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

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# nimbus-eth1
# 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.
{.push raises: [].}
import
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
results,
stew/[arrayops, endians2],
Aristo db update for short nodes key edge cases (#1887) * Aristo: Provide key-value list signature calculator detail: Simple wrappers around `Aristo` core functionality * Update new API for `CoreDb` details: + Renamed new API functions `contains()` => `hasKey()` or `hasPath()` which disables the `in` operator on non-boolean `contains()` functions + The functions `get()` and `fetch()` always return a not-found error if there is no item, available. The new functions `getOrEmpty()` and `mergeOrEmpty()` return an an empty `Blob` if there is no such key found. * Rewrite `core_apps.nim` using new API from `CoreDb` * Use `Aristo` functionality for calculating Merkle signatures details: For debugging, the `VerifyAristoForMerkleRootCalc` can be set so that `Aristo` results will be verified against the legacy versions. * Provide general interface for Merkle signing key-value tables details: Export `Aristo` wrappers * Activate `CoreDb` tests why: Now, API seems to be stable enough for general tests. * Update `toHex()` usage why: Byteutils' `toHex()` is superior to `toSeq.mapIt(it.toHex(2)).join` * Split `aristo_transcode` => `aristo_serialise` + `aristo_blobify` why: + Different modules for different purposes + `aristo_serialise`: RLP encoding/decoding + `aristo_blobify`: Aristo database encoding/decoding * Compacted representation of small nodes' links instead of Keccak hashes why: Ethereum 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.) Such a node is nor stored on key-value database. Rather the RLP encoded node value is stored instead of a lode link in a parent node instead. Only for the root hash, the top level node is always referred to by the hash. This feature needed an abstraction of the `HashKey` object which is now either a hash or a blob of length at most 31 bytes. This leaves two ways of representing an empty/void `HashKey` type, either as an empty blob of zero length, or the hash of an empty blob. * Update `CoreDb` interface (mainly reducing logger noise) * Fix copyright years (to make `Lint` happy)
2023-11-08 12:18:32 +00:00
./aristo_desc
export aristo_desc, results
No ext update (#2494) * 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>
2024-07-16 19:47:59 +00:00
# Allocation-free version short big-endian encoding that skips the leading
# zeroes
type
SbeBuf*[I] = object
buf*: array[sizeof(I), byte]
len*: byte
RVidBuf* = object
buf*: array[sizeof(SbeBuf[VertexID]) * 2, byte]
len*: byte
func significantBytesBE(val: openArray[byte]): byte =
for i in 0 ..< val.len:
if val[i] != 0:
return byte(val.len - i)
return 1
func blobify*(v: VertexID|uint64): SbeBuf[typeof(v)] =
let b = v.uint64.toBytesBE()
SbeBuf[typeof(v)](buf: b, len: significantBytesBE(b))
func blobify*(v: StUint): SbeBuf[typeof(v)] =
let b = v.toBytesBE()
SbeBuf[typeof(v)](buf: b, len: significantBytesBE(b))
template data*(v: SbeBuf): openArray[byte] =
let vv = v
vv.buf.toOpenArray(vv.buf.len - int(vv.len), vv.buf.high)
func blobify*(rvid: RootedVertexID): RVidBuf =
# Length-prefixed root encoding creates a unique and common prefix for all
# verticies sharing the same root
# TODO evaluate an encoding that colocates short roots (like VertexID(1)) with
# the length
let root = rvid.root.blobify()
result.buf[0] = root.len
assign(result.buf.toOpenArray(1, root.len), root.data())
if rvid.root == rvid.vid:
result.len = root.len + 1
else:
# We can derive the length of the `vid` from the total length
let vid = rvid.vid.blobify()
assign(result.buf.toOpenArray(root.len + 1, root.len + vid.len), vid.data())
result.len = root.len + 1 + vid.len
proc deblobify*[T: uint64|VertexID](data: openArray[byte], _: type T): Result[T,AristoError] =
if data.len < 1 or data.len > 8:
return err(Deblob64LenUnsupported)
var tmp = 0'u64
let start = 8 - data.len
for i in 0..<data.len:
tmp += uint64(data[i]) shl (8*(7-(i + start)))
ok T(tmp)
proc deblobify*(data: openArray[byte], _: type UInt256): Result[UInt256,AristoError] =
if data.len < 1 or data.len > 32:
return err(Deblob256LenUnsupported)
ok UInt256.fromBytesBE(data)
func deblobify*(data: openArray[byte], T: type RootedVertexID): Result[T, AristoError] =
let rlen = int(data[0])
if data.len < 2:
return err(DeblobRVidLenUnsupported)
if data.len < rlen + 1:
return err(DeblobRVidLenUnsupported)
let
root = ?deblobify(data.toOpenArray(1, rlen), VertexID)
vid = if data.len > rlen + 1:
?deblobify(data.toOpenArray(rlen + 1, data.high()), VertexID)
else:
root
ok (root, vid)
template data*(v: RVidBuf): openArray[byte] =
let vv = v
vv.buf.toOpenArray(0, vv.len - 1)
# ------------------------------------------------------------------------------
# Private helper
# ------------------------------------------------------------------------------
proc load64(data: openArray[byte]; start: var int, len: int): Result[uint64,AristoError] =
if data.len < start + len:
return err(Deblob256LenUnsupported)
let val = ?deblobify(data.toOpenArray(start, start + len - 1), uint64)
start += len
ok val
proc load256(data: openArray[byte]; start: var int, len: int): Result[UInt256,AristoError] =
if data.len < start + len:
return err(Deblob256LenUnsupported)
let val = ?deblobify(data.toOpenArray(start, start + len - 1), UInt256)
start += len
ok val
# ------------------------------------------------------------------------------
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
# ------------------------------------------------------------------------------
proc blobifyTo*(pyl: LeafPayload, data: var seq[byte]) =
case pyl.pType
of AccountData:
# `lens` holds `len-1` since `mask` filters out the zero-length case (which
# allows saving 1 bit per length)
var lens: uint16
var mask: byte
if 0 < pyl.account.nonce:
mask = mask or 0x01
let tmp = pyl.account.nonce.blobify()
lens += tmp.len - 1 # 3 bits
data &= tmp.data()
if 0 < pyl.account.balance:
mask = mask or 0x02
let tmp = pyl.account.balance.blobify()
lens += uint16(tmp.len - 1) shl 3 # 5 bits
data &= tmp.data()
if pyl.stoID.isValid:
mask = mask or 0x04
let tmp = pyl.stoID.vid.blobify()
lens += uint16(tmp.len - 1) shl 8 # 3 bits
data &= tmp.data()
if pyl.account.codeHash != EMPTY_CODE_HASH:
mask = mask or 0x08
data &= pyl.account.codeHash.data
data &= lens.toBytesBE()
data &= [mask]
of StoData:
data &= pyl.stoData.blobify().data
data &= [0x20.byte]
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
proc blobifyTo*(vtx: VertexRef, key: HashKey, data: var seq[byte]) =
## This function serialises the vertex argument to a database record.
## Contrary to RLP based serialisation, these records aim to align on
## fixed byte boundaries.
## ::
## Branch:
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
## <HashKey> -- optional hash key
## [VertexID, ..] -- list of up to 16 child vertices lookup keys
## seq[byte] -- hex encoded partial path (non-empty for extension nodes)
No ext update (#2494) * 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>
2024-07-16 19:47:59 +00:00
## uint64 -- lengths of each child vertex, each taking 4 bits
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
## 0x80 + xx -- marker(0/2) + pathSegmentLen(6)
##
## Leaf:
## seq[byte] -- opaque leaf data payload (might be zero length)
## seq[byte] -- hex encoded partial path (at least one byte)
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
## 0xc0 + yy -- marker(3) + partialPathLen(6)
##
## For a branch record, the bytes of the `access` array indicate the position
## of the Patricia Trie vertex reference. So the `vertexID` with index `n` has
## ::
## 8 * n * ((access shr (n * 4)) and 15)
##
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
doAssert vtx.isValid
No ext update (#2494) * 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>
2024-07-16 19:47:59 +00:00
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
bits =
case vtx.vType
of Branch:
let bits =
if key.isValid and key.len == 32:
# Shorter keys can be loaded from the vertex directly
data.add key.data()
0b10'u8
else:
0b00'u8
data.add vtx.startVid.blobify().data()
data.add toBytesBE(vtx.used)
bits
of Leaf:
vtx.lData.blobifyTo(data)
0b01'u8
pSegm =
if vtx.pfx.len > 0:
vtx.pfx.toHexPrefix(isleaf = vtx.vType == Leaf)
else:
default(HexPrefixBuf)
psLen = pSegm.len.byte
data &= pSegm.data()
data &= [(bits shl 6) or psLen]
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
proc blobify*(vtx: VertexRef, key: HashKey): seq[byte] =
## Variant of `blobify()`
result = newSeqOfCap[byte](128)
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
vtx.blobifyTo(key, result)
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
proc blobifyTo*(lSst: SavedState; data: var seq[byte]) =
## Serialise a last saved state record
data.add lSst.key.data
data.add lSst.serial.toBytesBE
data.add @[0x7fu8]
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
proc blobify*(lSst: SavedState): seq[byte] =
## Variant of `blobify()`
var data: seq[byte]
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
lSst.blobifyTo data
data
# -------------
proc deblobify(
data: openArray[byte];
pyl: var LeafPayload;
): Result[void,AristoError] =
if data.len == 0:
return err(DeblobVtxTooShort)
let mask = data[^1]
if (mask and 0x20) > 0: # Slot storage data
pyl = LeafPayload(
pType: StoData,
stoData: ?deblobify(data.toOpenArray(0, data.len - 2), UInt256))
ok()
elif (mask and 0xf0) == 0: # Only account fields set
pyl = LeafPayload(pType: AccountData)
var
start = 0
lens = uint16.fromBytesBE(data.toOpenArray(data.len - 3, data.len - 2))
if (mask and 0x01) > 0:
let len = lens and 0b111
pyl.account.nonce = ? load64(data, start, int(len + 1))
if (mask and 0x02) > 0:
let len = (lens shr 3) and 0b11111
pyl.account.balance = ? load256(data, start, int(len + 1))
if (mask and 0x04) > 0:
let len = (lens shr 8) and 0b111
pyl.stoID = (true, VertexID(? load64(data, start, int(len + 1))))
if (mask and 0x08) > 0:
if data.len() < start + 32:
return err(DeblobCodeLenUnsupported)
discard pyl.account.codeHash.data.copyFrom(data.toOpenArray(start, start + 31))
else:
pyl.account.codeHash = EMPTY_CODE_HASH
ok()
else:
err(DeblobUnknown)
Speed up initial MPT root computation after import (#2788) 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.
2024-10-27 11:08:37 +00:00
proc deblobifyType*(record: openArray[byte]; T: type VertexRef):
Result[VertexType, AristoError] =
if record.len < 3: # minimum `Leaf` record
return err(DeblobVtxTooShort)
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
ok if ((record[^1] shr 6) and 0b01'u8) > 0:
Leaf
Speed up initial MPT root computation after import (#2788) 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.
2024-10-27 11:08:37 +00:00
else:
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
Branch
Speed up initial MPT root computation after import (#2788) 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.
2024-10-27 11:08:37 +00:00
proc deblobify*(
record: openArray[byte];
T: type VertexRef;
): Result[T,AristoError] =
## De-serialise a data record encoded with `blobify()`. The second
## argument `vtx` can be `nil`.
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
if record.len < 3: # minimum `Leaf` record
Aristo db update for short nodes key edge cases (#1887) * Aristo: Provide key-value list signature calculator detail: Simple wrappers around `Aristo` core functionality * Update new API for `CoreDb` details: + Renamed new API functions `contains()` => `hasKey()` or `hasPath()` which disables the `in` operator on non-boolean `contains()` functions + The functions `get()` and `fetch()` always return a not-found error if there is no item, available. The new functions `getOrEmpty()` and `mergeOrEmpty()` return an an empty `Blob` if there is no such key found. * Rewrite `core_apps.nim` using new API from `CoreDb` * Use `Aristo` functionality for calculating Merkle signatures details: For debugging, the `VerifyAristoForMerkleRootCalc` can be set so that `Aristo` results will be verified against the legacy versions. * Provide general interface for Merkle signing key-value tables details: Export `Aristo` wrappers * Activate `CoreDb` tests why: Now, API seems to be stable enough for general tests. * Update `toHex()` usage why: Byteutils' `toHex()` is superior to `toSeq.mapIt(it.toHex(2)).join` * Split `aristo_transcode` => `aristo_serialise` + `aristo_blobify` why: + Different modules for different purposes + `aristo_serialise`: RLP encoding/decoding + `aristo_blobify`: Aristo database encoding/decoding * Compacted representation of small nodes' links instead of Keccak hashes why: Ethereum 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.) Such a node is nor stored on key-value database. Rather the RLP encoded node value is stored instead of a lode link in a parent node instead. Only for the root hash, the top level node is always referred to by the hash. This feature needed an abstraction of the `HashKey` object which is now either a hash or a blob of length at most 31 bytes. This leaves two ways of representing an empty/void `HashKey` type, either as an empty blob of zero length, or the hash of an empty blob. * Update `CoreDb` interface (mainly reducing logger noise) * Fix copyright years (to make `Lint` happy)
2023-11-08 12:18:32 +00:00
return err(DeblobVtxTooShort)
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
bits = record[^1] shr 6
vType = if (bits and 0b01'u8) > 0: Leaf else: Branch
hasKey = (bits and 0b10'u8) > 0
psLen = int(record[^1] and 0b00111111)
start = if hasKey: 32 else: 0
if psLen > record.len - 2 or start > record.len - 2 - psLen:
return err(DeblobBranchTooShort)
let
psPos = record.len - psLen - 1
(_, pathSegment) =
NibblesBuf.fromHexPrefix record.toOpenArray(psPos, record.len - 2)
ok case vType
of Branch:
var pos = start
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
2024-12-04 10:42:04 +00:00
svLen = psPos - pos - 2
startVid = VertexID(?load64(record, pos, svLen))
used = uint16.fromBytesBE(record.toOpenArray(pos, pos + 1))
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
pos += 2
VertexRef(vType: Branch, pfx: pathSegment, startVid: startVid, used: used)
of Leaf:
let vtx = VertexRef(vType: Leaf, pfx: pathSegment)
?record.toOpenArray(start, psPos - 1).deblobify(vtx.lData)
vtx
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
proc deblobify*(record: openArray[byte], T: type HashKey): Opt[HashKey] =
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
if record.len > 33 and (((record[^1] shr 6) and 0b10'u8) > 0):
HashKey.fromBytes(record.toOpenArray(0, 31))
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
else:
Opt.none(HashKey)
proc deblobify*(
data: openArray[byte];
T: type SavedState;
): Result[SavedState,AristoError] =
## De-serialise the last saved state data record previously encoded with
## `blobify()`.
if data.len != 41:
return err(DeblobWrongSize)
if data[^1] != 0x7f:
return err(DeblobWrongType)
ok(SavedState(
key: Hash32(array[32, byte].initCopyFrom(data.toOpenArray(0, 31))),
serial: uint64.fromBytesBE data.toOpenArray(32, 39)))
# ------------------------------------------------------------------------------
# End
# ------------------------------------------------------------------------------