I'm genuinely surprised that there isn't column-level shared-dictionary string compression built into SQLite, MySQL/MariaDB or Postgres, like this post is describing.
SQLite has no compression support, MySQL/MariaDB have page-level compression which doesn't work great and I've never seen anyone enable in production, and Postgres has per-value compression which is good for extremely long strings, but useless for short ones.
There are just so many string columns where values and substrings get repeated so much, whether you're storing names, URL's, or just regular text. And I have databases I know would be reduced in size by at least half.
Is it just really really hard to maintain a shared dictionary when constantly adding and deleting values? Is there just no established reference algorithm for it?
It still seems like it would be worth it even if it were something you had to manually set. E.g. wait until your table has 100,000 values, build a dictionary from those, and the dictionary is set in stone and used for the next 10,000,000 rows too unless you rebuild it in the future (which would be an expensive operation).
Strings in textual index are already compressed, with common prefix compression or other schemes. They are perfectly queryable. Not sure if their compression scheme is for index or data columns.
Global column dictionary has more complexity than normal. Now you are touching more pages than just the index pages and data page. The dictionary entries are sorted, so you need to worry about page expansion and contraction. They sidestep the problems by making it immutable, presumably building it up front by scanning all the data.
Not sure why using FSST is better than using a standard compression algorithm to compress the dictionary entries.
Storing the strings themselves as dictionary IDs is a good idea, as they can be processed quickly with SIMD.
There are some databases that can move an entire column into the index. But that's mostly going to work for schemas where the number of distinct values is <<< rowcount, so that you're effectively interning the rows.
1, complicates and slows down update, which is typically more important in OLTP than OLAP
2, is generally bad for high cardinality columns, which requires tracking cardinality to make decisions, which further complicates things.
lastly, additional operational complexity (like the table maintenance system you described in last paragraph) could reduce system reliability, and they might decide it's not worth the price or against their philosophy.
I've implemented a similar system based on the original 2020 paper,
but we applied it to the text log to try to "extract" similar features
from free-form text. It looked promising and even supported full
regex search, but the work was ultimately abandoned when we got
acquired.
I was evaluating it recently but it's not FOSS, so buyer beware. I'm totally fine with commercialization, but I hesitate to build on top of data stores with no escape hatches or maintenance plans–especially when they're venture backed. It is self-hostable, but not OSS.
It's probably going to be acquired. The last effort to commercialize the TUM (Technical University of Munich) database group's work was acquired by Snowflake and disappeared into that stack.
CedarDB is the commercialization of Umbra, the TUM group's in-memory database lead by professor Thomas Neumann. Umbra is a successor to HyPer, so this is the third generation of the system Neumann came up with.
Umbra/CedarDB isn't a completely new way of doing database stuff, but basically a combination of several things that rearchitect the query engine from the ground up for modern systems: A query compiler that generates native code, a buffer pool manager optimized for multi core, push-based DAG execution that divides work into batches ("morsels"), and in-memory Adaptive Radix Tries (never used in a database before, I think).
It also has an advanced query planner that embraces the latest theoretical advances in query optimization, especially some techniques to unnest complex multi-join query plans, especially with queries that have a ton of joins. The TUM group has published some great papers on this.
Umbra is not an in-memory database (Hyper was). TUM gave up on the feasibility of in-memory databases several years ago (when the price of RAM relative to storage stopped falling).
Yeah I think the way Umbra was pitched when I watched the talks and read the paper was as more as "hybrid" in the sense that it aimed for something close to in-memory performance while optimizing the page-in/page-out performance profile.
The part of Umbra I found interesting was the buffer pool, so that's where focused most of my attention when reading though.
SQLite has no compression support, MySQL/MariaDB have page-level compression which doesn't work great and I've never seen anyone enable in production, and Postgres has per-value compression which is good for extremely long strings, but useless for short ones.
There are just so many string columns where values and substrings get repeated so much, whether you're storing names, URL's, or just regular text. And I have databases I know would be reduced in size by at least half.
Is it just really really hard to maintain a shared dictionary when constantly adding and deleting values? Is there just no established reference algorithm for it?
It still seems like it would be worth it even if it were something you had to manually set. E.g. wait until your table has 100,000 values, build a dictionary from those, and the dictionary is set in stone and used for the next 10,000,000 rows too unless you rebuild it in the future (which would be an expensive operation).
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