OpenTelemetry vs Logstash - Which Logging Tool Is Right for You?

Modern applications produce vast amounts of logs, traces, and metrics, making observability tools essential for monitoring, troubleshooting, and optimizing system performance. Choosing the right tool, whether OpenTelemetry or Logstash, can greatly impact your observability strategy and data handling efficiency. Both are powerful solutions, each with distinct features and purposes suited to specific use cases. In this article, we’ll compare their capabilities, architecture, and how they can best meet your observability needs.

What are OpenTelemetry and Logstash?

OpenTelemetry is an open-source, vendor-neutral observability framework designed to collect and standardize telemetry data. It provides a unified method to instrument, generate, collect, and export telemetry across services. It is a universal translator, simplifying the data collection process for distributed systems by handling metrics, traces, and logs.

Logstash is part of the Elastic Stack (formerly known as the ELK Stack). It is a server-side data processing pipeline. It primarily focuses on log ingestion and processing, taking data from multiple sources, transforming it, and forwarding it to your required destination. Logstash operates like a data-processing assembly line, ideal for shaping, filtering, and routing logs with high flexibility.

Core Differences in architecture and approach

Let's take a look at the key differences between both of them for more clarity.

• Scope: OpenTelemetry supports distributed tracing, metrics, and logging, while Logstash is optimized for log data processing.
• Architecture: OpenTelemetry follows a collector-based model, while Logstash operates on a flexible pipeline architecture.
• Vendor Lock-in: OpenTelemetry is vendor-neutral and integrates well with cloud-native environments, while Logstash is most effective within the Elastic ecosystem.

Primary use cases

OpenTelemetry

• It is primarily used for complete observability in distributed systems, covering logs, metrics, and traces within a single, unified framework.
• It follows a vendor-neutral architecture, making it highly adaptable for cloud-native environments, especially suited for multi-service, microservices-based applications.
• It integrates easily with numerous monitoring backends, enabling teams to instrument applications with minimal vendor lock-in—ideal for organizations aiming to scale observability as they grow.

Logstash

• It works well in log-centric data processing workflows, particularly when paired with other Elastic Stack tools like Elasticsearch and Kibana.
• It is commonly used for centralized log management in large enterprises, where log aggregation and transformation are essential.
• It offers a vast plugin ecosystem that handles diverse log formats, filters data, and outputs to multiple destinations, making it an excellent choice for ETL pipelines focused on log data.

Key Features Comparison

Let's now have a look at the various key features of OpenTelemetry and Logstash and how they compare.

• Flexible metrics collection with multiple format support.
• Automatic instrumentation for popular frameworks.
• Supports formats like OTLP, Jaeger, and Zipkin. | - Ingests data from multiple sources (files, logs, events) through input plugins.
• Advanced log transformation via Grok parsing.
• Supports diverse formats (custom log formats, JSON). | | Processing and Transformation | Primarily for telemetry data transformation, with support for batching and processors to streamline data processing. | Customizable pipeline structure with filter plugins, including Grok parsing for data transformation based on patterns and formats. | | Integration Options | Vendor-neutral with easy integration to SigNoz, Prometheus, Jaeger, and others. | Works best within the Elastic Stack, but also integrates with Amazon S3, Google Cloud Storage, and Apache Kafka. | | Performance and Scalability | Efficient resource use and minimal configuration, scalable for cloud-native architectures. | Scales well but may require higher memory and CPU resources for complex pipelines or high-throughput setups. | | Data Retention and Storage Options | Primarily a data collection tool, with retention depending on backend integration and flexibility for various storage solutions. | Limited native storage, but can output to various destinations, allowing customizable retention policies. | | Security and Data Privacy | Secure data transport with support for gRPC and HTTP/JSON protocols, encryption options. | Role-based access control (with Elastic Stack), SSL/TLS support for secure data transfer and storage. |

Example OpenTelemetry Collector Configuration:

receivers:
  otlp:
    protocols:
      grpc:
        endpoint: "0.0.0.0:4317"

processors:
  batch:
    timeout: 1s

exporters:
  logging:
    loglevel: debug

Example Logstash Pipeline Configuration:

input {
  file {
    path => "/var/log/nginx/access.log"
    type => "nginx-access"
  }
}

filter {
  grok {
    match => { "message" => "%{COMBINEDAPACHELOG}" }
  }
}

Architecture Deep Dive

Let's now take a look at the architecture of OpenTelemetry and Logstash for better clarity.

OpenTelemetry's Collector-Based Approach

OpenTelemetry follows a flexible and scalable collector-based architecture:

• Collector: It acts as the central hub for receiving, processing, and exporting telemetry data to multiple backends.
• Protocol Support: It supports gRPC and HTTP/JSON for cloud-native interoperability and high-performance communication across environments.
• Resource Efficiency: It is optimized to handle high-throughput data efficiently, with minimal system overhead.

Logstash's Pipeline Architecture

Logstash relies on a powerful pipeline architecture that enables complex data transformations:

• Input Plugins: It supports data ingestion from diverse sources, including logs, files, and messaging systems like Kafka.
• Filter Plugins: It processes and enriches data through transformations using Grok, JSON, Mutate, and more.
• Output Plugins: It routes processed data to a variety of destinations, such as Elasticsearch, local storage, and external APIs.
• Persistent Queues: It enhances reliability with optional disk-based queues, which ensure data is retained even during service interruptions.

Protocol Support

OpenTelemetry and Logstash share support for the HTTP protocol, allowing interoperability with various systems. OpenTelemetry primarily operates with gRPC and HTTP/JSON for flexible backend integration, while Logstash extends compatibility further by supporting HTTP, TCP, and UDP, making it versatile for integration with a wider range of data sources and outputs.

Resource Requirements

Data Flow and Processing Efficiency

OpenTelemetry is optimized for efficient, high-throughput environments, handling data flow smoothly with minimal latency. But, Logstash is effective in processing high volumes, its complex pipelines can require more memory and CPU resources in large deployments.

Extensibility and Plugin Ecosystem

OpenTelemetry offers built-in extensibility for custom data processing, with community-driven processors and exporters to meet specialized observability needs.

Logstash features an extensive plugin ecosystem, with plugins for input, filter, and output, which enables deep customization and integration with nearly any data source or destination.

Implementation Considerations

Let's now look at the implementation considerations for OpenTelemetry and Logstash.

Setup Complexity and Learning Curve

OpenTelemetry:

• Pros:
  • Auto-Instrumentation Options: It simplifies telemetry data collection by automating instrumentation for supported libraries.
  • Standard SDK Across Languages: It provides consistent setup across different languages, ensuring a unified observability approach.
• Cons:
  • Complex Initial Configuration: The initial setup for OpenTelemetry especially for collectors and exporters, can be difficult.
  • Newer Ecosystem: OpenTelemetry is rapidly evolving but it is still building its community and resource base.

Logstash:

• Pros:
  • Extensive Documentation: Logstash provides ample guides and tutorials for setup.
  • Stability: It offers robust, tested plugins and a stable setup process.
• Cons:
  • Resource-Intensive: Logstash’s pipeline can demand higher memory and CPU, particularly for complex processing.
  • Complex Pipeline Configurations: Its multi-stage pipelines add operational overhead, especially for intricate data transformations.

Operational Overhead

OpenTelemetry requires lower operational overhead due to its collector model, but configuring multiple exporters can increase complexity.

Logstash requires high operational demand with resource-heavy configurations and ongoing maintenance, especially for high-throughput needs.

Community Support and Ecosystem

OpenTelemetry's community is growing with a focus on open standards and interoperability but its community is still in an early adoption phase.

Logstash has an established user base with a wealth of resources, including an extensive plugin ecosystem which makes it community support easily accessible.

Cost Implications

OpenTelemetry is open-source and vendor-neutral, which can reduce costs, especially for multi-cloud or hybrid setups.

Logstash is also open-source, and advanced Elastic Stack features may require licensing, potentially adding to costs if integrated deeply within Elastic’s ecosystem.

Integration with Existing Infrastructure

OpenTelemetry is highly adaptable for modern, cloud-native applications and easily integrates into diverse environments.

Logstash can integrate best within the Elastic Stack but can be adapted for standalone use in various log management systems.

Maintenance and Operational Requirements

OpenTelemetry is low-maintenance due to centralized data collection through the collector. However, its new updates may require ongoing attention as the ecosystem evolves.

Logstash's maintenance is ongoing. Especially for configuring and managing pipeline components and persistent queues to ensure data reliability.

Migration Strategies

Migrating from Logstash to OpenTelemetry requires a structured approach to maintain data integrity and minimize disruption.

Let's now look at some of the migration strategies for more clarity.

Moving from Logstash to OpenTelemetry

You should start by migrating a single, non-critical service with OpenTelemetry to identify any configuration or data consistency issues early on.

Running Both Systems in Parallel

During the transition, you should run Logstash and OpenTelemetry side by side. It enables data comparison in real time which will help you to verify data accuracy and ensure that essential metrics and logs are consistently captured across both systems.

You should also ensure that the telemetry data aligns between Logstash and OpenTelemetry by comparing output from both systems. It confirms that data integrity is preserved.

Data Transformation Considerations

OpenTelemetry’s processors allow custom data transformations. It enables compatibility with Logstash’s existing field mappings and semantic structures.

Example of Data Transformation in OpenTelemetry:

processors:
  transform:
    log_statements:
      - context: log
        statements:
          - set(body, ParseJSON(body))

Best Practices for Migration

• You should align Logstash’s field structures to OpenTelemetry’s semantic conventions, preserving data format consistency throughout the observability pipeline.
• You should implement batching in the OpenTelemetry collector to optimize performance, especially during high-volume data transfers.
• You should establish robust error-handling practices within OpenTelemetry to ensure issues are captured efficiently during data collection or transformation.

Hybrid Strategy

If your team is looking to use both tools then you can follow a hybrid approach as it would allow Logstash to focus on specific log transformations, while OpenTelemetry manages telemetry data collection and metrics.

Data Mapping and Consistency

In the case of migration, maintaining data consistency is very important.

• You should ensure Logstash’s fields are accurately mapped to OpenTelemetry, so data is consistently represented.
• You should conduct regular comparisons of key metrics and logs to validate that data quality remains high across both systems.

Modern Observability with SigNoz

When discussing observability, it's worth mentioning SigNoz, a notable alternative in the field.

Cloud-Native Approach to Logging

SigNoz offers a cloud-native observability solution tailored for modern, distributed systems. Let's take a look at its benefits:

• It is designed to seamlessly handle distributed environments.
• SigNoz captures logs, metrics, and traces from cloud-native applications with minimal overhead.
• It implements configurable data retention policies which allows organizations to balance storage costs and data accessibility.

OpenTelemetry Native Implementation

SigNoz is built with native OpenTelemetry support which makes integration quite easy. It can automatically collect and visualize OpenTelemetry data without complex configurations which allow teams to get started quickly.

• Example OpenTelemetry Configuration for SigNoz:

  exporters:
    otlp:
      endpoint: "https://<sigNoz_endpoint>"
      headers:
        api-key: "<api_key>"
  
  

SigNoz cloud is the easiest way to run SigNoz. Sign up for a free account and get 30 days of unlimited access to all features.

You can also install and self-host SigNoz yourself since it is open-source. With 19,000+ GitHub stars, open-source SigNoz is loved by developers. Find the instructions to self-host SigNoz.

Unified Observability Platform Benefits

SigNoz provides a single platform for full-stack observability.

• SigNoz enables end-to-end visibility and accelerates root cause analysis by unifying all telemetry data types.
• Its advanced querying and visualization tools simplify data analysis and troubleshooting.
• It is an OpenTelemetry-native tool. It allows flexibility without vendor lock-in, facilitating broader compatibility and adaptability.

If you use SigNoz, you can gain an all-in-one observability solution that enhances OpenTelemetry’s data collection capabilities, centralizing insights to improve system performance and streamline debugging.

Key Takeaways

• OpenTelemetry is ideal for comprehensive observability across logs, metrics, and traces.
• Logstash is best suited for mature, log-centric processing workflows, especially within the Elastic Stack.
• OpenTelemetry’s flexibility allows seamless integration across multiple platforms.
• Logstash works best with other Elastic tools for an all-in-one log processing solution.
• OpenTelemetry is generally lighter on resources due to its collector-based architecture.
• OpenTelemetry offers a future-proof architecture for evolving cloud-native requirements.

FAQs

Can OpenTelemetry completely replace Logstash?

Yes, OpenTelemetry can replace Logstash for most use cases, especially when comprehensive observability is a priority. OpenTelemetry not only supports logging but also collects metrics and traces, offering a more holistic observability solution than Logstash’s log-focused approach.

How do performance metrics compare between the two?

OpenTelemetry often uses fewer resources in high-throughput scenarios due to its efficient, collector-based architecture. This design enables optimized data routing and transformation with lower resource consumption compared to Logstash’s more resource-intensive pipeline architecture.

What are the cost implications of each solution?

OpenTelemetry is open-source and vendor-neutral, allowing flexibility in back-end storage and avoiding proprietary vendor costs. Logstash, on the other hand, may incur costs, especially if using Elastic’s licensed features or hosted services within the Elastic Stack.

Is it possible to use both tools together?

Yes, OpenTelemetry and Logstash can be used in tandem for different observability needs. OpenTelemetry can send logs to Logstash as part of a hybrid observability setup, or both tools can be used independently within the same infrastructure to meet specific logging or metric-tracing needs.