SingleStore

Formerly MemSQL

We were able to ingest and handle large amounts of data quickly and easily using many different methods. SingleStore's access to Kafka, AWS S3, etc. has proved to be very helpful in our data processing strategy.

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.

SingleStore has amazing performance compared to previous databases we have used. A lot faster with even more data

SingleStore is the fastest database we have ever used. (We have experience with MySQL, PostgreSQL, and MongoDB).

SingleStore's performance is incredible. Our predictive algorithms went from taking 24-48 HOURS down to 15 minutes allowing our team to run those much more frequently.

Previously, we were limited to about 60 requests per minute due to table locks. Implementing columnstore on SingleStore allowed us to receive 1000 requests per minute.

We give SingleStore the top score here given how performant it has been for us. We have used their self-managed option since day 1 which is now over 2 years ago.

We are running 100’s of millions of requests per month, performing queries across billions of records and the self-managed option has continued to hold up well for us without any need to move to their paid offering.

So far so good for us at FLOWD, SingleStore has performed flawlessly over the last 12 month in market.

Singlestore is a great solution for many workflows: it can ingest/write data very fast. It can also compute data/run calculations very fast. I also appreciate the level of accessibility SingleStore provides for retrieving data. They offer their own custom API for read/write operations, which has its own protections/synergies outside of traditional database connections

In almost all cases SingleStore is fast enough to run the workflows we need, which is better than some of the other database providers we've used in the past.

SingleStore's speeds are fast, reliable. Once in a while we get a long running query, but we get alerts about those and we can resolve those.

SingleStore is way faster than BigQuery, which we were using previously for our entire product.

SingleStore is very fast at most large analytical queries over millions of datapoints. Ingestion can be cumbersome at times. Annoying to size up workspace and need to remigrate to use all new shards

SingleStore ingests the data in a timely manner, which satisfies our requirements. The analytical queries we run also finish in an exceptional time, which has improved our app experience. By increasing our workspace size, we've also been able to increase the complexity and amount of data used in our queries, which has also improved our product.

The objective and goals of the POC we conducted with SingleStore were met.

Compare to previous organization's database SingleStore is much better in terms of data ingestion, faster query result

compared to postgres , the time taken to ingest and retrieve data improved by more than 10x . The time taken to run our complex analytics queries too improved by a huge margin.

Performance is the main benefit.

SingleStore is very powerful for analytical queries, however it can struggle under heavy write transactions, as the column store is not as fast as the row store for such workload.

When we moved to SingleStore, one of my jobs was to port our stored procedures (of which there were about 100) from our old system to SingleStore. This was quite straightforward, and something that I liked in SingleStore was that it was easy to predict which approach to a stored procedure would have the best performance. With our old provider, we had to carefully performance-test our code to see whether it would be faster to use a temporary table or a common table expression (CTE). In SingleStore, the CTE is always faster. This makes it faster for us to write new code. In our old system, we used to have to pepper the code with join hints (e.g., hash join instead of loop join) to ensure optimal performance. Two years after porting our code to SingleStore, I have never yet had to use a join hint in my code. This, together with the previous point, means that I can focus on developing features for our product rather than subtly tweaking my code for performance. SingleStore performance is consistently impressive.

fastest data ingesting platform I know of. Hands down

SingleStore is 3-5X faster than the previous solution we used and also performs better than other multi cloud competing solutions we have evaluated

SingleSore can perform transactions and operational analytics together in order to utilize their data and transform their business. SingleStore delivers a database that performs both functions. Before using SingleStore, we had different systems for OLTP queries and for OLAP analyses, and a number of ETL packages to bring data from the OLTP system to Reporting database.

I have used alot of ETL tools
I have even built spark based ETL scripts but using singlestoreDB's PIPELINE function makes it all easy.
Ingestion of data already present at file storage systems like S3 or GCS or Azure Blob is much much easier.
The set-up of a pipeline is really is easy to do and you can even set it to run in intervals with the requirements of your system

The mere speed of data load in SingleStore is phenomenal. That actually does things quite better than any other solutions we have had till now.