SingleStore Reviews

My main use case for SingleStore is real-time analytics and data storage and serving for my ML workloads. It enables me to run low-latency analytics and model-driven use cases at scale, which is quite difficult for OLAP and OLTP databases alone.

One specific example of how I use SingleStore for one of my model-driven use cases is as a data warehouse for fintech, which processes payments through TC worldwide. The integration with Azure Data Factory occurred without complications. It helps me in an excellent way since I am very fast in obtaining the data for my dashboards. Additionally, the compression of the information is accurate.

Regarding my main use cases with SingleStore, I think it is very useful for managing information for business intelligence processes and processing. Given that the information is brought quickly, it processes correctly when loaded into the dashboard.

The best features SingleStore offers include fast data recovery and data compression by 80 percent. Having the information in sheets helps me to process the information quickly. Simplicity in T-SQL is another aspect I appreciate.

Data recovery and sheet-based processing help my team on a day-to-day basis by enabling us to handle information efficiently.

I would like to add that the data compression by 80 percent helps us in an excellent way since we are very fast in obtaining the data for our dashboards and the compression of the information is great.

SingleStore has impacted my organization positively by enabling us to run low-latency analytics and model-driven use cases at scale, which is quite difficult for OLAP and OLTP databases alone. It has been very helpful because our internal clients are happy to have the data and make data-driven decisions easily. Making decisions based on data within a two-hour delay to the transactional database is excellent since we went from twenty-four hours to two hours. I think the best contribution is decision-making with data that is close to reality.

Reducing that delay from twenty-four hours to two hours has significantly affected my team and business outcomes by increasing productivity. We have been able to serve all our customers, and they are very happy. We can deliver to them on time.

SingleStore can be improved in the aspect that the Azure pipelines do not have many parameterization features compared to others, for example, AWS. Also, error handling needs attention. When it fails due to memory, it only indicates that but not exactly in which process it failed.

I would like to add that we need to know what the roadmap of SingleStore is.

An additional improvement needed is that the semantic layer can be better. Currently, it requires significant development experience to fine-tune queries.

I have been using SingleStore for six years and some few months.

SingleStore is stable as I find it extremely stable. I have not seen any downtime.

SingleStore's scalability is high and it can be used by any size of organization and can handle any needs of any organization.

The customer support is very proactive and responsive twenty-four hours per day, seven days per week.

Positive

I previously used a different solution, which was Oracle database.

I switched from Oracle to SingleStore because SingleStore outperforms based on my analysis with simple data sets within my organization.

I have seen a return on investment as real-time compute reduces the hassle of fine-tuning required, but it is minimal compared to other tools like Oracle or other RDBMS. Primarily, it was not client-focused. It was used for large enterprises with a significant amount of real-time data generation. The objective was to scale as data loads with high-performing query model responses.

My experience with pricing, setup cost, and licensing is that it can be a bit expensive for startups, but for my organization, the cost is affordable and cost-effective.

Before choosing SingleStore, I evaluated other options such as PostgreSQL.

My advice to others looking into using SingleStore is that it is extremely good for scenarios where large sets of data are generated in a day and the data is streamed. If you would like to run queries and analytics on such data, it would really scale and outperform TimesDB or Oracle in-memory options.

Before we wrap up, I have additional thoughts about SingleStore, stating it is a great tool that is very cost-effective. It shines as a unified solution for high OLTP and OLAP workloads. I rate this product a nine out of ten.

SingleStore is our main platform for handling high-performance real-time analytics of large data volumes, providing transactional and operational data on a single platform.

One specific example of how we use SingleStore for real-time analytics is real-time dashboard analytics for operational data. We ingest a stream of events and transactions into SingleStore and use it to power dashboards that need real-time updates. Before using SingleStore, we had noticeable delays. We also use multi-board architecture in SingleStore to store large volumes of data that power our dashboards.

Some of the most valuable features that SingleStore offers are its speed and scalability, delivering fast query performance even with large datasets and supporting real-time analytics with very low latency. I also appreciate how it handles both transactional and analytical workloads on a single platform, reducing the complexity of the data we deal with.

SingleStore's speed and scalability have made a significant difference for our team. For example, we use SingleStore to store all our customers' data into a database. The biggest impact happened when the data started growing significantly; previously, the query performance and dashboard response times were slow because the fetches were slow. With SingleStore, we scaled without major performance issues, and reports, dashboards, and data segments that previously took much longer to generate are now produced in real-time. The fetches are very fast, which overall improves the user experience and helps teams access insights faster for operational and decision-making.

SingleStore has positively impacted my organization by improving performance, reducing query response times, and enabling faster access to business insights. It has helped our teams make quicker decisions with real-time data visibility, reducing the complexity of our systems and operational workloads.

We have noticed improvements in both productivity and operational efficiency as query execution times were significantly reduced. This means teams spend less time waiting for data and more time acting on insights. We also benefit from consolidating workloads into a single platform, which reduces infrastructure complexity, and overall resource utilization is good.

SingleStore is very well at the current stage, but I see room for improvement in ease of administration, monitoring, and reducing troubleshooting time. Better documentation for advanced use cases, especially where data volumes are very high and queries are frequent, would help us manage the feature more efficiently. Expanding integrations and providing more built-in tools would also improve the overall experience.

For our use case, I would appreciate out-of-the-box integrations with commonly used data ecosystem tools and built-in monitoring and observability capabilities for SingleStore. The dashboards could be more intuitive for performance tracking, query optimization insights, and overall administrative management. This would make our day-to-day operations easier, allowing us to gain more insights and use SingleStore better with improved visibility on ongoing processes.

I have been using SingleStore for the past three years.

SingleStore is quite stable in my experience; we have faced some recent issues, but it handles large workloads and maintains consistent performance very well overall without any major stability issues.

SingleStore scales very well. As our data volumes grow, we can expand resources without significant performance degradation. It efficiently manages growing datasets and higher query loads.

I am not much aware of customer support for SingleStore, as I have not had the chance to interact directly. However, I have heard that the support team is responsive and very helpful in addressing and troubleshooting issues.

I cannot specify the previous solution we used before SingleStore, but I have shared the benefits and improvements we have experienced with SingleStore. Due to the increased data volumes, we needed a strong solution, and SingleStore meets all our requirements.

I cannot share much about how SingleStore is deployed due to company policy.

As a developer, I am not fully aware of the exact setup costs or licensing details; my focus is more on the implementation aspects at the developer level.

I cannot directly comment on money saved or fewer employees needed, but from a developer's perspective, I see a positive impact in query performance and real-time analytics during data processing. The efficiency has increased significantly, and our workflows are consolidated into a single platform, thus reducing the operational overhead we previously faced. I am not certain about exact financial metrics, but there are clear improvements in productivity and operational efficiency.

I cannot share much about how SingleStore is deployed due to company policy.

I am not entirely certain about the options we evaluated; I believe we considered Hive and other data platform options. However, we were focused on large-scale data processing and, when comparing performance and scalability, SingleStore stood out due to its strong query performance and ability to support multiple workloads on a single platform.

Others looking into using SingleStore can definitely benefit from it. I would rate this review an 8 out of 10.

My main use case for SingleStore is for our data retention and load management. We have infrastructure wherein the user's data are used for quick search, which means the searching functionality in our app is being handled with the help of SingleStore so that the searches are quick and easy for the users.

I decided on SingleStore for this use case because previously we were using AWS-related functionality, specifically EC2 instances, but those were not working well. I switched to SingleStore for the flexibility and infrastructure they provide, and it has helped us considerably.

The best features SingleStore offers in my experience are the excellent team support and the very good UI.

What makes the support stand out to me is that the UI is interactive and intuitive for first-time users. The team is also good at resolving issues, and I can reach out to them directly by creating a ticket whenever I face any issues.

SingleStore has impacted my organization positively because the output and performance of the app have been improved. I measured that improvement through both faster search time and better user experience based on customer reviews.

I have noticed that SingleStore can be improved because the integration of SingleStore with DataDog is not good. The data which is sent to DataDog sometimes does not match with the SingleStore dashboard.

I have been working in my current field for three years.

SingleStore is reasonably stable.

SingleStore offers good team support and their UI is interactive for first-time users. Their team is capable of resolving issues efficiently, allowing users to create tickets and receive support.

I did not previously use a different solution; SingleStore is the first solution which we implemented.

I did not purchase SingleStore through the AWS Marketplace. My experience with pricing, setup cost, and licensing is good.

I have seen a return on investment in terms of time saved.

The advice I would give to others looking into using SingleStore is that it is a good tool. The customer support is great. The integrations can sometimes be a problem that needs to be checked.

I use it for managing both transactional and analytical workloads within the same database. In my previous organization, I successfully implemented it for a banking system, where it accommodated transaction-based processes using row store tables and analytical requirements using column store tables. This dual functionality eliminates the need for separate databases for transactions and reporting, streamlining the overall architecture. With SingleStore's distributed architecture, it provides the scalability needed to support diverse workloads effectively.

Its in-memory storage, distributed architecture, scalability, and failover mechanisms collectively contribute to its exceptional performance and reliability, especially in demanding transactional environments like online and mobile banking systems. The ability to store data in memory is a standout feature, enhanced by robust failover mechanisms. Even in scenarios where all servers experience downtime, it ensures data safety by maintaining a copy on disk.

The critical challenge involves optimizing the distribution of data across partitions through careful design of the sharing key. Poor key distribution can significantly impact performance, requiring a backward approach in design rather than adding tables incrementally. Intricate use cases, especially those involving joins across multiple tables, pose challenges if sharing and distribution are not well-aligned. Unlike traditional databases where indexing may suffice, SingleStore may require redistributing the entire dataset, presenting a persistent challenge.

I have been using it for four years.

The stability of SingleStore varies depending on the use case. For a transaction-based system, I would rate it around eight out of ten. However, if it's utilized for an analytical system, I would give it a rating of around seven out of ten.

Scalability is its key strength. Adding servers for scalability is a straightforward process involving simply incorporating a few additional servers and recycling the cluster triggers automatic repartitioning and redistribution of data. For instance, if the initial database creation involved a hundred servers and later, four more servers are added, specific commands can be executed to increase the partitions to one hundred twenty. The data is then efficiently redistributed across the expanded partitions without the need for manual data movement, ensuring a seamless and efficient scalability process. In my current organization, approximately three projects involve the usage of SingleStore, with a team size ranging from ten to twenty individuals.

During the onboarding process at my previous organization, SingleStore provided dedicated support for five to six months, offering invaluable assistance. Presently, with our current service providers partnered with them, support involves raising a ticket, leading to the allocation of a dedicated person for assistance. This personalized approach enables an assessment of the issue, considering factors like data volume. Additionally, the forums serve as a helpful resource for addressing queries, although responses may take a few days. I would rate it eight out of ten.

Positive

We transitioned from using IBM Db2 to SingleStore due to a shift in our infrastructure plan. Initially designed for on-premise deployment, we sought optimized server capabilities for a banking process, with a primary goal of cost reduction compared to mainframe expenses. In our current project, SingleStore is predominantly employed for analysis and reporting purposes. Previously, Palantir and Vertica were used for reporting, but observations of drawbacks in these platforms led to the decision to migrate to SingleStore for more efficient analysis and reporting capabilities, which is proving successful in our current setup.

The initial setup is straightforward, with comprehensive tutorials available on its website. Beginners can easily follow the step-by-step guides, either for a local installation or on cloud platforms like Azure.

The installation process is user-friendly, requiring the selection of a cloud provider and a few configuration choices. Unlike on-premise solutions that involve server setup, SingleStore simplifies the process, making it accessible to a wide range of users. For on-premise installations, specifying server details and failover architecture is necessary, but once the server is prepared, the installation itself is uncomplicated. Database creation involves specifying configurations and requirements, and streamlining the overall setup process.

The platform's versatility allows it to cater to various use cases effectively. Unlike other databases that might require separate solutions for transactional and analytical needs, it offers a unified solution for both. This dual functionality appeals to organizations seeking cost-effective solutions, as they can invest in a single database to address multiple requirements.

Using it for analytical purposes can be cost-effective in the long run, especially in terms of infrastructure. While building an on-premise cluster incurs an initial cost for servers with ample RAM, it becomes a one-time investment with subsequent maintenance handled internally. For cloud deployments, the cost may be relatively higher due to instances offering lower RAM. Opting for higher RAM in cloud instances increases the per-server cost. However, it's important to note that this is a one-time expenditure, and maintenance becomes more straightforward.

I would advise individuals to consider it for transactional systems, particularly if their requirement is for millisecond-level performance. The row store feature is well-suited for such applications. However, it's essential to be mindful of the associated costs, whether deploying on the cloud or on-premise. Due to the need for substantial RAM to store data in memory, the cost can be significant, especially for larger datasets. Overall, I would rate it nine out of ten.

I worked for a company that outsourced tasks for SingleStore. I mainly worked with one customer who was a video platform. Their primary use case was storing metadata for their videos. They offered recording and playback services for TV shows in the US and Canada, and SingleStore efficiently managed the metadata for all these recordings.

 There's a wide range of professionals using SingleStore,  You can find more details on their website.

Since it is not as costly as  Oracle or other database counterparts, that's one benefit. And the speed is very fast. It supports in-memory data types, storing data on RAM for blazing-fast performance. That's the highlight; it's perfect for both OLTP and analytical workloads.

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. I heard a client processed 3.5 billion records in seven minutes! Their data ingestion is very high, and SingleStore even markets itself as the world's fastest database.

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. 

Also, technically,  SingleStore needs more features on the SQL part. Most SQL boards work in MySQL, and SingleStore integrated all its sequel with MySQL, so nearly 99% of MySQL code runs on SingleStore. But features like TVF and UDL lack depth. Users have to walk into it, and SingleStore has minimal features there.

IUDF, TVF, and stored procedures are not as advanced as SQL Server's. That's one thing I would like to see improved. 

I worked with SingleStore just two months before resigning from my previous company. I have over two years of experience with it.

It is a stable solution. Any downtime I've seen was due to application or software bugs, not SingleStore. Human errors happen, but the system itself is stable.

It is a highly scalable solution. SingleStore has servers, separated into two parts.  One aggregates queries (gateway nodes), and the other stores data (leaf nodes). 

To increase database size, you simply add more servers. There's minimal downtime during rebalancing, maybe a minute or two. You can add as many servers as you need without taking anything offline. That's what makes it highly scalable.

We mostly handled day-to-day maintenance. But for the real heavy lifting, there's a separate team in SingleStore. It's all handled through one ticketing tool, Zendesk. You're a registered customer, you log a ticket, and they prioritize and address them accordingly.

Positive

SingleStore offers cloud and bare-metal installations. 

Cloud hosting is simple; you pay hourly and follow their cloud UI instructions. Anyone, even someone less technical, can install it. 

For bare metal VMs, it might take a day for a new technical person, but an experienced one can do it in an hour. It's quite easy.

The customer I supported had an on-premises SingleStore cluster running on bare metal.

One technical person is enough for deployment if it's not a production-grade cluster. For testing, one or two days should be max. 

For production-grade clusters, you might need professional services. SingleStore also offers database architects and consultants to help design your cluster architecture, hardware selection, license units, etc. They basically do everything for you, from designing the blueprint to setting up hardware and licenses. So, professional services are highly recommended for production environments.

If you have many clusters (over 10-15), you might need a team of 3-4 people for maintenance. But for 1-2 servers, you can handle it yourself. It's easy after the initial learning curve. And best of all, no downtime! You can perform maintenance tasks online, like adding a new service ID or scaling your cluster, without impacting your business.

The product offers high availability. Two copies of data at all times, so even if one server goes down, your application stays up. That makes maintenance even easier. You can take a server down, fix it, and put it back without impacting users. This makes maintenance very easy. 

As long as one server is running, you're good. The only limitation to high availability is the increased cost. You need more servers, which means more money. Think of it like this: without high availability, you'd need X servers. With it, you'd need 2X servers. Hardware costs go up.

I had a customer I worked with for five years who kept adding new customers throughout. SingleStore provides significant cost-based value to companies.

The pricing is available on the website, https://www.singlestore.com/pr.... They have two main options: cloud installation and bare-metal installation, each with different pricing models.

For cloud services, they offer three tiers: Standard, Premium, and Dedicated. Standard starts at just $0.80 per hour. This is for the standard cloud service.

Now, for self-managed on-premises clusters, they provide free licensing up to four units. If you need premium features or enterprise support, like direct access to their support team, you'd need to purchase their enterprise license. For specific pricing on those, I recommend contacting their sales team directly.

I'm currently focusing on C++, building my resume for web development. I want to be a programmer and build things, not work in a service-based company. That's why I moved on. But I'm happy to help with any service-related tasks or where my two years of SingleStore experience might be useful.

Online resources are limited since it's a new database. So, first, read the documentation to understand the basics. Then, approach them directly and explain your specific use case for the database. 

Their sales team is very responsive and can help you get started. I highly recommend this database, but do your research first because online materials are scarce. Just understand the basic terms and policies in the docs, then contact them to set up your clusters.

Overall, I would rate the solution an eight out of ten. 

The issue we encountered was the inability to efficiently extract and evaluate our data from existing databases, causing limitations with Tableau, which struggled to handle datasets exceeding a hundred million records. Consequently, we explored alternative systems and initially attempted to access all data from target systems. In pursuit of faster and more effective data management, we considered Exasol as an alternative OLAP system. Ultimately, we opted for SingleStore.

The paramount advantage is the exceptional speed. Another noteworthy aspect of our experience is that, upon its setup, there aren't any issues that necessitate the intervention of a DBA. In terms of performance, it has proven impressive, particularly in handling complex joins without the need for data shortening. It excels in executing complex SQL operations swiftly.

There's a noteworthy consideration when it comes to collecting massive amounts of data. It is not the optimal choice for direct data collection through queries, and it's more suited for aggregation tasks. Attempting to use it for direct extraction, for instance, might lead to memory-related challenges. While MySQL version five might lack extensive SQL capabilities, SingleStore also has its constraints, requiring simpler SQL writing. This becomes evident when seeking advanced functionalities like window functions or JSON functions, where SingleStore doesn't offer an extensive toolkit, necessitating a more straightforward approach to SQL.

We have used it for two years.

I would rate its stability capabilities ten out of ten.

Regarding scalability, we did encounter occasional challenges as our data grew larger, resulting in potential out-of-memory issues. The resolution to this depends on the budget allocated. If budget constraints are not a concern, opting for a SaaS system is a viable solution, albeit with associated costs. Scaling the system incurs expenses for each additional node. I would rate it seven out of ten.

I use Exasol, and it has proven to be highly effective. However, when considering factors such as cost, financial aspects, SQL version capabilities, and insertion times, there are certain drawbacks. Specifically, the performance in terms of insertion times is notably subpar.

The initial setup was straightforward.

The deployment process was remarkably swift, taking only one or two days, and we encountered no issues throughout the entire setup.

If simplicity, quick data insertion, minimal complexity, and high availability are your primary concerns, and budget constraints aren't a major issue, then SingleStore is an excellent choice. I would rate it eight out of ten.

When I was working at Wag, MySQL had trouble scaling. We were using an M4-10X RDS instance and three replicas. These machines are expensive and with the growth that the company was experiencing, it was clear we would not be able to scale properly.

There were heavy writes done against MySQL that should not have been there; an event table for example. This table was close to 900M rows, taking more than 300GB. I decided to migrate this table to MemSQL, freeing up both lots of space and write resources on MySQL.

After the event table was migrated to MemSQL, using the MemSQL built-in S3 pipeline, the table was compressed down to 30GB.

Prior, the Support team would use MySQL to run queries against the very large MySQL event table. The query would take more than two minutes to run. After the table was migrated to MemSQL, queries would run under three seconds. 

Developers saw quickly the benefits of MemSQL and started using the memory engine to cache tables.

Moving the heavy writes to MemSQL freed up lots of write resources on MySQL. The replica lags, which would hover between two and ten seconds, were consistently below one second.

We were also able to ingest free weather data and enhance it to compare it against real-time data. (Weather affects dog walks). We then built some weather models to create some trends and predict weather patterns. 

The most valuable features are: 

• MySQL drop-in solution: MemSQL supports the MySQL protocol, and many functions are similar, so the learning curve is very short. Developers were able to code against MemSQL the same day. They configured MemSQL in Docker and were up and running.
• MemSQL built-in pipeline: You can connect MemSQL directly to an S3 bucket and it will ingest its data automatically. There is no need to create a lambda function. It just works. You can connect to Kafka, HDS, and other protocols.
• Support for CTE, Window, Stored Procedures, functions (UDF, Table) are all included. MemSQL is a full-blown database solution. 
• Replication: A MemSQL cluster can replicate to another cluster. This is a great feature, allowing you to have one cluster that does all the heavy work, and a Replica for reads. 
• MemSQL supports high concurrency, and scaling horizontally is limitless.
• I really like the MemSQL portal (portal.memsql.com). Creating more labs is the best way to introduce people to MemSQL.

There should be more pipelines available because I think that if MemSQL can connect to other services, that would be great. 

MySQL to MemSQL replication would be great. If it could replicate from MySQL then this would allow me to move all of the heavy counts, full-text search, aggregate, and analytical queries off of MySQL. We would not need many replicas, and you could have near-real-time (less than a few seconds) data you that can report on, and generate these reports in less than a few seconds.

I've been using MemSQL for the past four years

I have never had an issue with stability. A leaf node could go down and you can still operate. MemSQL provides full redundancy across the Cluster using a share nothing approach with its leaf nodes. Leaf nodes, that store the data, can be taken offline, maintained, upgraded, scaled, and put back in, while the cluster still server queries. 

MemSQL scales very well. I did some benchmarks with 128 concurrent users on an 80M row table doing counts "Where date between x and y, group by 1", and 75% of the queries would return in less than two seconds. 

Their support team is top-notch. They provide excellent support, and the feedback would be thorough. I have no complaints about the support.

I had been using MySQL before to run reports. I switched because MySQL could not deliver reports fast enough.

The setup is pretty straightforward, MemSQL provides excellent documentation.

Our implementation and deployment were done in-house.

ROI? Less headache, fewer worries about scaling. No learning curve and no need for specialized resources in a language. You can be up and running in a few minutes, or you can use the Cloud solution. 

I would advise users to try the free 128GB version. MemSQL also comes with "Cluster-in-a-box", which allows you to run it on a single instance. This is a great way to save money and get your hands dirty, until you need to scale bigger.

I looked at PostgreSQL but since I don't know it well, I decided to use MemSQL. The learning curve is short and once the cluster is up and running, you don't need to worry.

MemSQL supports backup, restore, and enterprise-grade fail-over. It's very stable and with one node down, you can still serve traffic.

I discovered this product at Strata Data in 2012 and followed the product evolution. I've tested many versions and recommended them to companies I've worked with.

MemSQL provides excellent documentation and they upgrade the product frequently. Their cloud solution offering, MemSQL Helios, allows you to create a cluster with a few clicks. MemSQL is a very powerful database.

There are a lot of solutions out there including ClickHouse, Aurora, and PostgreSQL, but none of them provide the rich features that MemSQL has. The pipeline by itself is awesome. 

I can write an SQL statement to create a pipeline and in less than 15 seconds I have data flowing into my tables (or stored procedure). You can ingest data into MemSQL stored procedures and do transformations on the fly. For example, counters.

So give it a try, you won't be disappointed.

The solution is primarily used for storing data.

The most valuable feature is the ability to create pipelines, streamline and extract data from the pipelines.

The documentation has room for improvement. There is not a large active community.

I would like to have step-by-step documentation to assist even a novice with installation.

Having the ability to migrate servers using a single command would be extremely beneficial. At present, there are numerous steps required to configure and migrate pipelines.

I am currently using SingleStore.

SingleStore is stable.

SingleStore is scalable.

The initial setup is straightforward for an experienced person with in-depth knowledge.

SingleStore is a bit expensive.

I give the solution an eight out of ten.

I recommend SingleStore to others.

The product can automatically reinstall and reconfigure in case of a shutdown. 

We don't get good discounts in Pakistan. 

I have been working with the product for three months. 

I would rate the product's stability a nine out of ten. 

I would rate the solution's scalability a ten out of ten. 

SingleStore's setup is difficult. 

The product's licensing is not expensive. It is comparable. 

I would rate SingleStore a ten out of ten.