Jaeger, a popular open-source distributed tracing system, offers flexibility in choosing a database for storing trace data. While Jaeger supports multiple storage backends, two primary options stand out: Elasticsearch and Cassandra. This article delves into Jaeger's database choices, comparing these options to help you make an informed decision for your tracing infrastructure.

Understanding Jaeger's Database Options

Jaeger's architecture allows for multiple storage backends to persist trace data. This flexibility enables users to choose a database that best fits their existing infrastructure and performance requirements. The two main contenders are:

  1. Elasticsearch: The recommended option by the Jaeger team.
  2. Cassandra: A historically popular choice, still widely used.

Additionally, Jaeger supports other options like Kafka for high-load ingestion scenarios. The choice of database significantly impacts your tracing system's performance, scalability, and query capabilities.

Elasticsearch as Jaeger's Recommended Backend

Elasticsearch has emerged as the preferred choice for Jaeger deployments. The Jaeger team officially recommends Elasticsearch due to its numerous advantages:

  • Powerful search capabilities: Elasticsearch excels at full-text search and complex queries.
  • Scalability: It easily handles large volumes of trace data in production environments.
  • Integration: Many organizations already use the ELK (Elasticsearch, Logstash, Kibana) stack.
  • Performance: Elasticsearch provides fast query response times for trace data analysis.

Setting Up Jaeger with Elasticsearch

To configure Jaeger with Elasticsearch:

  1. Install Elasticsearch and ensure it's running.
  2. Set the SPAN_STORAGE_TYPE environment variable to elasticsearch.
  3. Configure Jaeger components with Elasticsearch connection details.

Example configuration for Jaeger collector:

collector:
  app_name: jaeger-collector
  environment: production
  kafka:
    brokers: kafka:9092
    topic: jaeger-spans
  es:
    server-urls: <http://elasticsearch:9200>
    username: elastic
    password: changeme

For optimal performance:

  • Use dedicated Elasticsearch indices for Jaeger data.
  • Implement proper index management and data retention policies.
  • Tune Elasticsearch settings based on your hardware and data volume.

Cassandra as an Alternative Storage Backend

Cassandra was initially the primary storage option for Jaeger. While Elasticsearch is now recommended, Cassandra remains a viable choice in certain scenarios:

  • Existing Cassandra infrastructure: Organizations already using Cassandra may prefer to leverage their expertise.
  • Write-heavy workloads: Cassandra excels at handling high write throughput.
  • Global distribution: Cassandra's multi-datacenter replication can be advantageous for geographically distributed setups.

However, Cassandra has limitations when it comes to complex queries and full-text search capabilities compared to Elasticsearch.

Migrating from Cassandra to Elasticsearch

If you're considering migrating from Cassandra to Elasticsearch:

  1. Plan the migration carefully to minimize downtime.
  2. Use Jaeger's dual-write mode to write data to both databases during transition.
  3. Validate data integrity in Elasticsearch before completing the migration.
  4. Update Jaeger configuration to use Elasticsearch as the primary storage.

Kafka's Role in Jaeger's Data Pipeline

Kafka serves as a buffer for high-throughput tracing scenarios in Jaeger's architecture. It complements both Elasticsearch and Cassandra by:

  • Absorbing traffic spikes: Kafka can handle sudden increases in trace data ingestion.
  • Ensuring data durability: Traces are persisted in Kafka before being written to the storage backend.
  • Enabling parallel processing: Multiple consumers can process trace data from Kafka simultaneously.

To use Kafka with Jaeger:

  1. Set up a Kafka cluster.
  2. Configure Jaeger agents to send spans to Kafka instead of directly to the collector.
  3. Deploy Jaeger ingester to consume spans from Kafka and write them to the storage backend.

Choosing the Right Database for Your Jaeger Deployment

Consider these factors when selecting a storage backend for Jaeger:

  • Query requirements: Elasticsearch offers superior query capabilities for complex trace analysis.
  • Scalability needs: Both Elasticsearch and Cassandra can scale, but Elasticsearch may be easier to manage at large scales.
  • Existing infrastructure: Leverage your team's expertise with either database.
  • Data retention: Evaluate the cost and performance implications of long-term trace data storage.

For most new Jaeger deployments, Elasticsearch is the recommended choice due to its balance of performance, scalability, and query capabilities.

Key Takeaways

  • Elasticsearch is Jaeger's recommended database for trace data storage.
  • Cassandra remains a viable option, especially for existing deployments or write-heavy workloads.
  • Kafka can buffer high-volume trace data before storage, improving system resilience.
  • Database choice significantly impacts Jaeger's performance, scalability, and query capabilities.

FAQs

Can Jaeger work without a persistent database?

Yes, Jaeger can operate with in-memory storage for testing or development purposes. However, a persistent database is crucial for production deployments to ensure data durability and enable historical analysis.

How does Jaeger's database choice affect its performance?

The database choice impacts query performance, data ingestion rates, and overall system scalability. Elasticsearch generally offers better query performance, while Cassandra excels in write-heavy scenarios.

Is it possible to use both Elasticsearch and Cassandra with Jaeger?

While it's technically possible to use both databases simultaneously, it's not a common practice. Most organizations choose one primary storage backend for simplicity and consistency.

What are the minimum hardware requirements for running Jaeger with Elasticsearch?

The hardware requirements depend on your trace data volume and retention period. For small to medium deployments, start with:

  • 3 Elasticsearch nodes
  • 8GB RAM per node
  • 4 CPU cores per node
  • SSD storage for improved performance

Scale these resources as your tracing needs grow.

Consider SigNoz as an Alternative to Jaeger

While Jaeger is a popular choice for distributed tracing, it's worth considering SigNoz as an alternative solution. SigNoz is an open-source application performance monitoring (APM) and observability platform that offers:

  • Integrated metrics, traces, and logs in a single platform
  • Built-in support for ClickHouse as the storage backend, providing excellent query performance and data compression
  • User-friendly interface for visualizing and analyzing trace data
  • Easy setup and configuration, with both self-hosted and cloud options available

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.

Get Started - Free CTA

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.

If you're looking for a comprehensive observability solution that goes beyond just distributed tracing, SigNoz might be the right fit for your needs. It provides a seamless experience for developers and operations teams, combining the power of metrics, traces, and logs in one tool.

To learn more about how SigNoz compares to Jaeger and how it can enhance your observability stack, visit https://signoz.io.

Was this page helpful?