Databases & Storage Engines

-- Enable pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Query hybrid relational, JSONB, and semantic vector embeddings
SELECT 
    p.id, 
    p.metadata->>'title' AS article_title,
    1 - (p.embedding <=> '[0.021, -0.043, 0.812]'::vector) AS cosine_similarity
FROM documents p
WHERE p.metadata @> '{"status": "published"}'
ORDER BY p.embedding <=> '[0.021, -0.043, 0.812]'::vector
LIMIT 5;

-- Optimize SQLite for high-throughput concurrent reads and writes
PRAGMA journal_mode = WAL;          -- Enable Write-Ahead Logging
PRAGMA synchronous = NORMAL;        -- Safe durability with 10x write speedup
PRAGMA cache_size = -64000;         -- 64MB page cache in RAM
PRAGMA temp_store = MEMORY;         -- Store temp tables & indexes in RAM

-- Query with full window functions & CTEs
WITH RECURSIVE Fibonacci(n, a, b) AS (
    VALUES(1, 0, 1)
    UNION ALL
    SELECT n + 1, b, a + b FROM Fibonacci WHERE n < 10
)
SELECT n, a AS value FROM Fibonacci;

-- Directly query multi-gigabyte remote Parquet files with zero ingestion
SELECT 
    vendor_id,
    date_trunc('month', pickup_datetime) AS pickup_month,
    count(*) AS total_rides,
    quantile_cont(fare_amount, [0.5, 0.95, 0.99]) AS fare_percentiles
FROM 's3://yellow-taxi/year=2024/*.parquet'
WHERE passenger_count > 0
GROUP BY ALL
ORDER BY total_rides DESC
LIMIT 10;

// Open database with typed object store and secondary index
const request = indexedDB.open('OfflineAppDB', 2);

request.onupgradeneeded = (event) => {
  const db = event.target.result;
  const store = db.createObjectStore('documents', { keyPath: 'id' });
  store.createIndex('by_author', 'author', { unique: false });
  store.createIndex('by_created', 'createdAt', { unique: false });
};

request.onsuccess = (event) => {
  const db = event.target.result;
  const tx = db.transaction(['documents'], 'readwrite');
  const store = tx.objectStore('documents');
  
  store.put({ id: 'doc-404', author: 'guigo', title: 'DB Internals', createdAt: Date.now() });
  tx.oncomplete = () => console.log('Transaction committed safely to local disk!');
};

# Atomic token-bucket rate limiter via Redis script or pipeline
# Key: rate_limit:user:123 (Expires after 60 seconds)
MULTI
INCR rate_limit:user:123
EXPIRE rate_limit:user:123 60
EXEC

# Query high-scores leaderboard with O(log(N)) ranking
ZADD leaderboard:weekly 14850 "player_alpha" 19200 "player_beta"
ZREVRANGEBYSCORE leaderboard:weekly +inf -inf WITHSCORES LIMIT 0 5

-- High-performance MergeTree table with ZSTD compression
CREATE TABLE clickstream_events (
    timestamp DateTime64(3, 'UTC') CODEC(DoubleDelta, LZ4),
    user_id UUID,
    event_type LowCardinality(String),
    payload_bytes UInt32 CODEC(T64, ZSTD(3))
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY (event_type, timestamp, user_id);

-- Sub-second query scanning 50,000,000 rows
SELECT 
    event_type, 
    count() AS total_events, 
    uniqExact(user_id) AS unique_visitors
FROM clickstream_events
WHERE timestamp >= now() - INTERVAL 1 HOUR
GROUP BY event_type;

// Multi-stage document aggregation pipeline
db.orders.aggregate([
  { $match: { status: "completed", orderDate: { $gte: ISODate("2026-01-01") } } },
  { $unwind: "$items" },
  { $group: {
      _id: "$items.category",
      totalRevenue: { $sum: { $multiply: ["$items.price", "$items.quantity"] } },
      averageQuantity: { $avg: "$items.quantity" },
      uniqueBuyers: { $addToSet: "$customerId" }
  }},
  { $project: {
      category: "$_id",
      totalRevenue: 1,
      buyerCount: { $size: "$uniqueBuyers" }
  }},
  { $sort: { totalRevenue: -1 } }
]);

-- Cassandra table partitioned across cluster nodes by sensor_id
CREATE TABLE sensor_telemetry (
    sensor_id uuid,
    bucket_date text,
    reading_time timestamp,
    temperature double,
    humidity double,
    PRIMARY KEY ((sensor_id, bucket_date), reading_time)
) WITH CLUSTERING ORDER BY (reading_time DESC);

-- Tunable consistency query across the distributed ring
SELECT reading_time, temperature, humidity 
FROM sensor_telemetry 
WHERE sensor_id = 7a8f9c10-3d84-4e31-8933-4df45e7e1634 
  AND bucket_date = '2026-10-10' 
  AND reading_time >= '2026-10-10 12:00:00'
ORDER BY reading_time DESC 
LIMIT 100;

-- Clustered index table layout for ultra-fast point queries
CREATE TABLE customer_orders (
    order_id BIGINT UNSIGNED NOT NULL AUTO_INCREMENT,
    customer_id INT UNSIGNED NOT NULL,
    status ENUM('pending', 'paid', 'shipped') NOT NULL DEFAULT 'pending',
    order_total DECIMAL(10, 2) NOT NULL,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    PRIMARY KEY (order_id),
    KEY idx_customer_created (customer_id, created_at)
) ENGINE=InnoDB ROW_FORMAT=DYNAMIC;

-- Recursive CTE for hierarchical catalog browsing
WITH RECURSIVE CategoryTree AS (
    SELECT id, name, parent_id, 0 AS depth
    FROM categories WHERE parent_id IS NULL
    UNION ALL
    SELECT c.id, c.name, c.parent_id, ct.depth + 1
    FROM categories c JOIN CategoryTree ct ON c.parent_id = ct.id
)
SELECT * FROM CategoryTree ORDER BY depth, name;

-- Define global survival goal and regional data residency
ALTER DATABASE banking_app SET PRIMARY REGION "us-east1";
ALTER DATABASE banking_app ADD REGION "us-west1";
ALTER DATABASE banking_app ADD REGION "europe-west1";

-- Row-level data locality keeps EU accounts in Europe for GDPR compliance
CREATE TABLE user_accounts (
    account_id UUID NOT NULL DEFAULT gen_random_uuid(),
    region crdb_internal_region NOT NULL,
    balance DECIMAL(15, 2) NOT NULL,
    currency VARCHAR(3) NOT NULL,
    PRIMARY KEY (region, account_id)
) LOCALITY REGIONAL BY ROW;

// Detect circular money laundering cycles between bank accounts
MATCH path = (origin:Account)-[:TRANSFERRED_FUNDS*3..6]->(origin)
WHERE ALL(r IN relationships(path) WHERE r.amount > 10000)
RETURN [n IN nodes(path) | n.accountNumber] AS suspiciousCycle,
       length(path) AS cycleLength,
       reduce(total = 0, r IN relationships(path) | total + r.amount) AS totalAmountMoved
ORDER BY totalAmountMoved DESC
LIMIT 10;

// POST /collections/documents/points/search
{
  "vector": [0.034, -0.012, 0.981, 0.442, -0.115],
  "limit": 5,
  "with_payload": true,
  "filter": {
    "must": [
      { "key": "category", "match": { "value": "database_internals" } },
      { "key": "published_year", "range": { "gte": 2024 } }
    ]
  },
  "params": {
    "hnsw_ef": 128,
    "exact": false
  }
}

import { MeiliSearch } from 'meilisearch';

const client = new MeiliSearch({ host: 'http://localhost:7700', apiKey: 'masterKey' });
const index = client.index('products');

// Fast search-as-you-type query with typo-tolerance & facet filtering
const searchResults = await index.search('postgre sql', {
  limit: 10,
  attributesToHighlight: ['title', 'description'],
  filter: 'category = "databases" AND in_stock = true',
  sort: ['rating:desc', 'price:asc'],
  facets: ['brand', 'category']
});

console.log('Search latency:', searchResults.processingTimeMs, 'ms');

#include <rocksdb/db.h>
#include <rocksdb/options.h>

rocksdb::DB* db;
rocksdb::Options options;
options.create_if_missing = true;
options.compression = rocksdb::kLZ4Compression;

rocksdb::Status status = rocksdb::DB::Open(options, "/data/rocks_store", &db);

// Atomic multi-operation WriteBatch
rocksdb::WriteBatch batch;
batch.Put("user:1001:profile", "{'name':'Guigo'}");
batch.Delete("user:1001:temp_token");
rocksdb::WriteOptions write_options;
write_options.sync = true; // Guarantees durable disk fsync

db->Write(write_options, &batch);

-- Convert standard table into an automated hypertable
SELECT create_hypertable('server_metrics', 'timestamp', chunk_time_interval => INTERVAL '1 day');

-- Create real-time continuous aggregate materialized view
CREATE MATERIALIZED VIEW hourly_cpu_summary
WITH (timescaledb.continuous) AS
SELECT 
    time_bucket('1 hour', timestamp) AS bucket,
    server_id,
    avg(cpu_percent) AS avg_cpu,
    max(cpu_percent) AS max_cpu
FROM server_metrics
GROUP BY bucket, server_id;

-- Enable automated 90% columnar compression on data older than 7 days
ALTER TABLE server_metrics SET (timescaledb.compress, timescaledb.compress_segmentby = 'server_id');
SELECT add_compression_policy('server_metrics', INTERVAL '7 days');

-- Enable real-time columnar replica for an existing transactional table
ALTER TABLE ecommerce_orders SET TIFLASH REPLICA 1;

-- TiDB cost-based optimizer automatically routes transactional writes to TiKV 
-- and massive analytical aggregations to vectorized TiFlash columnar engine
SELECT 
    /*+ READ_FROM_STORAGE(TIFLASH[ecommerce_orders]) */
    category,
    sum(amount) AS total_sales,
    count(DISTINCT customer_id) AS active_buyers
FROM ecommerce_orders
WHERE order_date >= '2026-01-01'
GROUP BY category;

-- ScyllaDB table pinning partition key to CPU-core shards
CREATE KEYSPACE telemetry_feed WITH replication = {
    'class': 'NetworkTopologyStrategy', 
    'us-east': 3
};

CREATE TABLE telemetry_feed.ad_impressions (
    campaign_id uuid,
    ad_unit_id text,
    event_timestamp timestamp,
    user_fingerprint text,
    bid_price_micros bigint,
    PRIMARY KEY ((campaign_id, ad_unit_id), event_timestamp)
) WITH CLUSTERING ORDER BY (event_timestamp DESC);

from pymilvus import Collection

collection = Collection("knowledge_embeddings")

# Build HNSW index with M=16, efConstruction=200
index_params = {
    "metric_type": "COSINE",
    "index_type": "HNSW",
    "params": {"M": 16, "efConstruction": 200}
}
collection.create_index(field_name="vector", index_params=index_params)
collection.load()

# Execute fast k-NN vector search with scalar expression filter
results = collection.search(
    data=[[0.015, -0.045, 0.821, ...]], # query embedding
    anns_field="vector",
    param={"metric_type": "COSINE", "params": {"ef": 64}},
    limit=5,
    expr="year >= 2024 and category == 'ai_research'",
    output_fields=["title", "url"]
)

import chromadb
from chromadb.utils import embedding_functions

# Initialize persistent local embedded vector database
client = chromadb.PersistentClient(path="./chroma_data")
collection = client.get_or_create_collection(
    name="technical_docs",
    metadata={"hnsw:space": "cosine"}
)

# Insert documents with metadata
collection.add(
    documents=["DuckDB uses vectorized SIMD execution.", "PostgreSQL handles MVCC via heap files."],
    metadatas=[{"source": "duckdb_guide"}, {"source": "postgres_internals"}],
    ids=["doc1", "doc2"]
)

# Semantic retrieval for AI query
results = collection.query(
    query_texts=["How does vectorized column processing work?"],
    n_results=1,
    where={"source": "duckdb_guide"}
)

#include <lmdb.h>

MDB_env *env;
MDB_dbi dbi;
MDB_txn *txn;
MDB_val key, data;

mdb_env_create(&env);
mdb_env_set_mapsize(env, 10485760); // 10MB virtual address map
mdb_env_open(env, "./lmdb_store", 0, 0664);

// Zero-copy read transaction (runs concurrently with zero lock contention)
mdb_txn_begin(env, NULL, MDB_RDONLY, &txn);
mdb_dbi_open(txn, NULL, 0, &dbi);

key.mv_size = 4;
key.mv_data = "1001";
mdb_get(txn, dbi, &key, &data);

// data.mv_data points DIRECTLY into kernel page cache (zero copy)
printf("Record size: %zu bytes\n", data.mv_size);
mdb_txn_abort(txn);

import { PGlite } from '@electric-sql/pglite';
import { vector } from '@electric-sql/pglite/vector';

// Initialize PGlite with persistent IndexedDB backing & pgvector
const db = new PGlite('idb://my-local-postgres', {
  extensions: { vector }
});

await db.exec('CREATE EXTENSION IF NOT EXISTS vector;');
await db.exec(`
  CREATE TABLE IF NOT EXISTS notes (
    id SERIAL PRIMARY KEY,
    content TEXT,
    embedding vector(3)
  );
`);

// Execute transactional query in browser tab
await db.query(
  'INSERT INTO notes (content, embedding) VALUES ($1, $2)',
  ['Offline-first AI memory', '[0.1, 0.2, 0.3]']
);

const res = await db.query('SELECT * FROM notes;');
console.log('PGlite rows in browser:', res.rows);

-- Real-time slice-and-dice query using exact bitmap indexing
SELECT 
    TIME_FLOOR(__time, 'PT5M') AS time_interval,
    datacenter,
    country_code,
    COUNT(*) AS total_requests,
    APPROX_COUNT_DISTINCT_BUILTIN(user_id) AS approx_unique_users,
    APPROX_QUANTILE_DS(response_time_ms, 0.99) AS p99_latency_ms
FROM "telemetry_traffic"
WHERE __time >= CURRENT_TIMESTAMP - INTERVAL '1' HOUR
  AND status_code >= 500
GROUP BY 1, 2, 3
ORDER BY total_requests DESC;

-- User-facing high-QPS query accelerated by Star-Tree index
SELECT 
    member_country,
    company_industry,
    COUNT(*) AS profile_views,
    DISTINCTCOUNT(viewer_member_id) AS unique_viewers
FROM profile_impressions
WHERE viewed_timestamp >= now() - 86400000
  AND target_member_id = 948291
GROUP BY member_country, company_industry
ORDER BY profile_views DESC
LIMIT 10;

// POST /logs-application/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "multi_match": {
            "query": "database transaction deadlock",
            "fields": ["title^3", "message", "stack_trace"]
          }
        }
      ],
      "filter": [
        { "term": { "environment": "production" } },
        { "range": { "@timestamp": { "gte": "now-24h" } } }
      ]
    }
  },
  "highlight": {
    "fields": { "message": {} }
  },
  "aggs": {
    "errors_by_service": {
      "terms": { "field": "service.name.keyword", "size": 5 }
    }
  }
}

# Insert JSON document with revision control over standard HTTP
curl -X PUT http://admin:[email protected]:5984/inventory/item_884 \
  -H "Content-Type: application/json" \
  -d '{"name": "Database Internals Book", "stock": 42, "category": "engineering"}'

# JavaScript MapReduce View definition
function(doc) {
  if (doc.category && doc.stock) {
    emit(doc.category, doc.stock);
  }
}
// Reduce function
_sum

-- Create graph relation between users and products
RELATE user:guigo->purchased->product:db_course 
SET purchased_at = time::now(), discount = 15;

-- Traverse graph relationships with arrow syntax in SurrealQL
SELECT 
    name, 
    ->purchased->product.title AS courses_bought,
    ->purchased->product<-purchased<-user.name AS also_bought_by
FROM user:guigo;

-- Subscribe to real-time database mutations via WebSocket
LIVE SELECT * FROM order WHERE status = 'pending';

# High-throughput key set with 3600-second expiration
set session:user:4092 0 3600 24
{"role":"admin","v":1}
STORED

# Microsecond point retrieval
get session:user:4092
VALUE session:user:4092 0 24
{"role":"admin","v":1}
END

# Atomic increment (rate limiting / metrics counters)
incr api_hits:tenant_10 1

-- Connect Memgraph directly to live Kafka fraud event stream
CREATE KAFKA STREAM financial_stream 
TOPICS transaction_events 
TRANSFORM fraud_detection_module;

-- Detect real-time suspicious fund transfers across network
MATCH (sender:Account)-[t:TRANSACTION]->(receiver:Account)
WHERE t.amount > 50000 AND sender.risk_score > 0.85
CREATE (sender)-[:FLAGGED_FOR_AUDIT {timestamp: localDateTime()}]->(receiver);

-- Query high-cardinality IoT sensor fleet using standard SQL
SELECT 
    date_bin(INTERVAL '15 minutes', time) AS time_window,
    device_model,
    location_id,
    AVG(voltage) AS avg_voltage,
    MAX(temperature) AS max_temp
FROM "iot_sensors"
WHERE time >= NOW() - INTERVAL '24 hours'
  AND firmware_version = 'v2.4.1'
GROUP BY time_window, device_model, location_id
ORDER BY time_window DESC;

-- System-versioned temporal table tracks entire history automatically
CREATE TABLE employee_salaries (
    emp_id INT NOT NULL,
    salary DECIMAL(10, 2) NOT NULL,
    department VARCHAR(50) NOT NULL,
    PRIMARY KEY (emp_id)
) WITH SYSTEM VERSIONING;

-- Query employee salary as it was at a specific historical point in time
SELECT * FROM employee_salaries 
FOR SYSTEM_TIME AS OF '2025-01-01 00:00:00'
WHERE emp_id = 42;