This kind of data is not used except in tests where it is used only to
create databases that don't match actual usage of aristo.
Removing simplifies future optimizations that can focus on processing
specific leaf types more efficiently.
A casualty of this removal is some test code as well as some proof
generation code that is unused - on the surface, it looks like it should
be possible to port both of these to the more specific data types -
doing so would ensure that a database written by one part of the
codebase can interact with the other - as it stands, there is confusion
on this point since using the proof generation code will result in a
database of a shape that is incompatible with the rest of eth1.
When `nimbus import` runs, we end up with a database without MPT roots
leading to long startup times the first time one is needed.
Computing the state root is slow because the on-disk order based on
VertexID sorting does not match the trie traversal order and therefore
makes lookups inefficent.
Here we introduce a helper that speeds up this computation by traversing
the trie in on-disk order and computing the trie hashes bottom up
instead - even though this leads to some redundant reads of nodes that
we cannot yet compute, it's still a net win as leaves and "bottom"
branches make up the majority of the database.
This PR also addresses a few other sources of inefficiency largely due
to the separation of AriKey and AriVtx into their own column families.
Each column family is its own LSM tree that produces hundreds of SST
filtes - with a limit of 512 open files, rocksdb must keep closing and
opening files which leads to expensive metadata reads during random
access.
When rocksdb makes a lookup, it has to read several layers of files for
each lookup. Ribbon filters to skip over files that don't have the
requested data but when these filters are not in memory, reading them is
slow - this happens in two cases: when opening a file and when the
filter has been evicted from the LRU cache. Addressing the open file
limit solves one source of inefficiency, but we must also increase the
block cache size to deal with this problem.
* rocksdb.max_open_files increased to 2048
* per-file size limits increased so that fewer files are created
* WAL size increased to avoid partial flushes which lead to small files
* rocksdb block cache increased
All these increases of course lead to increased memory usage, but at
least performance is acceptable - in the future, we'll need to explore
options such as joining AriVtx and AriKey and/or reducing the row count
(by grouping branch layers under a single vertexid).
With this PR, the mainnet state root can be computed in ~8 hours (down
from 2-3 days) - not great, but still better.
Further, we write all keys to the database, also those that are less
than 32 bytes - because the mpt path is part of the input, it is very
rare that we actually hit a key like this (about 200k such entries on
mainnet), so the code complexity is not worth the benefit really, in the
current database layout / design.
* batch database key writes during `computeKey` calls
* log progress when there are many keys to update
* avoid evicting the vertex cache when traversing the trie for key
computation purposes
* avoid storing trivial leaf hashes that directly can be loaded from the
vertex
* move pfx out of variant which avoids pointless field type panic checks
and copies on access
* make `VertexRef` a non-inheritable object which reduces its memory
footprint and simplifies its use - it's also unclear from a semantic
point of view why inheritance makes sense for storing keys
* pre-allocate `blobify` data and remove redundant error handling
(cannot fail on correct data)
* use threadvar for temporary storage when decoding rdb, avoiding
closure env
* speed up database walkers by avoiding many temporaries
~5% perf improvement on block import, 100x on database iteration (useful
for building analysis tooling)
When lazily verifying state roots, we may end up with an entire state
without roots that gets computed for the whole database - in the current
design, that would result in hashes for the entire trie being held in
memory.
Since the hash depends only on the data in the vertex, we can store it
directly at the top-most level derived from the verticies it depends on
- be that memory or database - this makes the memory usage broadly
linear with respect to the already-existing in-memory change set stored
in the layers.
It also ensures that if we have multiple forks in memory, hashes get
cached in the correct layer maximising reuse between forks.
The same layer numbering scheme as elsewhere is reused, where -2 is the
backend, -1 is the balancer, then 0+ is the top of the stack and stack.
A downside of this approach is that we create many small batches - a
future improvement could be to collect all such writes in a single
batch, though the memory profile of this approach should be examined
first (where is the batch kept, exactly?).
* Remove cruft left-over from PR #2494
* TODO
* Update comments on `HashKey` type values
* Remove obsolete hash key conversion flag `forceRoot`
why:
Is treated implicitly by having vertex keys as `HashKey` type and
root vertex states converted to `Hash256`
* Imported/rebase from `no-ext`, PR #2485
Store extension nodes together with the branch
Extension nodes must be followed by a branch - as such, it makes sense
to store the two together both in the database and in memory:
* fewer reads, writes and updates to traverse the tree
* simpler logic for maintaining the node structure
* less space used, both memory and storage, because there are fewer
nodes overall
There is also a downside: hashes can no longer be cached for an
extension - instead, only the extension+branch hash can be cached - this
seems like a fine tradeoff since computing it should be fast.
TODO: fix commented code
* Fix merge functions and `toNode()`
* Update `merkleSignCommit()` prototype
why:
Result is always a 32bit hash
* Update short Merkle hash key generation
details:
Ethereum reference MPTs use Keccak hashes as node links if the size of
an RLP encoded node is at least 32 bytes. Otherwise, the RLP encoded
node value is used as a pseudo node link (rather than a hash.) This is
specified in the yellow paper, appendix D.
Different to the `Aristo` implementation, the reference MPT would not
store such a node on the key-value database. Rather the RLP encoded node value is stored instead of a node link in a parent node
is stored as a node link on the parent database.
Only for the root hash, the top level node is always referred to by the
hash.
* Fix/update `Extension` sections
why:
Were commented out after removal of a dedicated `Extension` type which
left the system disfunctional.
* Clean up unused error codes
* Update unit tests
* Update docu
---------
Co-authored-by: Jacek Sieka <jacek@status.im>
Introduce a new `StoData` payload type similar to `AccountData`
* slightly more efficient storage format
* typed api
* fewer seqs
* fix encoding docs - it wasn't rlp after all :)
The state and account MPT:s currenty share key space in the database
based on that vertex id:s are assigned essentially randomly, which means
that when two adjacent slot values from the same contract are accessed,
they might reside at large distance from each other.
Here, we prefix each vertex id by its root causing them to be sorted
together thus bringing all data belonging to a particular contract
closer together - the same effect also happens for the main state MPT
whose nodes now end up clustered together more tightly.
In the future, the prefix given to the storage keys can also be used to
perform range operations such as reading all the storage at once and/or
deleting an account with a batch operation.
Notably, parts of the API already supported this rooting concept while
parts didn't - this PR makes the API consistent by always working with a
root+vid.
* Remove `dirty` set from structural objects
why:
Not used anymore, the tree is dirty by default.
* Rename `aristo_hashify` -> `aristo_compute`
* Remove cruft, update comments, cosmetics, etc.
* Simplify `SavedState` object
why:
The key chaining have become obsolete after extra lazy hashing. There
is some available space for a state hash to be maintained in future.
details:
Accept the legacy `SavedState` object serialisation format for a
while (which will be overwritten by new format.)
Jacek Sieka
Jordan Hrycaj