I don't know anything here, but this seems like a good case for ahead of time compilation? Or at least caching your JIT results? I can image much of the time, you are getting more or less the same query again and again?
Some years ago we ported some code from querying out the data and tallying in Python (how many are in each bucket) to using SQL to do that. It didn't speed up the execution. I was surprised by that, but I guess the Postgres interpreter is roughly the same speed as Python, which when you think about it perhaps isn't that surprising.
But Python is truly general purpose while the core query stuff in SQL is really specialized (we were not using stored procedures). So if Pypy can get 5x speedup, it seems to me that it should be possible to get the same kind of speed up in Postgres. I guess it needs funding and someone as smart as the Pypy people.
And then the issue is not dissimilar to Postgres’s planner issues.
The problem though was that it had a single shared pool for all queries and it could only run a query if it was in the pool, which is how out DB machine would max out at 50% CPU and bandwidth. We had made some mistakes in our search code that I told the engineer not to make.
MS SQL still has prepared statements and they really haven't been used in 20 years since it gained the ability to cache plans based on statement text.
So there is not much to gain from JITing the query plan execution only.
JITing begins to make more sense, when the individual query plan steps (join, filter, ...) themselves be specialized/recompiled/improved/merged by knowing the context of the query plan.
- not all databases need migrations (or migrations without downtime)
- alternatively, ship the migrations as part of the binary
Adhoc modifications would still be more difficult but tbh that’s not necessarily a bug
The problems related to PostgreSQL are pretty much all described here. It's very difficult to do low-latency queries if you cannot cache the compiled code and do it over and over again. And once your JIT is slow you need a logic to decide whether to interpret or compile.
I think it would be the best to start interpreting the query and start compilation in another thread, and once the compilation is finished and interpreter still running, stop the interpreter and run the JIT compiled code. This would give you the best latency, because there would be no waiting for JIT compiler.
This is not too difficult, it just requires a different execution style. Salesforce's Hyper for example very heavily relies on JIT compilation, as does Umbra [1], which some people regard as one of the fastest databases right now. Umbra doesn't cache any IR or compiled code and still has an extremely low start-up latency; an interpreter exists but is practically never used.
Postgres is very robust and very powerful, but simply not designed for fast execution of queries.
Disclosure: I work in the group that develops Umbra.
The problem will always be queries where the compilation is orders of magnitude more expensive than the query itself. I can imagine indexed lookup of 1 or few entries, etc... Accessing indexed entries like these are very well optimized by SQL query engines and possibly make no sense JIT optimizing.
This technique is known as a "tiered JIT". It's how production virtual machines operate for high-level languages like JavaScript.
There can be many tiers, like an interpreter, baseline compiler, optimizing compiler, etc. The runtime switches into the faster tier once it becomes ready.
More info for the interested:
Most other DB's cache query plans including jitted code so they are basically precompiled from one request to the next with the same statement.
https://www.postgresql.org/docs/current/parallel-query.html
"PostgreSQL can devise query plans that can leverage multiple CPUs in order to answer queries faster."
If process based then they can send small parts of plan across processes.
[1]: https://pkg.go.dev/github.com/jackc/pgx/v5#hdr-Prepared_Stat...
[2]: https://www.psycopg.org/psycopg3/docs/advanced/prepare.html
An awful lot of people have tried to use it as a JIT now and had to backpedal. I'm not sure how the one lead to the other but here we are.
Really amazed to see not one but several generic JIT frameworks though, no idea that was a thing.
This compares to clickhouse where it constantly uses the whole hardware. Obviously it's easier to do that on a columnar database but it seems that postgres is actively designed to _not_ saturate multiple cores, which may be a good assumption in the past but definitely isn't a good one now IMO.
* Latency. LLM responses are measured in order of 1000s of milliseconds, where this project targets 10s of milliseconds, that's off by almost two orders of magnitute.
* Determinism. LLMs are inherently non-deterministic. Even with temperature=0, slight variations of the input lead to major changes in output. You really don't want your DB to be non-deterministic, ever.
This isn't true, and certainly not inherently so.
Changes to input leading to changes in output does not violate determinism.
From what I understand, in practice it often is true[1]:
Matrix multiplication should be “independent” along every element in the batch — neither the other elements in the batch nor how large the batch is should affect the computation results of a specific element in the batch. However, as we can observe empirically, this isn’t true.
In other words, the primary reason nearly all LLM inference endpoints are nondeterministic is that the load (and thus batch-size) nondeterministically varies! This nondeterminism is not unique to GPUs — LLM inference endpoints served from CPUs or TPUs will also have this source of nondeterminism.
[1]: https://thinkingmachines.ai/blog/defeating-nondeterminism-in...
"But why aren’t LLM inference engines deterministic? One common hypothesis is that some combination of floating-point non-associativity and concurrent execution leads to nondeterminism based on which concurrent core finishes first."
From https://thinkingmachines.ai/blog/defeating-nondeterminism-in...
Better known as "seconds"...
