Enjoy your observability: an industrial survey of microservice tracing and analysis
Open accessAbstract
Microservice systems are often deployed in complex cloud-based environments and may involve a large number of service instances being dynamically created and destroyed. It is thus essential to ensure observability to understand these microservice systems' behaviors and troubleshoot their problems. As an important means to achieve the observability, distributed tracing and analysis is known to be challenging. While many companies have started implementing distributed tracing and analysis for microservice systems, it is not clear whether existing approaches fulfill the required observability. In this article, we present our industrial survey on microservice tracing and analysis through interviewing developers and operation engineers of microservice systems from ten companies. Our survey results offer a number of findings. For example, large microservice systems commonly adopt a tracing and analysis pipeline, and the implementations of the pipeline in different companies reflect different tradeoffs among a variety of concerns. Visualization and statistic-based metrics are the most common means for trace analysis, while more advanced analysis techniques such as machine learning and data mining are seldom used. Microservice tracing and analysis is a new big data problem for software engineering, and its practices breed new challenges and opportunities.
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Trust must be established across the entire distributed diagnostic pipeline, including data collection, causal reasoning, and fault propagation analysis.
“Trust must be established across the entire distributed diagnostic pipeline, including data collection, causal reasoning, and fault propagation analysis [52,246,248].”
From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [248]
The Results sections on the tracing pipeline and root cause analysis describe data collection and the use of traces to identify error-propagation chains. The Discussion section on more intelligent trace analysis reports incomplete or erroneous traces. These passages support concern about diagnostic data quality, but the paper does not state that trust must be established across the entire pipeline or specifically address trust in causal reasoning. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMicroservice-based systems are inherently distributed, dynamic, and partially observable, where diagnostic outcomes emerge from the interaction of multiple services, heterogeneous telemetry sources, and continuously evolving system topologies.
“However, microservice-based systems are inherently distributed, dynamic, and partially observable, where diagnostic outcomes emerge from the interaction of multiple services, heterogeneous telemetry sources, and continuously evolving system topologies [243,248].”
From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [248]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredA change may be correct in its final state yet unsafe while it is being applied.
“A change may be correct in its final state yet unsafe while it is being applied, so the agent should stage the change, run a canary, validate the result, and only then widen the rollout [11], [73], [41].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [73]
The Results > Analysis passage reports observing a canary release and comparing service dependencies before and after a release to assess impact and possible rollback (page not provided). It does not say that a change can be correct in its final state but unsafe while being applied, or prescribe staging, validation, and widening the rollout in that sequence. (LLM, conf=0.82)
Experimental — accuracy not yet measuredNetOps practitioners assess risk using criteria that align with autonomy decisions, such as the number of affected devices, novelty of the change, the adequacy of verification procedures, and the history of similar changes causing incidents.
“NetOps practitioners assess risk using criteria that align with autonomy decisions, such as the number of affected devices, novelty of the change, the adequacy of verification procedures, and the history of similar changes causing incidents [73], [70], [65].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [73]
The Results > Analysis passage describes using service dependencies to assess release impact and rollback scope, but it does not describe how NetOps practitioners assess risk. Searches for affected devices, change novelty, verification procedures, and prior incidents found no passage stating those criteria. Page numbers are unavailable in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measuredTransient safety requires every intermediate state to satisfy the relevant invariants.
“Transient safety requires every intermediate state to satisfy the relevant invariants: ∀t ∈ [0, T ] : I(x t ) = 1, (25) where I combines the required reachability, isolation, waypointing, and loop-freedom checks.”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [73]
The Results > Logging passages discuss tracing implementation, while the Background passage describes service-invocation traces; neither addresses transient safety or invariants over intermediate states. Searches for those terms and for network invariants during reconfiguration found no passage supporting the attributed claim. The indexed passages provide no page numbers. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Rumo a uma Taxonomia de Observabilidade para Aplicações Baseadas em Microsserviços
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Assessing Database Contribution via Distributed Tracing for Microservice Systems
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VAMP: Visual Analytics for Microservices Performance
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TENSAI - Practical and Responsible Observability for Data Quality-aware Large-scale Analytics
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Using Observability to Detect Anti-patterns in Benchmarking Application with OpenTelemetry
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- Authors
- Li, B., Peng, X., Xiang, Q., Wang, H., Xie, T., Sun, J., & Liu, X.
- Affiliations
- Fudan University (CN)
- eBay (United States) (US)
- Peking University (CN)
- Singapore Management University (SG)
- Corresponding author
- Peng, Xin
- Venue
- Empirical Software Engineering
- Published
- 2021-11-30
- Volume
- 27
- Issue
- 1
- Pages
- 25-25
- ISSN
- 1382-3256 (ISSN-L)
- 1573-7616
- PMC
- PMC8629732
- OpenAlex
- W3216137074
- Cited by
- 121
- References
- 23
- Citations by year
- 2026: 34 · 2025: 26 · 2024: 27 · 2023: 21 · 2022: 13
- FWCI
- 6.84
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Empirical Software Engineering (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion)
- Funders
- National Key Research and Development Program of China (2018YFB1004803)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
- Keywords
- microservices, distributed tracing, trace analysis, observability, trace visualization, cloud systems
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