klag-exporter

A high-performance Apache Kafka® consumer group lag exporter written in Rust. Calculates both offset lag and time lag (latency in seconds) with accurate timestamp-based measurements.

Features

• Accurate Time Lag Calculation — Directly reads message timestamps from Kafka® partitions instead of interpolating from lookup tables
• Compaction & Retention Detection — Automatically detects when log compaction or retention deletion may affect time lag accuracy
• Data Loss Detection — Detects and quantifies message loss when consumers fall behind retention, with metrics for prevention alerts
• Dual Export Support — Native Prometheus HTTP endpoint (/metrics) and OpenTelemetry OTLP export
• Non-blocking Scrapes — Continuous background collection with instant metric reads (no Kafka® calls during Prometheus scrapes)
• Multi-cluster Support — Monitor multiple Kafka® clusters with independent collection loops and failure isolation
• Flexible Filtering — Regex-based whitelist/blacklist for consumer groups and topics
• Configurable Granularity — Topic-level (reduced cardinality) or partition-level metrics
• Custom Labels — Add environment, datacenter, or any custom labels to all metrics
• Full Authentication Support — SASL/PLAIN, SASL/SCRAM, SSL/TLS, and Kerberos via librdkafka
• Production Ready — Health (/health) and readiness (/ready) endpoints for Kubernetes deployments
• High Availability — Optional Kubernetes leader election for active-passive failover (see HA Guide)
• Resource Efficient — Written in Rust with async/await, minimal memory footprint, and bounded concurrency

Why klag-exporter?

Comparison with Existing Solutions

Time Lag Accuracy

Problem with interpolation (kafka-lag-exporter):

• Builds a lookup table of (offset, timestamp) pairs over time
• Interpolates to estimate lag — breaks when producers stop sending
• Shows 0 lag incorrectly for idle topics
• Requires many poll cycles to build accurate tables

Our approach (targeted timestamp sampling):

• Seeks directly to the consumer group's committed offset
• Reads actual message timestamp — always accurate
• Handles idle producers correctly (shows true message age)
• TTL-cached to prevent excessive broker load

Compaction and Retention Limitations

Both log compaction (cleanup.policy=compact) and retention-based deletion can affect time lag accuracy:

How it happens: When a consumer's committed offset points to a deleted message, Kafka returns the next available message instead. This message has a later timestamp, making time lag appear smaller than reality.

Detection: klag-exporter automatically detects these conditions and exposes:

• compaction_detected and data_loss_detected labels on kafka_consumergroup_group_lag_seconds
• kafka_lag_exporter_compaction_detected_total and kafka_lag_exporter_data_loss_partitions_total counters

Recommendations:

• For affected partitions, rely more on offset lag than time lag
• Alert on kafka_lag_exporter_compaction_detected_total > 0 or kafka_lag_exporter_data_loss_partitions_total > 0
• Investigate if detection counts are high — may indicate very lagging consumers or aggressive compaction/retention settings

See docs/compaction-detection.md for detailed technical explanation.

Data Loss Detection

When a consumer falls too far behind, Kafka® retention policies may delete messages before they're processed. klag-exporter detects and quantifies this:

How it works: Data loss occurs when a consumer group's committed offset falls below the partition's low watermark (earliest available offset). This means messages were deleted by retention before the consumer could process them.

Understanding lag_retention_ratio:

This metric shows what percentage of the available retention window is occupied by consumer lag:

                    current_lag
lag_retention_ratio = ─────────────────── × 100
                    retention_window

where:
  retention_window = high_watermark - low_watermark  (total offsets in partition)
  current_lag      = high_watermark - committed_offset

Example: If a partition has offsets 1000-2000 (retention window = 1000) and consumer is at offset 1200:

• current_lag = 2000 - 1200 = 800
• lag_retention_ratio = (800 / 1000) × 100 = 80% — consumer is 80% of the way to data loss

Metrics provided:

Prevention strategy:

• Set alerts on retention_margin approaching zero (e.g., < 10% of typical lag)
• Monitor lag_retention_ratio — values approaching 100% indicate imminent data loss
• Use messages_lost > 0 for post-incident detection
• Consider increasing topic retention or scaling consumers when ratios are high

Example Prometheus alerts:

# Imminent data loss warning
- alert: KafkaConsumerNearDataLoss
  expr: kafka_consumergroup_group_lag_retention_ratio > 80
  for: 5m
  labels:
    severity: warning
  annotations:
    summary: "Consumer {{ $labels.group }} approaching retention boundary"

# Data loss occurred
- alert: KafkaConsumerDataLoss
  expr: kafka_consumergroup_group_messages_lost > 0
  labels:
    severity: critical
  annotations:
    summary: "Consumer {{ $labels.group }} lost {{ $value }} messages"

Quick Start

Using Helm (Kubernetes)

# Install from OCI registry
helm install klag-exporter oci://ghcr.io/softwaremill/helm/klag-exporter \
  --set config.clusters[0].bootstrap_servers="kafka:9092" \
  --set config.clusters[0].name="my-cluster" \
  -n kafka --create-namespace

# Or with custom values file
helm install klag-exporter oci://ghcr.io/softwaremill/helm/klag-exporter \
  -f values.yaml \
  -n kafka --create-namespace

See helm/klag-exporter/Readme.md for detailed Helm chart documentation.

Using Docker

docker run -d \
  -p 8000:8000 \
  -v $(pwd)/config.toml:/etc/klag-exporter/config.toml \
  klag-exporter:latest \
  --config /etc/klag-exporter/config.toml

Using Binary

# Build from source
cargo build --release

# Run with config file
./target/release/klag-exporter --config config.toml

# Run with debug logging
./target/release/klag-exporter -c config.toml -l debug

Using Docker Compose (Demo Stack)

A complete demo environment with Kafka®, Prometheus, and Grafana is available in the test-stack/ directory:

cd test-stack
docker-compose up -d --build

# Access points:
# - Grafana:       http://localhost:3000 (admin/admin)
# - Prometheus:    http://localhost:9090
# - klag-exporter: http://localhost:8000/metrics
# - Kafka UI:      http://localhost:8080

Configuration

Create a config.toml file:

[exporter]
poll_interval = "30s"
http_port = 8000
http_host = "0.0.0.0"
granularity = "partition"  # "topic" or "partition"

[exporter.timestamp_sampling]
enabled = true
cache_ttl = "60s"
max_concurrent_fetches = 10

[exporter.otel]
enabled = false
endpoint = "http://localhost:4317"
export_interval = "60s"

[[clusters]]
name = "production"
bootstrap_servers = "kafka1:9092,kafka2:9092"
group_whitelist = [".*"]
group_blacklist = []
topic_whitelist = [".*"]
topic_blacklist = ["__.*"]  # Exclude internal topics

[clusters.consumer_properties]
"security.protocol" = "SASL_SSL"
"sasl.mechanism" = "PLAIN"
"sasl.username" = "${KAFKA_USER}"
"sasl.password" = "${KAFKA_PASSWORD}"

[clusters.labels]
environment = "production"
datacenter = "us-east-1"

Environment Variable Substitution

Use ${VAR_NAME} syntax in config values. The exporter will substitute with environment variable values at startup.

Metrics

Partition Offset Metrics

Consumer Group Metrics (Partition Level)

Data Loss Detection Metrics

Consumer Group Aggregate Metrics

Operational Metrics

HTTP Endpoints

Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│                              Main Application                                │
│  ┌──────────────┐  ┌──────────────────┐  ┌──────────────────────────────┐  │
│  │   Config     │  │   HTTP Server    │  │      Metrics Registry        │  │
│  │   Loader     │  │  (Prometheus +   │  │   (In-memory Gauge Store)    │  │
│  │              │  │   Health)        │  │                              │  │
│  └──────────────┘  └──────────────────┘  └──────────────────────────────┘  │
│                                                         ▲                   │
│  ┌──────────────────────────────────────────────────────┴────────────────┐  │
│  │                      Cluster Manager (per cluster)                    │  │
│  │  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────────┐   │  │
│  │  │  Offset         │  │  Timestamp      │  │  Metrics            │   │  │
│  │  │  Collector      │  │  Sampler        │  │  Calculator         │   │  │
│  │  │  (Admin API)    │  │  (Consumer)     │  │                     │   │  │
│  │  └─────────────────┘  └─────────────────┘  └─────────────────────┘   │  │
│  └───────────────────────────────────────────────────────────────────────┘  │
│                                                                             │
│  ┌───────────────────────────────────────────────────────────────────────┐  │
│  │                      Export Layer                                     │  │
│  │  ┌─────────────────────────┐  ┌─────────────────────────────────┐    │  │
│  │  │  Prometheus Exporter    │  │  OpenTelemetry Exporter         │    │  │
│  │  │  (HTTP /metrics)        │  │  (OTLP gRPC)                    │    │  │
│  │  └─────────────────────────┘  └─────────────────────────────────┘    │  │
│  └───────────────────────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────────────────┘

Building from Source

Prerequisites

• Rust 1.78 or later
• CMake (for librdkafka)
• OpenSSL development libraries
• SASL development libraries (optional, for Kerberos)

Build

# Debug build
cargo build

# Release build (optimized)
cargo build --release

# Release build with High Availability support
cargo build --release --features kubernetes

# Run tests
cargo test

# Run linter
cargo clippy

Docker Build

docker build -t klag-exporter:latest .

Kubernetes Deployment

For high availability with automatic failover, see the HA Guide. Below is a basic single-instance deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: klag-exporter
spec:
  replicas: 1
  selector:
    matchLabels:
      app: klag-exporter
  template:
    metadata:
      labels:
        app: klag-exporter
      annotations:
        prometheus.io/scrape: "true"
        prometheus.io/port: "8000"
        prometheus.io/path: "/metrics"
    spec:
      containers:
      - name: klag-exporter
        image: klag-exporter:latest
        ports:
        - containerPort: 8000
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 5
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 5
          periodSeconds: 10
        volumeMounts:
        - name: config
          mountPath: /etc/klag-exporter
        env:
        - name: KAFKA_USER
          valueFrom:
            secretKeyRef:
              name: kafka-credentials
              key: username
        - name: KAFKA_PASSWORD
          valueFrom:
            secretKeyRef:
              name: kafka-credentials
              key: password
      volumes:
      - name: config
        configMap:
          name: klag-exporter-config
---
apiVersion: v1
kind: Service
metadata:
  name: klag-exporter
  labels:
    app: klag-exporter
spec:
  ports:
  - port: 8000
    targetPort: 8000
    name: metrics
  selector:
    app: klag-exporter

Grafana Dashboard

A pre-built Grafana dashboard is included in test-stack/grafana/provisioning/dashboards/kafka-lag.json with:

• Total consumer lag overview
• Max time lag per consumer group
• Per-partition lag breakdown
• Offset progression over time
• Message rate calculations
• Exporter health status

Security Considerations

Timestamp Sampling and Message Content

To calculate accurate time lag, klag-exporter fetches messages from Kafka® at the consumer group's committed offset to read the message timestamp. Only the timestamp metadata is extracted — the message payload (key and value) is never read, logged, or stored.

For sensitive environments:

• Disable timestamp sampling with timestamp_sampling.enabled = false — you'll still get offset lag metrics
• Fetch size is limited to 256KB per partition to minimize data transfer
• Run klag-exporter with minimal privileges and restricted network access

Troubleshooting

Time Lag Shows Gaps in Grafana

This is expected when:

• Consumer catches up completely (lag = 0)
• Timestamp cache expires and refetch is in progress
• Kafka® fetch times out

Solutions:

• Increase cache_ttl in config
• Use Grafana's "Connect null values" option
• For alerting, use avg_over_time() or last_over_time()

High Memory Usage

• Reduce max_concurrent_fetches
• Use granularity = "topic" instead of "partition"
• Add more restrictive group_blacklist / topic_blacklist patterns

Connection Errors

• Verify bootstrap_servers are reachable
• Check authentication configuration in consumer_properties
• Ensure network policies allow connections to Kafka® brokers

License

MIT License — see LICENSE for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

Trademark Notice

Apache®, Apache Kafka®, and Kafka® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. This project is not affiliated with, endorsed by, or sponsored by the Apache Software Foundation. For more information about Apache trademarks, please see the Apache Trademark Policy.