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Medical Conditions DisordersTop 10 Best Ceph Tracing Software of 2026
Compare the Top 10 Ceph Tracing Software for 2026. Tracee, Parca, and Grafana Tempo included. Explore best picks for your cluster.
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
Continuous CPU profiling with aggregated, queryable flamegraphs
Built for ceph operators needing continuous profiling flamegraphs for CPU hotspot root-cause analysis.
Grafana Tempo
Tempo’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 comparison table evaluates Ceph tracing software options used to collect, transport, and query storage-system telemetry across clusters. It covers Tracee, Parca, Grafana Tempo, Jaeger, the OpenTelemetry Collector, and additional tools, focusing on data capture methods, trace ingestion and storage, query and visualization, and integration paths into existing observability stacks.
| # | Tool | Category | Overall | Features | Ease of Use | Value |
|---|---|---|---|---|---|---|
| 1 | Tracee Tracee provides eBPF-based syscall tracing to observe process and kernel activity with low overhead. | eBPF observability | 8.7/10 | 9.0/10 | 8.3/10 | 8.8/10 |
| 2 | Parca Parca generates continuous profiling and supports trace-like investigations via profiling data for Go, Java, and more workloads. | profiling-first | 8.1/10 | 8.5/10 | 7.6/10 | 7.9/10 |
| 3 | Grafana Tempo Grafana Tempo is a distributed tracing backend for OpenTelemetry traces used to locate latency and failure paths across services. | distributed tracing | 8.2/10 | 8.6/10 | 7.9/10 | 8.1/10 |
| 4 | Jaeger Jaeger collects, stores, and queries distributed tracing spans to visualize request flow across microservices. | distributed tracing | 7.9/10 | 8.2/10 | 7.4/10 | 8.0/10 |
| 5 | OpenTelemetry Collector The OpenTelemetry Collector receives, processes, and exports tracing data from instrumented applications. | telemetry pipeline | 8.0/10 | 8.6/10 | 7.4/10 | 7.9/10 |
| 6 | Elastic APM Elastic APM ingests traces and transaction events to correlate application performance issues across services. | APM tracing | 7.7/10 | 8.3/10 | 7.6/10 | 7.0/10 |
| 7 | Dynatrace Dynatrace provides end-to-end distributed tracing and dependency mapping for identifying slow or failing components. | enterprise APM | 8.3/10 | 8.7/10 | 8.2/10 | 8.0/10 |
| 8 | Datadog APM Datadog APM collects distributed traces and links them to logs and metrics for root-cause analysis. | cloud APM | 8.1/10 | 8.4/10 | 7.8/10 | 8.0/10 |
| 9 | New Relic Distributed Tracing New Relic distributed tracing correlates spans with transactions and services to diagnose performance issues. | enterprise tracing | 7.6/10 | 8.0/10 | 7.3/10 | 7.2/10 |
| 10 | Zipkin Zipkin receives and visualizes trace data to help trace requests through services and spot bottlenecks. | distributed tracing | 7.3/10 | 7.0/10 | 8.2/10 | 6.8/10 |
Tracee provides eBPF-based syscall tracing to observe process and kernel activity with low overhead.
Parca generates continuous profiling and supports trace-like investigations via profiling data for Go, Java, and more workloads.
Grafana Tempo is a distributed tracing backend for OpenTelemetry traces used to locate latency and failure paths across services.
Jaeger collects, stores, and queries distributed tracing spans to visualize request flow across microservices.
The OpenTelemetry Collector receives, processes, and exports tracing data from instrumented applications.
Elastic APM ingests traces and transaction events to correlate application performance issues across services.
Dynatrace provides end-to-end distributed tracing and dependency mapping for identifying slow or failing components.
Datadog APM collects distributed traces and links them to logs and metrics for root-cause analysis.
New Relic distributed tracing correlates spans with transactions and services to diagnose performance issues.
Zipkin receives and visualizes trace data to help trace requests through services and spot bottlenecks.
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.
Pros
- 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
Cons
- 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
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.
Pros
- 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
Cons
- 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
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.
Pros
- 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
Cons
- 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
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.
Pros
- 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
Cons
- 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.
Pros
- 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
Cons
- 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.
Pros
- 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
Cons
- 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.
Pros
- 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
- Flexible integrations support combining traces with Ceph metrics and logs
Cons
- 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.
Pros
- 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
Cons
- 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.
Pros
- 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
- Alerting on trace latency supports proactive incident response
Cons
- 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.
Pros
- 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
Cons
- 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
How to Choose the Right Ceph Tracing Software
This buyer's guide explains how to select Ceph Tracing Software tools for syscall-level visibility, continuous profiling, and OpenTelemetry-based distributed tracing across Ceph-adjacent services. It covers Tracee, Parca, Grafana Tempo, Jaeger, the OpenTelemetry Collector, Elastic APM, Dynatrace, Datadog APM, New Relic Distributed Tracing, and Zipkin. It also maps the most relevant capabilities and tradeoffs to the operational outcomes teams want during Ceph latency spikes, replication stalls, and CPU saturation.
What Is Ceph Tracing Software?
Ceph tracing software captures how work flows through a Ceph-based storage stack so latency, errors, and bottlenecks can be tied to requests, components, or system calls. It typically solves two problems. The first is translating performance symptoms into actionable evidence. The second is correlating Ceph activity with application behavior so slow Ceph storage paths can be connected to the user-facing transactions. Tools like Tracee focus on eBPF-based syscall and kernel event tracing. Tools like Grafana Tempo focus on distributed traces stored and searched via OpenTelemetry spans for end-to-end visibility around Ceph clients and gateways.
Key Features to Look For
The right Ceph tracing feature set determines whether investigations produce Ceph-level meaning fast or drown in raw telemetry noise.
eBPF syscall and kernel event tracing with flexible filters
Tracee captures system behavior without modifying Ceph services by using eBPF-driven dynamic syscall and kernel event tracing. This matters because Ceph operators need low-overhead visibility during incidents and require targeted filtering to avoid noisy outputs, especially around block IO and network paths.
Continuous CPU profiling with aggregated flamegraphs
Parca generates continuous profiling and renders aggregated, queryable flamegraphs from collected CPU and call-stack profiles. This matters for Ceph because long-lived OSD, MON, and client processes often show recurring CPU hotspots that are faster to isolate with flamegraphs than log-only approaches.
Distributed tracing backend with fast trace search and Grafana correlation
Grafana Tempo provides trace storage and trace search designed for high-cardinality observability workflows and pairs with Grafana Explore for incident triage correlation. This matters because Ceph tracing visibility frequently depends on where spans are emitted in Ceph-adjacent gateways, clients, and services.
Service graph views that infer request dependencies
Jaeger provides a service graph view that maps inferred request dependencies from trace data. Elastic APM and Datadog APM also provide service maps that reveal latency hot paths, which matters when Ceph-backed applications show cascading latency across services.
OTLP telemetry pipeline with attribute and resource enrichment
The OpenTelemetry Collector acts as a configurable telemetry pipeline that standardizes traces through OTLP ingestion and then enriches them using processors for resource and attribute alignment. This matters for Ceph because consistent span metadata and correlation with Ceph cluster context reduces investigation effort when multiple backends receive traces.
Automated correlation across application transactions and Ceph storage paths
Dynatrace emphasizes automated service topology discovery and Davis AI-driven root-cause analysis for correlated tracing across app and storage layers. Datadog APM, Elastic APM, and New Relic Distributed Tracing also focus on linking traces with logs and metrics, which matters when Ceph latency symptoms must be connected to specific failing or slow request paths.
How to Choose the Right Ceph Tracing Software
Selection should start with whether Ceph internals must be observed directly or whether Ceph-adjacent request spans are the primary evidence source.
Decide whether Ceph internals need syscall-level observability or span-level end-to-end traces
For direct Ceph host behavior without application instrumentation, Tracee is the strongest match because it uses eBPF-based dynamic syscall and kernel event tracing. For teams that already emit OpenTelemetry spans from Ceph gateway paths or Ceph-dependent services, Grafana Tempo or Jaeger deliver trace timelines and latency breakdowns for those request flows.
Choose a performance evidence model that matches the failure mode
For recurring CPU saturation patterns across long-lived Ceph processes, Parca provides continuous profiling and aggregated flamegraphs that help pinpoint CPU hotspots in OSD, MON, and client processes. For request-latency investigations across multiple services, Grafana Tempo, Jaeger, and Zipkin emphasize span-level durations and error surfacing along trace timelines.
Plan for correlation speed during incidents
Grafana Tempo speeds cross-service incident triage through trace search and Grafana Explore correlation. Datadog APM, Elastic APM, and New Relic Distributed Tracing accelerate root-cause workflows by linking distributed traces with logs and metrics so slow spans can be validated against infrastructure signals.
Validate how service dependencies are visualized for Ceph-backed request paths
Jaeger provides service graphs that map inferred request dependencies from trace data. Elastic APM and Datadog APM provide service maps to expose latency hot paths, while Dynatrace adds service topology discovery and Davis AI-driven root-cause analysis to connect observed anomalies to the impacted Ceph storage layer.
Ensure metadata consistency and control telemetry volume
The OpenTelemetry Collector is the best fit for teams needing an OTLP telemetry hub that standardizes traces and adds resource and attribute enrichment for consistent span metadata. For high trace volume environments, Grafana Tempo, Elastic APM, Dynatrace, and Datadog APM require careful sampling and tag cardinality control so query performance does not degrade and storage costs stay aligned with operational needs.
Who Needs Ceph Tracing Software?
Ceph tracing software benefits multiple roles based on whether Ceph internals must be observed directly or whether request evidence is already emitted by Ceph-adjacent services.
Ceph operators who need syscall-level observability with minimal instrumentation
Tracee is designed for Ceph operators because it captures eBPF-driven dynamic syscall and kernel event tracing without modifying Ceph services. This is a direct fit when live incident forensics requires low overhead and targeted filtering around Ceph-related block IO and network paths.
Ceph operators diagnosing recurring CPU hotspots across OSD, MON, and clients
Parca is the best match because it delivers continuous profiling and aggregated flamegraphs that make CPU hotspot discovery faster than log-only methods. This works especially well for long-lived Ceph processes where problems recur across time windows.
Observability teams tracing Ceph-adjacent request flows with fast search and dashboard correlation
Grafana Tempo excels for teams that want OpenTelemetry ingestion plus Tempo trace search paired with Grafana Explore correlation. This is a practical choice when Ceph end-to-end visibility comes from spans emitted around gateways, clients, and services that interact with Ceph.
Enterprises needing automated tracing correlation across application transactions and Ceph storage layers
Dynatrace fits enterprises because it provides automated service topology discovery and Davis AI-driven root-cause analysis for correlated tracing. This is ideal when multiple signals like traces, metrics, and logs must converge on the Ceph component causing user-perceived performance regression.
Common Mistakes to Avoid
Common failure patterns across Ceph tracing tools usually come from mismatched evidence models, uncontrolled telemetry cardinality, or missing span context propagation.
Assuming Ceph internals appear in distributed traces without instrumentation
Jaeger, Grafana Tempo, Zipkin, Datadog APM, Elastic APM, and New Relic Distributed Tracing all rely on spans emitted by the relevant services or gateways. Ceph internal behavior does not automatically show up in these tools unless Ceph-facing components or Ceph-touching application paths emit trace context.
Collecting high-volume raw events without filtering and sampling control
Tracee captures kernel and syscall events that can produce high event rates, so careful event selection is required to avoid noisy outputs. Grafana Tempo, Dynatrace, and Datadog APM similarly require tuning sampling and tag usage so trace search and query performance do not degrade under span cardinality pressure.
Using continuous profiling when the bottleneck is primarily memory stalls or IO waits
Parca is biased toward continuous CPU profiling, so memory stalls and IO waits still need other signals for complete diagnosis. Pairing Parca with trace and metrics evidence from tools like Elastic APM or Datadog APM can help when symptoms are not CPU-bound.
Creating inconsistent span metadata across Ceph services and collectors
Without consistent resource and attribute enrichment, OpenTelemetry traces become harder to correlate to Ceph cluster context. The OpenTelemetry Collector is built for processor pipelines that align Ceph metadata, while backends like Jaeger and Tempo depend on consistent span attributes for fast trace search and dependency exploration.
How We Selected and Ranked These Tools
we evaluated every tool on three sub-dimensions. Features carry weight 0.40, ease of use carries weight 0.30, and value carries weight 0.30. The overall rating is the weighted average calculated as overall = 0.40 × features + 0.30 × ease of use + 0.30 × value. Tracee separated itself from lower-ranked tools on the features dimension by providing eBPF-driven dynamic syscall and kernel event tracing with flexible filters, which directly supports low-overhead Ceph host incident forensics without requiring application instrumentation.
Frequently Asked Questions About Ceph Tracing Software
How can Ceph tracing capture storage and network behavior without modifying Ceph or application code?
Tracee captures kernel and userspace activity using eBPF, so Ceph-related system calls for block IO and network paths can be turned into traceable events without application instrumentation. This contrasts with Jaeger, Parca, and Tempo, which rely on spans or profiles generated by instrumented services.
Which tool is best for continuous CPU hotspot analysis during Ceph latency spikes?
Parca is optimized for continuous profiling and aggregated flamegraphs, which helps isolate CPU hotspots inside Ceph-related processes like OSD, MON, and client workloads. Jaeger and Elastic APM focus on span timelines and dependencies, which is useful for request flow debugging but does not replace CPU flamegraph analysis.
What is the fastest way to correlate Ceph-adjacent traces with metrics and logs in one workflow?
Grafana Tempo pairs trace storage and trace search with Grafana dashboards and Explore, enabling rapid correlation between trace findings and metrics or logs. Elastic APM also correlates traces with searchable logs and metrics using its unified data model, while Tempo’s strength is high-cardinality trace search speed.
When do service graphs matter for troubleshooting Ceph-backed applications?
Jaeger provides service graphs that infer request dependencies from trace data, which helps visualize how client traffic fans out to Ceph-adjacent components like RGW or MDS. Dynatrace adds automated topology discovery and correlation across app and infrastructure layers, which is useful for tracing cascaded failures affecting Ceph-backed user requests.
How does the OpenTelemetry pipeline support consistent Ceph trace metadata and routing?
OpenTelemetry Collector works as a telemetry pipeline that ingests OTLP spans, enriches them with processors for resource and attribute normalization, and forwards them to multiple tracing backends. This is more pipeline-centric than Zipkin’s compact trace storage and timeline UI, which assumes spans are already emitted by relevant components.
What tool helps most when Ceph-backed microservices need trace-to-logs dependency debugging?
Elastic APM ties distributed tracing, logs, and metrics into a shared Elastic data model, which supports span-level analysis and trace-driven root-cause workflows. Dynatrace also emphasizes automated correlation and root-cause narrowing, but Elastic’s service maps and log correlation are central for tracing where Ceph stalls inside the app dependency chain.
Why do Ceph internal operations often not appear in tracing platforms like Datadog and New Relic?
Datadog APM and New Relic Distributed Tracing show meaningful Ceph internal behavior only when Ceph client, gateway, or RADOS Gateway request paths emit compatible spans. Without instrumentation that threads Ceph operations into traced application requests, their trace analytics focus on Ceph-adjacent services rather than Ceph daemons’ internal execution.
Which approach is best for end-to-end request latency timelines across hops that include Ceph gateway paths?
Zipkin centers on compact trace timelines that show span-level duration and error surfacing across hops. Jaeger provides deeper span drilldowns and sampling controls for distributed traces, making it a strong option when hop-by-hop latency mapping must include multiple Ceph gateway or service layers.
What technical setup is required to use Tracee effectively in a Ceph environment?
Tracee relies on eBPF-based dynamic tracing, so it needs an environment where eBPF can attach to kernel and userspace hooks for system calls tied to Ceph traffic. This differs from Parca, Jaeger, Tempo, and Zipkin, which primarily require trace or profiling instrumentation from the processes that issue Ceph calls.
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.
Tools reviewed
Referenced in the comparison table and product reviews above.
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