Single Binary Mode

This guide will help you get Cortex running in single-binary mode using Docker Compose. In this mode, all Cortex components run in a single process, making it perfect for learning, development, and testing.

Time to complete: ~15 minutes

What You’ll Learn

How to run Cortex with Docker Compose
How to send metrics from Prometheus to Cortex using remote_write
How to query metrics stored in Cortex using Grafana
How to configure recording rules and alerting rules
How to set up the Cortex Alertmanager

Prerequisites

Software Requirements

Docker (v20.10+)
Docker Compose (v2.30+)

System Requirements

4GB RAM minimum
10GB disk space
Linux, macOS, or Windows with WSL2

Optional Tools

cortextool - For managing rules and alerts (we’ll use Docker to run this)

Architecture

This setup creates the following services:

┌─────────────┐     remote_write     ┌─────────────┐
│ Prometheus  │ ───────────────────> │   Cortex    │
│             │                      │  (single)   │
└─────────────┘                      └─────────────┘
                                             │
                                             │ stores blocks
                                             ▼
┌─────────────┐                      ┌─────────────┐
│   Grafana   │ ────── queries ────> │  SeaweedFS  │
│   Perses    │                      │     (S3)    │
└─────────────┘                      └─────────────┘

Components:

SeaweedFS: S3-compatible object storage for storing metric blocks
Cortex: Single-process Cortex instance with all components (distributor, ingester, querier, compactor, etc.)
Prometheus: Scrapes its own metrics and sends them to Cortex
Grafana: Visualizes metrics stored in Cortex
Perses: Modern dashboard alternative (optional)

Step 1: Clone the Repository

git clone https://github.com/cortexproject/cortex.git
cd cortex/docs/getting-started

The getting-started directory contains all the configuration files needed for this guide.

Step 2: Start the Services

docker compose up -d

This command starts all services in the background. Docker Compose will:

Pull required images (first time only)
Start SeaweedFS (S3-compatible storage)
Initialize S3 buckets
Start Cortex
Start Prometheus (configured to send metrics to Cortex)
Start Grafana (pre-configured with Cortex datasource)

What’s happening? Check the logs:

# View all logs
docker compose logs -f

# View Cortex logs only
docker compose logs -f cortex

Step 3: Verify Services Are Running

After ~30 seconds, all services should be healthy. Verify by checking:

docker compose ps

You should see all services with status “Up” or “healthy”.

Access the UIs

Open these URLs in your browser:

Cortex: http://localhost:9009 - Admin interface and ring status
Prometheus: http://localhost:9090 - Prometheus UI
Grafana: http://localhost:3000 - Dashboards (no auth needed)
SeaweedFS S3 API: http://localhost:8333 - S3-compatible API (use curl with --user any:any)

Step 4: Verify Data Flow

Let’s verify that metrics are flowing from Prometheus → Cortex → Grafana.

Check Prometheus is Sending Metrics

Open Prometheus
Go to Status → Targets
Verify the targets are UP
Go to Query - you should see prometheus_remote_storage_samples_total increasing

Query Metrics in Cortex

Test that Cortex is receiving metrics:

curl -H "X-Scope-OrgID: cortex" "http://localhost:9009/prometheus/api/v1/query?query=up" | jq

You should see JSON output with metrics data.

Note: The X-Scope-OrgID header specifies which tenant’s data to query. Cortex is multi-tenant by default. Prometheus automatically adds this header when writing metrics via remote_write.

View Metrics in Grafana

Open Grafana (login: admin / admin)
Go to Explore
Select the “Cortex” datasource
Run a query: up
You should see metrics from Prometheus!

View Cortex Dashboards

Pre-built dashboards are available at Dashboards:

Cortex / Writes: Monitor metric ingestion
Cortex / Reads: Monitor query performance
Cortex / Object Store: Monitor block storage

Step 5: Configure Recording and Alerting Rules (Optional)

Cortex can evaluate PromQL recording rules and alerting rules, similar to Prometheus. This is optional but demonstrates an important Cortex feature.

What are these?

Recording rules: Pre-compute expensive queries and store results as new metrics
Alerting rules: Define conditions that trigger alerts

The repository includes example rules in rules.yaml and alerts.yaml.

Load Rules into Cortex

For Linux users:

docker run --network host \
  -v "$(pwd):/workspace" -w /workspace \
  quay.io/cortexproject/cortex-tools:v0.17.0 \
  rules sync rules.yaml alerts.yaml --id cortex --address http://localhost:9009

For macOS/Windows users:

docker run --network cortex-docs-getting-started_default \
  -v "$(pwd):/workspace" -w /workspace \
  quay.io/cortexproject/cortex-tools:v0.17.0 \
  rules sync rules.yaml alerts.yaml --id cortex --address http://cortex:9009

Note: The --id cortex flag specifies the tenant ID. Cortex is multi-tenant, so rules are namespaced by tenant.

Verify Rules Are Loaded

View rules in Grafana: Alerting → Alert rules

Or check via API:

curl -H "X-Scope-OrgID: cortex" "http://localhost:9009/prometheus/api/v1/rules" | jq

Step 6: Configure Alertmanager (Optional)

Cortex includes a multi-tenant Alertmanager that receives alerts from the ruler.

Load Alertmanager Configuration

For Linux users:

docker run --network host \
  -v "$(pwd):/workspace" -w /workspace \
  quay.io/cortexproject/cortex-tools:v0.17.0 \
  alertmanager load alertmanager-config.yaml --id cortex --address http://localhost:9009

For macOS/Windows users:

docker run --network cortex-docs-getting-started_default \
  -v "$(pwd):/workspace" -w /workspace \
  quay.io/cortexproject/cortex-tools:v0.17.0 \
  alertmanager load alertmanager-config.yaml --id cortex --address http://cortex:9009

View Alertmanager in Grafana

Configure Alertmanager notification policies in Grafana: Alerting → Notification policies

Explore and Experiment

Now that everything is running, try these experiments to learn how Cortex works:

Experiment 1: Stop the Ingester

Cortex runs all components in one process, so stopping Cortex simulates an ingester failure.

docker compose stop cortex

Observe:

Prometheus continues running and queues samples
Grafana queries fail (no ingesters available)
Metrics are NOT lost - Prometheus will retry

Restart Cortex:

docker compose start cortex

Result: Prometheus catches up by sending queued samples. Check the Cortex / Writes dashboard to see the backlog being processed.

Experiment 2: Query Old vs Recent Data

Cortex stores recent data (last ~2 hours) in memory and older data in object storage (S3).

Query recent metrics (from ingester memory):

curl "http://localhost:9009/prometheus/api/v1/query?query=up" | jq

After 2+ hours, query old metrics (from S3 blocks):

curl "http://localhost:9009/prometheus/api/v1/query?query=up[24h]" | jq

Observe: Both queries work! Cortex seamlessly queries both sources.

Experiment 3: Compare Prometheus vs Cortex

In Prometheus: Query up

In Grafana (Cortex datasource): Query up

Are they the same? Initially yes, but after Prometheus sends data to Cortex via remote_write, the data diverges:

Prometheus has local storage (limited retention)
Cortex has long-term storage in S3

Experiment 4: Explore the Ring

Cortex uses a hash ring for consistent hashing of time series to ingesters.

View the ring status: http://localhost:9009/ring

In single-binary mode, you’ll see one ingester. In microservices mode, you’d see multiple ingesters.

Experiment 5: Inspect Object Storage

SeaweedFS stores Cortex blocks. You can inspect them using the S3 API:

List buckets:

curl --aws-sigv4 "aws:amz:local:seaweedfs" --user "any:any" http://localhost:8333

List objects in the cortex-blocks bucket:

curl --aws-sigv4 "aws:amz:local:seaweedfs" --user "any:any" http://localhost:8333/cortex-blocks?list-type=2

You’ll see:

cortex/ directory (tenant ID)
Block directories named by ULID (e.g., 01J8KRQ7M8...)
Each block contains index, chunks/, and meta.json

Tip: You can also use the AWS CLI with SeaweedFS:

export AWS_ACCESS_KEY_ID=any
export AWS_SECRET_ACCESS_KEY=any
aws --endpoint-url=http://localhost:8333 s3 ls s3://cortex-blocks/

Configuration Files

This setup uses several configuration files. Here’s what each does:

File	Purpose
`docker-compose.yaml`	Defines all services (Cortex, Prometheus, Grafana, SeaweedFS)
`cortex-config.yaml`	Cortex configuration (storage, limits, components)
`prometheus-config.yaml`	Prometheus configuration with remote_write to Cortex
`grafana-datasource-docker.yaml`	Grafana datasource pointing to Cortex
`rules.yaml`	Example recording rules
`alerts.yaml`	Example alerting rules
`alertmanager-config.yaml`	Alertmanager configuration

Want to customize? Edit these files and restart services:

docker compose restart cortex

Troubleshooting

Services won’t start

# Check logs
docker compose logs

# Check port conflicts
lsof -i :9009  # Cortex
lsof -i :9090  # Prometheus
lsof -i :3000  # Grafana

No metrics in Grafana

Check Prometheus is sending metrics: Status → Targets
Check Cortex is receiving metrics: curl "http://localhost:9009/prometheus/api/v1/query?query=up"
Check Grafana datasource: Settings → Data sources → Cortex → Test

cortextool fails on macOS/Windows

The --network host flag doesn’t work on macOS/Windows. Use the Docker network name instead:

docker run --network cortex-docs-getting-started_default ...

Out of memory errors

Increase Docker’s memory limit to 4GB or more:

Docker Desktop → Settings → Resources → Memory

Clean Up

When you’re done, stop and remove all services:

docker compose down -v

The -v flag removes volumes (stored data). Omit it to keep data between runs.

Next Steps

Congratulations! You’ve successfully run Cortex in single-binary mode. Here’s what to explore next:

Try Microservices Mode: Get started with microservices mode →
Learn the Architecture: Understand Cortex’s design →
Production Deployment: Run Cortex on Kubernetes →
Deep Dive into Blocks Storage: Learn about blocks storage →
Configure Multi-tenancy: Set up authentication →