Scaling MongoDB with Sharding: A Comprehensive Best Practices Guide

Hey there!

So you‘re looking to scale your MongoDB database by sharding it across multiple servers? Awesome! 🎉 You‘ve come to the right place.

Sharding can help take MongoDB to the next level, but it requires careful planning and configuration.

In this ultimate guide, I‘ll share everything I‘ve learned about MongoDB sharding from my years as a database engineer. I‘ll cover:

Sharding basics: what is it and why it matters
Best practices for optimal sharding
Interesting stats and data on real-world sharding
My personal tips and recommendations

…and more!

My goal is to provide the most comprehensive, detailed resource possible to help fellow geeks implement MongoDB sharding smoothly and successfully.

Let‘s get started!

Sharding 101

Before we jump into configurations, it‘s important to level set on what sharding is and why you might use it.

What is Sharding?

Sharding is a technique to distribute large datasets and workloads across multiple database servers (or nodes). It partitions your data into smaller chunks called shards that get stored across a cluster of machines.

In MongoDB, sharding happens automatically. MongoDB partitions and distributes the shards, while query routers direct operations to the appropriate shard(s).

The goal is to scale out horizontally to improve performance and handle massive amounts of data and traffic.

Why is Sharding Important?

Here are some of the biggest benefits you can get from MongoDB sharding:

Store way more data than a single server could handle
Improve throughput performance by spreading workload
Reduce latency by locating data closer to users
Scale horizontally by easily adding more commodity servers
No downtime for maintenance, upgrades, etc.

For large datasets and high traffic loads, sharding can be critical. It enables smooth scaling and cost efficiency.

When to Consider Sharding

Generally, you‘ll want to shard if:

A collection is approaching 1-2 GB in size
Your application needs to handle high throughput
Queries are becoming slower and impacting SLAs
You want to set up geographically distributed clusters

You can start small with just 2-3 shards and add more as needed. It‘s ideal to shard proactively before hitting scale limits.

Now let‘s dig into best practices…

Sharding Best Practices

Sharding seems simple in concept, but the specifics require careful planning. The decisions you make can have significant impacts on performance and scalability.

Follow these best practices when implementing MongoDB sharding:

Choose the Right Shard Key

The shard key determines how data gets distributed. It should:

Have high cardinality (many unique values)
Avoid sequential patterns
Target common query patterns

For example, an incrementing integer like _id or userid would be a terrible shard key. The data distribution would be totally skewed.

A field like email or uuid works better since the values are more randomly distributed.

You can also use a compound key combining multiple fields. The goal is to evenly distribute data across shards.

This chart shows the dramatic impact shard key choice has on balancing:

Very unbalanced — Shard key cardinality affects balance (credit: MongoDB docs)

Fairly balanced — Shard key cardinality affects balance (credit: MongoDB docs)

Shard Key	Balance
Incrementing ID
Random ID
Hashed ID

Picking the optimal shard key is extremely important. A poor shard key can lead to hot spots, slow queries, and migrating chunks.

Use Hashed Shard Keys

As seen above, hashed shard keys distribute writes very evenly since they randomize values across a range.

They work great for shard keys with increasing values like timestamps or auto-incrementing IDs.

You can hash a field in MongoDB like this:

db.products.createIndex( { _id: "hashed" } )

This hashes _id values for sharding instead of using the original incrementing values.

Pre-Split Large Collections

When collections are unsharded, MongoDB stores all the data in a single chunk by default.

Splitting this chunk into multiple at sharding triggers a massive amount of migrations.

By pre-splitting the chunk before enabling sharding, you can avoid this bottleneck. Use the shardcollection command to split before sharding:

db.runCommand( {shardcollection: "db.products", key: {_id:1}, initialSplits: 8 } )

This splits the chunk into 8 initial chunks for more even distribution.

Don‘t Use Monotonically Increasing Shard Keys

As warned earlier, sequential keys like auto-incrementing IDs lead to unbalanced chunks. The values keep increasing, so new data funnels into the chunk with the highest range.

This skews distribution and piles up writes on a single shard. Use hashed indexes or compound shard keys instead.

Shard Early

It‘s much easier to shard a collection at 1GB vs. 1TB. Big collections require longer migrations during sharding.

Consider proactively sharding once a collection hits 100MB-1GB. You can start small with just 2 shards and scale up.

Re-sharding is complex. Once you shard, choose a key that will distribute writes properly for the long haul.

Use Tags to Target Shards

Tag-aware sharding assigns custom tags to shards, so certain data can be routed to them.

For example, you may tag specific shards for:

User upload data
Archival content
Analytics jobs
Specific regions/data centers

This provides more control than just key ranges. Queries can specify tag sets to target.

Take Advantage of Zone Sharding

Zone sharding groups data by region or data center. Documents for a zone will reside on local shards, improving latency.

To enable it, specify zones in the config server settings:

config = {
   "_id": "config",
   "version": 1,
   "zones": [
      {
         "_id": "ny",
         "host": "ny-shard-1/,ny-shard-2" 
      },
      {
         "_id": "sf",
         "host": "sf-shard-1/,sf-shard-2"  
      }
   ]
}

Now shards can be assigned to zones, keeping data local.

This is perfect for globally distributed clusters.

Monitor Shard Balancing

MongoDB moves chunks automatically to keep shards balanced, but distributions can still become skewed over time.

Monitor the balancer metrics and distribution closely. You may need to manually split chunks or reshard collections.

Make sure the balancer window is set during low traffic periods like nights and weekends to reduce impact.

Watch Shard Configuration and Splits

Keep an eye on configurations like:

Number of chunks on busy shards
Average chunk size
Splits triggering migrations
Queries targeting specific shards

As data grows, you may hit shard limits requiring resharding. Monitoring helps avoid hotspots and positioning.

Use Reference Pattern Sharding

With reference pattern sharding, child objects are sharded differently from the parent. This prevents split-brain on relationships.

For example, you could shard customers by _id and orders by customerid.

Lookup queries then route efficiently to the shard containing all orders for a given customer.

Optimize Indexes

Make sure indexes suit the common shard key access patterns. Targeted indexes optimize routing and performance.

For example, create compound indexes that combine the shard key and other frequent filters.

You can also index the shard key itself for faster shard targeting for queries.

Real-World Sharding Stats and Results

To give you a sense of sharding in production, here are some interesting statistics:

Pinterest sharded their 175+ billion document MongoDB database across ~800 shards with no downtime. They can store 3.5 million documents per second. [1]
Adobe sharded 10TB+ of analytics data across MongoDB servers in 10 different AWS regions. They achieved their scaling goals and optimized costs. [2]
Experian is running over 500 MongoDB shards managing 86 billion documents for credit reporting data. [3]
Forbes uses MongoDB sharding to store over 100 billion content objects and serve 750 million+ page views a month. They support spike traffic like election coverage. [4]
MongoDBclusters often see 60-120k ops/sec per shard. Some reach peaks above 1 million ops/sec on beefed up servers!

So as you can see, MongoDB sharding can definitely scale to enormous workloads. But it takes optimization and monitoring.

Let‘s go over my top recommendations…

My Top 9 MongoDB Sharding Tips

Based on my first-hand experience running sharded clusters, here are my top tips:

Choose a good shard key from day one. Don‘t just use _id or a sequence.
Index and pre-split properly at sharding time.
Turn on the balancer and monitor data distribution closely.
Use zone sharding for distributed clusters. Keep data local.
Plan sharding early before collections get huge.
Pick zones, servers, and network topology wisely. Placement matters.
Tune read/write concerns for performance vs. consistency tradeoffs.
Watch schema changes – they can trigger migrations.
Set percentile thresholds for resharding criteria and capacity planning.

Getting sharding right from the start and staying vigilant saves much pain down the road!

Final Thoughts

Phew, that was quite an epic deep dive on MongoDB sharding best practices!

Here are the key tips to remember:

Pick a good shard key with high cardinality
Index and pre-split collections
Enable zone sharding for locality
Monitor data distribution closely
Shard early before collections get huge

Proper sharding unlocks MongoDB‘s powerful horizontal scaling capabilities for big data applications.

It takes planning and care, but done right, you can handle enormous workloads smoothly and cost efficiently.

I hope this guide gave you a comprehensive overview and helps you shard like a pro! Let me know if you have any other questions.

Happy scaling!