n8n Observability — Research & POC Findings

Background

As adoption for Common Hosted Workflow (CHW) grows, platform administrators and tenant teams need visibility into workflow health, execution performance, and failure patterns. This document captures the research, options explored, architecture decisions, and open questions from the observability POC.

Why Distributed Tracing for n8n

Before selecting tooling it is worth clarifying why distributed tracing is appropriate here. A typical workflow looks like:

Trigger → Node A → Node B → Node C

The value of tracing for n8n is intra-workflow visibility — understanding what happened inside a single execution:

Which workflows fail and why?
Which nodes are slow (HTTP Request timing out? SQL query taking 10 seconds)?
Which API calls timeout?
Which executions exceed SLA?
How long does the whole workflow take versus individual nodes?

The goal is node-to-node visibility within a workflow execution.

Observability Pillars

This implementation covers two of the three observability pillars. Grafana is the visualization layer across both — it is not itself a pillar.

Pillar	Storage	Collection path
Logs	Loki	n8n Log Streaming → syslog → Alloy `loki.source.syslog` → Loki
Traces	Tempo	n8n OTLP push → Alloy `otelcol.receiver.otlp` → Tempo

Metrics (out of scope for this POC): n8n exposes a Prometheus-compatible /metrics endpoint covering execution counts, active executions, and queue depth. Grafana cannot query this endpoint directly — /metrics only exposes the current instant's values with no history. A time-series storage layer is required: Prometheus scrapes the endpoint on a schedule, accumulates the data, and Grafana queries Prometheus for historical graphs. This adds one additional service but is otherwise straightforward. It is a natural addition to this stack for capacity planning and alerting once the core pillars are stable.

Selected Stack

Component	Role
Grafana Alloy	Telemetry pipeline — receives OTLP traces from n8n and Log Streaming events via syslog, forwards traces to Tempo and logs to Loki
Grafana Tempo	Distributed trace storage and TraceQL query capabilities consumed by Grafana
Grafana Loki	Log storage for n8n Log Streaming events (workflow lifecycle and audit events)
Grafana	Visualization layer — dashboards and alerting across logs and traces

Why this stack

All components are from the Grafana ecosystem — consistent versioning, shared support model
Native OpenTelemetry (OTLP) support — n8n's built-in OTLP exporter works without any adapter
TraceQL — Tempo's query language enables span-level filtering, metrics-from-traces, and time-series analysis not possible in other trace backends
Single visualization layer (Grafana) provides correlated views across logs and traces. Prometheus can be added later for metrics-based dashboards and alerting.

How n8n Connects

n8n connects to Alloy via two separate paths. Alloy is always the server — it does not poll n8n.

n8n  →  OTLP/HTTP (port 4318)  →  Alloy  →  Tempo   (traces)
n8n  →  syslog/TCP (port 5514) →  Alloy  →  Loki    (Log Streaming events)

chwf-alloy is the Kubernetes service name for the Alloy pod. Alloy listens on both ports:

// Path 1 — OTLP traces from n8n's built-in OpenTelemetry exporter
otelcol.receiver.otlp "default" {
  http { endpoint = "0.0.0.0:4318" }
  output {
    traces = [otelcol.exporter.otlp.tempo.input]
  }
}

n8n environment variables for traces

N8N_OTEL_ENABLED: 'true'
N8N_OTEL_EXPORTER_OTLP_ENDPOINT: 'http://chwf-alloy:4318'
N8N_OTEL_EXPORTER_SERVICE_NAME: 'chwf-n8n'
N8N_OTEL_TRACES_PRODUCTION_ONLY: 'true' # set false for dev/test
N8N_OTEL_TRACES_SAMPLE_RATE: '1.0' # send 100% to Alloy; Alloy does the filtering
N8N_OTEL_TRACES_INCLUDE_NODE_SPANS: 'true' # include per-node spans for node latency panels

Decision: Include node spans?

Setting N8N_OTEL_TRACES_INCLUDE_NODE_SPANS=true means each node in a workflow produces its own span. This enables the "Slowest Node Types" panel and node-level debugging. The trade-off is higher span volume.

Recommendation: keep enabled unless volume becomes a storage concern.

What Traces Contain

Each n8n workflow execution produces an OpenTelemetry trace. A trace is a tree of spans where:

The root span represents the full workflow execution
Child spans represent individual node executions within the workflow

Custom span attributes (Enterprise - needs verification)

Teams can attach additional attributes to node spans via Settings → Custom Span Attributes. n8n automatically prefixes them with n8n.node.custom.:

Key: customer_tier     Value: ={{ $json.customerTier }}   → n8n.node.custom.customer_tier
Key: response_status   Value: ={{ $response.statusCode }} → n8n.node.custom.response_status

See the Security section for PII restrictions on custom attributes.

Options Explored

Jaeger (considered, rejected)

Jaeger is a dedicated distributed tracing backend. It can replace Alloy + Tempo. n8n's documentation references it as a quick-start option.

Why Tempo was selected over Jaeger

The primary CHW observability requirement is not only viewing individual workflow traces, but also providing operational dashboards that analyze workflow execution behaviour over time:

Workflow failure trends
Execution volume
Workflow latency percentiles
Slow node identification
Workflow performance analysis

Tempo provides native TraceQL querying and TraceQL metrics integration with Grafana. This allows workflow-level metrics to be generated directly from trace data without introducing additional metric extraction components.

Dashboard Requirement	Tempo	Jaeger
Workflow execution count by outcome	Native TraceQL metrics (`count_over_time`)	Requires additional metrics extraction pipeline
Failed executions over time by workflow	Native TraceQL filtering and metrics	Requires additional aggregation tooling
Workflow duration percentiles (p95/p99)	Native TraceQL metrics	Requires additional metric generation
Slowest node types	Query node-level spans using TraceQL	Possible, but requires additional aggregation components

Jaeger capabilities considered

Jaeger provides several capabilities that are valuable in traditional distributed systems. The following were considered during evaluation:

Feature	Jaeger capability	Relevance to CHW
Trace comparison	Supports comparing individual traces during debugging	Useful for developers troubleshooting specific executions, but not required for platform dashboards
Adaptive sampling	Jaeger Collector supports adaptive sampling strategies	Useful for very high-volume tracing environments; CHW can achieve similar optimization using Alloy tail-based sampling

Platform comparison

Capability	Tempo	Jaeger
Grafana integration	Native Grafana datasource	Supported through Jaeger datasource plugin
Trace query language	TraceQL	Jaeger Query API
Object storage support	Native S3-compatible object storage support	Typically requires Elasticsearch, Cassandra, or Badger storage
Multi-tenancy	Native tenant isolation using `X-Scope-OrgID`	Possible but requires additional architecture
Sampling	OpenTelemetry Collector / Alloy processors	Jaeger Collector processors

Decision

Tempo was selected because the CHW observability requirements focus on workflow execution analytics, not only trace inspection.

The dashboard requirements depend on extracting operational insights from n8n execution traces, including:

Execution success and failure trends
Workflow duration analysis
Node-level performance analysis
Workflow filtering and tenant isolation

Using Tempo with Grafana provides these capabilities through native TraceQL queries and trace-derived metrics. Using Jaeger would require additional components to extract metrics from traces and would introduce additional complexity into the dashboard architecture.

Alloy remains the telemetry pipeline because it provides:

OTLP ingestion from n8n
Tail-based sampling
Attribute filtering (for example, PII removal)
Telemetry enrichment
Fan-out to multiple observability backends

SigNoz (considered, rejected)

SigNoz is an all-in-one OpenTelemetry observability platform built on ClickHouse. It provides logs and traces with its own UI and can also support metrics.

Service count comparison:

Grafana stack	SigNoz stack
Alloy	SigNoz OTel Collector
Tempo	ClickHouse (storage for everything)
Loki	SigNoz backend/query service
Grafana	—

Cons:

ClickHouse introduces additional operational and resource overhead compared with the specialized storage model used by Tempo and Loki.
Multi-tenancy is immature — significant gap for per-environment/per-project isolation
TraceQL-equivalent panels would need to be rebuilt in a different query language

Decision: SigNoz remains a capable OpenTelemetry observability platform, but the Grafana stack provides a better fit for CHW's isolation, dashboard, and operational requirements.

Elasticsearch (considered, rejected)

Elasticsearch was not selected because the CHW observability requirements are better aligned with a lightweight observability stack using Grafana, Tempo, Loki and Alloy.

Log Streaming

n8n has an enterprise feature called Log Streaming that emits structured workflow events (started, succeeded, failed) to external destinations. This is separate from stdout container logs and is required for audit events (user logins, credential changes, workflow lifecycle) to appear in Grafana.

Recommended path: Syslog → Alloy → Loki

This path is proposed but not yet tested. The exact syslog payload format emitted by n8n Enterprise Log Streaming must be validated before this approach is confirmed.

Alternative path:

If syslog is not suitable, the webhook destination requires an adapter because n8n's Log Streaming sends its own JSON format, not Loki's push format. No existing Grafana stack component accepts generic JSON webhooks.

Three options for the adapter:

Option A — Vector (recommended)

Vector is a lightweight log pipeline tool with a built-in HTTP source and Loki sink.

Option B — Small custom service A tiny HTTP server that receives the POST, wraps it in Loki format, and forwards. Another service to maintain.

Option C — Skip Log Streaming → Loki entirely Use Log Streaming only for alerting destinations (Sentry, Slack webhook) that accept arbitrary JSON.

Note: n8n Log Streaming is an Enterprise feature.

Sampling Strategy

Sending every trace from n8n provides the most complete troubleshooting information, but storing every successful execution can create unnecessary storage usage as workflow volume increases.

Recommendation: Configure n8n to send 100% of traces to Alloy:

N8N_OTEL_TRACES_SAMPLE_RATE: '1.0'

Failure Modes

Component down	Impact	Recovery
Alloy unavailable	n8n SDK buffers spans in memory briefly, retries, then drops.	Traces during the outage are lost if the outage is long.
Tempo unavailable	Alloy queues traces in memory (configurable size), replays on Tempo recovery.	Traces buffered up to queue limit, then dropped.
Grafana unavailable	Dashboard inaccessible. Telemetry still collected normally.	No data loss.
Object storage (S3) unavailable	Tempo and Loki ingestion fails after local WAL fills.	Critical — restore S3 access promptly.
n8n restart	In-memory OTel buffer lost (seconds of spans).	Minor — automatic on restart.

Configuring n8n's OTel buffer

n8n's OTel SDK has a built-in in-memory queue with retry. The default buffer is small — for longer Alloy outages, spans will be dropped. Configure the timeout:

OTEL_EXPORTER_OTLP_TIMEOUT: '10000' # 10 seconds before giving up on an export attempt

Configuring Alloy's buffer for Tempo outages

Add retry and persistent queue to the Alloy Tempo exporter so Alloy holds traces when Tempo is temporarily unavailable:

otelcol.exporter.otlp "tempo" {
  client {
    endpoint = "tempo:4317"
    tls { insecure = true }
  }
  sending_queue {
    enabled    = true
    num_consumers = 10
    queue_size = 10000
  }
  retry_on_failure {
    enabled          = true
    initial_interval = "5s"
    max_interval     = "30s"
    max_elapsed_time = "5m"
  }
}

Tempo also has a Write-Ahead Log (WAL) — mid-flight data is protected across Tempo pod restarts and replayed on startup.

Caching

Three layers of caching are available:

1. Tempo tag cache (already tried in POC)

The nginx proxy injects a time range on tag/tag-value endpoints so Tempo queries backend Parquet blocks. The Workflow dropdown is populated correctly without re-querying on every panel load.

2. Grafana datasource query cache

Grafana has built-in datasource query caching. Enable per datasource in datasources.yaml:

Repeated dashboard loads hit Grafana's memory cache instead of querying Tempo directly.

3. Tempo's built-in search cache

Tempo caches recent search results internally. For traceqlMetrics queries, results are computed once and reused for the same time window.

Security — PII in Traces

Traces must not contain personally identifiable information. Risk areas in n8n:

Custom span attributes added by teams (could accidentally include email addresses, customer IDs)
Node names or workflow names that embed customer identifiers

Policy: No PII, credentials, JWT tokens, or authorization headers in trace attributes.

Enforcement via Alloy — strip before traces reach Tempo

Dashboard

The POC dashboard (n8n-overview) is provisioned automatically by Grafana and covers:

Section	Panels	Data source
Health	Execution errors, workflow duration, recent failures	Tempo
Activity	Executions Over Time	Tempo
Executions	Recent Workflow Runs (table, clickable trace links)	Tempo
Performance	Duration p95, Error Rate trend, Throughput, Slowest Nodes	Tempo traceqlMetrics
Audit Events	User logins, credential changes, workflow created/deleted	Loki (Log Streaming)

A Workflow dropdown at the top filters all panels to a single workflow.

nginx Proxy (needed only if we need a per workflow filter within the dashboard)

nginx sits between Grafana and Tempo on port 8090.

Tag autocomplete fix

Grafana's Tempo plugin calls GET /api/v2/search/tags with no time range, so Tempo only returns intrinsic tags from the in-memory ingester and never includes span attributes like n8n.workflow.name. The nginx proxy injects a 7-day time range via an njs script, causing Tempo to query backend Parquet blocks and return all indexed attributes.

Storage and Retention

Backend recommendation

Environment	Tempo	Loki
Production	S3-compatible object storage	S3-compatible object storage

Tempo and Loki still needs a local PVC for its WAL even when using S3 for block storage.

Single S3 bucket with separate prefixes:

s3://chwf-observability/
  tempo/    ← trace blocks
  loki/     ← log chunks

Retention policies

Data	Recommended retention	Where configured
n8n execution history (PostgreSQL)	30 days	`EXECUTIONS_DATA_MAX_AGE` on n8n
Loki logs	30 days	`retention_period` in loki-config.yaml
Tempo traces	7 days	`compacted_block_retention` in tempo-config.yaml

These are three independent settings. Traces have the shortest retention because they are high volume and most troubleshooting uses recent data.

Execution pruning: n8n stores execution history in PostgreSQL separately from OTLP traces. Setting EXECUTIONS_DATA_MAX_AGE prunes the n8n database independently of Tempo — these are not the same data store and must be configured separately.

OpenShift Deployment Requirements

Items commonly missed until deployment:

Requirement	Notes
PVC for Tempo WAL	Required even when blocks go to S3 — WAL is always local
PVC for Loki WAL	Loki also has a WAL that requires a local PVC when using S3 for chunk storage
Route / Ingress	Grafana needs an OpenShift Route with TLS termination
NetworkPolicy	n8n → Alloy only; Alloy → Tempo, Loki; Grafana → all backends
Resource limits	All pods need CPU/memory requests and limits for OpenShift quota compliance

Full Architecture

                         dev/test/prod namespace

┌────────────────────────────────────────────────────────────┐
│                                                            │
│                                                            │
│  n8n ->  Alloy  ->  Tempo (S3 backend) ----------------|   │
│                 ->  Loki  (S3 backend)  ---------------|   │
│                                                        │   │
│  Grafana  <────────────────────────────────────────────┘   │
│  (single dashboard)                                        │
│                                                            │
└────────────────────────────────────────────────────────────┘

Data paths:

OTLP traces:       n8n → POST http://chwf-alloy:4318 → Alloy → Tempo
Log Streaming:     n8n → syslog tcp://chwf-alloy:5514 → Alloy → Loki

Implementation Tickets

1 — Core Infrastructure

Deploy Loki, Tempo, Alloy, Grafana
Configure nginx Tempo proxy (if needed)

2 — n8n Configuration

Enable OpenTelemetry tracing on all n8n instances
Configure execution data pruning (EXECUTIONS_DATA_MAX_AGE)
Configure Log Streaming syslog destination (Enterprise)

3 — Grafana Dashboards

Health Overview section (error rate, active workflows, avg duration)
Activity Timeline (executions over time, log volume by severity)
Workflow Executions table (clickable trace IDs → flame graph)
Performance Analysis section (traceqlMetrics panels)

4 — Multi-Tenancy and Access Control

Confirm primary isolation boundary (project) — drives all other decisions in this story
Choose and implement tenancy model (X-Scope-OrgID)
Design and implement query frontend enforcement layer — proxy that reads Keycloak JWT claims and injects correct X-Scope-OrgID before queries reach Tempo/Loki (required for real isolation; Tempo does not authenticate callers)
Configure Alloy to inject tenant identifier per environment
Configure Grafana datasources locked to environment tenant
Add n8n.project.id filter variable to dashboards
Configure Keycloak → Grafana role mapping

5 — Storage and Retention

Provision S3-compatible object storage bucket folder
Configure Tempo and Loki S3 backends
Define and document retention policies per data type

6 — Security

Add PII stripping processor to Alloy pipeline
Document permitted custom span attribute keys

7 — Documentation and Runbooks

Runbook: checking Alloy pipeline health (Alloy UI at port 12345)
Runbook: verifying traces are flowing end to end
Runbook: what to do when Tempo or Loki is full
Team guide: how to access Grafana, use the dashboard, add custom span attributes
Team guide: how to query Tempo/Loki via API key

Open Questions

Question	Notes
What does n8n Enterprise Log Streaming actually send over syslog?	Determines if `\| json` pipeline works or custom parsing is needed
What is the primary isolation boundary — environment or project?	Determines tenancy model; affects Alloy config, Grafana datasources, S3 layout
What alerting targets do teams use? (email, Slack, Teams, RocketChat?)	Needed to configure Grafana Alerting contact points
Should execution pruning be enabled and at what retention period?	Separate from trace retention — affects n8n PostgreSQL DB size
What is the SLA for the observability stack itself?	99.9% uptime requires HA replicas for Alloy, Tempo distributor, Loki distributor, and Grafana — single-pod deployments are insufficient
Are traces operational telemetry or records?	If a workflow trace contains a case ID or application reference, retention and security requirements may change significantly

Background​

Why Distributed Tracing for n8n​

Observability Pillars​

Selected Stack​

Why this stack​

How n8n Connects​

n8n environment variables for traces​

Decision: Include node spans?​

What Traces Contain​

Custom span attributes (Enterprise - needs verification)​

Options Explored​

Jaeger (considered, rejected)​

Why Tempo was selected over Jaeger​

Jaeger capabilities considered​

Platform comparison​

Decision​

SigNoz (considered, rejected)​

Elasticsearch (considered, rejected)​

Log Streaming​

Recommended path: Syslog → Alloy → Loki​

Alternative path:​

Sampling Strategy​

Failure Modes​

Configuring n8n's OTel buffer​

Configuring Alloy's buffer for Tempo outages​

Caching​

1. Tempo tag cache (already tried in POC)​

2. Grafana datasource query cache​

3. Tempo's built-in search cache​

Security — PII in Traces​

Dashboard​

nginx Proxy (needed only if we need a per workflow filter within the dashboard)​

Tag autocomplete fix​

Storage and Retention​

Backend recommendation​

Retention policies​

OpenShift Deployment Requirements​

Full Architecture​

Implementation Tickets​

1 — Core Infrastructure​

2 — n8n Configuration​

3 — Grafana Dashboards​

4 — Multi-Tenancy and Access Control​

5 — Storage and Retention​

6 — Security​

7 — Documentation and Runbooks​

Open Questions​