Sharding is a method for storing data across multiple machines. MongoDB uses sharding to support deployments with very large data sets and high throughput operations.
Database systems with large data sets and high throughput applications can challenge the capacity of a single server. High query rates can exhaust the CPU capacity of the server. Larger data sets exceed the storage capacity of a single machine. Finally, working set sizes larger than the system’s RAM stress the I/O capacity of disk drives.
To address these issues of scales, database systems have two basic approaches: vertical scaling andsharding.
Vertical scaling adds more CPU and storage resources to increase capacity. Scaling by adding capacity has limitations: high performance systems with large numbers of CPUs and large amount of RAM are disproportionately more expensive than smaller systems. Additionally, cloud-based providers may only allow users to provision smaller instances. As a result there is apractical maximum capability for vertical scaling.
Sharding, or horizontal scaling, by contrast, divides the data set and distributes the data over multiple servers, or shards. Each shard is an independent database, and collectively, the shards make up a single logical database.
Diagram of a large collection with data distributed across 4 shards.
Sharding addresses the challenge of scaling to support high throughput and large data sets:
Sharding reduces the number of operations each shard handles. Each shard processes fewer operations as the cluster grows. As a result, shared clusters can increase capacity and throughput horizontally.
For example, to insert data, the application only needs to access the shard responsible for that records.
Sharding reduces the amount of data that each server needs to store. Each shard stores less data as the cluster grows.
For example, if a database has a 1 terabyte data set, and there are 4 shards, then each shard might hold only 256GB of data. If there are 40 shards, then each shard might hold only 25GB of data.
MongoDB supports sharding through the configuration of a sharded clusters.
Diagram of a sample sharded cluster for production purposes. Contains exactly 3 config servers, 2 or more mongos query routers, and at least 2 shards. The shards are replica sets.
Sharded cluster has the following components: shards, query routers and config servers.
Shards store the data. To provide high availability and data consistency, in a production sharded cluster, each shard is areplica set [1]. For more information on replica sets, see Replica Sets.
Query Routers, or mongos instances, interface with client applications and direct operations to the appropriate shard or shards. The query router processes and targets operations to shards and then returns results to the clients. A sharded cluster can contain more than one query router to divide the client request load. A client sends requests to one query router. Most sharded cluster have many query routers.
Config servers store the cluster’s metadata. This data contains a mapping of the cluster’s data set to the shards. The query router uses this metadata to target operations to specific shards. Production sharded clusters have exactly 3 config servers.
| [1] | For development and testing purposes only, each shard can be a single mongod instead of a replica set. Do not deploy production clusters without 3 config servers. |
MongoDB distributes data, or shards, at the collection level. Sharding partitions a collection’s data by the shard key.
To shard a collection, you need to select a shard key. A shard key is either an indexed field or an indexed compound field that exists in every document in the collection. MongoDB divides the shard key values into chunks and distributes the chunksevenly across the shards. To divide the shard key values into chunks, MongoDB uses either range based partitioning andhash based partitioning. SeeShard Keys for more information.
For range-based sharding, MongoDB divides the data set into ranges determined by the shard key values to provide range based partitioning. Consider a numeric shard key: If you visualize a number line that goes from negative infinity to positive infinity, each value of the shard key falls at some point on that line. MongoDB partitions this line into smaller, non-overlapping ranges called chunks where a chunk is range of values from some minimum value to some maximum value.
Given a range based partitioning system, documents with “close” shard key values are likely to be in the same chunk, and therefore on the same shard.
Diagram of the shard key value space segmented into smaller ranges or chunks.
For hash based partitioning, MongoDB computes a hash of a field’s value, and then uses these hashes to create chunks.
With hash based partitioning, two documents with “close” shard key values are unlikely to be part of the same chunk. This ensures a more random distribution of a collection in the cluster.
Diagram of the hashed based segmentation.
Range based partitioning supports more efficient range queries. Given a range query on the shard key, the query router can easily determine which chunks overlap that range and route the query to only those shards that contain these chunks.
However, range based partitioning can result in an uneven distribution of data, which may negate some of the benefits of sharding. For example, if the shard key is a linearly increasing field, such as time, then all requests for a given time range will map to the same chunk, and thus the same shard. In this situation, a small set of shards may receive the majority of requests and the system would not scale very well.
Hash based partitioning, by contrast, ensures an even distribution of data at the expense of efficient range queries. Hashed key values results in random distribution of data across chunks and therefore shards. But random distribution makes it more likely that a range query on the shard key will not be able to target a few shards but would more likely query every shard in order to return a result.
The addition of new data or the addition of new servers can result in data distribution imbalances within the cluster, such as a particular shard contains significantly more chunks than another shard or a size of a chunk is significantly greater than other chunk sizes.
MongoDB ensures a balanced cluster using two background process: splitting and the balancer.
Splitting is a background process that keeps chunks from growing too large. When a chunk grows beyond a specified chunk size, MongoDB splits the chunk in half. Inserts and updates triggers splits. Splits are a efficient meta-data change. To create splits, MongoDB does not migrate any data or affect the shards.
Diagram of a shard with a chunk that exceeds the default chunk size of 64 MB and triggers a split of the chunk into two chunks.
The balancer is a background process that manages chunk migrations. The balancer runs in all of the query routers in a cluster.
When the distribution of a sharded collection in a cluster is uneven, the balancer process migrates chunks from the shard that has the largest number of chunks to the shard with the least number of chunks until the collection balances. For example: if collection users has 100 chunks on shard 1 and 50 chunks on shard 2, the balancer will migrate chunks from shard 1 to shard 2 until the collections achieves balance.
The shards manage chunk migrations as a background operation. During migration, all requests for a chunks data address the “origin” shard.
In a chunk migration, the destination shard receives all the documents in the chunk from the origin shard. Then, the destination shard captures and applies all changes made to the data during migration process. Finally, the destination shard updates the metadata regarding the location of the on config server.
If there’s an error during the migration, the balancer aborts the process leaving the chunk on the origin shard. MongoDB removes the chunks data from the origin shard after the migration completes successfully.
Diagram of a collection distributed across three shards. For this collection, the difference in the number of chunks between the shards reaches the migration thresholds (in this case, 2) and triggers migration.
Adding a shard to a cluster creates an imbalance since the new shard has no chunks. While MongoDB begins migrating data to the new shard immediately, it can take some time before the cluster balances.
When removing a shard, the balancer migrates all chunks from to other shards. After migrating all data and updating the meta data, you can safely remove the shard.
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