Profiling: Add Phlare and Parca datasources (#57809)
* Add phlare datasource * Rename * Add parca * Add self field to parca * Make sure phlare works with add to dashboard flow * Add profiling category and hide behind feature flag * Update description and logos * Update phlare icon * Cleanup logging * Clean up logging * Fix for shift+enter * onRunQuery to set label * Update type naming * Fix lint * Fix test and quality issues Co-authored-by: Joey Tawadrous <joey.tawadrous@grafana.com>
This commit is contained in:
Andrej Ocenas
co-authored by
Joey Tawadrous
Joey Tawadrous
parent
1d53a6f57e
commit
0845ac2f53
@@ -0,0 +1,246 @@
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package parca
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import (
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"context"
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"encoding/json"
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"fmt"
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"strings"
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"time"
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"github.com/bufbuild/connect-go"
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"github.com/grafana/grafana-plugin-sdk-go/backend"
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"github.com/grafana/grafana-plugin-sdk-go/data"
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v1alpha1 "github.com/parca-dev/parca/gen/proto/go/parca/query/v1alpha1"
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"google.golang.org/protobuf/types/known/timestamppb"
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)
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type queryModel struct {
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ProfileTypeID string `json:"profileTypeId"`
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LabelSelector string `json:"labelSelector"`
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}
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// These constants need to match the ones in the frontend.
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const queryTypeProfile = "profile"
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const queryTypeMetrics = "metrics"
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const queryTypeBoth = "both"
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// query processes single Fire query transforming the response to data.Frame packaged in DataResponse
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func (d *ParcaDatasource) query(ctx context.Context, pCtx backend.PluginContext, query backend.DataQuery) backend.DataResponse {
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var qm queryModel
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response := backend.DataResponse{}
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err := json.Unmarshal(query.JSON, &qm)
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if err != nil {
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response.Error = err
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return response
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}
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if query.QueryType == queryTypeMetrics || query.QueryType == queryTypeBoth {
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seriesResp, err := d.client.QueryRange(ctx, makeMetricRequest(qm, query))
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if err != nil {
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response.Error = err
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return response
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}
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response.Frames = append(response.Frames, seriesToDataFrame(seriesResp, qm.ProfileTypeID)...)
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}
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if query.QueryType == queryTypeProfile || query.QueryType == queryTypeBoth {
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logger.Debug("Querying SelectMergeStacktraces()", "queryModel", qm)
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resp, err := d.client.Query(ctx, makeProfileRequest(qm, query))
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if err != nil {
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response.Error = err
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return response
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}
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frame := responseToDataFrames(resp)
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response.Frames = append(response.Frames, frame)
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}
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return response
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}
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func makeProfileRequest(qm queryModel, query backend.DataQuery) *connect.Request[v1alpha1.QueryRequest] {
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return &connect.Request[v1alpha1.QueryRequest]{
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Msg: &v1alpha1.QueryRequest{
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Mode: v1alpha1.QueryRequest_MODE_MERGE,
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Options: &v1alpha1.QueryRequest_Merge{
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Merge: &v1alpha1.MergeProfile{
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Query: fmt.Sprintf("%s%s", qm.ProfileTypeID, qm.LabelSelector),
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Start: ×tamppb.Timestamp{
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Seconds: query.TimeRange.From.Unix(),
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},
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End: ×tamppb.Timestamp{
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Seconds: query.TimeRange.To.Unix(),
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},
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},
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},
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ReportType: v1alpha1.QueryRequest_REPORT_TYPE_FLAMEGRAPH_UNSPECIFIED,
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},
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}
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}
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func makeMetricRequest(qm queryModel, query backend.DataQuery) *connect.Request[v1alpha1.QueryRangeRequest] {
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return &connect.Request[v1alpha1.QueryRangeRequest]{
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Msg: &v1alpha1.QueryRangeRequest{
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Query: fmt.Sprintf("%s%s", qm.ProfileTypeID, qm.LabelSelector),
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Start: ×tamppb.Timestamp{
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Seconds: query.TimeRange.From.Unix(),
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},
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End: ×tamppb.Timestamp{
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Seconds: query.TimeRange.To.Unix(),
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},
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Limit: uint32(query.MaxDataPoints),
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},
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}
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}
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type CustomMeta struct {
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ProfileTypeID string
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}
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// responseToDataFrames turns fire response to data.Frame. We encode the data into a nested set format where we have
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// [level, value, label] columns and by ordering the items in a depth first traversal order we can recreate the whole
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// tree back.
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func responseToDataFrames(resp *connect.Response[v1alpha1.QueryResponse]) *data.Frame {
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if flameResponse, ok := resp.Msg.Report.(*v1alpha1.QueryResponse_Flamegraph); ok {
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frame := treeToNestedSetDataFrame(flameResponse.Flamegraph)
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frame.Meta = &data.FrameMeta{PreferredVisualization: "flamegraph"}
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return frame
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} else {
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panic("unknown report type returned from query")
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}
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}
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// treeToNestedSetDataFrame walks the tree depth first and adds items into the dataframe. This is a nested set format
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// where by ordering the items in depth first order and knowing the level/depth of each item we can recreate the
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// parent - child relationship without explicitly needing parent/child column and we can later just iterate over the
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// dataFrame to again basically walking depth first over the tree/profile.
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func treeToNestedSetDataFrame(tree *v1alpha1.Flamegraph) *data.Frame {
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frame := data.NewFrame("response")
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levelField := data.NewField("level", nil, []int64{})
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valueField := data.NewField("value", nil, []int64{})
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valueField.Config = &data.FieldConfig{Unit: normalizeUnit(tree.Unit)}
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selfField := data.NewField("self", nil, []int64{})
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selfField.Config = &data.FieldConfig{Unit: normalizeUnit(tree.Unit)}
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labelField := data.NewField("label", nil, []string{})
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frame.Fields = data.Fields{levelField, valueField, selfField, labelField}
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walkTree(tree.Root, func(level int64, value int64, name string, self int64) {
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levelField.Append(level)
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valueField.Append(value)
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labelField.Append(name)
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selfField.Append(self)
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})
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return frame
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}
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type Node struct {
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Node *v1alpha1.FlamegraphNode
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Level int64
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}
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func walkTree(tree *v1alpha1.FlamegraphRootNode, fn func(level int64, value int64, name string, self int64)) {
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var stack []*Node
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var childrenValue int64 = 0
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for _, child := range tree.Children {
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childrenValue += child.Cumulative
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stack = append(stack, &Node{Node: child, Level: 1})
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}
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fn(0, tree.Cumulative, "total", tree.Cumulative-childrenValue)
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for {
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if len(stack) == 0 {
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break
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}
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// shift stack
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node := stack[0]
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stack = stack[1:]
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childrenValue = 0
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if node.Node.Children != nil {
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var children []*Node
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for _, child := range node.Node.Children {
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childrenValue += child.Cumulative
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children = append(children, &Node{Node: child, Level: node.Level + 1})
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}
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// Put the children first so we do depth first traversal
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stack = append(children, stack...)
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}
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fn(node.Level, node.Node.Cumulative, nodeName(node.Node), node.Node.Cumulative-childrenValue)
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}
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}
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func nodeName(node *v1alpha1.FlamegraphNode) string {
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if node.Meta == nil {
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return "<unknown>"
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}
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mapping := ""
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if node.Meta.Mapping != nil && node.Meta.Mapping.File != "" {
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mapping = "[" + getLastItem(node.Meta.Mapping.File) + "] "
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}
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if node.Meta.Function != nil && node.Meta.Function.Name != "" {
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return mapping + node.Meta.Function.Name
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}
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address := ""
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if node.Meta.Location != nil {
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address = fmt.Sprintf("0x%x", node.Meta.Location.Address)
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}
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if mapping == "" && address == "" {
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return "<unknown>"
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} else {
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return mapping + address
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}
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}
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func getLastItem(path string) string {
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parts := strings.Split(path, "/")
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return parts[len(parts)-1]
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}
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func normalizeUnit(unit string) string {
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if unit == "nanoseconds" {
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return "ns"
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}
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if unit == "count" {
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return "short"
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}
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return unit
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}
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func seriesToDataFrame(seriesResp *connect.Response[v1alpha1.QueryRangeResponse], profileTypeID string) []*data.Frame {
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var frames []*data.Frame
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for _, series := range seriesResp.Msg.Series {
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frame := data.NewFrame("series")
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frame.Meta = &data.FrameMeta{PreferredVisualization: "graph"}
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frames = append(frames, frame)
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fields := data.Fields{}
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timeField := data.NewField("time", nil, []time.Time{})
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fields = append(fields, timeField)
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labels := data.Labels{}
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for _, label := range series.Labelset.Labels {
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labels[label.Name] = label.Value
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}
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valueField := data.NewField(strings.Split(profileTypeID, ":")[1], labels, []int64{})
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for _, sample := range series.Samples {
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timeField.Append(sample.Timestamp.AsTime())
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valueField.Append(sample.Value)
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}
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fields = append(fields, valueField)
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frame.Fields = fields
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}
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return frames
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}
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