Range-Based Sharding: Ordered But Uneven

Scaling Smart With Sorted Keys (and Hidden Pitfalls)

📌 Overview

Range-based sharding is one of the simplest and most intuitive ways to split data across servers — especially when your data has a natural order like timestamps, IDs, or numerical values. It’s a go-to strategy for systems that rely heavily on time-based queries or sorted ranges, such as logs, audit trails, or reporting systems.

But like all good things, it comes with trade-offs.

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🧠 What Is Range-Based Sharding?

In range-based sharding, you:

1. Choose a sharding key (like user_id, created_at, or order_total)

2. Define value ranges

3. Route each record to a shard based on where its value falls in those ranges

Example:

• IDs 1–10,000 → Shard 1

• IDs 10,001–20,000 → Shard 2

• IDs 20,001–30,000 → Shard 3

Each insert checks which range it belongs to and saves the record in that shard.

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🔁 Real-Life Analogy

Imagine a school splitting students into exam halls by last name:

• A–F → Hall 1

• G–L → Hall 2

• M–Z → Hall 3

Everything works well — unless 70% of students have the same surname. Suddenly, one hall becomes overcrowded while the others are mostly empty.

That’s the biggest risk in range-based sharding: data skew.

✅ Benefits of Range-Based Sharding

1. Great for Range Queries

Range-based sharding is excellent for queries like:

SELECT * FROM logs
WHERE timestamp BETWEEN '2024-01-01' AND '2024-01-31'

Since data is stored in sorted order, the system knows exactly which shard(s) to check.

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2. Predictable Distribution

You always know where to look for data. It’s clean and organized — ideal for analytical or time-based systems.

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3. Simple to Implement

No hash functions or modulo logic. Just define ranges and match values.

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🚫 Limitations of Range-Based Sharding

1. Hotspot Risk

If new data always falls into the highest range (e.g., latest timestamp), that shard becomes a write hotspot. It receives more load, while other shards sit idle.

2. Manual Range Management

Without automation, you’ll need to:

• Monitor usage patterns

• Add new ranges

• Migrate old data
  This can lead to operational overhead.

3. Skewed Traffic

If one user or customer contributes most of the data (e.g., a top e-commerce seller), and you’re sharding by customer_id, their shard can become overwhelmed.

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🏗 Use Case: Logging Systems

Time-series systems like Prometheus, ELK, and InfluxDB often use range-based sharding. Data is naturally ordered by time, and queries often request ranges.

However, they also use:

• Shard rotation

• Retention policies (TTL)

• Cold storage
  …to prevent write hotspots and overgrowth.

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When to Use Range-Based Sharding

Use it when:

• You mostly run range-based queries

• You’re working with time-series or ordered data

• Your load is predictable or split by time/customer/location

Avoid it if:

• Your data input is bursty or skewed

• You expect unpredictable growth

• You want auto-scaling or elastic architecture

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📊 Summary

Pros:

• Great for sorted or time-based data

• Easy range queries

• Simple logic

Cons:

• High chance of imbalance

• Manual scaling required

• Not ideal for bursty or random workloads

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🧭 Coming Up Next

In the next post, we’ll dive into Consistent Hashing — the smarter, scalable way to avoid rebalancing chaos and evenly distribute load, even as you grow.