Grafana Tempo’s storage backend isn’t just an object store; it’s a distributed log-structured merge-tree, and S3/GCS are just the transport layer for its immutable chunks.
Let’s see Tempo in action, storing traces in GCS. Imagine we have a simple application generating traces. We’ve configured Tempo to send these traces to GCS. Here’s a snippet of what that configuration might look like:
# tempo.yaml
storage:
trace:
backend: gcs
gcs:
bucket: "my-tempo-traces-bucket"
# Optional: specify a prefix within the bucket
# prefix: "traces/"
When Tempo receives a trace, it processes it and writes it out as a series of immutable "chunks" to the configured object storage. These chunks are small, typically a few megabytes, and contain serialized trace data. Tempo doesn’t update existing chunks; it only appends new ones. This immutability is key to its design.
The core problem Tempo solves is managing the sheer volume of trace data generated by modern distributed systems. Traditional tracing backends often struggle with scale, cost, and the operational overhead of managing large, mutable databases. Tempo’s approach decouples compute from storage, allowing you to scale them independently. By using commodity object storage like S3 or GCS, it leverages highly available, durable, and cost-effective infrastructure.
Internally, Tempo uses a Log-Structured Merge-Tree (LSM-tree) like structure for its chunk management. When traces are received, they are written to a local memory buffer and then flushed to disk as immutable sorted files (chunks). These chunks are then uploaded to object storage. When a query comes in, Tempo first consults its index (which can be a separate database like Cassandra, DynamoDB, or even Redis, or an in-memory index for smaller deployments) to find which chunks contain the requested trace IDs. It then retrieves these chunks from S3 or GCS and reconstructs the trace.
The backend and its specific configuration (s3 or gcs) tell Tempo where to physically store these immutable chunks. For S3, it would look like this:
# tempo.yaml
storage:
trace:
backend: s3
s3:
bucket: "my-tempo-traces-bucket"
endpoint: "s3.amazonaws.com" # For AWS
# region: "us-east-1" # For AWS
# For other S3-compatible storage, you might use:
# endpoint: "http://localhost:9000" # Example for MinIO
# access_key_id: "YOURACCESSKEY"
# secret_access_key: "YOURSECRETKEY"
The bucket is the primary container for your trace chunks. The endpoint is crucial for non-AWS S3 or if you’re using a specific region. For GCS, the configuration is similarly straightforward:
# tempo.yaml
storage:
trace:
backend: gcs
gcs:
bucket: "my-tempo-traces-bucket"
# Optional: specify a project ID if not using default
# project: "my-gcp-project-id"
The bucket in GCS is analogous to the S3 bucket. Tempo will create objects within this bucket to store the trace chunks. The project field is relevant if your Tempo instance is running in a different GCP project than the one where the GCS bucket resides.
A common point of confusion is how Tempo handles indexing. The object storage (S3/GCS) is only for the trace data itself (the chunks). The mapping of trace IDs to the specific chunks that contain them is handled by a separate indexing component. If you don’t configure an external index, Tempo defaults to an in-memory index, which is only suitable for very small deployments or testing. For production, you must configure an external index like cassandra, dynamodb, or redis. For example, with Redis:
# tempo.yaml
ingester:
# ... other ingester config
trace_ விகிதம்:
backend: redis
redis:
url: "redis://localhost:6379"
# For HA Redis Sentinel:
# sentinel_master: "mymaster"
# urls: ["redis-1:6379", "redis-2:6379"]
This trace_ விகிதம் (trace index) configuration is what allows Tempo to quickly look up which chunks to fetch from S3/GCS when you query for a specific trace. Without a robust index, queries would be impossibly slow, as Tempo would have to scan every object in your storage bucket.
The real magic of Tempo’s storage backend lies in its ability to leverage cloud-native object storage for massive scalability and cost-effectiveness. You don’t need to provision or manage complex distributed databases for trace data storage itself. Tempo writes immutable chunks, and object storage handles the rest with incredible durability and availability. The index, however, is a separate operational concern that needs to be managed for query performance.
When you’re setting up S3 or GCS, ensure your Tempo service account or IAM role has the necessary s3:PutObject, s3:GetObject, and s3:ListBucket (or equivalent GCS permissions) for the specified bucket. Without these, Tempo won’t be able to write or read trace data, leading to dropped traces or failed queries.
The next hurdle you’ll likely face is optimizing query performance, which hinges entirely on your chosen indexing strategy.