I'm designing an application where I want to cache million data each around 10kb.. I did some analysis and on the fence between using Redis vs memcached vs Scylla as Cache.. Can some experts suggests which might best suits my needs?
Full disclosure - I work on the Scylla project.
I think it is a question of latency and HA vs cost. As a RAM-based system, Redis will be the lowest latency. If you need < 1 millisecond response, then Redis or memcached are the choice.
Scylla is a disk-based system. Those values that are in Scylla's RAM will be low latency, but those that need to pull from disk will be slower. So your 99p latency is likely to be slower. How slow? Depends on your disk. NVME can be 99p 3-5 ms. SSD, maybe 5-10 ms. If this is an acceptable latency, then Scylla will be much less expensive, as even NVME is much cheaper than RAM.
As for HA - Redis and memcached are intended as a cache. While there are some features and frameworks that you can use to replicate data around, these are all bolt-ons and increase complexity. Scylla is a distributed system by design. So the replication to allow for multiple layers of HA is built-in (node, rack and DC-availability)
Redis (and to a lesser extend, memcached) are phenomenal caches. But, depending upon your use case, Scylla might be the right choice.
All three options you mentioned are open-source software, so the pricing is the same - zero :-) However, both Scylla and Redis are written and backed by companies (ScyllaDB and RedisLabs, respectively), so if your use case is mission-critical you may choose to pay these companies for enteprise-level support, you can inquire with these companies what are their prices.
The more interesting difference between the three is in the technology.
You described a use case where you have 10 GB of data in the cache. This amount can be easily held in memory, so a completely in-memory database like Memcached or Redis is a natural choice. However, there are still questions you need to ask yourself, which may lead you to a distributed database, such as Scylla depending on your answers:
Would you be using powerful many-core machines? If so, you should probably rule out Memcached - my experience (and others' - see Can memcached make full use of multi-core?) suggests that it does not scale well with many cores. On an 8-core machine you will not get anywhere close to 8 times the performance of a one-core machine. Redis is also not really meant for multi-core use - https://redis.io/topics/benchmarks says that Redis "is not designed to benefit from multiple CPU cores. People are supposed to launch several Redis instances to scale out on several cores if needed.". Scylla, on the other hand, thrives on multi-core machines. You should probably test the performance of all three products on your use case before making a decision.
How much of a disaster would be to suddenly lose the entire content of your cache? In some use cases, it just means you would need to query some slightly-slower backend server, so suddenly losing the cache on reboot is acceptable. In such cases, a memory-only cache like Memached or Redis is probably exactly what you need. However, in other cases, there may be a big penalty for starting from scratch with an empty cache - the backend server might be very slow, or maybe the original content is stored on a far-away server with a slow and expensive WAN. In such a case you would want a disk-backed cache, so if the memory cache is lost, you can still refresh it from disk and not from the backend server. Redis has a disk backing option, and in Scylla disk backing is the main way.
You mentioned a working set of 10 GB, which can easily fit memory of a single server. But is it possible this will grow and in a year you'll find yourself needing to cache 100 GB or 1 TB, which no longer fits the memory of a single server? In memcached you'll be out of luck. Redis used to have a "virtual memory" solution for this purpose, but it is deprecated and https://redis.io/topics/virtual-memory now states that Redis is "without considering at least for now the support for databases bigger than RAM". Scylla does handle this issue in two ways. First, your cache would be stored on disk which can be much larger than memory (and whatever amount of memory you have will be used to further speed up that cache, but it doesn't need to fit memory). Second, Scylla is a distributed server. It can distribute a 100 GB working set to 10 different nodes. Redis also has "replication", but it copies the entire data to all nodes - while Scylla can optionally store different subsets of the data on different nodes.
In-memory is actually a bad thing since RAM is expensive and not persistent. So Scylla will be a better option for K/V or columnar workloads. Scylla also has a limited Redis api with good results [1], using the CQL api will result in better results.
[1] https://medium.com/@siddharthc/redis-on-nvme-with-scylladb-5e12afd38dbc
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