This is a microservices dataset. For an exclusive explanation, please take a look at the paper and at the online appendix: https://github.com/darioamorosodaragona-tuni/Microservices-DatasetIn particular, this file contains all the projects labeled as:- Is it a microservices?: Yes- Archived: NoCopyright:Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). MSR ’24, April 15–16, 2024, Lisbon, Portugal © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0587-8/24/04 https://doi.org/10.1145/3643991.3644890

Prior works have noted that existing public traces on anomaly detection and bottleneck localization in microservices applications only contain single, severe bottlenecks that are not representative of real-world scenarios. When such a bottleneck is introduced, the resulting latency increases by an order of magnitude (100x), making it trivial to detect that single bottleneck using a simple grid search or threshold-based approaches.

To create a more realistic dataset that includes traces with multiple bottlenecks at different intensities, we carefully benchmarked the social networking application under different interference intensities and duration of interference. We chose intensities and duration values that degrade the application performance but do not cause any faults or errors that can be trivially detected. We induced interference on different VMs at different times and also simultaneously. A single VM could be induced with different types of interference (e.g., CPU and memory), resulting in the hosted microservices experiencing a mixture of interference patterns. The resulting dataset consists of around 40 million request traces along with corresponding time series of CPU, memory, I/O, and network metrics. The dataset also includes application, VM, and Kubernetes logs.

A detailed description of the files is provided in the Data Explorer section. Please reach out to gagan at cs dot stonybrook dot edu if you have any questions or concerns.

If you find the dataset useful, please cite our WWW'24 paper "GAMMA: Graph Neural Network-Based Multi-Bottleneck Localization for Microservices Applications." Citation format (bibtex):

author = {Somashekar, Gagan and Dutt, Anurag and Adak, Mainak and Lorido Botran, Tania and Gandhi, Anshul},
title = {GAMMA: Graph Neural Network-Based Multi-Bottleneck Localization for Microservices Applications.},
year = {2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589334.3645665},
doi = {10.1145/3589334.3645665},
booktitle = {Proceedings of the ACM Web Conference 2024},
location = {Singapore},
series = {WWW '24}
}```

A dataset containing system and service performance metrics

Welcome to Microservices and Distributed Systems - one of the most in-demand courses on the platform and for a good reason, microservices are quickly becoming the dominant framework in the field and a critical skill for a professional Java developer. The course will be a lot of hands-on, building a distributed application and learning the exact technology stack that makes a microservice tick. Join me today and master microservices with:​ •20-chapter roadmap made up of over 10 hours of video content: From fundamentals of the architecture to application deployment. •156 lessons: A step-by-step practical guide through every technology. •Microservices Application building: A comprehensive guide to give you all the knowledge needed to deploy components yourself. •Dedicated Discord Channel: Where you will be able to find answers to all your questions and chat with me and the students.

The Cloud Microservices Market report segments the industry into Deployment Mode (Platforms, Service), Enterprise Size (Small and Medium Enterprises, Large Enterprises), End-User Industry (BFSI, Retail, E-Commerce, Manufacturing, Telecommunications, IT and ITES, Healthcare, Other End-User Industries), and Geography (North America, Europe, Asia Pacific, Latin America, Middle East and Africa).

This repository contains two datasets used for anomaly detection in microservices-based systems. The datasets are summarized versions of several CSV files containing data from normal and anomalous instances. The two datasets provided here are representative of service and application anomalies. For more detailed information, please refer to the README file included in the repository.

The work contains ten datasets containing monitoring data (logs, Jaeger Traces and Prometheus KPI data). The datasets contain monitoring data from train-ticket, a benchmark system for microservices. The dataset includes a short description with explanations of identified anomalies.

The structure of the folder is as follows:

Each folder stores the respective data:

1. Logs:
   • original log file: LOGS_
   • parsed log files required for Loglizer (anomaly detection technique):
     • LOGS_
     • LOGS_
2. KPI Data: Monitoring_
3. Traces: Traces_

This dataset provides comprehensive microservice traces (around 1.4 million) collected from Uber microservice architecture, as described in our paper The Tale of Errors in Microservices, presented at SIGMETRICS 2025. This dataset enables researchers to study microservice behaviors, optimize performance, and investigate latency reduction techniques.

The data has been sanitized to protect proprietary information while retaining critical performance characteristics for academic research.

Artifact Structure and Decompression Instructions:

Due to Zenodo's file size constraints and upload issues, the large trace1-sanitized.tar.zst and trace2-sanitized.tar.zst files have been split into multiple pieces. The artifact is available in two parts (10.5281/zenodo.13947828 and 10.5281/zenodo.13952897). To access the sanitized microservice traces, download all the split parts from both artifacts. After downloading, reassemble the files using the following commands and then decompress the .zst files individually. Each .zst file will require 300-500GB of disk space to decompress.

Reassembling the split files:

For trace1-sanitized.tar.zst and trace2-sanitized.tar.zst, use the following commands to reassemble them:

Once reassembled, you can decompress the files:

Contents of the Traces:

• trace1-sanitized.tar.zst and trace2-sanitized.tar.zst (in 10.5281/zenodo.13952897) contain around 1.4 million traces that correspond to the data described in Sections 3 and 4 of the original paper. Note: The traces in this dataset were collected on different days than those used in the paper, so analysis results may vary slightly from what is reported in the publication.
• driver-sanitized.tar.zst contains the sanitized version of the original trace and corresponds to the App-Launch Use Case discussed in Section 6.3 and Figure 17 of the original paper.

Note

• Due to privacy and security concerns, most unrelated fields and tags are removed. However, error-related tags are retained.
• All trace is sanitized consistently. The same service or endpoint will have identical mapping across three directories. (i.e., service 1 represents the same service in all traces). However, the mapping is inconsistent with https://zenodo.org/records/13956078, so please do not mix the traces between the two artifacts.
• To preserve privacy, the start time of each trace has been randomly shifted. As a result, the start and end times in the traces do not reflect the actual collection times, and users should not attempt to infer when the traces were gathered.
• Within each trace, the relative durations and timestamps of all spans remained consistent, as the shift was applied uniformly across the entire trace.

If you use the traces in your research, please cite our paper

The Tale of Errors in Microservices
I-Ting Angelina Lee (Washington University in St. Louis); Zhizhou Zhang, Abhishek Parwal (Uber Technologies Inc.); Milind Chabbi (Uber Technologies)
SIGMETRICS 2025 https://doi.org/10.1145/3700436

If you have more questions, you can reach out to Chris(Zhizhou) Zhang.

The global microservices architecture market size reached USD 4.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 13.1 Billion by 2033, exhibiting a growth rate (CAGR) of 12.7% during 2025-2033. The increased demand for scalability, digital transformation initiatives, expanding e-commerce industry, and ongoing technological advancements are primarily driving the market's growth.

IMARC Group provides an analysis of the key trends in each segment of the global microservices architecture market report, along with forecasts at the global, regional, and country levels from 2025-2033. Our report has categorized the market based on component, deployment type, organization size, and industry vertical.

The global Cloud Microservices Market size is expected to reach USD 8.69 Billion in 2032 registering a CAGR of 22.9%. Our report provides a comprehensive overview of the industry, including key players, market share, growth opportunities and more.

Microservice Architecture Market Outlook

The global microservice architecture market size was valued at approximately USD 4.5 billion in 2023 and is projected to reach USD 16.7 billion by 2032, expanding at a compound annual growth rate (CAGR) of 15.6% during the forecast period. This robust growth is driven by the rising demand for scalable and efficient software development frameworks, which enable businesses to respond swiftly to market changes and customer demands. The surge in digital transformation initiatives across various industries and the need for agile and continuous delivery models are pivotal factors propelling this market's expansion.

One of the primary growth enablers for the microservice architecture market is the increasing emphasis on cloud-native application development. As enterprises aim to modernize their IT infrastructure, microservices provide a flexible architecture that facilitates the seamless integration of cloud services. The ability to scale services independently and the decoupled nature of microservices allow firms to innovate rapidly while reducing operational risks. Furthermore, the rise in containerization technology complements microservices, providing a lightweight, virtualized environment that enhances the deployment and management of microservices-based applications.

Moreover, the growing importance of DevOps practices in software development is another significant growth driver for the microservice architecture market. DevOps facilitates a culture of collaboration and integration between development and operations teams, enabling faster and more reliable software releases. Microservices architecture, with its modular structure, aligns perfectly with DevOps principles, fostering a continuous integration and continuous deployment (CI/CD) pipeline. This synergy reduces the time-to-market for new features and applications, encouraging more organizations to adopt microservices for their software development needs.

Additionally, the increasing need for business agility and flexibility in digital operations is contributing to the market's expansion. As markets become more competitive, businesses strive to improve their responsiveness to consumer demands and market trends. Microservices architecture supports this need by allowing for quick updates and enhancements of specific application components without affecting the entire system. This architecture also facilitates easier fault isolation and recovery, thus enhancing system reliability and uptime. Consequently, businesses across various sectors are increasingly investing in microservices to maintain competitive advantage and improve operational efficiency.

Regionally, North America holds the largest share in the microservice architecture market, driven by the presence of leading technology firms and software development companies. The region's early adoption of advanced technologies and its mature IT infrastructure support the rapid integration of microservices. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate, with a burgeoning IT industry and increasing investments in cloud services and digital transformation initiatives. European countries are also progressively adopting microservices, particularly in the BFSI and healthcare sectors, due to stringent compliance requirements and the need for innovative customer solutions.

The emergence of Cloud Microservices Platform is revolutionizing how businesses approach software development and deployment. By leveraging cloud-based microservices, organizations can achieve unparalleled scalability and flexibility, allowing them to respond to market demands with agility. These platforms provide a comprehensive suite of tools and services that streamline the development process, enabling faster time-to-market for new applications and features. As businesses increasingly migrate to the cloud, the integration of microservices into their IT strategies is becoming a critical factor for success. The ability to dynamically scale resources and optimize costs makes cloud microservices platforms an attractive option for enterprises aiming to enhance their digital capabilities.

Component Analysis

The microservice architecture market is segmented by component into platform and services. The platform segment is a fundamental enabler of microservices adoption, providing the necessary tools and solutions to design, develop, and deploy microservice-based applications. Platforms ofte

In 2021, ** percent of respondents state using microservices partially. Microservices, or microservice architecture, is an architectural style that enables frequent and reliable delivery of applications.

In 2022, ** percent of respondents currently using microservices state that it is extremely important for organizational operations. This was followed by majority of the respondents at ** percent agreeing that it is very important in the same year.

The microservices orchestration market size was valued at USD 4.7 billion in 2024 and is poised to reach USD 72.3 billion in 2037, witnessing around 23.4% CAGR during the forecast period i.e., between 2025-2037. North America industry is poised to account for a dominant revenue share of 24.9% owing to the early adoption of cloud-native architecture in the region.

Artifacts for the paper titled Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?.

This artifact repository contains 9 compressed folders, as follows:

Each zip file contains the generated/collected data from the corresponding data generator or microservice benchmark systems (e.g., online-boutique.zip contains metrics data collected from the Online Boutique system).

Details about the generation of our datasets

1. Synthetic datasets

We use three different synthetic data generators from three previous RCA studies [15, 25, 28] to create the synthetic datasets: CIRCA, RCD, and CausIL data generators. Their mechanisms are as follows:

1. CIRCA datagenerator [28] generates a random causal directed acyclic graph (DAG) based on a given number of nodes and edges. From this DAG, time series data for each node is generated using a vector auto-regression (VAR) model. A fault is injected into a node by altering the noise term in the VAR model for two timestamps.

2. RCD data generator [25] uses the pyAgrum package [3] to generate a random DAG based on a given number of nodes, subsequently generating discrete time series data for each node, with values ranging from 0 to 5. A fault is introduced into a node by changing its conditional probability distribution.

3. CausIL data generator [15] generates causal graphs and time series data that simulate the behavior of microservice systems. It first constructs a DAG of services and metrics based on domain knowledge, then generates metric data for each node of the DAG using regressors trained on real metrics data. Unlike the CIRCA and RCD data generators, the CausIL data generator does not have the capability to inject faults.

To create our synthetic datasets, we first generate 10 DAGs whose nodes range from 10 to 50 for each of the synthetic data generators. Next, we generate fault-free datasets using these DAGs with different seedings, resulting in 100 cases for the CIRCA and RCD generators and 10 cases for the CausIL generator. We then create faulty datasets by introducing ten faults into each DAG and generating the corresponding faulty data, yielding 100 cases for the CIRCA and RCD data generators. The fault-free datasets (e.g. `syn_rcd`, `syn_circa`) are used to evaluate causal discovery methods, while the faulty datasets (e.g. `rca_rcd`, `rca_circa`) are used to assess RCA methods.

2. Data collected from benchmark microservice systems

We deploy three popular benchmark microservice systems: Sock Shop [6], Online Boutique [4], and Train Ticket [8], on a four-node Kubernetes cluster hosted by AWS. Next, we use the Istio service mesh [2] with Prometheus [5] and cAdvisor [1] to monitor and collect resource-level and service-level metrics of all services, as in previous works [ 25 , 39, 59 ]. To generate traffic, we use the load generators provided by these systems and customise them to explore all services with 100 to 200 users concurrently. We then introduce five common faults (CPU hog, memory leak, disk IO stress, network delay, and packet loss) into five different services within each system. Finally, we collect metrics data before and after the fault injection operation. An overview of our setup is presented in the Figure below.

Code

The code to reproduce the experimental results in the paper is available at https://github.com/phamquiluan/RCAEval.

References

As in our paper.

We are releasing the tracing dataset of four microservice benchmarks deployed on our dedicated Kubernetes cluster consisting of 15 heterogeneous nodes. The dataset is not sampled and is from selected types of requests in each benchmark, i.e., compose-posts in the social network application, compose-reviews in the media service application, book-rooms in the hotel reservation application, and reserve-tickets in the train ticket booking application. The four microservice applications come from DeathStarBench and Train-Ticket. The performance anomaly injector is from FIRM. The dataset was preprocessed from the raw data generated in FIRM's tracing system. The dataset is separated by on which microservice component is the performance anomaly located (as the file name suggests). Each dataset is in CSV format and fields are separated by commas. Each line consists of the tracing ID and the duration (in 10^(-3) ms) of each component. Execution paths are specified in execution_paths.txt in each directory.

This dataset provides materials used and produced in the context of the research study leading to the article Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs. It includes materials used to conduct the study, as well as aggregated and anonymized data produced in its context.

We investigated how practitioners perceive the impact of 14 patterns on 7 quality attributes. In particular, we conducted 9 semi-structured interviews to collect industry expertise regarding (1) knowledge and adoption of software patterns, (2) the perceived architectural trade-offs of patterns, and (3) metrics professionals use to measure quality attributes.

Research Objective

Our objective with this work was to obtain insights on the relevance of design patterns in industry, how practitioners perceive their influence on software qualities as a consequence of their usage, and what metrics practitioners use, if any, to determine these derived effects, reflected as software qualities.

Research Questions

• RQ1: What is the rationale for the adoption of patterns in microservices systems?
• RQ2: How are QAs influenced as a result of applying microservice patterns?
• RQ3: How are QAs measured in microservices?

Interview Artifacts and Results

Materials used to conduct the study:

• interview-signup-form.pdf - Form used by participants to signup for the study. The data collected through this form was used to characterize the participant, and make an early assessment of her or his experience with software development and microservices.
• interview-guide.pdf - Notes used by the researcher to conduct the interviews, including the general structure to follow.
• interview-helper.pdf - Slides used by the researcher during the interviews, to illustrate each of the patterns.

Data produced in the context of the study:

• PreliminaryTradeoffAnalysis.csv - Benefits and liabilities gathered by the researchers from the description of the analyzed patterns. Those classified QAs as mixed when we considered that assigning a positive or negative value was a too simplistic judgement.
• Demographics.csv - Demographic data for each of the interviewees.
• InterviewsStats.csv - Simple statistics regarding the interviews, including number of words of the transcripts and duration of the interviews.
• Adoptions.csv - Reported patterns adopted by the interviewees.
• Gains.csv - Gains of each pattern as reported by interviewees.
• Pains.csv - Pains of each pattern as reported by interviewees.
• Indicators.csv - Reported indicators for measuring each QA.
• Techniques.csv - Reported techniques for addressing each QA.
• ThirdPartyMonitoringTools.csv - Reported third-party monitoring tools and the number of participants who use them.

The Cloud Microservices market is experiencing robust growth, driven by the increasing adoption of cloud-native architectures and the need for agile, scalable applications. The market's shift towards microservices is fueled by several key factors, including the desire for improved application development speed, enhanced scalability and resilience, and simplified deployment and maintenance processes. Organizations are increasingly leveraging cloud platforms like AWS, Azure, and Google Cloud to build and deploy microservices, benefiting from the inherent scalability, cost-effectiveness, and global reach of these services. The rising popularity of containerization technologies like Docker and Kubernetes further accelerates this market expansion, providing efficient mechanisms for managing and orchestrating microservices deployments. While initial investments in infrastructure and expertise can present a barrier to entry, the long-term benefits of increased efficiency, reduced operational costs, and enhanced innovation outweigh these challenges, fostering widespread adoption across various industries. The competitive landscape is highly dynamic, with established players like AWS, Microsoft, and IBM competing alongside specialized microservices providers and system integrators such as Infosys and TCS. The emergence of innovative tools and platforms for managing and monitoring microservices further intensifies competition. Significant regional variations exist, with North America and Europe currently dominating market share due to early adoption and mature technological infrastructure. However, Asia-Pacific and other emerging regions are expected to witness accelerated growth in the coming years driven by increasing digitalization and cloud adoption. The continued development of serverless computing and improved developer tooling will be critical factors shaping market evolution over the forecast period. Addressing security concerns and ensuring efficient data management within distributed microservices architectures will remain ongoing challenges and opportunities for market participants. We project continued double-digit growth throughout the forecast period, driven by factors detailed above.

Stress testing done with Locust, stressing the various microservice API endpoints available from the sockshop microservices demo found in https://microservices-demo.github.io/

Part of a master's thesis project

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