Question

Explain and give examples of how sharding and replication increase availablity and scalability.

Answer 1

How sharding and replication increase availablity and scalability.

- Sharding allows for horizontal scaling of data writes by partitioning data across multiple servers using a shard key

- Sharding is the process of storing data records across multiple machines and it is MongoDB's approach to meeting the demands of data growth. As the size of the data increases, a single machine may not be sufficient to store the data nor provide an acceptable read and write throughput.

- MongoDB supports horizontal scaling through Sharding , distributing data across several machines and facilitating high throughput operations with large sets of data.

- For larger render farms, scaling becomes a key performance issue. Having a large number of clients performing high-throughput operations can really test the limits of a single database instance. Sharding is a strategy that can mitigate this by distributing the database data across multiple machines. It is essentially a way to perform load balancing by routing operations to different database servers.

- Sharding is necessary if a dataset is too large to be stored in a single database. Moreover, many sharding strategies allow additional machines to be added. Sharding allows a database cluster to scale along with its data and traffic growth. Sharding is also referred as horizontal partitioning.

- Sharding is a method for distributing 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 or high throughput applications can challenge the capacity of a single server.

- The sharding strategy for different collections can be adjusted according to your own farm's particularities. For example, if your farm is distributed across a handful of regions across the world, you may want to split the Jobs collection such that each job resides on a shard in the same region as the node that will be rendering it. This can be done by choosing an appropriate shard key (such as the Group field) and assigning zones to each shard.

- A Shard Cluster requires the database data to be distributed among the shards in the cluster. There are many ways to decide how the data should be split up, and this choice can have significant performance implications. The method by which the data is distributed is called a sharding strategy. A bad sharding strategy can lead to one shard getting much more load than the others, or client application having to query several shards if it needs data from linked collections.

- Replication creates additional copies of the data and allows for automatic failover to another node.

- A replica set in MongoDB is a group of mongod processes that maintain the same data set. Replica sets provide redundancy and high availability, and are the basis for all production deployments.

- Replication refers to a database setup in which several copies of the same dataset are hosted on separate machines. The main reason to have replication is redundancy. If a single database host machine goes down, recovery is quick since one of the other machines hosting a replica of the same database can take over. A quick fail-over to a secondary machine minimizes downtime, and keeping an active copy of the database acts as a backup to minimize loss of data.

- Both replication and sharding can be used (individually or together) for horizontal scaling of a MongoDB installation. Sharding is MongoDB's solution for meeting the demands of data growth. This process copies and synchronizes the replica data set across multiple servers.

- There are two styles of distributing data: Sharding distributes different data across multiple servers. Each server acts as the single source for a subset of data. Replication copies data across multiple servers. Each bit of data can be found in multiple places.

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