Collector for Linux のデフォルト設定 🔗
Splunk Distribution of OpenTelemetry Collectorには、以下のコンポーネントとサービスがあります：
レシーバー: Collector にデータを取り込む方法を決定します。
プロセッサー ：エクスポートする前にデータに対してどのような処理を行うかを設定します。例えば、フィルターリングなど。
エクスポーター: データの送信先を設定します。1つまたは複数のバックエンドまたは送信先を指定できます。
拡張 ： Collector の機能を拡張します。
サービス。これは2つの要素で構成されています：
詳細は Collector コンポーネント を参照してください。
Collector の設定は YAML ファイル に保存され、さまざまなコンポーネントとサービスの動作を指定します。以下のセクションで、デフォルト設定の要素とパイプラインの概要を参照してください。
Collectorの設定方法については、Tutorial: Configure the Splunk Distribution of OpenTelemetry Collector on a Linux host を参照してください。
デフォルト設定 🔗
これは、Linux (Debian/RPM) インストーラコレクターパッケージのデフォルト設定ファイルです：
# Default configuration file for the Linux (deb/rpm) and Windows MSI collector packages
# If the collector is installed without the Linux/Windows installer script, the following
# environment variables are required to be manually defined or configured below:
# - SPLUNK_ACCESS_TOKEN: The Splunk access token to authenticate requests
# - SPLUNK_API_URL: The Splunk API URL, e.g. https://api.us0.signalfx.com
# - SPLUNK_BUNDLE_DIR: The path to the Smart Agent bundle, e.g. /usr/lib/splunk-otel-collector/agent-bundle
# - SPLUNK_COLLECTD_DIR: The path to the collectd config directory for the Smart Agent, e.g. /usr/lib/splunk-otel-collector/agent-bundle/run/collectd
# - SPLUNK_HEC_TOKEN: The Splunk HEC authentication token
# - SPLUNK_HEC_URL: The Splunk HEC endpoint URL, e.g. https://ingest.us0.signalfx.com/v1/log
# - SPLUNK_INGEST_URL: The Splunk ingest URL, e.g. https://ingest.us0.signalfx.com
# - SPLUNK_LISTEN_INTERFACE: The network interface the agent receivers listen on.
# - SPLUNK_TRACE_URL: The Splunk trace endpoint URL, e.g. https://ingest.us0.signalfx.com/v2/trace
extensions:
health_check:
endpoint: "${SPLUNK_LISTEN_INTERFACE}:13133"
http_forwarder:
ingress:
endpoint: "${SPLUNK_LISTEN_INTERFACE}:6060"
egress:
endpoint: "${SPLUNK_API_URL}"
# Use instead when sending to gateway
#endpoint: "${SPLUNK_GATEWAY_URL}"
smartagent:
bundleDir: "${SPLUNK_BUNDLE_DIR}"
collectd:
configDir: "${SPLUNK_COLLECTD_DIR}"
zpages:
#endpoint: "${SPLUNK_LISTEN_INTERFACE}:55679"
receivers:
fluentforward:
endpoint: "${SPLUNK_LISTEN_INTERFACE}:8006"
hostmetrics:
collection_interval: 10s
scrapers:
cpu:
disk:
filesystem:
memory:
network:
# System load average metrics https://en.wikipedia.org/wiki/Load_(computing)
load:
# Paging/Swap space utilization and I/O metrics
paging:
# Aggregated system process count metrics
processes:
# System processes metrics, disabled by default
# process:
jaeger:
protocols:
grpc:
endpoint: "${SPLUNK_LISTEN_INTERFACE}:14250"
thrift_binary:
endpoint: "${SPLUNK_LISTEN_INTERFACE}:6832"
thrift_compact:
endpoint: "${SPLUNK_LISTEN_INTERFACE}:6831"
thrift_http:
endpoint: "${SPLUNK_LISTEN_INTERFACE}:14268"
otlp:
protocols:
grpc:
endpoint: "${SPLUNK_LISTEN_INTERFACE}:4317"
http:
endpoint: "${SPLUNK_LISTEN_INTERFACE}:4318"
# This section is used to collect the OpenTelemetry Collector metrics
# Even if just a Splunk APM customer, these metrics are included
prometheus/internal:
config:
scrape_configs:
- job_name: 'otel-collector'
scrape_interval: 10s
static_configs:
- targets: ["${SPLUNK_LISTEN_INTERFACE}:8888"]
metric_relabel_configs:
- source_labels: [ __name__ ]
regex: 'otelcol_rpc_.*'
action: drop
- source_labels: [ __name__ ]
regex: 'otelcol_http_.*'
action: drop
- source_labels: [ __name__ ]
regex: 'otelcol_processor_batch_.*'
action: drop
smartagent/processlist:
type: processlist
signalfx:
endpoint: "${SPLUNK_LISTEN_INTERFACE}:9943"
# Whether to preserve incoming access token and use instead of exporter token
# default = false
#access_token_passthrough: true
zipkin:
endpoint: "${SPLUNK_LISTEN_INTERFACE}:9411"
processors:
batch:
# Enabling the memory_limiter is strongly recommended for every pipeline.
# Configuration is based on the amount of memory allocated to the collector.
# For more information about memory limiter, see
# https://github.com/open-telemetry/opentelemetry-collector/blob/main/processor/memorylimiter/README.md
memory_limiter:
check_interval: 2s
limit_mib: ${SPLUNK_MEMORY_LIMIT_MIB}
# Detect if the collector is running on a cloud system, which is important for creating unique cloud provider dimensions.
# Detector order is important: the `system` detector goes last so it can't preclude cloud detectors from setting host/os info.
# Resource detection processor is configured to override all host and cloud attributes because instrumentation
# libraries can send wrong values from container environments.
# https://docs.splunk.com/Observability/gdi/opentelemetry/components/resourcedetection-processor.html#ordering-considerations
resourcedetection:
detectors: [gcp, ecs, ec2, azure, system]
override: true
# Optional: The following processor can be used to add a default "deployment.environment" attribute to the logs and
# traces when it's not populated by instrumentation libraries.
# If enabled, make sure to enable this processor in a pipeline.
# For more information, see https://docs.splunk.com/Observability/gdi/opentelemetry/components/resource-processor.html
#resource/add_environment:
#attributes:
#- action: insert
#value: staging/production/...
#key: deployment.environment
exporters:
# Traces
sapm:
access_token: "${SPLUNK_ACCESS_TOKEN}"
endpoint: "${SPLUNK_TRACE_URL}"
# Metrics + Events
signalfx:
access_token: "${SPLUNK_ACCESS_TOKEN}"
api_url: "${SPLUNK_API_URL}"
ingest_url: "${SPLUNK_INGEST_URL}"
# Use instead when sending to gateway
#api_url: http://${SPLUNK_GATEWAY_URL}:6060
#ingest_url: http://${SPLUNK_GATEWAY_URL}:9943
sync_host_metadata: true
correlation:
# Logs
splunk_hec:
token: "${SPLUNK_HEC_TOKEN}"
endpoint: "${SPLUNK_HEC_URL}"
source: "otel"
sourcetype: "otel"
profiling_data_enabled: false
# Profiling
splunk_hec/profiling:
token: "${SPLUNK_ACCESS_TOKEN}"
endpoint: "${SPLUNK_INGEST_URL}/v1/log"
log_data_enabled: false
# Send to gateway
otlp:
endpoint: "${SPLUNK_GATEWAY_URL}:4317"
tls:
insecure: true
# Debug
debug:
verbosity: detailed
service:
telemetry:
metrics:
address: "${SPLUNK_LISTEN_INTERFACE}:8888"
extensions: [health_check, http_forwarder, zpages, smartagent]
pipelines:
traces:
receivers: [jaeger, otlp, zipkin]
processors:
- memory_limiter
- batch
- resourcedetection
#- resource/add_environment
exporters: [sapm, signalfx]
# Use instead when sending to gateway
#exporters: [otlp, signalfx]
metrics:
receivers: [hostmetrics, otlp, signalfx]
processors: [memory_limiter, batch, resourcedetection]
exporters: [signalfx]
# Use instead when sending to gateway
#exporters: [otlp]
metrics/internal:
receivers: [prometheus/internal]
processors: [memory_limiter, batch, resourcedetection]
# When sending to gateway, at least one metrics pipeline needs
# to use signalfx exporter so host metadata gets emitted
exporters: [signalfx]
logs/signalfx:
receivers: [signalfx, smartagent/processlist]
processors: [memory_limiter, batch, resourcedetection]
exporters: [signalfx]
logs:
receivers: [fluentforward, otlp]
processors:
- memory_limiter
- batch
- resourcedetection
#- resource/add_environment
exporters: [splunk_hec, splunk_hec/profiling]
# Use instead when sending to gateway
#exporters: [otlp]
デフォルトのパイプライン 🔗
デフォルトでは、取り込まれたデータはこれらのパイプラインに従います。
ログのデフォルト・パイプライン 🔗
以下の図は、デフォルトのログ・パイプラインを示しています：
flowchart LR
accTitle: Default logs pipeline diagram
accDescr: Receivers send logs to the logs/memory_limiter processor. The logs/memory_limiter processor sends logs to the batch processor, and the batch processor sends logs to the resource detection processor. The resource detection processor sends logs to the exporter. The SignalFx logs pipeline follows the same steps, but uses internal receivers, processors, and exporters to send logs.
%% LR indicates the direction (left-to-right)
%% You can define classes to style nodes and other elements
classDef receiver fill:#00FF00
classDef processor fill:#FF9900
classDef exporter fill:#FF33FF
%% Each subgraph determines what's in each category
subgraph Receivers
direction LR
logs/signalfx/signalfx/in:::receiver
logs/signalfx/smartagent/processlist:::receiver
logs/fluentforward:::receiver
logs/otlp:::receiver
end
subgraph Processor
direction LR
logs/signalfx/memory_limiter:::processor --> logs/signalfx/batch:::processor --> logs/signalfx/resourcedetection:::processor
logs/memory_limiter:::processor --> logs/batch:::processor --> logs/resourcedetection:::processor
end
subgraph Exporters
direction LR
logs/signalfx/signalfx/out:::exporter
logs/splunk_hec:::exporter
end
%% Connections beyond categories are added later
logs/signalfx/signalfx/in --> logs/signalfx/memory_limiter
logs/signalfx/resourcedetection --> logs/signalfx/signalfx/out
logs/signalfx/smartagent/processlist --> logs/signalfx/memory_limiter
logs/fluentforward --> logs/memory_limiter
logs/resourcedetection --> logs/splunk_hec
logs/otlp --> logs/memory_limiter
これらのレシーバーについて詳しく学びます：
これらのプロセッサーについて詳しく学びます：
これらのエクスポーターについて詳しく学びます：
メトリクスのデフォルトのパイプライン 🔗
次の図は、デフォルトのメトリクス・パイプラインを示しています：
flowchart LR
accTitle: Default metric pipeline diagram
accDescr: Receivers send logs to the metrics/memory_limiter processor. The metrics/memory_limiter processor sends metrics to the batch processor, and the batch processor sends metrics to the resource detection processor. The resource detection processor sends metrics to the exporter. The internal metrics pipeline follows the same steps, but uses internal receivers, processors, and exporters to send metrics.
%% LR indicates the direction (left-to-right)
%% You can define classes to style nodes and other elements
classDef receiver fill:#00FF00
classDef processor fill:#FF9900
classDef exporter fill:#FF33FF
%% Each subgraph determines what's in each category
subgraph Receivers
direction LR
metrics/hostmetrics:::receiver
metrics/otlp:::receiver
metrics/signalfx/in:::receiver
metrics/internal/prometheus/internal:::receiver
end
subgraph Processor
direction LR
metrics/memory_limiter:::processor --> metrics/batch:::processor --> metrics/resourcedetection:::processor
metrics/internal/memory_limiter:::processor --> metrics/internal/batch:::processor --> metrics/internal/resourcedetection:::processor
end
subgraph Exporters
direction LR
metrics/signalfx/out:::exporter
metrics/internal/signalfx/out:::exporter
end
%% Connections beyond categories are added later
metrics/hostmetrics --> metrics/memory_limiter
metrics/resourcedetection --> metrics/signalfx/out
metrics/otlp --> metrics/memory_limiter
metrics/signalfx/in --> metrics/memory_limiter
metrics/internal/prometheus/internal --> metrics/internal/memory_limiter
metrics/internal/resourcedetection --> metrics/internal/signalfx/out
これらのレシーバーについて詳しく学びます：
これらのプロセッサーについて詳しく学びます：
これらのエクスポーターについて詳しく学びます：
トレースのデフォルトのパイプライン 🔗
以下の図は、デフォルトのトレース・パイプラインを示しています：
flowchart LR
accTitle: Default traces pipeline diagram
accDescr: Receivers send traces to the traces/memory_limiter processor. The traces/memory_limiter processor sends traces to the batch processor, and the batch processor sends traces to the resource detection processor. The resource detection processor sends traces to the Splunk APM exporter and the SignalFx exporter.
%% LR indicates the direction (left-to-right)
%% You can define classes to style nodes and other elements
classDef receiver fill:#00FF00
classDef processor fill:#FF9900
classDef exporter fill:#FF33FF
%% Each subgraph determines what's in each category
subgraph Receivers
direction LR
traces/jaeger:::receiver
traces/otlp:::receiver
traces/zipkin:::receiver
end
subgraph Processor
direction LR
traces/memory_limiter:::processor --> traces/batch:::processor --> traces/resourcedetection:::processor
end
subgraph Exporters
direction LR
traces/sapm:::exporter
traces/signalfx/out:::exporter
end
%% Connections beyond categories are added later
traces/jaeger --> traces/memory_limiter
traces/otlp --> traces/memory_limiter
traces/zipkin --> traces/memory_limiter
traces/resourcedetection --> traces/sapm
traces/resourcedetection --> traces/signalfx/out
これらのレシーバーについて詳しく学びます：
これらのプロセッサーについて詳しく学びます：
これらのエクスポーターについて詳しく学びます：
さらに詳しく 🔗
以下の文書も参照してください：