Aerospike vs. ScyllaDB

We were developing an advertisement time auction application, where we had to store the client's personal details, advertisement-related details, location, and many other details. Moreover, we required a promotion, cookies, and a few more details from the front end. All this information is heavy in terms of size and cannot be lost if the server crash. So, we required an extremely fast disk database with high scalability and low throughput.

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Scylla is well suited for high-throughput scenarios where keyed data must be read or written with consistently low latency. It's less appropriate for use cases requiring relational queries, secondary indexes, or more structured data sets.

• Low latency
• Stable
• Highly configurable
• increasing features

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• Low-latency reads
• CQL has a familiar syntax
• Parity with Cassandra
• Practical features

• Load balancing per network segments.
• Reduction in price.
• Cross datacenter replication usage isn't so straightforward. Sometimes cross dc replication can have issues of bad data..

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• Better documentation for best practices (e.g., how to effectively use connection pooling)

If money isn't an issue, and you're not on the cloud, then I'd go with Aerospike. If you're the cloud ie, aws or azure, then i'd stick with dynamoDB or Cosmos then. Aerospike is definitely not something you want to put into the cloud. It doesn't work well w/ cross regions. If cross DC, you'll have to write some stuff for data integrity checks.

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Could be easier to use and install. Also developer experience needs some work

Very easy-to-understand syntax--uses CQL (same as Cassandra), which has many similarities to standard SQL. There are some gotchas, however, that must be known during schema development.

We used the community edition.

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The Scylla cloud support team is incredibly responsive and proactive.

Aerospike is much more performant than MongoDB, however there is much greater community adoption and support for mongo

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Scylla has a quick learning curve (same as Cassandra) compared to other proprietary solutions like BigTable. It supports higher throughput and lower latency that other NoSQL databases like MongoDB, which sacrifice those features for more flexibility and unique features.

• Being able to power the same workload on a fraction of the servers has led to better ROI for my application.
• Less servers needed also meant less time to manage the cluster, leading to savings in engineering time

• Addresses latency requirements of our platform