SDK Metrics

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Overview

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Prerequisites

• A GitHub integration connected to Corelayer (the repository you want to track must be linked)
• Node.js 18+ (for the SDK)

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Onboarding

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1. Create an API Key

1. In your Corelayer dashboard, go to Settings > API Keys
2. Click Create Key
3. Give the key a name (e.g., Production ETL) and set an expiration (optional)
4. Copy the key immediately — it will only be shown once

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2. Set the Environment Variable

export CORELAYER_API_KEY="clr_your_api_key_here"
export CORELAYER_BASE_URL="https://api.corelayer.com"

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3. Install the SDK

npm install @corelayer-ai/sdk

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4. Create a Metric in the Dashboard

1. Go to the Anomalies page in your Corelayer dashboard
2. Click Add Rule and select a codebase (repository)
3. On the metrics listing page, click Create Metric
4. Fill in the configuration:
   • Metric Name — A human-readable label (e.g., Daily Order Volume)
   • Frequency — How often data is reported (hourly, daily, weekly, monthly)
5. Copy the Metric ID and the SDK Snippet provided on the page
6. Click Save Metric

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5. Instrument Your Code

import { CorelayerClient } from '@corelayer-ai/sdk';

const client = new CorelayerClient({
  apiKey: process.env.CORELAYER_API_KEY,
  baseUrl: process.env.CORELAYER_BASE_URL,
});

client.trackMetric({
  metricId: "<your-metric-id>",
  partitions: [
    { keys: { "region": "us-east-1", "table": "orders" }, value: 1523 },
  ],
  timestamp: "2025-01-15T00:00:00Z",
});

// Call at process exit to flush remaining metrics
await client.shutdown();

• metricId — The UUID from step 4. Identifies which metric this data belongs to.
• partitions — An array of partition entries. Each entry has keys (an object of key-value pairs that define the partition) and value (the numeric count or measurement).
• timestamp — An ISO 8601 timestamp. Pass an explicit timestamp for batch jobs to ensure correct ordering, especially for reruns.

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6. Deploy and Monitor

1. Collect data to build a baseline for each partition (status shows Collecting)
2. Switch to active monitoring once the baseline is ready (status shows Ready)
3. Flag anomalies when values fall outside the expected range

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Environment Variables Reference

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Viewing Metrics

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Metrics Listing

• The metric name (or ID if no name was set)
• Baseline status (Collecting or Ready)

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Metric Detail Page

• Stat Cards — Total partitions, baselines ready, and anomalies detected in the selected time range
• Time-Series Chart — Visualize metric values over time for up to 10 partitions. Switch between count, min, max, and avg using the dropdown.
• Partition Table — Browse all partitions with search and pagination. Each row shows the partition keys, current metric values, baseline statistics, and anomaly count.

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Anomaly Findings

• Severity — Critical, High, Medium, Low, or Info
• Observed vs. Expected — The actual value compared to what the baseline predicted
• Bounds — The upper and lower thresholds that were breached
• Timestamp — When the anomaly was detected

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Tips

• Use descriptive partition keys — Keys like region, pipeline, or table_name make findings easier to diagnose.
• Set explicit timestamps for batch jobs — This ensures data is bucketed correctly, even during reruns.
• Call client.shutdown() — Always call shutdown before your process exits to flush any buffered metrics.
• One metric per logical measurement — Create separate metrics for different measurements (e.g., Order Volume and Payment Processing Time) rather than mixing them.

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Managing Metrics

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Deleting Metrics

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Rotating API Keys

1. Go to Settings > API Keys
2. Click the rotate icon next to the key
3. Copy the new key and update your environment variable
4. The old key is revoked immediately