package channeldb import ( "bytes" "io" "sort" "time" "github.com/btcsuite/btcwallet/walletdb" "github.com/lightningnetwork/lnd/channeldb/kvdb" "github.com/lightningnetwork/lnd/lnwire" ) var ( // forwardingLogBucket is the bucket that we'll use to store the // forwarding log. The forwarding log contains a time series database // of the forwarding history of a lightning daemon. Each key within the // bucket is a timestamp (in nano seconds since the unix epoch), and // the value a slice of a forwarding event for that timestamp. forwardingLogBucket = []byte("circuit-fwd-log") ) const ( // forwardingEventSize is the size of a forwarding event. The breakdown // is as follows: // // * 8 byte incoming chan ID || 8 byte outgoing chan ID || 8 byte value in // || 8 byte value out // // From the value in and value out, callers can easily compute the // total fee extract from a forwarding event. forwardingEventSize = 32 // MaxResponseEvents is the max number of forwarding events that will // be returned by a single query response. This size was selected to // safely remain under gRPC's 4MiB message size response limit. As each // full forwarding event (including the timestamp) is 40 bytes, we can // safely return 50k entries in a single response. MaxResponseEvents = 50000 ) // ForwardingLog returns an instance of the ForwardingLog object backed by the // target database instance. func (d *DB) ForwardingLog() *ForwardingLog { return &ForwardingLog{ db: d, } } // ForwardingLog is a time series database that logs the fulfilment of payment // circuits by a lightning network daemon. The log contains a series of // forwarding events which map a timestamp to a forwarding event. A forwarding // event describes which channels were used to create+settle a circuit, and the // amount involved. Subtracting the outgoing amount from the incoming amount // reveals the fee charged for the forwarding service. type ForwardingLog struct { db *DB } // ForwardingEvent is an event in the forwarding log's time series. Each // forwarding event logs the creation and tear-down of a payment circuit. A // circuit is created once an incoming HTLC has been fully forwarded, and // destroyed once the payment has been settled. type ForwardingEvent struct { // Timestamp is the settlement time of this payment circuit. Timestamp time.Time // IncomingChanID is the incoming channel ID of the payment circuit. IncomingChanID lnwire.ShortChannelID // OutgoingChanID is the outgoing channel ID of the payment circuit. OutgoingChanID lnwire.ShortChannelID // AmtIn is the amount of the incoming HTLC. Subtracting this from the // outgoing amount gives the total fees of this payment circuit. AmtIn lnwire.MilliSatoshi // AmtOut is the amount of the outgoing HTLC. Subtracting the incoming // amount from this gives the total fees for this payment circuit. AmtOut lnwire.MilliSatoshi } // encodeForwardingEvent writes out the target forwarding event to the passed // io.Writer, using the expected DB format. Note that the timestamp isn't // serialized as this will be the key value within the bucket. func encodeForwardingEvent(w io.Writer, f *ForwardingEvent) error { return WriteElements( w, f.IncomingChanID, f.OutgoingChanID, f.AmtIn, f.AmtOut, ) } // decodeForwardingEvent attempts to decode the raw bytes of a serialized // forwarding event into the target ForwardingEvent. Note that the timestamp // won't be decoded, as the caller is expected to set this due to the bucket // structure of the forwarding log. func decodeForwardingEvent(r io.Reader, f *ForwardingEvent) error { return ReadElements( r, &f.IncomingChanID, &f.OutgoingChanID, &f.AmtIn, &f.AmtOut, ) } // AddForwardingEvents adds a series of forwarding events to the database. // Before inserting, the set of events will be sorted according to their // timestamp. This ensures that all writes to disk are sequential. func (f *ForwardingLog) AddForwardingEvents(events []ForwardingEvent) error { // Before we create the database transaction, we'll ensure that the set // of forwarding events are properly sorted according to their // timestamp and that no duplicate timestamps exist to avoid collisions // in the key we are going to store the events under. makeUniqueTimestamps(events) var timestamp [8]byte return kvdb.Batch(f.db.Backend, func(tx kvdb.RwTx) error { // First, we'll fetch the bucket that stores our time series // log. logBucket, err := tx.CreateTopLevelBucket( forwardingLogBucket, ) if err != nil { return err } // With the bucket obtained, we can now begin to write out the // series of events. for _, event := range events { err := storeEvent(logBucket, event, timestamp[:]) if err != nil { return err } } return nil }) } // storeEvent tries to store a forwarding event into the given bucket by trying // to avoid collisions. If a key for the event timestamp already exists in the // database, the timestamp is incremented in nanosecond intervals until a "free" // slot is found. func storeEvent(bucket walletdb.ReadWriteBucket, event ForwardingEvent, timestampScratchSpace []byte) error { // First, we'll serialize this timestamp into our // timestamp buffer. byteOrder.PutUint64( timestampScratchSpace, uint64(event.Timestamp.UnixNano()), ) // Next we'll loop until we find a "free" slot in the bucket to store // the event under. This should almost never happen unless we're running // on a system that has a very bad system clock that doesn't properly // resolve to nanosecond scale. We try up to 100 times (which would come // to a maximum shift of 0.1 microsecond which is acceptable for most // use cases). If we don't find a free slot, we just give up and let // the collision happen. Something must be wrong with the data in that // case, even on a very fast machine forwarding payments _will_ take a // few microseconds at least so we should find a nanosecond slot // somewhere. const maxTries = 100 tries := 0 for tries < maxTries { val := bucket.Get(timestampScratchSpace) if val == nil { break } // Collision, try the next nanosecond timestamp. nextNano := event.Timestamp.UnixNano() + 1 event.Timestamp = time.Unix(0, nextNano) byteOrder.PutUint64(timestampScratchSpace, uint64(nextNano)) tries++ } // With the key encoded, we'll then encode the event // into our buffer, then write it out to disk. var eventBytes [forwardingEventSize]byte eventBuf := bytes.NewBuffer(eventBytes[0:0:forwardingEventSize]) err := encodeForwardingEvent(eventBuf, &event) if err != nil { return err } return bucket.Put(timestampScratchSpace, eventBuf.Bytes()) } // ForwardingEventQuery represents a query to the forwarding log payment // circuit time series database. The query allows a caller to retrieve all // records for a particular time slice, offset in that time slice, limiting the // total number of responses returned. type ForwardingEventQuery struct { // StartTime is the start time of the time slice. StartTime time.Time // EndTime is the end time of the time slice. EndTime time.Time // IndexOffset is the offset within the time slice to start at. This // can be used to start the response at a particular record. IndexOffset uint32 // NumMaxEvents is the max number of events to return. NumMaxEvents uint32 } // ForwardingLogTimeSlice is the response to a forwarding query. It includes // the original query, the set events that match the query, and an integer // which represents the offset index of the last item in the set of retuned // events. This integer allows callers to resume their query using this offset // in the event that the query's response exceeds the max number of returnable // events. type ForwardingLogTimeSlice struct { ForwardingEventQuery // ForwardingEvents is the set of events in our time series that answer // the query embedded above. ForwardingEvents []ForwardingEvent // LastIndexOffset is the index of the last element in the set of // returned ForwardingEvents above. Callers can use this to resume // their query in the event that the time slice has too many events to // fit into a single response. LastIndexOffset uint32 } // Query allows a caller to query the forwarding event time series for a // particular time slice. The caller can control the precise time as well as // the number of events to be returned. // // TODO(roasbeef): rename? func (f *ForwardingLog) Query(q ForwardingEventQuery) (ForwardingLogTimeSlice, error) { var resp ForwardingLogTimeSlice // If the user provided an index offset, then we'll not know how many // records we need to skip. We'll also keep track of the record offset // as that's part of the final return value. recordsToSkip := q.IndexOffset recordOffset := q.IndexOffset err := kvdb.View(f.db, func(tx kvdb.RTx) error { // If the bucket wasn't found, then there aren't any events to // be returned. logBucket := tx.ReadBucket(forwardingLogBucket) if logBucket == nil { return ErrNoForwardingEvents } // We'll be using a cursor to seek into the database, so we'll // populate byte slices that represent the start of the key // space we're interested in, and the end. var startTime, endTime [8]byte byteOrder.PutUint64(startTime[:], uint64(q.StartTime.UnixNano())) byteOrder.PutUint64(endTime[:], uint64(q.EndTime.UnixNano())) // If we know that a set of log events exists, then we'll begin // our seek through the log in order to satisfy the query. // We'll continue until either we reach the end of the range, // or reach our max number of events. logCursor := logBucket.ReadCursor() timestamp, events := logCursor.Seek(startTime[:]) for ; timestamp != nil && bytes.Compare(timestamp, endTime[:]) <= 0; timestamp, events = logCursor.Next() { // If our current return payload exceeds the max number // of events, then we'll exit now. if uint32(len(resp.ForwardingEvents)) >= q.NumMaxEvents { return nil } // If we're not yet past the user defined offset, then // we'll continue to seek forward. if recordsToSkip > 0 { recordsToSkip-- continue } currentTime := time.Unix( 0, int64(byteOrder.Uint64(timestamp)), ) // At this point, we've skipped enough records to start // to collate our query. For each record, we'll // increment the final record offset so the querier can // utilize pagination to seek further. readBuf := bytes.NewReader(events) for readBuf.Len() != 0 { var event ForwardingEvent err := decodeForwardingEvent(readBuf, &event) if err != nil { return err } event.Timestamp = currentTime resp.ForwardingEvents = append(resp.ForwardingEvents, event) recordOffset++ } } return nil }, func() { resp = ForwardingLogTimeSlice{ ForwardingEventQuery: q, } }) if err != nil && err != ErrNoForwardingEvents { return ForwardingLogTimeSlice{}, err } resp.LastIndexOffset = recordOffset return resp, nil } // makeUniqueTimestamps takes a slice of forwarding events, sorts it by the // event timestamps and then makes sure there are no duplicates in the // timestamps. If duplicates are found, some of the timestamps are increased on // the nanosecond scale until only unique values remain. This is a fix to // address the problem that in some environments (looking at you, Windows) the // system clock has such a bad resolution that two serial invocations of // time.Now() might return the same timestamp, even if some time has elapsed // between the calls. func makeUniqueTimestamps(events []ForwardingEvent) { sort.Slice(events, func(i, j int) bool { return events[i].Timestamp.Before(events[j].Timestamp) }) // Now that we know the events are sorted by timestamp, we can go // through the list and fix all duplicates until only unique values // remain. for outer := 0; outer < len(events)-1; outer++ { current := events[outer].Timestamp.UnixNano() next := events[outer+1].Timestamp.UnixNano() // We initially sorted the slice. So if the current is now // greater or equal to the next one, it's either because it's a // duplicate or because we increased the current in the last // iteration. if current >= next { next = current + 1 events[outer+1].Timestamp = time.Unix(0, next) } } }