Aerospike vs. kdb+

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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kdb is well suited for real time tick data and time series analytics.

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• Low latency
• Stable
• Highly configurable
• increasing features

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• Time series analysis. The built-in vector operations are extremely fast. Also with the q language you can code up any customized analytical ideas quickly.
• The database are all file based, very easy to maintain.
• Very solid and fast interface to websocket, so you can interface with javascript easily.

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• 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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• Run time error message readability, particularly for new users.
• Backwards compatibility between versions.

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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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switching costs

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

its ok

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We used the community edition.

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We don't use it.

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

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Python is very commonly used for large data analysis and in general is much easier to pickup than kdb+. The biggest drawback of kdb+ is the learning curve.

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• 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

• It perfectly solves most of our real time tick data needs.
• Finding good kdb resources is slightly difficult. Also new people trying to learn kdb experience a relatively longer learning curve.

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