OTel

Understanding Jaeger and Tempo for Distributed Tracing

techhadoop monitoring, OTel

If OpenTelemetry is the camera that takes the pictures (traces), Jaeger and Tempo are the albums where you store them. They are both open-source backends designed for distributed tracing, helping you visualize exactly how a single user request flows across multiple microservices.

However, they are built with completely opposite engineering philosophies.

Jaeger: The Rich, Standalone Heavyweight

Created by Uber and hosted by the CNCF, Jaeger is the older, battle-tested veteran of distributed tracing.

  • How it works: Jaeger loves databases. It is designed to index everything. When you search for a trace, it queries database backends like Elasticsearch, OpenSearch, or Cassandra.
  • The Interface: It has its own dedicated, standalone web UI.
  • Key Strength (Powerful Searching): Because it indexes everything, Jaeger’s search capability is top-tier. You can effortlessly hunt for traces using custom tags, status codes (e.g., http.status_code=500), specific services, or durations right out of the box.
  • The Catch (High Cost): Managing Elasticsearch or Cassandra at a massive scale is an operational nightmare. Indexing every single trace requires huge amounts of RAM and disk space, making Jaeger very expensive to run if you have terabytes of trace volume.

Grafana Tempo: The Cheap, High-Scale Disruptor

Created by Grafana Labs, Tempo is the modern challenger built specifically to fix Jaeger’s massive storage bills.

  • How it works: Tempo is completely index-free. Instead of setting up massive, complex databases, Tempo batches traces together and throws them directly into Object Storage (like Amazon S3, Google Cloud Storage, or Azure Blob).
  • The Interface: It doesn’t have its own UI; it uses Grafana natively.
  • Key Strength (Insanely Cheap & Scalable): Object storage is incredibly cheap. Tempo can run with 10x to 100x lower storage costs than Jaeger because it doesn’t waste resources building heavy search indexes.
  • The Catch (How do you find a trace?): Because there are no indexes, you traditionally couldn’t just “search” Tempo for a random trace attribute easily. Instead, it relies on a “Metrics-to-Logs-to-Traces” workflow. You look at a Prometheus chart, click a spike, find the log line in Grafana Loki, click the trace_id embedded in the log, and Tempo instantly pulls it up via that ID. (Note: Tempo now includes a query language called TraceQL to allow searching, but it still heavily relies on object storage scanning).

Side-by-Side Comparison

FeatureJaegerGrafana Tempo
Primary Storage BackendElasticsearch, Cassandra, OpenSearchAmazon S3, Google Cloud Storage, Azure Blob
Cost at ScaleHigh (Requires heavy compute/RAM for DBs)Extremely Low (Relies on cheap object storage)
User InterfaceStandalone Jaeger UINatively integrated inside Grafana
Search MechanismFull-text, database-indexed searchTraceQL or structural discovery via logs/metrics
EcosystemStandalone CNCF toolTightly bound to the Grafana LGTM stack (Loki/Grafana/Tempo/Mimir)

Which one should you pick?

  • Choose Jaeger if: You want a standalone, rock-solid tracing tool, you have a massive budget for Elasticsearch, or your engineers absolutely need to query traces using complex tag filtering without depending on logs or Grafana.
  • Choose Tempo if: You are already using Grafana, you want to view metrics, logs, and traces side-by-side in a single window, and you want to scale up tracing across millions of requests without destroying your cloud infrastructure budget.

OpenTelemetry vs. Prometheus: Understanding Their Roles

techhadoop OTel, Prometheus

No, OpenTelemetry (OTel) will not replace Prometheus. Instead, they have essentially joined forces.

The relationship between the two is one of the most misunderstood topics in DevOps and SRE, but the reality is beautifully collaborative: OpenTelemetry is the industry standard for generating and collecting data, while Prometheus is the industry standard for storing and querying metrics.

The Core Difference

FeaturePrometheusOpenTelemetry (OTel)
What is it?A full monitoring system (scraper, database, querying, alerting).A standardized framework/API for application instrumentation.
Data ScopeMetrics only (CPU, memory, request counts).The “Three Pillars”: Metrics, Traces, and Logs.
StorageHas its own built-in, highly efficient local database (TSDB).No storage. It only collects and routes data; it cannot store it.
Data FlowTraditionally Pull-based (scrapes apps).Push or Pull (via the OTel Collector).

Why OTel Won’t Replace Prometheus (They “Grew Up” Together)

Historically, there was real friction. If you instrumented an application using OTel, it used dot-notation (http.server.request.duration), but Prometheus only accepted underscores (http_server_request_duration). It caused massive formatting headaches.

However, the community completely solved this. With the widespread adoption of Prometheus 3.0, the two systems are perfectly intertwined:

  1. Prometheus Speaks OTel Natively: Prometheus natively ingests OTLP (OpenTelemetry’s native protocol) and fully supports OTel’s naming conventions (like dots and dashes).
  2. OTel Lacks a Backend: Because OTel explicitly refuses to build a storage database or a query language, it needs backends. When OTel collects metrics from your applications, it frequently sends them directly into a Prometheus backend.
  3. Infrastructure vs. Application: * Prometheus remains king for infrastructure monitoring. Thousands of tools (like Kubernetes, Linux Node Exporter, databases) natively output Prometheus metrics.
    • OTel is the new king for application monitoring (APM), because it allows developers to write code once and seamlessly correlate metrics with deep distributed distributed traces.

The Winning Architecture

Most modern engineering teams don’t choose between them—they use them together in a hybrid pipeline:

[ App / Code ] ──(Traces & Metrics)──> [ OTel Collector ]
                                              │
                    ┌─────────────────────────┴────────────────────────┐
                    ▼                                                  ▼
     [ Prometheus / Grafana ]                                  [ Jaeger / Tempo ]
      (Stores & Alerts on Metrics)                              (Stores & Analyzes Traces)