mempool

Documentation

Index

• func GetBlockReward(baseReward, medianWeight, fees, weight uint64) uint64
• type Entry
  • func (t *Entry) Compare(o *Entry) int
• type Mempool
  • func (m Mempool) Fees() (r uint64)
  • func (m Mempool) PerfectSum(targetFee uint64) chan Mempool
  • func (m Mempool) Pick(baseReward, minerTxWeight, medianWeight uint64) Mempool
  • func (m Mempool) Sort()
  • func (m Mempool) Weight() (r uint64)
  • func (m Mempool) WeightAndFees() (weight, fees uint64)

Functions

func GetBlockReward

func GetBlockReward(baseReward, medianWeight, fees, weight uint64) uint64

GetBlockReward Faster and limited version of block.GetBlockReward

Types

type Entry

type Entry struct {
	Id           types.Hash `json:"id"`
	BlobSize     uint64     `json:"blob_size"`
	Weight       uint64     `json:"weight"`
	Fee          uint64     `json:"fee"`
	TimeReceived time.Time  `json:"-"`
}

func (*Entry) Compare

func (t *Entry) Compare(o *Entry) int

Compare returns -1 if self is preferred over o, 0 if equal, 1 if o is preferred over self

type Mempool

type Mempool []*Entry

func (Mempool) Fees

func (m Mempool) Fees() (r uint64)

func (Mempool) PerfectSum

func (m Mempool) PerfectSum(targetFee uint64) chan Mempool

func (Mempool) Pick

func (m Mempool) Pick(baseReward, minerTxWeight, medianWeight uint64) Mempool

Pick Selects transactions semi-optimally

Picking all transactions will result in the base reward penalty Use a heuristic algorithm to pick transactions and get the maximum possible reward Testing has shown that this algorithm is very close to the optimal selection Usually no more than 0.5 micronero away from the optimal discrete knapsack solution Sometimes it even finds the optimal solution

func (Mempool) Sort

func (m Mempool) Sort()

func (Mempool) Weight

func (m Mempool) Weight() (r uint64)

func (Mempool) WeightAndFees

func (m Mempool) WeightAndFees() (weight, fees uint64)