고급 자바 8 교육 (6일 중 6일차)
티맥스소프트 연구소에 연구소장으로 재직 중이던 2013년 10월에 진행한 자바 언어 강의 내용입니다.
JVM에 대한 이해와 Java 8에 대한 소개를 포함하려고 노력하였습니다.
아래 강의 동영상이 있습니다.
http://javadom.blogspot.com/2017/07/8-6.html
This document provides an introduction and overview of Apache Mesos. It begins by describing Mesos' origins at companies like Google and how it enables fine-grained resource sharing in data centers. It then discusses concepts like schedulers, executors, frameworks and how Mesos allows building distributed applications and data center infrastructure. The document also covers Mesos concepts such as resources, attributes, roles, constraints and how tools like Marathon interact with Mesos. Finally, it provides examples of Mesos frameworks for technologies like Kafka and Cassandra.
Anatomy of the libvirt virtualization library
http://www.ibm.com/developerworks/library/l-libvirt/
libvirt
http://libvirt.org/index.html
Scheduling
http://docs.openstack.org/icehouse/config-reference/content/section_compute-scheduler.html
Openstack Zoning – Region/Availability Zone/Host Aggregate
https://kimizhang.wordpress.com/2013/08/26/openstack-zoning-regionavailability-zonehost-aggregate/
Availability Zones and Host Aggregates in OpenStack Compute (Nova)
http://blog.russellbryant.net/2013/05/21/availability-zones-and-host-aggregates-in-openstack-compute-nova/
An Introduction to Droplet Metadata
https://www.digitalocean.com/community/tutorials/an-introduction-to-droplet-metadata
HOW WE USE CLOUDINIT IN OPENSTACK HEAT
http://sdake.io/2013/03/03/how-we-use-cloudinit-in-openstack-heat/
How to inject file/meta/ssh key/root password/userdata/config drive to a VM during nova boot
https://kimizhang.wordpress.com/2014/03/18/how-to-inject-filemetassh-keyroot-passworduserdataconfig-drive-to-a-vm-during-nova-boot/
Cloud-init
https://cloudinit.readthedocs.org/en/latest/
Kubernetes can orchestrate and manage container workloads through components like Pods, Deployments, DaemonSets, and StatefulSets. It schedules containers across a cluster based on resource needs and availability. Services enable discovery and network access to Pods, while ConfigMaps and Secrets allow injecting configuration and credentials into applications.
This document discusses using MongoDB as a message queue. It summarizes the author's prior use of RabbitMQ, how MongoDB is now used at About.me for asynchronous and periodic tasks, and the benefits MongoDB provides as a message queue including sharding and durability. It also compares features of MongoDB queues to AMQP queues and provides code samples and performance benchmarks for using MongoDB as a message queue with Celery and Python.
This document is a presentation from OpenStack Summit Sydney. It describes how to easily install OpenStack on Kubernetes. It explains Kubernetes and OpenStack-Helm.
The document discusses an agenda for a meetup about Redis Labs and Kubernetes operators. Key points:
- An introduction to Redis Enterprise architecture and Redis Labs products.
- A discussion of "double orchestration" using Kubernetes and PKS to manage Redis clusters for performance and resource management.
- An overview of Redis Labs' Kubernetes solution using StatefulSets, services, and a custom controller.
- An introduction to operators, how they provide lifecycle management and simplify deployments compared to static YAML files or Helm.
- Details on Redis Labs' operator development process and challenges in building idempotent APIs and handling state changes and validation in the reconciliation loop.
Kubernetes advanced sheduling
- Taint and tolerant
- Affinity (Node & inter pod)
Learn how to place Pod like (same or different) node, rack, zone, region
This document discusses Kubernetes monitoring concepts and architectures. It describes how cAdvisor collects metrics from nodes and pods and sends them to Heapster for storage and visualization. It also covers the Kubernetes dashboard for visualizing cluster resources and creating/modifying resources, as well as logging with ELK or Stackdriver. Finally, it briefly mentions Datadog as a monitoring solution for Kubernetes.
ARCHITECTING TENANT BASED QOS IN MULTI-TENANT CLOUD PLATFORMSArun prasath
Achieving QOS in a multi-tenant cloud platforms is still a difficult task and many companies follow different approaches to solve this problem. Here in this document I tried architecting a simple solution for achieving different QOS for different tenants in a Multi-tenant cloud environment based on my experiments with containers , docker and cgroup on Openstack.
This document provides an overview of using Prometheus for monitoring and alerting. It discusses using Node Exporters and other exporters to collect metrics, storing metrics in Prometheus, querying metrics using PromQL, and configuring alert rules and the Alertmanager for notifications. Key aspects covered include scraping configs, common exporters, data types and selectors in PromQL, operations and functions, and setting up alerts and the Alertmanager for routing alerts.
EFK Stack이란 ElasticSearch, Fluentd, Kibana라는 오픈소스의 조합으로, 방대한 양의 데이터를 신속하고 실시간으로 수집/저장/분석/시각화 할 수 있는 솔루션입니다. 특히 컨테이너 환경에서 로그 수집을 위해 주로 사용되는 기술 스택입니다.
Elasitc Stack에 대한 소개와 EFK Stack 설치 방법에 대해 설명합니다.
Presto generates Java bytecode at runtime to optimize query execution. Key query operations like filtering, projections, joins and aggregations are compiled into efficient Java methods using libraries like ASM and Fastutil. This bytecode generation improves performance by 30% through techniques like compiling row hashing for join lookups directly into machine instructions.
The document discusses OpenNebula, an open-source cloud computing platform. It provides an overview of OpenNebula's architecture and components, and how developers can create custom drivers to integrate new virtualization technologies, storage solutions, and cloud interfaces. The presentation also explains how OpenNebula uses drivers to manage tasks like virtual machine deployment and image transfer between components.
Orchestration tool roundup - OpenStack Israel summit - kubernetes vs. docker...Uri Cohen
It’s no news that containers represent a portable unit of deployment, and OpenStack has proven an ideal environment for running container workloads. However, where it usually becomes more complex is that many times an application is often built out of multiple containers. What’s more, setting up a cluster of container images can be fairly cumbersome because you need to make one container aware of another and expose intimate details that are required for them to communicate which is not trivial especially if they’re not on the same host.
These scenarios have instigated the demand for some kind of orchestrator. The list of container orchestrators is growing fairly fast. This session will compare the different orchestation projects out there - from Heat to Kubernetes to TOSCA - and help you choose the right tool for the job.
[OpenInfra Days Korea 2018] Day 2 - E6 - OpenInfra monitoring with PrometheusOpenStack Korea Community
This document discusses using Prometheus for open infrastructure and cloud monitoring. It introduces Prometheus as a time series database and monitoring tool. Key features covered include metrics collection, service discovery, graphing, and alerting. The architecture of Prometheus is explained, including scrapping metrics directly or via exporters. A demo of Prometheus and Grafana is proposed to monitor Kubernetes clusters and visualize CPU usage. Alerting configuration and routes in Prometheus and Alertmanager are also summarized.
The document describes Onyx, a new flexible and extensible data processing system. It discusses limitations of existing frameworks in new resource environments like resource disaggregation and transient resources. The Onyx architecture includes a compiler that transforms dataflow programs into optimized physical execution plans using passes, and a runtime that executes the plans across cluster resources. It provides examples of compiling and running MapReduce and ALS jobs, and handling dynamic data skew through runtime optimization.
OSv is a new, high-performance OS for virtual machines in the cloud. Designed to run one application per guest with minimal overhead, OSv eliminates important bottlenecks for NoSQL applications through improvements in memory management, network I/O, and scheduling. And many important bottlenecks for NoSQL applications are tunable on a conventional OS, but do not require tuning in the OSv environment.
OSv is fully stateless and can be configured at runtime with cloud-init or through a REST API, with zero configuration files. OSv offers unified tracing from the application layer through the JVM and the OS kernel. Attendees will learn how to boot Cassandra in one second, and create a simple cluster in a minute.
Deploying Docker Containers at Scale with Mesos and MarathonDiscover Pinterest
Connor Doyle from Mesosphere.
Deploying Docker Containers at Scale with Mesos and Marathon
The norm these days is to operate apps at web scale. But that’s out of reach for most companies. Deploying Docker containers with Mesos and Marathon makes it easier. See how they help deploy and manage Docker containers at scale and how the Mesos cluster scheduler builds highly-available, fault-tolerant web scale apps.
Distributed Tests on Pulsar with Fallout - Pulsar Summit NA 2021StreamNative
Fallout is an open source testing framework based on Jepsen. In this talk we will see how distributed testing works and how to use these tools to verify Pulsar quality. We will see how we can easily deploy a reproducible Pulsar cluster on K8S and how to use ChaosMesh to inject failures. We will also cover integrated metrics reporting tools, very useful to verify the behaviour of the system for any Pulsar version, system environment and especially during maintenance operations (rollout restarts/upgrades) and unexpected failures.
1) The Docker Volume Isolator in Mesos allows containers to use external distributed storage volumes for persistent data, providing availability and scale beyond a single host.
2) It uses a driver-based model where volume drivers like Convoy and Rex-ray manage the external storage, and the isolator handles mounting volumes into containers.
3) The isolator mounts and unmounts volumes using the driver name and volume name, providing operators flexibility in their choice of storage platforms like IBM Spectrum Scale, AWS EBS, or GCE.
The document discusses best practices for backing up MongoDB databases. It explains that backups are important for availability and recovery from data loss. There are two main types of backups: one-time snapshots that capture a point-in-time view and incremental backups that capture changes since the last snapshot. The key steps for taking a one-time snapshot of a sharded cluster are to stop the balancer, take snapshots of the config servers and secondaries of each shard, restart the balancer, and copy the snapshots. Important considerations for backups include minimizing downtime and optimizing storage efficiency.
The Collection API provides classes and interfaces that support operations on collections of objects, such as HashSet, HashMap, ArrayList, and LinkedList. It replaces vectors, arrays, and hashtables. Iterator is an interface used to iterate through elements of a Collection. The differences between an abstract class and interface are that interfaces provide multiple inheritance while abstract classes do not, and interfaces only define public methods without implementation.
The document discusses Scala, a programming language designed to be scalable. It can be used for both small and large programs. Scala combines object-oriented and functional programming. It interoperates seamlessly with Java but allows developers to add new constructs like actors through libraries. The Scala community is growing, with industrial adoption starting at companies like Twitter.
Kubernetes advanced sheduling
- Taint and tolerant
- Affinity (Node & inter pod)
Learn how to place Pod like (same or different) node, rack, zone, region
This document discusses Kubernetes monitoring concepts and architectures. It describes how cAdvisor collects metrics from nodes and pods and sends them to Heapster for storage and visualization. It also covers the Kubernetes dashboard for visualizing cluster resources and creating/modifying resources, as well as logging with ELK or Stackdriver. Finally, it briefly mentions Datadog as a monitoring solution for Kubernetes.
ARCHITECTING TENANT BASED QOS IN MULTI-TENANT CLOUD PLATFORMSArun prasath
Achieving QOS in a multi-tenant cloud platforms is still a difficult task and many companies follow different approaches to solve this problem. Here in this document I tried architecting a simple solution for achieving different QOS for different tenants in a Multi-tenant cloud environment based on my experiments with containers , docker and cgroup on Openstack.
This document provides an overview of using Prometheus for monitoring and alerting. It discusses using Node Exporters and other exporters to collect metrics, storing metrics in Prometheus, querying metrics using PromQL, and configuring alert rules and the Alertmanager for notifications. Key aspects covered include scraping configs, common exporters, data types and selectors in PromQL, operations and functions, and setting up alerts and the Alertmanager for routing alerts.
EFK Stack이란 ElasticSearch, Fluentd, Kibana라는 오픈소스의 조합으로, 방대한 양의 데이터를 신속하고 실시간으로 수집/저장/분석/시각화 할 수 있는 솔루션입니다. 특히 컨테이너 환경에서 로그 수집을 위해 주로 사용되는 기술 스택입니다.
Elasitc Stack에 대한 소개와 EFK Stack 설치 방법에 대해 설명합니다.
Presto generates Java bytecode at runtime to optimize query execution. Key query operations like filtering, projections, joins and aggregations are compiled into efficient Java methods using libraries like ASM and Fastutil. This bytecode generation improves performance by 30% through techniques like compiling row hashing for join lookups directly into machine instructions.
The document discusses OpenNebula, an open-source cloud computing platform. It provides an overview of OpenNebula's architecture and components, and how developers can create custom drivers to integrate new virtualization technologies, storage solutions, and cloud interfaces. The presentation also explains how OpenNebula uses drivers to manage tasks like virtual machine deployment and image transfer between components.
Orchestration tool roundup - OpenStack Israel summit - kubernetes vs. docker...Uri Cohen
It’s no news that containers represent a portable unit of deployment, and OpenStack has proven an ideal environment for running container workloads. However, where it usually becomes more complex is that many times an application is often built out of multiple containers. What’s more, setting up a cluster of container images can be fairly cumbersome because you need to make one container aware of another and expose intimate details that are required for them to communicate which is not trivial especially if they’re not on the same host.
These scenarios have instigated the demand for some kind of orchestrator. The list of container orchestrators is growing fairly fast. This session will compare the different orchestation projects out there - from Heat to Kubernetes to TOSCA - and help you choose the right tool for the job.
[OpenInfra Days Korea 2018] Day 2 - E6 - OpenInfra monitoring with PrometheusOpenStack Korea Community
This document discusses using Prometheus for open infrastructure and cloud monitoring. It introduces Prometheus as a time series database and monitoring tool. Key features covered include metrics collection, service discovery, graphing, and alerting. The architecture of Prometheus is explained, including scrapping metrics directly or via exporters. A demo of Prometheus and Grafana is proposed to monitor Kubernetes clusters and visualize CPU usage. Alerting configuration and routes in Prometheus and Alertmanager are also summarized.
The document describes Onyx, a new flexible and extensible data processing system. It discusses limitations of existing frameworks in new resource environments like resource disaggregation and transient resources. The Onyx architecture includes a compiler that transforms dataflow programs into optimized physical execution plans using passes, and a runtime that executes the plans across cluster resources. It provides examples of compiling and running MapReduce and ALS jobs, and handling dynamic data skew through runtime optimization.
OSv is a new, high-performance OS for virtual machines in the cloud. Designed to run one application per guest with minimal overhead, OSv eliminates important bottlenecks for NoSQL applications through improvements in memory management, network I/O, and scheduling. And many important bottlenecks for NoSQL applications are tunable on a conventional OS, but do not require tuning in the OSv environment.
OSv is fully stateless and can be configured at runtime with cloud-init or through a REST API, with zero configuration files. OSv offers unified tracing from the application layer through the JVM and the OS kernel. Attendees will learn how to boot Cassandra in one second, and create a simple cluster in a minute.
Deploying Docker Containers at Scale with Mesos and MarathonDiscover Pinterest
Connor Doyle from Mesosphere.
Deploying Docker Containers at Scale with Mesos and Marathon
The norm these days is to operate apps at web scale. But that’s out of reach for most companies. Deploying Docker containers with Mesos and Marathon makes it easier. See how they help deploy and manage Docker containers at scale and how the Mesos cluster scheduler builds highly-available, fault-tolerant web scale apps.
Distributed Tests on Pulsar with Fallout - Pulsar Summit NA 2021StreamNative
Fallout is an open source testing framework based on Jepsen. In this talk we will see how distributed testing works and how to use these tools to verify Pulsar quality. We will see how we can easily deploy a reproducible Pulsar cluster on K8S and how to use ChaosMesh to inject failures. We will also cover integrated metrics reporting tools, very useful to verify the behaviour of the system for any Pulsar version, system environment and especially during maintenance operations (rollout restarts/upgrades) and unexpected failures.
1) The Docker Volume Isolator in Mesos allows containers to use external distributed storage volumes for persistent data, providing availability and scale beyond a single host.
2) It uses a driver-based model where volume drivers like Convoy and Rex-ray manage the external storage, and the isolator handles mounting volumes into containers.
3) The isolator mounts and unmounts volumes using the driver name and volume name, providing operators flexibility in their choice of storage platforms like IBM Spectrum Scale, AWS EBS, or GCE.
The document discusses best practices for backing up MongoDB databases. It explains that backups are important for availability and recovery from data loss. There are two main types of backups: one-time snapshots that capture a point-in-time view and incremental backups that capture changes since the last snapshot. The key steps for taking a one-time snapshot of a sharded cluster are to stop the balancer, take snapshots of the config servers and secondaries of each shard, restart the balancer, and copy the snapshots. Important considerations for backups include minimizing downtime and optimizing storage efficiency.
The Collection API provides classes and interfaces that support operations on collections of objects, such as HashSet, HashMap, ArrayList, and LinkedList. It replaces vectors, arrays, and hashtables. Iterator is an interface used to iterate through elements of a Collection. The differences between an abstract class and interface are that interfaces provide multiple inheritance while abstract classes do not, and interfaces only define public methods without implementation.
The document discusses Scala, a programming language designed to be scalable. It can be used for both small and large programs. Scala combines object-oriented and functional programming. It interoperates seamlessly with Java but allows developers to add new constructs like actors through libraries. The Scala community is growing, with industrial adoption starting at companies like Twitter.
The document discusses several topics related to optimizing Java program performance including:
1. Using buffering and non-blocking I/O for file reading/writing to improve efficiency.
2. Minimizing network calls by retrieving related data in one call and using design patterns like the session facade.
3. Reusing objects when possible rather than constantly creating new instances to reduce garbage collection overhead.
The document discusses new features in Java 8 including lambda expressions, method references, functional interfaces, default methods, streams, Optional class, and the new date/time API. It provides examples and explanations of how these features work, common use cases, and how they improve functionality and code readability in Java. Key topics include lambda syntax, functional interfaces, default interface methods, Stream API operations like filter and collect, CompletableFuture for asynchronous programming, and the replacement of java.util.Date with the new date/time classes.
This document provides an introduction to arrays and collections in Java. It discusses course objectives which include creating, compiling and running Java programs, using primitive data types, control flow, methods, arrays, object-oriented programming and core Java classes. It then covers generic programming using generic classes, working with arrays including copying and sorting arrays, and the advantages and disadvantages of arrays. Finally, it discusses Java collections including the different collection types, implementations like ArrayList and HashMap, adding and retrieving items from collections using iterators, and additional collection utility methods.
The document discusses 24 interview questions related to Java Collections Framework. It covers common questions about the key interfaces and classes in Collections like HashMap, ArrayList, Vector, Iterator, differences between HashMap and Hashtable, fail-fast property, and more. Example questions include what is difference between Iterator and ListIterator, when to use Hashtable vs HashMap, how to make a HashMap synchronized, and importance of hashCode() and equals() methods.
고급 자바 8 교육 (6일 중 4일차)
티맥스소프트 연구소에 연구소장으로 재직 중이던 2013년 10월에 진행한 자바 언어 강의 내용입니다.
JVM에 대한 이해와 Java 8에 대한 소개를 포함하려고 노력하였습니다.
아래 강의 동영상이 있습니다.
http://javadom.blogspot.com/2017/07/8-6.html
The document provides an overview of Java collections and exceptions. It discusses core collection interfaces like Collection, Set, List, Queue, and Map. It also covers exception handling in Java and common exception classes. The document contains examples and tips for using collections and exceptions in code. Key collection implementations like ArrayList, HashSet, and HashMap are described.
Clojure is a Lisp dialect designed for the Java Virtual Machine that focuses on functional programming and concurrency. It allows easy interoperability with Java code and provides persistent data structures and software transactional memory for writing concurrent programs that avoid deadlocks. The document provides an overview of Clojure's syntax, data structures, higher-order functions, Java interoperability features, and approach to concurrency through immutable objects and managed references.
GPars (Groovy Parallel Systems) is an open-source concurrency and parallelism library for Java and Groovy that gives you a number of high-level abstractions for writing concurrent and parallel code in Groovy (map/reduce, fork/join, asynchronous closures, actors, agents, dataflow concurrency and other concepts), which can make your Java and Groovy code concurrent and/or parallel with little effort.
Scala is a multi-paradigm programming language that blends object-oriented and functional programming. It is designed to express common programming patterns in a concise, elegant, and type-safe way. Scala runs on the Java Virtual Machine and interoperates seamlessly with Java, but also integrates concepts from languages such as Haskell, ML and Ruby. Some key features of Scala include support for functional programming, a static type system with type inference, pattern matching, actors and immutable data structures.
This document provides summaries of common Java interview questions. It discusses the differences between abstract classes and interfaces, checked and unchecked exceptions, user-defined exceptions, differences between C++ and Java, Java statements, JAR files, JNI, serialization, null interfaces, synchronized methods, singleton classes, compilation units, resource bundles, transient variables, the Collection API, iterators, observers and observables, synchronization, locks on classes, thread states, anonymous classes, primitive data types and their ranges.
This document introduces Clojure for Java developers with little Clojure experience. It discusses why Clojure is a good option, what Clojure is, its core concepts like immutability and functional programming, and how to interact with Java from Clojure. It also provides an overview of managing Clojure projects and deploying Clojure applications to the cloud.
Can two JVM languages, none of them Java, work harmoniously, side by side? Should they? In this lecture we’ll see how code written in Groovy and Scala work with Java and how they can be made to work with each other. The lecture is based on the experience of introducing Scala to an actual Groovy project.
The document contains questions and answers related to Java interview questions. It discusses topics like access modifiers, differences between abstract classes and interfaces, garbage collection, constructors vs methods, inheritance, polymorphism, exceptions and more. The questions aim to test the interviewee's understanding of core Java concepts.
This presentation describes key concepts in Java. I call it The Java Quicky.
This is part of a series of presentations to cover the Java programming language and its new offerings and versions in depth.
This is my attempt to compose a brief and cursory introduction to concepts in Java programming language. I call it Java Quicky.
I plan to extend and enhance it over time.
This document discusses different types of interfaces in Java, including functional interfaces, marker interfaces, and examples. It covers the key characteristics of functional interfaces like having only one abstract method. Marker interfaces are described as having no methods or fields. Examples are provided of serializable and cloneable marker interfaces. The document also provides examples of using serializable interfaces to serialize and deserialize objects.
고급 자바 8 교육 (6일 중 5일차)
티맥스소프트 연구소에 연구소장으로 재직 중이던 2013년 10월에 진행한 자바 언어 강의 내용입니다.
JVM에 대한 이해와 Java 8에 대한 소개를 포함하려고 노력하였습니다.
아래 강의 동영상이 있습니다.
http://javadom.blogspot.com/2017/07/8-6.html
고급 자바 8 교육 (6일 중 3일차)
티맥스소프트 연구소에 연구소장으로 재직 중이던 2013년 10월에 진행한 자바 언어 강의 내용입니다.
JVM에 대한 이해와 Java 8에 대한 소개를 포함하려고 노력하였습니다.
아래 강의 동영상이 있습니다.
http://javadom.blogspot.com/2017/07/8-6.html
고급 자바 8 교육 (6일 중 2일차)
티맥스소프트 연구소에 연구소장으로 재직 중이던 2013년 10월에 진행한 자바 언어 강의 내용입니다.
JVM에 대한 이해와 Java 8에 대한 소개를 포함하려고 노력하였습니다.
아래 강의 동영상이 있습니다.
http://javadom.blogspot.com/2017/07/8-6.html
고급 자바 8 교육 (6일 중 1일차)
티맥스소프트 연구소에 연구소장으로 재직 중이던 2013년 10월에 진행한 자바 언어 강의 내용입니다.
JVM에 대한 이해와 Java 8에 대한 소개를 포함하려고 노력하였습니다.
아래 강의 동영상이 있습니다.
http://javadom.blogspot.com/2017/07/8-6.html
Lecture on Java Concurrency Day 3 on Feb 11, 2009.Kyung Koo Yoon
The document discusses various worker thread models and provides examples of using concurrency in Java. It also summarizes the java.util.concurrent package, which provides common high-level concurrency classes like ConcurrentHashMap, CopyOnWriteArrayList, BlockingQueue, CountDownLatch and Executor. The package was introduced in Java 5 to make concurrent programming easier and more robust.
Lecture on Java Concurrency Day 2 on Feb 4, 2009.Kyung Koo Yoon
Lecture on Java Concurrency Day 2 on Feb 4, 2009. (in Korean)
Lectures are 4 days in all.
See http://javadom.blogspot.com/2011/06/lecture-on-java-concurrency-day-2.html
Lecture on Java Concurrency Day 4 on Feb 18, 2009.Kyung Koo Yoon
Lecture on Java Concurrency Day 4 on Feb 18, 2009. (in Korean)
Lectures are 4 days in all.
See http://javadom.blogspot.com/2011/06/lecture-on-java-concurrency-day-4.html
Lecture on Java Concurrency Day 1 on Jan 21, 2009.Kyung Koo Yoon
Lecture on Java Concurrency Day 1 on Jan 21, 2009. (in Korean)
Lectures are 4 days in all.
See http://javadom.blogspot.com/2011/06/lecture-on-java-concurrency-day-1.html
Discover why Wi-Fi 7 is set to transform wireless networking and how Router Architects is leading the way with next-gen router designs built for speed, reliability, and innovation.
Download YouTube By Click 2025 Free Full Activatedsaniamalik72555
Copy & Past Link 👉👉
https://dr-up-community.info/
"YouTube by Click" likely refers to the ByClick Downloader software, a video downloading and conversion tool, specifically designed to download content from YouTube and other video platforms. It allows users to download YouTube videos for offline viewing and to convert them to different formats.
Not So Common Memory Leaks in Java WebinarTier1 app
This SlideShare presentation is from our May webinar, “Not So Common Memory Leaks & How to Fix Them?”, where we explored lesser-known memory leak patterns in Java applications. Unlike typical leaks, subtle issues such as thread local misuse, inner class references, uncached collections, and misbehaving frameworks often go undetected and gradually degrade performance. This deck provides in-depth insights into identifying these hidden leaks using advanced heap analysis and profiling techniques, along with real-world case studies and practical solutions. Ideal for developers and performance engineers aiming to deepen their understanding of Java memory management and improve application stability.
Scaling GraphRAG: Efficient Knowledge Retrieval for Enterprise AIdanshalev
If we were building a GenAI stack today, we'd start with one question: Can your retrieval system handle multi-hop logic?
Trick question, b/c most can’t. They treat retrieval as nearest-neighbor search.
Today, we discussed scaling #GraphRAG at AWS DevOps Day, and the takeaway is clear: VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval.
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Societal challenges of AI: biases, multilinguism and sustainabilityJordi Cabot
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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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2. 자바 교육 계획
1일 : Language, String,
ClassLoader, Proxy
2일 : GC, Collections
3일 : Thread, Java Memory
Model
4일 : AQS, ForkJoin,
Concurrent Utils
5일 : IO, Generics, Annotation,
RMI
6일 : Unsafe, Lambda (Java 8)
2
내겐 바스켓만 보여 (Flow 상태)
213년 10월 22일 화
5. sun.misc.Unsafe
5
A collection of methods for performing low-level, unsafe operations
park, unpark
direct memory manipulation
provide wrappers for malloc, realloc, free, memset, memcpy
(not Java Heap)
direct object memory manipulation
CAS operations : compareAndSwap
defineClass, defineAnonymousClass
513년 10월 22일 화
6. Java is not safe with Unsafe
6
can easily corrupt memory
fast serialization
super array (non-Java heap)
allocateMemory(long size)
can cause JVM crash
613년 10월 22일 화
7. Lambda
7
The
issue
being
debated
is
not
whether
closures
are
a
good
idea
-‐
because
they
clearly
are
-‐
but
whether
the
benefits
of
retrofi7ng
the
Java
language
with
closures
are
worth
the
costs.
-‐
Brian
Goetz
713년 10월 22일 화
8. Functional Programming
8
lambda
anonymous function with a single argument
closure
a block of code that may contain free (unbound)
variables
currying
handling multiple argument function with lambda
function
813년 10월 22일 화
9. Java 8 Lambda Expression
SAM (single abstract method) of an interface
Java lambda expression
a list of formal parameters and a body—an expression
or block—expressed in terms of those parameters
e.g,
s -> s.toUpperCase()
(int a, int b) -> a + b
9
913년 10월 22일 화
10. Method Reference expression
method reference : refer to a method without invoking it
System::getProperty
"abc"::length
constructor reference : refer to a constructor without creating
a new instance of the named class or array type
ArrayList::new
int[]::new
e.g, Arrays.sort(myIntegerArray, Integer::compare)
10
1013년 10월 22일 화
11. Java 8 Closure
For both lambda bodies and inner classes, local
variables in the enclosing context can only be
referenced if they are final or effectively final.
A variable is effectively final if it is never assigned to
after its initialization.
11
1113년 10월 22일 화
12. Java 8 Currying
Currying :A technique of transforming a multi-argument
function in such a way that it can be called as a chain
of functions, each with a single argument.
12
1213년 10월 22일 화
13. New Object-oriented feature
mixin
a class which contains a combination of methods
from other classes.
can also be viewed as an interface with implemented
methods.
Mixins encourage code reuse and avoid well-known
pathologies associated with multiple inheritance.
13
1313년 10월 22일 화
14. Java 8 Default Method
Java’s language addition of mixin-like thing
provides a default implementation for any class that
implements the interface without overriding the
method.
allows new functionality to be added to existing (and
perhaps already widely-distributed) interfaces.
More generally, it provides a mechanism for multiple
inheritance of behavior.
14
1413년 10월 22일 화
15. Java 8 Changes to Interface
Now, interface method can be default, static and
abstract
all the non-default, non-static methods in the
interface are abstract as before
Default methods of super-interface can be accessed
<InterfaceName>.super.<methodName>()
15
1513년 10월 22일 화
16. Java 8 Project Lambda
Language Changes
Still, No Function Type
Functional Interface
Default Method
Streams
invokeDynamic
java.util.Spliterator
16
1613년 10월 22일 화
17. Java Lambda ABC
@interface java.lang.FunctionalInterface
Just a hint for compiler (compile error if does not have
SAM)
any interface which has SAM is functional interface
package java.util.function
lambda function does not create additional classes unlike
anonymous inner classes
it creates a private static lambda method
17
1713년 10월 22일 화
18. Streams
handle aggregate operations DECLARATIVELY
stream pipeline
1 source : collection, array, generator ftn, IO, ...
0 or more intermediate operations : filter, map, ...
1 terminal operation : forEach, reduce, sum, ...
18
1813년 10월 22일 화
19. Stages of Streams
txns.stream()
.filter(txn -> txn.getBuyer().getAge() >= 65)
.map(txn -> txn.getSeller())
.distinct()
.sort(comparing(seller -> seller.getName()))
.forEach(seller ->
System.out.println(seller.getName());
19
source
intermediate
intermediate
intermediate
intermediate
terminal
intermediate ops just setup pipeline
and returns new stream
1913년 10월 22일 화
20. Parallel Streams
A stream pipeline can be created with an orientation of either
serial or parallel
Parallelism may be faster but introduces non-determinism
Internally uses fork-join framework
Collection c = ...;
List r1 = c.stream().map(...).collect(toList());
List r2 = c.parallelStream().map(...).collect(toList());
20
2013년 10월 22일 화
22. MethodHandle
java.lang.invoke 패키지
can store references to methods in the constant pool, load
with LDC (load constant)
can obtain a method handle for any method (or field access)
But
MethodHandle invocation performance is not good
Lambda is language level method, MethodHandle is VM
level method
22
2213년 10월 22일 화
23. Lambda Invocation
desugar implementation method
private static boolean lambda$1(int minAge, Person p) { ... }
lambda metafactory에 MethodHandle argument로 전달
invokeDynamic 호출
Predicate $p = invokedynamic[bootstrap=LambdaMetaFactory, ... ]
moving more work from static compiler to runtime
performance
linkage, capture 비용은 inner class보다 싸지만 invoke 비용은 비싸다!
23
2313년 10월 22일 화