Hashed Sharding

Hashed sharding uses a hashed index to partition data across your shared cluster. Hashed indexes compute the hash value of a single field as the index value; this value is used as your shard key. [1]

Diagram of the hashed based segmentation.

Hashed sharding provides more even data distribution across the sharded cluster at the cost of reducing Query Isolation. Post-hash, documents with “close” shard key values are unlikely to be on the same chunk or shard - the mongos is more likely to perform Broadcast Operations to fulfill a given ranged query. mongos can target queries with equality matches to a single shard.

If you shard an empty collection using a hashed shard key, MongoDB automatically creates two empty chunks per shard, to cover the entire range of the hashed shard key value across the cluster. You can control how many chunks MongoDB creates with the numInitialChunks parameter to shardCollection or by manually creating chunks on the empty collection using the split command.


MongoDB automatically computes the hashes when resolving queries using hashed indexes. Applications do not need to compute hashes.

[1]Starting in version 4.0, the mongo shell provides the method convertShardKeyToHashed(). This method uses the same hashing function as the hashed index and can be used to see what the hashed value would be for a key.

Hashed Sharding Shard Key

The field you choose as your hashed shard key should have a good cardinality, or large number of different values. Hashed keys are ideal for shard keys with fields that change monotonically like ObjectId values or timestamps. A good example of this is the default _id field, assuming it only contains ObjectID values.

To shard a collection using a hashed shard key, see Deploy Sharded Cluster using Hashed Sharding.

Hashed vs Ranged Sharding

Given a collection using a monotonically increasing value X as the shard key, using ranged sharding results in a distribution of incoming inserts similar to the following:

Diagram of poor shard key distribution due to monotonically increasing or decreasing shard key

Since the value of X is always increasing, the chunk with an upper bound of maxKey receives the majority incoming writes. This restricts insert operations to the single shard containing this chunk, which reduces or removes the advantage of distributed writes in a sharded cluster.

By using a hashed index on X, the distribution of inserts is similar to the following:

Diagram of hashed shard key distribution

Since the data is now distributed more evenly, inserts are efficiently distributed throughout the cluster.

Shard the Collection

Use the sh.shardCollection() method, specifying the full namespace of the collection and the target hashed index to use as the shard key.

sh.shardCollection( "database.collection", { <field> : "hashed" } )

Specify the Initial Number of Chunks

If you shard an empty collection using a hashed shard key, MongoDB automatically creates and migrates empty chunks so that each shard has two chunks. To control how many chunks MongoDB creates when sharding the collection, use shardCollection with the numInitialChunks parameter.


MongoDB hashed indexes truncate floating point numbers to 64-bit integers before hashing. For example, a hashed index would store the same value for a field that held a value of 2.3, 2.2, and 2.9. To prevent collisions, do not use a hashed index for floating point numbers that cannot be reliably converted to 64-bit integers (and then back to floating point). MongoDB hashed indexes do not support floating point values larger than 253.

To see what the hashed value would be for a key, see convertShardKeyToHashed().