Walking Through Spring Cloud Data Flow
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This document provides an overview and safe harbor statement for the "Walking Through Spring Cloud Data Flow" presentation at SpringOne 2020. It outlines that any information provided is intended for informational purposes only and is subject to change. The presentation will cover topics like Spring Cloud Stream for event-driven applications, Spring Cloud Task for batch applications, application development, deployment and monitoring using Spring Cloud Data Flow. It also provides details about the presenters and includes a sample demo data.
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Explaining GitHub Actions Failures with Large Language Models Challenges, In...
Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185 GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers’ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise.
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Meet the Agents: How AI Is Learning to Think, Plan, and Collaborate
Meet the Agents: How AI Is Learning to Think, Plan, and CollaborateMaxim Salnikov Imagine if apps could think, plan, and team up like humans. Welcome to the world of AI agents and agentic user interfaces (UI)! In this session, we'll explore how AI agents make decisions, collaborate with each other, and create more natural and powerful experiences for users.
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Walking Through Spring Cloud Data Flow
- 1. Walking Through Spring Cloud Data Flow September 2–3, 2020 springone.io
- 2. Safe Harbor Statement The following is intended to outline the general direction of VMware's offerings. It is intended for information purposes only and may not be incorporated into any contract. Any information regarding pre-release of VMware offerings, future updates or other planned modifications is subject to ongoing evaluation by VMware and is subject to change. This information is provided without warranty or any kind, express or implied, and is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions regarding VMware's offerings. These purchasing decisions should only be based on features currently available. The development, release, and timing of any features or functionality described for VMware's offerings in this presentation remain at the sole discretion of Pivotal. Pivotal has no obligation to update forward looking information in this presentation. 2
- 3. 3 Glenn Renfro Twitter: @cppwfs Github: github.com/cppwfs Ilayaperumal Gopinathan Twitter: @ilayaperumalg Github: github.com/ilayaperumalg
- 4. Data In the Enterprise
- 7. ?
- 8. 8
- 9. Application Development Spring Cloud Stream Event-driven, long-lived streaming apps Spring Cloud Task Ephemeral, batch apps Continuous Integration Kubernetes / Cloud Foundry Message Broker / / / / / Application Deployment Streaming applications Task (batch) applications Monitor Performance Track Lifecycle Deploy Streams Orchestration using Spring Cloud Data Flow Launch Jobs
- 10. Demo Time!
- 13. Demo!
- 14. What’s New! Single Step Batch Job Auto Configuration
- 15. @EnableBatchProcessing @EnableTask @Configuration public class MyBatchConfiguration { … @Bean public Job myBatchJob(Step step) { return jobBuilderFactory.get(this.properties.getJobName()). … } … @Bean public ItemReader<Map<Object, Object>> itemReader() { return new JdbcCurserItemReaderBuilder(). … } @Bean public ItemWriter<Map<Object,Object>> itemWriter(DataSource dataSource) { return new JdbcBatchItemWriterBuilder<Map<Object, Object>>(). … }
- 16. What’s New! Single Step Batch Job Auto Configuration spring.batch.job.jobName=job spring.batch.job.stepName=step1 spring.batch.job.chunkSize=5 spring.batch.job.jdbccursorreader.name=fooReader spring.batch.job.jdbccursorreader.sql=select item_name from item_sample spring.batch.job.jdbcwriter.name=jdbcWriter spring.batch.job.jdbcwriter.sql=INSERT INTO item (item_name) VALUES (:ITEM_NAME) <dependency> <groupId>org.springframework.cloud</groupId> <artifactId>spring-cloud-starter-single-step-batch-job</artifactId> </dependency>
- 17. What’s new! Spring Cloud Data Flow OSS • 2.6.1 RELEASE ● Task Improvements ● OOTB Functional applications ● Wavefront monitoring support ● Multi platform scheduling support ● Bitnami helm charts for SCDF
- 18. Commercial Offering Spring Cloud Data Flow For Kubernetes • Built on top of SCDF OSS • SCDF Pro Server and Pro Dashboard UI • Packaging • Certified Container Images • Image Relocation • Provisioning of SCDF components • Multi IO Stream DSL and Editor
- 21. What’s Up Next! Spring Cloud Data Flow OSS • 2.7.0 in the works ○ Dashboard redesign using Clarity ○ Observability support ○ Single Step Batch Job support
- 22. What’s Up Next! Spring Cloud Data Flow for Kubernetes (SCDF4K8s) • Container Registry Integration • Single Step Batch Job Auto Config • Multi IO support enhancements
- 23. Want to learn more? #springone@s1p SCDF microsite SCDF for Kubernetes
- 24. Stay Connected. https://springone.io/2020/sessions/iot-scale-event-stream-processing-for- predictive-maintenance-at-penske #springone@s1p IoT Scale Event-Stream Processing for Predictive Maintenance at Penske