ClickHouse vs. MonetDB vs. MongoDB

The most important thing when using ClickHouse is to be clear that the scenarios in which you want to use it really are the right ones. Many users think that when a database is very fast for a specific use case, it can be extrapolated to other contexts (most of the time different) in which a previous analysis has not been carried out.

ClickHouse is an analytical database, as such, it should be used for such purposes, where the information is stored correctly, the data volumes are really large and the queries to be performed are not the typical traditional queries on several columns with multiple aggregations. ClickHouse is not the solution for this.

On the other hand, if your case is not one of the above, it is quite possible that ClickHouse can help you. Where ClickHouse shines is when you are looking for aggregation over a particular column in large volumes of data.

Incentivized

MonetDB is great when you are performing adhoc queries on a large set of data. For example, if you store data in a typical RDBMS such as MySQL or Postgres and want to join large tables for analytics but the query runs unacceptably slow then MonetDB can act as a second database to offload complex queries. Based on my experience, it may not be a production-ready database since there aren't many DBAs familiar with it and due to lack of documentation, maintenance can become a little tricky.

Incentivized

If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.

Incentivized

• Their MergeTree table engine provide impressive performance for data insert in bulk
• Not only data insert but also the way MergeTree engine uses Primary Keys to sort the data and perform data skipping based on the granules its also their secret for ridiculous fast queries
• Data compression its also great
• They provide especial table engines that allow you to read data directly from other sources like S3
• Since its written with C++ you have very granular data types and especial ones like enum, LowCardinality and etc, they save you a lot of storage since are stored as integer values
• ClickHouse functions besides the ones that respect ANSI Standards are also awesome and useful

Incentivized

• It is easy to use.
• You are able to input lots of data and it understands and populates information.
• Able to change settings on the fly to use with your needs.

Incentivized

• Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
• You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
• Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.

Incentivized

• Avro data manipulation
• Kafka consistency
• DDL operations errors (by replica configuration)

Incentivized

• This is an open source software so there are obvious drawbacks, the biggest of which is a lack of documentation.
• MonetDB does not seem to be well known outside of the academic environment so there is less information when you are searching for answers of any type.
• I'd like to see more use cases and/or best practices so that commercial companies like ours can optimally use all of its highly performant features.
• The code is written in C/C++ and this can be negative if you are a mainly java-shop and need UDF - User Defined Function.

Incentivized

• An aggregate pipeline can be a bit overwhelming as a newcomer.
• There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
• Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.

Incentivized

I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.

Incentivized

NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.

Incentivized

Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.

Incentivized

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.

Incentivized

ClickHouse outperforms, especially in costs, since its compression/indexing engines are so smart, and even with very low computing power, you can already perform huge analyses of the data.

Incentivized

We have used Five9 in my previous company but on a much smaller scale. It was more expensive, however we were using it for a max of 50 employees, now we need a much bigger platform. We also used Five9 for other things, like phone dialers etc. so it was a little different.

Incentivized

We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.

• Queries that used to take more than 2 minutes now take less than 1 second
• Possibility to analyze use cases in real time (before was impossible)
• The applications are more complete and the users decisions are better

Incentivized

• If you are familiar with a general database concept and played with open source products before then MonetDB will give you immediate return in terms of productivity since developers can quickly develop and verify their test cases involving back-end database with a large sample data set.
• There is a stiff learning curve due to lack of documentation and sparse information available on the internet.
• Overall experience has been positive since MonetDB gives you another option when it comes to building out a data warehouse.

Incentivized

• Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
• You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB