MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.
$0
per month
SingleStore
Score 7.5 out of 10
N/A
SingleStore aims to enable organizations to scale from one to one million customers, handling SQL, JSON, full text and vector workloads in one unified platform.
$0.69
per hour
Pricing
MongoDB
SingleStore
Editions & Modules
Shared
$0
per month
Serverless
$0.10million reads
million reads
Dedicated
$57
per month
OnDemand
$0.69
per hour
Offerings
Pricing Offerings
MongoDB
SingleStore
Free Trial
Yes
Yes
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Optional
Additional Details
Fully managed, global cloud database on AWS, Azure, and GCP
MongoDB [is] great at storing JSON data grouped into "collections". In this format, you can store any JSON documents and conveniently categorize them by collections. The JSON document contained in MongoDB is called binary JSON or BSON and, like any other document in this format, is unstructured. Therefore, unlike traditional DBMS, any kind of data can be stored in collections, and this flexibility is combined with the horizontal scalability of the database. It should be noted that MongoDB does not have links between documents and “collections” (this is partially compensated by the Database Reference - links in the DBMS, but this does not completely solve the problem). As a result, a situation arises in which there is a certain set of data that is not related to other information in the database, and there is no way to combine data from different documents. In SQL systems, this would be an elementary task.
Well-Suited Scenarios: Real-Time Analytics: Financial trading platforms requiring instant insights. Operational Dashboards: Retail businesses monitoring live sales. IoT Data Processing: Smart device monitoring with high data ingestion. Fraud Detection: Banks detect suspicious transactions instantly. Less Appropriate Scenarios: Archival Storage: Cold data storage with infrequent access. Low-Volume Workloads: Small-scale apps with minimal data processing needs. Complex ETL Pipelines: Heavy data transformations without real-time demands.
Easy to learn. When I picked up MongoDB for the first time, I had little background in database management or modeling. If you have a background in javascript (and JSON)... then you can figure out how to use MongoDB pretty fast.
Fast performance.
It's relatively easy to set up in certain environments because there are lots of ready-made solutions out there.
There's a lot of support in the existing ecosystem for it —, especially in the node.js realm.
Query syntax is pretty simple to grasp and utilize.
Aggregate functions are powerful.
Scaling options.
Documentation is quite good and versioned for each release.
It does not release a patch to have back porting; it just releases a new version and stops support; it's difficult to keep up to that pace.
Support engineers lack expertise, but they seem to be improving organically.
Lacks enterprise CDC capability: Change data capture (CDC) is a process that tracks and records changes made to data in a database and then delivers those changes to other systems in real time.
For enterprise-level backup & restore capability, we had to implement our model via Velero snapshot backup.
MongoDB is one of the most famous non-relational databases in the world, there are famous active projects that use this database. I think that the same company that develops the database gives you the online induction totally free is something that really is very positive. Accounts with a first-class support to be able to relate the correct implementation of the database, in addition to teaching you the best practices to optimize your projects, I believe that with this decision it is more than obvious which is the best decision at the time of seeing with which database to work.
It is one of the reasons why we prefer it to store documents in a JSON-style format, to access the desired document very quickly regardless of its size, to be readable by human eyes, and to be easily scalable and manageable.
[Until it is] supported on AWS ECS containers, I will reserve a higher rating for SingleStore. Right now it works well on EC2 and serves our current purpose, [but] would look forward to seeing SingleStore respond to our urge of feature in a shorter time period with high quality and security.
When it comes to ingestion speed, SingleStore is probably at the top. Being able to create pipelines using SQL to ingest data from S3, Kafka, and other sources, is a great advantages. This means you can dynamically ingest data by customizing your SQL queries. SingleStore pipelines are pretty sophisticated, yet very simple. Few lines of codes and you are ingesting data, while still able to perform analytical queries on your billions of row tables.
I have reached multiple times to the MongoDB community for the help and they have provided each and easy solution for every problem. Over the internet and on stack overflow many people responds over the challenges. Now this tool is very much used in every company and projects so internally many people are there to give a support.
The support deep dives into our most complexed queries and bizarre issues that sometimes only we get comparing to other clients. Our special workload (thousands of Kafka pipelines + high concurrency of queries). The response match to the priority of the request, P1 gets immediate return call. Missing features are treated, they become a client request and being added to the roadmap after internal consideration on all client needs and priority. Bugs are patched quite fast, depends on the impact and feasible temporary workarounds. There is no issue that we haven't got a proper answer, resolution or reasoning
While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.
We allowed 2-3 months for a thorough evaluation. We saw pretty quickly that we were likely to pick SingleStore, so we ported some of our stored procedures to SingleStore in order to take a deeper look. Two SingleStore people worked closely with us to ensure that we did not have any blocking problems. It all went remarkably smoothly.
The environment I work in is somewhat unique in that we use both MySQL and MongoDB. However, each is used for specific purposes that the other is not well suited for. MongoDB is not a relational database like MySQL, so it serves as the perfect place to dump key bits of data for quick retrieval later. This is something we can't easily do with MySQL. On this smaller database, MongoDB also lets us retrieve data more quickly with its fast and efficient querying.
Reduces database sprawl, ETL costs, infrastructure expenses, etc. Supports horizontal scaling, unlike PostgreSQL & Aurora, and real-time analytics and fast transactions (HTAP), unlike Snowflake & ClickHouse.Handles high-volume workloads with thousands of concurrent queries. No need for ETL processes, unlike BigQuery & Snowflake. Works with JSON, relational, and key-value data, unlike ClickHouse.
We can make more open and flexible systems due to its easy adaptation to new evolutions in web applications.
In the latest versions it offers support for different transactions and we could carry out real tests related to the concurrency of the application.
MongoDB allows you to have distributed clusters, which improves the speed of the queries by reducing the latency that exists between the database cluster and the service that executes the query.
Lower operational complexity - Installation and maintenance is pretty easy
Object scale when used can compete with Traditional Warehouse Systems like Teradata, Netezza, Greenplum
Adds lot of value to the business like couple of operations which never worked in traditional DBMS including HANA, Oracle In Memory, SQL Server In Memory just flew in SingleStore
