MonetDB is an open source column-oriented relational database management system issued and supported by the Dutch MonetDB development team.
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SingleStore
Score 8.2 out of 10
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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.
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.
There is a plethora of choices when it comes to NoSQL and columnar based databases. We use not one but sometimes 2 or 3 of them to carry out a specific purpose. We chose MonetDB because our engineering team enjoys working with open source software and appreciates its simplicity …
Effective solution around data even when it comes to real time info. Knowing you can access the data from any country is effective solution to extract the info needed.
I can only compare it with Exasol, which I have used a similar base, which manages the Hadoop scheme and is very similar to SingleStore. SingleStore has many advantages: being in the cloud, with just a couple of clicks I can increase the capacity, the configuration is super …
first of all SingleStoreis a cluster with high availability and easy to use. you need to design you tables / procedures such in a way that your SingleStore perform well and with handle heavy load
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 …
SingleStore (memsql) out performs based on our analysis with sample data sets within org. We could see limitations with other products which SingleStore can overcall like scaling with data while performing with similar SLA. It also has the advantage of row store and column …
SingleStore outperforms Snowflake in real-time analytics and transactional workloads but lags in large-scale batch processing. Compared to MongoDB Atlas, SingleStore excels in complex SQL queries and joins, while MongoDB handles unstructured, document-based data better. Its …
Greenplum is good in handling very large amount of data. Concurrency in Greenplum was a major problem. Features available in SingleStore like Pipelines and in memory features are not available in Greenplum.
Gemfire was not scaling well like SingleStore. Support of both …
We previously used Bigquery for our application, and a single store gave us very good performance over Bigquery. But the comparison is not apples to apples, as Bigquery is more of a data warehousing solution.
SingleStore is just a bigger engine with more capability. Its ability to handle larger data sets with ease is its biggest advantage. These other database solutions are great for smaller scale projects that don't include large data sets but Singlestore greatly out performs in …
It has more APIs and other access methods. It has a multi-version concurrency control (MVCC) Distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in …
Its easier to query and faster. Ingestion is for the most part easier to understand and monitor and directly integrated with other storage solution products we use such as AWS S3. Singlestore overall is a better database to serve up an application large amounts of data very …
SingleStore is built for fast data ingestion and fast queries against large tables (> billions of rows). This is possible because of the column store engine that SingleStore uses. SingleStore also support a memory engine. Pipelines is also another big advantage. Being able to …
We knew early on that MySQL (Amazon Aurora) would not be suitable for this workload as it cannot query our time series data as fast as SingleStore. We also use MongoDB Atlas for another application but we could not achieve the raw speed we saw from SingleStore. Our technical …
We were initially using AWS Aurora which worked well at the time but as we grew it just felt like a lumbering beast - even with tier upgrades. We started looking at caching & search options to help make searches faster - we were already using redis. We looked at and even …
Timescale was the biggest alternative option we looked at for SingleStore, however the requirement to learn a new syntax (due to not being SQL compatible) was our biggest pain point.
Supporting a new language would require alterations to the Laravel framework, as this only …
SingleStore provides an abstraction layer in managing a sharded database solution reducing complexity for the FLOWD team. Coupled with the SingleStore Managed Service, we are partnering with SingleStore to provide FLOWD services to various utilities & councils.
SingleStore is eons faster than other database providers, and it absolutely crushes calculations & aggregations. While other providers may have a few quality of life enhancements over SingleStore, the speed benefits of SS far outweigh the cons. At the end of the day, speed …
I think for what we use it for, mainly scheduling, forecast and to compare against payroll, it works well. I think there are some other things that could be added to it to use, like more expansive ways to use the forecasting tool and an easier way to pull previous data from prior years. This would help making the forecast for scheduling in the future.
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.
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.
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.
[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.
Solutions are based around a business needs and even when implementing such solution, real time insights are also followed through showing the updates the business are implementing while informing the end users as what is new with technology.
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.
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
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.
There is a plethora of choices when it comes to NoSQL and columnar based databases. We use not one but sometimes 2 or 3 of them to carry out a specific purpose. We chose MonetDB because our engineering team enjoys working with open source software and appreciates its simplicity although becoming familiar with it did take time. I would not deploy MonetDB to production but it's a great backup option.
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.
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.
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