Item: SingleStore
Rating: 9
Author: Ziv Fridfertig

Overall Satisfaction with SingleStore

Use Cases and Deployment Scope

We use SingleStore for a super fast client experience, running real time analytics on billions of events arriving every day from various publishers channels.

Pros and Cons

Pros

Performance - Milliseconds response of 80 tables Joined queries
Scalability - Ability to grow with no downtimes
Client success - Attentive to business needs, deep level support, patches and fixes
Efficiency - Built-in Kafka / S3 / MySQL integrations well adjusted to leverage SingleStore architecture and hardware
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Cons

Add Iceberg tables / files Pipeline
CDC out in form of logfile / binlog / producer to Kafka
Efficiency with multi shard-key use case: Joined three tables when one of them holds both shard keys of the other two.
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Return on Investment

Graded as the top leaders in the field
Client satisfaction
Innovate with each new major release

Our setup includes hundreds/thousands Kafka pipelines per cluster, pushing billions of rows daily into the system with replication lag average of less than 3 seconds.
Alongside that, we have the client operations which consist of complexed queries running on hundreds of tables in high concurrency.

Before SingleStore, client workloads on MySQL took much longer, a lot of effort was needed from devs on data re-modeling , query tuning and solving performance issues.

Consolidation

Latest versions reduced the gap of transactional and online operations in the OLAP engine, this has enabled us to shift more data and workloads into SingleStore and consider it as the single source of truth. Mostly for operational client facing use cases.

Key Insights

Do you think SingleStore delivers good value for the price?

Not sure

Are you happy with SingleStore's feature set?

Yes

Did SingleStore live up to sales and marketing promises?

Yes

Did implementation of SingleStore go as expected?

Yes

Would you buy SingleStore again?

Yes

Other Software Used

Confluent, Snowflake

Likelihood to Recommend

SingleStore shines as a unified solution of high OLTP & OLAP workloads.
The technology suits big data systems with mutual identity (shard key/s), fast JSON processing, vector search for AI features and streaming.

The client success attentiveness and the consistent support of experts in any matter shows the company maturity and their vision for success.
No doubt this is a long term partnership.

SingleStore Support

SingleStore Customer Support Pros and Cons

Pros	Cons
Quick Resolution Good followup Knowledgeable team Problems get solved Kept well informed No escalation required Immediate help available Support understands my problem Support cares about my success Quick Initial Response	None

Premium Support

Yes. We use SingleStore as our main core product backend DB. Issues in this critical business path must have specialists support, even more, when our workloads push the limits of the technology and the unique SingleStore architecture.

Support Rating

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.

Bug Resolution

Yes - Yes they were.
Multiple times and on multiple scale and impact.

All of them got a proper investigation on demand, with a recovery plan from day one, in some cases after few days of deeper analysis on less pressing matters.

Exceptional Examples of SingleStore Support

Yes, one of the major version upgrade included a feature for better resiliency on hardware failures. behind the scenes this feature included a dedicated snapshot logs for each DB partition, with 2 copies for HA and a minimum of 1-2 GB file size. For our use case we have tens of schemas on each cluster, each has 16/60/120 DB partitions, which made the upgrade much more expensive than we thought, storage wise.

We involved the support immediately, with a simple question, why do we need to add ~40 TB of storage after upgrading to the newest version.

SingleStore tailored us a patch while we were in the middle of gradual upgrade rollout (5 weeks, 50 clusters). With the new patch + special settings we could adjust the snapshot logs size and continue the rollout with addition of only ~4 TB instead of 40.

Comments

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