Oracle TimesTen In-Memory Database (TimesTen) delivers real time application performance by changing the assumptions around where data resides at runtime. By managing data in memory, and optimizing data structures and access algorithms, database operations execute achieve gains in responsiveness and throughput. With TimesTen Scaleout, a shared nothing scale-out architecture based on the existing in-memory technology, TimesTen allows databases to scale across hosts, reach hundreds of terabytes in…
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SingleStore
Score 8.3 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.
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 …
TimesTen is well suited for applications using smaller data or smaller data stores and where transaction response times are not as business critical. TimesTen is good for applications already accessing Oracle and need to cache data for quick read/write operations. TimesTen is not appropriate for large data dependent applications or applications requiring fast response times. In these cases, using Oracle database or Exadata is better
Good for Applications needing instant insights on large, streaming datasets. Applications processing continuous data streams with low latency. When a multi-cloud, high-availability database is required When NOT to Use Small-scale applications with limited budgets Projects that do not require real-time analytics or distributed scaling Teams without experience in distributed databases and HTAP architectures.
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.
SingleStore excels in real-time analytics and low-latency transactions, making it ideal for operational analytics and mixed workloads. Snowflake shines in batch analytics and data warehousing with strong scalability for large datasets. SingleStore offers faster data ingestion and query execution for real-time use cases, while Snowflake is better for complex analytical queries on historical data.
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.
Sybase does not have an in-memory database until version 15 so TimesTen was ideal for caching data. TimesTen has reliable replication and backing up mechanisms. Oracle takes longer to set up and use for most applications where as TimesTen is a smaller DBMS that is quick and easy to set up and use. TimesTen can connect to Oracle for caching data so using Oracle as a backend makes sense
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 Greenplum and Gemfire was not good. Product team did not help us much like the ones in SingleStore who helped us getting started on our first cluster very fast.
TimesTen has had a positive impact from a developer's perspective because implementing TimesTen is quick and easy. The benefits of TimesTen can be seen almost instantly. For instance, the application start up time is faster, the data is easy to maintain and the performance is fast for TimesTen clients.
TimesTen has had a positive impact for the business because it can be made accessible to users via a GUI. This gives users transparency to the data at any time.
The negative impact is that once the TimesTen database has grown too large, the application should move to using Oracle database or else it suffers from performance degradation and stability issues.
As the overall performance and functionality were expanded, we are able to deliver our data much faster than before, which increases the demand for data.
Metadata is available in the platform by default, like metadata on the pipelines. Also, the information schema has lots of metadata, making it easy to load our assets to the data catalog.
