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Version: 0.8.2

Metrics

Starknet Devnet can expose Prometheus-compatible metrics for monitoring and observability. This feature allows you to track RPC calls, block creation, transactions, and upstream forking calls.

Enabling Metrics​

To enable metrics, start Devnet with the --metrics-host parameter:

$ starknet-devnet --metrics-host <IP_ADDRESS>

By default, the metrics server will listen on port 9090. You can customize the port with the --metrics-port parameter:

$ starknet-devnet --metrics-host 127.0.0.1 --metrics-port 8080

Or using environment variables:

$ METRICS_HOST=127.0.0.1 METRICS_PORT=8080 starknet-devnet

If running with Docker:

$ docker run -e METRICS_HOST=0.0.0.0 -e METRICS_PORT=9090 -p 9090:9090 starknetfoundation/starknet-devnet-rs

Accessing Metrics​

Once the metrics server is running, you can access the metrics endpoint at:

http://<metrics-host>:<metrics-port>/metrics

For example:

$ curl http://127.0.0.1:9090/metrics

The metrics are exposed in Prometheus text format, which can be scraped by Prometheus or other compatible monitoring systems.

Available Metrics​

RPC Metrics​

rpc_call_duration_seconds​

Type: Histogram

Description: Duration of RPC calls in seconds

Labels:

  • method: The RPC method name (e.g., starknet_getBlockWithTxs, starknet_call)

Buckets: 0.00005, 0.0001, 0.00025, 0.0005, 0.001, 0.0025, 0.005, 0.01, 0.015, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0 seconds

rpc_call_count​

Type: Counter

Description: Total number of RPC calls

Labels:

  • method: The RPC method name
  • status: Either success or error

Starknet Core Metrics​

starknet_transaction_count​

Type: Counter

Description: Total number of transactions in Starknet

This counter is incremented when a transaction is added to the network and decremented when blocks are aborted.

starknet_block_count​

Type: Counter

Description: Total number of blocks in Starknet

This counter is incremented when a new block is created and decremented when blocks are aborted.

starknet_block_creation_duration_seconds​

Type: Histogram

Description: Duration of block creation in seconds

Buckets: 0.00005, 0.0001, 0.00025, 0.0005, 0.001, 0.0025, 0.005, 0.01 seconds

This metric tracks how long it takes to generate a new block and transition from the pre-confirmed state.

Upstream Forking Metrics​

These metrics are only relevant when running Devnet in forking mode.

starknet_upstream_call_duration_seconds​

Type: Histogram

Description: Duration of upstream forking origin calls in seconds

Labels:

  • method: The RPC method called on the upstream network
  • status: Either success or error

Buckets: 0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0 seconds

starknet_upstream_call_count​

Type: Counter

Description: Total number of upstream forking origin calls

Labels:

  • method: The RPC method called on the upstream network
  • status: Either success or error

These metrics help monitor the performance and reliability of interactions with the forked network.

Integrating with Prometheus​

To scrape these metrics with Prometheus, add the following job to your prometheus.yml configuration:

scrape_configs:
  - job_name: 'starknet-devnet'
    static_configs:
      - targets: ['localhost:9090']

Adjust the target address to match your Devnet metrics server configuration.

Example Queries​

Here are some example PromQL queries you can use:

Average RPC call duration by method​

rate(rpc_call_duration_seconds_sum[5m]) / rate(rpc_call_duration_seconds_count[5m])

RPC call rate by method​

rate(rpc_call_count[5m])

RPC error rate​

rate(rpc_call_count{status="error"}[5m]) / rate(rpc_call_count[5m])

Block creation rate​

rate(starknet_block_count[5m])

Transaction throughput​

rate(starknet_transaction_count[5m])

Upstream call error rate (forking mode)​

rate(starknet_upstream_call_count{status="error"}[5m]) / rate(starknet_upstream_call_count[5m])

95th percentile block creation time​

histogram_quantile(0.95, rate(starknet_block_creation_duration_seconds_bucket[5m]))

Visualization with Grafana​

You can visualize these metrics using Grafana by:

  1. Adding Prometheus as a data source
  2. Creating dashboards with panels using the PromQL queries above
  3. Setting up alerts based on metric thresholds