jumprope-rs VS RocksDB

Thanks for all the work in bootstrapping this part of the ecosystem! I opened an issue[1] on the memory issue for jumprope. It seems to really come down to the large size of skiplist nodes relative to the text.

I did some testing with JumpRopeBuf, but ultimately did not include it because I was comparing things from an "interactive editor" perspective where edits are applied immediately instead of a collaborative/CRDT use case where edits are async. But it did perform very well as you said! I feel like JumpRopeBuf feels similar to a piece table, where edits are stored separately and then joined reading.

[1] https://github.com/josephg/jumprope-rs/issues/5

Every piece of a large program should be tested like this. And if you can, test your whole program like this too. (Doable for most libraries, databases, compilers, etc. This is much harder for graphics engines or UI code.)

I've been doing this for years and I can't remember a single time I set something like this up and didn't find bugs. I'm constantly humbled by how effective fuzzy bois are.

This sounds complex, but code like this will usually be much smaller and easier to maintain than a thorough unit testing suite.

Here's an example from a rope (complex string) library I maintain. The library lets you insert or delete characters in a string at arbitrary locations. The randomizer loop is here[1]. I make Rope and a String, then in a loop make random changes and then call check() to make sure the contents match. And I check and all the expected internal invariants in the rope data structure hold:

[1] https://github.com/josephg/jumprope-rs/blob/ae2a3f3c2bc7fc1f...

When I first ran this test, it found a handful of bugs in my code. I also ran this same code on a few rust rope libraries in cargo, and about half of them fail this test.

Jumprope author here. Thanks for the quick test! I just updated the benchmarks in jumprope/rope_benches to include Crop, and it looks to me like jumprope is about 2x faster than crop:

I’d go further and say that writing most software without fuzz testing is insane. Fuzz testing is one of those things they should teach in school. They’re a super useful technique - up there with TDD and it’s a tragedy they aren’t more wildly used.

Fuzzers are so good because they find so many bugs relative to programmer effort (lines of code). They’re some of the most efficient testing you can do. If I had to choose between a full test suite and a fuzzer, I’d choose the fuzzer.

I use fuzzers whenever I have a self contained “machine” in my code which should have well defined behaviour. For example, a b-tree. I write little custom fuzzers each time. The fuzzing code randomly mutates the data structure and keeps a list of the expected btree content. Then periodically I verify that the list and the btree agree on what should be contained inside the list. In the project I’m working on at the moment, I have about 6 different fuzzers sprinkled throughout my testing code. (Btree fuzzer, rope fuzzer, file serialisation fuzzer, a few crdt fuzzers, and so on).

Writing fuzzers is quite devastating for the ego. Usually the first time I point a fuzzer at my code, even when my code has a lot of tests, the fuzzer throws an assertion failure instantly. “Iteration 2 … the state doesn’t match what was expected”.

Getting a fuzzer running all night without finding any bugs is a balm for the soul.

The code looks like this, if anyone is curious. Here’s a fuzzer for a rope (fancy string) implementation: https://github.com/josephg/jumprope-rs/blob/master/tests/tes...

Yep. A few years ago I implemented a skip list based rope library in C[1], and after learning rust I eventually ported it over[2].

The rust implementation was much less code than the C version. It generated a bigger assembly but it ran 20% faster or so. (I don't know why it ran faster than the C version - this was before the noalias analysis was turned on in the compiler).

Its now about 3x faster than C, thanks to some use of clever layered data structures. I could implement those optimizations in C, but I find rust easier to work with.

C has advantages, but performance is a bad reason to choose C over rust. In my experience, the runtime bounds checks it adds are remarkably cheap from a performance perspective. And its more than offset by the extra optimizations the rust compiler can do thanks to the extra knowledge the compiler has about your program. If my experience is anything to go by, naively porting C programs to rust would result in faster code a lot of the time.

And I find it easier to optimize rust code compared to C code, thanks to generics and the (excellent) crates ecosystem. If I was optimizing for runtime speed, I'd pick rust over C every time.

[1] https://github.com/josephg/librope

[2] https://github.com/josephg/jumprope-rs

Linked lists are also the basis for skip lists - which are awesome. One of the only data structures I know of which needs a random number generator to work correctly. I have a rope implementation that I tidied up over the last few days which uses a skip list. Its several times faster than the next fastest library I know of (ropey). They're both O(log n), but for some reason jumprope (with skip lists) still ended up several times faster than ropey's b-trees.

RocksDB: A high-performance embedded database optimized for multi-core CPUs and fast storage like SSDs. Its use of a log-structured merge-tree (LSM tree) makes it suitable for applications requiring high throughput and efficient storage, such as streaming data processing.

[RocksDB](https://rocksdb.org/) isn’t a distributed storage system, fwiw. It’s an embedded KV engine similar to LevelDB, LMDB, or really sqlite (though that’s full SQL, not just KV)

To output the top 3 rocks, our engine has to first store all the rocks in some sorted way. To do this, we of course picked RocksDB, an embedded lexicographically sorted key-value store, which acts as the sorting operation's persistent state. In our RocksDB state, the diffs are keyed by the value of weight, and since RocksDB is sorted, our stored diffs are automatically sorted by their weight.

The in-memory version of Memgraph uses Delta storage to support multi-version concurrency control (MVCC). However, for larger-than-memory storage, we decided to use the Optimistic Concurrency Control Protocol (OCC) since we assumed conflicts would rarely happen, and we could make use of RocksDB’s transactions without dealing with the custom layer of complexity like in the case of Delta storage.

I was looking at https://github.com/facebook/rocksdb/ but it seems to not allow queries by value, as my last requirmenet.

Tuning RocksDB well is a very very hard challenge, and one that I am happy to not do day to day anymore. RocksDB is very powerful but it comes with other very sharp edges. Compaction is one of those, and all answers are likely workload dependent.

If you are worried about write amplification then leveled compactions are sub-optimal. I would try the universal compaction.

- https://github.com/facebook/rocksdb/wiki/Universal-Compactio...

It's fairly challenging to write a KV database, and takes several years of development to get the balance right between performance and reliability and avoiding data loss. Maybe read through the documentation for RocksDB https://github.com/facebook/rocksdb/wiki/RocksDB-Overview and watch the video on why it was developed and that may give you an impression of what is involved.

LMDB is much more sain in the sense that it supports real ACID transactions instead of savepoints for RocksDB. The latter is heavy and consumes a lot more memory for a lot less read throughput. However, RocksDB has a much better parallel and concurrent write story, where you can merge entries with merge functions and therefore write from multiple CPUs.

https://github.com/facebook/rocksdb/issues?q=is%3Aissue+clic...

Here are some bugs in JeMalloc: