Optimal plan construction is math-heavy, algorithm-heavy and vary even by workload. There are options like creating just-in-time indexes, so solution space grows even faster than article presents. Sometimes it is the query planner which is the slow part of total execution time.
LLM is kind of blunt weapon to use here. I am waiting rather for alphago style neural net heuristic.
I also wondered why an LLM would be the right starting point. Why would Balzac or billions of lines of rwir code or reddit be relevant to mapping this smallish, well-defined language (SQL) to this other tiny constrained specification language (the query plan suggestions)? You could make a (relatively) tiny network and then actually pass it some relevant features of the actual data, like as numbers, not just as text returned from a tool call.
It’s ultimately based on a lot of hand-written heuristics. Google has some non-LLM based machine learning technique to guide optimization heuristics in LLVM; that would be closer to what you are looking for.
You create formulas to estimate the cost of running a given query plan. Use statistics collected about the tables (e.g. how many rows) to try to be accurate. The topic is "Cost Based Optimization".
Only in the worst case when the plans are equivalent: If one plan is significantly faster, then it'll finish first, and the loser can get canceled before it finishes.
Good and bad plans can have orders of magnitude performance difference. The bad one can easily do enough damage cutting the performance in half before it is canceled.
At least in theory, the drivers being linked to the kernel and using the GPL symbols from the kernel means the drivers could be covered by the GPL whatever Google's intended license was. I'm sure they have $$$$ lawyers who have looked into this.
Many essential metabolic enzymes require metal ions for catalysis, structure, or substrate binding. So in some sense the dependence is still there, we just learned to take bits of substrate with us while travelling.
Does encrypted memory give ECC-like detection benefit, along with rowhammer protection, for cheaper cost (only additional computation), and 100% reliable?
We did try planet-scale geo engineering. It has worked so far, predictions were met. Heating through CO_2 and other heavy molecules works as predicted. Cooling through Sulfur compounds works even better than predicted, as evidenced by the sudden rise in temperatures after cessation of sulfur-rich fuels.
And in the end, all solutions we can attempt are technical and in some aspect untested. We just assume that the consequences of some of those solutions are small enough, but none have really been tested at scale.
The latest moves from google are the damning smoking gun evidence that anti monopoly court ever needs. "Do this or else". Recaptcha, gmail, google suite, android, chrome, colab and even google play must be viable businesses on their own, separate from google ads machine. Gmail must start competing for users with other email providers. And, yes, recaptcha must pay its infrastructure cost in full only from recaptcha revenue. This is the good way to level playing field, silence all the critics and let air into the room.
I like how they argued AI was a massive threat to their search empire and subsequently advertising empire ... and then a few weeks after the judge issued their astonishingly light measures to address Google's monopoly abuse, they partnered with Apple and achieved ~100% of default AI agents on smartphones being powered by Google!
LLM is kind of blunt weapon to use here. I am waiting rather for alphago style neural net heuristic.
reply