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bitcoin/src/txgraph.cpp
Anthony Towns ade0397f59 txgraph: drop move assignment operator
2025-11-25 07:36:50 -05:00

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// Copyright (c) The Bitcoin Core developers
// Distributed under the MIT software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include <txgraph.h>
#include <cluster_linearize.h>
#include <random.h>
#include <util/bitset.h>
#include <util/check.h>
#include <util/feefrac.h>
#include <util/vector.h>
#include <compare>
#include <functional>
#include <memory>
#include <set>
#include <span>
#include <unordered_set>
#include <utility>
namespace {
using namespace cluster_linearize;
/** The maximum number of levels a TxGraph can have (0 = main, 1 = staging). */
static constexpr int MAX_LEVELS{2};
// Forward declare the TxGraph implementation class.
class TxGraphImpl;
/** Position of a DepGraphIndex within a Cluster::m_linearization. */
using LinearizationIndex = uint32_t;
/** Position of a Cluster within TxGraphImpl::ClusterSet::m_clusters. */
using ClusterSetIndex = uint32_t;
/** Quality levels for cached cluster linearizations. */
enum class QualityLevel
{
/** This is a singleton cluster consisting of a transaction that individually exceeds the
* cluster size limit. It cannot be merged with anything. */
OVERSIZED_SINGLETON,
/** This cluster may have multiple disconnected components, which are all NEEDS_RELINEARIZE. */
NEEDS_SPLIT,
/** This cluster may have multiple disconnected components, which are all ACCEPTABLE. */
NEEDS_SPLIT_ACCEPTABLE,
/** This cluster has undergone changes that warrant re-linearization. */
NEEDS_RELINEARIZE,
/** The minimal level of linearization has been performed, but it is not known to be optimal. */
ACCEPTABLE,
/** The linearization is known to be optimal. */
OPTIMAL,
/** This cluster is not registered in any ClusterSet::m_clusters.
* This must be the last entry in QualityLevel as ClusterSet::m_clusters is sized using it. */
NONE,
};
/** Information about a transaction inside TxGraphImpl::Trim. */
struct TrimTxData
{
// Fields populated by Cluster::AppendTrimData(). These are immutable after TrimTxData
// construction.
/** Chunk feerate for this transaction. */
FeePerWeight m_chunk_feerate;
/** GraphIndex of the transaction. */
TxGraph::GraphIndex m_index;
/** Size of the transaction. */
uint32_t m_tx_size;
// Fields only used internally by TxGraphImpl::Trim():
/** Number of unmet dependencies this transaction has. -1 if the transaction is included. */
uint32_t m_deps_left;
/** Number of dependencies that apply to this transaction as child. */
uint32_t m_parent_count;
/** Where in deps_by_child those dependencies begin. */
uint32_t m_parent_offset;
/** Number of dependencies that apply to this transaction as parent. */
uint32_t m_children_count;
/** Where in deps_by_parent those dependencies begin. */
uint32_t m_children_offset;
// Fields only used internally by TxGraphImpl::Trim()'s union-find implementation, and only for
// transactions that are definitely included or definitely rejected.
//
// As transactions get processed, they get organized into trees which form partitions
// representing the would-be clusters up to that point. The root of each tree is a
// representative for that partition. See
// https://en.wikipedia.org/wiki/Disjoint-set_data_structure.
//
/** Pointer to another TrimTxData, towards the root of the tree. If this is a root, m_uf_parent
* is equal to this itself. */
TrimTxData* m_uf_parent;
/** If this is a root, the total number of transactions in the partition. */
uint32_t m_uf_count;
/** If this is a root, the total size of transactions in the partition. */
uint64_t m_uf_size;
};
/** A grouping of connected transactions inside a TxGraphImpl::ClusterSet. */
class Cluster
{
friend class TxGraphImpl;
friend class BlockBuilderImpl;
protected:
using GraphIndex = TxGraph::GraphIndex;
using SetType = BitSet<MAX_CLUSTER_COUNT_LIMIT>;
/** The quality level of m_linearization. */
QualityLevel m_quality{QualityLevel::NONE};
/** Which position this Cluster has in TxGraphImpl::ClusterSet::m_clusters[m_quality]. */
ClusterSetIndex m_setindex{ClusterSetIndex(-1)};
/** Sequence number for this Cluster (for tie-breaking comparison between equal-chunk-feerate
transactions in distinct clusters). */
uint64_t m_sequence;
explicit Cluster(uint64_t sequence) noexcept : m_sequence(sequence) {}
public:
// Provide virtual destructor, for safe polymorphic usage inside std::unique_ptr.
virtual ~Cluster() = default;
// Cannot move or copy (would invalidate Cluster* in Locator and ClusterSet). */
Cluster(const Cluster&) = delete;
Cluster& operator=(const Cluster&) = delete;
Cluster(Cluster&&) = delete;
Cluster& operator=(Cluster&&) = delete;
// Generic helper functions.
/** Whether the linearization of this Cluster can be exposed. */
bool IsAcceptable(bool after_split = false) const noexcept
{
return m_quality == QualityLevel::ACCEPTABLE || m_quality == QualityLevel::OPTIMAL ||
(after_split && m_quality == QualityLevel::NEEDS_SPLIT_ACCEPTABLE);
}
/** Whether the linearization of this Cluster is optimal. */
bool IsOptimal() const noexcept
{
return m_quality == QualityLevel::OPTIMAL;
}
/** Whether this cluster is oversized. Note that no changes that can cause oversizedness are
* ever applied, so the only way a materialized Cluster object can be oversized is by being
* an individually oversized transaction singleton. */
bool IsOversized() const noexcept { return m_quality == QualityLevel::OVERSIZED_SINGLETON; }
/** Whether this cluster requires splitting. */
bool NeedsSplitting() const noexcept
{
return m_quality == QualityLevel::NEEDS_SPLIT ||
m_quality == QualityLevel::NEEDS_SPLIT_ACCEPTABLE;
}
/** Get the smallest number of transactions this Cluster is intended for. */
virtual DepGraphIndex GetMinIntendedTxCount() const noexcept = 0;
/** Get the maximum number of transactions this Cluster supports. */
virtual DepGraphIndex GetMaxTxCount() const noexcept = 0;
/** Total memory usage currently for this Cluster, including all its dynamic memory, plus Cluster
* structure itself, and ClusterSet::m_clusters entry. */
virtual size_t TotalMemoryUsage() const noexcept = 0;
/** Determine the range of DepGraphIndexes used by this Cluster. */
virtual DepGraphIndex GetDepGraphIndexRange() const noexcept = 0;
/** Get the number of transactions in this Cluster. */
virtual LinearizationIndex GetTxCount() const noexcept = 0;
/** Get the total size of the transactions in this Cluster. */
virtual uint64_t GetTotalTxSize() const noexcept = 0;
/** Given a DepGraphIndex into this Cluster, find the corresponding GraphIndex. */
virtual GraphIndex GetClusterEntry(DepGraphIndex index) const noexcept = 0;
/** Append a transaction with given GraphIndex at the end of this Cluster and its
* linearization. Return the DepGraphIndex it was placed at. */
virtual DepGraphIndex AppendTransaction(GraphIndex graph_idx, FeePerWeight feerate) noexcept = 0;
/** Add dependencies to a given child in this cluster. */
virtual void AddDependencies(SetType parents, DepGraphIndex child) noexcept = 0;
/** Invoke visitor_fn for each transaction in the cluster, in linearization order, then wipe this Cluster. */
virtual void ExtractTransactions(const std::function<void (DepGraphIndex, GraphIndex, FeePerWeight, SetType)>& visit_fn) noexcept = 0;
/** Figure out what level this Cluster exists at in the graph. In most cases this is known by
* the caller already (see all "int level" arguments below), but not always. */
virtual int GetLevel(const TxGraphImpl& graph) const noexcept = 0;
/** Only called by TxGraphImpl::SwapIndexes. */
virtual void UpdateMapping(DepGraphIndex cluster_idx, GraphIndex graph_idx) noexcept = 0;
/** Push changes to Cluster and its linearization to the TxGraphImpl Entry objects. */
virtual void Updated(TxGraphImpl& graph, int level) noexcept = 0;
/** Create a copy of this Cluster in staging, returning a pointer to it (used by PullIn). */
virtual Cluster* CopyToStaging(TxGraphImpl& graph) const noexcept = 0;
/** Get the list of Clusters in main that conflict with this one (which is assumed to be in staging). */
virtual void GetConflicts(const TxGraphImpl& graph, std::vector<Cluster*>& out) const noexcept = 0;
/** Mark all the Entry objects belonging to this staging Cluster as missing. The Cluster must be
* deleted immediately after. */
virtual void MakeStagingTransactionsMissing(TxGraphImpl& graph) noexcept = 0;
/** Remove all transactions from a (non-empty) Cluster. */
virtual void Clear(TxGraphImpl& graph, int level) noexcept = 0;
/** Change a Cluster's level from 1 (staging) to 0 (main). */
virtual void MoveToMain(TxGraphImpl& graph) noexcept = 0;
/** Minimize this Cluster's memory usage. */
virtual void Compact() noexcept = 0;
// Functions that implement the Cluster-specific side of internal TxGraphImpl mutations.
/** Apply all removals from the front of to_remove that apply to this Cluster, popping them
* off. There must be at least one such entry. */
virtual void ApplyRemovals(TxGraphImpl& graph, int level, std::span<GraphIndex>& to_remove) noexcept = 0;
/** Split this cluster (must have a NEEDS_SPLIT* quality). Returns whether to delete this
* Cluster afterwards. */
[[nodiscard]] virtual bool Split(TxGraphImpl& graph, int level) noexcept = 0;
/** Move all transactions from cluster to *this (as separate components). */
virtual void Merge(TxGraphImpl& graph, int level, Cluster& cluster) noexcept = 0;
/** Given a span of (parent, child) pairs that all belong to this Cluster, apply them. */
virtual void ApplyDependencies(TxGraphImpl& graph, int level, std::span<std::pair<GraphIndex, GraphIndex>> to_apply) noexcept = 0;
/** Improve the linearization of this Cluster. Returns how much work was performed and whether
* the Cluster's QualityLevel improved as a result. */
virtual std::pair<uint64_t, bool> Relinearize(TxGraphImpl& graph, int level, uint64_t max_iters) noexcept = 0;
/** For every chunk in the cluster, append its FeeFrac to ret. */
virtual void AppendChunkFeerates(std::vector<FeeFrac>& ret) const noexcept = 0;
/** Add a TrimTxData entry (filling m_chunk_feerate, m_index, m_tx_size) for every
* transaction in the Cluster to ret. Implicit dependencies between consecutive transactions
* in the linearization are added to deps. Return the Cluster's total transaction size. */
virtual uint64_t AppendTrimData(std::vector<TrimTxData>& ret, std::vector<std::pair<GraphIndex, GraphIndex>>& deps) const noexcept = 0;
// Functions that implement the Cluster-specific side of public TxGraph functions.
/** Process elements from the front of args that apply to this cluster, and append Refs for the
* union of their ancestors to output. */
virtual void GetAncestorRefs(const TxGraphImpl& graph, std::span<std::pair<Cluster*, DepGraphIndex>>& args, std::vector<TxGraph::Ref*>& output) noexcept = 0;
/** Process elements from the front of args that apply to this cluster, and append Refs for the
* union of their descendants to output. */
virtual void GetDescendantRefs(const TxGraphImpl& graph, std::span<std::pair<Cluster*, DepGraphIndex>>& args, std::vector<TxGraph::Ref*>& output) noexcept = 0;
/** Populate range with refs for the transactions in this Cluster's linearization, from
* position start_pos until start_pos+range.size()-1, inclusive. Returns whether that
* range includes the last transaction in the linearization. */
virtual bool GetClusterRefs(TxGraphImpl& graph, std::span<TxGraph::Ref*> range, LinearizationIndex start_pos) noexcept = 0;
/** Get the individual transaction feerate of a Cluster element. */
virtual FeePerWeight GetIndividualFeerate(DepGraphIndex idx) noexcept = 0;
/** Modify the fee of a Cluster element. */
virtual void SetFee(TxGraphImpl& graph, int level, DepGraphIndex idx, int64_t fee) noexcept = 0;
// Debugging functions.
virtual void SanityCheck(const TxGraphImpl& graph, int level) const = 0;
};
/** An implementation of Cluster that uses a DepGraph and vectors, to support arbitrary numbers of
* transactions up to MAX_CLUSTER_COUNT_LIMIT. */
class GenericClusterImpl final : public Cluster
{
friend class TxGraphImpl;
/** The DepGraph for this cluster, holding all feerates, and ancestors/descendants. */
DepGraph<SetType> m_depgraph;
/** m_mapping[i] gives the GraphIndex for the position i transaction in m_depgraph. Values for
* positions i that do not exist in m_depgraph shouldn't ever be accessed and thus don't
* matter. m_mapping.size() equals m_depgraph.PositionRange(). */
std::vector<GraphIndex> m_mapping;
/** The current linearization of the cluster. m_linearization.size() equals
* m_depgraph.TxCount(). This is always kept topological. */
std::vector<DepGraphIndex> m_linearization;
public:
/** The smallest number of transactions this Cluster implementation is intended for. */
static constexpr DepGraphIndex MIN_INTENDED_TX_COUNT{2};
/** The largest number of transactions this Cluster implementation supports. */
static constexpr DepGraphIndex MAX_TX_COUNT{SetType::Size()};
GenericClusterImpl() noexcept = delete;
/** Construct an empty GenericClusterImpl. */
explicit GenericClusterImpl(uint64_t sequence) noexcept;
size_t TotalMemoryUsage() const noexcept final;
constexpr DepGraphIndex GetMinIntendedTxCount() const noexcept final { return MIN_INTENDED_TX_COUNT; }
constexpr DepGraphIndex GetMaxTxCount() const noexcept final { return MAX_TX_COUNT; }
DepGraphIndex GetDepGraphIndexRange() const noexcept final { return m_depgraph.PositionRange(); }
LinearizationIndex GetTxCount() const noexcept final { return m_linearization.size(); }
uint64_t GetTotalTxSize() const noexcept final;
GraphIndex GetClusterEntry(DepGraphIndex index) const noexcept final { return m_mapping[index]; }
DepGraphIndex AppendTransaction(GraphIndex graph_idx, FeePerWeight feerate) noexcept final;
void AddDependencies(SetType parents, DepGraphIndex child) noexcept final;
void ExtractTransactions(const std::function<void (DepGraphIndex, GraphIndex, FeePerWeight, SetType)>& visit_fn) noexcept final;
int GetLevel(const TxGraphImpl& graph) const noexcept final;
void UpdateMapping(DepGraphIndex cluster_idx, GraphIndex graph_idx) noexcept final { m_mapping[cluster_idx] = graph_idx; }
void Updated(TxGraphImpl& graph, int level) noexcept final;
Cluster* CopyToStaging(TxGraphImpl& graph) const noexcept final;
void GetConflicts(const TxGraphImpl& graph, std::vector<Cluster*>& out) const noexcept final;
void MakeStagingTransactionsMissing(TxGraphImpl& graph) noexcept final;
void Clear(TxGraphImpl& graph, int level) noexcept final;
void MoveToMain(TxGraphImpl& graph) noexcept final;
void Compact() noexcept final;
void ApplyRemovals(TxGraphImpl& graph, int level, std::span<GraphIndex>& to_remove) noexcept final;
[[nodiscard]] bool Split(TxGraphImpl& graph, int level) noexcept final;
void Merge(TxGraphImpl& graph, int level, Cluster& cluster) noexcept final;
void ApplyDependencies(TxGraphImpl& graph, int level, std::span<std::pair<GraphIndex, GraphIndex>> to_apply) noexcept final;
std::pair<uint64_t, bool> Relinearize(TxGraphImpl& graph, int level, uint64_t max_iters) noexcept final;
void AppendChunkFeerates(std::vector<FeeFrac>& ret) const noexcept final;
uint64_t AppendTrimData(std::vector<TrimTxData>& ret, std::vector<std::pair<GraphIndex, GraphIndex>>& deps) const noexcept final;
void GetAncestorRefs(const TxGraphImpl& graph, std::span<std::pair<Cluster*, DepGraphIndex>>& args, std::vector<TxGraph::Ref*>& output) noexcept final;
void GetDescendantRefs(const TxGraphImpl& graph, std::span<std::pair<Cluster*, DepGraphIndex>>& args, std::vector<TxGraph::Ref*>& output) noexcept final;
bool GetClusterRefs(TxGraphImpl& graph, std::span<TxGraph::Ref*> range, LinearizationIndex start_pos) noexcept final;
FeePerWeight GetIndividualFeerate(DepGraphIndex idx) noexcept final;
void SetFee(TxGraphImpl& graph, int level, DepGraphIndex idx, int64_t fee) noexcept final;
void SanityCheck(const TxGraphImpl& graph, int level) const final;
};
/** An implementation of Cluster that only supports 1 transaction. */
class SingletonClusterImpl final : public Cluster
{
friend class TxGraphImpl;
/** The feerate of the (singular) transaction in this Cluster. */
FeePerWeight m_feerate;
/** Constant to indicate that this Cluster is empty. */
static constexpr auto NO_GRAPH_INDEX = GraphIndex(-1);
/** The GraphIndex of the transaction. NO_GRAPH_INDEX if this Cluster is empty. */
GraphIndex m_graph_index = NO_GRAPH_INDEX;
public:
/** The smallest number of transactions this Cluster implementation is intended for. */
static constexpr DepGraphIndex MIN_INTENDED_TX_COUNT{1};
/** The largest number of transactions this Cluster implementation supports. */
static constexpr DepGraphIndex MAX_TX_COUNT{1};
SingletonClusterImpl() noexcept = delete;
/** Construct an empty SingletonClusterImpl. */
explicit SingletonClusterImpl(uint64_t sequence) noexcept : Cluster(sequence) {}
size_t TotalMemoryUsage() const noexcept final;
constexpr DepGraphIndex GetMinIntendedTxCount() const noexcept final { return MIN_INTENDED_TX_COUNT; }
constexpr DepGraphIndex GetMaxTxCount() const noexcept final { return MAX_TX_COUNT; }
LinearizationIndex GetTxCount() const noexcept final { return m_graph_index != NO_GRAPH_INDEX; }
DepGraphIndex GetDepGraphIndexRange() const noexcept final { return GetTxCount(); }
uint64_t GetTotalTxSize() const noexcept final { return GetTxCount() ? m_feerate.size : 0; }
GraphIndex GetClusterEntry(DepGraphIndex index) const noexcept final { Assume(index == 0); Assume(GetTxCount()); return m_graph_index; }
DepGraphIndex AppendTransaction(GraphIndex graph_idx, FeePerWeight feerate) noexcept final;
void AddDependencies(SetType parents, DepGraphIndex child) noexcept final;
void ExtractTransactions(const std::function<void (DepGraphIndex, GraphIndex, FeePerWeight, SetType)>& visit_fn) noexcept final;
int GetLevel(const TxGraphImpl& graph) const noexcept final;
void UpdateMapping(DepGraphIndex cluster_idx, GraphIndex graph_idx) noexcept final { Assume(cluster_idx == 0); m_graph_index = graph_idx; }
void Updated(TxGraphImpl& graph, int level) noexcept final;
Cluster* CopyToStaging(TxGraphImpl& graph) const noexcept final;
void GetConflicts(const TxGraphImpl& graph, std::vector<Cluster*>& out) const noexcept final;
void MakeStagingTransactionsMissing(TxGraphImpl& graph) noexcept final;
void Clear(TxGraphImpl& graph, int level) noexcept final;
void MoveToMain(TxGraphImpl& graph) noexcept final;
void Compact() noexcept final;
void ApplyRemovals(TxGraphImpl& graph, int level, std::span<GraphIndex>& to_remove) noexcept final;
[[nodiscard]] bool Split(TxGraphImpl& graph, int level) noexcept final;
void Merge(TxGraphImpl& graph, int level, Cluster& cluster) noexcept final;
void ApplyDependencies(TxGraphImpl& graph, int level, std::span<std::pair<GraphIndex, GraphIndex>> to_apply) noexcept final;
std::pair<uint64_t, bool> Relinearize(TxGraphImpl& graph, int level, uint64_t max_iters) noexcept final;
void AppendChunkFeerates(std::vector<FeeFrac>& ret) const noexcept final;
uint64_t AppendTrimData(std::vector<TrimTxData>& ret, std::vector<std::pair<GraphIndex, GraphIndex>>& deps) const noexcept final;
void GetAncestorRefs(const TxGraphImpl& graph, std::span<std::pair<Cluster*, DepGraphIndex>>& args, std::vector<TxGraph::Ref*>& output) noexcept final;
void GetDescendantRefs(const TxGraphImpl& graph, std::span<std::pair<Cluster*, DepGraphIndex>>& args, std::vector<TxGraph::Ref*>& output) noexcept final;
bool GetClusterRefs(TxGraphImpl& graph, std::span<TxGraph::Ref*> range, LinearizationIndex start_pos) noexcept final;
FeePerWeight GetIndividualFeerate(DepGraphIndex idx) noexcept final;
void SetFee(TxGraphImpl& graph, int level, DepGraphIndex idx, int64_t fee) noexcept final;
void SanityCheck(const TxGraphImpl& graph, int level) const final;
};
/** The transaction graph, including staged changes.
*
* The overall design of the data structure consists of 3 interlinked representations:
* - The transactions (held as a vector of TxGraphImpl::Entry inside TxGraphImpl).
* - The clusters (Cluster objects in per-quality vectors inside TxGraphImpl::ClusterSet).
* - The Refs (TxGraph::Ref objects, held externally by users of the TxGraph class)
*
* The Clusters are kept in one or two ClusterSet objects, one for the "main" graph, and one for
* the proposed changes ("staging"). If a transaction occurs in both, they share the same Entry,
* but there will be a separate Cluster per graph.
*
* Clusters and Refs contain the index of the Entry objects they refer to, and the Entry objects
* refer back to the Clusters and Refs the corresponding transaction is contained in.
*
* While redundant, this permits moving all of them independently, without invalidating things
* or costly iteration to fix up everything:
* - Entry objects can be moved to fill holes left by removed transactions in the Entry vector
* (see TxGraphImpl::Compact).
* - Clusters can be rewritten continuously (removals can cause them to split, new dependencies
* can cause them to be merged).
* - Ref objects can be held outside the class, while permitting them to be moved around, and
* inherited from.
*/
class TxGraphImpl final : public TxGraph
{
friend class Cluster;
friend class SingletonClusterImpl;
friend class GenericClusterImpl;
friend class BlockBuilderImpl;
private:
/** Internal RNG. */
FastRandomContext m_rng;
/** This TxGraphImpl's maximum cluster count limit. */
const DepGraphIndex m_max_cluster_count;
/** This TxGraphImpl's maximum cluster size limit. */
const uint64_t m_max_cluster_size;
/** The number of linearization improvement steps needed per cluster to be considered
* acceptable. */
const uint64_t m_acceptable_iters;
/** Information about one group of Clusters to be merged. */
struct GroupEntry
{
/** Where the clusters to be merged start in m_group_clusters. */
uint32_t m_cluster_offset;
/** How many clusters to merge. */
uint32_t m_cluster_count;
/** Where the dependencies for this cluster group in m_deps_to_add start. */
uint32_t m_deps_offset;
/** How many dependencies to add. */
uint32_t m_deps_count;
};
/** Information about all groups of Clusters to be merged. */
struct GroupData
{
/** The groups of Clusters to be merged. */
std::vector<GroupEntry> m_groups;
/** Which clusters are to be merged. GroupEntry::m_cluster_offset indexes into this. */
std::vector<Cluster*> m_group_clusters;
};
/** The collection of all Clusters in main or staged. */
struct ClusterSet
{
/** The vectors of clusters, one vector per quality level. ClusterSetIndex indexes into each. */
std::array<std::vector<std::unique_ptr<Cluster>>, int(QualityLevel::NONE)> m_clusters;
/** Which removals have yet to be applied. */
std::vector<GraphIndex> m_to_remove;
/** Which dependencies are to be added ((parent,child) pairs). GroupData::m_deps_offset indexes
* into this. */
std::vector<std::pair<GraphIndex, GraphIndex>> m_deps_to_add;
/** Information about the merges to be performed, if known. */
std::optional<GroupData> m_group_data = GroupData{};
/** Which entries were removed in this ClusterSet (so they can be wiped on abort). This
* includes all entries which have an (R) removed locator at this level (staging only),
* plus optionally any transaction in m_unlinked. */
std::vector<GraphIndex> m_removed;
/** Total number of transactions in this graph (sum of all transaction counts in all
* Clusters, and for staging also those inherited from the main ClusterSet). */
GraphIndex m_txcount{0};
/** Total number of individually oversized transactions in the graph. */
GraphIndex m_txcount_oversized{0};
/** Whether this graph is oversized (if known). */
std::optional<bool> m_oversized{false};
/** The combined TotalMemoryUsage of all clusters in this level (only Clusters that
* are materialized; in staging, implicit Clusters from main are not counted), */
size_t m_cluster_usage{0};
ClusterSet() noexcept = default;
};
/** The main ClusterSet. */
ClusterSet m_main_clusterset;
/** The staging ClusterSet, if any. */
std::optional<ClusterSet> m_staging_clusterset;
/** Next sequence number to assign to created Clusters. */
uint64_t m_next_sequence_counter{0};
/** Information about a chunk in the main graph. */
struct ChunkData
{
/** The Entry which is the last transaction of the chunk. */
mutable GraphIndex m_graph_index;
/** How many transactions the chunk contains (-1 = singleton tail of cluster). */
LinearizationIndex m_chunk_count;
ChunkData(GraphIndex graph_index, LinearizationIndex chunk_count) noexcept :
m_graph_index{graph_index}, m_chunk_count{chunk_count} {}
};
/** Compare two Cluster* by their m_sequence value (while supporting nullptr). */
static std::strong_ordering CompareClusters(Cluster* a, Cluster* b) noexcept
{
// The nullptr pointer compares before everything else.
if (a == nullptr || b == nullptr) {
return (a != nullptr) <=> (b != nullptr);
}
// If neither pointer is nullptr, compare the Clusters' sequence numbers.
Assume(a == b || a->m_sequence != b->m_sequence);
return a->m_sequence <=> b->m_sequence;
}
/** Compare two entries (which must both exist within the main graph). */
std::strong_ordering CompareMainTransactions(GraphIndex a, GraphIndex b) const noexcept
{
Assume(a < m_entries.size() && b < m_entries.size());
const auto& entry_a = m_entries[a];
const auto& entry_b = m_entries[b];
// Compare chunk feerates, and return result if it differs.
auto feerate_cmp = FeeRateCompare(entry_b.m_main_chunk_feerate, entry_a.m_main_chunk_feerate);
if (feerate_cmp < 0) return std::strong_ordering::less;
if (feerate_cmp > 0) return std::strong_ordering::greater;
// Compare Cluster m_sequence as tie-break for equal chunk feerates.
const auto& locator_a = entry_a.m_locator[0];
const auto& locator_b = entry_b.m_locator[0];
Assume(locator_a.IsPresent() && locator_b.IsPresent());
if (locator_a.cluster != locator_b.cluster) {
return CompareClusters(locator_a.cluster, locator_b.cluster);
}
// As final tie-break, compare position within cluster linearization.
return entry_a.m_main_lin_index <=> entry_b.m_main_lin_index;
}
/** Comparator for ChunkData objects in mining order. */
class ChunkOrder
{
const TxGraphImpl* const m_graph;
public:
explicit ChunkOrder(const TxGraphImpl* graph) : m_graph(graph) {}
bool operator()(const ChunkData& a, const ChunkData& b) const noexcept
{
return m_graph->CompareMainTransactions(a.m_graph_index, b.m_graph_index) < 0;
}
};
/** Definition for the mining index type. */
using ChunkIndex = std::set<ChunkData, ChunkOrder>;
/** Index of ChunkData objects, indexing the last transaction in each chunk in the main
* graph. */
ChunkIndex m_main_chunkindex;
/** Number of index-observing objects in existence (BlockBuilderImpls). */
size_t m_main_chunkindex_observers{0};
/** Cache of discarded ChunkIndex node handles to reuse, avoiding additional allocation. */
std::vector<ChunkIndex::node_type> m_main_chunkindex_discarded;
/** A Locator that describes whether, where, and in which Cluster an Entry appears.
* Every Entry has MAX_LEVELS locators, as it may appear in one Cluster per level.
*
* Each level of a Locator is in one of three states:
*
* - (P)resent: actually occurs in a Cluster at that level.
*
* - (M)issing:
* - In the main graph: the transaction does not exist in main.
* - In the staging graph: the transaction's existence is the same as in main. If it doesn't
* exist in main, (M) in staging means it does not exist there
* either. If it does exist in main, (M) in staging means the
* cluster it is in has not been modified in staging, and thus the
* transaction implicitly exists in staging too (without explicit
* Cluster object; see PullIn() to create it in staging too).
*
* - (R)emoved: only possible in staging; it means the transaction exists in main, but is
* removed in staging.
*
* The following combinations are possible:
* - (M,M): the transaction doesn't exist in either graph.
* - (P,M): the transaction exists in both, but only exists explicitly in a Cluster object in
* main. Its existence in staging is inherited from main.
* - (P,P): the transaction exists in both, and is materialized in both. Thus, the clusters
* and/or their linearizations may be different in main and staging.
* - (M,P): the transaction is added in staging, and does not exist in main.
* - (P,R): the transaction exists in main, but is removed in staging.
*
* When staging does not exist, only (M,M) and (P,M) are possible.
*/
struct Locator
{
/** Which Cluster the Entry appears in (nullptr = missing). */
Cluster* cluster{nullptr};
/** Where in the Cluster it appears (if cluster == nullptr: 0 = missing, -1 = removed). */
DepGraphIndex index{0};
/** Mark this Locator as missing (= same as lower level, or non-existing if level 0). */
void SetMissing() noexcept { cluster = nullptr; index = 0; }
/** Mark this Locator as removed (not allowed in level 0). */
void SetRemoved() noexcept { cluster = nullptr; index = DepGraphIndex(-1); }
/** Mark this Locator as present, in the specified Cluster. */
void SetPresent(Cluster* c, DepGraphIndex i) noexcept { cluster = c; index = i; }
/** Check if this Locator is missing. */
bool IsMissing() const noexcept { return cluster == nullptr && index == 0; }
/** Check if this Locator is removed. */
bool IsRemoved() const noexcept { return cluster == nullptr && index == DepGraphIndex(-1); }
/** Check if this Locator is present (in some Cluster). */
bool IsPresent() const noexcept { return cluster != nullptr; }
};
/** Internal information about each transaction in a TxGraphImpl. */
struct Entry
{
/** Pointer to the corresponding Ref object if any, or nullptr if unlinked. */
Ref* m_ref{nullptr};
/** Iterator to the corresponding ChunkData, if any, and m_main_chunkindex.end() otherwise.
* This is initialized on construction of the Entry, in AddTransaction. */
ChunkIndex::iterator m_main_chunkindex_iterator;
/** Which Cluster and position therein this Entry appears in. ([0] = main, [1] = staged). */
Locator m_locator[MAX_LEVELS];
/** The chunk feerate of this transaction in main (if present in m_locator[0]). */
FeePerWeight m_main_chunk_feerate;
/** The position this transaction has in the main linearization (if present). */
LinearizationIndex m_main_lin_index;
};
/** The set of all transactions (in all levels combined). GraphIndex values index into this. */
std::vector<Entry> m_entries;
/** Set of Entries which have no linked Ref anymore. */
std::vector<GraphIndex> m_unlinked;
public:
/** Construct a new TxGraphImpl with the specified limits. */
explicit TxGraphImpl(DepGraphIndex max_cluster_count, uint64_t max_cluster_size, uint64_t acceptable_iters) noexcept :
m_max_cluster_count(max_cluster_count),
m_max_cluster_size(max_cluster_size),
m_acceptable_iters(acceptable_iters),
m_main_chunkindex(ChunkOrder(this))
{
Assume(max_cluster_count >= 1);
Assume(max_cluster_count <= MAX_CLUSTER_COUNT_LIMIT);
}
/** Destructor. */
~TxGraphImpl() noexcept;
// Cannot move or copy (would invalidate TxGraphImpl* in Ref, MiningOrder, EvictionOrder).
TxGraphImpl(const TxGraphImpl&) = delete;
TxGraphImpl& operator=(const TxGraphImpl&) = delete;
TxGraphImpl(TxGraphImpl&&) = delete;
TxGraphImpl& operator=(TxGraphImpl&&) = delete;
// Simple helper functions.
/** Swap the Entry referred to by a and the one referred to by b. */
void SwapIndexes(GraphIndex a, GraphIndex b) noexcept;
/** If idx exists in the specified level ClusterSet (explicitly, or in the level below and not
* removed), return the Cluster it is in. Otherwise, return nullptr. */
Cluster* FindCluster(GraphIndex idx, int level) const noexcept { return FindClusterAndLevel(idx, level).first; }
/** Like FindCluster, but also return what level the match was found in (-1 if not found). */
std::pair<Cluster*, int> FindClusterAndLevel(GraphIndex idx, int level) const noexcept;
/** Extract a Cluster from its ClusterSet, and set its quality to QualityLevel::NONE. */
std::unique_ptr<Cluster> ExtractCluster(int level, QualityLevel quality, ClusterSetIndex setindex) noexcept;
/** Delete a Cluster. */
void DeleteCluster(Cluster& cluster, int level) noexcept;
/** Insert a Cluster into its ClusterSet. */
ClusterSetIndex InsertCluster(int level, std::unique_ptr<Cluster>&& cluster, QualityLevel quality) noexcept;
/** Change the QualityLevel of a Cluster (identified by old_quality and old_index). */
void SetClusterQuality(int level, QualityLevel old_quality, ClusterSetIndex old_index, QualityLevel new_quality) noexcept;
/** Get the index of the top level ClusterSet (staging if it exists, main otherwise). */
int GetTopLevel() const noexcept { return m_staging_clusterset.has_value(); }
/** Get the specified level (staging if it exists and level is TOP, main otherwise). */
int GetSpecifiedLevel(Level level) const noexcept { return level == Level::TOP && m_staging_clusterset.has_value(); }
/** Get a reference to the ClusterSet at the specified level (which must exist). */
ClusterSet& GetClusterSet(int level) noexcept;
const ClusterSet& GetClusterSet(int level) const noexcept;
/** Make a transaction not exist at a specified level. It must currently exist there.
* oversized_tx indicates whether the transaction is an individually-oversized one
* (OVERSIZED_SINGLETON). */
void ClearLocator(int level, GraphIndex index, bool oversized_tx) noexcept;
/** Find which Clusters in main conflict with ones in staging. */
std::vector<Cluster*> GetConflicts() const noexcept;
/** Clear an Entry's ChunkData. */
void ClearChunkData(Entry& entry) noexcept;
/** Give an Entry a ChunkData object. */
void CreateChunkData(GraphIndex idx, LinearizationIndex chunk_count) noexcept;
/** Create an empty GenericClusterImpl object. */
std::unique_ptr<GenericClusterImpl> CreateEmptyGenericCluster() noexcept
{
return std::make_unique<GenericClusterImpl>(m_next_sequence_counter++);
}
/** Create an empty SingletonClusterImpl object. */
std::unique_ptr<SingletonClusterImpl> CreateEmptySingletonCluster() noexcept
{
return std::make_unique<SingletonClusterImpl>(m_next_sequence_counter++);
}
/** Create an empty Cluster of the appropriate implementation for the specified (maximum) tx
* count. */
std::unique_ptr<Cluster> CreateEmptyCluster(DepGraphIndex tx_count) noexcept
{
if (tx_count >= SingletonClusterImpl::MIN_INTENDED_TX_COUNT && tx_count <= SingletonClusterImpl::MAX_TX_COUNT) {
return CreateEmptySingletonCluster();
}
if (tx_count >= GenericClusterImpl::MIN_INTENDED_TX_COUNT && tx_count <= GenericClusterImpl::MAX_TX_COUNT) {
return CreateEmptyGenericCluster();
}
assert(false);
return {};
}
// Functions for handling Refs.
/** Only called by Ref's move constructor/assignment to update Ref locations. */
void UpdateRef(GraphIndex idx, Ref& new_location) noexcept final
{
auto& entry = m_entries[idx];
Assume(entry.m_ref != nullptr);
entry.m_ref = &new_location;
}
/** Only called by Ref::~Ref to unlink Refs, and Ref's move assignment. */
void UnlinkRef(GraphIndex idx) noexcept final
{
auto& entry = m_entries[idx];
Assume(entry.m_ref != nullptr);
Assume(m_main_chunkindex_observers == 0 || !entry.m_locator[0].IsPresent());
entry.m_ref = nullptr;
// Mark the transaction as to be removed in all levels where it explicitly or implicitly
// exists.
bool exists_anywhere{false};
bool exists{false};
for (int level = 0; level <= GetTopLevel(); ++level) {
if (entry.m_locator[level].IsPresent()) {
exists_anywhere = true;
exists = true;
} else if (entry.m_locator[level].IsRemoved()) {
exists = false;
}
if (exists) {
auto& clusterset = GetClusterSet(level);
clusterset.m_to_remove.push_back(idx);
// Force recomputation of grouping data.
clusterset.m_group_data = std::nullopt;
// Do not wipe the oversized state of main if staging exists. The reason for this
// is that the alternative would mean that cluster merges may need to be applied to
// a formerly-oversized main graph while staging exists (to satisfy chunk feerate
// queries into main, for example), and such merges could conflict with pulls of
// some of their constituents into staging.
if (level == GetTopLevel() && clusterset.m_oversized == true) {
clusterset.m_oversized = std::nullopt;
}
}
}
m_unlinked.push_back(idx);
if (!exists_anywhere) Compact();
}
// Functions related to various normalization/application steps.
/** Get rid of unlinked Entry objects in m_entries, if possible (this changes the GraphIndex
* values for remaining Entry objects, so this only does something when no to-be-applied
* operations or staged removals referring to GraphIndexes remain). */
void Compact() noexcept;
/** If cluster is not in staging, copy it there, and return a pointer to it.
* Staging must exist, and this modifies the locators of its
* transactions from inherited (P,M) to explicit (P,P). */
Cluster* PullIn(Cluster* cluster, int level) noexcept;
/** Apply all removals queued up in m_to_remove to the relevant Clusters (which get a
* NEEDS_SPLIT* QualityLevel) up to the specified level. */
void ApplyRemovals(int up_to_level) noexcept;
/** Split an individual cluster. */
void Split(Cluster& cluster, int level) noexcept;
/** Split all clusters that need splitting up to the specified level. */
void SplitAll(int up_to_level) noexcept;
/** Populate m_group_data based on m_deps_to_add in the specified level. */
void GroupClusters(int level) noexcept;
/** Merge the specified clusters. */
void Merge(std::span<Cluster*> to_merge, int level) noexcept;
/** Apply all m_deps_to_add to the relevant Clusters in the specified level. */
void ApplyDependencies(int level) noexcept;
/** Make a specified Cluster have quality ACCEPTABLE or OPTIMAL. */
void MakeAcceptable(Cluster& cluster, int level) noexcept;
/** Make all Clusters at the specified level have quality ACCEPTABLE or OPTIMAL. */
void MakeAllAcceptable(int level) noexcept;
// Implementations for the public TxGraph interface.
Ref AddTransaction(const FeePerWeight& feerate) noexcept final;
void RemoveTransaction(const Ref& arg) noexcept final;
void AddDependency(const Ref& parent, const Ref& child) noexcept final;
void SetTransactionFee(const Ref&, int64_t fee) noexcept final;
bool DoWork(uint64_t iters) noexcept final;
void StartStaging() noexcept final;
void CommitStaging() noexcept final;
void AbortStaging() noexcept final;
bool HaveStaging() const noexcept final { return m_staging_clusterset.has_value(); }
bool Exists(const Ref& arg, Level level) noexcept final;
FeePerWeight GetMainChunkFeerate(const Ref& arg) noexcept final;
FeePerWeight GetIndividualFeerate(const Ref& arg) noexcept final;
std::vector<Ref*> GetCluster(const Ref& arg, Level level) noexcept final;
std::vector<Ref*> GetAncestors(const Ref& arg, Level level) noexcept final;
std::vector<Ref*> GetDescendants(const Ref& arg, Level level) noexcept final;
std::vector<Ref*> GetAncestorsUnion(std::span<const Ref* const> args, Level level) noexcept final;
std::vector<Ref*> GetDescendantsUnion(std::span<const Ref* const> args, Level level) noexcept final;
GraphIndex GetTransactionCount(Level level) noexcept final;
bool IsOversized(Level level) noexcept final;
std::strong_ordering CompareMainOrder(const Ref& a, const Ref& b) noexcept final;
GraphIndex CountDistinctClusters(std::span<const Ref* const> refs, Level level) noexcept final;
std::pair<std::vector<FeeFrac>, std::vector<FeeFrac>> GetMainStagingDiagrams() noexcept final;
std::vector<Ref*> Trim() noexcept final;
std::unique_ptr<BlockBuilder> GetBlockBuilder() noexcept final;
std::pair<std::vector<Ref*>, FeePerWeight> GetWorstMainChunk() noexcept final;
size_t GetMainMemoryUsage() noexcept final;
void SanityCheck() const final;
};
TxGraphImpl::ClusterSet& TxGraphImpl::GetClusterSet(int level) noexcept
{
if (level == 0) return m_main_clusterset;
Assume(level == 1);
Assume(m_staging_clusterset.has_value());
return *m_staging_clusterset;
}
const TxGraphImpl::ClusterSet& TxGraphImpl::GetClusterSet(int level) const noexcept
{
if (level == 0) return m_main_clusterset;
Assume(level == 1);
Assume(m_staging_clusterset.has_value());
return *m_staging_clusterset;
}
/** Implementation of the TxGraph::BlockBuilder interface. */
class BlockBuilderImpl final : public TxGraph::BlockBuilder
{
/** Which TxGraphImpl this object is doing block building for. It will have its
* m_main_chunkindex_observers incremented as long as this BlockBuilderImpl exists. */
TxGraphImpl* const m_graph;
/** Cluster sequence numbers which we're not including further transactions from. */
std::unordered_set<uint64_t> m_excluded_clusters;
/** Iterator to the current chunk in the chunk index. end() if nothing further remains. */
TxGraphImpl::ChunkIndex::const_iterator m_cur_iter;
/** Which cluster the current chunk belongs to, so we can exclude further transactions from it
* when that chunk is skipped. */
Cluster* m_cur_cluster;
/** Whether we know that m_cur_iter points to the last chunk of m_cur_cluster. */
bool m_known_end_of_cluster;
// Move m_cur_iter / m_cur_cluster to the next acceptable chunk.
void Next() noexcept;
public:
/** Construct a new BlockBuilderImpl to build blocks for the provided graph. */
BlockBuilderImpl(TxGraphImpl& graph) noexcept;
// Implement the public interface.
~BlockBuilderImpl() final;
std::optional<std::pair<std::vector<TxGraph::Ref*>, FeePerWeight>> GetCurrentChunk() noexcept final;
void Include() noexcept final;
void Skip() noexcept final;
};
void TxGraphImpl::ClearChunkData(Entry& entry) noexcept
{
if (entry.m_main_chunkindex_iterator != m_main_chunkindex.end()) {
Assume(m_main_chunkindex_observers == 0);
// If the Entry has a non-empty m_main_chunkindex_iterator, extract it, and move the handle
// to the cache of discarded chunkindex entries.
m_main_chunkindex_discarded.emplace_back(m_main_chunkindex.extract(entry.m_main_chunkindex_iterator));
entry.m_main_chunkindex_iterator = m_main_chunkindex.end();
}
}
void TxGraphImpl::CreateChunkData(GraphIndex idx, LinearizationIndex chunk_count) noexcept
{
auto& entry = m_entries[idx];
if (!m_main_chunkindex_discarded.empty()) {
// Reuse an discarded node handle.
auto& node = m_main_chunkindex_discarded.back().value();
node.m_graph_index = idx;
node.m_chunk_count = chunk_count;
auto insert_result = m_main_chunkindex.insert(std::move(m_main_chunkindex_discarded.back()));
Assume(insert_result.inserted);
entry.m_main_chunkindex_iterator = insert_result.position;
m_main_chunkindex_discarded.pop_back();
} else {
// Construct a new entry.
auto emplace_result = m_main_chunkindex.emplace(idx, chunk_count);
Assume(emplace_result.second);
entry.m_main_chunkindex_iterator = emplace_result.first;
}
}
size_t GenericClusterImpl::TotalMemoryUsage() const noexcept
{
return // Dynamic memory allocated in this Cluster.
memusage::DynamicUsage(m_mapping) + memusage::DynamicUsage(m_linearization) +
// Dynamic memory usage inside m_depgraph.
m_depgraph.DynamicMemoryUsage() +
// Memory usage of the allocated Cluster itself.
memusage::MallocUsage(sizeof(GenericClusterImpl)) +
// Memory usage of the ClusterSet::m_clusters entry.
sizeof(std::unique_ptr<Cluster>);
}
size_t SingletonClusterImpl::TotalMemoryUsage() const noexcept
{
return // Memory usage of the allocated SingletonClusterImpl itself.
memusage::MallocUsage(sizeof(SingletonClusterImpl)) +
// Memory usage of the ClusterSet::m_clusters entry.
sizeof(std::unique_ptr<Cluster>);
}
uint64_t GenericClusterImpl::GetTotalTxSize() const noexcept
{
uint64_t ret{0};
for (auto i : m_linearization) {
ret += m_depgraph.FeeRate(i).size;
}
return ret;
}
DepGraphIndex GenericClusterImpl::AppendTransaction(GraphIndex graph_idx, FeePerWeight feerate) noexcept
{
Assume(graph_idx != GraphIndex(-1));
auto ret = m_depgraph.AddTransaction(feerate);
m_mapping.push_back(graph_idx);
m_linearization.push_back(ret);
return ret;
}
DepGraphIndex SingletonClusterImpl::AppendTransaction(GraphIndex graph_idx, FeePerWeight feerate) noexcept
{
Assume(!GetTxCount());
m_graph_index = graph_idx;
m_feerate = feerate;
return 0;
}
void GenericClusterImpl::AddDependencies(SetType parents, DepGraphIndex child) noexcept
{
m_depgraph.AddDependencies(parents, child);
}
void SingletonClusterImpl::AddDependencies(SetType parents, DepGraphIndex child) noexcept
{
// Singletons cannot have any dependencies.
Assume(child == 0);
Assume(parents == SetType{} || parents == SetType::Fill(0));
}
void GenericClusterImpl::ExtractTransactions(const std::function<void (DepGraphIndex, GraphIndex, FeePerWeight, SetType)>& visit_fn) noexcept
{
for (auto pos : m_linearization) {
visit_fn(pos, m_mapping[pos], FeePerWeight::FromFeeFrac(m_depgraph.FeeRate(pos)), m_depgraph.GetReducedParents(pos));
}
// Purge this Cluster, now that everything has been moved.
m_depgraph = DepGraph<SetType>{};
m_linearization.clear();
m_mapping.clear();
}
void SingletonClusterImpl::ExtractTransactions(const std::function<void (DepGraphIndex, GraphIndex, FeePerWeight, SetType)>& visit_fn) noexcept
{
if (GetTxCount()) {
visit_fn(0, m_graph_index, m_feerate, SetType{});
m_graph_index = NO_GRAPH_INDEX;
}
}
int GenericClusterImpl::GetLevel(const TxGraphImpl& graph) const noexcept
{
// GetLevel() does not work for empty Clusters.
if (!Assume(!m_linearization.empty())) return -1;
// Pick an arbitrary Entry that occurs in this Cluster.
const auto& entry = graph.m_entries[m_mapping[m_linearization.front()]];
// See if there is a level whose Locator matches this Cluster, if so return that level.
for (int level = 0; level < MAX_LEVELS; ++level) {
if (entry.m_locator[level].cluster == this) return level;
}
// Given that we started with an Entry that occurs in this Cluster, one of its Locators must
// point back to it.
assert(false);
return -1;
}
int SingletonClusterImpl::GetLevel(const TxGraphImpl& graph) const noexcept
{
// GetLevel() does not work for empty Clusters.
if (!Assume(GetTxCount())) return -1;
// Get the Entry in this Cluster.
const auto& entry = graph.m_entries[m_graph_index];
// See if there is a level whose Locator matches this Cluster, if so return that level.
for (int level = 0; level < MAX_LEVELS; ++level) {
if (entry.m_locator[level].cluster == this) return level;
}
// Given that we started with an Entry that occurs in this Cluster, one of its Locators must
// point back to it.
assert(false);
return -1;
}
void TxGraphImpl::ClearLocator(int level, GraphIndex idx, bool oversized_tx) noexcept
{
auto& entry = m_entries[idx];
auto& clusterset = GetClusterSet(level);
Assume(entry.m_locator[level].IsPresent());
// Change the locator from Present to Missing or Removed.
if (level == 0 || !entry.m_locator[level - 1].IsPresent()) {
entry.m_locator[level].SetMissing();
} else {
entry.m_locator[level].SetRemoved();
clusterset.m_removed.push_back(idx);
}
// Update the transaction count.
--clusterset.m_txcount;
clusterset.m_txcount_oversized -= oversized_tx;
// If clearing main, adjust the status of Locators of this transaction in staging, if it exists.
if (level == 0 && GetTopLevel() == 1) {
if (entry.m_locator[1].IsRemoved()) {
entry.m_locator[1].SetMissing();
} else if (!entry.m_locator[1].IsPresent()) {
--m_staging_clusterset->m_txcount;
m_staging_clusterset->m_txcount_oversized -= oversized_tx;
}
}
if (level == 0) ClearChunkData(entry);
}
void GenericClusterImpl::Updated(TxGraphImpl& graph, int level) noexcept
{
// Update all the Locators for this Cluster's Entry objects.
for (DepGraphIndex idx : m_linearization) {
auto& entry = graph.m_entries[m_mapping[idx]];
// Discard any potential ChunkData prior to modifying the Cluster (as that could
// invalidate its ordering).
if (level == 0) graph.ClearChunkData(entry);
entry.m_locator[level].SetPresent(this, idx);
}
// If this is for the main graph (level = 0), and the Cluster's quality is ACCEPTABLE or
// OPTIMAL, compute its chunking and store its information in the Entry's m_main_lin_index
// and m_main_chunk_feerate. These fields are only accessed after making the entire graph
// ACCEPTABLE, so it is pointless to compute these if we haven't reached that quality level
// yet.
if (level == 0 && IsAcceptable()) {
const LinearizationChunking chunking(m_depgraph, m_linearization);
LinearizationIndex lin_idx{0};
// Iterate over the chunks.
for (unsigned chunk_idx = 0; chunk_idx < chunking.NumChunksLeft(); ++chunk_idx) {
auto chunk = chunking.GetChunk(chunk_idx);
auto chunk_count = chunk.transactions.Count();
Assume(chunk_count > 0);
// Iterate over the transactions in the linearization, which must match those in chunk.
while (true) {
DepGraphIndex idx = m_linearization[lin_idx];
GraphIndex graph_idx = m_mapping[idx];
auto& entry = graph.m_entries[graph_idx];
entry.m_main_lin_index = lin_idx++;
entry.m_main_chunk_feerate = FeePerWeight::FromFeeFrac(chunk.feerate);
Assume(chunk.transactions[idx]);
chunk.transactions.Reset(idx);
if (chunk.transactions.None()) {
// Last transaction in the chunk.
if (chunk_count == 1 && chunk_idx + 1 == chunking.NumChunksLeft()) {
// If this is the final chunk of the cluster, and it contains just a single
// transaction (which will always be true for the very common singleton
// clusters), store the special value -1 as chunk count.
chunk_count = LinearizationIndex(-1);
}
graph.CreateChunkData(graph_idx, chunk_count);
break;
}
}
}
}
}
void SingletonClusterImpl::Updated(TxGraphImpl& graph, int level) noexcept
{
// Don't do anything if this is empty.
if (GetTxCount() == 0) return;
auto& entry = graph.m_entries[m_graph_index];
// Discard any potential ChunkData prior to modifying the Cluster (as that could
// invalidate its ordering).
if (level == 0) graph.ClearChunkData(entry);
entry.m_locator[level].SetPresent(this, 0);
// If this is for the main graph (level = 0), compute its chunking and store its information in
// the Entry's m_main_lin_index and m_main_chunk_feerate.
if (level == 0 && IsAcceptable()) {
entry.m_main_lin_index = 0;
entry.m_main_chunk_feerate = m_feerate;
// Always use the special LinearizationIndex(-1), indicating singleton chunk at end of
// Cluster, here.
graph.CreateChunkData(m_graph_index, LinearizationIndex(-1));
}
}
void GenericClusterImpl::GetConflicts(const TxGraphImpl& graph, std::vector<Cluster*>& out) const noexcept
{
for (auto i : m_linearization) {
auto& entry = graph.m_entries[m_mapping[i]];
// For every transaction Entry in this Cluster, if it also exists in a lower-level Cluster,
// then that Cluster conflicts.
if (entry.m_locator[0].IsPresent()) {
out.push_back(entry.m_locator[0].cluster);
}
}
}
void SingletonClusterImpl::GetConflicts(const TxGraphImpl& graph, std::vector<Cluster*>& out) const noexcept
{
// Empty clusters have no conflicts.
if (GetTxCount() == 0) return;
auto& entry = graph.m_entries[m_graph_index];
// If the transaction in this Cluster also exists in a lower-level Cluster, then that Cluster
// conflicts.
if (entry.m_locator[0].IsPresent()) {
out.push_back(entry.m_locator[0].cluster);
}
}
std::vector<Cluster*> TxGraphImpl::GetConflicts() const noexcept
{
Assume(GetTopLevel() == 1);
auto& clusterset = GetClusterSet(1);
std::vector<Cluster*> ret;
// All main Clusters containing transactions in m_removed (so (P,R) ones) are conflicts.
for (auto i : clusterset.m_removed) {
auto& entry = m_entries[i];
if (entry.m_locator[0].IsPresent()) {
ret.push_back(entry.m_locator[0].cluster);
}
}
// Then go over all Clusters at this level, and find their conflicts (the (P,P) ones).
for (int quality = 0; quality < int(QualityLevel::NONE); ++quality) {
auto& clusters = clusterset.m_clusters[quality];
for (const auto& cluster : clusters) {
cluster->GetConflicts(*this, ret);
}
}
// Deduplicate the result (the same Cluster may appear multiple times).
std::sort(ret.begin(), ret.end(), [](Cluster* a, Cluster* b) noexcept { return CompareClusters(a, b) < 0; });
ret.erase(std::unique(ret.begin(), ret.end()), ret.end());
return ret;
}
Cluster* GenericClusterImpl::CopyToStaging(TxGraphImpl& graph) const noexcept
{
// Construct an empty Cluster.
auto ret = graph.CreateEmptyGenericCluster();
auto ptr = ret.get();
// Copy depgraph, mapping, and linearization.
ptr->m_depgraph = m_depgraph;
ptr->m_mapping = m_mapping;
ptr->m_linearization = m_linearization;
// Insert the new Cluster into the graph.
graph.InsertCluster(/*level=*/1, std::move(ret), m_quality);
// Update its Locators.
ptr->Updated(graph, /*level=*/1);
// Update memory usage.
graph.GetClusterSet(/*level=*/1).m_cluster_usage += ptr->TotalMemoryUsage();
return ptr;
}
Cluster* SingletonClusterImpl::CopyToStaging(TxGraphImpl& graph) const noexcept
{
// Construct an empty Cluster.
auto ret = graph.CreateEmptySingletonCluster();
auto ptr = ret.get();
// Copy data.
ptr->m_graph_index = m_graph_index;
ptr->m_feerate = m_feerate;
// Insert the new Cluster into the graph.
graph.InsertCluster(/*level=*/1, std::move(ret), m_quality);
// Update its Locators.
ptr->Updated(graph, /*level=*/1);
// Update memory usage.
graph.GetClusterSet(/*level=*/1).m_cluster_usage += ptr->TotalMemoryUsage();
return ptr;
}
void GenericClusterImpl::ApplyRemovals(TxGraphImpl& graph, int level, std::span<GraphIndex>& to_remove) noexcept
{
// Iterate over the prefix of to_remove that applies to this cluster.
Assume(!to_remove.empty());
SetType todo;
graph.GetClusterSet(level).m_cluster_usage -= TotalMemoryUsage();
do {
GraphIndex idx = to_remove.front();
Assume(idx < graph.m_entries.size());
auto& entry = graph.m_entries[idx];
auto& locator = entry.m_locator[level];
// Stop once we hit an entry that applies to another Cluster.
if (locator.cluster != this) break;
// - Remember it in a set of to-remove DepGraphIndexes.
todo.Set(locator.index);
// - Remove from m_mapping. This isn't strictly necessary as unused positions in m_mapping
// are just never accessed, but set it to -1 here to increase the ability to detect a bug
// that causes it to be accessed regardless.
m_mapping[locator.index] = GraphIndex(-1);
// - Remove its linearization index from the Entry (if in main).
if (level == 0) {
entry.m_main_lin_index = LinearizationIndex(-1);
}
// - Mark it as missing/removed in the Entry's locator.
graph.ClearLocator(level, idx, m_quality == QualityLevel::OVERSIZED_SINGLETON);
to_remove = to_remove.subspan(1);
} while(!to_remove.empty());
auto quality = m_quality;
Assume(todo.Any());
// Wipe from the Cluster's DepGraph (this is O(n) regardless of the number of entries
// removed, so we benefit from batching all the removals).
m_depgraph.RemoveTransactions(todo);
m_mapping.resize(m_depgraph.PositionRange());
// First remove all removals at the end of the linearization.
while (!m_linearization.empty() && todo[m_linearization.back()]) {
todo.Reset(m_linearization.back());
m_linearization.pop_back();
}
if (todo.None()) {
// If no further removals remain, and thus all removals were at the end, we may be able
// to leave the cluster at a better quality level.
if (IsAcceptable(/*after_split=*/true)) {
quality = QualityLevel::NEEDS_SPLIT_ACCEPTABLE;
} else {
quality = QualityLevel::NEEDS_SPLIT;
}
} else {
// If more removals remain, filter those out of m_linearization.
m_linearization.erase(std::remove_if(
m_linearization.begin(),
m_linearization.end(),
[&](auto pos) { return todo[pos]; }), m_linearization.end());
quality = QualityLevel::NEEDS_SPLIT;
}
Compact();
graph.GetClusterSet(level).m_cluster_usage += TotalMemoryUsage();
graph.SetClusterQuality(level, m_quality, m_setindex, quality);
Updated(graph, level);
}
void SingletonClusterImpl::ApplyRemovals(TxGraphImpl& graph, int level, std::span<GraphIndex>& to_remove) noexcept
{
// We can only remove the one transaction this Cluster has.
Assume(!to_remove.empty());
Assume(GetTxCount());
Assume(to_remove.front() == m_graph_index);
// Pop all copies of m_graph_index from the front of to_remove (at least one, but there may be
// multiple).
do {
to_remove = to_remove.subspan(1);
} while (!to_remove.empty() && to_remove.front() == m_graph_index);
// Clear this cluster.
graph.ClearLocator(level, m_graph_index, m_quality == QualityLevel::OVERSIZED_SINGLETON);
m_graph_index = NO_GRAPH_INDEX;
graph.SetClusterQuality(level, m_quality, m_setindex, QualityLevel::NEEDS_SPLIT);
// No need to account for m_cluster_usage changes here, as SingletonClusterImpl has constant
// memory usage.
}
void GenericClusterImpl::Clear(TxGraphImpl& graph, int level) noexcept
{
Assume(GetTxCount());
graph.GetClusterSet(level).m_cluster_usage -= TotalMemoryUsage();
for (auto i : m_linearization) {
graph.ClearLocator(level, m_mapping[i], m_quality == QualityLevel::OVERSIZED_SINGLETON);
}
m_depgraph = {};
m_linearization.clear();
m_mapping.clear();
}
void SingletonClusterImpl::Clear(TxGraphImpl& graph, int level) noexcept
{
Assume(GetTxCount());
graph.GetClusterSet(level).m_cluster_usage -= TotalMemoryUsage();
graph.ClearLocator(level, m_graph_index, m_quality == QualityLevel::OVERSIZED_SINGLETON);
m_graph_index = NO_GRAPH_INDEX;
}
void GenericClusterImpl::MoveToMain(TxGraphImpl& graph) noexcept
{
for (auto i : m_linearization) {
GraphIndex idx = m_mapping[i];
auto& entry = graph.m_entries[idx];
entry.m_locator[1].SetMissing();
}
auto quality = m_quality;
// Subtract memory usage from staging and add it to main.
graph.GetClusterSet(/*level=*/1).m_cluster_usage -= TotalMemoryUsage();
graph.GetClusterSet(/*level=*/0).m_cluster_usage += TotalMemoryUsage();
// Remove cluster itself from staging and add it to main.
auto cluster = graph.ExtractCluster(1, quality, m_setindex);
graph.InsertCluster(/*level=*/0, std::move(cluster), quality);
Updated(graph, /*level=*/0);
}
void SingletonClusterImpl::MoveToMain(TxGraphImpl& graph) noexcept
{
if (GetTxCount()) {
auto& entry = graph.m_entries[m_graph_index];
entry.m_locator[1].SetMissing();
}
auto quality = m_quality;
graph.GetClusterSet(/*level=*/1).m_cluster_usage -= TotalMemoryUsage();
auto cluster = graph.ExtractCluster(/*level=*/1, quality, m_setindex);
graph.InsertCluster(/*level=*/0, std::move(cluster), quality);
graph.GetClusterSet(/*level=*/0).m_cluster_usage += TotalMemoryUsage();
Updated(graph, /*level=*/0);
}
void GenericClusterImpl::Compact() noexcept
{
m_linearization.shrink_to_fit();
m_mapping.shrink_to_fit();
m_depgraph.Compact();
}
void SingletonClusterImpl::Compact() noexcept
{
// Nothing to compact; SingletonClusterImpl is constant size.
}
void GenericClusterImpl::AppendChunkFeerates(std::vector<FeeFrac>& ret) const noexcept
{
auto chunk_feerates = ChunkLinearization(m_depgraph, m_linearization);
ret.reserve(ret.size() + chunk_feerates.size());
ret.insert(ret.end(), chunk_feerates.begin(), chunk_feerates.end());
}
void SingletonClusterImpl::AppendChunkFeerates(std::vector<FeeFrac>& ret) const noexcept
{
if (GetTxCount()) {
ret.push_back(m_feerate);
}
}
uint64_t GenericClusterImpl::AppendTrimData(std::vector<TrimTxData>& ret, std::vector<std::pair<GraphIndex, GraphIndex>>& deps) const noexcept
{
const LinearizationChunking linchunking(m_depgraph, m_linearization);
LinearizationIndex pos{0};
uint64_t size{0};
auto prev_index = GraphIndex(-1);
// Iterate over the chunks of this cluster's linearization.
for (unsigned i = 0; i < linchunking.NumChunksLeft(); ++i) {
const auto& [chunk, chunk_feerate] = linchunking.GetChunk(i);
// Iterate over the transactions of that chunk, in linearization order.
auto chunk_tx_count = chunk.Count();
for (unsigned j = 0; j < chunk_tx_count; ++j) {
auto cluster_idx = m_linearization[pos];
// The transaction must appear in the chunk.
Assume(chunk[cluster_idx]);
// Construct a new element in ret.
auto& entry = ret.emplace_back();
entry.m_chunk_feerate = FeePerWeight::FromFeeFrac(chunk_feerate);
entry.m_index = m_mapping[cluster_idx];
// If this is not the first transaction of the cluster linearization, it has an
// implicit dependency on its predecessor.
if (pos != 0) deps.emplace_back(prev_index, entry.m_index);
prev_index = entry.m_index;
entry.m_tx_size = m_depgraph.FeeRate(cluster_idx).size;
size += entry.m_tx_size;
++pos;
}
}
return size;
}
uint64_t SingletonClusterImpl::AppendTrimData(std::vector<TrimTxData>& ret, std::vector<std::pair<GraphIndex, GraphIndex>>& deps) const noexcept
{
if (!GetTxCount()) return 0;
auto& entry = ret.emplace_back();
entry.m_chunk_feerate = m_feerate;
entry.m_index = m_graph_index;
entry.m_tx_size = m_feerate.size;
return m_feerate.size;
}
bool GenericClusterImpl::Split(TxGraphImpl& graph, int level) noexcept
{
// This function can only be called when the Cluster needs splitting.
Assume(NeedsSplitting());
// Determine the new quality the split-off Clusters will have.
QualityLevel new_quality = IsAcceptable(/*after_split=*/true) ? QualityLevel::ACCEPTABLE
: QualityLevel::NEEDS_RELINEARIZE;
// If we're going to produce ACCEPTABLE clusters (i.e., when in NEEDS_SPLIT_ACCEPTABLE), we
// need to post-linearize to make sure the split-out versions are all connected (as
// connectivity may have changed by removing part of the cluster). This could be done on each
// resulting split-out cluster separately, but it is simpler to do it once up front before
// splitting. This step is not necessary if the resulting clusters are NEEDS_RELINEARIZE, as
// they will be post-linearized anyway in MakeAcceptable().
if (new_quality == QualityLevel::ACCEPTABLE) {
PostLinearize(m_depgraph, m_linearization);
}
/** Which positions are still left in this Cluster. */
auto todo = m_depgraph.Positions();
/** Mapping from transaction positions in this Cluster to the Cluster where it ends up, and
* its position therein. */
std::vector<std::pair<Cluster*, DepGraphIndex>> remap(m_depgraph.PositionRange());
std::vector<Cluster*> new_clusters;
bool first{true};
// Iterate over the connected components of this Cluster's m_depgraph.
while (todo.Any()) {
auto component = m_depgraph.FindConnectedComponent(todo);
auto component_size = component.Count();
auto split_quality = component_size <= 2 ? QualityLevel::OPTIMAL : new_quality;
if (first && component == todo && SetType::Fill(component_size) == component && component_size >= MIN_INTENDED_TX_COUNT) {
// The existing Cluster is an entire component, without holes. Leave it be, but update
// its quality. If there are holes, we continue, so that the Cluster is reconstructed
// without holes, reducing memory usage. If the component's size is below the intended
// transaction count for this Cluster implementation, continue so that it can get
// converted.
Assume(todo == m_depgraph.Positions());
graph.SetClusterQuality(level, m_quality, m_setindex, split_quality);
// If this made the quality ACCEPTABLE or OPTIMAL, we need to compute and cache its
// chunking.
Updated(graph, level);
return false;
}
first = false;
// Construct a new Cluster to hold the found component.
auto new_cluster = graph.CreateEmptyCluster(component_size);
new_clusters.push_back(new_cluster.get());
// Remember that all the component's transactions go to this new Cluster. The positions
// will be determined below, so use -1 for now.
for (auto i : component) {
remap[i] = {new_cluster.get(), DepGraphIndex(-1)};
}
graph.InsertCluster(level, std::move(new_cluster), split_quality);
todo -= component;
}
// We have to split the Cluster up. Remove accounting for the existing one first.
graph.GetClusterSet(level).m_cluster_usage -= TotalMemoryUsage();
// Redistribute the transactions.
for (auto i : m_linearization) {
/** The cluster which transaction originally in position i is moved to. */
Cluster* new_cluster = remap[i].first;
// Copy the transaction to the new cluster's depgraph, and remember the position.
remap[i].second = new_cluster->AppendTransaction(m_mapping[i], FeePerWeight::FromFeeFrac(m_depgraph.FeeRate(i)));
}
// Redistribute the dependencies.
for (auto i : m_linearization) {
/** The cluster transaction in position i is moved to. */
Cluster* new_cluster = remap[i].first;
// Copy its parents, translating positions.
SetType new_parents;
for (auto par : m_depgraph.GetReducedParents(i)) new_parents.Set(remap[par].second);
new_cluster->AddDependencies(new_parents, remap[i].second);
}
// Update all the Locators of moved transactions, and memory usage.
for (Cluster* new_cluster : new_clusters) {
new_cluster->Updated(graph, level);
new_cluster->Compact();
graph.GetClusterSet(level).m_cluster_usage += new_cluster->TotalMemoryUsage();
}
// Wipe this Cluster, and return that it needs to be deleted.
m_depgraph = DepGraph<SetType>{};
m_mapping.clear();
m_linearization.clear();
return true;
}
bool SingletonClusterImpl::Split(TxGraphImpl& graph, int level) noexcept
{
Assume(NeedsSplitting());
Assume(!GetTxCount());
graph.GetClusterSet(level).m_cluster_usage -= TotalMemoryUsage();
return true;
}
void GenericClusterImpl::Merge(TxGraphImpl& graph, int level, Cluster& other) noexcept
{
/** Vector to store the positions in this Cluster for each position in other. */
std::vector<DepGraphIndex> remap(other.GetDepGraphIndexRange());
// Iterate over all transactions in the other Cluster (the one being absorbed).
other.ExtractTransactions([&](DepGraphIndex pos, GraphIndex idx, FeePerWeight feerate, SetType other_parents) noexcept {
// Copy the transaction into this Cluster, and remember its position.
auto new_pos = m_depgraph.AddTransaction(feerate);
// Since this cluster must have been made hole-free before being merged into, all added
// transactions should appear at the end.
Assume(new_pos == m_mapping.size());
remap[pos] = new_pos;
m_mapping.push_back(idx);
m_linearization.push_back(new_pos);
// Copy the transaction's dependencies, translating them using remap. Note that since
// pos iterates in linearization order, which is topological, all parents of pos should
// already be in remap.
SetType parents;
for (auto par : other_parents) {
parents.Set(remap[par]);
}
m_depgraph.AddDependencies(parents, remap[pos]);
// Update the transaction's Locator. There is no need to call Updated() to update chunk
// feerates, as Updated() will be invoked by Cluster::ApplyDependencies on the resulting
// merged Cluster later anyway.
auto& entry = graph.m_entries[idx];
// Discard any potential ChunkData prior to modifying the Cluster (as that could
// invalidate its ordering).
if (level == 0) graph.ClearChunkData(entry);
entry.m_locator[level].SetPresent(this, new_pos);
});
}
void SingletonClusterImpl::Merge(TxGraphImpl&, int, Cluster&) noexcept
{
// Nothing can be merged into a singleton; it should have been converted to GenericClusterImpl first.
Assume(false);
}
void GenericClusterImpl::ApplyDependencies(TxGraphImpl& graph, int level, std::span<std::pair<GraphIndex, GraphIndex>> to_apply) noexcept
{
// This function is invoked by TxGraphImpl::ApplyDependencies after merging groups of Clusters
// between which dependencies are added, which simply concatenates their linearizations. Invoke
// PostLinearize, which has the effect that the linearization becomes a merge-sort of the
// constituent linearizations. Do this here rather than in Cluster::Merge, because this
// function is only invoked once per merged Cluster, rather than once per constituent one.
// This concatenation + post-linearization could be replaced with an explicit merge-sort.
PostLinearize(m_depgraph, m_linearization);
// Sort the list of dependencies to apply by child, so those can be applied in batch.
std::sort(to_apply.begin(), to_apply.end(), [](auto& a, auto& b) { return a.second < b.second; });
// Iterate over groups of to-be-added dependencies with the same child.
auto it = to_apply.begin();
while (it != to_apply.end()) {
auto& first_child = graph.m_entries[it->second].m_locator[level];
const auto child_idx = first_child.index;
// Iterate over all to-be-added dependencies within that same child, gather the relevant
// parents.
SetType parents;
while (it != to_apply.end()) {
auto& child = graph.m_entries[it->second].m_locator[level];
auto& parent = graph.m_entries[it->first].m_locator[level];
Assume(child.cluster == this && parent.cluster == this);
if (child.index != child_idx) break;
parents.Set(parent.index);
++it;
}
// Push all dependencies to the underlying DepGraph. Note that this is O(N) in the size of
// the cluster, regardless of the number of parents being added, so batching them together
// has a performance benefit.
m_depgraph.AddDependencies(parents, child_idx);
}
// Finally fix the linearization, as the new dependencies may have invalidated the
// linearization, and post-linearize it to fix up the worst problems with it.
FixLinearization(m_depgraph, m_linearization);
PostLinearize(m_depgraph, m_linearization);
Assume(!NeedsSplitting());
Assume(!IsOversized());
if (IsAcceptable()) {
graph.SetClusterQuality(level, m_quality, m_setindex, QualityLevel::NEEDS_RELINEARIZE);
}
// Finally push the changes to graph.m_entries.
Updated(graph, level);
}
void SingletonClusterImpl::ApplyDependencies(TxGraphImpl&, int, std::span<std::pair<GraphIndex, GraphIndex>>) noexcept
{
// Nothing can actually be applied.
Assume(false);
}
TxGraphImpl::~TxGraphImpl() noexcept
{
// If Refs outlive the TxGraphImpl they refer to, unlink them, so that their destructor does not
// try to reach into a non-existing TxGraphImpl anymore.
for (auto& entry : m_entries) {
if (entry.m_ref != nullptr) {
GetRefGraph(*entry.m_ref) = nullptr;
}
}
}
std::unique_ptr<Cluster> TxGraphImpl::ExtractCluster(int level, QualityLevel quality, ClusterSetIndex setindex) noexcept
{
Assume(quality != QualityLevel::NONE);
auto& clusterset = GetClusterSet(level);
auto& quality_clusters = clusterset.m_clusters[int(quality)];
Assume(setindex < quality_clusters.size());
// Extract the Cluster-owning unique_ptr.
std::unique_ptr<Cluster> ret = std::move(quality_clusters[setindex]);
ret->m_quality = QualityLevel::NONE;
ret->m_setindex = ClusterSetIndex(-1);
// Clean up space in quality_cluster.
auto max_setindex = quality_clusters.size() - 1;
if (setindex != max_setindex) {
// If the cluster was not the last element of quality_clusters, move that to take its place.
quality_clusters.back()->m_setindex = setindex;
quality_clusters[setindex] = std::move(quality_clusters.back());
}
// The last element of quality_clusters is now unused; drop it.
quality_clusters.pop_back();
return ret;
}
ClusterSetIndex TxGraphImpl::InsertCluster(int level, std::unique_ptr<Cluster>&& cluster, QualityLevel quality) noexcept
{
// Cannot insert with quality level NONE (as that would mean not inserted).
Assume(quality != QualityLevel::NONE);
// The passed-in Cluster must not currently be in the TxGraphImpl.
Assume(cluster->m_quality == QualityLevel::NONE);
// Append it at the end of the relevant TxGraphImpl::m_cluster.
auto& clusterset = GetClusterSet(level);
auto& quality_clusters = clusterset.m_clusters[int(quality)];
ClusterSetIndex ret = quality_clusters.size();
cluster->m_quality = quality;
cluster->m_setindex = ret;
quality_clusters.push_back(std::move(cluster));
return ret;
}
void TxGraphImpl::SetClusterQuality(int level, QualityLevel old_quality, ClusterSetIndex old_index, QualityLevel new_quality) noexcept
{
Assume(new_quality != QualityLevel::NONE);
// Don't do anything if the quality did not change.
if (old_quality == new_quality) return;
// Extract the cluster from where it currently resides.
auto cluster_ptr = ExtractCluster(level, old_quality, old_index);
// And re-insert it where it belongs.
InsertCluster(level, std::move(cluster_ptr), new_quality);
}
void TxGraphImpl::DeleteCluster(Cluster& cluster, int level) noexcept
{
// Extract the cluster from where it currently resides.
auto cluster_ptr = ExtractCluster(level, cluster.m_quality, cluster.m_setindex);
// And throw it away.
cluster_ptr.reset();
}
std::pair<Cluster*, int> TxGraphImpl::FindClusterAndLevel(GraphIndex idx, int level) const noexcept
{
Assume(level >= 0 && level <= GetTopLevel());
auto& entry = m_entries[idx];
// Search the entry's locators from top to bottom.
for (int l = level; l >= 0; --l) {
// If the locator is missing, dig deeper; it may exist at a lower level and therefore be
// implicitly existing at this level too.
if (entry.m_locator[l].IsMissing()) continue;
// If the locator has the entry marked as explicitly removed, stop.
if (entry.m_locator[l].IsRemoved()) break;
// Otherwise, we have found the topmost ClusterSet that contains this entry.
return {entry.m_locator[l].cluster, l};
}
// If no non-empty locator was found, or an explicitly removed was hit, return nothing.
return {nullptr, -1};
}
Cluster* TxGraphImpl::PullIn(Cluster* cluster, int level) noexcept
{
int to_level = GetTopLevel();
Assume(to_level == 1);
Assume(level <= to_level);
// Copy the Cluster from main to staging, if it's not already there.
if (level == 0) {
// Make the Cluster Acceptable before copying. This isn't strictly necessary, but doing it
// now avoids doing double work later.
MakeAcceptable(*cluster, level);
cluster = cluster->CopyToStaging(*this);
}
return cluster;
}
void TxGraphImpl::ApplyRemovals(int up_to_level) noexcept
{
Assume(up_to_level >= 0 && up_to_level <= GetTopLevel());
for (int level = 0; level <= up_to_level; ++level) {
auto& clusterset = GetClusterSet(level);
auto& to_remove = clusterset.m_to_remove;
// Skip if there is nothing to remove in this level.
if (to_remove.empty()) continue;
// Pull in all Clusters that are not in staging.
if (level == 1) {
for (GraphIndex index : to_remove) {
auto [cluster, cluster_level] = FindClusterAndLevel(index, level);
if (cluster != nullptr) PullIn(cluster, cluster_level);
}
}
// Group the set of to-be-removed entries by Cluster::m_sequence.
std::sort(to_remove.begin(), to_remove.end(), [&](GraphIndex a, GraphIndex b) noexcept {
Cluster* cluster_a = m_entries[a].m_locator[level].cluster;
Cluster* cluster_b = m_entries[b].m_locator[level].cluster;
return CompareClusters(cluster_a, cluster_b) < 0;
});
// Process per Cluster.
std::span to_remove_span{to_remove};
while (!to_remove_span.empty()) {
Cluster* cluster = m_entries[to_remove_span.front()].m_locator[level].cluster;
if (cluster != nullptr) {
// If the first to_remove_span entry's Cluster exists, hand to_remove_span to it, so it
// can pop off whatever applies to it.
cluster->ApplyRemovals(*this, level, to_remove_span);
} else {
// Otherwise, skip this already-removed entry. This may happen when
// RemoveTransaction was called twice on the same Ref, for example.
to_remove_span = to_remove_span.subspan(1);
}
}
to_remove.clear();
}
Compact();
}
void TxGraphImpl::SwapIndexes(GraphIndex a, GraphIndex b) noexcept
{
Assume(a < m_entries.size());
Assume(b < m_entries.size());
// Swap the Entry objects.
std::swap(m_entries[a], m_entries[b]);
// Iterate over both objects.
for (int i = 0; i < 2; ++i) {
GraphIndex idx = i ? b : a;
Entry& entry = m_entries[idx];
// Update linked Ref, if any exists.
if (entry.m_ref) GetRefIndex(*entry.m_ref) = idx;
// Update linked chunk index entries, if any exist.
if (entry.m_main_chunkindex_iterator != m_main_chunkindex.end()) {
entry.m_main_chunkindex_iterator->m_graph_index = idx;
}
// Update the locators for both levels. The rest of the Entry information will not change,
// so no need to invoke Cluster::Updated().
for (int level = 0; level < MAX_LEVELS; ++level) {
Locator& locator = entry.m_locator[level];
if (locator.IsPresent()) {
locator.cluster->UpdateMapping(locator.index, idx);
}
}
}
}
void TxGraphImpl::Compact() noexcept
{
// We cannot compact while any to-be-applied operations or staged removals remain as we'd need
// to rewrite them. It is easier to delay the compaction until they have been applied.
if (!m_main_clusterset.m_deps_to_add.empty()) return;
if (!m_main_clusterset.m_to_remove.empty()) return;
Assume(m_main_clusterset.m_removed.empty()); // non-staging m_removed is always empty
if (m_staging_clusterset.has_value()) {
if (!m_staging_clusterset->m_deps_to_add.empty()) return;
if (!m_staging_clusterset->m_to_remove.empty()) return;
if (!m_staging_clusterset->m_removed.empty()) return;
}
// Release memory used by discarded ChunkData index entries.
ClearShrink(m_main_chunkindex_discarded);
// Sort the GraphIndexes that need to be cleaned up. They are sorted in reverse, so the last
// ones get processed first. This means earlier-processed GraphIndexes will not cause moving of
// later-processed ones during the "swap with end of m_entries" step below (which might
// invalidate them).
std::sort(m_unlinked.begin(), m_unlinked.end(), std::greater{});
auto last = GraphIndex(-1);
for (GraphIndex idx : m_unlinked) {
// m_unlinked should never contain the same GraphIndex twice (the code below would fail
// if so, because GraphIndexes get invalidated by removing them).
Assume(idx != last);
last = idx;
// Make sure the entry is unlinked.
Entry& entry = m_entries[idx];
Assume(entry.m_ref == nullptr);
// Make sure the entry does not occur in the graph.
for (int level = 0; level < MAX_LEVELS; ++level) {
Assume(!entry.m_locator[level].IsPresent());
}
// Move the entry to the end.
if (idx != m_entries.size() - 1) SwapIndexes(idx, m_entries.size() - 1);
// Drop the entry for idx, now that it is at the end.
m_entries.pop_back();
}
m_unlinked.clear();
}
void TxGraphImpl::Split(Cluster& cluster, int level) noexcept
{
// To split a Cluster, first make sure all removals are applied (as we might need to split
// again afterwards otherwise).
ApplyRemovals(level);
bool del = cluster.Split(*this, level);
if (del) {
// Cluster::Split reports whether the Cluster is to be deleted.
DeleteCluster(cluster, level);
}
}
void TxGraphImpl::SplitAll(int up_to_level) noexcept
{
Assume(up_to_level >= 0 && up_to_level <= GetTopLevel());
// Before splitting all Cluster, first make sure all removals are applied.
ApplyRemovals(up_to_level);
for (int level = 0; level <= up_to_level; ++level) {
for (auto quality : {QualityLevel::NEEDS_SPLIT, QualityLevel::NEEDS_SPLIT_ACCEPTABLE}) {
auto& queue = GetClusterSet(level).m_clusters[int(quality)];
while (!queue.empty()) {
Split(*queue.back().get(), level);
}
}
}
}
void TxGraphImpl::GroupClusters(int level) noexcept
{
auto& clusterset = GetClusterSet(level);
// If the groupings have been computed already, nothing is left to be done.
if (clusterset.m_group_data.has_value()) return;
// Before computing which Clusters need to be merged together, first apply all removals and
// split the Clusters into connected components. If we would group first, we might end up
// with inefficient and/or oversized Clusters which just end up being split again anyway.
SplitAll(level);
/** Annotated clusters: an entry for each Cluster, together with the sequence number for the
* representative for the partition it is in (initially its own, later that of the
* to-be-merged group). */
std::vector<std::pair<Cluster*, uint64_t>> an_clusters;
/** Annotated dependencies: an entry for each m_deps_to_add entry (excluding ones that apply
* to removed transactions), together with the sequence number of the representative root of
* Clusters it applies to (initially that of the child Cluster, later that of the
* to-be-merged group). */
std::vector<std::pair<std::pair<GraphIndex, GraphIndex>, uint64_t>> an_deps;
// Construct an an_clusters entry for every oversized cluster, including ones from levels below,
// as they may be inherited in this one.
for (int level_iter = 0; level_iter <= level; ++level_iter) {
for (auto& cluster : GetClusterSet(level_iter).m_clusters[int(QualityLevel::OVERSIZED_SINGLETON)]) {
auto graph_idx = cluster->GetClusterEntry(0);
auto cur_cluster = FindCluster(graph_idx, level);
if (cur_cluster == nullptr) continue;
an_clusters.emplace_back(cur_cluster, cur_cluster->m_sequence);
}
}
// Construct a an_clusters entry for every parent and child in the to-be-applied dependencies,
// and an an_deps entry for each dependency to be applied.
an_deps.reserve(clusterset.m_deps_to_add.size());
for (const auto& [par, chl] : clusterset.m_deps_to_add) {
auto par_cluster = FindCluster(par, level);
auto chl_cluster = FindCluster(chl, level);
// Skip dependencies for which the parent or child transaction is removed.
if (par_cluster == nullptr || chl_cluster == nullptr) continue;
an_clusters.emplace_back(par_cluster, par_cluster->m_sequence);
// Do not include a duplicate when parent and child are identical, as it'll be removed
// below anyway.
if (chl_cluster != par_cluster) an_clusters.emplace_back(chl_cluster, chl_cluster->m_sequence);
// Add entry to an_deps, using the child sequence number.
an_deps.emplace_back(std::pair{par, chl}, chl_cluster->m_sequence);
}
// Sort and deduplicate an_clusters, so we end up with a sorted list of all involved Clusters
// to which dependencies apply, or which are oversized.
std::sort(an_clusters.begin(), an_clusters.end(), [](auto& a, auto& b) noexcept { return a.second < b.second; });
an_clusters.erase(std::unique(an_clusters.begin(), an_clusters.end()), an_clusters.end());
// Sort an_deps by applying the same order to the involved child cluster.
std::sort(an_deps.begin(), an_deps.end(), [&](auto& a, auto& b) noexcept { return a.second < b.second; });
// Run the union-find algorithm to to find partitions of the input Clusters which need to be
// grouped together. See https://en.wikipedia.org/wiki/Disjoint-set_data_structure.
{
/** Each PartitionData entry contains information about a single input Cluster. */
struct PartitionData
{
/** The sequence number of the cluster this holds information for. */
uint64_t sequence;
/** All PartitionData entries belonging to the same partition are organized in a tree.
* Each element points to its parent, or to itself if it is the root. The root is then
* a representative for the entire tree, and can be found by walking upwards from any
* element. */
PartitionData* parent;
/** (only if this is a root, so when parent == this) An upper bound on the height of
* tree for this partition. */
unsigned rank;
};
/** Information about each input Cluster. Sorted by Cluster::m_sequence. */
std::vector<PartitionData> partition_data;
/** Given a Cluster, find its corresponding PartitionData. */
auto locate_fn = [&](uint64_t sequence) noexcept -> PartitionData* {
auto it = std::lower_bound(partition_data.begin(), partition_data.end(), sequence,
[](auto& a, uint64_t seq) noexcept { return a.sequence < seq; });
Assume(it != partition_data.end());
Assume(it->sequence == sequence);
return &*it;
};
/** Given a PartitionData, find the root of the tree it is in (its representative). */
static constexpr auto find_root_fn = [](PartitionData* data) noexcept -> PartitionData* {
while (data->parent != data) {
// Replace pointers to parents with pointers to grandparents.
// See https://en.wikipedia.org/wiki/Disjoint-set_data_structure#Finding_set_representatives.
auto par = data->parent;
data->parent = par->parent;
data = par;
}
return data;
};
/** Given two PartitionDatas, union the partitions they are in, and return their
* representative. */
static constexpr auto union_fn = [](PartitionData* arg1, PartitionData* arg2) noexcept {
// Find the roots of the trees, and bail out if they are already equal (which would
// mean they are in the same partition already).
auto rep1 = find_root_fn(arg1);
auto rep2 = find_root_fn(arg2);
if (rep1 == rep2) return rep1;
// Pick the lower-rank root to become a child of the higher-rank one.
// See https://en.wikipedia.org/wiki/Disjoint-set_data_structure#Union_by_rank.
if (rep1->rank < rep2->rank) std::swap(rep1, rep2);
rep2->parent = rep1;
rep1->rank += (rep1->rank == rep2->rank);
return rep1;
};
// Start by initializing every Cluster as its own singleton partition.
partition_data.resize(an_clusters.size());
for (size_t i = 0; i < an_clusters.size(); ++i) {
partition_data[i].sequence = an_clusters[i].first->m_sequence;
partition_data[i].parent = &partition_data[i];
partition_data[i].rank = 0;
}
// Run through all parent/child pairs in an_deps, and union the partitions their Clusters
// are in.
Cluster* last_chl_cluster{nullptr};
PartitionData* last_partition{nullptr};
for (const auto& [dep, _] : an_deps) {
auto [par, chl] = dep;
auto par_cluster = FindCluster(par, level);
auto chl_cluster = FindCluster(chl, level);
Assume(chl_cluster != nullptr && par_cluster != nullptr);
// Nothing to do if parent and child are in the same Cluster.
if (par_cluster == chl_cluster) continue;
Assume(par != chl);
if (chl_cluster == last_chl_cluster) {
// If the child Clusters is the same as the previous iteration, union with the
// tree they were in, avoiding the need for another lookup. Note that an_deps
// is sorted by child Cluster, so batches with the same child are expected.
last_partition = union_fn(locate_fn(par_cluster->m_sequence), last_partition);
} else {
last_chl_cluster = chl_cluster;
last_partition = union_fn(locate_fn(par_cluster->m_sequence), locate_fn(chl_cluster->m_sequence));
}
}
// Update the sequence numbers in an_clusters and an_deps to be those of the partition
// representative.
auto deps_it = an_deps.begin();
for (size_t i = 0; i < partition_data.size(); ++i) {
auto& data = partition_data[i];
// Find the sequence of the representative of the partition Cluster i is in, and store
// it with the Cluster.
auto rep_seq = find_root_fn(&data)->sequence;
an_clusters[i].second = rep_seq;
// Find all dependencies whose child Cluster is Cluster i, and annotate them with rep.
while (deps_it != an_deps.end()) {
auto [par, chl] = deps_it->first;
auto chl_cluster = FindCluster(chl, level);
Assume(chl_cluster != nullptr);
if (chl_cluster->m_sequence > data.sequence) break;
deps_it->second = rep_seq;
++deps_it;
}
}
}
// Sort both an_clusters and an_deps by sequence number of the representative of the
// partition they are in, grouping all those applying to the same partition together.
std::sort(an_deps.begin(), an_deps.end(), [](auto& a, auto& b) noexcept { return a.second < b.second; });
std::sort(an_clusters.begin(), an_clusters.end(), [](auto& a, auto& b) noexcept { return a.second < b.second; });
// Translate the resulting cluster groups to the m_group_data structure, and the dependencies
// back to m_deps_to_add.
clusterset.m_group_data = GroupData{};
clusterset.m_group_data->m_group_clusters.reserve(an_clusters.size());
clusterset.m_deps_to_add.clear();
clusterset.m_deps_to_add.reserve(an_deps.size());
clusterset.m_oversized = false;
auto an_deps_it = an_deps.begin();
auto an_clusters_it = an_clusters.begin();
while (an_clusters_it != an_clusters.end()) {
// Process all clusters/dependencies belonging to the partition with representative rep.
auto rep = an_clusters_it->second;
// Create and initialize a new GroupData entry for the partition.
auto& new_entry = clusterset.m_group_data->m_groups.emplace_back();
new_entry.m_cluster_offset = clusterset.m_group_data->m_group_clusters.size();
new_entry.m_cluster_count = 0;
new_entry.m_deps_offset = clusterset.m_deps_to_add.size();
new_entry.m_deps_count = 0;
uint32_t total_count{0};
uint64_t total_size{0};
// Add all its clusters to it (copying those from an_clusters to m_group_clusters).
while (an_clusters_it != an_clusters.end() && an_clusters_it->second == rep) {
clusterset.m_group_data->m_group_clusters.push_back(an_clusters_it->first);
total_count += an_clusters_it->first->GetTxCount();
total_size += an_clusters_it->first->GetTotalTxSize();
++an_clusters_it;
++new_entry.m_cluster_count;
}
// Add all its dependencies to it (copying those back from an_deps to m_deps_to_add).
while (an_deps_it != an_deps.end() && an_deps_it->second == rep) {
clusterset.m_deps_to_add.push_back(an_deps_it->first);
++an_deps_it;
++new_entry.m_deps_count;
}
// Detect oversizedness.
if (total_count > m_max_cluster_count || total_size > m_max_cluster_size) {
clusterset.m_oversized = true;
}
}
Assume(an_deps_it == an_deps.end());
Assume(an_clusters_it == an_clusters.end());
Compact();
}
void TxGraphImpl::Merge(std::span<Cluster*> to_merge, int level) noexcept
{
Assume(!to_merge.empty());
// Nothing to do if a group consists of just a single Cluster.
if (to_merge.size() == 1) return;
// Move the largest Cluster to the front of to_merge. As all transactions in other to-be-merged
// Clusters will be moved to that one, putting the largest one first minimizes the number of
// moves.
size_t max_size_pos{0};
DepGraphIndex max_size = to_merge[max_size_pos]->GetTxCount();
GetClusterSet(level).m_cluster_usage -= to_merge[max_size_pos]->TotalMemoryUsage();
DepGraphIndex total_size = max_size;
for (size_t i = 1; i < to_merge.size(); ++i) {
GetClusterSet(level).m_cluster_usage -= to_merge[i]->TotalMemoryUsage();
DepGraphIndex size = to_merge[i]->GetTxCount();
total_size += size;
if (size > max_size) {
max_size_pos = i;
max_size = size;
}
}
if (max_size_pos != 0) std::swap(to_merge[0], to_merge[max_size_pos]);
size_t start_idx = 1;
Cluster* into_cluster = to_merge[0];
if (total_size > into_cluster->GetMaxTxCount()) {
// The into_merge cluster is too small to fit all transactions being merged. Construct a
// a new Cluster using an implementation that matches the total size, and merge everything
// in there.
auto new_cluster = CreateEmptyCluster(total_size);
into_cluster = new_cluster.get();
InsertCluster(level, std::move(new_cluster), QualityLevel::OPTIMAL);
start_idx = 0;
}
// Merge all further Clusters in the group into the result (first one, or new one), and delete
// them.
for (size_t i = start_idx; i < to_merge.size(); ++i) {
into_cluster->Merge(*this, level, *to_merge[i]);
DeleteCluster(*to_merge[i], level);
}
into_cluster->Compact();
GetClusterSet(level).m_cluster_usage += into_cluster->TotalMemoryUsage();
}
void TxGraphImpl::ApplyDependencies(int level) noexcept
{
auto& clusterset = GetClusterSet(level);
// Do not bother computing groups if we already know the result will be oversized.
if (clusterset.m_oversized == true) return;
// Compute the groups of to-be-merged Clusters (which also applies all removals, and splits).
GroupClusters(level);
Assume(clusterset.m_group_data.has_value());
// Nothing to do if there are no dependencies to be added.
if (clusterset.m_deps_to_add.empty()) return;
// Dependencies cannot be applied if it would result in oversized clusters.
if (clusterset.m_oversized == true) return;
// For each group of to-be-merged Clusters.
for (const auto& group_entry : clusterset.m_group_data->m_groups) {
auto cluster_span = std::span{clusterset.m_group_data->m_group_clusters}
.subspan(group_entry.m_cluster_offset, group_entry.m_cluster_count);
// Pull in all the Clusters that contain dependencies.
if (level == 1) {
for (Cluster*& cluster : cluster_span) {
cluster = PullIn(cluster, cluster->GetLevel(*this));
}
}
// Invoke Merge() to merge them into a single Cluster.
Merge(cluster_span, level);
// Actually apply all to-be-added dependencies (all parents and children from this grouping
// belong to the same Cluster at this point because of the merging above).
auto deps_span = std::span{clusterset.m_deps_to_add}
.subspan(group_entry.m_deps_offset, group_entry.m_deps_count);
Assume(!deps_span.empty());
const auto& loc = m_entries[deps_span[0].second].m_locator[level];
Assume(loc.IsPresent());
loc.cluster->ApplyDependencies(*this, level, deps_span);
}
// Wipe the list of to-be-added dependencies now that they are applied.
clusterset.m_deps_to_add.clear();
Compact();
// Also no further Cluster mergings are needed (note that we clear, but don't set to
// std::nullopt, as that would imply the groupings are unknown).
clusterset.m_group_data = GroupData{};
}
std::pair<uint64_t, bool> GenericClusterImpl::Relinearize(TxGraphImpl& graph, int level, uint64_t max_iters) noexcept
{
// We can only relinearize Clusters that do not need splitting.
Assume(!NeedsSplitting());
// No work is required for Clusters which are already optimally linearized.
if (IsOptimal()) return {0, false};
// Invoke the actual linearization algorithm (passing in the existing one).
uint64_t rng_seed = graph.m_rng.rand64();
auto [linearization, optimal, cost] = Linearize(m_depgraph, max_iters, rng_seed, m_linearization);
// Postlinearize if the result isn't optimal already. This guarantees (among other things)
// that the chunks of the resulting linearization are all connected.
if (!optimal) PostLinearize(m_depgraph, linearization);
// Update the linearization.
m_linearization = std::move(linearization);
// Update the Cluster's quality.
bool improved = false;
if (optimal) {
graph.SetClusterQuality(level, m_quality, m_setindex, QualityLevel::OPTIMAL);
improved = true;
} else if (max_iters >= graph.m_acceptable_iters && !IsAcceptable()) {
graph.SetClusterQuality(level, m_quality, m_setindex, QualityLevel::ACCEPTABLE);
improved = true;
}
// Update the Entry objects.
Updated(graph, level);
return {cost, improved};
}
std::pair<uint64_t, bool> SingletonClusterImpl::Relinearize(TxGraphImpl& graph, int level, uint64_t max_iters) noexcept
{
// All singletons are optimal, oversized, or need splitting. Each of these precludes
// Relinearize from being called.
assert(false);
return {0, false};
}
void TxGraphImpl::MakeAcceptable(Cluster& cluster, int level) noexcept
{
// Relinearize the Cluster if needed.
if (!cluster.NeedsSplitting() && !cluster.IsAcceptable() && !cluster.IsOversized()) {
cluster.Relinearize(*this, level, m_acceptable_iters);
}
}
void TxGraphImpl::MakeAllAcceptable(int level) noexcept
{
ApplyDependencies(level);
auto& clusterset = GetClusterSet(level);
if (clusterset.m_oversized == true) return;
auto& queue = clusterset.m_clusters[int(QualityLevel::NEEDS_RELINEARIZE)];
while (!queue.empty()) {
MakeAcceptable(*queue.back().get(), level);
}
}
GenericClusterImpl::GenericClusterImpl(uint64_t sequence) noexcept : Cluster{sequence} {}
TxGraph::Ref TxGraphImpl::AddTransaction(const FeePerWeight& feerate) noexcept
{
Assume(m_main_chunkindex_observers == 0 || GetTopLevel() != 0);
Assume(feerate.size > 0);
// Construct a new Ref.
Ref ret;
// Construct a new Entry, and link it with the Ref.
auto idx = m_entries.size();
m_entries.emplace_back();
auto& entry = m_entries.back();
entry.m_main_chunkindex_iterator = m_main_chunkindex.end();
entry.m_ref = &ret;
GetRefGraph(ret) = this;
GetRefIndex(ret) = idx;
// Construct a new singleton Cluster (which is necessarily optimally linearized).
bool oversized = uint64_t(feerate.size) > m_max_cluster_size;
auto cluster = CreateEmptyCluster(1);
cluster->AppendTransaction(idx, feerate);
auto cluster_ptr = cluster.get();
int level = GetTopLevel();
auto& clusterset = GetClusterSet(level);
InsertCluster(level, std::move(cluster), oversized ? QualityLevel::OVERSIZED_SINGLETON : QualityLevel::OPTIMAL);
cluster_ptr->Updated(*this, level);
clusterset.m_cluster_usage += cluster_ptr->TotalMemoryUsage();
++clusterset.m_txcount;
// Deal with individually oversized transactions.
if (oversized) {
++clusterset.m_txcount_oversized;
clusterset.m_oversized = true;
clusterset.m_group_data = std::nullopt;
}
// Return the Ref.
return ret;
}
void TxGraphImpl::RemoveTransaction(const Ref& arg) noexcept
{
// Don't do anything if the Ref is empty (which may be indicative of the transaction already
// having been removed).
if (GetRefGraph(arg) == nullptr) return;
Assume(GetRefGraph(arg) == this);
Assume(m_main_chunkindex_observers == 0 || GetTopLevel() != 0);
// Find the Cluster the transaction is in, and stop if it isn't in any.
int level = GetTopLevel();
auto cluster = FindCluster(GetRefIndex(arg), level);
if (cluster == nullptr) return;
// Remember that the transaction is to be removed.
auto& clusterset = GetClusterSet(level);
clusterset.m_to_remove.push_back(GetRefIndex(arg));
// Wipe m_group_data (as it will need to be recomputed).
clusterset.m_group_data.reset();
if (clusterset.m_oversized == true) clusterset.m_oversized = std::nullopt;
}
void TxGraphImpl::AddDependency(const Ref& parent, const Ref& child) noexcept
{
// Don't do anything if either Ref is empty (which may be indicative of it having already been
// removed).
if (GetRefGraph(parent) == nullptr || GetRefGraph(child) == nullptr) return;
Assume(GetRefGraph(parent) == this && GetRefGraph(child) == this);
Assume(m_main_chunkindex_observers == 0 || GetTopLevel() != 0);
// Don't do anything if this is a dependency on self.
if (GetRefIndex(parent) == GetRefIndex(child)) return;
// Find the Cluster the parent and child transaction are in, and stop if either appears to be
// already removed.
int level = GetTopLevel();
auto par_cluster = FindCluster(GetRefIndex(parent), level);
if (par_cluster == nullptr) return;
auto chl_cluster = FindCluster(GetRefIndex(child), level);
if (chl_cluster == nullptr) return;
// Remember that this dependency is to be applied.
auto& clusterset = GetClusterSet(level);
clusterset.m_deps_to_add.emplace_back(GetRefIndex(parent), GetRefIndex(child));
// Wipe m_group_data (as it will need to be recomputed).
clusterset.m_group_data.reset();
if (clusterset.m_oversized == false) clusterset.m_oversized = std::nullopt;
}
bool TxGraphImpl::Exists(const Ref& arg, Level level_select) noexcept
{
if (GetRefGraph(arg) == nullptr) return false;
Assume(GetRefGraph(arg) == this);
size_t level = GetSpecifiedLevel(level_select);
// Make sure the transaction isn't scheduled for removal.
ApplyRemovals(level);
auto cluster = FindCluster(GetRefIndex(arg), level);
return cluster != nullptr;
}
void GenericClusterImpl::GetAncestorRefs(const TxGraphImpl& graph, std::span<std::pair<Cluster*, DepGraphIndex>>& args, std::vector<TxGraph::Ref*>& output) noexcept
{
/** The union of all ancestors to be returned. */
SetType ancestors_union;
// Process elements from the front of args, as long as they apply.
while (!args.empty()) {
if (args.front().first != this) break;
ancestors_union |= m_depgraph.Ancestors(args.front().second);
args = args.subspan(1);
}
Assume(ancestors_union.Any());
// Translate all ancestors (in arbitrary order) to Refs (if they have any), and return them.
for (auto idx : ancestors_union) {
const auto& entry = graph.m_entries[m_mapping[idx]];
Assume(entry.m_ref != nullptr);
output.push_back(entry.m_ref);
}
}
void SingletonClusterImpl::GetAncestorRefs(const TxGraphImpl& graph, std::span<std::pair<Cluster*, DepGraphIndex>>& args, std::vector<TxGraph::Ref*>& output) noexcept
{
Assume(GetTxCount());
while (!args.empty()) {
if (args.front().first != this) break;
args = args.subspan(1);
}
const auto& entry = graph.m_entries[m_graph_index];
Assume(entry.m_ref != nullptr);
output.push_back(entry.m_ref);
}
void GenericClusterImpl::GetDescendantRefs(const TxGraphImpl& graph, std::span<std::pair<Cluster*, DepGraphIndex>>& args, std::vector<TxGraph::Ref*>& output) noexcept
{
/** The union of all descendants to be returned. */
SetType descendants_union;
// Process elements from the front of args, as long as they apply.
while (!args.empty()) {
if (args.front().first != this) break;
descendants_union |= m_depgraph.Descendants(args.front().second);
args = args.subspan(1);
}
Assume(descendants_union.Any());
// Translate all descendants (in arbitrary order) to Refs (if they have any), and return them.
for (auto idx : descendants_union) {
const auto& entry = graph.m_entries[m_mapping[idx]];
Assume(entry.m_ref != nullptr);
output.push_back(entry.m_ref);
}
}
void SingletonClusterImpl::GetDescendantRefs(const TxGraphImpl& graph, std::span<std::pair<Cluster*, DepGraphIndex>>& args, std::vector<TxGraph::Ref*>& output) noexcept
{
// In a singleton cluster, the ancestors or descendants are always just the entire cluster.
GetAncestorRefs(graph, args, output);
}
bool GenericClusterImpl::GetClusterRefs(TxGraphImpl& graph, std::span<TxGraph::Ref*> range, LinearizationIndex start_pos) noexcept
{
// Translate the transactions in the Cluster (in linearization order, starting at start_pos in
// the linearization) to Refs, and fill them in range.
for (auto& ref : range) {
Assume(start_pos < m_linearization.size());
const auto& entry = graph.m_entries[m_mapping[m_linearization[start_pos++]]];
Assume(entry.m_ref != nullptr);
ref = entry.m_ref;
}
// Return whether start_pos has advanced to the end of the Cluster.
return start_pos == m_linearization.size();
}
bool SingletonClusterImpl::GetClusterRefs(TxGraphImpl& graph, std::span<TxGraph::Ref*> range, LinearizationIndex start_pos) noexcept
{
Assume(!range.empty());
Assume(GetTxCount());
Assume(start_pos == 0);
const auto& entry = graph.m_entries[m_graph_index];
Assume(entry.m_ref != nullptr);
range[0] = entry.m_ref;
return true;
}
FeePerWeight GenericClusterImpl::GetIndividualFeerate(DepGraphIndex idx) noexcept
{
return FeePerWeight::FromFeeFrac(m_depgraph.FeeRate(idx));
}
FeePerWeight SingletonClusterImpl::GetIndividualFeerate(DepGraphIndex idx) noexcept
{
Assume(GetTxCount());
Assume(idx == 0);
return m_feerate;
}
void GenericClusterImpl::MakeStagingTransactionsMissing(TxGraphImpl& graph) noexcept
{
// Mark all transactions of a Cluster missing, needed when aborting staging, so that the
// corresponding Locators don't retain references into aborted Clusters.
for (auto ci : m_linearization) {
GraphIndex idx = m_mapping[ci];
auto& entry = graph.m_entries[idx];
entry.m_locator[1].SetMissing();
}
}
void SingletonClusterImpl::MakeStagingTransactionsMissing(TxGraphImpl& graph) noexcept
{
if (GetTxCount()) {
auto& entry = graph.m_entries[m_graph_index];
entry.m_locator[1].SetMissing();
}
}
std::vector<TxGraph::Ref*> TxGraphImpl::GetAncestors(const Ref& arg, Level level_select) noexcept
{
// Return the empty vector if the Ref is empty.
if (GetRefGraph(arg) == nullptr) return {};
Assume(GetRefGraph(arg) == this);
// Apply all removals and dependencies, as the result might be incorrect otherwise.
size_t level = GetSpecifiedLevel(level_select);
ApplyDependencies(level);
// Ancestry cannot be known if unapplied dependencies remain.
Assume(GetClusterSet(level).m_deps_to_add.empty());
// Find the Cluster the argument is in, and return the empty vector if it isn't in any.
auto [cluster, cluster_level] = FindClusterAndLevel(GetRefIndex(arg), level);
if (cluster == nullptr) return {};
// Dispatch to the Cluster.
std::pair<Cluster*, DepGraphIndex> match = {cluster, m_entries[GetRefIndex(arg)].m_locator[cluster_level].index};
auto matches = std::span(&match, 1);
std::vector<TxGraph::Ref*> ret;
cluster->GetAncestorRefs(*this, matches, ret);
return ret;
}
std::vector<TxGraph::Ref*> TxGraphImpl::GetDescendants(const Ref& arg, Level level_select) noexcept
{
// Return the empty vector if the Ref is empty.
if (GetRefGraph(arg) == nullptr) return {};
Assume(GetRefGraph(arg) == this);
// Apply all removals and dependencies, as the result might be incorrect otherwise.
size_t level = GetSpecifiedLevel(level_select);
ApplyDependencies(level);
// Ancestry cannot be known if unapplied dependencies remain.
Assume(GetClusterSet(level).m_deps_to_add.empty());
// Find the Cluster the argument is in, and return the empty vector if it isn't in any.
auto [cluster, cluster_level] = FindClusterAndLevel(GetRefIndex(arg), level);
if (cluster == nullptr) return {};
// Dispatch to the Cluster.
std::pair<Cluster*, DepGraphIndex> match = {cluster, m_entries[GetRefIndex(arg)].m_locator[cluster_level].index};
auto matches = std::span(&match, 1);
std::vector<TxGraph::Ref*> ret;
cluster->GetDescendantRefs(*this, matches, ret);
return ret;
}
std::vector<TxGraph::Ref*> TxGraphImpl::GetAncestorsUnion(std::span<const Ref* const> args, Level level_select) noexcept
{
// Apply all dependencies, as the result might be incorrect otherwise.
size_t level = GetSpecifiedLevel(level_select);
ApplyDependencies(level);
// Ancestry cannot be known if unapplied dependencies remain.
Assume(GetClusterSet(level).m_deps_to_add.empty());
// Translate args to matches.
std::vector<std::pair<Cluster*, DepGraphIndex>> matches;
matches.reserve(args.size());
for (auto arg : args) {
Assume(arg);
// Skip empty Refs.
if (GetRefGraph(*arg) == nullptr) continue;
Assume(GetRefGraph(*arg) == this);
// Find the Cluster the argument is in, and skip if none is found.
auto [cluster, cluster_level] = FindClusterAndLevel(GetRefIndex(*arg), level);
if (cluster == nullptr) continue;
// Append to matches.
matches.emplace_back(cluster, m_entries[GetRefIndex(*arg)].m_locator[cluster_level].index);
}
// Group by Cluster.
std::sort(matches.begin(), matches.end(), [](auto& a, auto& b) noexcept { return CompareClusters(a.first, b.first) < 0; });
// Dispatch to the Clusters.
std::span match_span(matches);
std::vector<TxGraph::Ref*> ret;
while (!match_span.empty()) {
match_span.front().first->GetAncestorRefs(*this, match_span, ret);
}
return ret;
}
std::vector<TxGraph::Ref*> TxGraphImpl::GetDescendantsUnion(std::span<const Ref* const> args, Level level_select) noexcept
{
// Apply all dependencies, as the result might be incorrect otherwise.
size_t level = GetSpecifiedLevel(level_select);
ApplyDependencies(level);
// Ancestry cannot be known if unapplied dependencies remain.
Assume(GetClusterSet(level).m_deps_to_add.empty());
// Translate args to matches.
std::vector<std::pair<Cluster*, DepGraphIndex>> matches;
matches.reserve(args.size());
for (auto arg : args) {
Assume(arg);
// Skip empty Refs.
if (GetRefGraph(*arg) == nullptr) continue;
Assume(GetRefGraph(*arg) == this);
// Find the Cluster the argument is in, and skip if none is found.
auto [cluster, cluster_level] = FindClusterAndLevel(GetRefIndex(*arg), level);
if (cluster == nullptr) continue;
// Append to matches.
matches.emplace_back(cluster, m_entries[GetRefIndex(*arg)].m_locator[cluster_level].index);
}
// Group by Cluster.
std::sort(matches.begin(), matches.end(), [](auto& a, auto& b) noexcept { return CompareClusters(a.first, b.first) < 0; });
// Dispatch to the Clusters.
std::span match_span(matches);
std::vector<TxGraph::Ref*> ret;
while (!match_span.empty()) {
match_span.front().first->GetDescendantRefs(*this, match_span, ret);
}
return ret;
}
std::vector<TxGraph::Ref*> TxGraphImpl::GetCluster(const Ref& arg, Level level_select) noexcept
{
// Return the empty vector if the Ref is empty (which may be indicative of the transaction
// having been removed already.
if (GetRefGraph(arg) == nullptr) return {};
Assume(GetRefGraph(arg) == this);
// Apply all removals and dependencies, as the result might be incorrect otherwise.
size_t level = GetSpecifiedLevel(level_select);
ApplyDependencies(level);
// Cluster linearization cannot be known if unapplied dependencies remain.
Assume(GetClusterSet(level).m_deps_to_add.empty());
// Find the Cluster the argument is in, and return the empty vector if it isn't in any.
auto [cluster, cluster_level] = FindClusterAndLevel(GetRefIndex(arg), level);
if (cluster == nullptr) return {};
// Make sure the Cluster has an acceptable quality level, and then dispatch to it.
MakeAcceptable(*cluster, cluster_level);
std::vector<TxGraph::Ref*> ret(cluster->GetTxCount());
cluster->GetClusterRefs(*this, ret, 0);
return ret;
}
TxGraph::GraphIndex TxGraphImpl::GetTransactionCount(Level level_select) noexcept
{
size_t level = GetSpecifiedLevel(level_select);
ApplyRemovals(level);
return GetClusterSet(level).m_txcount;
}
FeePerWeight TxGraphImpl::GetIndividualFeerate(const Ref& arg) noexcept
{
// Return the empty FeePerWeight if the passed Ref is empty.
if (GetRefGraph(arg) == nullptr) return {};
Assume(GetRefGraph(arg) == this);
// Find the cluster the argument is in (the level does not matter as individual feerates will
// be identical if it occurs in both), and return the empty FeePerWeight if it isn't in any.
Cluster* cluster{nullptr};
int level;
for (level = 0; level <= GetTopLevel(); ++level) {
// Apply removals, so that we can correctly report FeePerWeight{} for non-existing
// transactions.
ApplyRemovals(level);
if (m_entries[GetRefIndex(arg)].m_locator[level].IsPresent()) {
cluster = m_entries[GetRefIndex(arg)].m_locator[level].cluster;
break;
}
}
if (cluster == nullptr) return {};
// Dispatch to the Cluster.
return cluster->GetIndividualFeerate(m_entries[GetRefIndex(arg)].m_locator[level].index);
}
FeePerWeight TxGraphImpl::GetMainChunkFeerate(const Ref& arg) noexcept
{
// Return the empty FeePerWeight if the passed Ref is empty.
if (GetRefGraph(arg) == nullptr) return {};
Assume(GetRefGraph(arg) == this);
// Apply all removals and dependencies, as the result might be inaccurate otherwise.
ApplyDependencies(/*level=*/0);
// Chunk feerates cannot be accurately known if unapplied dependencies remain.
Assume(m_main_clusterset.m_deps_to_add.empty());
// Find the cluster the argument is in, and return the empty FeePerWeight if it isn't in any.
auto [cluster, cluster_level] = FindClusterAndLevel(GetRefIndex(arg), /*level=*/0);
if (cluster == nullptr) return {};
// Make sure the Cluster has an acceptable quality level, and then return the transaction's
// chunk feerate.
MakeAcceptable(*cluster, cluster_level);
const auto& entry = m_entries[GetRefIndex(arg)];
return entry.m_main_chunk_feerate;
}
bool TxGraphImpl::IsOversized(Level level_select) noexcept
{
size_t level = GetSpecifiedLevel(level_select);
auto& clusterset = GetClusterSet(level);
if (clusterset.m_oversized.has_value()) {
// Return cached value if known.
return *clusterset.m_oversized;
}
ApplyRemovals(level);
if (clusterset.m_txcount_oversized > 0) {
clusterset.m_oversized = true;
} else {
// Find which Clusters will need to be merged together, as that is where the oversize
// property is assessed.
GroupClusters(level);
}
Assume(clusterset.m_oversized.has_value());
return *clusterset.m_oversized;
}
void TxGraphImpl::StartStaging() noexcept
{
// Staging cannot already exist.
Assume(!m_staging_clusterset.has_value());
// Apply all remaining dependencies in main before creating a staging graph. Once staging
// exists, we cannot merge Clusters anymore (because of interference with Clusters being
// pulled into staging), so to make sure all inspectors are available (if not oversized), do
// all merging work now. Call SplitAll() first, so that even if ApplyDependencies does not do
// any thing due to knowing the result is oversized, splitting is still performed.
SplitAll(0);
ApplyDependencies(0);
// Construct the staging ClusterSet.
m_staging_clusterset.emplace();
// Copy statistics, precomputed data, and to-be-applied dependencies (only if oversized) to
// the new graph. To-be-applied removals will always be empty at this point.
m_staging_clusterset->m_txcount = m_main_clusterset.m_txcount;
m_staging_clusterset->m_txcount_oversized = m_main_clusterset.m_txcount_oversized;
m_staging_clusterset->m_deps_to_add = m_main_clusterset.m_deps_to_add;
m_staging_clusterset->m_group_data = m_main_clusterset.m_group_data;
m_staging_clusterset->m_oversized = m_main_clusterset.m_oversized;
Assume(m_staging_clusterset->m_oversized.has_value());
m_staging_clusterset->m_cluster_usage = 0;
}
void TxGraphImpl::AbortStaging() noexcept
{
// Staging must exist.
Assume(m_staging_clusterset.has_value());
// Mark all removed transactions as Missing (so the staging locator for these transactions
// can be reused if another staging is created).
for (auto idx : m_staging_clusterset->m_removed) {
m_entries[idx].m_locator[1].SetMissing();
}
// Do the same with the non-removed transactions in staging Clusters.
for (int quality = 0; quality < int(QualityLevel::NONE); ++quality) {
for (auto& cluster : m_staging_clusterset->m_clusters[quality]) {
cluster->MakeStagingTransactionsMissing(*this);
}
}
// Destroy the staging ClusterSet.
m_staging_clusterset.reset();
Compact();
if (!m_main_clusterset.m_group_data.has_value()) {
// In case m_oversized in main was kept after a Ref destruction while staging exists, we
// need to re-evaluate m_oversized now.
if (m_main_clusterset.m_to_remove.empty() && m_main_clusterset.m_txcount_oversized > 0) {
// It is possible that a Ref destruction caused a removal in main while staging existed.
// In this case, m_txcount_oversized may be inaccurate.
m_main_clusterset.m_oversized = true;
} else {
m_main_clusterset.m_oversized = std::nullopt;
}
}
}
void TxGraphImpl::CommitStaging() noexcept
{
// Staging must exist.
Assume(m_staging_clusterset.has_value());
Assume(m_main_chunkindex_observers == 0);
// Delete all conflicting Clusters in main, to make place for moving the staging ones
// there. All of these have been copied to staging in PullIn().
auto conflicts = GetConflicts();
for (Cluster* conflict : conflicts) {
conflict->Clear(*this, /*level=*/0);
DeleteCluster(*conflict, /*level=*/0);
}
// Mark the removed transactions as Missing (so the staging locator for these transactions
// can be reused if another staging is created).
for (auto idx : m_staging_clusterset->m_removed) {
m_entries[idx].m_locator[1].SetMissing();
}
// Then move all Clusters in staging to main.
for (int quality = 0; quality < int(QualityLevel::NONE); ++quality) {
auto& stage_sets = m_staging_clusterset->m_clusters[quality];
while (!stage_sets.empty()) {
stage_sets.back()->MoveToMain(*this);
}
}
// Move all statistics, precomputed data, and to-be-applied removals and dependencies.
m_main_clusterset.m_deps_to_add = std::move(m_staging_clusterset->m_deps_to_add);
m_main_clusterset.m_to_remove = std::move(m_staging_clusterset->m_to_remove);
m_main_clusterset.m_group_data = std::move(m_staging_clusterset->m_group_data);
m_main_clusterset.m_oversized = std::move(m_staging_clusterset->m_oversized);
m_main_clusterset.m_txcount = std::move(m_staging_clusterset->m_txcount);
m_main_clusterset.m_txcount_oversized = std::move(m_staging_clusterset->m_txcount_oversized);
// Delete the old staging graph, after all its information was moved to main.
m_staging_clusterset.reset();
Compact();
}
void GenericClusterImpl::SetFee(TxGraphImpl& graph, int level, DepGraphIndex idx, int64_t fee) noexcept
{
// Make sure the specified DepGraphIndex exists in this Cluster.
Assume(m_depgraph.Positions()[idx]);
// Bail out if the fee isn't actually being changed.
if (m_depgraph.FeeRate(idx).fee == fee) return;
// Update the fee, remember that relinearization will be necessary, and update the Entries
// in the same Cluster.
m_depgraph.FeeRate(idx).fee = fee;
if (m_quality == QualityLevel::OVERSIZED_SINGLETON) {
// Nothing to do.
} else if (!NeedsSplitting()) {
graph.SetClusterQuality(level, m_quality, m_setindex, QualityLevel::NEEDS_RELINEARIZE);
} else {
graph.SetClusterQuality(level, m_quality, m_setindex, QualityLevel::NEEDS_SPLIT);
}
Updated(graph, level);
}
void SingletonClusterImpl::SetFee(TxGraphImpl& graph, int level, DepGraphIndex idx, int64_t fee) noexcept
{
Assume(GetTxCount());
Assume(idx == 0);
m_feerate.fee = fee;
Updated(graph, level);
}
void TxGraphImpl::SetTransactionFee(const Ref& ref, int64_t fee) noexcept
{
// Don't do anything if the passed Ref is empty.
if (GetRefGraph(ref) == nullptr) return;
Assume(GetRefGraph(ref) == this);
Assume(m_main_chunkindex_observers == 0);
// Find the entry, its locator, and inform its Cluster about the new feerate, if any.
auto& entry = m_entries[GetRefIndex(ref)];
for (int level = 0; level < MAX_LEVELS; ++level) {
auto& locator = entry.m_locator[level];
if (locator.IsPresent()) {
locator.cluster->SetFee(*this, level, locator.index, fee);
}
}
}
std::strong_ordering TxGraphImpl::CompareMainOrder(const Ref& a, const Ref& b) noexcept
{
// The references must not be empty.
Assume(GetRefGraph(a) == this);
Assume(GetRefGraph(b) == this);
// Apply dependencies in main.
ApplyDependencies(0);
Assume(m_main_clusterset.m_deps_to_add.empty());
// Make both involved Clusters acceptable, so chunk feerates are relevant.
const auto& entry_a = m_entries[GetRefIndex(a)];
const auto& entry_b = m_entries[GetRefIndex(b)];
const auto& locator_a = entry_a.m_locator[0];
const auto& locator_b = entry_b.m_locator[0];
Assume(locator_a.IsPresent());
Assume(locator_b.IsPresent());
MakeAcceptable(*locator_a.cluster, /*level=*/0);
MakeAcceptable(*locator_b.cluster, /*level=*/0);
// Invoke comparison logic.
return CompareMainTransactions(GetRefIndex(a), GetRefIndex(b));
}
TxGraph::GraphIndex TxGraphImpl::CountDistinctClusters(std::span<const Ref* const> refs, Level level_select) noexcept
{
size_t level = GetSpecifiedLevel(level_select);
ApplyDependencies(level);
auto& clusterset = GetClusterSet(level);
Assume(clusterset.m_deps_to_add.empty());
// Build a vector of Clusters that the specified Refs occur in.
std::vector<Cluster*> clusters;
clusters.reserve(refs.size());
for (const Ref* ref : refs) {
Assume(ref);
if (GetRefGraph(*ref) == nullptr) continue;
Assume(GetRefGraph(*ref) == this);
auto cluster = FindCluster(GetRefIndex(*ref), level);
if (cluster != nullptr) clusters.push_back(cluster);
}
// Count the number of distinct elements in clusters.
std::sort(clusters.begin(), clusters.end(), [](Cluster* a, Cluster* b) noexcept { return CompareClusters(a, b) < 0; });
Cluster* last{nullptr};
GraphIndex ret{0};
for (Cluster* cluster : clusters) {
ret += (cluster != last);
last = cluster;
}
return ret;
}
std::pair<std::vector<FeeFrac>, std::vector<FeeFrac>> TxGraphImpl::GetMainStagingDiagrams() noexcept
{
Assume(m_staging_clusterset.has_value());
MakeAllAcceptable(0);
Assume(m_main_clusterset.m_deps_to_add.empty()); // can only fail if main is oversized
MakeAllAcceptable(1);
Assume(m_staging_clusterset->m_deps_to_add.empty()); // can only fail if staging is oversized
// For all Clusters in main which conflict with Clusters in staging (i.e., all that are removed
// by, or replaced in, staging), gather their chunk feerates.
auto main_clusters = GetConflicts();
std::vector<FeeFrac> main_feerates, staging_feerates;
for (Cluster* cluster : main_clusters) {
cluster->AppendChunkFeerates(main_feerates);
}
// Do the same for the Clusters in staging themselves.
for (int quality = 0; quality < int(QualityLevel::NONE); ++quality) {
for (const auto& cluster : m_staging_clusterset->m_clusters[quality]) {
cluster->AppendChunkFeerates(staging_feerates);
}
}
// Sort both by decreasing feerate to obtain diagrams, and return them.
std::sort(main_feerates.begin(), main_feerates.end(), [](auto& a, auto& b) { return a > b; });
std::sort(staging_feerates.begin(), staging_feerates.end(), [](auto& a, auto& b) { return a > b; });
return std::make_pair(std::move(main_feerates), std::move(staging_feerates));
}
void GenericClusterImpl::SanityCheck(const TxGraphImpl& graph, int level) const
{
// There must be an m_mapping for each m_depgraph position (including holes).
assert(m_depgraph.PositionRange() == m_mapping.size());
// The linearization for this Cluster must contain every transaction once.
assert(m_depgraph.TxCount() == m_linearization.size());
// Unless a split is to be applied, the cluster cannot have any holes.
if (!NeedsSplitting()) {
assert(m_depgraph.Positions() == SetType::Fill(m_depgraph.TxCount()));
}
// Compute the chunking of m_linearization.
LinearizationChunking linchunking(m_depgraph, m_linearization);
// Verify m_linearization.
SetType m_done;
LinearizationIndex linindex{0};
DepGraphIndex chunk_pos{0}; //!< position within the current chunk
assert(m_depgraph.IsAcyclic());
for (auto lin_pos : m_linearization) {
assert(lin_pos < m_mapping.size());
const auto& entry = graph.m_entries[m_mapping[lin_pos]];
// Check that the linearization is topological.
m_done.Set(lin_pos);
assert(m_done.IsSupersetOf(m_depgraph.Ancestors(lin_pos)));
// Check that the Entry has a locator pointing back to this Cluster & position within it.
assert(entry.m_locator[level].cluster == this);
assert(entry.m_locator[level].index == lin_pos);
// For main-level entries, check linearization position and chunk feerate.
if (level == 0 && IsAcceptable()) {
assert(entry.m_main_lin_index == linindex);
++linindex;
if (!linchunking.GetChunk(0).transactions[lin_pos]) {
linchunking.MarkDone(linchunking.GetChunk(0).transactions);
chunk_pos = 0;
}
assert(entry.m_main_chunk_feerate == linchunking.GetChunk(0).feerate);
// Verify that an entry in the chunk index exists for every chunk-ending transaction.
++chunk_pos;
bool is_chunk_end = (chunk_pos == linchunking.GetChunk(0).transactions.Count());
assert((entry.m_main_chunkindex_iterator != graph.m_main_chunkindex.end()) == is_chunk_end);
if (is_chunk_end) {
auto& chunk_data = *entry.m_main_chunkindex_iterator;
if (m_done == m_depgraph.Positions() && chunk_pos == 1) {
assert(chunk_data.m_chunk_count == LinearizationIndex(-1));
} else {
assert(chunk_data.m_chunk_count == chunk_pos);
}
}
// If this Cluster has an acceptable quality level, its chunks must be connected.
assert(m_depgraph.IsConnected(linchunking.GetChunk(0).transactions));
}
}
// Verify that each element of m_depgraph occurred in m_linearization.
assert(m_done == m_depgraph.Positions());
}
void SingletonClusterImpl::SanityCheck(const TxGraphImpl& graph, int level) const
{
// All singletons are optimal, oversized, or need splitting.
Assume(IsOptimal() || IsOversized() || NeedsSplitting());
if (GetTxCount()) {
const auto& entry = graph.m_entries[m_graph_index];
// Check that the Entry has a locator pointing back to this Cluster & position within it.
assert(entry.m_locator[level].cluster == this);
assert(entry.m_locator[level].index == 0);
// For main-level entries, check linearization position and chunk feerate.
if (level == 0 && IsAcceptable()) {
assert(entry.m_main_lin_index == 0);
assert(entry.m_main_chunk_feerate == m_feerate);
assert(entry.m_main_chunkindex_iterator != graph.m_main_chunkindex.end());
auto& chunk_data = *entry.m_main_chunkindex_iterator;
assert(chunk_data.m_chunk_count == LinearizationIndex(-1));
}
}
}
void TxGraphImpl::SanityCheck() const
{
/** Which GraphIndexes ought to occur in m_unlinked, based on m_entries. */
std::set<GraphIndex> expected_unlinked;
/** Which Clusters ought to occur in ClusterSet::m_clusters, based on m_entries. */
std::set<const Cluster*> expected_clusters[MAX_LEVELS];
/** Which GraphIndexes ought to occur in ClusterSet::m_removed, based on m_entries. */
std::set<GraphIndex> expected_removed[MAX_LEVELS];
/** Which Cluster::m_sequence values have been encountered. */
std::set<uint64_t> sequences;
/** Which GraphIndexes ought to occur in m_main_chunkindex, based on m_entries. */
std::set<GraphIndex> expected_chunkindex;
/** Whether compaction is possible in the current state. */
bool compact_possible{true};
// Go over all Entry objects in m_entries.
for (GraphIndex idx = 0; idx < m_entries.size(); ++idx) {
const auto& entry = m_entries[idx];
if (entry.m_ref == nullptr) {
// Unlinked Entry must have indexes appear in m_unlinked.
expected_unlinked.insert(idx);
} else {
// Every non-unlinked Entry must have a Ref that points back to it.
assert(GetRefGraph(*entry.m_ref) == this);
assert(GetRefIndex(*entry.m_ref) == idx);
}
if (entry.m_main_chunkindex_iterator != m_main_chunkindex.end()) {
// Remember which entries we see a chunkindex entry for.
assert(entry.m_locator[0].IsPresent());
expected_chunkindex.insert(idx);
}
// Verify the Entry m_locators.
bool was_present{false}, was_removed{false};
for (int level = 0; level < MAX_LEVELS; ++level) {
const auto& locator = entry.m_locator[level];
// Every Locator must be in exactly one of these 3 states.
assert(locator.IsMissing() + locator.IsRemoved() + locator.IsPresent() == 1);
if (locator.IsPresent()) {
// Once removed, a transaction cannot be revived.
assert(!was_removed);
// Verify that the Cluster agrees with where the Locator claims the transaction is.
assert(locator.cluster->GetClusterEntry(locator.index) == idx);
// Remember that we expect said Cluster to appear in the ClusterSet::m_clusters.
expected_clusters[level].insert(locator.cluster);
was_present = true;
} else if (locator.IsRemoved()) {
// Level 0 (main) cannot have IsRemoved locators (IsMissing there means non-existing).
assert(level > 0);
// A Locator can only be IsRemoved if it was IsPresent before, and only once.
assert(was_present && !was_removed);
// Remember that we expect this GraphIndex to occur in the ClusterSet::m_removed.
expected_removed[level].insert(idx);
was_removed = true;
}
}
}
// For all levels (0 = main, 1 = staged)...
for (int level = 0; level <= GetTopLevel(); ++level) {
assert(level < MAX_LEVELS);
auto& clusterset = GetClusterSet(level);
std::set<const Cluster*> actual_clusters;
size_t recomputed_cluster_usage{0};
// For all quality levels...
for (int qual = 0; qual < int(QualityLevel::NONE); ++qual) {
QualityLevel quality{qual};
const auto& quality_clusters = clusterset.m_clusters[qual];
// ... for all clusters in them ...
for (ClusterSetIndex setindex = 0; setindex < quality_clusters.size(); ++setindex) {
const auto& cluster = *quality_clusters[setindex];
// The number of transactions in a Cluster cannot exceed m_max_cluster_count.
assert(cluster.GetTxCount() <= m_max_cluster_count);
// The level must match the Cluster's own idea of what level it is in (but GetLevel
// can only be called for non-empty Clusters).
assert(cluster.GetTxCount() == 0 || level == cluster.GetLevel(*this));
// The sum of their sizes cannot exceed m_max_cluster_size, unless it is an
// individually oversized transaction singleton. Note that groups of to-be-merged
// clusters which would exceed this limit are marked oversized, which means they
// are never applied.
assert(cluster.IsOversized() || cluster.GetTotalTxSize() <= m_max_cluster_size);
// OVERSIZED clusters are singletons.
assert(!cluster.IsOversized() || cluster.GetTxCount() == 1);
// Transaction counts cannot exceed the Cluster implementation's maximum
// supported transactions count.
assert(cluster.GetTxCount() <= cluster.GetMaxTxCount());
// Unless a Split is yet to be applied, the number of transactions must not be
// below the Cluster implementation's intended transaction count.
if (!cluster.NeedsSplitting()) {
assert(cluster.GetTxCount() >= cluster.GetMinIntendedTxCount());
}
// Check the sequence number.
assert(cluster.m_sequence < m_next_sequence_counter);
assert(sequences.count(cluster.m_sequence) == 0);
sequences.insert(cluster.m_sequence);
// Remember we saw this Cluster (only if it is non-empty; empty Clusters aren't
// expected to be referenced by the Entry vector).
if (cluster.GetTxCount() != 0) {
actual_clusters.insert(&cluster);
}
// Sanity check the cluster, according to the Cluster's internal rules.
cluster.SanityCheck(*this, level);
// Check that the cluster's quality and setindex matches its position in the quality list.
assert(cluster.m_quality == quality);
assert(cluster.m_setindex == setindex);
// Count memory usage.
recomputed_cluster_usage += cluster.TotalMemoryUsage();
}
}
// Verify memory usage.
assert(clusterset.m_cluster_usage == recomputed_cluster_usage);
// Verify that all to-be-removed transactions have valid identifiers.
for (GraphIndex idx : clusterset.m_to_remove) {
assert(idx < m_entries.size());
// We cannot assert that all m_to_remove transactions are still present: ~Ref on a
// (P,M) transaction (present in main, inherited in staging) will cause an m_to_remove
// addition in both main and staging, but a subsequence ApplyRemovals in main will
// cause it to disappear from staging too, leaving the m_to_remove in place.
}
// Verify that all to-be-added dependencies have valid identifiers.
for (auto [par_idx, chl_idx] : clusterset.m_deps_to_add) {
assert(par_idx != chl_idx);
assert(par_idx < m_entries.size());
assert(chl_idx < m_entries.size());
}
// Verify that the actually encountered clusters match the ones occurring in Entry vector.
assert(actual_clusters == expected_clusters[level]);
// Verify that the contents of m_removed matches what was expected based on the Entry vector.
std::set<GraphIndex> actual_removed(clusterset.m_removed.begin(), clusterset.m_removed.end());
for (auto i : expected_unlinked) {
// If a transaction exists in both main and staging, and is removed from staging (adding
// it to m_removed there), and consequently destroyed (wiping the locator completely),
// it can remain in m_removed despite not having an IsRemoved() locator. Exclude those
// transactions from the comparison here.
actual_removed.erase(i);
expected_removed[level].erase(i);
}
assert(actual_removed == expected_removed[level]);
// If any GraphIndex entries remain in this ClusterSet, compact is not possible.
if (!clusterset.m_deps_to_add.empty()) compact_possible = false;
if (!clusterset.m_to_remove.empty()) compact_possible = false;
if (!clusterset.m_removed.empty()) compact_possible = false;
// For non-top levels, m_oversized must be known (as it cannot change until the level
// on top is gone).
if (level < GetTopLevel()) assert(clusterset.m_oversized.has_value());
}
// Verify that the contents of m_unlinked matches what was expected based on the Entry vector.
std::set<GraphIndex> actual_unlinked(m_unlinked.begin(), m_unlinked.end());
assert(actual_unlinked == expected_unlinked);
// If compaction was possible, it should have been performed already, and m_unlinked must be
// empty (to prevent memory leaks due to an ever-growing m_entries vector).
if (compact_possible) {
assert(actual_unlinked.empty());
}
// Finally, check the chunk index.
std::set<GraphIndex> actual_chunkindex;
FeeFrac last_chunk_feerate;
for (const auto& chunk : m_main_chunkindex) {
GraphIndex idx = chunk.m_graph_index;
actual_chunkindex.insert(idx);
auto chunk_feerate = m_entries[idx].m_main_chunk_feerate;
if (!last_chunk_feerate.IsEmpty()) {
assert(FeeRateCompare(last_chunk_feerate, chunk_feerate) >= 0);
}
last_chunk_feerate = chunk_feerate;
}
assert(actual_chunkindex == expected_chunkindex);
}
bool TxGraphImpl::DoWork(uint64_t iters) noexcept
{
uint64_t iters_done{0};
// First linearize everything in NEEDS_RELINEARIZE to an acceptable level. If more budget
// remains after that, try to make everything optimal.
for (QualityLevel quality : {QualityLevel::NEEDS_RELINEARIZE, QualityLevel::ACCEPTABLE}) {
// First linearize staging, if it exists, then main.
for (int level = GetTopLevel(); level >= 0; --level) {
// Do not modify main if it has any observers.
if (level == 0 && m_main_chunkindex_observers != 0) continue;
ApplyDependencies(level);
auto& clusterset = GetClusterSet(level);
// Do not modify oversized levels.
if (clusterset.m_oversized == true) continue;
auto& queue = clusterset.m_clusters[int(quality)];
while (!queue.empty()) {
if (iters_done >= iters) return false;
// Randomize the order in which we process, so that if the first cluster somehow
// needs more work than what iters allows, we don't keep spending it on the same
// one.
auto pos = m_rng.randrange<size_t>(queue.size());
auto iters_now = iters - iters_done;
if (quality == QualityLevel::NEEDS_RELINEARIZE) {
// If we're working with clusters that need relinearization still, only perform
// up to m_acceptable_iters iterations. If they become ACCEPTABLE, and we still
// have budget after all other clusters are ACCEPTABLE too, we'll spend the
// remaining budget on trying to make them OPTIMAL.
iters_now = std::min(iters_now, m_acceptable_iters);
}
auto [cost, improved] = queue[pos].get()->Relinearize(*this, level, iters_now);
iters_done += cost;
// If no improvement was made to the Cluster, it means we've essentially run out of
// budget. Even though it may be the case that iters_done < iters still, the
// linearizer decided there wasn't enough budget left to attempt anything with.
// To avoid an infinite loop that keeps trying clusters with minuscule budgets,
// stop here too.
if (!improved) return false;
}
}
}
// All possible work has been performed, so we can return true. Note that this does *not* mean
// that all clusters are optimally linearized now. It may be that there is nothing to do left
// because all non-optimal clusters are in oversized and/or observer-bearing levels.
return true;
}
void BlockBuilderImpl::Next() noexcept
{
// Don't do anything if we're already done.
if (m_cur_iter == m_graph->m_main_chunkindex.end()) return;
while (true) {
// Advance the pointer, and stop if we reach the end.
++m_cur_iter;
m_cur_cluster = nullptr;
if (m_cur_iter == m_graph->m_main_chunkindex.end()) break;
// Find the cluster pointed to by m_cur_iter.
const auto& chunk_data = *m_cur_iter;
const auto& chunk_end_entry = m_graph->m_entries[chunk_data.m_graph_index];
m_cur_cluster = chunk_end_entry.m_locator[0].cluster;
m_known_end_of_cluster = false;
// If we previously skipped a chunk from this cluster we cannot include more from it.
if (!m_excluded_clusters.contains(m_cur_cluster->m_sequence)) break;
}
}
std::optional<std::pair<std::vector<TxGraph::Ref*>, FeePerWeight>> BlockBuilderImpl::GetCurrentChunk() noexcept
{
std::optional<std::pair<std::vector<TxGraph::Ref*>, FeePerWeight>> ret;
// Populate the return value if we are not done.
if (m_cur_iter != m_graph->m_main_chunkindex.end()) {
ret.emplace();
const auto& chunk_data = *m_cur_iter;
const auto& chunk_end_entry = m_graph->m_entries[chunk_data.m_graph_index];
if (chunk_data.m_chunk_count == LinearizationIndex(-1)) {
// Special case in case just a single transaction remains, avoiding the need to
// dispatch to and dereference Cluster.
ret->first.resize(1);
Assume(chunk_end_entry.m_ref != nullptr);
ret->first[0] = chunk_end_entry.m_ref;
m_known_end_of_cluster = true;
} else {
Assume(m_cur_cluster);
ret->first.resize(chunk_data.m_chunk_count);
auto start_pos = chunk_end_entry.m_main_lin_index + 1 - chunk_data.m_chunk_count;
m_known_end_of_cluster = m_cur_cluster->GetClusterRefs(*m_graph, ret->first, start_pos);
// If the chunk size was 1 and at end of cluster, then the special case above should
// have been used.
Assume(!m_known_end_of_cluster || chunk_data.m_chunk_count > 1);
}
ret->second = chunk_end_entry.m_main_chunk_feerate;
}
return ret;
}
BlockBuilderImpl::BlockBuilderImpl(TxGraphImpl& graph) noexcept : m_graph(&graph)
{
// Make sure all clusters in main are up to date, and acceptable.
m_graph->MakeAllAcceptable(0);
// There cannot remain any inapplicable dependencies (only possible if main is oversized).
Assume(m_graph->m_main_clusterset.m_deps_to_add.empty());
// Remember that this object is observing the graph's index, so that we can detect concurrent
// modifications.
++m_graph->m_main_chunkindex_observers;
// Find the first chunk.
m_cur_iter = m_graph->m_main_chunkindex.begin();
m_cur_cluster = nullptr;
if (m_cur_iter != m_graph->m_main_chunkindex.end()) {
// Find the cluster pointed to by m_cur_iter.
const auto& chunk_data = *m_cur_iter;
const auto& chunk_end_entry = m_graph->m_entries[chunk_data.m_graph_index];
m_cur_cluster = chunk_end_entry.m_locator[0].cluster;
}
}
BlockBuilderImpl::~BlockBuilderImpl()
{
Assume(m_graph->m_main_chunkindex_observers > 0);
// Permit modifications to the main graph again after destroying the BlockBuilderImpl.
--m_graph->m_main_chunkindex_observers;
}
void BlockBuilderImpl::Include() noexcept
{
// The actual inclusion of the chunk is done by the calling code. All we have to do is switch
// to the next chunk.
Next();
}
void BlockBuilderImpl::Skip() noexcept
{
// When skipping a chunk we need to not include anything more of the cluster, as that could make
// the result topologically invalid. However, don't do this if the chunk is known to be the last
// chunk of the cluster. This may significantly reduce the size of m_excluded_clusters,
// especially when many singleton clusters are ignored.
if (m_cur_cluster != nullptr && !m_known_end_of_cluster) {
m_excluded_clusters.insert(m_cur_cluster->m_sequence);
}
Next();
}
std::unique_ptr<TxGraph::BlockBuilder> TxGraphImpl::GetBlockBuilder() noexcept
{
return std::make_unique<BlockBuilderImpl>(*this);
}
std::pair<std::vector<TxGraph::Ref*>, FeePerWeight> TxGraphImpl::GetWorstMainChunk() noexcept
{
std::pair<std::vector<Ref*>, FeePerWeight> ret;
// Make sure all clusters in main are up to date, and acceptable.
MakeAllAcceptable(0);
Assume(m_main_clusterset.m_deps_to_add.empty());
// If the graph is not empty, populate ret.
if (!m_main_chunkindex.empty()) {
const auto& chunk_data = *m_main_chunkindex.rbegin();
const auto& chunk_end_entry = m_entries[chunk_data.m_graph_index];
Cluster* cluster = chunk_end_entry.m_locator[0].cluster;
if (chunk_data.m_chunk_count == LinearizationIndex(-1) || chunk_data.m_chunk_count == 1) {
// Special case for singletons.
ret.first.resize(1);
Assume(chunk_end_entry.m_ref != nullptr);
ret.first[0] = chunk_end_entry.m_ref;
} else {
ret.first.resize(chunk_data.m_chunk_count);
auto start_pos = chunk_end_entry.m_main_lin_index + 1 - chunk_data.m_chunk_count;
cluster->GetClusterRefs(*this, ret.first, start_pos);
std::reverse(ret.first.begin(), ret.first.end());
}
ret.second = chunk_end_entry.m_main_chunk_feerate;
}
return ret;
}
std::vector<TxGraph::Ref*> TxGraphImpl::Trim() noexcept
{
int level = GetTopLevel();
Assume(m_main_chunkindex_observers == 0 || level != 0);
std::vector<TxGraph::Ref*> ret;
// Compute the groups of to-be-merged Clusters (which also applies all removals, and splits).
auto& clusterset = GetClusterSet(level);
if (clusterset.m_oversized == false) return ret;
GroupClusters(level);
Assume(clusterset.m_group_data.has_value());
// Nothing to do if not oversized.
Assume(clusterset.m_oversized.has_value());
if (clusterset.m_oversized == false) return ret;
// In this function, would-be clusters (as precomputed in m_group_data by GroupClusters) are
// trimmed by removing transactions in them such that the resulting clusters satisfy the size
// and count limits.
//
// It works by defining for each would-be cluster a rudimentary linearization: at every point
// the highest-chunk-feerate remaining transaction is picked among those with no unmet
// dependencies. "Dependency" here means either a to-be-added dependency (m_deps_to_add), or
// an implicit dependency added between any two consecutive transaction in their current
// cluster linearization. So it can be seen as a "merge sort" of the chunks of the clusters,
// but respecting the dependencies being added.
//
// This rudimentary linearization is computed lazily, by putting all eligible (no unmet
// dependencies) transactions in a heap, and popping the highest-feerate one from it. Along the
// way, the counts and sizes of the would-be clusters up to that point are tracked (by
// partitioning the involved transactions using a union-find structure). Any transaction whose
// addition would cause a violation is removed, along with all their descendants.
//
// A next invocation of GroupClusters (after applying the removals) will compute the new
// resulting clusters, and none of them will violate the limits.
/** All dependencies (both to be added ones, and implicit ones between consecutive transactions
* in existing cluster linearizations), sorted by parent. */
std::vector<std::pair<GraphIndex, GraphIndex>> deps_by_parent;
/** Same, but sorted by child. */
std::vector<std::pair<GraphIndex, GraphIndex>> deps_by_child;
/** Information about all transactions involved in a Cluster group to be trimmed, sorted by
* GraphIndex. It contains entries both for transactions that have already been included,
* and ones that have not yet been. */
std::vector<TrimTxData> trim_data;
/** Iterators into trim_data, treated as a max heap according to cmp_fn below. Each entry is
* a transaction that has not yet been included yet, but all its ancestors have. */
std::vector<std::vector<TrimTxData>::iterator> trim_heap;
/** The list of representatives of the partitions a given transaction depends on. */
std::vector<TrimTxData*> current_deps;
/** Function to define the ordering of trim_heap. */
static constexpr auto cmp_fn = [](auto a, auto b) noexcept {
// Sort by increasing chunk feerate, and then by decreasing size.
// We do not need to sort by cluster or within clusters, because due to the implicit
// dependency between consecutive linearization elements, no two transactions from the
// same Cluster will ever simultaneously be in the heap.
return a->m_chunk_feerate < b->m_chunk_feerate;
};
/** Given a TrimTxData entry, find the representative of the partition it is in. */
static constexpr auto find_fn = [](TrimTxData* arg) noexcept {
while (arg != arg->m_uf_parent) {
// Replace pointer to parent with pointer to grandparent (path splitting).
// See https://en.wikipedia.org/wiki/Disjoint-set_data_structure#Finding_set_representatives.
auto par = arg->m_uf_parent;
arg->m_uf_parent = par->m_uf_parent;
arg = par;
}
return arg;
};
/** Given two TrimTxData entries, union the partitions they are in, and return the
* representative. */
static constexpr auto union_fn = [](TrimTxData* arg1, TrimTxData* arg2) noexcept {
// Replace arg1 and arg2 by their representatives.
auto rep1 = find_fn(arg1);
auto rep2 = find_fn(arg2);
// Bail out if both representatives are the same, because that means arg1 and arg2 are in
// the same partition already.
if (rep1 == rep2) return rep1;
// Pick the lower-count root to become a child of the higher-count one.
// See https://en.wikipedia.org/wiki/Disjoint-set_data_structure#Union_by_size.
if (rep1->m_uf_count < rep2->m_uf_count) std::swap(rep1, rep2);
rep2->m_uf_parent = rep1;
// Add the statistics of arg2 (which is no longer a representative) to those of arg1 (which
// is now the representative for both).
rep1->m_uf_size += rep2->m_uf_size;
rep1->m_uf_count += rep2->m_uf_count;
return rep1;
};
/** Get iterator to TrimTxData entry for a given index. */
auto locate_fn = [&](GraphIndex index) noexcept {
auto it = std::lower_bound(trim_data.begin(), trim_data.end(), index, [](TrimTxData& elem, GraphIndex idx) noexcept {
return elem.m_index < idx;
});
Assume(it != trim_data.end() && it->m_index == index);
return it;
};
// For each group of to-be-merged Clusters.
for (const auto& group_data : clusterset.m_group_data->m_groups) {
trim_data.clear();
trim_heap.clear();
deps_by_child.clear();
deps_by_parent.clear();
// Gather trim data and implicit dependency data from all involved Clusters.
auto cluster_span = std::span{clusterset.m_group_data->m_group_clusters}
.subspan(group_data.m_cluster_offset, group_data.m_cluster_count);
uint64_t size{0};
for (Cluster* cluster : cluster_span) {
size += cluster->AppendTrimData(trim_data, deps_by_child);
}
// If this group of Clusters does not violate any limits, continue to the next group.
if (trim_data.size() <= m_max_cluster_count && size <= m_max_cluster_size) continue;
// Sort the trim data by GraphIndex. In what follows, we will treat this sorted vector as
// a map from GraphIndex to TrimTxData via locate_fn, and its ordering will not change
// anymore.
std::sort(trim_data.begin(), trim_data.end(), [](auto& a, auto& b) noexcept { return a.m_index < b.m_index; });
// Add the explicitly added dependencies to deps_by_child.
deps_by_child.insert(deps_by_child.end(),
clusterset.m_deps_to_add.begin() + group_data.m_deps_offset,
clusterset.m_deps_to_add.begin() + group_data.m_deps_offset + group_data.m_deps_count);
// Sort deps_by_child by child transaction GraphIndex. The order will not be changed
// anymore after this.
std::sort(deps_by_child.begin(), deps_by_child.end(), [](auto& a, auto& b) noexcept { return a.second < b.second; });
// Fill m_parents_count and m_parents_offset in trim_data, as well as m_deps_left, and
// initially populate trim_heap. Because of the sort above, all dependencies involving the
// same child are grouped together, so a single linear scan suffices.
auto deps_it = deps_by_child.begin();
for (auto trim_it = trim_data.begin(); trim_it != trim_data.end(); ++trim_it) {
trim_it->m_parent_offset = deps_it - deps_by_child.begin();
trim_it->m_deps_left = 0;
while (deps_it != deps_by_child.end() && deps_it->second == trim_it->m_index) {
++trim_it->m_deps_left;
++deps_it;
}
trim_it->m_parent_count = trim_it->m_deps_left;
// If this transaction has no unmet dependencies, and is not oversized, add it to the
// heap (just append for now, the heapification happens below).
if (trim_it->m_deps_left == 0 && trim_it->m_tx_size <= m_max_cluster_size) {
trim_heap.push_back(trim_it);
}
}
Assume(deps_it == deps_by_child.end());
// Construct deps_by_parent, sorted by parent transaction GraphIndex. The order will not be
// changed anymore after this.
deps_by_parent = deps_by_child;
std::sort(deps_by_parent.begin(), deps_by_parent.end(), [](auto& a, auto& b) noexcept { return a.first < b.first; });
// Fill m_children_offset and m_children_count in trim_data. Because of the sort above, all
// dependencies involving the same parent are grouped together, so a single linear scan
// suffices.
deps_it = deps_by_parent.begin();
for (auto& trim_entry : trim_data) {
trim_entry.m_children_count = 0;
trim_entry.m_children_offset = deps_it - deps_by_parent.begin();
while (deps_it != deps_by_parent.end() && deps_it->first == trim_entry.m_index) {
++trim_entry.m_children_count;
++deps_it;
}
}
Assume(deps_it == deps_by_parent.end());
// Build a heap of all transactions with 0 unmet dependencies.
std::make_heap(trim_heap.begin(), trim_heap.end(), cmp_fn);
// Iterate over to-be-included transactions, and convert them to included transactions, or
// skip them if adding them would violate resource limits of the would-be cluster.
//
// It is possible that the heap empties without ever hitting either cluster limit, in case
// the implied graph (to be added dependencies plus implicit dependency between each
// original transaction and its predecessor in the linearization it came from) contains
// cycles. Such cycles will be removed entirely, because each of the transactions in the
// cycle permanently have unmet dependencies. However, this cannot occur in real scenarios
// where Trim() is called to deal with reorganizations that would violate cluster limits,
// as all added dependencies are in the same direction (from old mempool transactions to
// new from-block transactions); cycles require dependencies in both directions to be
// added.
while (!trim_heap.empty()) {
// Move the best remaining transaction to the end of trim_heap.
std::pop_heap(trim_heap.begin(), trim_heap.end(), cmp_fn);
// Pop it, and find its TrimTxData.
auto& entry = *trim_heap.back();
trim_heap.pop_back();
// Initialize it as a singleton partition.
entry.m_uf_parent = &entry;
entry.m_uf_count = 1;
entry.m_uf_size = entry.m_tx_size;
// Find the distinct transaction partitions this entry depends on.
current_deps.clear();
for (auto& [par, chl] : std::span{deps_by_child}.subspan(entry.m_parent_offset, entry.m_parent_count)) {
Assume(chl == entry.m_index);
current_deps.push_back(find_fn(&*locate_fn(par)));
}
std::sort(current_deps.begin(), current_deps.end());
current_deps.erase(std::unique(current_deps.begin(), current_deps.end()), current_deps.end());
// Compute resource counts.
uint32_t new_count = 1;
uint64_t new_size = entry.m_tx_size;
for (TrimTxData* ptr : current_deps) {
new_count += ptr->m_uf_count;
new_size += ptr->m_uf_size;
}
// Skip the entry if this would violate any limit.
if (new_count > m_max_cluster_count || new_size > m_max_cluster_size) continue;
// Union the partitions this transaction and all its dependencies are in together.
auto rep = &entry;
for (TrimTxData* ptr : current_deps) rep = union_fn(ptr, rep);
// Mark the entry as included (so the loop below will not remove the transaction).
entry.m_deps_left = uint32_t(-1);
// Mark each to-be-added dependency involving this transaction as parent satisfied.
for (auto& [par, chl] : std::span{deps_by_parent}.subspan(entry.m_children_offset, entry.m_children_count)) {
Assume(par == entry.m_index);
auto chl_it = locate_fn(chl);
// Reduce the number of unmet dependencies of chl_it, and if that brings the number
// to zero, add it to the heap of includable transactions.
Assume(chl_it->m_deps_left > 0);
if (--chl_it->m_deps_left == 0) {
trim_heap.push_back(chl_it);
std::push_heap(trim_heap.begin(), trim_heap.end(), cmp_fn);
}
}
}
// Remove all the transactions that were not processed above. Because nothing gets
// processed until/unless all its dependencies are met, this automatically guarantees
// that if a transaction is removed, all its descendants, or would-be descendants, are
// removed as well.
for (const auto& trim_entry : trim_data) {
if (trim_entry.m_deps_left != uint32_t(-1)) {
ret.push_back(m_entries[trim_entry.m_index].m_ref);
clusterset.m_to_remove.push_back(trim_entry.m_index);
}
}
}
clusterset.m_group_data.reset();
clusterset.m_oversized = false;
Assume(!ret.empty());
return ret;
}
size_t TxGraphImpl::GetMainMemoryUsage() noexcept
{
// Make sure splits/merges are applied, as memory usage may not be representative otherwise.
SplitAll(/*up_to_level=*/0);
ApplyDependencies(/*level=*/0);
// Compute memory usage
size_t usage = /* From clusters */
m_main_clusterset.m_cluster_usage +
/* From Entry objects. */
sizeof(Entry) * m_main_clusterset.m_txcount +
/* From the chunk index. */
memusage::DynamicUsage(m_main_chunkindex);
return usage;
}
} // namespace
TxGraph::Ref::~Ref()
{
if (m_graph) {
// Inform the TxGraph about the Ref being destroyed.
m_graph->UnlinkRef(m_index);
m_graph = nullptr;
}
}
TxGraph::Ref::Ref(Ref&& other) noexcept
{
// Inform the TxGraph of other that its Ref is being moved.
if (other.m_graph) other.m_graph->UpdateRef(other.m_index, *this);
// Actually move the contents.
std::swap(m_graph, other.m_graph);
std::swap(m_index, other.m_index);
}
std::unique_ptr<TxGraph> MakeTxGraph(unsigned max_cluster_count, uint64_t max_cluster_size, uint64_t acceptable_iters) noexcept
{
return std::make_unique<TxGraphImpl>(max_cluster_count, max_cluster_size, acceptable_iters);
}
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