Redis VS cockroach

Redis stands for REmote DIctionary Server. It is an open source, fast NoSQL database written in ANSI C and optimized for speed. Redis is an in-memory database that means that all data in Redis is stored in RAM, delivering the fastest possible access times to the data for both read and write requests.

Here, have some cache

Redis is an in-memory key-value store which can save abstract data structures with high performance. The open-source software is typically used for database, messaging, and caching functions.

According to its GitHub repository, Redis (stands for REmote DIctionary Server) is an in-memory data structure store. It is a disk-persistent key-value database with support for multiple data structures such as strings, hashes, lists, sets, bitmaps, etc.

Redis offers highly performant and efficient read and write along with few operations mentioned in this article. There are lots more. For more information, you can visit the documentation.

If you need distributed access then redis

Redis is another database commonly used for caching. You can download it from here.

import { ClientOpts, RedisClient } from "redis"; // TODO: When (if ever) redis supports expiration of hash entries we should remove this prefix and use a hash instead. (See https://github.com/redis/redis/issues/167) const KEY_PREFIX = "SESSION_BLACKLIST:"; const redisOptions: ClientOpts = { host: process.env.REDIS_HOST, // password: process.env.REDIS_PASSWORD, port: (process.env.REDIS_PORT && parseInt(process.env.REDIS_PORT)) || 6379, }; class TokenBlacklist { private readonly client: RedisClient; constructor() { this.client = new RedisClient(redisOptions); } public add(sessionId: string, expiresIn: number): Promise { return new Promise((resolve, reject) => { this.client.setex( KEY_PREFIX + sessionId, expiresIn + (2 * 60 * 1000), // Add 2 minute buffer "", (error: Error | null) => { if (error) { return reject(error); } resolve(); } ); }); } public check(sessionId: string): Promise { return new Promise((resolve, reject) => { this.client.exists( KEY_PREFIX + sessionId, (error: Error | null, exists: number) => { if (error) { return reject(error); } return resolve(exists === 1); } ); }); } public close(): Promise { return new Promise((resolve, reject) => { this.client.quit((error: Error | null) => { if (error) { return reject(error); } return resolve(); }); }); } }

If you're looking to save data like a hashmap, look into redis, although this is only an in-memory DB, if I remember correctly. You can try using it in combination with H2, which someone else already mentioned.

We developed the backend in Kotlin with the Spring Boot 2.5.3 framework and the Hibernate 5.4.32.Final ORM (Object Relational Mapping). We deployed it on an 8GB 4 CPU VPS through a Docker container managed by Dokku. The initial heap size was set to 2GB and limited to 7GB, while we allocated the remaining GB of RAM to a Redis-based caching system. We built the web application with performance in mind. Specifically, it's based on the multi-layered Spring Boot architecture described here and involves multi-thread processing. ​

Cockroach DB is a great example, it’s a PostgreSQL compatible database made to be distributed: https://github.com/cockroachdb/cockroach

But if you're looking for a highly scalable relational db then you can check YugaByteDB/CockroachDB (=both based upon PostGreSql) would also do the job well.

No matter how you cut it, my guess is that the most difficult aspect will be syncing up your data. You could into whether something like CockroachDB works with Wireguard. That'd help you simplify a good bit.

Uff... Let's go give a look at the licenses: https://github.com/cockroachdb/cockroach/blob/master/LICENSE

So many licenses, including the Business Source License which is non-free license.

Also there's this CockroachDB Community License (CCL), which I guess is a made-up license that's going to include some whatever terms.

I might still accept this mix of licenses however I can't find the definition of "foundational features" -- what do I "lose" by using the OSS version instead of one of their managed offerings?

From a quick overview, it's soft-no from me (in the sense that it probably wouldn't be my first choice).

Recently a colleague, Nathan, reflecting on CockroachDB, remarked (paraphrased from memory) that the key data structure is the interval btree. The story of Nathan’s addition of the first interval btree to cockroach and the power of copy-on-write data structures is worthy of its own blog post for another day. It’s Nathan’s hand-specialization of that data structure that provided the basis (and tests) for the generalization I’ll be presenting here. The reason for this specialization was as much for the performance wins of avoiding excessive allocations, pointer chasing, and cost of type assertions when using interface boxing.

I'm surprised Reviewable[0] hasn't come up in this discussion. It does a great job of allowing stacked code reviews and even handles rebases nicely; the reviewer sees the diff between commit #1 and commit #1' (prime = after rebase).

CockroachDB[1] has been using it since very early in the project.

[0] https://reviewable.io/

[1] https://github.com/cockroachdb/cockroach

And even if you did want to run your database on a bunch of untrusted machines, a blockchain, being a linked list, is not a particularly efficient implementation. Its size increases linearly with the number of operations, which, for any rapid-fire application such as banking, means you have a tremendously inefficient marginal computational and storage cost per operation. You’d be considerably better off running something like Cockroach, or FoundationDB, or more ‘out-there’ offerings like Hypercore.

And here: https://github.com/cockroachdb/cockroach/issues/16790

As you now know, we do not want to achieve this by restricting payments of some merchants to certain machines, as this would mean the machines are no longer linearly scalable. The information needs to be available locally, so we eventually decided on integrating Cockroach, a distributed database, with our PALs.