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01ca415721
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.
123 lines
4.2 KiB
Nim
123 lines
4.2 KiB
Nim
# nimbus-eth1
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# Copyright (c) 2023-2024 Status Research & Development GmbH
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# Licensed under either of
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# * Apache License, version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or
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# http://www.apache.org/licenses/LICENSE-2.0)
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# * MIT license ([LICENSE-MIT](LICENSE-MIT) or
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# http://opensource.org/licenses/MIT)
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# at your option. This file may not be copied, modified, or distributed
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# except according to those terms.
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## Rocks DB internal driver descriptor
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## ===================================
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{.push raises: [].}
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import
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std/os,
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std/concurrency/atomics,
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eth/common,
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rocksdb,
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stew/endians2,
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../../aristo_desc,
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../init_common,
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minilru
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export minilru
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type
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RdbWriteEventCb* =
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proc(session: WriteBatchRef): bool {.gcsafe, raises: [].}
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## Call back closure function that passes the the write session handle
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## to a guest peer right after it was opened. The guest may store any
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## data on its own column family and return `true` if that worked
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## all right. Then the `Aristo` handler will stor its own columns and
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## finalise the write session.
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##
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## In case of an error when `false` is returned, `Aristo` will abort the
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## write session and return a session error.
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RdbInst* = object
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admCol*: ColFamilyReadWrite ## Admin column family handler
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vtxCol*: ColFamilyReadWrite ## Vertex column family handler
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session*: WriteBatchRef ## For batched `put()`
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# Note that the key type `VertexID` for LRU caches requires that there is
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# strictly no vertex ID re-use.
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#
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# Otherwise, in some fringe cases one might remove a vertex with key
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# `(root1,vid)` and insert another vertex with key `(root2,vid)` while
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# re-using the vertex ID `vid`. Without knowledge of `root1` and `root2`,
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# the LRU cache will return the same vertex for `(root2,vid)` also for
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# `(root1,vid)`.
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#
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# The other alternaive would be to use the key type `RootedVertexID` which
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# is less memory and time efficient (the latter one due to internal LRU
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# handling of the longer key.)
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#
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rdKeyLru*: LruCache[VertexID,HashKey] ## Read cache
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rdKeySize*: int
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rdVtxLru*: LruCache[VertexID,VertexRef] ## Read cache
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rdVtxSize*: int
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basePath*: string ## Database directory
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trgWriteEvent*: RdbWriteEventCb ## Database piggiback call back handler
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AristoCFs* = enum
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## Column family symbols/handles and names used on the database
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AdmCF = "AriAdm" ## Admin column family name
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VtxCF = "AriVtx" ## Vertex column family name
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RdbLruCounter* = array[bool, Atomic[uint64]]
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RdbStateType* = enum
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Account
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World
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const
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BaseFolder* = "nimbus" ## Same as for Legacy DB
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DataFolder* = "aristo" ## Legacy DB has "data"
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var
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# Hit/miss counters for LRU cache - global so as to integrate easily with
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# nim-metrics and `uint64` to ensure that increasing them is fast - collection
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# happens from a separate thread.
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# TODO maybe turn this into more general framework for LRU reporting since
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# we have lots of caches of this sort
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rdbVtxLruStats*: array[RdbStateType, array[VertexType, RdbLruCounter]]
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rdbKeyLruStats*: array[RdbStateType, RdbLruCounter]
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# ------------------------------------------------------------------------------
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# Public functions
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# ------------------------------------------------------------------------------
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template logTxt*(info: static[string]): static[string] =
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"RocksDB/" & info
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template baseDb*(rdb: RdbInst): RocksDbReadWriteRef =
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rdb.admCol.db
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func baseDir*(rdb: RdbInst): string =
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rdb.basePath / BaseFolder
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func dataDir*(rdb: RdbInst): string =
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rdb.baseDir / DataFolder
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template toOpenArray*(xid: AdminTabID): openArray[byte] =
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xid.uint64.toBytesBE.toOpenArray(0,7)
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template to*(v: RootedVertexID, T: type RdbStateType): RdbStateType =
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if v.root == VertexID(1): RdbStateType.World else: RdbStateType.Account
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template inc*(v: var RdbLruCounter, hit: bool) =
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discard v[hit].fetchAdd(1, moRelaxed)
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template get*(v: RdbLruCounter, hit: bool): uint64 =
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v[hit].load(moRelaxed)
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# ------------------------------------------------------------------------------
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# End
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# ------------------------------------------------------------------------------
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