Grafana Tempo, the distributed tracing backend, can be upgraded with minimal downtime, but the key is understanding how its internal storage, particularly its reliance on object storage, dictates the migration process.
Let’s see Tempo in action, specifically how it handles trace data and how a configuration change might look after an upgrade.
Imagine you have a simple Go application sending traces to Tempo. Here’s a snippet of how that might look:
package main
import (
"context"
"log"
"net/http"
"os"
"time"
"go.opentelemetry.io/otel"
"go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracegrpc"
"go.opentelemetry.io/otel/sdk/resource"
sdktrace "go.opentelemetry.io/otel/sdk/trace"
semconv "go.opentelemetry.io/otel/semconv/v1.20.0" // Use a specific version
)
var tracerProvider *sdktrace.TracerProvider
func initTracer() {
ctx := context.Background()
res, err := resource.New(ctx,
resource.WithAttributes(
semconv.ServiceName("my-tempered-app"),
semconv.ServiceVersion("1.0.0"),
),
)
if err != nil {
log.Fatalf("failed to create resource: %v", err)
}
// Replace with your Tempo OTLP endpoint
tempoEndpoint := "localhost:4317" // Default OTLP gRPC port
traceClient, err := otlptracegrpc.New(ctx,
otlptracegrpc.WithEndpoint(tempoEndpoint),
otlptracegrpc.WithInsecure(), // Use WithTLSCredentials for secure connections
)
if err != nil {
log.Fatalf("failed to create OTLP trace client: %v", err)
}
// For new versions of Tempo, you might be using a different protocol or endpoint.
// This example sticks to OTLP gRPC for broad compatibility.
bsp := sdktrace.NewBatchSpanProcessor(traceClient)
tracerProvider = sdktrace.NewTracerProvider(
sdktrace.WithResource(res),
sdktrace.WithSpanProcessor(bsp),
)
otel.SetTracerProvider(tracerProvider)
}
func main() {
initTracer()
defer func() {
ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
defer cancel()
if err := tracerProvider.Shutdown(ctx); err != nil {
log.Fatalf("failed to shutdown TracerProvider: %v", err)
}
}()
http.HandleFunc("/", func(w http.ResponseWriter, r *http.Request) {
ctx := r.Context()
tr := otel.Tracer("my-component")
_, span := tr.Start(ctx, "main-handler")
defer span.End()
span.AddEvent("handling request")
time.Sleep(100 * time.Millisecond) // Simulate work
w.Write([]byte("Hello, Tempo!"))
})
log.Println("Starting server on :8080")
log.Fatal(http.ListenAndServe(":8080", nil))
}
When you upgrade Tempo, the core challenge isn’t usually migrating the data itself, but ensuring your configuration and client libraries are compatible with the new version. Tempo’s data is stored in object storage (like S3, GCS, or MinIO) in a specific format. Older versions might write data in a way that newer versions can still read, but the reverse isn’t always true, and new features might require new configuration settings.
The primary problem Tempo solves is making distributed tracing accessible and cost-effective. Unlike traditional tracing systems that might require complex indexing or large databases, Tempo is designed to be simple, relying heavily on object storage for its data. It doesn’t index traces; instead, it uses external systems like Prometheus (for head-based metrics) and a key-value store (like etcd or Consul) for indexing head chunks. When you query a trace, Tempo reconstructs it from the object storage.
Here’s a look at a hypothetical tempo.yaml configuration file, highlighting some areas that might change between versions.
# Example tempo.yaml for a recent version
server:
http_listen_port: 3100
grpc_listen_port: 9095
common:
path:
# Used by the service if it needs to write local state.
# For example, if using the file backend for logs.
data_dir: /tmp/tempo
# This section defines how Tempo communicates with its dependencies.
# The specific backend for head-based metrics and the index store are critical.
# For an upgrade, you might be changing the version of these dependencies
# or their configuration.
#
# Prometheus is often used for head-based metrics (e.g., active traces).
# The object storage configuration is for where trace spans are persisted.
#
# Example for Prometheus remote write:
# prometheus:
# remote_write_client:
# url: http://prometheus:9090/api/v1/write
# Key-value store for indexing head chunks. etcd is common.
# In newer versions, you might see changes in how this is configured,
# or a move towards a different backend.
#
# Example for etcd:
# instance:
# name: tempo-instance
#
# storage:
# trace:
# backend: s3 # or gcs, azure, s3compat, filesystem
# s3:
# bucket: my-tempo-bucket
# endpoint: s3.amazonaws.com
# region: us-east-1
# access_key_id: YOUR_ACCESS_KEY_ID
# secret_access_key: YOUR_SECRET_ACCESS_KEY
# sse: AES256
#
# # The index configuration is crucial for trace retrieval.
# # Older versions might have different index types or configurations.
# # For example, a change from 'boltdb-shipper' to a new index type.
# index:
# # Example for a newer index type, possibly using a different backend
# # than the older 'boltdb-shipper' which relied on local filesystem.
# # Check the changelog for specific index backend changes.
# # This might look like:
# # type: cassandra # or other supported index backends
# # cassandra:
# # ... cassandra connection details ...
#
# # The head_unset configuration is for how Tempo handles traces
# # that have no explicit end span.
# head_unset:
# enabled: true
# max_unset_age: 10m # Example: 10 minutes
# For newer versions, you might encounter new experimental features
# or changes in how components are enabled.
#
# Example: New experimental tracing backend configuration
# experimental_tracing_backend:
# enabled: true
# type: jaeger # or other types
# config:
# ... specific config for the experimental backend ...
# If you are upgrading from a very old version, you might have
# been using a different set of components or a monolithic binary.
# Tempo has evolved to be more modular.
# The key to a smooth upgrade is reading the release notes and changelog
# for the specific versions you are moving between. Pay close attention
# to any changes in the `storage`, `index`, and `common.path` sections,
# as these are most likely to impact data storage and retrieval.
The most surprising true thing about Tempo upgrades is that you often don’t "migrate" data in the traditional sense; you simply point the new version at your existing object storage. The complexity lies in ensuring compatibility of the index and head chunk storage, and that your Prometheus/agent configurations are updated if Tempo’s endpoint or protocol changes.
When you upgrade Tempo, the next thing you’ll likely encounter is ensuring your Grafana instance is configured correctly to query the new Tempo version, especially if there are changes in the query API or authentication methods.