We compared MongoDB and SingleStore based on our users' reviews across four parameters. After reading all of the collected data, you can find our conclusion below.
MongoDB stands out for its flexibility, scalability, powerful query language, replication capabilities, and integration potential. SingleStore is praised for its high performance, scalability, real-time analytics, ease of use, and integration capabilities. Users of both platforms mention the need for enhancements in areas such as query execution, scalability, documentation, and user interface.
Features: MongoDB's valuable features include flexibility for working with dynamic data, scalability for large amounts of data, powerful query language, and reliable replication. SingleStore focuses on high performance, scalability, real-time analytics, seamless integration, ease of use, and intuitive interface.
Pricing and ROI: The setup cost for MongoDB is user-friendly and seamless. SingleStore is also straightforward and hassle-free. MongoDB's licensing process is described as straightforward, and SingleStore's is transparent and fair. Both products offer flexible pricing options catering to different budgets and needs. Users reported positive outcomes and benefits from adopting MongoDB. SingleStore users praised its performance, scalability, ease of use, and integration capabilities.
Room for Improvement: MongoDB users have suggested improvements to the query language, error handling, documentation, and performance optimization. SingleStore users have focused on enhancements to performance, data replication, scalability, and ease of use.
Deployment and customer support: The initial setup of MongoDB is straightforward, especially for those with prior experience in databases. It can be deployed in a couple of hours or even less, either on-premises or on the cloud. Some mention that cluster deployment can be a bit more complex and may take a couple of days. The availability of community support and the ease of learning the initial setup process makes it accessible to users without prior experience with MongoDB or traditional SQL databases. The duration required for deployment of SingleStore can vary depending on the experience of the person performing it. Cloud installations are simple and can be done by anyone (even someone less technical) in a matter of hours by following the provided instructions. Bare-metal installations might take a day for a new technical person, however, an experienced one can do it in an hour. MongoDB's customer service is consistently praised for exceptional assistance, responsiveness, and expertise. Users highlight prompt issue resolution, knowledgeable support staff, and effective communication. SingleStore customers express satisfaction with prompt and helpful assistance, knowledgeable staff, and responsive services.
The summary above is based on two interviews we conducted with MongoDB and SingleStore users. To access the review's full transcripts, download our report.
"One of the biggest benefits is the speed and flexibility of the documents, especially when it comes to modifications."
"One of the most valuable features of MongoDB is it is Its open source."
"MongoDB is extremely developer-friendly because when you are starting, there is very little time needed upfront in terms of planning."
"It's super easy to develop a couple of solutions for clients with MongoDB, like a quick web page with no clear data structure that they need to spin up quickly to validate some sort of MDTP."
"I find the integration with other tools very easy."
"The installation is very easy to do and understand."
"It is easy to set up."
"The solution does not hold data in tabular format like SQL does but rather clusters data so that it can link on a large scale."
"The product can automatically reinstall and reconfigure in case of a shutdown."
"The paramount advantage is the exceptional speed."
"The most valuable feature is the ability to create pipelines, streamline and extract data from the pipelines."
"MemSQL supports the MySQL protocol, and many functions are similar, so the learning curve is very short."
"The ability to store data in memory is a standout feature, enhanced by robust failover mechanisms."
"The product's initial setup phase was pretty straightforward, with no complex processes."
"It's a distributed relational database, so it does not have a single server, it has multiple servers. Its architecture itself is fast because it has multiple nodes to distribute the workload and process large amounts of data."
"The transaction could use improvement. From MySQL, for example, you cannot create a transaction if you are reading and writing a document at the same time."
"MongoDB should not be used for reporting, analytics, or number-crunching tasks."
"The MongoDB documentation can be a little complicated sometimes."
"A normal Oracle or database tester will take some time to gear up to MongoDB because the way of writing queries is different in MongoDB. There should be some kind of midway where a person who is coming from an Oracle background can write a query and get a response by using something like a select * statement or other such things. There should be some way for MongoDB to interpret these commands rather than making a person learn MongoDB commands and writing them. I struggled while writing these MongoDB commands. I had not seen such queries before. It was pretty difficult to get them. This is one of the areas where it would help from the improvement standpoint."
"The solution should have better integration."
"I'd like to see an ID generator. It's very technical but I don't think it has one, so we have to go to great lengths to work around that."
"The solution could include more integrations with other platforms."
"I think it would be good to have more search options such as an index resource. This will provide more options and resources to do advance searches."
"The product can be developed further to provide more appropriate output to users as it is one of the areas where there are shortcomings."
"We don't get good discounts in Pakistan."
"It is not the optimal choice for direct data collection through queries, and it's more suited for aggregation tasks."
"Having the ability to migrate servers using a single command would be extremely beneficial."
"Poor key distribution can significantly impact performance, requiring a backward approach in design rather than adding tables incrementally."
"There should be more pipelines available because I think that if MemSQL can connect to other services, that would be great."
"For new customers, it's very tough to start. Their documentation isn't organized, and there's no online training available. SingleStore is working on it, but that's a major drawback."
MongoDB is ranked 1st in NoSQL Databases with 69 reviews while SingleStore is ranked 6th in Database as a Service with 7 reviews. MongoDB is rated 8.2, while SingleStore is rated 8.8. The top reviewer of MongoDB writes "Lightweight with good flexibility and very fast performance for searching data". On the other hand, the top reviewer of SingleStore writes "A reasonably priced product that offers good speed and seamless support". MongoDB is most compared with InfluxDB, Couchbase, ScyllaDB, Oracle NoSQL and Cassandra, whereas SingleStore is most compared with SQL Server, MySQL, Teradata, Oracle Database and YugabyteDB.
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SQreamDB is a GPU DB. It is not suitable for real-time oltp of course.
Cassandra is best suited for OLTP database use cases, when you need a scalable database (instead of SQL server, Postgres)
SQream is a GPU database suited for OLAP purposes. It's the best suite for a very large data warehouse, very large queries needed mass parallel activity since GPU is great in massive parallel workload.
Also, SQream is quite cheap since we need only one server with a GPU card, the best GPU card the better since we will have more CPU activity. It's only for a very big data warehouse, not for small ones.
Your best DB for 40+ TB is Apache Spark, Drill and the Hadoop stack, in the cloud.
Use the public cloud provider's elastic store (S3, Azure BLOB, google drive) and then stand up Apache Spark on a cluster sized to run your queries within 20 minutes. Based on my experience (Azure BLOB store, Databricks, PySpark) you may need around 500 32GB nodes for reading 40 TB of data.
Costs can be contained by running your own clusters but Databricks manage clusters for you.
I would recommend optimizing your 40TB data store into the Databricks delta format after an initial parse.
Morten, the most popular comparisons of SQream can be found here: www.itcentralstation.com
The top ones include Cassandra, MemSQL, MongoDB, and Vertica.