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

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# nimbus-eth1
Core db update storage root management for sub tries (#1964) * Aristo: Re-phrase `LayerDelta` and `LayerFinal` as object references why: Avoids copying in some cases * Fix copyright header * Aristo: Verify `leafTie.root` function argument for `merge()` proc why: Zero root will lead to inconsistent DB entry * Aristo: Update failure condition for hash labels compiler `hashify()` why: Node need not be rejected as long as links are on the schedule. In that case, `redo[]` is to become `wff.base[]` at a later stage. This amends an earlier fix, part of #1952 by also testing against the target nodes of the `wff.base[]` sets. * Aristo: Add storage root glue record to `hashify()` schedule why: An account leaf node might refer to a non-resolvable storage root ID. Storage root node chains will end up at the storage root. So the link `storage-root->account-leaf` needs an extra item in the schedule. * Aristo: fix error code returned by `fetchPayload()` details: Final error code is implied by the error code form the `hikeUp()` function. * CoreDb: Discard `createOk` argument in API `getRoot()` function why: Not needed for the legacy DB. For the `Arsto` DB, a lazy approach is implemented where a stprage root node is created on-the-fly. * CoreDb: Prevent `$$` logging in some cases why: Logging the function `$$` is not useful when it is used for internal use, i.e. retrieving an an error text for logging. * CoreDb: Add `tryHashFn()` to API for pretty printing why: Pretty printing must not change the hashification status for the `Aristo` DB. So there is an independent API wrapper for getting the node hash which never updated the hashes. * CoreDb: Discard `update` argument in API `hash()` function why: When calling the API function `hash()`, the latest state is always wanted. For a version that uses the current state as-is without checking, the function `tryHash()` was added to the backend. * CoreDb: Update opaque vertex ID objects for the `Aristo` backend why: For `Aristo`, vID objects encapsulate a numeric `VertexID` referencing a vertex (rather than a node hash as used on the legacy backend.) For storage sub-tries, there might be no initial vertex known when the descriptor is created. So opaque vertex ID objects are supported without a valid `VertexID` which will be initalised on-the-fly when the first item is merged. * CoreDb: Add pretty printer for opaque vertex ID objects * Cosmetics, printing profiling data * CoreDb: Fix segfault in `Aristo` backend when creating MPT descriptor why: Missing initialisation error * CoreDb: Allow MPT to inherit shared context on `Aristo` backend why: Creates descriptors with different storage roots for the same shared `Aristo` DB descriptor. * Cosmetics, update diagnostic message items for `Aristo` backend * Fix Copyright year
2024-01-11 19:11:38 +00:00
# Copyright (c) 2023-2024 Status Research & Development GmbH
# 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 builder, raw node insertion
## ======================================================
##
## This module merges `PathID` values as hexary lookup paths into the
## `Patricia Trie`. When changing vertices (aka nodes without Merkle hashes),
## associated (but separated) Merkle hashes will be deleted unless locked.
## Instead of deleting locked hashes error handling is applied.
##
## Also, nodes (vertices plus merkle hashes) can be added which is needed for
## boundary proofing after `snap/1` download. The vertices are split from the
## nodes and stored as-is on the table holding `Patricia Trie` entries. The
## hashes are stored iin a separate table and the vertices are labelled
## `locked`.
{.push raises: [].}
import
std/typetraits,
eth/common,
results,
"."/[aristo_desc, aristo_fetch, aristo_get, aristo_layers, aristo_vid]
proc layersPutLeaf(
db: AristoDbRef, rvid: RootedVertexID, path: NibblesBuf, payload: LeafPayload
): VertexRef =
let vtx = VertexRef(vType: Leaf, pfx: path, lData: payload)
db.layersPutVtx(rvid, vtx)
vtx
proc mergePayloadImpl(
db: AristoDbRef, # Database, top layer
root: VertexID, # MPT state root
path: Hash32, # Leaf item to add to the database
leaf: Opt[VertexRef],
payload: LeafPayload, # Payload value
): Result[(VertexRef, VertexRef, VertexRef), AristoError] =
## Merge the argument `(root,path)` key-value-pair into the top level vertex
## table of the database `db`. The `path` argument is used to address the
## leaf vertex with the payload. It is stored or updated on the database
## accordingly.
##
var
path = NibblesBuf.fromBytes(path.data)
cur = root
(vtx, _) = db.getVtxRc((root, cur)).valueOr:
if error != GetVtxNotFound:
return err(error)
# We're at the root vertex and there is no data - this must be a fresh
# VertexID!
return ok (db.layersPutLeaf((root, cur), path, payload), nil, nil)
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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vids: ArrayBuf[NibblesBuf.high + 1, VertexID]
vtxs: ArrayBuf[NibblesBuf.high + 1, VertexRef]
template resetKeys() =
# Reset cached hashes of touched verticies
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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for i in 2..vids.len:
db.layersResKey((root, vids[^i]), vtxs[^i])
while path.len > 0:
# Clear existing merkle keys along the traversal path
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.
2024-11-20 08:56:27 +00:00
vids.add cur
vtxs.add vtx
let n = path.sharedPrefixLen(vtx.pfx)
case vtx.vType
of Leaf:
let res =
if n == vtx.pfx.len:
# Same path - replace the current vertex with a new payload
if vtx.lData == payload:
return err(MergeNoAction)
let leafVtx = if root == VertexID(1):
var payload = payload.dup()
# TODO can we avoid this hack? it feels like the caller should already
# have set an appropriate stoID - this "fixup" feels risky,
# specially from a caching point of view
payload.stoID = vtx.lData.stoID
db.layersPutLeaf((root, cur), path, payload)
else:
db.layersPutLeaf((root, cur), path, payload)
(leafVtx, nil, nil)
else:
# Turn leaf into a branch (or extension) then insert the two leaves
# into the branch
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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let branch = VertexRef(vType: Branch, pfx: path.slice(0, n), startVid: db.vidFetch(16))
let other = block: # Copy of existing leaf node, now one level deeper
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
2024-12-04 10:42:04 +00:00
let local = branch.setUsed(vtx.pfx[n], true)
db.layersPutLeaf((root, local), vtx.pfx.slice(n + 1), vtx.lData)
let leafVtx = block: # Newly inserted leaf node
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
2024-12-04 10:42:04 +00:00
let local = branch.setUsed(path[n], true)
db.layersPutLeaf((root, local), path.slice(n + 1), payload)
# Put the branch at the vid where the leaf was
db.layersPutVtx((root, cur), branch)
# We need to return vtx here because its pfx member hasn't yet been
# sliced off and is therefore shared with the hike
(leafVtx, vtx, other)
resetKeys()
return ok(res)
of Branch:
if vtx.pfx.len == n:
# The existing branch is a prefix of the new entry
let
nibble = path[vtx.pfx.len]
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
2024-12-04 10:42:04 +00:00
next = vtx.bVid(nibble)
if next.isValid:
cur = next
path = path.slice(n + 1)
vtx =
if leaf.isSome and leaf[].isValid and leaf[].pfx == path:
leaf[]
else:
(?db.getVtxRc((root, next)))[0]
else:
# There's no vertex at the branch point - insert the payload as a new
# leaf and update the existing branch
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
2024-12-04 10:42:04 +00:00
let brDup = vtx.dup()
let local = brDup.setUsed(nibble, true)
db.layersPutVtx((root, cur), brDup)
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
2024-12-04 10:42:04 +00:00
let
leafVtx = db.layersPutLeaf((root, local), path.slice(n + 1), payload)
resetKeys()
return ok((leafVtx, nil, nil))
else:
# Partial path match - we need to split the existing branch at
# the point of divergence, inserting a new branch
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
2024-12-04 10:42:04 +00:00
let branch = VertexRef(vType: Branch, pfx: path.slice(0, n), startVid: db.vidFetch(16))
block: # Copy the existing vertex and add it to the new branch
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
2024-12-04 10:42:04 +00:00
let local = branch.setUsed(vtx.pfx[n], true)
db.layersPutVtx(
(root, local),
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
2024-12-04 10:42:04 +00:00
VertexRef(vType: Branch, pfx: vtx.pfx.slice(n + 1), startVid: vtx.startVid, used: vtx.used),
)
let leafVtx = block: # add the new entry
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
2024-12-04 10:42:04 +00:00
let local = branch.setUsed(path[n], true)
db.layersPutLeaf((root, local), path.slice(n + 1), payload)
db.layersPutVtx((root, cur), branch)
resetKeys()
return ok((leafVtx, nil, nil))
err(MergeHikeFailed)
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
# ------------------------------------------------------------------------------
# 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
2024-06-27 09:01:26 +00:00
proc mergeAccountRecord*(
db: AristoDbRef; # Database, top layer
accPath: Hash32; # Even nibbled byte path
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
accRec: AristoAccount; # Account data
): Result[bool,AristoError] =
## Merge the key-value-pair argument `(accKey,accRec)` as an account
## ledger value, i.e. the the sub-tree starting at `VertexID(1)`.
##
## On success, the function returns `true` if the `accRec` argument was
## not on the database already or different from `accRec`, and `false`
## otherwise.
##
let
pyl = LeafPayload(pType: AccountData, account: accRec)
updated = db.mergePayloadImpl(
VertexID(1), accPath, db.cachedAccLeaf(accPath), pyl).valueOr:
if error == MergeNoAction:
return ok false
return err(error)
# Update leaf cache both of the merged value and potentially the displaced
# leaf resulting from splitting a leaf into a branch with two leaves
db.layersPutAccLeaf(accPath, updated[0])
if updated[1].isValid:
let otherPath = Hash32(getBytes(
NibblesBuf.fromBytes(accPath.data).replaceSuffix(updated[1].pfx)))
db.layersPutAccLeaf(otherPath, updated[2])
ok 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
proc mergeStorageData*(
db: AristoDbRef; # Database, top layer
accPath: Hash32; # Needed for accounts payload
stoPath: Hash32; # Storage data path (aka key)
stoData: UInt256; # Storage data payload value
): Result[void,AristoError] =
## Store the `stoData` data argument on the storage area addressed by
## `(accPath,stoPath)` where `accPath` is the account key (into the MPT)
## and `stoPath` is the slot path of the corresponding storage area.
##
var accHike: Hike
db.fetchAccountHike(accPath,accHike).isOkOr:
return err(MergeStoAccMissing)
let
stoID = accHike.legs[^1].wp.vtx.lData.stoID
# Provide new storage ID when needed
useID =
if stoID.isValid: stoID # Use as is
elif stoID.vid.isValid: (true, stoID.vid) # Re-use previous vid
else: (true, db.vidFetch()) # Create new vid
mixPath = mixUp(accPath, stoPath)
# Call merge
pyl = LeafPayload(pType: StoData, stoData: stoData)
updated = db.mergePayloadImpl(
useID.vid, stoPath, db.cachedStoLeaf(mixPath), pyl).valueOr:
if error == MergeNoAction:
assert stoID.isValid # debugging only
return ok()
return err(error)
# Mark account path Merkle keys for update
db.layersResKeys(accHike)
# Update leaf cache both of the merged value and potentially the displaced
# leaf resulting from splitting a leaf into a branch with two leaves
db.layersPutStoLeaf(mixPath, updated[0])
if updated[1].isValid:
let otherPath = Hash32(getBytes(
NibblesBuf.fromBytes(stoPath.data).replaceSuffix(updated[1].pfx)))
db.layersPutStoLeaf(mixUp(accPath, otherPath), updated[2])
if not stoID.isValid:
# Make sure that there is an account that refers to that storage trie
let leaf = accHike.legs[^1].wp.vtx.dup # Dup on modify
leaf.lData.stoID = useID
db.layersPutAccLeaf(accPath, leaf)
db.layersPutVtx((VertexID(1), accHike.legs[^1].wp.vid), leaf)
ok()
# ------------------------------------------------------------------------------
# End
# ------------------------------------------------------------------------------