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Terminal.skills
Use Cases/Build a Database Sharding Layer

Build a Database Sharding Layer

Build a database sharding layer with consistent hashing, automatic shard routing, cross-shard queries, rebalancing, and monitoring for horizontally scaling PostgreSQL.

#redis#caching#database#pub-sub#queues
Works with:claude-codeopenai-codexgemini-clicursor

Skills stack · 5 skills

Avg quality 93/100·All SAFE
>

typescript

v

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redis

v1.0.0

Build applications with Redis — caching, session storage, pub/sub, streams, rate limiting, leaderboards, and queues. Use when tasks involve in-memory data storage, real-time messaging, distributed locking, or performance optimization with caching layers.

93/100 quality
1.81× impact
SAFE
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postgresql

v1.0.0

Assists with designing schemas, writing performant queries, managing indexes, and operating PostgreSQL databases. Use when working with JSONB, full-text search, window functions, CTEs, row-level security, replication, or performance tuning. Trigger words: postgresql, postgres, sql, database, jsonb, rls, window functions, cte.

87/100 quality
1.53× impact
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hono

v1.0.0

You are an expert in Hono, the ultrafast web framework for the edge. You help developers build APIs and web applications that run on Cloudflare Workers, Deno, Bun, Node.js, AWS Lambda, and Vercel Edge — with a tiny footprint (~14KB), middleware ecosystem, JSX support, RPC client, and Web Standards API compatibility that makes code truly portable across runtimes.

93/100 quality
3.00× impact
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zod

v1.0.0

You are an expert in Zod, the TypeScript-first schema declaration and validation library. You help developers define schemas that validate data at runtime AND infer TypeScript types at compile time — eliminating the need to write types and validators separately. Used for API input validation, form validation, environment variables, config files, and any data boundary.

100/100 quality
1.21× impact
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$

The Problem

Omar leads engineering at a 30-person SaaS with 500M rows in their main events table. PostgreSQL queries that took 50ms now take 5 seconds. Vertical scaling hit the ceiling at 64 cores / 256GB RAM. Partitioning helps for time-series queries but not for tenant-based access patterns. They need horizontal sharding: split data across multiple PostgreSQL instances by tenant ID, route queries automatically, handle cross-shard queries for analytics, and rebalance when shards get hot.

Step 1: Build the Sharding Layer

typescript
// src/sharding/router.ts — Database sharding with consistent hashing and cross-shard queries
import { Pool } from "pg";
import { Redis } from "ioredis";
import { createHash } from "node:crypto";

const redis = new Redis(process.env.REDIS_URL!);

interface ShardConfig {
  id: string;
  host: string;
  port: number;
  database: string;
  weight: number;            // for weighted distribution
  status: "active" | "draining" | "readonly" | "offline";
  maxConnections: number;
}

interface ShardMap {
  shards: ShardConfig[];
  virtualNodes: number;      // consistent hashing ring resolution
  shardKey: string;          // e.g., "tenant_id"
}

const pools = new Map<string, Pool>();
let hashRing: Array<{ hash: number; shardId: string }> = [];
let shardMap: ShardMap;

// Initialize shard connections
export async function initialize(config: ShardMap): Promise<void> {
  shardMap = config;

  // Create connection pools for each shard
  for (const shard of config.shards) {
    if (shard.status === "offline") continue;
    pools.set(shard.id, new Pool({
      host: shard.host, port: shard.port, database: shard.database,
      max: shard.maxConnections, user: process.env.DB_USER, password: process.env.DB_PASSWORD,
    }));
  }

  // Build consistent hash ring
  buildHashRing(config.shards, config.virtualNodes);
}

function buildHashRing(shards: ShardConfig[], virtualNodes: number): void {
  hashRing = [];
  for (const shard of shards) {
    if (shard.status === "offline") continue;
    // Create virtual nodes proportional to weight
    const nodes = Math.ceil(virtualNodes * shard.weight);
    for (let i = 0; i < nodes; i++) {
      const hash = hashKey(`${shard.id}:${i}`);
      hashRing.push({ hash, shardId: shard.id });
    }
  }
  hashRing.sort((a, b) => a.hash - b.hash);
}

function hashKey(key: string): number {
  const hash = createHash("md5").update(key).digest();
  return hash.readUInt32BE(0);
}

// Route query to correct shard
export function getShard(shardKeyValue: string): string {
  const hash = hashKey(shardKeyValue);
  // Binary search on hash ring
  let lo = 0, hi = hashRing.length - 1;
  while (lo < hi) {
    const mid = (lo + hi) >> 1;
    if (hashRing[mid].hash < hash) lo = mid + 1;
    else hi = mid;
  }
  // Wrap around if past the last node
  const idx = lo < hashRing.length ? lo : 0;
  return hashRing[idx].shardId;
}

// Execute query on correct shard (single-shard)
export async function query(
  shardKeyValue: string,
  sql: string,
  params?: any[]
): Promise<any> {
  const shardId = getShard(shardKeyValue);
  const pool = pools.get(shardId);
  if (!pool) throw new Error(`Shard ${shardId} not available`);

  const start = Date.now();
  const result = await pool.query(sql, params);

  // Track shard metrics
  await redis.hincrby(`shard:metrics:${shardId}`, "queries", 1);
  await redis.hincrby(`shard:metrics:${shardId}`, "totalLatency", Date.now() - start);

  return result;
}

// Execute query across all shards (scatter-gather)
export async function queryAllShards(
  sql: string,
  params?: any[]
): Promise<{ shardId: string; rows: any[] }[]> {
  const results = await Promise.all(
    Array.from(pools.entries())
      .filter(([id]) => shardMap.shards.find((s) => s.id === id)?.status === "active")
      .map(async ([shardId, pool]) => {
        const result = await pool.query(sql, params);
        return { shardId, rows: result.rows };
      })
  );
  return results;
}

// Aggregate results from cross-shard queries
export async function aggregateQuery(
  sql: string,
  params: any[],
  aggregation: "sum" | "count" | "avg" | "merge"
): Promise<any> {
  const shardResults = await queryAllShards(sql, params);

  switch (aggregation) {
    case "count":
      return shardResults.reduce((sum, r) => sum + parseInt(r.rows[0]?.count || "0"), 0);
    case "sum":
      return shardResults.reduce((sum, r) => sum + parseFloat(r.rows[0]?.sum || "0"), 0);
    case "avg": {
      let totalSum = 0, totalCount = 0;
      for (const r of shardResults) {
        totalSum += parseFloat(r.rows[0]?.sum || "0");
        totalCount += parseInt(r.rows[0]?.count || "0");
      }
      return totalCount > 0 ? totalSum / totalCount : 0;
    }
    case "merge":
      return shardResults.flatMap((r) => r.rows);
    default:
      return shardResults;
  }
}

// Rebalance: migrate data between shards
export async function rebalance(fromShardId: string, toShardId: string, tenantIds: string[]): Promise<{
  migrated: number; errors: number;
}> {
  const fromPool = pools.get(fromShardId);
  const toPool = pools.get(toShardId);
  if (!fromPool || !toPool) throw new Error("Shard not found");

  let migrated = 0, errors = 0;

  for (const tenantId of tenantIds) {
    const client = await fromPool.connect();
    try {
      // Read data from source
      const { rows } = await client.query(
        "SELECT * FROM events WHERE tenant_id = $1", [tenantId]
      );

      // Write to destination
      const toClient = await toPool.connect();
      try {
        await toClient.query("BEGIN");
        for (const row of rows) {
          const keys = Object.keys(row);
          const values = Object.values(row);
          const placeholders = keys.map((_, i) => `$${i + 1}`).join(", ");
          await toClient.query(
            `INSERT INTO events (${keys.join(", ")}) VALUES (${placeholders}) ON CONFLICT DO NOTHING`,
            values
          );
        }
        await toClient.query("COMMIT");

        // Remove from source
        await client.query("DELETE FROM events WHERE tenant_id = $1", [tenantId]);
        migrated += rows.length;
      } finally {
        toClient.release();
      }
    } catch (e: any) {
      errors++;
    } finally {
      client.release();
    }
  }

  // Update shard routing
  for (const tenantId of tenantIds) {
    await redis.set(`shard:override:${tenantId}`, toShardId);
  }

  return { migrated, errors };
}

// Shard health monitoring
export async function getShardHealth(): Promise<Array<{
  shardId: string; status: string; rowCount: number;
  avgLatency: number; connections: number;
}>> {
  const results = [];
  for (const [shardId, pool] of pools) {
    const metrics = await redis.hgetall(`shard:metrics:${shardId}`);
    const queries = parseInt(metrics.queries || "0");
    const totalLatency = parseInt(metrics.totalLatency || "0");

    const { rows: [{ count }] } = await pool.query("SELECT COUNT(*) as count FROM events");

    results.push({
      shardId,
      status: shardMap.shards.find((s) => s.id === shardId)?.status || "unknown",
      rowCount: parseInt(count),
      avgLatency: queries > 0 ? Math.round(totalLatency / queries) : 0,
      connections: (pool as any).totalCount || 0,
    });
  }
  return results;
}

Results

  • Query latency: 5s → 50ms — data split across 4 shards; each shard handles 125M rows instead of 500M; queries hit single shard via tenant routing
  • Horizontal scaling — added 2 more shards when traffic doubled; consistent hashing redistributes only 25% of data; minimal disruption
  • Cross-shard analytics work — scatter-gather pattern queries all shards in parallel; aggregation merges results; reporting dashboard still sees all data
  • Hot shard rebalancing — enterprise tenant consuming 40% of shard 1; migrated to dedicated shard 5; other tenants on shard 1 saw 60% latency improvement
  • Consistent hashing — adding/removing shard only moves ~1/N of data; no full reshuffling; virtual nodes ensure even distribution