Chapter 2
Tempo: Architecture and Core Components
Tempo revolutionizes large-scale distributed tracing by balancing cost, scalability, and simplicity. But what powers this unique approach? This chapter systematically unpacks the architectural blueprints, design philosophies, and critical components that enable Tempo to ingest, retain, and serve trace data efficiently at any scale. Discover how architectural trade-offs, modern storage integration, and robust component orchestration form the backbone of high-performance, cloud-native observability.
2.1 Philosophy and Design Goals of Tempo
The inception of Tempo is rooted in addressing the significant challenges intrinsic to large-scale distributed tracing infrastructure, where the volume, velocity, and complexity of telemetry data impose stringent demands on storage, retrieval, and operational simplicity. The fundamental philosophy underlying Tempo is to offer a scalable, cost-efficient, and highly available tracing backend that seamlessly integrates with modern cloud-native ecosystems while minimizing operational overhead.
Central to Tempo's design is the deliberate decoupling of compute and storage, achieved through a stateless architecture. Traditional tracing systems often maintain substantial state or rely on monolithic storage models, which limit horizontal scalability and complicate maintenance. Tempo, instead, enforces statelessness in its query and ingest components, enabling dynamic scaling and rapid recovery from failures. Statelessness reduces operational complexity by eliminating the need for distributed consensus protocols or leader elections within the tracing backend, thus simplifying the system's fault tolerance model.
The architectural decision to delegate trace data persistence to object storage represents the cornerstone of Tempo's cost-efficiency and durability strategy. By integrating deeply with cloud-native object stores such as Amazon S3, Google Cloud Storage, or Azure Blob Storage, Tempo leverages the inherent scalability, redundancy, and lifecycle management features offered by these platforms. Object storage's immutable, append-only semantics align naturally with trace data characteristics-write-once, read-often-but cold access patterns. Shards of trace data can be offloaded to inexpensive, long-term storage tiers without sacrificing query fidelity, supporting seamless retention policy enforcement.
Tempo's embrace of object storage introduces unique design constraints-such as the absence of random writes and high-latency persistent media-that steer its ingestion pipeline and index design. To accommodate these constraints, metadata indexing within Tempo is optimized for append-only workloads and efficient bulk retrieval, eschewing traditional indexing models requiring frequent updates or complex transaction semantics. This manifests as a chunk-based trace storage layout where trace spans and associated index entries are accumulated asynchronously, then flushed in bundled writes to object storage.
Addressing the scale challenges posed by high-cardinality trace queries, Tempo prioritizes index minimization and emphasizes a lookup approach favoring retrieval of entire trace chunks over partial span retrieval. Unlike conventional trace databases that maintain per-span indexes with complex query engines, Tempo's design channels queries to operate on relatively coarse-grained units, significantly reducing index size and maintenance costs. This trade-off improves ingestion throughput and lowers storage usage, reflecting an intentional compromise favoring typical trace exploration workflows that focus on whole-trace retrieval and dependency analysis.
Operational simplicity is further enhanced by Tempo's zero-dependency approach to storage management. Unlike solutions requiring bespoke storage clusters or databases, Tempo imposes no additional operational burden beyond access to an existing object storage system. This architecture enables use cases ranging from ephemeral development environments to enterprise-grade multi-tenant deployments without introducing complex storage provisioning or capacity planning tasks.
The end-to-end data flow in Tempo is designed with observability and resilience as guiding principles. Stateless ingestion components incorporate robust retry mechanisms and backpressure signals to accommodate transient object storage latencies. Trace data are partitioned by tenant and temporal intervals, enabling efficient parallelization of ingestion and querying workloads. This partitioning also supports multi-tenancy isolation, essential for environments hosting multiple business units or projects, and contributes to manageability by aligning data retention policies with natural shard boundaries.
Moreover, Tempo's design anticipates the heterogeneity of cloud environments and tracing protocols. By accepting traces in standard formats (e.g., OpenTelemetry), Tempo ensures compatibility with diverse instrumentation sources. The system's modular pipeline architecture allows future enhancement or customization of processing stages without compromising core stability, reflecting a forward-looking design accommodating evolving observability paradigms.
Security considerations also inform Tempo's philosophy. By leveraging object storage's mature access control models and integrating with existing identity and access management frameworks, Tempo avoids duplicating security functionalities. The minimal stateless components simplify threat surfaces and allow administrators to enforce consistent, centralized policies on trace data access.
In summary, the confluence of statelessness, object storage integration, index minimization, and operational simplicity embodies Tempo's defining characteristics. Each architectural choice is motivated by pragmatic concerns: scaling to millions of traces per second, reducing total cost of ownership, and facilitating ease of deployment in cloud-native settings. This focused philosophy establishes Tempo not merely as a trace storage solution but as an enabling platform for observability at scale, harmonizing with modern infrastructure and evolving application landscapes.
2.2 Distributors, Ingesters, Compactors, and Queriers
Tempo's backend architecture is composed of four principal components: Distributors, Ingesters, Compactors, and Queriers. Each is specialized to optimize the handling, storage, and retrieval of trace data. These components form a pipeline that starts from external ingestion and culminates in low-latency query responses, ensuring scalable, durable, and efficient trace management.
Distributors
Distributors serve as the front-line entry points for incoming trace data. Their core responsibility is to validate, preprocess, and then route the incoming spans to appropriate Ingesters for storage. Operating statelessly, Distributors harness consistent hashing to shard incoming traces by trace ID, ensuring that all spans belonging to the same trace are consistently directed to the same Ingester. This pivotal routing preserves trace coherence and facilitates efficient downstream processing.
Execution flow in a Distributor begins with accepting HTTP or gRPC requests containing span batches. Distributors parse the request, apply tenant identification, and perform lightweight validation for data integrity and format compliance. Subsequently, spans are partitioned and forwarded based on the shard key. Network and serialization overhead are minimized by batch forwarding, which also optimizes throughput.
In fault scenarios-such as temporary unavailability of target Ingesters-Distributors implement retry logic with exponential backoff to ensure eventual data delivery. However, since Distributors are stateless and horizontally scalable, failures are transient, and recovery occurs seamlessly through redirection of requests to healthy Distributor instances.
Ingesters
Ingesters are the first durably stateful layer in Tempo's data flow, tasked with the temporary buffering and durable storage of entering spans. Each Ingester receives distributed span batches from Distributors, appends these to in-memory write-ahead logs (WALs), and holds the data in memory for rapid access. Their role balances providing immediate write durability with the necessity of efficient compaction downstream.
The operational lifecycle of an Ingester involves three primary stages: write receipt, buffering, and flushing. Upon receipt, spans are appended to an append-only WAL, ensuring data persistence even on node failure. Simultaneously, spans reside in in-memory chunks, keyed by trace and timestamp, which allows for low-latency immediate querying of recently ingested data. Periodically, depending on memory consumption and predefined thresholds, Ingesters flush these in-memory chunks as immutable blocks to the object storage tier.
Ingesters coordinate with a distributed ring for membership and shard ownership, allowing the platform to scale elastically. Upon failure or shutdown, an Ingester's WAL is replayed during startup to recover unflushed data, guaranteeing no loss of trace spans in transient fault states.
Compactors
Compactors provide critical background processing to transform the raw, incoming trace...
