Scaling MongoDB – A Guide to Sharding and Best Practices

So you‘ve built an amazing application powered by MongoDB. Users are thrilled that you can add features and iterate so quickly thanks to the flexible data model. But now things are taking off! You have millions of active users, terabytes of data, and your database server is swapping like crazy…

First of all, congratulations! Second, it‘s time to explore scaling your MongoDB database before performance grinds to a halt.

You likely already know MongoDB for its flexible schemas and developer agility. But MongoDB‘s automatic sharding and horizontal scaling capabilities are equally important long-term. As your data and traffic grows, sharding allows seamlessly distributing data across low-cost commodity servers to multiply capacity and throughput.

In this comprehensive guide, you‘ll learn:

• Exactly how MongoDB sharding works – components, architecture
• Critical best practices to effectively shard your cluster
• Hardware provisioning and deployment strategies for sharded environments
• Capacity planning and testing considerations
• Available alternatives like fully-managed cloud services

If you intend to scale a production MongoDB cluster, understanding sharding is imperative. Apply these best practices properly, and sharding allows building mammoth systems. Ignore them, and you construct data glass houses that easily shatter under load!

I‘ve helped dozens of early stage startups scale successfully with MongoDB sharding after viral growth kicks in. As an experienced database infrastructure advisor, I‘m sharing everything I wish I knew years ago when starting out as an engineer.

Let‘s get started with the foundations…

What is MongoDB Sharding?

We‘ll briefly cover some sharding basics before diving into best practices.

Why is Sharding Used?

There are several motivations for sharding a MongoDB cluster:

Storage capacity – Sharding partitions data across cheaper commodity infrastructure allowing for greater cumulative storage and IOPS.

Throughput – Spreading reads/writes across shards parallelizes operations increasing total throughput.

Geographic data locality – Data can be sharded by location proximity for user-facing performance needs.

Linear scalability – Capacity, operations per second, and geographic coverage can be extended by adding additional shards.

Without sharding, MongoDB is constrained to the storage capacity, computing power, and network bandwidth of a single server. Sharding removes those limitations so systems can scale linearly with steady operational effort.

Components of a Sharded Cluster

A MongoDB sharded cluster consists of three core components:

Shards store partitioned data as MongoDB database instances. Each shard is typically a replica set for redundancy. In some large clusters, shards themselves are sharded.

Query Routers (mongos processes) handle client app read/write requests, directing operations to appropriate shards. Adds transparency for apps.

Config Servers store metadata about overall cluster state, chunk mappings, shard locations, balancer activity. Cluster "brain."

MongoDB sharded cluster architecture. Image source: MongoDB

This separation of roles allows the shards to focus solely on efficient data storage and operations. The query routers abstract and manage interactions with the distributed data. And config servers provide coordination and orchestration logic.

Challenges with Sharding

While sharding solves serious scale problems, it also introduces new complexities:

• Picking a good shard key – The shard key largely determines distribution of data across the cluster. A poor choice can lead to hotspots, slow queries, and imbalance.

• Chunk splitting and migration – As data volumes, throughput or cluster resources change, chunks split and migrate constantly. This requires planning and oversight.

• Query routing – Mongos must analyze queries and efficiently route reads/writes only to applicable shards. More complex with multi-shard queries.

There are also challenges around deploying and managing a sharded cluster:

• Careful hardware capacity planning for shards and components
• Configuring replica sets properly on each shard for redundancy
• Monitoring data distribution and resource utilization
• Adding capacity via new shards
• Backup and recovery processes

In short – sharding enables scale, but requires forethought and oversight to maximize benefits long-term.

Now let‘s jump into those best practices and considerations…

Best Practices for MongoDB Sharding

While MongoDB abstracts much of the sharding complexity from developers and users, operating a sharded cluster takes more planning and care than a single MongoDB instance.

The recommendations in this section aim to ensure your cluster scales smoothly while avoiding common pain points as capacity needs increase over time.

Choose a Good Shard Key

The shard key is the most important decision when sharding MongoDB. The shard key selection determines:

• How data is distributed across the cluster‘s shards
• The efficiency of the chunk splitting and migration process

Choosing an optimal shard key involves studying:

• Application query patterns
• Dataset cardinality across fields
• Document size distributions
• Projected deployment geography if location-based sharding

Some key principles for picking a MongoDB shard key:

High cardinality – Choose a field or compound index with high number of distinct values to distribute writes broadly. For example:

Non-Ideal:     user_id  (monotonic, low cardinality)  

Better:        email    (high cardinality assuming random addresses)

Ideal:     hashed_user_id (deterministically pseudorandom)

Location affinity – If deploying regionally, include a location identifier in shard key to minimize cross-region queries which reduces latency.

Avoid growing monotonically – Auto-incrementing keys or timestamps lead to uneven chunk distribution. Use hashed shard keys instead.

Uniform data access – Distribute queries evenly via cardinal fields users query, not just insertion volume fields. Improves later query performance.

Match query patterns – Structure key to align with predominant query filters and sorts to isolate queries to single shards.

Some example shard keys for different access scenarios:

• Hashed customer ID + Country
• Content type (article, video, etc.) + Published date range
• Random GUID

Spend adequate time analyzing your evolving data and access patterns when selecting a shard key. Like application indexes, it has dramatic long-term impacts on performance, scaling trajectory, and operational overhead.

And re-sharding a large cluster is exceedingly painful, so choose carefully from the start!

Shard Early

Many MongoDB operators make the mistake of drastically undershading clusters, waiting to shard until datasets are multiple terabytes and sharding is unavoidable. This delay causes major downsides:

• Migrating terabytes/petabytes of data across a cluster is complex, slow and disruptive. Business-impacting downtime.
• Identification and correction of data issues becomes difficult at scale.
• Performance and capacity planning is reactive instead of strategic.

Industry best practice is to deploy MongoDB sharding early – during initial production rollout or certainly before hitting ~500GB of data per mongod. Benefits include:

Incremental data distribution – Smooth expansion of data volume across shards as your app and user base grow. Effortless organic scaling.

Consistent query performance – Avoids perf drops and firefights as data volumes increase.

Operational agility – Gradual shard additions lets you monitor and learn vs. emergency scaling when overloaded.

Many successfully sharded systems begin with as few as 2-3 shards for early data redundancy. This smaller cluster is easier to manage as you evaluate data patterns and optimize performance.

Additional shards get added incrementally to match your application‘s observed growth trajectory – both data volume and throughput-based.

For example, a shard could be added quarterly while monitoring key usage metrics like:

• DB size per shard
• Read/write operations per shard
• Chunk distribution per shard_id
• Hardware utilization like CPU, RAM, IOPS

This aligns cluster capacity with your uptake rather than risky guessing.

In short, go ahead and dip your toe into MongoDB sharding from the start. Adding a couple shards is straightforward to evaluate and prepare for growth. Just be sure to follow the other operational best practices outlined here!

Manage the Balancing Process

In a sharded cluster, MongoDB automatically handles balancing chunks across shards as documents are inserted, updated or deleted over time. This distributes data uniformly based on volume.

But balancing takes network and server resources so it requires oversight:

• Restrict balancing window – Use the balancer window to restrict activity to approved low-traffic periods like nights and weekends. This is configured under sh.getBalancerState().

• Monitor data distribution – Check the config database .chunks collection, sh.status() output and Atlas charts to watch for fast growing "jumbo" chunks indicating hotspots.

• Pre-split large chunks before migrations – Avoidoverly taxing networks and shards by pre-splitting chunks before migrating entire large chunks. Configured via the split command.

• Occasionally stop balancers – During hardware swaps, network events, or initial cluster setups,停止balancer几小时避免无谓的数据移动。

With planning, MongoDB efficiently rebalances shards during normal operations. But overlook imbalanced shards, and certain chunks receive disproportionate load leading to hotspots.

Deploying Sharded Clusters

Now that we‘ve covered critical logical sharding considerations like choosing a good key and balancing, let‘s discuss options for physically deploying MongoDB shard topologies.

Hardware Selection Guidelines

For projects with sufficient capital expenditure (CapEx) budgets, bare metal servers provide the highest performance and control. This allows tailoring server specifications precisely to the anticipated workload. Some hardware selection principles:

Commodity shard servers – Since data is distributed across shards, less performant individual shards are often more cost-effective than fewer premium shards.

All-flash storage – SSDs deliver markedly faster and more consistent throughput and IOPS than traditional HDD arrays. Worth the premium for primary shards.

10GbE clubbed network – Faster interconnect between physically separate shards reduces impact of migrations and cross-shard queries.

Match server specs to shard type – Allocate more CPU, RAM and I/O priority to config servers and query routers.

Of course bare metal requires significant ops effort for provisioning, networking, scaling and other DBA responsibilities relative to managed services. This tradeoff depends on your operational constraints.

Cloud-hosted Options

For teams favoring operational simplicity over custom hardware control, managed MongoDB services like Atlas radically reduce administration burdens. The manual work mentioned in this guide around deploying shards, balancing chunks, scaling capacity etc. is all handled automatically.

Some advantages:

• Instant provisioning of complex multi-region, sharded clusters. No networking headaches.
• Elastic scaling of clusters via API/UI rather than manual shard additions.
• Granular monitoring, alerts and event logging bundled.
• Automated patching, upgrades, and backup/restore.

The simplicity does come at a cost premium over self-managed hardware of equivalent capability. Check pricing calculators to assess feasibility for your project.

For many teams, Atlas or competitor services provide the best turnkey sharding experience by offloading operational complexity. But plan budgets accordingly.

Development/Test Environments

For pre-production environments, teams often opt for public cloud VMs over dedicated hardware since fluctuating workloads make small instances more economical.

I generally recommend mimicking the production shard topology in dev/test environments – same shard key, number of shards, geographic distribution etc. This surfaces issues before reaching business-critical systems.

Costs are still reduced given smaller data volumes plus leveraging auto-scaling groups, spot instances and ephemeral storage options.

Capacity Planning Guidelines

Scaling MongoDB beyond a single replica set introduces additional planning and forecasting complexity. Here are capacity guidelines for smooth sharding operations:

Predict data growth – Analyze early ingestion and query trends then map futures growth to plan shard needs.

Consider access patterns – Allow for uneven distribution based on shard key spreads and effects of normalizing or denormalizing data structures.

Model throughput needs – Estimate capacity for reads, writes and aggregation pipeline operations.

Account for replicas – Replica sets reduce net available database capacity by replication factor. Plan accordingly.

Determine shard allocation ratios – Apportion percentages of overall resources per shard role – primaries first, then secondaries.

Add shard headroom – Overestimate shard resourcing 10-20% for inevitable surprises as clusters scale.

Regularly reevaluate projections – Update estimates as application usage data matures over time.

While detailed to model, these forecasts set expectations for required hardware, ratios of shard types, replica set sizes, and timeline to add capacity.

Some sharding-specific metrics to monitor closely across shards:

• Disk capacity %
• Ops counters per category
• Replication lag
• Chunk distribution per shard
• Network utilization
• Hardware saturation – CPU, RAM, IOPS

As metrics approach thresholds, add shards or improve specifications accordingly.

Testing Sharded Deployments

Given the operational intricacies of sharding, MongoDB experts strongly emphasize thoroughly testing shard topologies before business-critical reliance in production.

I advise teams to run sharded development/QA environments early then graduate to staging environments mirrored off production infrastructure specifications.

Why rigorous testing? A few reasons…

Validate shard key selection – Assess options against real dataset spreads, query patterns and data lifecycles before locking in long term.

Refine capacity planning – Update projections for hardware, shards counts and specifications based on observations.

Prove operational procedures – Exercise failure scenarios, management functions, data integrity checks, and other procedures.

Performance and load testing – Utilize with production-like test data and query load. Confirm sufficient headroom.

Disaster recovery testing – Practice failure scenarios and complete restores. Build operator knowledge and confidence.

No amount of documentation substitutes for empirical, hands-on testing before relying on sharded production systems. Treat testing shards with the same rigor as proper disaster recovery or security penetration testing.

With testing enhancing runbooks and evidence of resiliency, production shards and operational teams run far more smoothly.

Alternatives to Custom Sharding

While essential for large-scale MongoDB systems, self-managed sharding may be overkill for some teams. A few options to consider:

Managed MongoDB offerings – As mentioned for Atlas, cloud services handle all sharding complexity behind easy APIs/UIs.

Vertical scaling – For smaller datasets, beefier individual systems may suffice vs. sharding.

Caching layers – Systems like Redis offload read traffic from primaries.

Hybrid architectures – Mix transactional and analytical paths across different technologies optimized per need.

Evaluate your team experience managing distributed systems, capacity forecasting abilities, risk appetite and finances when determining a MongoDB scaling approach.

Conclusion and Key Recommendations

MongoDB makes sharding infrastructure far easier than traditional SQL databases, enabling horizontal scaling to multiples of capacity, throughput and geographic reach.

But improper planning and oversight of sharded clusters quickly degrades system stability as chunks split and migrate to balance data distribution. Careful capacity forecasting and operational governance prevents those issues.

Here are my core recommendations again in review:

🔑Pick the optimal shard key during initial rollout – High cardinality, aligned with primary access patterns. Difficult to change later.

🚀Start sharding early with just 2 shards during development or initial launch. Incrementally add shards matching observed growth trends.

🕰️Restrict the balancer window during off-peak hours to reduce user-facing disruption. Monitor chunks actively regardless.

⚙️Model expected capacity rigorously incorporating replica set needs and headroom buffers of 20%+.

🧪Test shard deployments extensively on pre-production environments before launching business-critical production shards. Validate resiliency.

INTERNAL: Thank you for this comprehensive discussion of MongoDB sharding concepts and recommendations! Let‘s wrap up now with any final thoughts for the reader.

You now have all the tools needed to successfully scale MongoDB databases over years as uptake expands! Apply these guidelines diligently to reap maximum value from MongoDB‘s distribution capabilities.

As always, feel free to reach out if any questions pop up on your sharding journey!