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Medical Conditions DisordersTop 10 Best Ceph Tracing Software of 2026
Compare the Top 10 Ceph Tracing Software options for 2026, with Tracee, Parca, and Grafana Tempo ranked for cluster observability.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Tracee
eBPF-driven dynamic syscall and kernel event tracing with flexible filters
Built for ceph operators needing syscall-level observability with minimal instrumentation.
Parca
Editor pickContinuous CPU profiling with aggregated, queryable flamegraphs
Built for ceph operators needing continuous profiling flamegraphs for CPU hotspot root-cause analysis.
Grafana Tempo
Editor pickTempo’s trace search and aggregation with Grafana Explore for rapid cross-service incident analysis
Built for observability teams needing fast trace search and Grafana correlation for Ceph-adjacent services.
Related reading
Comparison Table
This table compares Ceph tracing tools by integration depth, data model, and the automation and API surface used to ingest spans, metrics, and logs. It also maps admin and governance controls such as RBAC, configuration provisioning, and audit log coverage across Tracee, Parca, Grafana Tempo, Jaeger, OpenTelemetry Collector, and other options. Use the comparison to evaluate schema choices, extensibility paths, and how each system handles Ceph workload throughput under real pipeline configurations.
Tracee
eBPF observabilityTracee provides eBPF-based syscall tracing to observe process and kernel activity with low overhead.
eBPF-driven dynamic syscall and kernel event tracing with flexible filters
Tracee uniquely focuses on eBPF-based tracing that turns kernel and userspace activity into rich events without requiring application instrumentation. For Ceph environments, it can capture storage and network related system calls to connect performance behavior with workload actions.
It provides flexible filtering and event selection to target noisy subsystems such as block IO and network paths used by Ceph components. Collected traces can be analyzed and exported through its event-driven output and integrations.
- +eBPF tracing captures system behavior without modifying Ceph services
- +Powerful event filtering targets Ceph-related syscalls and workloads
- +Low overhead tracing helps observe live Ceph clusters during incidents
- +Consistent event model simplifies building repeatable investigations
- –Kernel and eBPF prerequisites can add setup complexity in Ceph hosts
- –Interpreting raw syscall events to Ceph-level meaning takes expertise
- –High event rates require careful selection to avoid noisy outputs
Ceph performance engineers
Identify Ceph slowdowns without app instrumentation
Faster root-cause for incidents
SRE operations teams
Triage noisy block IO stalls
Reduced time to mitigate
Show 2 more scenarios
Network reliability engineers
Debug intermittent Ceph networking issues
Clearer network failure patterns
Tracee traces network activity to link transport behavior with workload actions across Ceph nodes.
Platform security teams
Monitor Ceph process behavior changes
Early detection of anomalies
Event streams support auditing suspicious syscall patterns tied to Ceph daemons and clients.
Best for: Ceph operators needing syscall-level observability with minimal instrumentation
More related reading
Parca
profiling-firstParca generates continuous profiling and supports trace-like investigations via profiling data for Go, Java, and more workloads.
Continuous CPU profiling with aggregated, queryable flamegraphs
Parca stands out by focusing on continuous profiling and aggregated flamegraphs, which fits Ceph performance investigation across noisy, long-lived workloads. It captures CPU and call-stack profiles, then visualizes them as interactive flamegraphs tied to binary and symbol resolution.
For Ceph clusters, it supports pinpointing hotspots in OSD, MON, and client processes using low-friction instrumentation that pairs well with existing observability pipelines. The result is faster root-cause narrowing for latency spikes, replication stalls, and CPU saturation than log-only approaches.
- +Aggregates continuous CPU profiles into flamegraphs for quick hotspot discovery
- +Works well for long-running Ceph processes where incidents recur across time
- +Uses symbolization and binary metadata to make stack traces readable
- –Biases toward CPU profiling, so memory stalls and IO waits need other signals
- –Requires careful symbol and binary setup to avoid unhelpful stack names
- –Correlation to specific Ceph events still needs external timestamps and tooling
Ceph SREs and performance engineers
OSD CPU saturation root-cause analysis
Reduce time to diagnosis
Storage reliability engineers
Latency spikes during replication backlog
Stabilize tail latency
Show 2 more scenarios
Platform observability leads
Profiling for noisy multi-tenant workloads
Identify regression early
Leads aggregate profiles over long runs to isolate regressions despite overlapping traffic patterns.
On-call incident responders
MON and client slowdown triage
Faster incident mitigation
Responders use flamegraphs to pinpoint slow functions across MON and client binaries quickly.
Best for: Ceph operators needing continuous profiling flamegraphs for CPU hotspot root-cause analysis
Grafana Tempo
distributed tracingGrafana Tempo is a distributed tracing backend for OpenTelemetry traces used to locate latency and failure paths across services.
Tempo’s trace search and aggregation with Grafana Explore for rapid cross-service incident analysis
Grafana Tempo stands out by pairing Tempo for trace storage with Grafana dashboards and Tempo’s trace search designed for fast, high-cardinality observability workflows. It supports OpenTelemetry ingestion and spans routing through Tempo, making it practical for instrumented microservices and Kubernetes environments that need end-to-end request visibility.
Tempo integrates with Grafana’s explore experience to correlate trace findings with metrics and logs, reducing time spent pivoting between tools. For Ceph tracing, the biggest strengths come from capturing request spans around gateways, clients, and services that interact with Ceph rather than from tracing Ceph internals directly.
- +OpenTelemetry ingestion supports standard spans and attributes without custom exporters
- +Grafana trace search enables quick correlation with dashboards during incident triage
- +Native integrations fit Kubernetes workflows using common collectors and exporters
- –Ceph end-to-end visibility depends on where spans are emitted
- –Throughput tuning for trace retention and storage can be operationally demanding
- –Query performance degrades when span cardinality and tag usage are not controlled
SRE teams running Ceph clusters
Diagnose slow Ceph client requests
Shorter incident time
Platform engineers on Kubernetes
Correlate Ceph operations across services
Faster root cause
Show 2 more scenarios
Storage observability owners
Track high-cardinality request flows
Better visibility at scale
Tempo’s trace search supports high-cardinality patterns for Ceph-facing APIs and microservice interactions.
Performance engineers for distributed systems
Compare retries and routing paths
Improved request reliability
Tempo highlights routing and retry spans around Ceph interactions to explain throughput drops.
Best for: Observability teams needing fast trace search and Grafana correlation for Ceph-adjacent services
More related reading
Jaeger
distributed tracingJaeger collects, stores, and queries distributed tracing spans to visualize request flow across microservices.
Service graph view that maps inferred request dependencies from trace data
Jaeger stands out with its end-to-end distributed tracing model built around spans, traces, and service graphs. It can ingest telemetry via Jaeger clients and common OpenTelemetry or OpenTracing pathways, then visualize request flows and latencies.
For Ceph environments, it is useful for instrumenting RGW, MDS, RADOS Gateway components, or related application services and correlating downstream calls across microservices. It also supports trace sampling, search, and span-level drilldowns that help pinpoint latency hotspots in a multi-service stack.
- +Powerful trace search with span drilldowns and latency breakdowns
- +Works with OpenTelemetry and Jaeger protocol ingestion for flexible instrumentation
- +Supports service graphs to expose dependencies across traced services
- –Ceph-specific tracing requires manual instrumentation of Ceph-facing components
- –Operational setup for storage, query, and ingestion tuning adds complexity
- –High-volume tracing needs careful sampling to avoid index and retention pressure
Best for: Teams instrumenting Ceph-adjacent services to visualize latency and dependencies
OpenTelemetry Collector
telemetry pipelineThe OpenTelemetry Collector receives, processes, and exports tracing data from instrumented applications.
Processor pipelines with attribute and resource enrichment for consistent span metadata
OpenTelemetry Collector stands out by acting as a configurable telemetry pipeline that can ingest Ceph-related logs, metrics, and traces and forward them to multiple backends. It supports OTLP end to end, so Ceph tracing spans can be normalized, enriched, and routed consistently before storage. It also includes a large set of receiver, processor, and exporter components, which helps standardize observability across heterogeneous Ceph deployments.
- +Modular receivers, processors, and exporters support flexible Ceph telemetry routing
- +OTLP-first pipeline standardizes traces and metrics formats across multiple backends
- +Batching, memory limiting, and retry logic improve reliability under telemetry spikes
- +Resource and attribute processors help align Ceph cluster metadata for correlation
- –Achieving correct Ceph trace context propagation requires careful instrumentation mapping
- –Configuration complexity rises quickly when adding multiple processors and exporters
- –Debugging dropped spans is harder than with purpose-built Ceph tracing dashboards
- –Transforms can be limited for deep Ceph-specific semantics without custom logic
Best for: Ceph operators needing an OTLP telemetry hub for tracing plus metrics correlation
Elastic APM
APM tracingElastic APM ingests traces and transaction events to correlate application performance issues across services.
Service maps with trace-driven dependency visualization
Elastic APM stands out for combining distributed tracing with searchable logs and metrics in a single Elastic data model. It provides service maps, trace sampling controls, and span-level analysis for pinpointing where Ceph-related services stall or fail.
Intake supports common instrumentation paths for Java, Python, Node.js, and OpenTelemetry, which simplifies capturing Ceph gateway, controller, and client behavior. Correlation with infrastructure metrics helps relate storage latency spikes to trace spans across dependent components.
- +Span-level distributed tracing with rich dependency views for Ceph call chains
- +OpenTelemetry support enables consistent instrumentation across Ceph-adjacent services
- +Correlates traces with logs and metrics for faster root-cause analysis
- –High-cardinality fields can inflate storage and indexing costs for trace data
- –Service-map accuracy depends on correct propagation across Ceph-facing components
- –Fine-grained tuning of sampling and retention adds operational overhead
Best for: Teams tracing microservice paths that depend on Ceph storage latency
More related reading
Dynatrace
enterprise APMDynatrace provides end-to-end distributed tracing and dependency mapping for identifying slow or failing components.
Service topology discovery with Davis AI-driven root-cause analysis for correlated tracing
Dynatrace stands out with end-to-end distributed tracing driven by intelligent request correlation and automated service topology discovery. It captures traces across microservices and infrastructure so Ceph-related latency and failure cascades can be tied to application transactions.
Native support for observability workflows like anomaly detection and root-cause analysis helps narrow which Ceph component impacts user-perceived performance. Deep metrics and log integration improves verification of trace findings across Ceph daemons and storage operations.
- +Auto-discovered service maps connect Ceph storage events to app transactions
- +End-to-end tracing correlates latency spikes across distributed systems
- +Anomaly detection highlights abnormal trends affecting Ceph and request flows
- +Root-cause analysis reduces investigation time for performance regressions
- –Ceph-specific instrumentation needs careful mapping of storage operations
- –High-cardinality traces can create heavy dashboard and query overhead
- –Deep configuration of agents and collectors can be time-consuming
- –Cross-domain correlation requires consistent context propagation across services
Best for: Enterprises needing automated tracing correlation across app and Ceph storage layers
Datadog APM
cloud APMDatadog APM collects distributed traces and links them to logs and metrics for root-cause analysis.
Service maps with distributed traces across services
Datadog APM stands out with deep distributed tracing that ties spans to services, endpoints, and logs for fast root-cause workflows. It provides an end-to-end view of request traces, with searchable trace analytics and service maps for identifying latency and dependency issues across microservices.
For Ceph tracing, it is strongest when Ceph client, gateway, and supporting apps emit compatible spans so Datadog can correlate Ceph-related operations with application traffic. Without that instrumentation, Ceph internal behavior will not appear as meaningful traces.
- +Correlates traces with logs and metrics for faster Ceph-adjacent incident triage
- +Service maps and dependency views reveal latency hot paths across traced components
- +Powerful trace search supports pinpointing slow spans and error patterns
- –Effective Ceph tracing depends on correct instrumentation for Ceph-related spans
- –High trace volume can increase ingestion overhead without careful sampling
- –Service map usefulness drops when Ceph components do not emit trace context
Best for: Platform teams tracing microservices plus Ceph-adjacent workflows for rapid root-cause
More related reading
New Relic Distributed Tracing
enterprise tracingNew Relic distributed tracing correlates spans with transactions and services to diagnose performance issues.
Distributed tracing with service maps plus trace-to-logs correlation for rapid dependency troubleshooting
New Relic Distributed Tracing stands out for end-to-end trace visibility built on OpenTelemetry instrumentation and New Relic agent support. It collects spans, correlates them with logs and metrics, and drives interactive latency and dependency analysis across microservices.
For Ceph-backed applications, it can trace calls that touch Ceph gateway services, RADOS Gateway endpoints, or client RPC flows when those operations are instrumented. Deep Ceph storage internals only show up when Ceph components are instrumented or linked through traced application requests rather than from the Ceph stack automatically.
- +OpenTelemetry compatibility supports spans for Ceph-touching application services
- +Trace-to-logs and trace-to-metrics correlation accelerates root-cause analysis
- +Built-in service maps highlight slow or failing request paths
- +High-cardinality filtering and search improve pinpointing problematic spans
- –Ceph internal operations are not automatically traced without custom instrumentation
- –Accurate correlation depends on consistent trace propagation across services
- –Deep RADOS Gateway versus librados causality can be hard to model from spans
- –Troubleshooting requires familiarity with distributed tracing concepts
Best for: Teams instrumenting Ceph-dependent microservices for trace-driven latency diagnosis
Zipkin
distributed tracingZipkin receives and visualizes trace data to help trace requests through services and spot bottlenecks.
Trace timeline UI with span-level duration and error surfacing
Zipkin distinctively focuses on end-to-end distributed tracing with a compact trace data model and visual trace timelines. It supports common instrumentation patterns and can ingest spans from applications to enable correlation across services.
For Ceph tracing, it pairs well with tracing-enabled RADOS or gateway request paths when spans are emitted from relevant components. Its core workflow centers on collecting spans, searching by trace and service attributes, and analyzing latency and failure propagation across hops.
- +Fast trace timeline visualization with span ordering and timing breakdowns
- +Strong search by service name, trace ID, and timing attributes
- +Lightweight deployment options for span collection and query
- +Fits well with OpenTelemetry and common tracing instrumentation pipelines
- –Ceph-specific tracing requires custom span emission in Ceph components or gateways
- –Advanced analytics like service dependency modeling needs external tooling
- –Large-scale retention and high-cardinality metadata can strain storage backends
Best for: Teams tracing microservice calls that include Ceph gateway or storage paths
Conclusion
After evaluating 10 medical conditions disorders, Tracee stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Ceph Tracing Software
This buyer’s guide covers Tracee, Parca, Grafana Tempo, Jaeger, OpenTelemetry Collector, Elastic APM, Dynatrace, Datadog APM, New Relic Distributed Tracing, and Zipkin for Ceph-adjacent observability and Ceph-focused investigations.
The guidance focuses on integration depth, the tracing or profiling data model, automation and API surface, and admin and governance controls that affect day-to-day operations in Ceph clusters.
Ceph tracing for syscall, CPU flamegraphs, and request spans that map to Ceph performance symptoms
Ceph tracing software captures timing and context so investigations can connect latency, stalls, and failures to Ceph components and Ceph-adjacent services. Tracee targets Ceph hosts by collecting eBPF-based syscall and kernel events, which can relate storage and network behavior to workload actions without application instrumentation.
Parca targets long-lived Ceph processes by producing continuous CPU profiling and aggregated flamegraphs, which helps isolate CPU hotspots in OSD, MON, and client workloads. Grafana Tempo and Jaeger focus on distributed tracing spans for request flows, which helps trace gateway and service calls that touch Ceph rather than automatically instrumenting Ceph internals.
Evaluation criteria that map to integration, data model control, automation, and governance in Ceph tracing
Ceph investigations fail when tracing signals cannot be correlated to Ceph operations, which makes integration depth and the data model decisive. Tracee and Parca reduce that gap on Ceph hosts by capturing kernel and CPU behavior directly, while Tempo and Jaeger require that spans are emitted by Ceph-facing gateways and clients.
Automation and API surface matter because trace pipelines often need enrichment, routing, and controlled sampling under telemetry spikes. OpenTelemetry Collector is a concrete example because it can standardize OTLP spans through receiver, processor, and exporter components with attribute and resource enrichment, while higher-volume backends like Tempo and Elastic APM depend on cardinality control for stable search and throughput.
Host-side Ceph signals via eBPF syscall tracing
Tracee captures kernel and userspace activity through eBPF-driven dynamic syscall and kernel event tracing with flexible filtering for Ceph-related subsystems like block IO and network paths. This reduces reliance on app instrumentation and supports live incident observation on Ceph hosts with minimal overhead.
Continuous CPU profiling with aggregated, queryable flamegraphs
Parca generates continuous CPU profiling and visualizes aggregated flamegraphs with binary and symbol resolution, which helps pinpoint CPU hotspots in OSD, MON, and client processes. This works especially well when incidents recur across time, while profiling-aware triage complements log-only approaches.
OpenTelemetry ingest and trace routing with standardized span metadata
Grafana Tempo and Jaeger support OpenTelemetry or compatible ingestion paths, which enables consistent trace search on distributed request flows that touch Ceph gateways and services. OpenTelemetry Collector adds governance-like control through processor pipelines for resource and attribute enrichment plus batching, retry logic, and memory limiting for ingestion reliability.
Trace search performance under span cardinality and retention pressure
Grafana Tempo emphasizes fast trace search and aggregation with Grafana Explore for cross-service incident triage, but query performance degrades when span cardinality and tag usage are not controlled. Elastic APM and Dynatrace similarly rely on correct context propagation and careful handling of high-cardinality fields to prevent storage and query overhead from dominating investigations.
Dependency and service topology visualization for request-to-Ceph call chains
Jaeger provides service graph views inferred from trace data, which supports mapping request dependencies across traced services that call Ceph. Elastic APM provides service maps with trace-driven dependency visualization, while Dynatrace adds automated service topology discovery to connect Ceph-related latency and failure cascades to application transactions.
Automation and governance control points for sampling, enrichment, and routing
Tempo supports trace search and aggregation for operational triage, while Jaeger supports sampling controls to limit index and retention pressure during high-volume tracing. OpenTelemetry Collector adds explicit governance levers through configurable receiver, processor, and exporter pipelines that enrich Ceph cluster metadata for correlation.
A Ceph-focused decision framework for selecting tracing tooling by signal source and control depth
Start by selecting the signal source that matches the Ceph symptom type. Tracee is a direct fit when the goal is Ceph-host syscall and kernel behavior visibility, while Parca is a direct fit when the goal is continuous CPU hotspot identification in long-lived Ceph daemons.
Next, align the integration and data model to how spans or profiling context will be produced and governed. Tempo and Jaeger require instrumented Ceph-facing request paths, while OpenTelemetry Collector can normalize and enrich OTLP traces into backends that support search and correlation.
Match tracing signal to Ceph symptom behavior
If investigations need to connect Ceph storage and network behavior to live kernel and syscall activity, choose Tracee because it captures eBPF-driven syscall and kernel events with flexible filters. If investigations need CPU hotspot narrowing in OSD, MON, or client workloads over time, choose Parca because it produces continuous CPU profiles and aggregated flamegraphs.
Pick the data model that fits correlation requirements
For request-level causality across services that touch Ceph gateways, choose Grafana Tempo or Jaeger because both are built on distributed tracing spans and trace search. For Ceph-host behavior that does not rely on application spans, choose Tracee or Parca because they capture kernel or CPU behavior without needing Ceph services to emit trace context.
Decide where span normalization and enrichment should run
Choose OpenTelemetry Collector when trace metadata needs consistent normalization and enrichment for correlation, because it provides processor pipelines that align resource and attribute metadata and forwards OTLP to multiple backends. Choose Tempo or Elastic APM when the goal is fast search and correlation in a single observability workflow, because Tempo pairs trace search with Grafana Explore and Elastic APM correlates traces with logs and metrics.
Define automation levers for sampling and high-throughput control
If high-volume tracing risks query and retention pressure, use Jaeger sampling controls to cap telemetry intensity and keep indexing and retention manageable. If ingest spikes cause dropped signals, use OpenTelemetry Collector batching, memory limiting, and retry logic so telemetry pipelines remain stable under load.
Validate dependency visualization against Ceph-facing call paths
For teams that need inferred request dependencies, choose Jaeger service graphs or Elastic APM service maps because both build dependency views from trace data. For organizations that want automated topology discovery to connect application transactions to Ceph impacts, choose Dynatrace because it performs service topology discovery and ties correlated tracing with anomaly detection.
Avoid mismatched instrumentation coverage for Ceph internals
If only application spans exist and Ceph internals are not instrumented, Tempo and Jaeger will show Ceph-adjacent latency but will not automatically expose Ceph internal operations. If Ceph internals are the target, choose Tracee for eBPF syscall capture or Parca for CPU profiling because both reduce dependency on trace context emitted from Ceph components.
Which teams get the fastest value from each Ceph tracing approach
Ceph tracing needs vary by whether the bottleneck shows up as host-level behavior or request-level latency in Ceph-facing services. Host-side tools reduce reliance on instrumentation, while distributed tracing tools excel when request spans are already emitted across gateways, clients, and microservices.
The recommended choices below map directly to each tool’s stated best-for use case and standout signal type.
Ceph operators needing syscall-level observability without modifying Ceph services
Tracee fits this workload because it captures eBPF-based syscall and kernel events with low overhead and flexible filtering for Ceph-related subsystems. This reduces instrumentation work on Ceph hosts during incidents.
Ceph performance teams needing continuous CPU hotspot analysis for recurring incidents
Parca fits teams that need continuous profiling and aggregated, queryable flamegraphs tied to symbolization. This helps isolate CPU saturation and call-stack hotspots in long-lived OSD, MON, and client processes.
Observability teams correlating Ceph-adjacent request paths across Kubernetes services
Grafana Tempo fits teams that already emit OpenTelemetry traces because it provides trace storage and trace search with Grafana Explore correlation. This supports rapid cross-service incident analysis for gateways and services that interact with Ceph.
Platform teams tracing microservices that depend on Ceph gateway and storage paths
Elastic APM and Datadog APM fit teams that can produce compatible spans so traces can link to logs and metrics, which accelerates triage of Ceph-related stalls. Both tools focus on span analytics and service maps built from traced request paths.
Enterprises needing automated service topology discovery and anomaly-driven correlation across app and Ceph layers
Dynatrace fits enterprises because it performs service topology discovery and uses anomaly detection to highlight abnormal trends tied to Ceph impacts. This helps shorten investigation time when the Ceph component impact propagates across distributed systems.
Ceph tracing pitfalls caused by instrumentation gaps, schema misuse, and throughput blind spots
Many Ceph tracing failures come from mismatched signal coverage, because Ceph internal operations appear only when the tool is configured to capture them or when Ceph-facing request spans exist. Another frequent issue is unbounded metadata cardinality, which harms search speed and storage efficiency in span-based systems.
The pitfalls below connect directly to the concrete failure modes listed across the reviewed tools.
Assuming distributed tracing automatically reveals Ceph internal operations
Tempo and Jaeger show Ceph-adjacent latency based on where spans are emitted from gateways, clients, and services, not from Ceph internals by default. Tracee and Parca avoid this gap by capturing kernel syscall events or continuous CPU profiling directly on Ceph hosts.
Over-tagging spans and creating unmanageable cardinality
Tempo’s query performance degrades when span cardinality and tag usage are not controlled, and Elastic APM warns that high-cardinality fields inflate storage and indexing costs. Apply strict attribute and resource enrichment rules in OpenTelemetry Collector to keep metadata controlled.
Skipping symbol and binary preparation for profiling stacks
Parca depends on symbolization and binary metadata so stack traces become readable. If symbol and binary setup is incomplete, flamegraphs can produce unhelpful stack names that slow root-cause narrowing.
Running eBPF tracing without handling kernel prerequisites and event-rate control
Tracee works through kernel and eBPF prerequisites, which adds host setup complexity if those prerequisites are not planned. Tracee also needs careful event selection because high event rates can create noisy outputs that drown Ceph-related signals.
Expecting one tool to cover CPU, IO waits, and kernel behavior equally
Parca biases toward CPU profiling, so memory stalls and IO waits still require other signals to explain latency spikes. Tracee covers syscall and kernel behavior, so pairing signals across tools is often necessary when incidents involve both CPU and IO wait components.
How We Selected and Ranked These Tools
We evaluated Tracee, Parca, Grafana Tempo, Jaeger, OpenTelemetry Collector, Elastic APM, Dynatrace, Datadog APM, New Relic Distributed Tracing, and Zipkin using the same scoring signals: features, ease of use, and value, with features carrying the most weight while ease of use and value each contribute equally to the overall ordering. We rated each tool by mapping the stated capabilities to integration depth, data model control, automation and API surface, and operational friction in Ceph-focused workflows.
Tracee separated itself with eBPF-driven dynamic syscall and kernel event tracing plus flexible filtering designed to target Ceph-related syscalls and workloads without modifying Ceph services. That host-level signal capture raised the features score and supported its ease-of-use position when the goal is live incident observability on Ceph hosts.
Frequently Asked Questions About Ceph Tracing Software
How does Tracee differ from Tempo, Jaeger, and OpenTelemetry Collector for Ceph tracing coverage?
When investigating CPU saturation in Ceph, which tool is better aligned: Parca or distributed tracing tools?
Which setup fits gateway-focused request visibility for Ceph-backed services: Grafana Tempo, Jaeger, or Elastic APM?
What role does OpenTelemetry Collector play when multiple backends must receive Ceph traces?
How do SSO and RBAC controls typically differ across these tools for trace access?
Which tool is most suitable for diagnosing Ceph performance issues without modifying application code?
How can auditability be handled when tracing requires change approvals in regulated environments?
Why do Ceph internal behaviors sometimes not appear in Tempo, Datadog APM, or New Relic traces?
What admin controls matter most for high-throughput Ceph tracing: sampling, filtering, or both?
How do extensibility and integrations work across these tools when Ceph runs alongside Kubernetes and existing observability stacks?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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