core/bloombits: use general filters instead of addresses and topics

This commit is contained in:
Zsolt Felfoldi 2017-09-06 02:33:10 +02:00 committed by Péter Szilágyi
parent 6ff2c02991
commit 451ffdb62b
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GPG Key ID: E9AE538CEDF8293D
3 changed files with 34 additions and 47 deletions

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@ -24,7 +24,6 @@ import (
"sync/atomic"
"time"
"github.com/ethereum/go-ethereum/common"
"github.com/ethereum/go-ethereum/common/bitutil"
"github.com/ethereum/go-ethereum/crypto"
)
@ -68,8 +67,7 @@ type Retrieval struct {
type Matcher struct {
sectionSize uint64 // Size of the data batches to filter on
addresses []bloomIndexes // Addresses the system is filtering for
topics [][]bloomIndexes // Topics the system is filtering for
filters [][]bloomIndexes // Filter the system is matching for
schedulers map[uint]*scheduler // Retrieval schedulers for loading bloom bits
retrievers chan chan uint // Retriever processes waiting for bit allocations
@ -82,7 +80,8 @@ type Matcher struct {
// NewMatcher creates a new pipeline for retrieving bloom bit streams and doing
// address and topic filtering on them.
func NewMatcher(sectionSize uint64, addresses []common.Address, topics [][]common.Hash) *Matcher {
func NewMatcher(sectionSize uint64, filters [][][]byte) *Matcher {
// Create the matcher instance
m := &Matcher{
sectionSize: sectionSize,
schedulers: make(map[uint]*scheduler),
@ -91,48 +90,25 @@ func NewMatcher(sectionSize uint64, addresses []common.Address, topics [][]commo
retrievals: make(chan chan *Retrieval),
deliveries: make(chan *Retrieval),
}
m.setAddresses(addresses)
m.setTopics(topics)
return m
}
// Calculate the bloom bit indexes for the groups we're interested in
m.filters = nil
// setAddresses configures the matcher to only return logs that are generated
// from addresses that are included in the given list.
func (m *Matcher) setAddresses(addresses []common.Address) {
// Calculate the bloom bit indexes for the addresses we're interested in
m.addresses = make([]bloomIndexes, len(addresses))
for i, address := range addresses {
m.addresses[i] = calcBloomIndexes(address.Bytes())
for _, filter := range filters {
bloomBits := make([]bloomIndexes, len(filter))
for i, clause := range filter {
bloomBits[i] = calcBloomIndexes(clause)
}
m.filters = append(m.filters, bloomBits)
}
// For every bit, create a scheduler to load/download the bit vectors
for _, bloomIndexList := range m.addresses {
for _, bloomIndex := range bloomIndexList {
m.addScheduler(bloomIndex)
}
}
}
// setTopics configures the matcher to only return logs that have topics matching
// the given list.
func (m *Matcher) setTopics(topicsList [][]common.Hash) {
// Calculate the bloom bit indexes for the topics we're interested in
m.topics = nil
for _, topics := range topicsList {
bloomBits := make([]bloomIndexes, len(topics))
for i, topic := range topics {
bloomBits[i] = calcBloomIndexes(topic.Bytes())
}
m.topics = append(m.topics, bloomBits)
}
// For every bit, create a scheduler to load/download the bit vectors
for _, bloomIndexLists := range m.topics {
for _, bloomIndexLists := range m.filters {
for _, bloomIndexList := range bloomIndexLists {
for _, bloomIndex := range bloomIndexList {
m.addScheduler(bloomIndex)
}
}
}
return m
}
// addScheduler adds a bit stream retrieval scheduler for the given bit index if
@ -250,14 +226,10 @@ func (m *Matcher) run(begin, end uint64, buffer int, session *MatcherSession) ch
}
}()
// Assemble the daisy-chained filtering pipeline
blooms := m.topics
if len(m.addresses) > 0 {
blooms = append([][]bloomIndexes{m.addresses}, blooms...)
}
next := source
dist := make(chan *request, buffer)
for _, bloom := range blooms {
for _, bloom := range m.filters {
next = m.subMatch(next, dist, bloom, session)
}
// Start the request distribution

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@ -94,10 +94,8 @@ func testMatcherBothModes(t *testing.T, filter [][]bloomIndexes, blocks uint64,
// number of requests made for cross validation between different modes.
func testMatcher(t *testing.T, filter [][]bloomIndexes, blocks uint64, intermittent bool, retrievals uint32, maxReqCount int) uint32 {
// Create a new matcher an simulate our explicit random bitsets
matcher := NewMatcher(testSectionSize, nil, nil)
matcher.addresses = filter[0]
matcher.topics = filter[1:]
matcher := NewMatcher(testSectionSize, nil)
matcher.filters = filter
for _, rule := range filter {
for _, topic := range rule {

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@ -60,6 +60,23 @@ type Filter struct {
// New creates a new filter which uses a bloom filter on blocks to figure out whether
// a particular block is interesting or not.
func New(backend Backend, begin, end int64, addresses []common.Address, topics [][]common.Hash) *Filter {
// Flatten the address and topic filter clauses into a single filter system
var filters [][][]byte
if len(addresses) > 0 {
filter := make([][]byte, len(addresses))
for i, address := range addresses {
filter[i] = address.Bytes()
}
filters = append(filters, filter)
}
for _, topicList := range topics {
filter := make([][]byte, len(topicList))
for i, topic := range topicList {
filter[i] = topic.Bytes()
}
filters = append(filters, filter)
}
// Assemble and return the filter
size, _ := backend.BloomStatus()
return &Filter{
@ -69,7 +86,7 @@ func New(backend Backend, begin, end int64, addresses []common.Address, topics [
addresses: addresses,
topics: topics,
db: backend.ChainDb(),
matcher: bloombits.NewMatcher(size, addresses, topics),
matcher: bloombits.NewMatcher(size, filters),
}
}