IBM Storage Ceph vs. Riak

Large scale data storage: Red Hat Ceph Storage is designed to be highly scalable and can handle large amounts of data. It's well suited for organizations that need to store and manage large amounts of data, such as backups, images, videos, and other types of multimedia content.Cloud-based deployments: Red Hat Ceph Storage can provide object storage services for cloud-based applications such as SaaS and PaaS offerings. It is well suited for organizations that are looking to build their own cloud storage infrastructure or to use it as a storage backend for their cloud-based applications.High-performance computing: Red Hat Ceph Storage can be used to provide storage for high-performance computing (HPC) applications, such as scientific simulations and other types of compute-intensive workloads. It's well suited for organizations that need to store

Incentivized

Riak is well suited to applications such as:

Transaction logging e.g. financial transactions and/or exchange rates.

Storing time series data, especially IoT.

Storing massive amounts of data e.g. corporation wide backups, data lakes etc.

A fully s3 compatible replacement for Amazon s3 ensuring data privacy.

Riak is not as well suited to:

Traditional RDBMS functions, especially those that join the outputs of one or more queries together to produce the desired result.

• Highly resilient, almost every time we attempted to destroy the cluster it was able to recover from a failure. It struggled to when the nodes where down to about 30%(3 replicas on 10 nodes)
• The cache tiering feature of Ceph is especially nice. We attached solid state disks and assigned them as the cache tier. Our sio benchmarks beat the our Netapp when we benchmarked it years ago (no traffic, clean disks) by a very wide margin.
• Ceph effectively allows the admin to control the entire stack from top to bottom instead of being tied to any one storage vendor. The cluster can be decentralized and replicated across data centers if necessary although we didn't try that feature ourselves, it gave us some ideas for a disaster recovery solution. We really liked the idea that since we control the hardware and the software, we have infinite upgradability with off the shelf parts which is exactly what it was built for.

Incentivized

• Highly available: If nodes go offline for any reason, the system still operates.
• Highly scalable: There is a minimum of 5 nodes, which can handle a lot by themselves. When scaling is required, it can be done easily, with minimal to no downtime on large scales.
• Very fast searching: Riak has SOLR indexing built-into the core product, which makes querying for data very fast.

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• GUI based mainetenence should be developed
• Unable to detect storage latencies
• VM to disk mapping should be visible so as to save some critical applications data in case of HDD failures

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• Deletes!!! We've seen on numerous occasions where Riak has "resurrected" deleted data. We've worked with Basho numerous times and tried multiple changes to the way we interact with Riak to prevent the problem but it still remains. The deletes seem to reappear weeks, even months, after the delete was issued. We've had to work around this issue by providing a "deleted" flag for all data objects stored in Riak. Thus, we do no delete but simply flip the flag. Excess baggage we would really like to not have to worry about.
• Search. Currently there's no way to tell what data you have in Riak without already knowing a particular bucket/key. There is a way to list the keys for a given bucket but due to performance implications, this is not a viable method to lookup data. Especially when you have a large amount of keys in the bucket.

Incentivized

Right now, I'm on a project where we need databases that can run on embedded systems. Riak isn't necessarily the best fit for that scenario. But when we need a clustered database, that's where we'd start considering Riak.

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Despite Basho going bankrupt and the project becoming fully open-source, community support is reasonably good, albeit a little slow at times. Paid enterprise-grade support is also available from former Basho engineers but the same company also contributes to the community support for free for basic questions or specific knowledge areas.

MongoDB offers better search ability compared to Red Hat Ceph Storage but it’s more optimized for large number of object while Red Hat Ceph Storage is preferred if you need to store binary data or large individual objects. To get acceptable search functionality you really need to compile Red Hat Ceph Storage with another database where the search metadata related to Red Hat Ceph Storage objects are stored.

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Because of the RESTful HTTP interface, the consistency model, and because of the catalog-driven data model, Riak was an easy win over Redis and Memcached.

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• Ceph allows my customer to scale out very fast.
• Ceph allows distributing storage objects through multiple server rooms.
• Ceph is fault-taulerant, meaning the customer can lose a server room and would still be able to access the storage.

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• Riak has been a key part of our company's build process for our client's search backend. It is valuable for is in that it provides a reliable way to view the current search index.

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