2018-02-28 09:05:58 +03:00
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package channeldb
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import (
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"bytes"
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"io"
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"sort"
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"time"
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2020-07-31 11:32:34 +03:00
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"github.com/btcsuite/btcwallet/walletdb"
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2021-04-26 20:08:11 +03:00
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"github.com/lightningnetwork/lnd/kvdb"
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2018-02-28 09:05:58 +03:00
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"github.com/lightningnetwork/lnd/lnwire"
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)
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var (
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// forwardingLogBucket is the bucket that we'll use to store the
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// forwarding log. The forwarding log contains a time series database
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// of the forwarding history of a lightning daemon. Each key within the
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// bucket is a timestamp (in nano seconds since the unix epoch), and
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// the value a slice of a forwarding event for that timestamp.
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forwardingLogBucket = []byte("circuit-fwd-log")
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)
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const (
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// forwardingEventSize is the size of a forwarding event. The breakdown
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// is as follows:
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//
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// * 8 byte incoming chan ID || 8 byte outgoing chan ID || 8 byte value in
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// || 8 byte value out
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//
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// From the value in and value out, callers can easily compute the
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// total fee extract from a forwarding event.
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forwardingEventSize = 32
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// MaxResponseEvents is the max number of forwarding events that will
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// be returned by a single query response. This size was selected to
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// safely remain under gRPC's 4MiB message size response limit. As each
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// full forwarding event (including the timestamp) is 40 bytes, we can
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// safely return 50k entries in a single response.
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MaxResponseEvents = 50000
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)
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// ForwardingLog returns an instance of the ForwardingLog object backed by the
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// target database instance.
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func (d *DB) ForwardingLog() *ForwardingLog {
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return &ForwardingLog{
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db: d,
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}
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}
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// ForwardingLog is a time series database that logs the fulfilment of payment
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// circuits by a lightning network daemon. The log contains a series of
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// forwarding events which map a timestamp to a forwarding event. A forwarding
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// event describes which channels were used to create+settle a circuit, and the
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// amount involved. Subtracting the outgoing amount from the incoming amount
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// reveals the fee charged for the forwarding service.
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type ForwardingLog struct {
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db *DB
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}
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// ForwardingEvent is an event in the forwarding log's time series. Each
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// forwarding event logs the creation and tear-down of a payment circuit. A
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// circuit is created once an incoming HTLC has been fully forwarded, and
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// destroyed once the payment has been settled.
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type ForwardingEvent struct {
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// Timestamp is the settlement time of this payment circuit.
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Timestamp time.Time
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// IncomingChanID is the incoming channel ID of the payment circuit.
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IncomingChanID lnwire.ShortChannelID
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// OutgoingChanID is the outgoing channel ID of the payment circuit.
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OutgoingChanID lnwire.ShortChannelID
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// AmtIn is the amount of the incoming HTLC. Subtracting this from the
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// outgoing amount gives the total fees of this payment circuit.
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AmtIn lnwire.MilliSatoshi
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// AmtOut is the amount of the outgoing HTLC. Subtracting the incoming
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// amount from this gives the total fees for this payment circuit.
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AmtOut lnwire.MilliSatoshi
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}
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// encodeForwardingEvent writes out the target forwarding event to the passed
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// io.Writer, using the expected DB format. Note that the timestamp isn't
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// serialized as this will be the key value within the bucket.
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func encodeForwardingEvent(w io.Writer, f *ForwardingEvent) error {
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2018-06-28 02:30:47 +03:00
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return WriteElements(
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2018-02-28 09:05:58 +03:00
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w, f.IncomingChanID, f.OutgoingChanID, f.AmtIn, f.AmtOut,
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)
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}
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// decodeForwardingEvent attempts to decode the raw bytes of a serialized
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// forwarding event into the target ForwardingEvent. Note that the timestamp
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// won't be decoded, as the caller is expected to set this due to the bucket
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// structure of the forwarding log.
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func decodeForwardingEvent(r io.Reader, f *ForwardingEvent) error {
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2018-06-28 02:30:47 +03:00
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return ReadElements(
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2018-02-28 09:05:58 +03:00
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r, &f.IncomingChanID, &f.OutgoingChanID, &f.AmtIn, &f.AmtOut,
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)
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}
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// AddForwardingEvents adds a series of forwarding events to the database.
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// Before inserting, the set of events will be sorted according to their
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// timestamp. This ensures that all writes to disk are sequential.
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func (f *ForwardingLog) AddForwardingEvents(events []ForwardingEvent) error {
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// Before we create the database transaction, we'll ensure that the set
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// of forwarding events are properly sorted according to their
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2020-07-31 11:32:34 +03:00
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// timestamp and that no duplicate timestamps exist to avoid collisions
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// in the key we are going to store the events under.
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makeUniqueTimestamps(events)
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2018-02-28 09:05:58 +03:00
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var timestamp [8]byte
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2019-12-13 05:22:19 +03:00
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return kvdb.Batch(f.db.Backend, func(tx kvdb.RwTx) error {
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2018-02-28 09:05:58 +03:00
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// First, we'll fetch the bucket that stores our time series
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// log.
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2019-12-13 05:22:19 +03:00
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logBucket, err := tx.CreateTopLevelBucket(
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2018-02-28 09:05:58 +03:00
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forwardingLogBucket,
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)
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if err != nil {
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return err
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}
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// With the bucket obtained, we can now begin to write out the
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// series of events.
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for _, event := range events {
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2020-07-31 11:32:34 +03:00
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err := storeEvent(logBucket, event, timestamp[:])
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2018-02-28 09:05:58 +03:00
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if err != nil {
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return err
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}
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}
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return nil
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})
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}
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2020-07-31 11:32:34 +03:00
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// storeEvent tries to store a forwarding event into the given bucket by trying
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// to avoid collisions. If a key for the event timestamp already exists in the
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// database, the timestamp is incremented in nanosecond intervals until a "free"
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// slot is found.
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func storeEvent(bucket walletdb.ReadWriteBucket, event ForwardingEvent,
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timestampScratchSpace []byte) error {
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// First, we'll serialize this timestamp into our
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// timestamp buffer.
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byteOrder.PutUint64(
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timestampScratchSpace, uint64(event.Timestamp.UnixNano()),
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)
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// Next we'll loop until we find a "free" slot in the bucket to store
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// the event under. This should almost never happen unless we're running
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// on a system that has a very bad system clock that doesn't properly
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// resolve to nanosecond scale. We try up to 100 times (which would come
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// to a maximum shift of 0.1 microsecond which is acceptable for most
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// use cases). If we don't find a free slot, we just give up and let
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// the collision happen. Something must be wrong with the data in that
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// case, even on a very fast machine forwarding payments _will_ take a
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// few microseconds at least so we should find a nanosecond slot
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// somewhere.
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const maxTries = 100
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tries := 0
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for tries < maxTries {
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val := bucket.Get(timestampScratchSpace)
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if val == nil {
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break
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}
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// Collision, try the next nanosecond timestamp.
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nextNano := event.Timestamp.UnixNano() + 1
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event.Timestamp = time.Unix(0, nextNano)
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byteOrder.PutUint64(timestampScratchSpace, uint64(nextNano))
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tries++
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}
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// With the key encoded, we'll then encode the event
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// into our buffer, then write it out to disk.
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var eventBytes [forwardingEventSize]byte
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eventBuf := bytes.NewBuffer(eventBytes[0:0:forwardingEventSize])
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err := encodeForwardingEvent(eventBuf, &event)
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if err != nil {
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return err
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}
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return bucket.Put(timestampScratchSpace, eventBuf.Bytes())
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}
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2018-02-28 09:05:58 +03:00
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// ForwardingEventQuery represents a query to the forwarding log payment
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// circuit time series database. The query allows a caller to retrieve all
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// records for a particular time slice, offset in that time slice, limiting the
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// total number of responses returned.
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type ForwardingEventQuery struct {
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// StartTime is the start time of the time slice.
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StartTime time.Time
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// EndTime is the end time of the time slice.
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EndTime time.Time
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// IndexOffset is the offset within the time slice to start at. This
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// can be used to start the response at a particular record.
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IndexOffset uint32
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// NumMaxEvents is the max number of events to return.
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NumMaxEvents uint32
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}
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// ForwardingLogTimeSlice is the response to a forwarding query. It includes
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// the original query, the set events that match the query, and an integer
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// which represents the offset index of the last item in the set of retuned
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// events. This integer allows callers to resume their query using this offset
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// in the event that the query's response exceeds the max number of returnable
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// events.
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type ForwardingLogTimeSlice struct {
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ForwardingEventQuery
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// ForwardingEvents is the set of events in our time series that answer
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// the query embedded above.
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ForwardingEvents []ForwardingEvent
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// LastIndexOffset is the index of the last element in the set of
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// returned ForwardingEvents above. Callers can use this to resume
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// their query in the event that the time slice has too many events to
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// fit into a single response.
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LastIndexOffset uint32
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}
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// Query allows a caller to query the forwarding event time series for a
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// particular time slice. The caller can control the precise time as well as
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// the number of events to be returned.
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//
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// TODO(roasbeef): rename?
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func (f *ForwardingLog) Query(q ForwardingEventQuery) (ForwardingLogTimeSlice, error) {
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2020-10-20 17:18:40 +03:00
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var resp ForwardingLogTimeSlice
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2018-02-28 09:05:58 +03:00
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// If the user provided an index offset, then we'll not know how many
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// records we need to skip. We'll also keep track of the record offset
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// as that's part of the final return value.
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recordsToSkip := q.IndexOffset
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recordOffset := q.IndexOffset
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2020-05-07 01:45:50 +03:00
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err := kvdb.View(f.db, func(tx kvdb.RTx) error {
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2018-02-28 09:05:58 +03:00
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// If the bucket wasn't found, then there aren't any events to
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// be returned.
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2019-12-13 05:22:19 +03:00
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logBucket := tx.ReadBucket(forwardingLogBucket)
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2018-02-28 09:05:58 +03:00
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if logBucket == nil {
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return ErrNoForwardingEvents
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}
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// We'll be using a cursor to seek into the database, so we'll
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// populate byte slices that represent the start of the key
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// space we're interested in, and the end.
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var startTime, endTime [8]byte
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byteOrder.PutUint64(startTime[:], uint64(q.StartTime.UnixNano()))
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byteOrder.PutUint64(endTime[:], uint64(q.EndTime.UnixNano()))
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// If we know that a set of log events exists, then we'll begin
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// our seek through the log in order to satisfy the query.
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// We'll continue until either we reach the end of the range,
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// or reach our max number of events.
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2019-12-13 05:22:19 +03:00
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logCursor := logBucket.ReadCursor()
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2018-02-28 09:05:58 +03:00
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timestamp, events := logCursor.Seek(startTime[:])
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for ; timestamp != nil && bytes.Compare(timestamp, endTime[:]) <= 0; timestamp, events = logCursor.Next() {
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// If our current return payload exceeds the max number
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// of events, then we'll exit now.
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if uint32(len(resp.ForwardingEvents)) >= q.NumMaxEvents {
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return nil
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}
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// If we're not yet past the user defined offset, then
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// we'll continue to seek forward.
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if recordsToSkip > 0 {
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recordsToSkip--
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continue
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}
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currentTime := time.Unix(
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0, int64(byteOrder.Uint64(timestamp)),
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)
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// At this point, we've skipped enough records to start
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// to collate our query. For each record, we'll
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// increment the final record offset so the querier can
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// utilize pagination to seek further.
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readBuf := bytes.NewReader(events)
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for readBuf.Len() != 0 {
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var event ForwardingEvent
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err := decodeForwardingEvent(readBuf, &event)
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if err != nil {
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return err
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}
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event.Timestamp = currentTime
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resp.ForwardingEvents = append(resp.ForwardingEvents, event)
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recordOffset++
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}
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}
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return nil
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2020-10-20 17:18:40 +03:00
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}, func() {
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resp = ForwardingLogTimeSlice{
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ForwardingEventQuery: q,
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}
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2018-02-28 09:05:58 +03:00
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})
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if err != nil && err != ErrNoForwardingEvents {
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return ForwardingLogTimeSlice{}, err
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}
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resp.LastIndexOffset = recordOffset
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return resp, nil
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}
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2020-07-31 11:32:34 +03:00
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// makeUniqueTimestamps takes a slice of forwarding events, sorts it by the
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// event timestamps and then makes sure there are no duplicates in the
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// timestamps. If duplicates are found, some of the timestamps are increased on
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// the nanosecond scale until only unique values remain. This is a fix to
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// address the problem that in some environments (looking at you, Windows) the
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// system clock has such a bad resolution that two serial invocations of
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// time.Now() might return the same timestamp, even if some time has elapsed
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// between the calls.
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func makeUniqueTimestamps(events []ForwardingEvent) {
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sort.Slice(events, func(i, j int) bool {
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return events[i].Timestamp.Before(events[j].Timestamp)
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})
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// Now that we know the events are sorted by timestamp, we can go
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// through the list and fix all duplicates until only unique values
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// remain.
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for outer := 0; outer < len(events)-1; outer++ {
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current := events[outer].Timestamp.UnixNano()
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next := events[outer+1].Timestamp.UnixNano()
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// We initially sorted the slice. So if the current is now
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// greater or equal to the next one, it's either because it's a
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// duplicate or because we increased the current in the last
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// iteration.
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if current >= next {
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next = current + 1
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events[outer+1].Timestamp = time.Unix(0, next)
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}
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}
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}
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