, origin, destination, status,
     carrier_id, weight_kg, eta)
  VALUES ($1, $2, $3, 'in_transit',
          $4, $5, $6)
`, [shipmentId, origin, dest,
    carrierId, weight, eta]);

// Real-time status update via Redis protocol
await redis.hset(`shipment:${shipmentId}`, {
  lat: gpsData.lat,
  lng: gpsData.lng,
  speed: gpsData.speed,
  updated: Date.now()
});

// Analytical query — columnar-optimized scan
// No ETL, no warehouse, just query directly
const stats = await db.query(`
  SELECT
    carrier_id,
    COUNT(*)             AS total_shipments,
    AVG(weight_kg)       AS avg_weight,
    PERCENTILE_CONT(0.95)
      WITHIN GROUP
      (ORDER BY delivery_hours)
                         AS p95_delivery_time,
    SUM(CASE WHEN status = 'delayed'
        THEN 1 ELSE 0 END)::FLOAT
      / COUNT(*)         AS delay_rate
  FROM shipments
  WHERE created_at > NOW() - INTERVAL '24h'
  GROUP BY carrier_id
  ORDER BY delay_rate DESC
`);

# Store document + auto-generate embedding
import requests

def store_doc(doc_id, content):
    # 1. Generate embedding via built-in API
    emb = requests.post(
        "http://synergydb:8080/v1/embeddings",
        json={"input": content,
              "model": "text-embedding-3-small"}
    ).json()["data"][0]["embedding"]

    # 2. Store everything in one transaction
    requests.post(
        "http://synergydb:8080/api/v1/query",
        json={"query": f"""
          INSERT INTO documents
            (id, content, embedding)
          VALUES ('{doc_id}', '{content}',
                  '{emb}')
        """})

-- Hybrid search: vector similarity + full-text
-- All in one query, one engine, one index scan

SELECT id, content,
  vector_distance(
    embedding,
    embed('machine learning best practices'),
    'cosine'
  ) AS similarity,
  bm25_score(content, 'machine learning')
    AS text_score
FROM documents
WHERE vector_distance(embedding,
  embed('machine learning best practices'),
  'cosine') < 0.3
ORDER BY similarity ASC
LIMIT 10;

-- Feed results directly to your LLM
-- No sync pipelines. No stale data.