hash-db VS multiversion-concurrency-control

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.

The code is in this repository https://github.com/samsquire/multiversion-concurrency-control in MultiplexingThread.java and MultiplexProgramParser.java

https://github.com/samsquire/multiversion-concurrency-contro...

And I implemented a 3 way text diff with myers algorithm based on https://blog.jcoglan.com/2017/02/12/the-myers-diff-algorithm...

https://github.com/samsquire/text-diff

I implemented an eventually consistent mesh protocol that uses timestamps to provide last write wins

I think I lean towards per-thread sharding instead of mutex based or lock free data structures except for lockfree ringbuffers.

You can get embarassingly parallel performance if you split your data by thread and aggregate periodically.

If you need a consistent view of your entire set of data, that is slow path with sharding.

In my experiments with multithreaded software I simulate a bank where many bankaccounts are randomly withdrawn from and deposited to. https://github.com/samsquire/multiversion-concurrency-contro...

I get 700 million requests per second due to the sharding of money over accounts.

If you want some sample code to implement MVCC, I implemented MVCC in multithreaded Java as a toy example

https://github.com/samsquire/multiversion-concurrency-contro...

First read TransactionC.java then read MVCC.java

I wrote an unrolled switch statement in Java to simulate eager async/await across treads.

https://github.com/samsquire/multiversion-concurrency-contro...

The goal is that a compiler should generate this for you. This code is equivalent to the following:

   task1:

https://doc.rust-lang.org/book/ch16-00-concurrency.html

I've been working on implementing Java async/await state machine with switch statements and a scheduling loop. If the user doesn't await the async task handle, then the task's returnvalue is never handled. This is similar to the Go problem with the go statement.

https://github.com/samsquire/multiversion-concurrency-contro...

If your async call returns a handle and

yield value2++

https://github.com/samsquire/multiversion-concurrency-contro...

I am still working on allowing multiple coroutines to be in flight in parallel at the same time. At the moment the tasks share the same background thread.

I asked this stackoverflow question regarding C++ coroutines, as I wanted to use coroutines with a thread pool.

https://stackoverflow.com/questions/74520133/how-can-i-pass-...

This is very interesting. Thank you for submitting this and thank you for working on this.

I am highly interested in parallelism and high concurrency. I implemented multiversion concurrency control in Java.

https://github.com/samsquire/multiversion-concurrency-contro...

I am curious how to handle replication with high concurrency. I'm not sure how you detect dangerous reads+writes to the same key (tuples/fields) across different replica machines. In other words, multiple master.

I am aware Google uses truetime and some form of timestamp ordering and detection of interfering timestamps. But I'm not sure how to replicate that.

I began working on an algorithm to synchronize database records, do a sort, then a hash for each row where hash(row) = hash(previous_row.hash + row.data)

Then do a binary search on hashes matching/not matching. This is a synchronization algorithm I'm designing that requires minimal data transfer but multiple round trips.

The binary search would check the end of the data set for hash(replica_a.row[last]) == hash(replica_b.row[last]) then split the hash list in half and check the middle item, this shall tell you which row and which columns are different.