nimbus-eth1/nimbus/db/aristo/aristo_delete.nim

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# nimbus-eth1
# Copyright (c) 2023-2024 Status Research & Development GmbH
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# Licensed under either of
# * Apache License, version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or
# http://www.apache.org/licenses/LICENSE-2.0)
# * MIT license ([LICENSE-MIT](LICENSE-MIT) or
# http://opensource.org/licenses/MIT)
# at your option. This file may not be copied, modified, or distributed
# except according to those terms.
## Aristo DB -- Patricia Trie delete funcionality
## ==============================================
##
{.push raises: [].}
import
std/typetraits,
eth/common,
results,
Store keys together with node data (#2849) Currently, computed hash keys are stored in a separate column family with respect to the MPT data they're generated from - this has several disadvantages: * A lot of space is wasted because the lookup key (`RootedVertexID`) is repeated in both tables - this is 30% of the `AriKey` content! * rocksdb must maintain in-memory bloom filters and LRU caches for said keys, doubling its "minimal efficient cache size" * An extra disk traversal must be made to check for existence of cached hash key * Doubles the amount of files on disk due to each column family being its own set of files Here, the two CFs are joined such that both key and data is stored in `AriVtx`. This means: * we save ~30% disk space on repeated lookup keys * we save ~2gb of memory overhead that can be used to cache data instead of indices * we can skip storing hash keys for MPT leaf nodes - these are trivial to compute and waste a lot of space - previously they had to present in the `AriKey` CF to avoid having to look in two tables on the happy path. * There is a small increase in write amplification because when a hash value is updated for a branch node, we must write both key and branch data - previously we would write only the key * There's a small shift in CPU usage - instead of performing lookups in the database, hashes for leaf nodes are (re)-computed on the fly * We can return to slightly smaller on-disk SST files since there's fewer of them, which should reduce disk traffic a bit Internally, there are also other advantages: * when clearing keys, we no longer have to store a zero hash in memory - instead, we deduce staleness of the cached key from the presence of an updated VertexRef - this saves ~1gb of mem overhead during import * hash key cache becomes dedicated to branch keys since leaf keys are no longer stored in memory, reducing churn * key computation is a lot faster thanks to the skipped second disk traversal - a key computation for mainnet can be completed in 11 hours instead of ~2 days (!) thanks to better cache usage and less read amplification - with additional improvements to the on-disk format, we can probably get rid of the initial full traversal method of seeding the key cache on first start after import All in all, this PR reduces the size of a mainnet database from 160gb to 110gb and the peak memory footprint during import by ~1-2gb.
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./aristo_delete/delete_subtree,
"."/[aristo_desc, aristo_fetch, aristo_get, aristo_hike, aristo_layers]
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# ------------------------------------------------------------------------------
# Private heplers
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# ------------------------------------------------------------------------------
Pre-allocate vids for branches (#2882) Each branch node may have up to 16 sub-items - currently, these are given VertexID based when they are first needed leading to a mostly-random order of vertexid for each subitem. Here, we pre-allocate all 16 vertex ids such that when a branch subitem is filled, it already has a vertexid waiting for it. This brings several important benefits: * subitems are sorted and "close" in their id sequencing - this means that when rocksdb stores them, they are likely to end up in the same data block thus improving read efficiency * because the ids are consequtive, we can store just the starting id and a bitmap representing which subitems are in use - this reduces disk space usage for branches allowing more of them fit into a single disk read, further improving disk read and caching performance - disk usage at block 18M is down from 84 to 78gb! * the in-memory footprint of VertexRef reduced allowing more instances to fit into caches and less memory to be used overall. Because of the increased locality of reference, it turns out that we no longer need to iterate over the entire database to efficiently generate the hash key database because the normal computation is now faster - this significantly benefits "live" chain processing as well where each dirtied key must be accompanied by a read of all branch subitems next to it - most of the performance benefit in this branch comes from this locality-of-reference improvement. On a sample resync, there's already ~20% improvement with later blocks seeing increasing benefit (because the trie is deeper in later blocks leading to more benefit from branch read perf improvements) ``` blocks: 18729664, baseline: 190h43m49s, contender: 153h59m0s Time (total): -36h44m48s, -19.27% ``` Note: clients need to be resynced as the PR changes the on-disk format R.I.P. little bloom filter - your life in the repo was short but valuable
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proc branchStillNeeded(vtx: VertexRef, removed: int8): Result[int8,void] =
## Returns the nibble if there is only one reference left.
Pre-allocate vids for branches (#2882) Each branch node may have up to 16 sub-items - currently, these are given VertexID based when they are first needed leading to a mostly-random order of vertexid for each subitem. Here, we pre-allocate all 16 vertex ids such that when a branch subitem is filled, it already has a vertexid waiting for it. This brings several important benefits: * subitems are sorted and "close" in their id sequencing - this means that when rocksdb stores them, they are likely to end up in the same data block thus improving read efficiency * because the ids are consequtive, we can store just the starting id and a bitmap representing which subitems are in use - this reduces disk space usage for branches allowing more of them fit into a single disk read, further improving disk read and caching performance - disk usage at block 18M is down from 84 to 78gb! * the in-memory footprint of VertexRef reduced allowing more instances to fit into caches and less memory to be used overall. Because of the increased locality of reference, it turns out that we no longer need to iterate over the entire database to efficiently generate the hash key database because the normal computation is now faster - this significantly benefits "live" chain processing as well where each dirtied key must be accompanied by a read of all branch subitems next to it - most of the performance benefit in this branch comes from this locality-of-reference improvement. On a sample resync, there's already ~20% improvement with later blocks seeing increasing benefit (because the trie is deeper in later blocks leading to more benefit from branch read perf improvements) ``` blocks: 18729664, baseline: 190h43m49s, contender: 153h59m0s Time (total): -36h44m48s, -19.27% ``` Note: clients need to be resynced as the PR changes the on-disk format R.I.P. little bloom filter - your life in the repo was short but valuable
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var nibble = -1'i8
for n in 0'i8 .. 15'i8:
if n == removed:
continue
Pre-allocate vids for branches (#2882) Each branch node may have up to 16 sub-items - currently, these are given VertexID based when they are first needed leading to a mostly-random order of vertexid for each subitem. Here, we pre-allocate all 16 vertex ids such that when a branch subitem is filled, it already has a vertexid waiting for it. This brings several important benefits: * subitems are sorted and "close" in their id sequencing - this means that when rocksdb stores them, they are likely to end up in the same data block thus improving read efficiency * because the ids are consequtive, we can store just the starting id and a bitmap representing which subitems are in use - this reduces disk space usage for branches allowing more of them fit into a single disk read, further improving disk read and caching performance - disk usage at block 18M is down from 84 to 78gb! * the in-memory footprint of VertexRef reduced allowing more instances to fit into caches and less memory to be used overall. Because of the increased locality of reference, it turns out that we no longer need to iterate over the entire database to efficiently generate the hash key database because the normal computation is now faster - this significantly benefits "live" chain processing as well where each dirtied key must be accompanied by a read of all branch subitems next to it - most of the performance benefit in this branch comes from this locality-of-reference improvement. On a sample resync, there's already ~20% improvement with later blocks seeing increasing benefit (because the trie is deeper in later blocks leading to more benefit from branch read perf improvements) ``` blocks: 18729664, baseline: 190h43m49s, contender: 153h59m0s Time (total): -36h44m48s, -19.27% ``` Note: clients need to be resynced as the PR changes the on-disk format R.I.P. little bloom filter - your life in the repo was short but valuable
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if vtx.bVid(uint8 n).isValid:
if 0 <= nibble:
return ok(-1)
nibble = n
if 0 <= nibble:
return ok(nibble)
# Oops, degenerated branch node
err()
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# ------------------------------------------------------------------------------
# Private functions
# ------------------------------------------------------------------------------
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proc deleteImpl(
db: AristoDbRef; # Database, top layer
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hike: Hike; # Fully expanded path
): Result[VertexRef,AristoError] =
## Removes the last node in the hike and returns the updated leaf in case
## a branch collapsed
Core db aristo hasher profiling and timing improvement (#1938) * Explicitly use shared `Kvt` table on `Ledger` and `Clique` lookup. why: Speeds up lookup time with `Aristo` backend. For writing `Clique` data, the `Companion` model allows to write `Clique` data past the database locked by evm transactions. * Implement `CoreDb` profiling with API tracking why: Chasing time spent per APT procs ... * Implement `Ledger` profiling with API tracking why: Chasing time spent per APT procs ... * Always hashify when commiting or storing why: A dirty cache makes no sense when committing * Make sure that a zero key is created when adding/updating vertices why: This is an error fix mainly for edge cases. A typical error was that the root key got deleted when there were only a few vertices left on the DB. * Need all created and changed vertices zero-keyed on the cache why: A zero key (i.e. empty Merkle hash) indicates that a vertex key needs to be updated. This would not be needed immediately after a merge as there is an actual leaf path on the cache layer. But after subsequent merge and delete operations this information might get blurred. * Re-org hashing algorithm why: Apart from errors, the previous implementation was too slow for two reasons: + some control hashes were calculated for debugging (now all verification is done in `aristo_check` module) + the leaf paths stored on the cache are used to build the labelling (aka hashing) schedule; there paths were accumulated over successive hash sessions although it is clear that all keys were generated, already
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# Remove leaf entry
let lf = hike.legs[^1].wp
if lf.vtx.vType != Leaf:
return err(DelLeafExpexted)
Store keys together with node data (#2849) Currently, computed hash keys are stored in a separate column family with respect to the MPT data they're generated from - this has several disadvantages: * A lot of space is wasted because the lookup key (`RootedVertexID`) is repeated in both tables - this is 30% of the `AriKey` content! * rocksdb must maintain in-memory bloom filters and LRU caches for said keys, doubling its "minimal efficient cache size" * An extra disk traversal must be made to check for existence of cached hash key * Doubles the amount of files on disk due to each column family being its own set of files Here, the two CFs are joined such that both key and data is stored in `AriVtx`. This means: * we save ~30% disk space on repeated lookup keys * we save ~2gb of memory overhead that can be used to cache data instead of indices * we can skip storing hash keys for MPT leaf nodes - these are trivial to compute and waste a lot of space - previously they had to present in the `AriKey` CF to avoid having to look in two tables on the happy path. * There is a small increase in write amplification because when a hash value is updated for a branch node, we must write both key and branch data - previously we would write only the key * There's a small shift in CPU usage - instead of performing lookups in the database, hashes for leaf nodes are (re)-computed on the fly * We can return to slightly smaller on-disk SST files since there's fewer of them, which should reduce disk traffic a bit Internally, there are also other advantages: * when clearing keys, we no longer have to store a zero hash in memory - instead, we deduce staleness of the cached key from the presence of an updated VertexRef - this saves ~1gb of mem overhead during import * hash key cache becomes dedicated to branch keys since leaf keys are no longer stored in memory, reducing churn * key computation is a lot faster thanks to the skipped second disk traversal - a key computation for mainnet can be completed in 11 hours instead of ~2 days (!) thanks to better cache usage and less read amplification - with additional improvements to the on-disk format, we can probably get rid of the initial full traversal method of seeding the key cache on first start after import All in all, this PR reduces the size of a mainnet database from 160gb to 110gb and the peak memory footprint during import by ~1-2gb.
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db.layersResVtx((hike.root, lf.vid))
if hike.legs.len == 1:
# This was the last node in the trie, meaning we don't have any branches or
# leaves to update
return ok(nil)
if hike.legs[^2].wp.vtx.vType != Branch:
return err(DelBranchExpexted)
# Get current `Branch` vertex `br`
let
br = hike.legs[^2].wp
nbl = br.vtx.branchStillNeeded(hike.legs[^2].nibble).valueOr:
return err(DelBranchWithoutRefs)
# Clear all Merkle hash keys up to the root key
for n in 0 .. hike.legs.len - 2:
Store keys together with node data (#2849) Currently, computed hash keys are stored in a separate column family with respect to the MPT data they're generated from - this has several disadvantages: * A lot of space is wasted because the lookup key (`RootedVertexID`) is repeated in both tables - this is 30% of the `AriKey` content! * rocksdb must maintain in-memory bloom filters and LRU caches for said keys, doubling its "minimal efficient cache size" * An extra disk traversal must be made to check for existence of cached hash key * Doubles the amount of files on disk due to each column family being its own set of files Here, the two CFs are joined such that both key and data is stored in `AriVtx`. This means: * we save ~30% disk space on repeated lookup keys * we save ~2gb of memory overhead that can be used to cache data instead of indices * we can skip storing hash keys for MPT leaf nodes - these are trivial to compute and waste a lot of space - previously they had to present in the `AriKey` CF to avoid having to look in two tables on the happy path. * There is a small increase in write amplification because when a hash value is updated for a branch node, we must write both key and branch data - previously we would write only the key * There's a small shift in CPU usage - instead of performing lookups in the database, hashes for leaf nodes are (re)-computed on the fly * We can return to slightly smaller on-disk SST files since there's fewer of them, which should reduce disk traffic a bit Internally, there are also other advantages: * when clearing keys, we no longer have to store a zero hash in memory - instead, we deduce staleness of the cached key from the presence of an updated VertexRef - this saves ~1gb of mem overhead during import * hash key cache becomes dedicated to branch keys since leaf keys are no longer stored in memory, reducing churn * key computation is a lot faster thanks to the skipped second disk traversal - a key computation for mainnet can be completed in 11 hours instead of ~2 days (!) thanks to better cache usage and less read amplification - with additional improvements to the on-disk format, we can probably get rid of the initial full traversal method of seeding the key cache on first start after import All in all, this PR reduces the size of a mainnet database from 160gb to 110gb and the peak memory footprint during import by ~1-2gb.
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let wp = hike.legs[n].wp
db.layersResKey((hike.root, wp.vid), wp.vtx)
if 0 <= nbl:
# Branch has only one entry - move that entry to where the branch was and
# update its path
# Get child vertex (there must be one after a `Branch` node)
let
Pre-allocate vids for branches (#2882) Each branch node may have up to 16 sub-items - currently, these are given VertexID based when they are first needed leading to a mostly-random order of vertexid for each subitem. Here, we pre-allocate all 16 vertex ids such that when a branch subitem is filled, it already has a vertexid waiting for it. This brings several important benefits: * subitems are sorted and "close" in their id sequencing - this means that when rocksdb stores them, they are likely to end up in the same data block thus improving read efficiency * because the ids are consequtive, we can store just the starting id and a bitmap representing which subitems are in use - this reduces disk space usage for branches allowing more of them fit into a single disk read, further improving disk read and caching performance - disk usage at block 18M is down from 84 to 78gb! * the in-memory footprint of VertexRef reduced allowing more instances to fit into caches and less memory to be used overall. Because of the increased locality of reference, it turns out that we no longer need to iterate over the entire database to efficiently generate the hash key database because the normal computation is now faster - this significantly benefits "live" chain processing as well where each dirtied key must be accompanied by a read of all branch subitems next to it - most of the performance benefit in this branch comes from this locality-of-reference improvement. On a sample resync, there's already ~20% improvement with later blocks seeing increasing benefit (because the trie is deeper in later blocks leading to more benefit from branch read perf improvements) ``` blocks: 18729664, baseline: 190h43m49s, contender: 153h59m0s Time (total): -36h44m48s, -19.27% ``` Note: clients need to be resynced as the PR changes the on-disk format R.I.P. little bloom filter - your life in the repo was short but valuable
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vid = br.vtx.bVid(uint8 nbl)
nxt = db.getVtx (hike.root, vid)
if not nxt.isValid:
return err(DelVidStaleVtx)
Store keys together with node data (#2849) Currently, computed hash keys are stored in a separate column family with respect to the MPT data they're generated from - this has several disadvantages: * A lot of space is wasted because the lookup key (`RootedVertexID`) is repeated in both tables - this is 30% of the `AriKey` content! * rocksdb must maintain in-memory bloom filters and LRU caches for said keys, doubling its "minimal efficient cache size" * An extra disk traversal must be made to check for existence of cached hash key * Doubles the amount of files on disk due to each column family being its own set of files Here, the two CFs are joined such that both key and data is stored in `AriVtx`. This means: * we save ~30% disk space on repeated lookup keys * we save ~2gb of memory overhead that can be used to cache data instead of indices * we can skip storing hash keys for MPT leaf nodes - these are trivial to compute and waste a lot of space - previously they had to present in the `AriKey` CF to avoid having to look in two tables on the happy path. * There is a small increase in write amplification because when a hash value is updated for a branch node, we must write both key and branch data - previously we would write only the key * There's a small shift in CPU usage - instead of performing lookups in the database, hashes for leaf nodes are (re)-computed on the fly * We can return to slightly smaller on-disk SST files since there's fewer of them, which should reduce disk traffic a bit Internally, there are also other advantages: * when clearing keys, we no longer have to store a zero hash in memory - instead, we deduce staleness of the cached key from the presence of an updated VertexRef - this saves ~1gb of mem overhead during import * hash key cache becomes dedicated to branch keys since leaf keys are no longer stored in memory, reducing churn * key computation is a lot faster thanks to the skipped second disk traversal - a key computation for mainnet can be completed in 11 hours instead of ~2 days (!) thanks to better cache usage and less read amplification - with additional improvements to the on-disk format, we can probably get rid of the initial full traversal method of seeding the key cache on first start after import All in all, this PR reduces the size of a mainnet database from 160gb to 110gb and the peak memory footprint during import by ~1-2gb.
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db.layersResVtx((hike.root, vid))
let vtx =
case nxt.vType
of Leaf:
VertexRef(
vType: Leaf,
pfx: br.vtx.pfx & NibblesBuf.nibble(nbl.byte) & nxt.pfx,
lData: nxt.lData)
of Branch:
VertexRef(
vType: Branch,
pfx: br.vtx.pfx & NibblesBuf.nibble(nbl.byte) & nxt.pfx,
Pre-allocate vids for branches (#2882) Each branch node may have up to 16 sub-items - currently, these are given VertexID based when they are first needed leading to a mostly-random order of vertexid for each subitem. Here, we pre-allocate all 16 vertex ids such that when a branch subitem is filled, it already has a vertexid waiting for it. This brings several important benefits: * subitems are sorted and "close" in their id sequencing - this means that when rocksdb stores them, they are likely to end up in the same data block thus improving read efficiency * because the ids are consequtive, we can store just the starting id and a bitmap representing which subitems are in use - this reduces disk space usage for branches allowing more of them fit into a single disk read, further improving disk read and caching performance - disk usage at block 18M is down from 84 to 78gb! * the in-memory footprint of VertexRef reduced allowing more instances to fit into caches and less memory to be used overall. Because of the increased locality of reference, it turns out that we no longer need to iterate over the entire database to efficiently generate the hash key database because the normal computation is now faster - this significantly benefits "live" chain processing as well where each dirtied key must be accompanied by a read of all branch subitems next to it - most of the performance benefit in this branch comes from this locality-of-reference improvement. On a sample resync, there's already ~20% improvement with later blocks seeing increasing benefit (because the trie is deeper in later blocks leading to more benefit from branch read perf improvements) ``` blocks: 18729664, baseline: 190h43m49s, contender: 153h59m0s Time (total): -36h44m48s, -19.27% ``` Note: clients need to be resynced as the PR changes the on-disk format R.I.P. little bloom filter - your life in the repo was short but valuable
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startVid: nxt.startVid,
used: nxt.used)
# Put the new vertex at the id of the obsolete branch
db.layersPutVtx((hike.root, br.vid), vtx)
if vtx.vType == Leaf:
ok(vtx)
else:
ok(nil)
else:
# Clear the removed leaf from the branch (that still contains other children)
let brDup = br.vtx.dup
Pre-allocate vids for branches (#2882) Each branch node may have up to 16 sub-items - currently, these are given VertexID based when they are first needed leading to a mostly-random order of vertexid for each subitem. Here, we pre-allocate all 16 vertex ids such that when a branch subitem is filled, it already has a vertexid waiting for it. This brings several important benefits: * subitems are sorted and "close" in their id sequencing - this means that when rocksdb stores them, they are likely to end up in the same data block thus improving read efficiency * because the ids are consequtive, we can store just the starting id and a bitmap representing which subitems are in use - this reduces disk space usage for branches allowing more of them fit into a single disk read, further improving disk read and caching performance - disk usage at block 18M is down from 84 to 78gb! * the in-memory footprint of VertexRef reduced allowing more instances to fit into caches and less memory to be used overall. Because of the increased locality of reference, it turns out that we no longer need to iterate over the entire database to efficiently generate the hash key database because the normal computation is now faster - this significantly benefits "live" chain processing as well where each dirtied key must be accompanied by a read of all branch subitems next to it - most of the performance benefit in this branch comes from this locality-of-reference improvement. On a sample resync, there's already ~20% improvement with later blocks seeing increasing benefit (because the trie is deeper in later blocks leading to more benefit from branch read perf improvements) ``` blocks: 18729664, baseline: 190h43m49s, contender: 153h59m0s Time (total): -36h44m48s, -19.27% ``` Note: clients need to be resynced as the PR changes the on-disk format R.I.P. little bloom filter - your life in the repo was short but valuable
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discard brDup.setUsed(uint8 hike.legs[^2].nibble, false)
db.layersPutVtx((hike.root, br.vid), brDup)
ok(nil)
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# ------------------------------------------------------------------------------
# Public functions
# ------------------------------------------------------------------------------
Update storage tree admin (#2419) * Tighten `CoreDb` API for accounts why: Apart from cruft, the way to fetch the accounts state root via a `CoreDbColRef` record was unnecessarily complicated. * Extend `CoreDb` API for accounts to cover storage tries why: In future, this will make the notion of column objects obsolete. Storage trees will then be indexed by the account address rather than the vertex ID equivalent like a `CoreDbColRef`. * Apply new/extended accounts API to ledger and tests details: This makes the `distinct_ledger` module obsolete * Remove column object constructors why: They were needed as an abstraction of MPT sub-trees including storage trees. Now, storage trees are handled by the account (e.g. via address) they belong to and all other trees can be identified by a constant well known vertex ID. So there is no need for column objects anymore. Still there are some left-over column object methods wnich will be removed next. * Remove `serialise()` and `PayloadRef` from default Aristo API why: Not needed. `PayloadRef` was used for unstructured/unknown payload formats (account or blob) and `serialise()` was used for decodng `PayloadRef`. Now it is known in advance what the payload looks like. * Added query function `hasStorageData()` whether a storage area exists why: Useful for supporting `slotStateEmpty()` of the `CoreDb` API * In the `Ledger` replace `storage.stateEmpty()` by `slotStateEmpty()` * On Aristo, hide the storage root/vertex ID in the `PayloadRef` why: The storage vertex ID is fully controlled by Aristo while the `AristoAccount` object is controlled by the application. With the storage root part of the `AristoAccount` object, there was a useless administrative burden to keep that storage root field up to date. * Remove cruft, update comments etc. * Update changed MPT access paradigms why: Fixes verified proxy tests * Fluffy cosmetics
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proc deleteAccountRecord*(
db: AristoDbRef;
accPath: Hash32;
): Result[void,AristoError] =
## Delete the account leaf entry addressed by the argument `path`. If this
## leaf entry referres to a storage tree, this one will be deleted as well.
##
var accHike: Hike
db.fetchAccountHike(accPath, accHike).isOkOr:
if error == FetchAccInaccessible:
return err(DelPathNotFound)
return err(error)
let
stoID = accHike.legs[^1].wp.vtx.lData.stoID
# Delete storage tree if present
if stoID.isValid:
? db.delStoTreeImpl((stoID.vid, stoID.vid), accPath)
let otherLeaf = ?db.deleteImpl(accHike)
db.layersPutAccLeaf(accPath, nil)
if otherLeaf.isValid:
db.layersPutAccLeaf(
Hash32(getBytes(NibblesBuf.fromBytes(accPath.data).replaceSuffix(otherLeaf.pfx))),
otherLeaf)
ok()
proc deleteGenericData*(
db: AristoDbRef;
root: VertexID;
path: openArray[byte];
): Result[bool,AristoError] =
## Delete the leaf data entry addressed by the argument `path`. The MPT
## sub-tree the leaf data entry is subsumed under is passed as argument
## `root` which must be greater than `VertexID(1)` and smaller than
## `LEAST_FREE_VID`.
Core db and aristo maintenance update (#2014) * Aristo: Update error return code why: Failing of `Aristo` function `delete()` might fail because there is no such data item on the db. This must return a single error code as is done with `fetch()`. * Ledger: Better error handling why: The `expect()` clauses have been replaced by raising asserts indicating the error from the database backend. Also, `delete()` failures are legitimate if the item to delete does not exist. * Aristo: Delete function must always leave a label on DB for `hashify()` why: The `hashify()` uses the labels left bu `merge()` and `delete()` to compile (and optimise) a scheduler for subsequent hashing. Originally, the labels were not used for deleted entries and `delete()` still had some edge case where the deletion label was not properly handled. * Aristo: Update `hashify()` scheduler, remove buggy optimisation why: Was left over from version without virtual state roots which did not know about account payload leaf vertices referring to storage roots. * Aristo: Label storage trie account in `delete()` similar to `merge()` details; The `delete()` function applied to a non-static state root (assumed to be a storage root) will check the payload of an accounts leaf and mark its Merkle keys to be re-checked when runninh `hashify()` * Aristo: Clean up and re-org recycled vertex IDs in `hashify()` why: Re-organising the recycled vertex IDs list intends to reduce the size of the list. This list is organised as a LIFO (or stack.) By reorganising it in a way so that the least vertex ID numbers are on top, the list will be kept smaller as observed on some examples (less than 30%.) * CoreDb: Accept storage trie deletion requests in non-initialised state why: Due to lazy initialisation, the root vertex ID might not yet exist. So the `Aristo` database handlers would reject this call with an error and this condition needs to be handled by the API (which realises the lazy feature.) * Cosmetics & code massage, prettify logging * fix missing import
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##
## The return value is `true` if the argument `path` deleted was the last
## one and the tree does not exist anymore.
Aristo db api extensions for use as core db backend (#1754) * Update docu * Update Aristo/Kvt constructor prototype why: Previous version used an `enum` value to indicate what backend is to be used. This was replaced by using the backend object type. * Rewrite `hikeUp()` return code into `Result[Hike,(Hike,AristoError)]` why: Better code maintenance. Previously, the `Hike` object was returned. It had an internal error field so partial success was also available on a failure. This error field has been removed. * Use `openArray[byte]` rather than `Blob` in functions prototypes * Provide synchronised multi instance transactions why: The `CoreDB` object was geared towards the legacy DB which used a single transaction for the key-value backend DB. Different state roots are provided by the backend database, so all instances work directly on the same backend. Aristo db instances have different in-memory mappings (aka different state roots) and the transactions are on top of there mappings. So each instance might run different transactions. Multi instance transactions are a compromise to converge towards the legacy behaviour. The synchronised transactions span over all instances available at the time when base transaction was opened. Instances created later are unaffected. * Provide key-value pair database iterator why: Needed in `CoreDB` for `replicate()` emulation also: Some update of internal code * Extend API (i.e. prototype variants) why: Needed for `CoreDB` geared towards the legacy backend which has a more basic API than Aristo.
2023-09-15 15:23:53 +00:00
##
# Verify that `root` is neither an accounts tree nor a strorage tree.
if not root.isValid:
return err(DelRootVidMissing)
elif root == VertexID(1):
return err(DelAccRootNotAccepted)
elif LEAST_FREE_VID <= root.distinctBase:
return err(DelStoRootNotAccepted)
var hike: Hike
path.hikeUp(root, db, Opt.none(VertexRef), hike).isOkOr:
if error[1] in HikeAcceptableStopsNotFound:
return err(DelPathNotFound)
return err(error[1])
2023-06-02 19:21:46 +00:00
discard ?db.deleteImpl(hike)
ok(not db.getVtx((root, root)).isValid)
proc deleteGenericTree*(
db: AristoDbRef; # Database, top layer
root: VertexID; # Root vertex
): Result[void,AristoError] =
## Variant of `deleteGenericData()` for purging the whole MPT sub-tree.
Aristo db api extensions for use as core db backend (#1754) * Update docu * Update Aristo/Kvt constructor prototype why: Previous version used an `enum` value to indicate what backend is to be used. This was replaced by using the backend object type. * Rewrite `hikeUp()` return code into `Result[Hike,(Hike,AristoError)]` why: Better code maintenance. Previously, the `Hike` object was returned. It had an internal error field so partial success was also available on a failure. This error field has been removed. * Use `openArray[byte]` rather than `Blob` in functions prototypes * Provide synchronised multi instance transactions why: The `CoreDB` object was geared towards the legacy DB which used a single transaction for the key-value backend DB. Different state roots are provided by the backend database, so all instances work directly on the same backend. Aristo db instances have different in-memory mappings (aka different state roots) and the transactions are on top of there mappings. So each instance might run different transactions. Multi instance transactions are a compromise to converge towards the legacy behaviour. The synchronised transactions span over all instances available at the time when base transaction was opened. Instances created later are unaffected. * Provide key-value pair database iterator why: Needed in `CoreDB` for `replicate()` emulation also: Some update of internal code * Extend API (i.e. prototype variants) why: Needed for `CoreDB` geared towards the legacy backend which has a more basic API than Aristo.
2023-09-15 15:23:53 +00:00
##
# Verify that `root` is neither an accounts tree nor a strorage tree.
if not root.isValid:
return err(DelRootVidMissing)
elif root == VertexID(1):
return err(DelAccRootNotAccepted)
elif LEAST_FREE_VID <= root.distinctBase:
return err(DelStoRootNotAccepted)
db.delSubTreeImpl root
proc deleteStorageData*(
Aristo db api extensions for use as core db backend (#1754) * Update docu * Update Aristo/Kvt constructor prototype why: Previous version used an `enum` value to indicate what backend is to be used. This was replaced by using the backend object type. * Rewrite `hikeUp()` return code into `Result[Hike,(Hike,AristoError)]` why: Better code maintenance. Previously, the `Hike` object was returned. It had an internal error field so partial success was also available on a failure. This error field has been removed. * Use `openArray[byte]` rather than `Blob` in functions prototypes * Provide synchronised multi instance transactions why: The `CoreDB` object was geared towards the legacy DB which used a single transaction for the key-value backend DB. Different state roots are provided by the backend database, so all instances work directly on the same backend. Aristo db instances have different in-memory mappings (aka different state roots) and the transactions are on top of there mappings. So each instance might run different transactions. Multi instance transactions are a compromise to converge towards the legacy behaviour. The synchronised transactions span over all instances available at the time when base transaction was opened. Instances created later are unaffected. * Provide key-value pair database iterator why: Needed in `CoreDB` for `replicate()` emulation also: Some update of internal code * Extend API (i.e. prototype variants) why: Needed for `CoreDB` geared towards the legacy backend which has a more basic API than Aristo.
2023-09-15 15:23:53 +00:00
db: AristoDbRef;
accPath: Hash32; # Implies storage data tree
stoPath: Hash32;
): Result[bool,AristoError] =
## For a given account argument `accPath`, this function deletes the
## argument `stoPath` from the associated storage tree (if any, at all.) If
## the if the argument `stoPath` deleted was the last one on the storage tree,
## account leaf referred to by `accPath` will be updated so that it will
## not refer to a storage tree anymore. In the latter case only the function
## will return `true`.
Aristo db api extensions for use as core db backend (#1754) * Update docu * Update Aristo/Kvt constructor prototype why: Previous version used an `enum` value to indicate what backend is to be used. This was replaced by using the backend object type. * Rewrite `hikeUp()` return code into `Result[Hike,(Hike,AristoError)]` why: Better code maintenance. Previously, the `Hike` object was returned. It had an internal error field so partial success was also available on a failure. This error field has been removed. * Use `openArray[byte]` rather than `Blob` in functions prototypes * Provide synchronised multi instance transactions why: The `CoreDB` object was geared towards the legacy DB which used a single transaction for the key-value backend DB. Different state roots are provided by the backend database, so all instances work directly on the same backend. Aristo db instances have different in-memory mappings (aka different state roots) and the transactions are on top of there mappings. So each instance might run different transactions. Multi instance transactions are a compromise to converge towards the legacy behaviour. The synchronised transactions span over all instances available at the time when base transaction was opened. Instances created later are unaffected. * Provide key-value pair database iterator why: Needed in `CoreDB` for `replicate()` emulation also: Some update of internal code * Extend API (i.e. prototype variants) why: Needed for `CoreDB` geared towards the legacy backend which has a more basic API than Aristo.
2023-09-15 15:23:53 +00:00
##
let
mixPath = mixUp(accPath, stoPath)
stoLeaf = db.cachedStoLeaf(mixPath)
if stoLeaf == Opt.some(nil):
return err(DelPathNotFound)
var accHike: Hike
db.fetchAccountHike(accPath, accHike).isOkOr:
if error == FetchAccInaccessible:
return err(DelStoAccMissing)
return err(error)
let
wpAcc = accHike.legs[^1].wp
Update storage tree admin (#2419) * Tighten `CoreDb` API for accounts why: Apart from cruft, the way to fetch the accounts state root via a `CoreDbColRef` record was unnecessarily complicated. * Extend `CoreDb` API for accounts to cover storage tries why: In future, this will make the notion of column objects obsolete. Storage trees will then be indexed by the account address rather than the vertex ID equivalent like a `CoreDbColRef`. * Apply new/extended accounts API to ledger and tests details: This makes the `distinct_ledger` module obsolete * Remove column object constructors why: They were needed as an abstraction of MPT sub-trees including storage trees. Now, storage trees are handled by the account (e.g. via address) they belong to and all other trees can be identified by a constant well known vertex ID. So there is no need for column objects anymore. Still there are some left-over column object methods wnich will be removed next. * Remove `serialise()` and `PayloadRef` from default Aristo API why: Not needed. `PayloadRef` was used for unstructured/unknown payload formats (account or blob) and `serialise()` was used for decodng `PayloadRef`. Now it is known in advance what the payload looks like. * Added query function `hasStorageData()` whether a storage area exists why: Useful for supporting `slotStateEmpty()` of the `CoreDb` API * In the `Ledger` replace `storage.stateEmpty()` by `slotStateEmpty()` * On Aristo, hide the storage root/vertex ID in the `PayloadRef` why: The storage vertex ID is fully controlled by Aristo while the `AristoAccount` object is controlled by the application. With the storage root part of the `AristoAccount` object, there was a useless administrative burden to keep that storage root field up to date. * Remove cruft, update comments etc. * Update changed MPT access paradigms why: Fixes verified proxy tests * Fluffy cosmetics
2024-06-27 09:01:26 +00:00
stoID = wpAcc.vtx.lData.stoID
if not stoID.isValid:
return err(DelStoRootMissing)
let stoNibbles = NibblesBuf.fromBytes(stoPath.data)
var stoHike: Hike
stoNibbles.hikeUp(stoID.vid, db, stoLeaf, stoHike).isOkOr:
if error[1] in HikeAcceptableStopsNotFound:
return err(DelPathNotFound)
return err(error[1])
# Mark account path Merkle keys for update
db.layersResKeys accHike
let otherLeaf = ?db.deleteImpl(stoHike)
db.layersPutStoLeaf(mixPath, nil)
if otherLeaf.isValid:
let leafMixPath = mixUp(
accPath,
Hash32(getBytes(stoNibbles.replaceSuffix(otherLeaf.pfx))))
db.layersPutStoLeaf(leafMixPath, otherLeaf)
# If there was only one item (that got deleted), update the account as well
if stoHike.legs.len > 1:
return ok(false)
# De-register the deleted storage tree from the account record
let leaf = wpAcc.vtx.dup # Dup on modify
leaf.lData.stoID.isValid = false
db.layersPutAccLeaf(accPath, leaf)
db.layersPutVtx((accHike.root, wpAcc.vid), leaf)
ok(true)
proc deleteStorageTree*(
db: AristoDbRef; # Database, top layer
accPath: Hash32; # Implies storage data tree
): Result[void,AristoError] =
## Variant of `deleteStorageData()` for purging the whole storage tree
## associated to the account argument `accPath`.
##
var accHike: Hike
db.fetchAccountHike(accPath, accHike).isOkOr:
if error == FetchAccInaccessible:
return err(DelStoAccMissing)
return err(error)
let
wpAcc = accHike.legs[^1].wp
Update storage tree admin (#2419) * Tighten `CoreDb` API for accounts why: Apart from cruft, the way to fetch the accounts state root via a `CoreDbColRef` record was unnecessarily complicated. * Extend `CoreDb` API for accounts to cover storage tries why: In future, this will make the notion of column objects obsolete. Storage trees will then be indexed by the account address rather than the vertex ID equivalent like a `CoreDbColRef`. * Apply new/extended accounts API to ledger and tests details: This makes the `distinct_ledger` module obsolete * Remove column object constructors why: They were needed as an abstraction of MPT sub-trees including storage trees. Now, storage trees are handled by the account (e.g. via address) they belong to and all other trees can be identified by a constant well known vertex ID. So there is no need for column objects anymore. Still there are some left-over column object methods wnich will be removed next. * Remove `serialise()` and `PayloadRef` from default Aristo API why: Not needed. `PayloadRef` was used for unstructured/unknown payload formats (account or blob) and `serialise()` was used for decodng `PayloadRef`. Now it is known in advance what the payload looks like. * Added query function `hasStorageData()` whether a storage area exists why: Useful for supporting `slotStateEmpty()` of the `CoreDb` API * In the `Ledger` replace `storage.stateEmpty()` by `slotStateEmpty()` * On Aristo, hide the storage root/vertex ID in the `PayloadRef` why: The storage vertex ID is fully controlled by Aristo while the `AristoAccount` object is controlled by the application. With the storage root part of the `AristoAccount` object, there was a useless administrative burden to keep that storage root field up to date. * Remove cruft, update comments etc. * Update changed MPT access paradigms why: Fixes verified proxy tests * Fluffy cosmetics
2024-06-27 09:01:26 +00:00
stoID = wpAcc.vtx.lData.stoID
if not stoID.isValid:
return err(DelStoRootMissing)
# Mark account path Merkle keys for update
db.layersResKeys accHike
? db.delStoTreeImpl((stoID.vid, stoID.vid), accPath)
# De-register the deleted storage tree from the accounts record
let leaf = wpAcc.vtx.dup # Dup on modify
leaf.lData.stoID.isValid = false
db.layersPutAccLeaf(accPath, leaf)
db.layersPutVtx((accHike.root, wpAcc.vid), leaf)
ok()
2023-06-02 19:21:46 +00:00
# ------------------------------------------------------------------------------
# End
# ------------------------------------------------------------------------------