hash-db VS crdt-benchmarks

https://github.com/samsquire/hash-db

I need to combine the ideas in each of these projects into a cohesive solution.

I did some work on trying to implement the YATA algorithm, poorly.

From an SQL database implementation perspective, in my toy Python barebones SQL database that barely supports inner joins (https://github.com/samsquire/hash-db) I tested by testing on postgresql and seeing if my query with two joins produces the same results.

I ought to produce unit tests that prove that tuples from each join operation produces the correct dataset.

For a user perspective, I guess you could write some tooling that loads example data into a database and does an incremental join with each part of the join statement added.

I wrote a toy dynamodb, SQL, Cypher graph and document storage database engine in Python for the learning.

https://github.com/samsquire/hash-db

I am interested in database internals. Btrees come up with regard to designing database systems that are efficient to query on disk. Postgres uses them for its indexes. Radix trees are memory efficient tries which are useful for answering prefix queries. They're also called prefix trees. I use them to get a list of prefixes of a string. Useful for simple intellisense style forms or dynamodb style querying. I've also been studying LSM trees which are used in Leveldb and RocksDB.

I experiment with database technology in my experimental project hash-db https://github.com/samsquire/hash-db The code should be readable.

I need to change my search tree to be self balancing currently it grows to the left or right without balancing. I think I need to use tree rotation depending on which branch has the highest height.

Author of Yjs here. I'm all for faster data structures. But only benchmarking one dimension looks quite fishy to me. A CRDT needs to be adequate at multiple dimensions. At least you should describe the tradeoffs in your article.

The time to insert characters is the least interesting property of a CRDT. It doesn't matter to the user whether a character is inserted within .1ms or .000000001ms. No human can type that fast.

It would be much more interesting to benchmark the time it takes to load a document containing X operations. Yjs & Yrs are pretty performant and conservative on memory here because they don't have to build an index (it's a tradeoff that we took consciously).

When benchmarking it is important to measure the right things and interpret the results somehow so that you can give recommendations when to use your algorithm / implementation. Some things can't be fast/low enough (e.g. time to load a document, time to apply updates, memory consumption, ..) other things only need to be adequate (e.g. time to insert a character into a document).

Unfortunately, a lot of academic papers set a bad trend of only measuring one dimension. Yeah, it's really easy to succeed in one dimension (e.g. memory or insertion-time) and it is very nice click-bait. But that doesn't make your CRDT a viable option in practice.

I maintain a set of benchmarks that tests multiple dimensions [1]. I'd love to receive a PR from you.

[1]: https://github.com/dmonad/crdt-benchmarks

Diamond types author here! Congratulations on getting your crdt working! It’s lovely to see a new generation of CRDTs which have decent performance.

And nice stuff implementing peritext! I’d love to do the same in diamond types at some point. You beat me to it!

Im building a little repository of real world collaborative editing traces to use when benchmarking, comparing and optimising text based CRDTs[1]. The automerge-perf editing trace isn’t enough on its own. And we’re increasingly converging on a format for multi user concurrent editing traces too[2]. It’d be great to add some rich text editing traces in the mix if you’re interested in recording something, so we can also compare how peritext performs in different systems.

Anyway, welcome to the community! Love to have more implementations around!

https://github.com/josephg/crdt-benchmarks

https://github.com/dmonad/crdt-benchmarks/issues/20

So yes, a particularly large document is not the norm but it can happen.

JavaScript CRDTs can be quite performant, see the Yjs benchmarks: https://github.com/dmonad/crdt-benchmarks