Meter Explorer

The Meter Explorer provides advanced capabilities to analyze and understand your observability costs through detailed meter metrics. With the powerful SigNoz query interface, you can drill deeper into each meter metric to extract meaningful insights about your ingested telemetry data and optimize your observability spend.

What you'll learn:

• How to query and analyze meter metrics for cost insights
• Understanding the structure and components of meter data
• How to break down signal meter data by specific label dimensions
• Creating saved views for recurring cost analysis

Overview

Meter Explorer enables you to monitor and control your observability costs by providing granular visibility into your telemetry data ingestion patterns. By leveraging the familiar SigNoz query interface, you can analyze meter metrics to understand consumption trends, identify cost drivers, and make informed decisions about your observability strategy.

Prerequisites

Before using Meter Explorer, ensure you have:

• Familiarity with how metrics work in SigNoz
• A running SigNoz instance with telemetry data being ingested
• Understanding of the SigNoz query interface (helpful but not required)

Understanding Meter Metrics

Meter metrics provide detailed information about your telemetry data ingestion, helping you understand consumption patterns and costs. Let's explore the structure and components of meter data.

Namespace

All meter data exposed by SigNoz follows a consistent namespace pattern and is queryable through the standard metrics interface. Each meter metric is namespaced with signoz.meter and follows standard metric aggregation patterns.

Available Metrics

SigNoz exposes five core meter metrics that provide comprehensive visibility into your telemetry data ingestion:

Metric Labels

Each meter metric includes a fixed set of labels that enable you to slice and dice your data across different dimensions:

• service.name: The name of the service generating the telemetry data
• deployment.environment: The deployment environment (e.g., production, staging, development)
• host.name: The hostname of the system generating the data
• signoz.workspace.key.id: The ID of the ingestion key used for ingesting telemetry data

These labels allow you to analyze your metering data across multiple dimensions, helping you identify which services, environments, or hosts are contributing most to your observability costs.

Data Retention

Meter data is retained for a period of 1 year. This retention policy ensures that you have access to historical data for long-term analysis and trend identification. By maintaining a year-long history, you can make more informed decisions about your observability strategy and cost optimization efforts over time.

Querying Meter Data

Meter Explorer uses the same powerful query interface as other SigNoz components, making it easy to analyze your cost data with familiar patterns and syntax.

Time Range Considerations

Query Patterns

The querying patterns for meter data are identical to standard metric querying patterns. You can use the same operators, functions, and syntax that you're familiar with from other SigNoz metrics.

For detailed information about metric querying, refer to our Metrics Management Guide.

Example Queries

1. Log Size Analysis by Service

To find the total size of log records ingested by a specific service over the last day:

signoz.meter.log.size{service.name="demo-app"}

This query will show you the total log size for the demo-app service, helping you understand which services are generating the most log data.

2. Span Count by Environment

To analyze span ingestion across different environments:

signoz.meter.span.count{deployment.environment="otel-demo"}

This helps you understand the volume of trace data being generated in your environment.

3. Multi-Service Cost Comparison

To compare log ingestion across multiple services:

signoz.meter.log.size{group-by:service.name}

4. Host-Level Analysis

To identify which hosts are generating the most telemetry data:

signoz.meter.log.size{host.name = "x"}

This aggregation helps you identify hosts that might be generating excessive log data.

5. Ingestion ID-Based Analysis

To analyze telemetry data based on specific ingestion IDs:

signoz.meter.log.size{signoz.workspace.key.id = "01963e56-0bbb-7df5-b26c-2c4aa48b291a"}

Creating Saved Views

Meter Explorer supports saved views, allowing you to create and reuse common queries for recurring cost analysis.

You can checkout the Saved View Documentation for more details on creating and managing saved views.

For additional support and discussion, visit our Community Forum. For advanced topics and comprehensive guides, explore our Documentation.