googlecloudexporter

README

Google Cloud Exporter

Status
Stability	beta: traces, metrics, logs
Distributions	contrib
Issues
Code Owners	@aabmass, @dashpole, @jsuereth, @punya, @psx95

This exporter can be used to send metrics to Google Cloud Monitoring (formerly Stackdriver), traces to Google Cloud Trace, and logs to Google Cloud Logging.

To learn more about instrumentation and observability, including opinionated recommendations for Google Cloud Observability, visit Instrumentation and observability.

Getting started

Prerequisite: Authenticating

In general, authenticating with the Collector exporter follows the same steps as any other app using the steps documented for Application Default Credentials. This section explains the specific use cases relevant to the exporter.

The exporter relies on GCP client libraries to send data to Google Cloud. Use of these libraries requires the caller (the Collector) to be authenticated with a GCP account and project. This should be done using a GCP service account with at minimum the following IAM roles (depending on the type of data you wish to send):

• Metrics: roles/monitoring.metricWriter
• Traces: roles/cloudtrace.agent
• Logs: roles/logging.logWriter

The Compute Engine default service account has all of these permissions by default, but if you are running on a different platform or with a different GCP service account you will need to ensure your service account has these permissions.

Options for different environments

Depending on the environment where your Collector is running, you can authenticate one of several ways:

GCE instances

On GCE it is recommended to use the GCP service account associated with your instance. If this is the Compute Engine default service account or another GCP service account with the sufficient IAM permissions, then there is nothing additional you need to do to authenticate the Collector process. Simply run the Collector on your instance, and it will inherit these permissions.

GKE / Workload Identity

On GKE clusters with Workload Identity enabled (including GKE Autopilot), follow the steps to configure a Workload Identity ServiceAccount in your cluster (if you do not already have one). Then, deploy the Collector as you would with any other workload, setting the serviceAccountName field in the Collector Pod’s .spec to the WI-enabled ServiceAccount.

In non-WI clusters, you can use the GCP service account associated with the node the same way as in the instructions for GCE instances above.

Non-GCP (AWS, Azure, on-prem, etc.) or alternative service accounts

In non-GCP environments, a service account key or credentials file is required. The exporter will automatically look for this file using the GOOGLE_APPLICATION_CREDENTIALS environment variable or, if that is unset, one of the other known locations. Note that when using this approach, you may need to explicitly set the project option in the exporter’s config.

When running the Collector in a Docker container, a credentials file can be passed to the container via volume mounts and environment variables at runtime like so:

docker run \
  --volume ~/service-account-key.json:/etc/otelcol-contrib/key.json \
  --volume $(pwd)/config.yaml:/etc/otelcol-contrib/config.yaml \
  --env GOOGLE_APPLICATION_CREDENTIALS=/etc/otelcol-contrib/key.json \
  --expose 4317 \
  --expose 55681 \
  --rm \
  otel/opentelemetry-collector-contrib

Using gcloud auth application-default login

Using gcloud auth application-default login to authenticate is not recommended for production use. Instead, it’s best to use a GCP service account through one of the methods listed above. The gcloud auth command can be useful for development and testing on a user account, and authenticating with it follows the same approach as the service account key method above.

These instructions are to get you up and running quickly with the GCP exporter in a local development environment. We'll also point out alternatives that may be more suitable for CI or production.

1. Obtain a Collector binary. Pull a binary or Docker image for the OpenTelemetry contrib collector which includes the GCP exporter plugin through one of the following:

   • Download a binary or package of the OpenTelemetry Collector Contrib that is appropriate for your platform, and includes the Google Cloud exporter.
   • Pull a Docker image with docker pull otel/opentelemetry-collector-contrib
   • Create your own main package in Go, that pulls in just the plugins you need.
   • Use the OpenTelemetry Collector Builder to generate the Go main package and go.mod.

2. Create a configuration file config.yaml. The example below shows a minimal recommended configuration that receives OTLP and sends data to GCP, in addition to verbose logging to help understand what is going on. It uses application default credentials (which we will set up in the next step).

   Note that this configuration includes the recommended memory_limiter and batch plugins, which avoid high latency for reporting telemetry, and ensure that the collector itself will stay stable (not run out of memory) by dropping telemetry if needed.

   receivers:
     otlp:
       protocols:
         grpc:
         http:
   exporters:
     googlecloud:
       log:
         default_log_name: opentelemetry.io/collector-exported-log
   processors:
     memory_limiter:
       check_interval: 1s
       limit_percentage: 65
       spike_limit_percentage: 20
     batch:
     resourcedetection:
       detectors: [gcp]
       timeout: 10s
   service:
     pipelines:
       traces:
         receivers: [otlp]
         processors: [memory_limiter, batch]
         exporters: [googlecloud]
       metrics:
         receivers: [otlp]
         processors: [memory_limiter, batch]
         exporters: [googlecloud]
       logs:
         receivers: [otlp]
         processors: [memory_limiter, batch]
         exporters: [googlecloud]
   

3. Set up credentials.

   1. Enable billing in your GCP project.

   2. Enable the Cloud Metrics and Cloud Trace APIs.

   3. Ensure that your user GCP user has (at minimum) roles/monitoring.metricWriter and roles/cloudtrace.agent. You can learn about metric-related and trace-related IAM in the GCP documentation.

   4. Obtain credentials using one of the methods in the Authenticating section above.

4. Run the collector. The following runs the collector in the foreground, so please execute it in a separate terminal.

   ./otelcol-contrib --config=config.yaml
   

   Alternatives

   If you obtained OS-specific packages or built your own binary in step 1, you'll need to follow the appropriate conventions for running the collector.

5. Gather telemetry. Run an application that can submit OTLP-formatted metrics and traces, and configure it to send them to 127.0.0.1:4317 (for gRPC) or 127.0.0.1:55681 (for HTTP).

   Alternatives

   • Set up the host metrics receiver, which will gather telemetry from the host without needing an external application to submit telemetry.

   • Set up an application-specific receiver, such as the Nginx receiver, and run the corresponding application.

   • Set up a receiver for some other protocol (such Prometheus, StatsD, Zipkin or Jaeger), and run an application that speaks one of those protocols.

6. View telemetry in GCP. Use the GCP metrics explorer and trace overview to view your newly submitted telemetry.

Configuration reference

The following configuration options are supported:

• project (default = Fetch from Credentials): GCP project identifier.
• destination_project_quota (optional): Counts quota for traces and metrics against the project to which the data is sent (as opposed to the project associated with the Collector's service account. For example, when setting project_id or using multi-project export. (default = false)
• user_agent (default = opentelemetry-collector-contrib {{version}}): Override the user agent string sent on requests to Cloud Monitoring (currently only applies to metrics). Specify {{version}} to include the application version number.
• impersonate (optional): Configuration for service account impersonation
  • target_principal: TargetPrincipal is the email address of the service account to impersonate.
  • subject: (optional) Subject is the sub field of a JWT. This field should only be set if you wish to impersonate as a user. This feature is useful when using domain wide delegation.
  • delegates: (default = []) Delegates are the service account email addresses in a delegation chain. Each service account must be granted roles/iam.serviceAccountTokenCreatoron the next service account in the chain.
• metric (optional): Configuration for sending metrics to Cloud Monitoring.
  • prefix (default = workload.googleapis.com): The prefix to add to metrics.
  • endpoint (default = monitoring.googleapis.com): Endpoint where metric data is going to be sent to.
  • compression (optional): Compression format for Metrics gRPC requests. Supported values: [gzip]. Defaults to no compression.
  • grpc_pool_size (optional): Sets the size of the connection pool in the GCP client. Defaults to a single connection.
  • use_insecure (default = false): If true, disables gRPC client transport security. Only has effect if Endpoint is not "".
  • known_domains (default = [googleapis.com, kubernetes.io, istio.io, knative.dev]): If a metric belongs to one of these domains it does not get a prefix.
  • skip_create_descriptor (default = false): If set to true, do not send metric descriptors to GCM.
  • instrumentation_library_labels (default = true): If true, set the instrumentation_source and instrumentation_version labels.
  • create_service_timeseries (default = false): If true, this will send all timeseries using CreateServiceTimeSeries. Implicitly, this sets skip_create_descriptor to true.
  • create_metric_descriptor_buffer_size (default = 10): Buffer size for the channel which asynchronously calls CreateMetricDescriptor.
  • service_resource_labels (default = true): If true, the exporter will copy OTel's service.name, service.namespace, and service.instance.id resource attributes into the GCM timeseries metric labels.
  • resource_filters (default = []): If provided, resource attributes matching any filter will be included in metric labels. Can be defined by prefix, regex, or prefix AND regex.
    • prefix: Match resource keys by prefix.
    • regex: Match resource keys by regex.
  • cumulative_normalization (default = true): If true, normalizes cumulative metrics without start times or with explicit reset points by subtracting subsequent points from the initial point. It is enabled by default. Since it caches starting points, it may result inincreased memory usage.
  • sum_of_squared_deviation (default = false): If true, enables calculation of an estimated sum of squared deviation. It is an estimate, and is not exact.
  • experimental_wal (default = []): If provided, enables use of a write ahead log for time series requests.
    • directory (default = ./): Path to local directory for WAL file.
    • max_backoff (default = 1h): Max duration to retry requests on network errors (UNAVAILABLE or DEADLINE_EXCEEDED).
• trace (optional): Configuration for sending traces to Cloud Trace.
  • endpoint (default = cloudtrace.googleapis.com): Endpoint where trace data is going to be sent to.
  • grpc_pool_size (optional): Sets the size of the connection pool in the GCP client. Defaults to a single connection.
  • use_insecure (default = false): If true, disables gRPC client transport security. Only has effect if Endpoint is not "".
  • attribute_mappings (optional): AttributeMappings determines how to map from OpenTelemetry attribute keys to Google Cloud Trace keys. By default, it changes http and service keys so that they appear more prominently in the UI.
    • key: Key is the OpenTelemetry attribute key
    • replacement: Replacement is the attribute sent to Google Cloud Trace
• log (optional): Configuration for sending metrics to Cloud Logging.
  • endpoint (default = logging.googleapis.com): Endpoint where log data is going to be sent to.
  • compression (optional): Compression format for Metrics gRPC requests. Supported values: [gzip]. Defaults to no compression.
  • grpc_pool_size (optional): Sets the size of the connection pool in the GCP client. Defaults to a single connection.
  • use_insecure (default = false): If true, disables gRPC client transport security. Only has effect if Endpoint is not "".
  • default_log_name (optional): Defines a default name for log entries. If left unset, and a log entry does not have the gcp.log_name attribute set, the exporter will return an error processing that entry.
  • resource_filters (default = []): If provided, resource attributes matching any filter will be included in log labels. Can be defined by prefix, regex, or prefix AND regex.
    • prefix: Match resource keys by prefix.
    • regex: Match resource keys by regex.
  • compression (optional): Enable gzip compression for gRPC requests (valid vlaues: gzip).
• sending_queue (optional): Configuration for how to buffer traces before sending.
  • enabled (default = true)
  • num_consumers (default = 10): Number of consumers that dequeue batches; ignored if enabled is false
  • queue_size (default = 1000): Maximum number of batches kept in memory before data; ignored if enabled is false; User should calculate this as num_seconds * requests_per_second where:
    • num_seconds is the number of seconds to buffer in case of a backend outage
    • requests_per_second is the average number of requests per seconds.

Note: The sending_queue is provided (and documented) by the Exporter Helper

Beyond standard YAML configuration as outlined in the sections that follow, exporters that leverage the net/http package (all do today) also respect the following proxy environment variables:

• HTTP_PROXY
• HTTPS_PROXY
• NO_PROXY

If set at Collector start time then exporters, regardless of protocol, will or will not proxy traffic as defined by these environment variables.

Monitored Resources

For metrics and logs, this exporter maps the OpenTelemetry Resource to a Google Cloud Logging or Monitoring Monitored Resource.

The complete mapping logic can be found here. That may be the most helpful reference if you want to map to a specific monitored resource.

On GCP

If running on GCP, using the GCP resource detector, as shown above, will populate the resource attributes required to map to the appropriate monitored resource.

Off GCP

If you are not running on GCP, you still need to choose a GCP zone or region to send telemetry to by setting cloud.availability_zone or cloud.region. In addition, you should use the detector associated with other cloud providers, if applicable.

If running on Kubernetes, it is recommended to additionally set k8s.pod.name, k8s.namespace.name, and k8s.container.name using the k8sattributes processor.

If you are getting "duplicate timeseries encountered" errors, it is likely because you are missing a required resource attribute, causing a metric from two different instances of an application to end up with the same monitored resource.

Preventing metric label collisions

The metrics exporter can add metric labels to timeseries, such as when setting metric.service_resource_labels, metric.instrumentation_library_labels (both on by default), or when using metric.resource_filters to convert resource attributes to metric labels.

However, if your metrics already contain any of these labels they will fail to export to Google Cloud with a Duplicate label key encountered error. Such labels from the default features above include:

• service_name
• service_namespace
• service_instance_id
• instrumentation_source
• instrumentation_version

(Note that these are the sanitized versions of OpenTelemetry attributes, with . replaced by _ to be compatible with Cloud Monitoring. For example, service_name comes from the service.name resource attribute.)

To prevent this, it's recommended to use the transform processor in your collector config to rename existing metric labels to preserve them, for example:

processors:
  transform:
    metric_statements:
    - context: datapoint
      statements:
      - set(attributes["exported_service_name"], attributes["service_name"])
      - delete_key(attributes, "service_name")
      - set(attributes["exported_service_namespace"], attributes["service_namespace"])
      - delete_key(attributes, "service_namespace")
      - set(attributes["exported_service_instance_id"], attributes["service_instance_id"])
      - delete_key(attributes, "service_instance_id")
      - set(attributes["exported_instrumentation_source"], attributes["instrumentation_source"])
      - delete_key(attributes, "instrumentation_source")
      - set(attributes["exported_instrumentation_version"], attributes["instrumentation_version"])
      - delete_key(attributes, "instrumentation_version")

Note It is not recommended to use these transformations with the googlecloud exporter in a logging or trace pipeline.

The same method can be used for any resource attributes being filtered to metric labels, or metric labels which might collide with the GCP monitored resource used with resource detection.

Keep in mind that your conflicting attributes may contain dots instead of underscores (eg, service.name), but these will still collide once all attributes are normalized to metric labels. In this case you will need to update the collector config above appropriately.

Logging Example

The logging exporter processes OpenTelemetry log entries and exports them to GCP Cloud Logging. Logs can be collected using one of the opentelemetry-collector-contrib log receivers, such as the filelogreceiver.

Log entries must contain any Cloud Logging-specific fields as a matching OpenTelemetry attribute (as shown in examples from the logs data model). These attributes can be parsed using the various log operators available upstream.

For example, the following config parses the HTTPRequest field from Apache log entries saved in /var/log/apache.log. It also parses out the timestamp and inserts a non-default log_name attribute and GCP MonitoredResource attribute.

receivers:
  filelog:
    include: [ /var/log/apache.log ]
    start_at: beginning
    operators:
      - id: http_request_parser
        type: regex_parser
        regex: '(?m)^(?P<remoteIp>[^ ]*) (?P<host>[^ ]*) (?P<user>[^ ]*) \[(?P<time>[^\]]*)\] "(?P<requestMethod>\S+)(?: +(?P<requestUrl>[^\"]*?)(?: +(?P<protocol>\S+))?)?" (?P<status>[^ ]*) (?P<responseSize>[^ ]*)(?: "(?P<referer>[^\"]*)" "(?P<userAgent>[^\"]*)")?$'
        parse_to: attributes["gcp.http_request"]
        timestamp:
          parse_from: attributes["gcp.http_request"].time
          layout_type: strptime
          layout: '%d/%b/%Y:%H:%M:%S %z'
    converter:
      max_flush_count: 100
      flush_interval: 100ms

exporters:
  googlecloud:
    project: my-gcp-project
    log:
      default_log_name: opentelemetry.io/collector-exported-log

processors:
  memory_limiter:
    check_interval: 1s
    limit_percentage: 65
    spike_limit_percentage: 20
  resourcedetection:
    detectors: [gcp]
    timeout: 10s
  attributes:
    # Override the default log name.  `gcp.log_name` takes precedence
    # over the `default_log_name` specified in the exporter.
    actions:
      - key: gcp.log_name
        action: insert
        value: apache-access-log

service:
    logs:
      receivers: [filelog]
      processors: [memory_limiter, resourcedetection, attributes]
      exporters: [googlecloud]

This would parse logs of the following example structure:

127.0.0.1 - - [26/Apr/2022:22:53:36 +0800] "GET / HTTP/1.1" 200 1247

To the following GCP entry structure:

        {
          "logName": "projects/my-gcp-project/logs/apache-access-log",
          "resource": {
            "type": "gce_instance",
            "labels": {
              "instance_id": "",
              "zone": ""
            }
          },
          "textPayload": "127.0.0.1 - - [26/Apr/2022:22:53:36 +0800] \"GET / HTTP/1.1\" 200 1247",
          "timestamp": "2022-05-02T12:16:14.574548493Z",
          "httpRequest": {
            "requestMethod": "GET",
            "requestUrl": "/",
            "status": 200,
            "responseSize": "1247",
            "remoteIp": "127.0.0.1",
            "protocol": "HTTP/1.1"
          }
        }

The logging exporter also supports the full range of GCP log severity levels, which differ from the available OpenTelemetry log severity levels. To accommodate this, the following mapping is used to equate an incoming OpenTelemetry SeverityNumber to a matching GCP log severity:

OTel SeverityNumber/Name	GCP severity level
Undefined	Default
1-4 / Trace	Debug
5-8 / Debug	Debug
9-10 / Info	Info
11-12 / Info	Notice
13-16 / Warn	Warning
17-20 / Error	Error
21-22 / Fatal	Critical
23 / Fatal	Alert
24 / Fatal	Emergency

The upstream severity parser (along with the regex parser) allows for additional flexibility in parsing log severity from incoming entries.

Multi-Project exporting

By default, the exporter sends telemetry to the project specified by project in the configuration. This can be overridden on a per-metrics basis using the gcp.project.id resource attribute. For example, if a metric has a label project, you could use the groupbyattrs processor to promote it to a resource label, and the resource processor to rename the attribute from project to gcp.project.id.

Multi-Project quota usage

The gcp.project.id label can be combined with the destination_project_quota option to attribute quota usage to the project parsed by the label. This feature is currently only available for traces and metrics. The Collector's default service account will need roles/serviceusage.serviceUsageConsumer IAM permissions in the destination quota project.

Note that this option will not work if a quota project is already defined in your Collector's GCP credentials. In this case, the telemetry will fail to export with a "project not found" error. This can be done by manually editing your ADC file (if it exists) to remove the quota_project_id entry line.

Features and Feature-Gates

See the Collector feature gates for an overview of feature gates in the collector.

Documentation

Index

• func NewFactory() exporter.Factory
• type Config
  • func (cfg *Config) Validate() error

Functions

func NewFactory

func NewFactory() exporter.Factory

NewFactory creates a factory for the googlecloud exporter

Types

type Config

type Config struct {
	collector.Config `mapstructure:",squash"`

	// Timeout for all API calls. If not set, defaults to 12 seconds.
	TimeoutSettings exporterhelper.TimeoutConfig `mapstructure:",squash"` // squash ensures fields are correctly decoded in embedded struct.
	QueueSettings   exporterhelper.QueueConfig   `mapstructure:"sending_queue"`
}

Config defines configuration for Google Cloud exporter.

func (*Config) Validate

func (cfg *Config) Validate() error