Export metrics

How to export metrics from Baker

Baker can publish various kind of metrics that may be used to monitor a pipeline in execution. The metrics exported range from numbers giving an high-level overview of the ongoing pipeline (total processed records, current speed in records per second, etc.) or per-component metrics such as the number of files read or written, to performance statistics published by the Go runtime in order to monitor lower level information (objects, memory, garbage collection, etc.).

Configuring metrics in TOML

Baker configuration TOML files may have a [metrics] section dedicated to the configuration of a metrics client. [metrics.name] specifies the metrics client to use, from the list of all registered baker.MetricsClient. [metrics.config] specifies some configuration settings which are specific to the client you’re using.

For example, this is what the [metrics] section would look like with the Datadog metrics client:


    host="localhost:8125"                  # address of the dogstatsd client to which send metrics to
    prefix="myapp.baker."                  # prefix for all exported metric names
    send_logs=true                         # whether we should log messages (as Dogstatd events) or not 
    tags=["env:prod", "region:eu-west-1"]  # extra tags to associate to all exported metrics 

Disabling metrics

If you don’t want to publish any metrics, it’s enough to not provide the [metrics] TOML section in Baker configuration file.

Implementing a custom MetricsClient

The metrics example shows an example implementation of baker.MetricsClient and how to register it within Baker so that it can be selected in the [metrics.name] TOML section.

In order to be selected from TOML, you should first register a baker.MetricsDesc instance within baker.Components.

var fooBarDesc = baker.MetricsDesc{
	Name:   "MyMetrics",
	Config: &myyMetricsConfig{},
	New:    newMyMetrics,

where newMyMetrics is a constructor-like function receiving an interface{}, which is guaranteed to be of the type of the Config field value. This function should either return a ready to use baker.MetricsClient or an error saying why it can’t.

func newMyMetrics(icfg interface{}) (baker.MetricsClient, error)

How to publish statistics from a component

All components need to implement a Stats method where they can expose metrics, called by Baker once per second. Each of the different component types support a set of predefined metrics. In particular, the following counters are defined:

  • Input:
    • NumProcessedLines, the total number of processed records since the component creation.
  • Filter:
    • NumFilteredLines, the number of filtered out (i.e. discarded) records.
  • Output:
    • NumProcessedLines, the total number of processed records since the component creation.
    • NumErrorLines, the number of records that have produced an error.
  • Upload:
    • NumProcessedFiles, the total number of processed files since the component creation.
    • NumErrorFiles, the number of files that have produced an error.

Due to historical reasons, these fields have the word lines in them but they do mean the number of records.

The code example of how-to-create-filter shows how to correctly report metrics in a custom Filter.

Report custom metrics

In addition to the record counters described above, components can report custom metrics giving a more specific view about the component health or performance. Baker supports two ways of exposing custom metrics:

The two mechanisms follow the pull vs push approaches, respectively.

The MetricsBag should be returned by the Stats method along with the default statistics and is collected once per second by Baker.

The MetricsClient instance configured by your topology is passed to your component during instantiation, in the ComponentsParam structure. You can copy it so as to use it at any point in your component code since well-behaved MetricsClient implementations are safe for concurrent use by multiple goroutines.

Both MetricsBag and MetricsClient Metrics support the most common metric types, namely:

  • RawCounter: a cumulative counter that can only increase.
  • DeltaCounter: the total number of event occurrences in a unit time.
  • Gauge: a snapshot of an event in the last unit time.
  • Histogram: statistical distribution of a set of values in one unit of time.
  • Duration or Timing: like a histogram but with time durations.

If there is no particular requirement the MetricsBag approach is preferred. Indeed, the MetricsBag pull approach is simpler and it better integrates with the other default metric gathering. In addition to that, the MetricsClient requires special attention to avoid too many calls to the client during record processing. Indeed, the MetricsClient is a low-level and direct way to communicate with your metrics system, thus it permits extra flexibility, but it skips the additional aggregation/buffering layer of the MetricsBag.

Moreover, the MetricsClient supports also the runtime tagging of the published metrics. Tags (or labels) are additional dimensions on metrics and allow them to be filtered, aggregated, and compared in different visualizations. Therefore, if tags are only known at runtime and you need to add dynamic tags to your metrics, the MetricsClient should be used rather than MetricsBag (see the MetricsClient example).

In summary, the go-to way is to implement custom statistics with a MetricsBag, but there are some situations where a MetricsClient is preferred, for example:

  • Your component needs to publish the metrics using a set of tag that changes at runtime.
  • You can’t centralize metrics collection in the Stats method since the metrics to expose are produced by different worker goroutines running inside your component multiple goroutines.

MetricsBag Example

Let’s say our filter needs to perform HTTP requests in order to decide whether a record should be discarded, we might want to keep track of the requests' durations in an histogram. In this case, we would probably record a slice of time.Duration in our filter and call AddTimings on the returned MetricsBag.

An important point is that Baker may call Process and Stats from different goroutines so access to the shared data must be properly synchronized (atomic, lock, channel, etc.)

func (f *MyFilter) Process(r Record, next func(Record)) {
    // Perform http request

    f.mu.Lock() // keep track of its duration
    f.requestDurations = append(f.requestDurations, duration)

    if (/* filter logic*/) {
        // Discard record
        atomic.AddInt64(&f.filteredLines, 1)

func (f *MyFilter) Stats() baker.FilterStats {
    // Copy the slice of durations
    requestDurations := f.requestDurations[:]

    bag := make(baker.MetricsBag)
    bag.AddTimings("myfilter_http_request_duration", requestDurations)
    return baker.FilterStats{
        NumFilteredLines: atomic.LoadInt64(&f.filteredLines),
        Metrics: bag,

MetricsClient Example

Once a baker.MetricsClient instance has been successfully created, it’s made available to and can be used by a Baker pipeline to report metrics. During construction, components receive the MetricsClient instance. baker.MetricsClient provides a set of methods to report the most common type of metric types (gauges, counters and histograms)

Let’s now consider we want to publish additional tags alongside the duration slice of our previous MetricsBag example.

func NewMyFilter(cfg baker.InputParams) (baker.Input, error) {
    return MyFilter{metrics: cfg.Metrics}

func (f *MyFilter) Process(r Record, next func(Record)) {
 /*... */

func (f *MyFilter) Stats() baker.FilterStats {
    for _, duration := range f.requestDurations {
            []string{"tag1", "tag2"},

    return baker.FilterStats{
        NumFilteredLines: atomic.LoadInt64(&f.filteredLines),

Here, we chose to use the MetricsClient in the Stats method to limit the number of calls to the client. Indeed, since Stats gets called once per second, the MetricsClient is also called once per second. We could have directly called DurationWithTags from the Process method but we would have probably - depending on the frequency of incoming records - called it way more often, and this could have potentially introduce some performance degradation.

Last modified November 3, 2020