Setting Up a Sequencer Performance Monitoring Dashboard

A sequencer is the primary node in a rollup architecture responsible for ordering transactions, executing them, and submitting compressed data to a base layer like Ethereum. Its performance directly impacts user experience through metrics like transaction finality time, throughput, and liveness. Monitoring these metrics is essential for developers and node operators to ensure network reliability and quickly diagnose issues.

To build an effective dashboard, you need to collect data from several sources. The core data comes from the sequencer's own RPC endpoints and log outputs. You should also monitor the associated data availability layer and the base layer settlement contract. Key performance indicators (KPIs) to track include: average block time, transactions per second (TPS), pending transaction queue size, gas usage on L1, and error rates from RPC calls.

A practical setup involves using open-source monitoring tools. You can deploy Prometheus to scrape metrics from your sequencer node if it exposes a metrics endpoint, and use Grafana to visualize the data. For sequencers based on Geth or similar clients, you can enable metrics with flags like --metrics and --metrics.addr. Custom metrics, such as the time between receiving a transaction and including it in a block, may require instrumenting your sequencer's code.

Here is a basic example of a Prometheus configuration to scrape a sequencer's metrics endpoint:

yamlCopy
scrape_configs:
  - job_name: 'sequencer'
    static_configs:
      - targets: ['localhost:6060']

This assumes your sequencer is exposing metrics on port 6060. In Grafana, you can then create panels to graph metrics like sequencer_block_height or rpc_duration_seconds.

Beyond basic metrics, you should set up alerts for critical failures. Alert rules in Prometheus can notify your team if the sequencer stops producing blocks, if the transaction backlog exceeds a safe threshold, or if the latency to L1 submission spikes. Integrating with Alertmanager can route these alerts to Slack, PagerDuty, or email, enabling a rapid response to outages.

Finally, consider logging and tracing for deeper diagnostics. Structured logs from your sequencer, ingested into a system like Loki or Elasticsearch, can help you trace the lifecycle of specific failed transactions. Combining metrics, logs, and traces in a single observability platform gives you a comprehensive view of your sequencer's health and is a best practice for operating production-grade rollup infrastructure.

To monitor a sequencer, you need access to its data. For a rollup like Arbitrum, Optimism, or Base, this typically means running an archive node or using a node provider service like Alchemy, Infura, or QuickNode. An archive node is essential as it provides full historical state data, allowing you to query past blocks and transactions for metrics like inclusion time and gas usage. For real-time monitoring, you'll also need access to the sequencer's RPC endpoint for submitting transactions and listening for new blocks via WebSocket subscriptions.

Your development environment should include Node.js (v18 or later) and a package manager like npm or yarn. You will use libraries such as ethers.js or viem to interact with the blockchain. For data storage and visualization, set up a time-series database like InfluxDB or Prometheus, paired with a dashboard tool such as Grafana. These tools are industry standards for monitoring because they handle high-frequency metric ingestion and provide powerful querying and alerting capabilities.

You must also understand the key performance indicators (KPIs) you want to track. Common sequencer metrics include: - Block Production Latency: Time between receiving a transaction and including it in a block. - Transaction Throughput: Transactions processed per second (TPS). - Gas Efficiency: Cost of L2 transactions relative to L1. - Finality Time: Time for a transaction to be considered final on L1. - RPC Endpoint Health: Uptime and response latency of the sequencer's RPC. Defining these metrics upfront dictates the data you need to collect.

For programmatic data collection, you'll write scripts that poll the sequencer at regular intervals. A basic script using ethers might fetch the latest block number and timestamp, calculate the time since the previous block, and push that latency metric to your database. You should also listen for new pending transactions to measure mempool inclusion time. Structuring your code with error handling and retry logic is crucial for maintaining a reliable data pipeline, as RPC endpoints can be unstable.

Finally, ensure you have a basic understanding of the rollup's architecture. Know the difference between sequencer batches, state roots, and fraud proofs or validity proofs. This context is necessary to interpret your data correctly; for example, a spike in finality time might indicate congestion on the base layer (Ethereum) rather than a sequencer issue. Official documentation for chains like Arbitrum and Optimism is the best source for these architectural details.

A sequencer performance dashboard is essential for maintaining network reliability and user trust. It provides real-time visibility into key operational metrics, allowing teams to detect issues like transaction backlogs, latency spikes, or hardware failures before they impact end-users. For rollups and high-throughput chains, this proactive monitoring is non-negotiable. The core components of such a dashboard typically include a data collection agent (like Prometheus), a time-series database (e.g., TimescaleDB), and a visualization layer (such as Grafana). This tutorial will walk through integrating these tools to create a comprehensive monitoring solution.

The first step is instrumenting your sequencer node to expose metrics. Most modern clients support the Prometheus format. For a Geth-based sequencer, you would enable metrics with flags like --metrics and --metrics.expensive. For a custom sequencer, you'll need to instrument your code. Here's a basic example using the Go prometheus client library to track processed blocks:

goCopy
import "github.com/prometheus/client_golang/prometheus"

var blocksProcessed = prometheus.NewCounter(
    prometheus.CounterOpts{
        Name: "sequencer_blocks_processed_total",
        Help: "Total number of blocks processed by the sequencer",
    })

func init() {
    prometheus.MustRegister(blocksProcessed)
}

// Call this when a block is sequenced
func processBlock() {
    // ... sequencing logic
    blocksProcessed.Inc()
}

Expose these metrics on an HTTP endpoint (e.g., :6060/metrics) for Prometheus to scrape.

Next, configure Prometheus to scrape your sequencer endpoint. Your prometheus.yml configuration should define the target. A robust setup includes alerting rules for critical thresholds, such as a sequencer being down or transaction pool saturation.

yamlCopy
scrape_configs:
  - job_name: 'sequencer_node'
    static_configs:
      - targets: ['sequencer-host:6060']
        labels:
          instance: 'primary-sequencer-01'
    scrape_interval: 15s

rule_files:
  - "sequencer_alerts.yml"

The sequencer_alerts.yml file would contain rules that trigger alerts to platforms like Slack or PagerDuty when, for instance, sequencer_up == 0 for over a minute.

With data flowing into Prometheus, use Grafana to build the visualization dashboard. Create panels for the core health metrics. Essential panels include: Sequencer Uptime (using the up metric), Block Production Rate & Latency, Transaction Pool Size (pending transactions), Gas Usage per Block, Peer Count, and System Resources (CPU, Memory, Disk I/O). For rollups, add specific metrics like Batch Submission Latency to L1 and State Root Update Frequency. Grafana's query language, PromQL, allows for powerful aggregations. For example, to calculate the average block time over the last 5 minutes: rate(sequencer_blocks_processed_total[5m]).

To move beyond basic availability, track performance SLOs (Service Level Objectives). Define key user-impacting metrics like Transaction Finality Time (from submission to L1 confirmation) and Sequencer Inclusion Delay. Set SLOs (e.g., "99% of transactions included within 2 seconds") and create Grafana panels that show error budgets. This transforms your dashboard from a simple status page into a tool for managing reliability engineering. Integrate logs from your sequencer (using Loki) and traces (using Tempo or Jaeger) with Grafana for a unified observability stack, enabling you to drill from a high-level latency spike down to a specific slow database query.

Finally, ensure your dashboard is actionable. Place the most critical RED metrics (Rate, Errors, Duration) at the top. Use color coding (green/yellow/red) for quick status checks. Share the dashboard with your DevOps and engineering teams, and set up automated reports. Regularly review the data to identify trends, such as gradual increases in memory usage that could indicate a memory leak. A well-maintained sequencer dashboard is not just a monitoring tool; it's a foundational component for ensuring network stability, planning capacity, and providing transparency to your users and community.

Instrumenting your sequencer node is the process of embedding telemetry code to collect internal metrics and expose them for external monitoring. This is a prerequisite for building any performance dashboard. For a typical L2 sequencer built with a framework like the OP Stack or Arbitrum Nitro, this involves configuring the node's underlying execution client (e.g., Geth, Erigon) and the rollup-specific components to emit Prometheus-formatted metrics. The goal is to capture a comprehensive view of node health, including block production latency, transaction pool depth, gas usage, peer connections, and system resource consumption (CPU, memory, disk I/O).

The primary tool for this is Prometheus, a time-series database designed for monitoring. You must first enable metrics export in your sequencer's configuration. For a Geth-based sequencer, you would start the node with flags like --metrics and --metrics.addr 0.0.0.0. For OP Stack nodes, you configure the metrics section in your rollup.json or node service file to enable the endpoint. This exposes an HTTP endpoint (typically on port 6060 or 9100) that serves metrics in the plain-text format Prometheus can scrape. It is critical to secure this endpoint in production, often by restricting access with a firewall or using Prometheus's service discovery and authentication features.

Beyond base client metrics, you should instrument your application logic. This involves using a library like Prometheus's client for your node's language (Go, Rust, etc.) to create custom metrics. Key rollup-specific metrics to track include sequencer_batch_submission_duration_seconds, l1_rollup_cost_wei for transaction cost to Ethereum, pending_tx_count in the sequencer mempool, and state_root_calculation_time. Here is a simplified Go example for a custom metric:

goCopy
import "github.com/prometheus/client_golang/prometheus"

var batchSubmissionDuration = prometheus.NewHistogram(prometheus.HistogramOpts{
    Name: "sequencer_batch_submission_duration_seconds",
    Help: "Time taken to submit a batch to L1",
    Buckets: prometheus.DefBuckets,
})

func init() {
    prometheus.MustRegister(batchSubmissionDuration)
}

// In your batch submission function
start := time.Now()
// ... submit batch logic ...
batchSubmissionDuration.Observe(time.Since(start).Seconds())

Once your node is emitting metrics, you need a Prometheus server to collect them. You deploy Prometheus on a separate machine or container and configure a scrape_config in its prometheus.yml file to target your sequencer's metrics endpoint. Prometheus will then pull data at regular intervals (e.g., every 15 seconds) and store it. For a robust setup, consider running Prometheus in a high-availability pair and defining alerting rules within the same configuration to trigger on conditions like the sequencer being down for 5 minutes or batch submission latency exceeding a 2-minute threshold. This completes the data collection layer, creating a time-series database of your sequencer's performance ready for visualization in the next step.

Prometheus operates on a pull-based model, requiring you to define targets it will scrape metrics from. For a sequencer monitoring setup, your primary target is the sequencer node itself, typically exposing metrics on a port like 7300. You must also consider scraping from the L1 execution and consensus clients your sequencer depends on, as their performance directly impacts sequencer health. The configuration is managed in a YAML file, usually prometheus.yml, where you define scrape_configs.

A basic job configuration for a sequencer node looks like this:

yamlCopy
scrape_configs:
  - job_name: 'sequencer-node'
    static_configs:
      - targets: ['sequencer-hostname:7300']
    metrics_path: '/metrics'
    scheme: 'http'

This tells Prometheus to scrape the sequencer's metrics endpoint every 15 seconds (the default). You should add similar jobs for your L1 Geth (port 6060) and Lighthouse/ Prysm (port 5054) clients. Using static_configs is fine for fixed infrastructure, but for dynamic environments, you can integrate with service discovery tools like Consul or Kubernetes.

For production reliability, configure scrape timeouts and relabeling. Timeouts prevent a slow target from blocking the entire scrape cycle. Relabeling is powerful: you can add custom labels like chain="sepolia" or instance_type="c6a.2xlarge" to every metric from a job, which is essential for filtering and aggregating data in Grafana. For example, relabel_configs can be used to set a job label from an environment variable, differentiating between dev and prod deployments.

After updating prometheus.yml, restart the Prometheus service. Verify the configuration by checking the Prometheus web UI (default port 9090) under Status > Targets. All defined targets should show as UP. If a target is down, check the node's metrics endpoint is accessible and that any required firewall rules are in place. The Graph page can be used to test a simple query like up{job="sequencer-node"} to confirm data is flowing.

This configured Prometheus instance now acts as the centralized time-series database for your sequencer's operational data. It collects raw metrics like sequencer_batch_submission_duration_seconds, geth_chain_head_block, and cpu_usage_percent. In the next step, you will configure Grafana to query this data and build the visual dashboard that transforms these numbers into actionable insights.

With Prometheus successfully scraping metrics from your sequencer, you can now visualize this data in Grafana. Log into your Grafana instance and navigate to Dashboards > New > New Dashboard. Click Add visualization to create your first panel. In the query editor, select your Prometheus data source (e.g., Prometheus-Local). You can now write PromQL queries to graph key metrics. A good starting panel is Block Production Rate. Use the query rate(op_node_blocks_created_total[5m]) to visualize the number of blocks your sequencer is producing per second over a 5-minute rolling window.

For effective monitoring, organize your dashboard into logical sections. Create a System Health row with panels for CPU usage (rate(process_cpu_seconds_total[5m])), memory consumption (process_resident_memory_bytes), and disk I/O. Next, add a Sequencer Core Metrics row. Essential panels here include: op_node_unconfirmed_proofs (queued proofs), op_node_batch_tx_ingestion_queue_size (batch queue depth), and op_node_p2p_peers (network connectivity). Use Stat visualizations for single-number displays and Time series graphs for trends. Set appropriate Min and Max axis values and meaningful Unit formats (e.g., short for numbers, bytes for memory).

Configure Alerting directly from your dashboard panels. Click on a panel title, select Edit, then navigate to the Alert tab. Create a rule to notify you if critical thresholds are breached. For example, set an alert when op_node_unconfirmed_proofs exceeds 50 for more than 5 minutes, indicating a backup in proof generation. Use notification channels like Slack, PagerDuty, or email. Finally, make your dashboard actionable by adding Annotations. Use Grafana's built-in annotation system or query metrics like op_node_events_total{event="sequencer\_block"} to mark new block events directly on your graphs, correlating system load with on-chain activity.

Effective monitoring is proactive, not reactive. After building your dashboard to visualize sequencer health, the next critical step is to define alerting rules that notify your team of anomalies. These rules are logical conditions evaluated against your metrics data. For example, you might create an alert that triggers if the block_production_latency_seconds metric exceeds a 2-second threshold for more than 5 minutes, indicating a significant slowdown in block finality. Rules are defined using a query language like PromQL (for Prometheus) or the native query syntax of your monitoring service (e.g., Datadog, Grafana Cloud).

When designing alerts, focus on actionable signals rather than noise. Key metrics to alert on include: - High block latency - Missed slots or skipped blocks - RPC endpoint error rate spikes - Unusual gas price volatility - Sudden drop in transaction throughput. Each alert should have a clear severity level (e.g., warning, critical) and be routed to the appropriate on-call channel, such as PagerDuty, Slack, or email. Avoid alert fatigue by setting sensible thresholds and using features like alert grouping and snoozing for known maintenance windows.

Here is a concrete example of a Prometheus alerting rule for high sequencer latency, defined in a YAML configuration file:

yamlCopy
groups:
  - name: sequencer_alerts
    rules:
    - alert: HighSequencerLatency
      expr: avg_over_time(block_production_latency_seconds[5m]) > 2
      for: 5m
      labels:
        severity: critical
        component: sequencer
      annotations:
        summary: "Sequencer block production latency is critically high"
        description: "Average block latency over 5m is {{ $value }}s (threshold: 2s)."

This rule calculates the 5-minute average of the latency metric and triggers a critical alert if it remains above 2 seconds for a continuous 5-minute period.

For teams using a managed service like Grafana Cloud or Datadog, you can configure similar rules through their web UI. The core principles remain: define the metric, set a threshold and duration, and configure notifications. Advanced setups can include multi-window alerts (e.g., latency is high for 2m and error rate is rising) or forecast-based alerts that predict a threshold breach before it happens using machine learning. Always document your alert runbooks, specifying the exact steps an engineer should take when an alert fires, such as checking specific dashboard panels or restarting services.

Finally, test your alerts thoroughly. Use your monitoring system's tools to simulate alert conditions and verify that notifications are delivered correctly to all configured channels. Periodically review alert logs to identify rules that fire too frequently (requiring threshold adjustment) or not at all (potentially broken). A well-tuned alerting system transforms your dashboard from a passive observability tool into an active guardian of your sequencer's reliability and user experience.

Public verifiability transforms monitoring from a trusted report into a cryptographically secured truth. For a sequencer—the node responsible for ordering transactions in a rollup—this means publishing performance metrics like block production time, transaction inclusion latency, and uptime in a way that any third party can independently verify. Instead of relying on the sequencer's honesty, verifiers can check attestations or zero-knowledge proofs against the canonical chain data. This is critical for decentralized sequencer sets, cross-chain bridges assessing liveness, and users verifying service-level agreements.

The foundation is publishing raw data and proofs to a persistent, immutable data availability layer. For Ethereum rollups, this is often the L1 itself, using calldata or blobs. For other systems, it could be Celestia, EigenDA, or a decentralized storage network like Arweave or IPFS. The key is that the data commitment (e.g., a Merkle root) is posted on-chain. A basic flow involves: the sequencer node collecting metrics, generating a signed attestation or a succinct proof (like a zkSNARK) of the state transition, and publishing the proof and the data root to the DA layer in a predictable format.

Here's a simplified conceptual example of a signed attestation schema that could be published. The signature ensures the data originated from the sequencer's known key, while the on-chain root allows verification of the data's integrity.

solidityCopy
struct PerformanceAttestation {
    uint256 blockNumber;
    uint256 timestamp;
    uint256 avgLatencyMs;
    uint256 uptimePercentage;
    bytes32 dataRoot; // Merkle root of the full dataset
    bytes signature; // Signed by sequencer key
}

A dashboard frontend would fetch these attestations, verify the signature against the known sequencer address, and optionally verify that the underlying detailed data matches the committed dataRoot by fetching it from the DA layer and checking a Merkle proof.

For more robust verification, especially for complex computations, integrate zero-knowledge proofs. A zk circuit can prove that given the previous state and the incoming transactions, the new state (including computed metrics like average latency) is correct, without revealing the raw transactions. Frameworks like Circom, Halo2, or Noir can be used to create these circuits. The resulting proof is posted on-chain. The dashboard can then simply verify the on-chain proof contract, providing users with a cryptographic guarantee of data correctness with minimal trust.

To build the dashboard, connect a frontend to two primary data sources: the blockchain (to read the posted commitments and proofs) and the data availability layer (to retrieve the detailed datasets). Use a standard stack like Next.js or Vite with Ethers.js or Viem. The core logic involves: 1) polling for new attestations or proof submissions, 2) running local signature and Merkle proof verification, 3) aggregating historical data, and 4) visualizing trends in latency, uptime, and block production. Public verifiability is achieved by open-sourcing the dashboard's verification logic, allowing anyone to audit the process.

Implementing this creates a trust-minimized transparency tool. Applications include: staking dashboards for delegators to monitor sequencer performance, bridge watchdogs that pause withdrawals if sequencer liveness proofs fail, and user-facing status pages with cryptographic backing. By moving from opaque logging to verifiable data streams, you strengthen the security assumptions of the entire rollup ecosystem and provide a concrete foundation for decentralized sequencer rotation and slashing mechanisms based on objectively verifiable performance.

Your dashboard now provides a real-time view of your sequencer's operational state. Key metrics like block production rate, transaction pool depth, and peer count are essential for detecting liveness issues. Alerts for missed slots or high mempool backlog enable proactive intervention before user experience degrades. Integrating this with an incident management platform like PagerDuty or Opsgenie creates a closed-loop system for reliability.

To deepen your analysis, consider implementing historical data tracking. Tools like TimescaleDB or ClickHouse can store time-series metrics for long-term trend analysis, such as tracking gas usage patterns or identifying gradual performance degradation. Correlating sequencer metrics with RPC node performance and layer-1 base chain conditions (e.g., Ethereum mainnet gas prices) provides context for anomalies, distinguishing internal issues from external network congestion.

The next logical step is to expand monitoring to the full node network that submits transactions to your sequencer. Monitor node synchronization status, request error rates, and geographic latency distribution. For rollup sequencers, add specific metrics for batch submission latency to the L1 and state root finalization time. Implementing distributed tracing with Jaeger or OpenTelemetry can map the entire transaction journey from user wallet to finalization.

Finally, establish a regular review process. Schedule weekly reports on Mean Time Between Failures (MTBF) and Mean Time To Recovery (MTTR). Use your dashboard to run failure scenario drills, testing your team's response to simulated sequencer halts or fee market spikes. Continuously update alert thresholds based on historical data to reduce false positives. Your monitoring stack is a living system that must evolve with your network's usage and complexity.

For further learning, explore the Prometheus documentation for advanced querying with PromQL and the Grafana Labs tutorials for building public dashboards. The Ethereum Execution Client Specifications (e.g., for Geth or Nethermind) provide insight into the metrics exposed by the nodes your sequencer interacts with, enabling even more granular instrumentation.