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MongoDB is an open source, document-oriented, and cross-platform database. It is primarily written in C++. It is also the leading NoSQL database and tied with the SQL database in the fifth position after PostgreSQL. It provides high performance, high availability, and easy scalability. MongoDB uses JSON-like documents with schema. MongoDB, developed by MongoDB Inc., is free to use. It is published under a combination of the GNU Affero General Public License and the Apache License.

In this article, we look at the indexing, replication and sharding features offered by MongoDB.

The following excerpt is taken from the book ‘Seven NoSQL Databases in a Week‘ written by Aaron Ploetz et al.

Introduction to MongoDB indexing

Indexes allow efficient execution of MongoDB queries. If we don’t have indexes, MongoDB has to scan all the documents in the collection to select those documents that match the criteria. If proper indexing is used, MongoDB can limit the scanning of documents and select documents efficiently. Indexes are a special data structure that store some field values of documents in an easy-to-traverse way.

Indexes store the values of specific fields or sets of fields, ordered by the values of fields. The ordering of field values allows us to apply effective algorithms of traversing, such as the mid-search algorithm, and also supports range-based operations effectively. In addition, MongoDB can return sorted results easily.

Indexes in MongoDB are the same as indexes in other database systems. MongoDB defines indexes at the collection level and supports indexes on fields and sub-fields of documents.

The default _id index

MongoDB creates the default _id index when creating a document. The _id index prevents users from inserting two documents with the same _id value. You cannot drop an index on an _id field.

The following syntax is used to create an index in MongoDB:

>db.collection.createIndex(<key and index type specification>, <options>);

The preceding method creates an index only if an index with the same specification does not exist. MongoDB indexes use the B-tree data structure.

The following are the different types of indexes:

  • Single field: In addition to the _id field index, MongoDB allows the creation of an index on any single field in ascending or descending order. For a single field index, the order of the index does not matter as MongoDB can traverse indexes in any order. The following is an example of creating an index on the single field where we are creating an index on the firstName field of the user_profiles collection:
The query gives acknowledgment after creating the index:
This will create an ascending index on the firstName field. To create a descending index, we have to provide -1 instead of 1.
  • Compound index: MongoDB also supports user-defined indexes on multiple fields. The order of fields defined while creating an index has a significant effect. For example, a compound index defined as {firstName:1, age:-1} will sort data by firstName first and then each firstName with age.
  • Multikey index: MongoDB uses multi-key indexes to index the content in the array. If you index the field that contains the array values, MongoDB creates an index for each field in the object of an array. These indexes allow queries to select the document by matching the element or set of elements of the array. MongoDB automatically decides whether to create multi-key indexes or not.
  • Text indexes: MongoDB provides text indexes that support the searching of string contents in the MongoDB collection. To create text indexes, we have to use the db.collection.createIndex() method, but we need to pass a text string literal in the query:
You can also create text indexes on multiple fields, for example:
Once the index is created, we get an acknowledgment:
Compound indexes can be used with text indexes to define an ascending or descending order of the index.
  • Hashed index: To support hash-based sharding, MongoDB supports hashed indexes. In this approach, indexes store the hash value and query, and the select operation checks the hashed indexes. Hashed indexes can support only equality-based operations. They are limited in their performance of range-based operations.

Indexes have the following properties:

  • Unique indexes: Indexes should maintain uniqueness. This makes MongoDB drop the duplicate value from indexes.
  • Partial Indexes: Partial indexes apply the index on documents of a collection that match a specified condition. By applying an index on the subset of documents in the collection, partial indexes have a lower storage requirement as well as a reduced performance cost.
  • Sparse index: In the sparse index, MongoDB includes only those documents in the index in which the index field is present, other documents are discarded. We can combine unique indexes with a sparse index to reject documents that have duplicate values but ignore documents that have an indexed key.
  • TTL index: TTL indexes are a special type of indexes where MongoDB will automatically remove the document from the collection after a certain amount of time. Such indexes are ideal to remove machine-generated data, logs, and session information that we need for a finite duration. The following TTL index will automatically delete data from the log table after 3000 seconds:
Once the index is created, we get an acknowledgment message:

The limitations of indexes:

  • A single collection can have up to 64 indexes only.
  • The qualified index name is <database-name>.<collection-name>.$<index-name> and cannot have more than 128 characters. By default, the index name is a combination of index type and field name. You can specify an index name while using the createIndex() method to ensure that the fully-qualified name does not exceed the limit.
  • There can be no more than 31 fields in the compound index.
  • The query cannot use both text and geospatial indexes. You cannot combine the $text operator, which requires text indexes, with some other query operator required for special indexes. For example, you cannot combine the $text operator with the $near operator.
  • Fields with 2d sphere indexes can only hold geometry data. 2d sphere indexes are specially provided for geometric data operations. For example, to perform operations on co-ordinate, we have to provide data as points on a planer co-ordinate system, [x, y]. For non-geometries, the data query operation will fail.

The limitation on data:

  • The maximum number of documents in a capped collection must be less than 2^32. We should define it by the max parameter while creating it. If you do not specify, the capped collection can have any number of documents, which will slow down the queries.
  • The MMAPv1 storage engine will allow 16,000 data files per database, which means it provides the maximum size of 32 TB.
    We can set the storage.mmapv1.smallfile parameter to reduce the size of the database to 8 TB only.
  • Replica sets can have up to 50 members.
  • Shard keys cannot exceed 512 bytes.

Replication in MongoDB

A replica set is a group of MongoDB instances that store the same set of data. Replicas are basically used in production to ensure a high availability of data.

Redundancy and data availability: because of replication, we have redundant data across the MongoDB instances. We are using replication to provide a high availability of data to the application. If one instance of MongoDB is unavailable, we can serve data from another instance.

Replication also increases the read capacity of applications as reading operations can be sent to different servers and retrieve data faster. By maintaining data on different servers, we can increase the locality of data and increase the availability of data for distributed applications. We can use the replica copy for backup, reporting, as well as disaster recovery.

Working with replica sets

A replica set is a group of MongoDB instances that have the same dataset. A replica set has one arbiter node and multiple data-bearing nodes. In data-bearing nodes, one node is considered the primary node while the other nodes are considered the secondary nodes.

All write operations happen at the primary node. Once a write occurs at the primary node, the data is replicated across the secondary nodes internally to make copies of the data available to all nodes and to avoid data inconsistency.

If a primary node is not available for the operation, secondary nodes use election algorithms to select one of their nodes as a primary node. A special node, called an arbiter node, is added in the replica set. This arbiter node does not store any data. The arbiter is used to maintain a quorum in the replica set by responding to a heartbeat and election request sent by the secondary nodes in replica sets. As an arbiter does not store data, it is a cost-effective resource used in the election process.

If votes in the election process are even, the arbiter adds a voice to choose a primary node. The arbiter node is always the arbiter, it will not change its behavior, unlike a primary or secondary node. The primary node can step down and work as secondary node, while secondary nodes can be elected to perform as primary nodes. Secondary nodes apply read/write operations from a primary node to secondary nodes asynchronously.

Automatic failover in replication

Primary nodes always communicate with other members every 10 seconds. If it fails to communicate with the others in 10 seconds, other eligible secondary nodes hold an election to choose a primary-acting node among them. The first secondary node that holds the election and receives the majority of votes is elected as a primary node. If there is an arbiter node, its vote is taken into consideration while choosing primary nodes.

Read operations

Basically, the read operation happens at the primary node only, but we can specify the read operation to be carried out from secondary nodes also. A read from a secondary node does not affect data at the primary node. Reading from secondary nodes can also give inconsistent data.

Sharding in MongoDB

Sharding is a methodology to distribute data across multiple machines. Sharding is basically used for deployment with a large dataset and high throughput operations. The single database cannot handle a database with large datasets as it requires larger storage, and bulk query operations can use most of the CPU cycles, which slows down processing. For such scenarios, we need more powerful systems.

One approach is to add more capacity to a single server, such as adding more memory and processing units or adding more RAM on the single server, this is also called vertical scaling. Another approach is to divide a large dataset across multiple systems and serve a data application to query data from multiple servers. This approach is called horizontal scaling. MongoDB handles horizontal scaling through sharding.

Sharded clusters

MongoDB’s sharding consists of the following components:

  • Shard: Each shard stores a subset of sharded data. Also, each shard can be deployed as a replica set.
  • Mongos: Mongos provide an interface between a client application and sharded cluster to route the query.
  • Config server: The configuration server stores the metadata and configuration settings for the cluster. The MongoDB data is sharded at the collection level and distributed across sharded clusters.
  • Shard keys: To distribute documents in collections, MongoDB partitions the collection using the shard key. MongoDB shards data into chunks. These chunks are distributed across shards in sharded clusters.

Advantages of sharding

Here are some of the advantages of sharding:

  • When we use sharding, the load of the read/write operations gets distributed across sharded clusters.
  • As sharding is used to distribute data across a shard cluster, we can increase the storage capacity by adding shards horizontally.
  • MongoDB allows continuing the read/write operation even if one of the shards is unavailable. In the production environment, shards should deploy with a replication mechanism to maintain high availability and add fault tolerance in a system.

Indexing, sharding and replication are three of the most important tasks to perform on any database, as they ensure optimal querying and database performance. In this article, we saw how MongoDB facilitates these tasks and makes them as easy as possible for the administrators to take care of.

If you found the excerpt to be useful, make sure you check out our book Seven NoSQL Databases in a Week to learn more about the different database administration techniques in MongoDB, as well as the other popularly used NoSQL databases such as Redis, HBase, Neo4j, and more.

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