Unlike MongoDB, Cassandra has its own query language called CQL . Essentially, the database has a different way of storing and recovering data due to it being non-relational. One of the most significant differences between MongoDB and Cassandra is their strategy concerning data availability. This feature dependents on the number of master slaves in a cluster. This ability to redefine a shard key online will be welcomed by those who maintain larger distributed systems and in particular, those who want to transition a large cluster to a geographically distributed topology. While this is definitely of use to a smaller subset of users, those users are the ones running the largest and presumably most expensive MongoDB deployments today.

What’s more, the load characteristic of the application your database needs to support also plays a crucial role. If you are expecting heavy load input, Cassandra, with its multiple master nodes, will give better results. https://globalcloudteam.com/ With heavy load output both MongoDB and Cassandra will show good performance. All of Couchbase’s key-value data retrieval and processing operations happen in memory, resulting in sub-millisecond performance.

While particularly useful for time series data, Windowing functions have very broad applicability in analytical contexts and can be used across any collection type. I expect to see MongoDB charts introducing strong new analytic visualizations based on the Windowing functions. The ability to detect trends in data can also be used to build self-learning adaptive applications.

Long-running queries within a transaction can now extend to five minutes by default or longer if configured. Previously, transactional statements were limited to just one minute. Under the hoods, transaction snapshots are now written to disk storage rather than memory, allowing for larger transaction windows. The unique feature of Redis is that it supports the “Lua script”, due to which it is known as an intelligent cache; so it can be used for high computations. Azure Cosmos DB is also a NoSQL database, which is used to store the data of a website and is released by Microsoft as its name indicates.

You may also enable inference of implicit relationship in the data. DBAs can define a view of a collection that’s generated from an aggregation over another collection or view. These documents contain the data we want to store in the MongoDB database and a single collection can contain multiple documents and you are schema-less means it is not necessary that one document is similar to another. The MongoDB database contains collections just like the MYSQL database contains tables. You are allowed to create multiple databases and multiple collections. As we know that MongoDB is a database server and the data is stored in these databases.

BSON is a binary serialization format used to store documents and make remote procedure calls in MongoDB. You are not allowed to store more than 16MB data in the documents. If you’re interested in learning more about N1QL and Couchbase, I encourage you to try it out for yourself with this free online N1QL tutorial. Or, compare N1QL to MongoDB’s query language in this third-party evaluation. Just compare the MongoDB query language with Couchbase N1QL side by side in the image below. The second reason many enterprises switch from MongoDB to Couchbase is because of N1QL, the Couchbase query language.

Community Edition users can take full benefit of the majority of new features. The versioned API is intended to allow developers to seamlessly upgrade the backend database to the latest version of the database, while maintaining app compatibility. As a result, the overhead of migrating an application to a new version of the database should be radically reduced. It’s inevitable that MongoDB will eventually End of Life old versions, and that forced upgrades to new versions are not always pain-free.

Mongodb 5 0: Worth The Wait

Even novice users can query the database using Full-Text Search, with geospatial capabilities. The in-memory key-value managed cache delivers millisecond performance without needing a separate caching product. Couchbase easily scales by simply adding necessary nodes one at a time, while MongoDB needs to add at least three new nodes at a time in order to introduce a new shard to the cluster, which drives up your total cost of ownership . Meanwhile Couchbase automatically creates shards and rebalances distribution to all available nodes.

MongoDB Key Features

It facilitates static typing and demands the categorization and definition of columns beforehand. Hence, Cassandra and MongoDB have significant differences between their writing scalabilities. Accordingly, Cassandra was released in 2008, as one of these NoSQL databases.

Data Types

If you need to store data using rows and columns, in a structured format, stick to one of the many available relational databases. Perhaps the most notable technical innovation in 5.0 is native time series support. These Time series datasets are typically subject to very distinct analytic queries involving trending and aggregation across time boundaries.

Upon creation, these columns are assigned one of the available Cassandra data types, ultimately relying more on data structure. This is a great feature as it does not restrict any data to be inserted in the database on the basis of data types. A database management system is used to store and manage the data of a website just like a warehouse is used to store the data of any shopping store.

MongoDB Key Features

Sharding allows a write-intensive workload to be scaled out across multiple replica sets or to geographically distribute an application across multiple regions. However, it’s historically been very difficult to change the shard “key” for an existing cluster. Redis can be used in companies where troubleshooting is not an important factor whereas, in companies in which performance is considered strictly, MongoDB will be recommended.

Secondary Indexes

In the future, these upgrades will take significantly less work as the database drivers and database will be able to continue to support previous major releases even when the back-end database is upgraded. From 5.0 onwards, MongoDB are committing to a major release every year and quarterly Rapid Releases. However, while minor releases will be available for download, they will only be certified for production use on Atlas. This may cause consternation amongst users of the community edition or those running on-premise.

In this write-up, we will discuss the top competitors of MongoDB by comparing them with it. Hackolade dynamically generates the Mongoose script based on model attributes and constraints. To distribute the documents in a collection, MongoDB partitions the collection using the shard key. The shard key consists of an immutable field or fields that exist in every document in the target collection. A good indexing system will also contribute to better performance of your database. Keep in mind that MongoDB has a limit of 32MB in holding documents for a sort operation.

  • Cassandra is an open-source database management system that is used to manage the data of NoSQL databases in the form of a cluster model.
  • From 5.0 onwards, MongoDB are committing to a major release every year and quarterly Rapid Releases.
  • Before-starting, everyone should consider the cardinality of the relation.
  • Data scientists using Python will welcome the PyMongoArrow API, which converts MongoDB query results to python formats popular in machine learning and statistical analysis.
  • Its queries are limited to single columns and equality comparisons.
  • The ability to detect trends in data can also be used to build self-learning adaptive applications.
  • If you are going to manage hundreds of documents then it will be better to use MongoDB as it contains a high processing speed than PostgreSQL, because it can use a horizontal scaling approach.

In both of them, MongoDB is preferred by the small business whereas the Microsoft Azure Cosmos DB is preferred by the large business due to the feature of high-level scalability. Moreover, MongoDB only contains the document storage model whereas the Microsoft Azure Cosmos DB contains the storage engines too along with the document storage models. This nesting of data allows you to create complex relations between data and store them in the same document which makes the working and fetching of data extremely efficient as compared to SQL.


For developers that means less to learn, code, integrate and maintain. For DevOps teams, the result is fewer tools to license, deploy and support. If the master node goes down, one of the slave nodes takes over its role. Although the strategy of automatic failover does ensure recovery, it may take up to a minute for the slave to become the master. During this time, the database isn’t able to respond to requests. Most importantly, Cassandra and MongoDB are classified as NoSQL databases.

MongoDB Key Features

Before-starting, everyone should consider the cardinality of the relation. Denormalization – is storing multiple data in a single JSON document. For example, you can have a document for persons in which you also embed the addresses of each person. Denormalization will perform better on reads but will be slower on writes and take up more space. To get the best out of MongoDB, you have to understand and follow some basic database design principles. Before getting to some tips on design & performance, we have to first understand how MongoDB structures the data.

Visualize Data & Schema

PostgreSQL is an RDMS that is used to manage data of relational databases, stored in the form of tables. The data inserted is dependent on the schema which is designed before the creation of the table so the data inserted in the database should follow that schema strictly. MongoDB is a non-relational database, which follows the BSON model to store data in which data is stored in the form of documents, these documents combine to form the collections and these collections ultimately combine to form a database.

Since traditional databases weren’t able to handle a lot of unstructured data in real-time, NoSQL databases took up the challenge by scaling horizontally. It’s worth noting that while MongoDB Atlas remains a cornerstone of MongoDB’s revenue strategy, the company seems to have resisted the temptation to make these new features Atlas-only. To be sure, there are many features added to Atlas, but generally, these are features that only make sense within the fully managed Database as a Service paradigm.

Creating virtual foreign keys for MongoDB can be also very helpful for data visualization. One of Couchbase’s key advantages is exceptional throughput and low latency at scale. The MEAN stack is an open-source JavaScript framework used for developing robust web applications…

Instead of having one master node, it utilizes multiple masters inside a cluster. Cassandra was launched in 2008 by Google, in 2009 it became a part of an incubator project and later on, in 2010 it was known as the top-level project of the database. Cassandra MongoDB vs PostgreSQL is an open-source database management system that is used to manage the data of NoSQL databases in the form of a cluster model. The MongoDB database is developed and managed by MongoDB.Inc under SSPL and initially released in February 2009.

Integrated Services For Flexible Development & Data Access

Deciding between MongoDB or Cassandra may also come down to whether or not you want a built-in aggregation framework. On the basis of the query, if it depends on the primary index then Cassandra will be recommended and if it is a secondary index then MongoDB will be preferred. The nesting of data in BSON is also limited you are not allowed to nest data more than 100 levels.

The Hackolade process for reverse-engineering of MongoDB databases is different depending on the MongoDB version. For versions prior to 3.2, collections are queried with a random function. Starting with version 3.2, Hackolade uses $sample syntax to perform the statistical sampling followed by the schema inference. You may define a custom sampling with a specific aggregation pipeline query and sort.

Approaching Mongodb Design

Such flexibility means the database can input documents of different structures and interpret them once in the software. MongoDB’s data model is categorized as object and document-oriented. This means it can represent any kind of object structures which can have properties or even be nested for multiple levels.

Comparison Between Dynamodb And Mongodb

In SQL, you need to write complex joins to get the data from table 1 and table 2. In MongoDB, you can search by field, range query and it also supports regular expression searches. MongoDB offers more options for modeling “One-to-N” relations than a Relational Database. In the beginning, you can be very attracted to denormalize data by embedding an array of documents into the parent table, but this is not always the best move. As we’ve seen above, understanding when to use the two concepts is the key.