- In-house developed A/B testing analysis tool uses this to fetch raw data in real-time when end …
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SingleStore (formerly MemSQL) is a Cloud DB for the Data-Intensive Era.™ The data platform market is vast and growing. SingleStore is a late-stage (Series F) scaleup, with a solution that runs on the Big 3 cloud providers and a customer base spanning every vertical, including many Fortune/Global 500 brands. SingleStore is headquartered in San Francisco with offices around the globe.
SingleStore states they are dedicated to helping businesses adapt more quickly, embrace diverse data, and accelerate digital innovation by operationalizing all data through a single database for all of their moments that matter.
- Amazon S3 (Simple Storage Service)
- a Logi Analytics company
- Data Virtuality Pipes
|Deployment Types||On-premise, SaaS|
- UI design
- Perfect SQL editor and faster query execution
- Ease of pipeline creation for data loads
- Need to have a place to create an admin user for the first information schema database. Because when we log in, we do not have admin access by default to the system.
- The state of the pipeline is not available if the currently available load is finished, there is extra work to check if all your files in the current load are complete.
- The Dashboard can be a bit more simplified than having a lot of details.
2. The places where it is not suited are, when the data load is very low and there is not much difference achieved while execution of data loads.
- I was amazed at the ability to connect to the cluster with third-party clients, including native vendor-supplied MySQL command line tools.
- The ease of exporting data from an existing MySQL database and loading it into SingleStore is impressive.
- Speed and ease of use. Everything is streamlined for you to hit the ground running with minimal learning curve.
- The mysqldump file required some manual massaging before it could be ingested. This was expected, and I am surprised how little manual modification is needed, but nonetheless -- this could be improved.
- Some GUI database tools (such as MySQL Workbench) have trouble connecting and need additional configuration.
- Faster query result as Relational Database.
- High Availability.
- Load data from one or more data sources.
- Loads external data in real-time.
- Distributed and partition feature with master-Slave architecture.
- Load data from a file that is located on the filesystem.
- Power up legacy database and support massive workload.
- MySql Client can access MemSql with same query experience.
- SingleStore has this unique ingest capability it can do parallel ingest of data from sources like S3, Azure Blob, GCS, Kafka.
- Can be integration with Tableau for Data Visualization.
- There should be powerful Data Visualization.
- There can be a better Query Builder UI.
- No implicit ordering of results by primary key.
- Compatibility with S3 and data formats such as Parquet, JSON, and CSV
- Can be Deployed on a Kubernetes Cluster and can be scaled seamlessly.
- Great UI and support documentation available to look and work around.
- Scale out capability
- Fast data ingestion and queries
- Can be used to lower the latencies of various services
- Did not find support for User Defined Functions.
- It opens a new query result tab and that feels irritating to me personally.
- Lot of RAM required while running Developer instances locally.
- super fast
- execute complex query
- the use of several types of database
- open many SQL Editor in same time
- wizard for object creation (table, view, procedure...)
- link table from other database
• data analysis
• migration preparation
• for data processing•......
- Handing and processing Json data types fastly ( Actually I worked on it particularly )
- It is fast and reliable
- Pay as you use. This is quite a good feature for the customers who have load only during peak hours and doesn't have load during non-peak hours.
- It is easy to access and can be accessed from anywhere without VPN issues as it is a cloud-based solution.
- It is very user friendly and all options are easily navigatable.
- Errors are easily understandable if we go wrong anywhere while doing our operations
- Fast retrieving and reading data with JSON Formats.
- super fast data ingestion and queries
- commonly used formats such as csv, json and parquet are well supported
- MySQL engine allowing the customers also to work easily
- minimum administration needed
- support team is quick and helpful
- Every time i run a new query, it opens a new query result tab
- Lot of RAM required while running Developer instances locally
- limited information on the running queries
- Responsive and intuitive UI.
- Query engine is very fast.
- Very easy to deploy clusters and use the database.
- Execution plan can be shown when the query is running so that the user might get information of their running queries and where to optimize them
- Fast on intense data load
- Few clicks away to have the environment setup
- Easiness/speed to load data from different pipelines
- Great speed on running complex queries
- SQL Editor opens a new result tab on every query run
- Consumption of RAM memory through browser if you have many queries
- Simple and easy interface
- Similar SQL as we are used to with other RDBMS, so easy to start with
- The performance is quite awesome
- At some point I would love to enhance, i.e. it should have an indicator for progress so we are aware if something is running or is completed.
- Very easy to configure the SingleStore database.
- Loading data was extremely easy and super fast. I loaded millions of records and it took a few seconds.
- SingleStore seems to be an ideal database for real-time dashboards.
- Not too many issues. Only one aggregate function I ran in the database took more than a minute to run, otherwise, performance on queries for millions of records was great.
We offer end to end reporting & analytics services and use SingleStore to power our dashboards and reports.
- Relational online analytical processing (ROLAP).
- SQL completeness (e.g. triggers).
- Performance tuning insides.
- Data replication.
- Small data set processing in memory.
- User defined functions.
- Providing optimization options.
- In-house developed A/B testing analysis tool uses this to fetch raw data in real-time when end users analyse the tests using the UI.
- Machine learning models fetch the raw data and calculate needed features in real-time instead of preprocessing the data by the data engineers.
- Return results of complex queries scanning TBs of data in sub-seconds.
- Customer support team answer tickets quickly and provide guidance.
- MySQL engine which allows to query using simple MySQL drivers from different clients.
- Queries profiling is easy to use and helps investigating performance.
- Loading batch data using pipelines is complex.
- No native monitoring and alerts of resources (CPU, RAM, etc.).
- Performance is strongly coupled with number of nodes (sometimes extra nodes hurt the performance).
- Very expensive compared to other databases.
- Real-time calculation.
- Massive data storage.
- Real-time updates and integration.
- Scaling up (adding memory) takes a lot of time.
- Real-time information about lags is limited.
If you're looking only for storage, I would pass.
- Ingesting data from kafka at very high rates.
- Easy to understand performance characteristics.
- Hosted solution is bulletproof and always up.
- Programming languages for ingest logic.
- Ability to run in our own cloud accounts to save transit costs.
Right now, MemSQL is our main DB and serve all of our customers.
We are transferring data with MemSQL pipelines.
In addition we are collaborating multi DBs into clusters. It is very comfortable instead of MySQL
- Query time
- Very easy to MySQL customers to work with
- Easy to transfer data from other data sources
- IN operator at where clause
- Multi key index at columnstore
- Enable to run cross clusters query
2. If you need to improve your queries performance.
3. If your you don't need to create a lot of tables at online flow (means to create a table for another query).
- We use it as a replacement for having a separate in-memory cache and a traditional disk-based database because SingleStore does both in a single package.
- We also use the row store for storing transactional state and the column store for storing time series data. Having both in the same product is nice.
- We get 7x compression for the time series data, which is really good for scaling.
- We wish the product had better support for High Availability of the aggregator. Currently the indexes generated by the two different aggregators are not in the same sequential space and so our apps have more burden to deal with HA.
- More tools for debugging issues such as high memory usage would be good.
- The price was the one that kept us away from purchasing for the first few years. Now we are able to afford due to a promotion that gives it at 25% of the list price. Not sure if we'll continue after the promotion offer expires in another 2 years.
With SingleStore, we are able to replace multiple other databases and started migrating from on-premise to SAAS. As SingleStore provides both scenarios, the migration to the cloud will be a smart and simple one. We now use SingleStore for our data and analytics environment. As we now can use the pipelines towards our data lakes and send data via procedures into our tables, we have achieved the next step in our data management by adding additional metadata during the ingestion. Using text search and the geospatial functions adds more functionality than we had before in our previous databases. The performance and scalability are beyond our expectations.
- The scenario to migrate on-premise and scale up to the cloud as a SAAS.
- Using HTAP means we can now use our database for transactions and analytics.
- The main language is similar to MYSQL, so our organization has already the knowledge to master the system.
- The SingleStore academy is a good program to support our organization to master the environment.
- Performance on large sets of data for analytics.
- Row based and column based can be used and mixed.
- Time series, geo spatial and text search function to add to our platform.
- Scalability, so we can continue to deliver the same performance.
- Does not yet support foreign keys.
UCB Biopharma wanted to offer a solution enabling scientists to "self-service" research data rapidly with the aim to accelerate the early discovery process.
○ Scientists define the data set that is relevant to their investigation in a ‘data collection & preparation’ tool, execute the resulting query against SingleStore, and visualize it in a data-analytics tool.
○ Data alerts are generated with the help of SingleStore queries to inform end-users about the arrival of fresh information.
UCB selected SingleStore for raw query performance and for concurrency. we serve on a daily basis between 220-800x scientists in several countries.
- Query performance execution
- Concurrent users
- Simplified data pipeline
- Metrics collection/visualization for queries execution.
- Ease of use (installation, managed service, pipelines)
- Hybrid configuration (on-premise & cloud), analytical and transactional workload
- ANSI SQL as a standard
- Runs on any major cloud platform
- More use cases needed that have their origin outside the USA
Less appropriate: simple data sets, low volumes.
- First and foremost with the use of Aggregator master and leaf nodes the query processing time for DB are reduced significantly from hundred of msec to now just below 50msec.
- Supports very high level of Real time transaction processing, for our project the OLTP via real time messages were ~500 to max of 800 TPS, the SingleStore was able to process those very efficiently with significant lower sever side utilization.
- One of the most powerful tool for SingleSore monitoring is Planecache query, this helped in measuring performance of the stored procedures and evaluating the optimization required. The use was simple and helped analyst to provide the outcome in shortest interval of time.
- Features related on [SingleStore (formerly MemSQL)] studio UI for monitoring needed some improvement when we used. The SingleStore studio support team helped us in resolving the issues with relation on running the different sets of indexing queries. Challenges here being the detailed logs were not available and we needed to connect with DBA for more detail drill down.
- SingleStore can have some more improvement in setting up more examples on help documentation for the beginners and early learners.
- where the components are processing very large amounts of data and requires very low latency.
- Columnstore compression of data reduces the time to respond. Compression resulted in quick responses which are not achieved using the other DB tools.
- The concept of Rowstore and implementation on frequently used tables results in support of high OLTP.
Not suited/less appropriate
- The In-memory(Rowstore) and col-store does not share the same language compatibility. When required the transition form other table type more efforts are required.
- SingleStore DB (formerly MemSQL) connection between AWS cloud failed when partitioning is higher for data processing.
- Administration is sometime bit confusing when providing layered access to different teams.
- OLAP workloads
- Fast query responses
- Multiple use cases in one single database
- Does not provide adequate support for data discovery apps, i.e. Power BI.
- It would be great to have a native load balancing component for dealing with aggregator failure. Otherwise having a Child Aggregator becomes optional since not all the customers can afford an external balancing solution and does not feel confortable with switching between aggregators manually.
- They used to have certifications and training in development and administration. That is very important to have, since other competitors does provide access to those sort of things and although they have free tutorials/videos, that doesn't provide an in-depth understanding.
It's highly recommended for BI and Analytics use cases though
- The fastest speed for querying compared with traditional relational database
- Support JSON and full text search which can be used by API’s
- Nearly zero admin tasks once it’s running
- You can use it’s data streaming pipeline with Kafka
- It doesn’t provide redistribution when you reach the maximum node capacity
- The Graphical Interface could have a revamp, it’s a little bit laggy
- You cannot run it local so development should be made always using cloud instances
- Processes large amounts of data with very low latency.
- The support department is fantastic.
- The developer experience is lacking. Running developer instances locally requires a lot of RAM.