Sharding mongodb

Sharding is a concept in MongoDB, which splits large data sets into small data sets across multiple MongoDB instances. Sometimes the data within MongoDB will be so huge, that queries against such big data sets can cause a lot of CPU utilization on the server. To tackle this situation, MongoDB has a concept of Sharding, which is basically the splitting of data sets across multiple MongoDB instances.

The collection which could be large in size is actually split across multiple collections or Shards as they are called.

sharding mongodb

Logically all the shards work as one collection. How to Implement Sharding Shards are implemented by using clusters which are nothing but a group of MongoDB instances. The components of a Shard include A Shard — This is the basic thing, and this is nothing but a MongoDB instance which holds the subset of the data.

sharding mongodb

In production environments, all shards need to be part of replica sets. Config server — This is a mongodb instance which holds metadata about the cluster, basically information about the various mongodb instances which will hold the shard data.

A Router — This is a mongodb instance which basically is responsible to re-directing the commands send by the client to the right servers.

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Suppose if we have a server named Server D which would be our configuration server, we would need to run the below command to configure the server as a configuration server.

So if we need to shard the Employeedb database, issue the below command sh. So if we need to shard the Employee collection, issue the below command Sh. What is MongoDB? What is NoSQL? It supports You do not need install the MongoDB server and configure it. You can deploy MongoDB Atlas server Home Testing. Must Learn! Big Data. Live Projects. There are many MongoDB management tools available in the market.

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I have been reading about scalable architectures recently. In that context, two words that keep on showing up with regards to databases are sharding and partitioning.

I looked up descriptions but still ended up confused. Partitioning is more a generic term for dividing data across tables or databases. Sharding is one specific type of partitioning, part of what is called horizontal partitioning. Here you replicate the schema across typically multiple instances or servers, using some kind of logic or identifier to know which instance or server to look for the data.

An identifier of this kind is often called a "Shard Key". A common, key-less logic is to use the alphabet to divide the data.

A-D is instance 1, E-G is instance 2 etc. Customer data is well suited for this, but will be somewhat misrepresented in size across instances if the partitioning does not take in to account that some letters are more common than others.

Another common technique is to use a key-synchronization system or logic that ensures unique keys across the instances. A well known example you can study is how Instagram solved their partitioning in the early days see link below. They started out partitioned on very few servers, using Postgres to divide the data from the get-go.

I believe it was several thousand logical shards on those few physical shards. Horizontal partitioning splits one or more tables by row, usually within a single instance of a schema and a database server. It may offer an advantage by reducing index size and thus search effort provided that there is some obvious, robust, implicit way to identify in which table a particular row will be found, without first needing to search the index, e.

Sharding goes beyond this: it partitions the problematic table s in the same way, but it does this across potentially multiple instances of the schema.

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The obvious advantage would be that search load for the large partitioned table can now be split across multiple servers logical or physicalnot just multiple indexes on the same logical server. 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. Sharding solves the problem with horizontal scaling. With sharding, you add more machines to support data growth and the demands of read and write operations.

I've been diving into this as well and although I'm by far the reference on the matter, there are few key facts that I've gathered and points that I'd like to share:. A partition is a division of a logical database or its constituent elements into distinct independent parts. Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing.

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Sharding is a type of partitioning, such as Horizontal Partitioning HP. There is also Vertical Partitioning VP whereby you split a table into smaller distinct parts. Normalization also involves this splitting of columns across tables, but vertical partitioning goes beyond that and partitions columns even when already normalized. I really like Tony Baco's answer on Quora where he makes you think in terms of schema rather than columns and rows. He states that Oracle's Database Partitioning Guide has some nice figures.

I have copied a few excerpts from the article. Partition pruning is the simplest and also the most substantial means to improve performance using partitioning. Partition pruning can often improve query performance by several orders of magnitude.Enables sharding on the specified database. Once you enabled sharding for a database, you can use sh.

The mongo shell method sh. The sh. The database for which you wish to enable sharding. The primary shard for the database; the primary shard contains the unsharded collection for this database.

In general, rather than explicitly specifying the primary shard, it is recommended to let the balancer select the primary shard instead. In general, you should not need to specify the primary shard. Allow the balancer to select the primary shard instead. In general, you should not need to specify the primaryShard in the command. Instead, allow the balancer to select the primary shard. However, if you do specify the primary shard for a database and the database is already enabled for sharding with a different primary shard, the operation returns an error and the primary shard for the database remains as before.

To change the primary shard for a database, use the movePrimary command instead. The following example, run from a mongosenables sharding for the test database:. To verify, run sh. Available starting in MongoDB 4. Tip In general, you should not need to specify the primary shard. See also sh.Sharding is one of the more complex features provided by MongoDB and getting comfortable with it can take some time.

Sharding is a mechanism for scaling writes by distributing them across multiple shards. Each document contains an associated shard key field that decides on which shard the document lives. In MongoDB sharding happens at the collection level.

That is to say you can have a combination of sharded and unsharded collections. The application does not directly talk to the shards but instead talks through the sharding proxy Mongos.

Mongos is responsible for the routing of writes and queries to the shards. One of the typical errors people commit is to shard too early.

This can lead reads to be inefficiently spread out across shards causing unnecessary IO and CPU usage in order to retrieve the data. Once the collection is sharded, it can be very time consuming to undo it, as all the data will have to be migrated from one sharded collection to another by reinserting all the documents into the new collection.

Your Working Set no longer fits in the memory of your server. In this case, sharding can help make more of your Working Set stay in memory by pooling the RAM of all the shards. Scaling the write IO. You need to perform more write operations than what a single server can handle. By Sharding you can scale out the writes across multiple computers, increasing the total write throughput. There are a couple of tips that can help in the quest to identify the right shard key.

When discussing cardinality in the context of MongoDB, we refer to the ability to partition data into chunks distributed across all the shards. In our documents the state field contains the US state for a specific address. This field is considered low cardinalityas all documents containing the same state will have to reside on the same shard.

Since the key space for states is a limited set of values, MongoDB might end up distributing the data unevenly across a small set of fixed chunks. This might cause some unintentional side effects. If we pick the post code field as a shard key, the number of possible post codes is higher than when using the state state field.

Ask the Experts: Sharding How to's

So the shard key is considered to have a higher cardinality than when using the state field.Comment 0. Scalability is a characteristic of a system that describes its capability to perform under an increased workload. A system that scales well will be able to maintain and increase its level of performance under larger operational demands.

MongoDB supports horizontal scaling through Shardingdistributing data across several machines and facilitating high throughput operations with large sets of data. An advantage of horizontal scalability is that it can provide system administrators with the ability to increase capacity on the fly, being only limited by how many machines can be connected successfully.

As we can see, sharding is implemented through three different components:. The configuration serverwhich must be deployed as a replica set in order to ensure redundancy and high availability. The query routers mongoswhich are the instances you have to connect in order to retrieve data. The client application does not work directly with the shards. Shard instances are the ones persisting the data.

Each shard handling only a part of the data, the one that belongs to a certain subset of shard keys. A single shard is usually composed of a Replica Set when deployed in production. Note : This article will not explain what a Replica Set is. Sharding in MongoDB happens at the collection level and, as a result, the collection data will be distributed across the servers in the cluster.

The first step is to initialize the Config Servers, as they must be available before any router or shard instances are running. To do this:. After this, the Config Server will start listening for connections from the routers.

Config Servers can be started with a Replica Set, in which case, after all the Config Servers are running, connect to one of them by running mongo in a console.

sharding mongodb

The Mongo Shell will start, and now the rs. A Query Router is started up with a service called mongos, and the command looks like the following:. In this case, configserver1. If there are more Query Routers to initialize, then each one of them must be started using a command like the one above. Note : In a production configuration Replica Set Shards must be added to the Clusterin which case a replica set with at least the members must be used.

Note : If you are configuring a production cluster with replication sets, you have to specify the replication set name and a replication set member to configure each set as a distinct shard. MongoDB organizes information into databases, and inside each database, data is split into collections.

A collection is similar to a table from a relational database. At this point, we have to decide on a sharding strategy. Sharding is using a Shard key to split data between shards. Shard keys are used to distribute the documents from a collection on multiple servers.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. Sharding solves the problem with horizontal scaling. With sharding, you add more machines to support data growth and the demands of read and write operations. They provide high availability and data consistency. In production environment, each shard is a separate replica set.

Divide and Conquer: High Scalability With MongoDB Sharding

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. In production environment, sharded clusters have exactly 3 config servers.

The query router processes and targets the 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. Generally, a sharded cluster have many query routers. MongoDB - Sharding Advertisements.

Previous Page. Next Page. Previous Page Print Page.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Replication seems to be a lot simpler than sharding, unless I am missing the benefits of what sharding is actually trying to achieve. Don't they both provide horizontal scaling?

Replication may help with horizontal scaling of reads if you are OK to read data that potentially isn't the latest. It's important to choose a good shard key. For example, a poor choice of shard key could lead to "hot spots" of data only being written on a single shard. A sharded environment does add more complexity because MongoDB now has to manage distributing data and requests between shards -- additional configuration and routing processes are added to manage those aspects. Replication and sharding are typically combined to created a sharded cluster where each shard is supported by a replica set.

Consider you have a great music collection on your hard disk, you store the music in logical order based on year of release in different folders.

MongoDB Sharding Tutorial - What is Sharding - MongoDB Tutorial for Beginners - Simplilearn

You are concerned that your collection will be lost if drive fails. So you get a new disk and occasionally copy the entire collection keeping the same folder structure. It is a reasonably simple tool. It's primarily meant for redundancy, although you can scale reads by adding replica set members. That's a little complicated, but works very well for some apps. Sharding sits on top of replication, usually. Your application will connect to the router, issue queries, and it will decide which replica set shard to forward things on to.

It's significantly more complex than a single replica set because you have the router and config servers to deal with these keep track of what data is stored where. If you want to scale Mongo horizontally, you'd shard. It's possible to do a more ghetto form of sharding where you have the app decide which DB to write to as well. Sharding is a technique of splitting up a large collection amongst multiple servers.

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When we shard, we deploy multiple mongod servers. And in the front, mongos which is a router. The application talks to this router. This router then talks to various servers, the mongod s. The application and the mongos are usually co-located on the same server.

We can have multiple mongos services running on the same machine. It's also recommended to keep set of multiple mongod s together called replica setinstead of one single mongod on each server. A replica set keeps the data in sync across several different instances so that if one of them goes down, we won't lose any data.


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