Chronicle Map VS H2

Chronicle-Map: Chronicle Map is an in-memory, key-value store, designed for low-latency, and/or multi-process applications.

https://github.com/OpenHFT/Chronicle-Map - Maybe a better offheap map

I've wrangled data sets in the ~600gb range using nothing but plain old Java and a few beefy boxes. This can all be kept in memory, but you have to go off-heap. You can use Chronicle Map and Chronicle Values to model this data and work with it off-heap in a way that's still very clean and object oriented. 128gb of RAM is cheap these days, whether you're in the cloud or not.

> ...than it would be to learn the exact syntax and quirks and possibly bugs of someone else's implementation...

Yup. Also, having deep knowledge of the language is required.

SQLite's grammar is neat. Creating a compatible parser would make a fun project. Here's a pretty good example: https://github.com/bkiers/sqlite-parser (Actual ANTLR 4 grammar: https://github.com/bkiers/sqlite-parser/blob/master/src/main... )

Postgres, which tries to be compliant with the latest standards, however...

SQL-2016 is a beast. Not to mention all the dialects.

I'm updating my personal (soon to be FOSS) grammar from ANTLR 3 LL(k) to ANTLR 4 ALL().

I've long had a working knowledge of SQL-92, with some SQL-1999 (eg common table expressions).

But the new structures and extensions are a bit overwhelming.

Fortunately, ANTLR project has ~dozen FOSS grammars to learn from. https://github.com/antlr/grammars-v4/tree/master/sql

They mostly mechanically translate BNFs to LL(k) with some ALL(). Meaning few take advantage of left-recursion. https://github.com/antlr/antlr4/blob/master/doc/left-recursi...

Honestly, I struggled to understand these grammars. Plus, not being conversant with the SQL-2016 was a huge impediment. Just finding a succinct corbis of test cases was a huge hurdle for me.

Fortunately, the H2 Database project is a great resource. https://github.com/h2database/h2database/tree/master/h2/src/...

Now for the exciting conclusion...

My ANTLR grammar which passes all of H2's tests looks nothing like any of the official or product specific BNFs.

Further, I found discrepancy between the product specific BNFs and their implementations.

So a lot of trial & error is required for a "real world" parser. Which would explain why the professional SQL parsing tools charge money.

I still think creating a parser for SQLite is a great project.

H2 - Free, Embedded & Open source

H2 2.1

H2 for mocking relational database connections

Firstly, let's set up a basic Spring Boot application. We can use the Spring Initializr with Spring Data JPA, H2 Database, Lombok added. H2 Database will provide us with a simple database, and Spring Data JPA will allow us to easily interact with it using Hibernate. Lombok will make it easier to write concise and readable classes.

Sorting chunks by similarity: commonly used tools don't do that. Most archive tools only sort by file type.

I wrote a tool that chunks the data (into variable-sized blocks, to re-sync if there are multiple files that have different length prefixes, but that's another story), and then sorts the chunks by LSH (locality sensitive hash). LSH is used by search engines to detect similar text. It can compress directories that contain multiple version of e.g. source code very well (e.g. trunk, branches). https://github.com/h2database/h2database/blob/master/h2/src/...

I discussed this approach with a researcher in this area in January 2020. AFAIK there is active research in this area, specially to compress DNA sequences. But he also wasn't aware of papers or research in this area for general-purpose data compression.

So, I think this area is largely uncharted. I would be interested (as a hobby side project) to help, if somebody is interested.