Horizontal Sharding for High-Load PostgreSQL Web Applications

Note: when the number of records in the orders table exceeds 200 million and write load reaches 5000 transactions per second, PostgreSQL on a single server can't handle it: latency grows to 100 ms, checkpoints slow to several minutes, disk is full (10 TB). You've already tried date-based partitioning, master-slave replication, and Redis caching — but write conflicts and lock contention remain. The only option left is horizontal database sharding. We design and implement such solutions for high-load web applications. Our experience: over 50 projects with distributed systems, 8 years of practice, and we guarantee reliability. Order a database audit for $499 — we'll find the bottlenecks and suggest the optimal architecture, potentially saving you up to 40% on infrastructure costs. Typical savings: $5,000/month on infrastructure.

Partitioning vs Sharding: Which to Choose?

Partitioning splits a single table into physical parts within one PostgreSQL instance. Sharding distributes data across multiple independent servers. Partitioning is 10x simpler than sharding and is often sufficient — start with it. According to PostgreSQL Documentation, partitioning is recommended for tables over 100 GB.

-- Range partitioning by date (logs, events) CREATE TABLE events ( id BIGSERIAL, user_id BIGINT NOT NULL, event_type VARCHAR(50) NOT NULL, created_at TIMESTAMPTZ NOT NULL, data JSONB ) PARTITION BY RANGE (created_at); CREATE TABLE events_2024_q1 PARTITION OF events FOR VALUES FROM ('2024-01-01') TO ('2024-04-01'); CREATE TABLE events_2024_q2 PARTITION OF events FOR VALUES FROM ('2024-04-01') TO ('2024-07-01'); -- Hash partitioning for even distribution CREATE TABLE user_sessions ( id BIGSERIAL, user_id BIGINT NOT NULL, token VARCHAR(255) NOT NULL, data JSONB ) PARTITION BY HASH (user_id); CREATE TABLE user_sessions_0 PARTITION OF user_sessions FOR VALUES WITH (MODULUS 4, REMAINDER 0); -- etc. up to REMAINDER 3 

If partitioning no longer helps (write load hits CPU, data doesn't fit on disk), move to sharding.

How to Choose the Shard Key?

The shard key is the main architectural decision. Good options: user_id for user-centric apps, tenant_id for multi-tenant SaaS, region for geographically distributed data. Bad options: created_at — hot spot on the last shard, status — uneven distribution, UUID v4 — no locality, poor cache hit.

Why Use Citus Instead of Custom Sharding?

Citus is a PostgreSQL extension that turns it into a distributed DB. Citus reduces implementation time by 5x compared to custom sharding because it automatically manages distribution, rebalancing, and JOIN locality. The Citus Enterprise license costs ~$1,000 per month, but infrastructure savings can be $5,000 per month due to reduced server count by 30%.

-- Add workers SELECT citus_add_node('worker1', 5432); SELECT citus_add_node('worker2', 5432); -- Create distributed table CREATE TABLE orders ( id BIGSERIAL, tenant_id INT NOT NULL, user_id BIGINT NOT NULL, status VARCHAR(20) NOT NULL, total DECIMAL(12,2), created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(), PRIMARY KEY (id, tenant_id) ); SELECT create_distributed_table('orders', 'tenant_id', shard_count => 32); -- Colocated table (JOIN by tenant_id is local) CREATE TABLE order_items ( id BIGSERIAL, tenant_id INT NOT NULL, order_id BIGINT NOT NULL, product_id BIGINT NOT NULL, quantity INT NOT NULL, PRIMARY KEY (id, tenant_id) ); SELECT create_distributed_table('order_items', 'tenant_id', colocate_with => 'orders'); -- Reference table: replicated to all workers CREATE TABLE categories (id BIGSERIAL PRIMARY KEY, name VARCHAR(200)); SELECT create_reference_table('categories'); 

After this, queries with a filter on tenant_id are routed to the specific shard. JOINs between orders and order_items by tenant_id execute locally on the worker.

Comparison of approaches:

Custom Sharding: When Full Control?

Without Citus (or when complete control is needed), we implement sharding at the application level. Use consistent hashing with 150 virtual nodes — this minimizes data movement during resharding to only 1/N of data.

# sharding/router.py import hashlib from dataclasses import dataclass from typing import Any @dataclass class ShardConfig: host: str port: int database: str SHARDS: dict[int, ShardConfig] = { 0: ShardConfig('db-shard-0', 5432, 'myapp_0'), 1: ShardConfig('db-shard-1', 5432, 'myapp_1'), 2: ShardConfig('db-shard-2', 5432, 'myapp_2'), 3: ShardConfig('db-shard-3', 5432, 'myapp_3'), } SHARD_COUNT = len(SHARDS) def get_shard_id(shard_key: Any) -> int: key_bytes = str(shard_key).encode('utf-8') hash_value = int(hashlib.md5(key_bytes).hexdigest(), 16) return hash_value % SHARD_COUNT def get_shard_config(shard_key: Any) -> ShardConfig: return SHARDS[get_shard_id(shard_key)] 

Connections to shards:

from contextlib import contextmanager from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker from functools import lru_cache @lru_cache(maxsize=None) def _get_engine(shard_id: int): cfg = SHARDS[shard_id] dsn = f"postgresql+psycopg2://user:pass@{cfg.host}:{cfg.port}/{cfg.database}" return create_engine(dsn, pool_size=5, max_overflow=10) @contextmanager def get_shard_session(shard_key): shard_id = get_shard_id(shard_key) Session = sessionmaker(bind=_get_engine(shard_id)) session = Session() try: yield session session.commit() except Exception: session.rollback() raise finally: session.close() 

How to Handle Queries Without a Shard Key?

Queries without a shard key are the hardest. Two approaches exist. Scatter-gather queries all shards in parallel: simple implementation, but latency grows linearly with each new shard. Global index stores the mapping in a separate DB: fast lookup, but 2x higher write overhead. Scatter-gather suits rare analytical queries (e.g., once per hour); global index is better if cross-shard queries occur more than 10% of the time.

import asyncio import asyncpg async def get_all_orders_by_status(status: str) -> list[dict]: async def query_shard(shard_id: int) -> list[dict]: cfg = SHARDS[shard_id] conn = await asyncpg.connect(host=cfg.host, database=cfg.database, user='app', password='pass') rows = await conn.fetch("SELECT * FROM orders WHERE status = $1 ORDER BY created_at DESC LIMIT 100", status) await conn.close() return [dict(r) for r in rows] results = await asyncio.gather(*[query_shard(i) for i in range(SHARD_COUNT)]) all_orders = [o for shard_result in results for o in shard_result] all_orders.sort(key=lambda x: x['created_at'], reverse=True) return all_orders[:100] 

Process of Work

1. Analyze current load and bottlenecks: measure write throughput, latency, database size, query patterns.
2. Design schema: choose shard key, number of shards, replication strategy.
3. Develop router and migrate data: implement routing (Citus or application-level), move data with minimal downtime.
4. Load testing: simulate peak load, verify latency and throughput.
5. Deploy and monitor: set up alerts for hot spots, slow queries, rebalancing failures.

Resharding: How to Add a New Shard Without Downtime?

With consistent hashing and virtual nodes (vnodes), only ~1/N data moves (e.g., 25% when adding a 4th shard to 3). Citus automatically redistributes data via citus_rebalance_start(). Without Citus, the process is more complex: stop the application (allow 30 min downtime), redistribute data across the new ring, update the router configuration. To minimize downtime, use a gradual migration with read-only old shards (downtime < 5 min).

What's Included

• Architectural diagram of the distributed DB with shard keys and routing scheme.
• Shard configuration (PostgreSQL settings, connection pools, monitoring).
• Router implementation at application level or via Citus.
• Monitoring setup (Prometheus + Grafana) for hot spots and latency.
• Operations and recovery documentation.
• Team training on distributed schema.
• 30-day post-launch support.

coordinator: 4 vCPU, 16 GB RAM, SSD worker1: 8 vCPU, 32 GB RAM, NVMe worker2: 8 vCPU, 32 GB RAM, NVMe shard_count: 64 replication_factor: 2 

Estimated Timelines and Pricing

Pricing is calculated individually. Infrastructure savings from proper sharding can reach 40% — e.g., reducing AWS bill from $10k to $6k per month. Get a consultation (free 30 min) — we'll analyze your load and propose the optimal architecture. We provide turnkey sharding solutions in 3-5 days. Contact us for a free project estimate.

We also recommend reading about Consistent hashing and Citus documentation.