I like this strategy a lot, but the performance of read queries suffer if they span partitions, correct?
The issue I'm facing is a very large table, that is both write and read heavy, and the reads do not fall into a specific range of values for any particular column, so I don't think partitioning is an option.
Yes, partitioning will decrease a bit the read performance of queries not correlated with the partition key. That's why you need to periodically merge smaller partitions, so that you can keep the overall partition count bounded.
It is a lot of admin work, but if you really need to scale up Postgres write throughput, I don't see many other options without increasing hardware costs.
I assume you have already picked the low-hanging fruit discussed in the neighboring comments - batch writes, make sure you are using COPY instead of INSERT, tune Postgres parameters adequately and use the fastest disk you can grab for the WAL.
Partitioning is not all that expensive. It is definitely worth testing for your specific workload. We use TimescaleDB, which relies heavily on postgres partitions, have a bit under 100 million rows in our active set (last 90 days), across 120 partitions (device*time), and it works nicely. Over 100 partitions is probably a bit many for this workload, but since it works OK we have not changed it.
2. Deriving the private key(s) from the public key(s)
3. Creating and broadcasting its own transaction using the stolen keypairs before the original transaction confirms (presumably with a higher fee to win the confirmation race).
Please correct me if I'm wrong.
EDIT: correction: every transaction completely spends any selected UTXO of an associated keypair, not all of the "source keypairs' funds". Thus the attack vector also includes being able to steal from any keypair that has ever made a transaction and also has UTXOs.
The newest transaction mechanism (taproot; P2TR) exposes the public key of the receiver as part of the transaction. If it becomes more commonly used, the supply of bitcoins with exposed public keys would start going up again. See figure 5 of https://arxiv.org/pdf/2603.28846#page=14 .
Slightly off-topic, but I wish more OSS projects and maintainers would advertise cryptocurrency donation addresses. It's probably the easiest way for end users to donate.
I have done that for years, and so far have received the equivalent of $25 (through three mBTC transactions) on my Bitcoin address, and maybe $90 through whatever the token is Brave uses (BAT?).
I still get random donations through an old PayPal email address that's listed on the same page as my bitcoin address, and that totals more like $100 (a year, not over the lifetime).
That's what is suggested here but according to the Giant Impact Hypothesis the impact happened about 4.5 billion years ago and formed the Moon from debris, and it likely vaporized much of any existing water on proto-Earth rather than delivering it...
More investigations needed ...
The issue I'm facing is a very large table, that is both write and read heavy, and the reads do not fall into a specific range of values for any particular column, so I don't think partitioning is an option.