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
1) The document discusses various techniques for analyzing thread dumps to troubleshoot issues like CPU spikes, out of memory errors, and unresponsive applications or databases.
2) It provides examples of common thread dump analysis patterns that indicate specific problems, such as the "thread mill" pattern revealing infinite looping or the "leprechaun trap" pattern indicating issues with finalization.
3) The document also describes how to take thread dumps from a Java process using tools like jstack and jcmd and explains the anatomy of a typical thread dump.
A brief look at a few IP cameras, wireless extenders and routers - a significant number have unpatched issues such as cross-site scripting, cross-site request forgery and authentication bypass, meaning we can take control of devices, change wireless settings and obtain a copy of all video on certain devices, all by having the unsuspecting user visit a web page we control.
The document discusses Java 5 concurrency features including locks, conditions, atomic variables, blocking queues, concurrent hash maps, synchronizers like semaphores and mutexes, and the executor framework. Key points include:
- Locks provide an alternative to synchronized blocks and methods, and allow more flexible locking behavior. ReentrantLock is a common lock implementation.
- Conditions (condition variables) allow threads to wait/signal and are used with locks rather than synchronized monitors.
- Atomic variables ensure thread-safe operations on single variables without locking.
Мастер-класс "Логическая репликация и Avito" / Константин Евтеев, Михаил Тюр...Ontico
HighLoad++ 2017
Зал «Кейптаун», 7 ноября, 12:00
Тезисы:
http://www.highload.ru/2017/abstracts/2868.html
В Avito объявления хранятся в базах данных Postgres. При этом уже на протяжении многих лет активно применяется логическая репликация. С помощью неё успешно решаются вопросы роста объема данных и количества запросов к ним, масштабирования и распределения нагрузки, доставки данных в DWH и поисковые подсистемы, меж-базные и меж-сервисные синхронизации данных и пр.
...
This document summarizes the major changes and new features introduced in each version of Java from Java 8 to Java 17. It discusses key enhancements like modules in Java 9, switch expressions in Java 12, text blocks in Java 13, records and pattern matching in Java 14, sealed classes in Java 15 and strong encapsulation in Java 16. It also provides code examples to illustrate many of the new Java features.
The document discusses the Disruptor, an ultrafast communication framework. It describes the problems with traditional concurrent queues, such as lock contention. The Disruptor uses a novel ring buffer approach with barriers to allow producers to add events and consumers to process them in parallel without synchronization overhead. This architecture enables it to achieve throughput of over 6 million transactions per second. The Disruptor exploits hardware characteristics to achieve extremely low latency communication between threads.
The document discusses various issues that can occur in multi-threaded applications and how thread dumps can help identify and diagnose them. It describes problems that can arise from I/O blocking, inefficient algorithms, lock contention, deadlocks, and non-thread-safe code. It provides examples like a HashMap causing 100% CPU usage. It also covers thread dump formats, tools for analyzing thread dumps, and questions that thread dumps may help answer about sporadic application behavior and performance problems.
Non-blocking synchronization — what is it and why we (don't?) need itAlexey Fyodorov
This document contains notes from a presentation on non-blocking synchronization. It introduces concepts like concurrency, locking, compare-and-swap (CAS) operations, and how they are implemented in Java using classes like AtomicLong. CAS allows updating shared memory without locking, but can involve retries if the expected value changes. Java provides various atomic data structures like scalars, arrays, and accumulators that have lock-free or obstruction-free guarantees. Non-blocking data structures like lock-free stacks and queues are also discussed.
The document discusses the Disruptor, a high performance inter-thread messaging library created by LMAX. It introduces key concepts like the ring buffer, producer-consumer pattern, and consumer dependency graphs. It then explains the Disruptor architecture which uses a multithreaded producer to populate a ring buffer of event data which is consumed by handler threads in a specified order without blocking. Finally, it outlines examples of unicast, multicast and pipelined consumer configurations and references additional resources to learn more about the Disruptor and related concurrency concepts.
This document discusses thread dumps, heap dumps, and best practices for generating and analyzing them. It defines a thread dump as a snapshot of all Java threads with information like name, state, and ID. A heap dump snapshots the Java heap showing object references and usage. The document recommends generating thread/heap dumps when full GC is frequent, memory isn't being reclaimed, or response times decrease. It provides commands to generate dumps and JVM options for tuning garbage collection and heap size. Finally, it includes links for further reading on Java memory management and performance tuning.
Slides from #PromCon2018 Munich.
https://promcon.io/2018-munich/talks/thanos-prometheus-at-scale/
Bartłomiej Płotka
Fabian Reinartz
The Prometheus Monitoring system has been thriving for several years. Along with its powerful data model, operational simplicity and reliability have been a key factor in its success. However, some questions were still largely unaddressed to this day. How can we store historical data at the order of petabytes in a reliable and cost-efficient way? Can we do so without sacrificing responsive query times? And what about a global view of all our metrics and transparent handling of HA setups?
Thanos takes Prometheus' strong foundations and extends it into a clustered, yet coordination free, globally scalable metric system. It retains Prometheus's simple operational model and even simplifies deployments further. Under the hood, Thanos uses highly cost-efficient object storage that's available in virtually all environments today. By building directly on top of the storage format introduced with Prometheus 2.0, Thanos achieves near real-time responsiveness even for cold queries against historical data. All while having virtually no cost overhead beyond that of the underlying object storage.
We will show the theoretical concepts behind Thanos and demonstrate how it seamlessly integrates into existing Prometheus setups.
The document discusses several high performance architectural patterns:
- Event sourcing, which models data as a sequence of immutable events and allows reconstructing past states;
- CQRS, which separates read and write operations into different models for improved performance;
- LMAX Disruptor, a high performance inter-thread messaging library that enables lock-free, wait-free, bounded queues for passing data between threads.
When combined, these patterns enable building highly scalable and available systems by allowing separate scaling of read and write workloads, easy evolution of data models, and maximizing throughput with disruption-free concurrency.
In-depth caching in Varnish - GOG Varnish Meetup, march 2019GOG.com dev team
You think Varnish can cache responses by URL only? Not even close. Learn all different caching strategies available in Varnish, their benefits and consequences of usage. Learn how to and when to queue requests for the same endpoint, how to handle requests with conditional caching headers and how to have two levels of cache by tagging the responses.
La scalabilité des applications est une préoccupation importante. Beaucoup de pertes en scalabilité proviennent de code contenant des locks qui produisent une importante contention en cas de forte charge.
Dans cette présentation nous allons aborder différentes techniques (striping, copy-on-write, ring buffer, spinning, ...) qui vont nous permettre de réduire cette contention ou d'obtenir un code sans lock. Nous expliquerons aussi les concepts de Compare-And-Swap et de barrières mémoires.
The document discusses using AMQP/MQ Light as a microservices transport. It compares using MQ Light to using REST for communication between microservices. MQ Light uses a publish-subscribe model where services subscribe to topics to receive messages, rather than services calling each other directly. This allows for looser coupling between services and avoids services needing to know about changes to other services.
Distributed system coordination by zookeeper and introduction to kazoo python...Jimmy Lai
Zookeeper is a coordination tool to let people build distributed systems easier. In this slides, the author summarizes the usage of zookeeper and provides Kazoo Python library as example.
The document discusses common concurrency problems in Java like shared mutable state, visibility issues, inconsistent synchronization, and unsafe publication and provides examples of how to properly implement threading concepts like locking, waiting and notifying with synchronization, volatile variables, atomic classes and safe initialization techniques to avoid concurrency bugs. It also cautions against unsafe practices like synchronizing on the wrong objects or misusing threading methods that can lead to deadlocks, race conditions and other concurrency problems.
The document discusses performance tuning of Java applications. It covers identifying bottlenecks in Java applications, techniques for performance engineering like defining problems, breaking down into sections, isolating issues and finding bottlenecks. It also provides examples of common bottlenecks like lock contention, deadlocks and waiting for I/O responses. Specific cases discussed include threads waiting for locks, circular waiting conditions causing hangs, and threads blocked waiting for database or network responses.
The document discusses concurrency programming in Java. It covers the scope of concurrency including multi-threading, multi-core, and distributed systems. It then discusses key aspects of concurrency programming like shared data/coordination for correctness and performance. It provides examples of thread-safety issues and how to address them using locks, volatile fields, and final fields to safely publish objects between threads.
pg / shardman: шардинг в PostgreSQL на основе postgres / fdw, pg / pathman и ...Ontico
Pg_shardman provides PostgreSQL sharding via partitioning and foreign data wrappers (FDW), with high availability through logical replication and ACID transactions. It aims for horizontal scalability of reads and writes with an OLTP workload. Key features include hash-sharding data across nodes, replication for high availability, and distributed two-phase commit with a distributed snapshot manager to provide transactions across shards. It utilizes partitioning, FDW, and logical replication capabilities in PostgreSQL to provide a sharded cluster with transactions.
Java Core | Understanding the Disruptor: a Beginner's Guide to Hardcore Concu...JAX London
2011-11-02 | 05:45 PM - 06:35 PM | Victoria
The Disruptor is new open-source concurrency framework, designed as a high performance mechanism for inter-thread messaging. It was developed at LMAX as part of our efforts to build the world's fastest financial exchange. Using the Disruptor as an example, this talk will explain of some of the more detailed and less understood areas of concurrency, such as memory barriers and cache coherency. These concepts are often regarded as scary complex magic only accessible by wizards like Doug Lea and Cliff Click. Our talk will try and demystify them and show that concurrency can be understood by us mere mortal programmers.
This document discusses refactoring C++ code to make it easier to test. It begins by describing hard-to-test code patterns like tight coupling, dependence on global states, and high complexity. It then discusses refactoring techniques like abstracting dependencies through interfaces, injecting dependencies, and avoiding hard-coded values. The document provides examples refactoring files, odometers, timers, and domain logic to be more testable. It concludes by emphasizing writing unit tests that are fast and focus on verifying code through abstraction and injection.
The document discusses slowdowns, hangs, and thread dumps in troubleshooting Java applications. It explains that thread dumps provide a snapshot of thread states and what each thread is doing at a point in time. This can help identify issues like threads getting stuck or competing for resources. The document also demonstrates example output of a thread dump with threads in different states like waiting and provides tips for analyzing thread dumps to diagnose performance problems.
This document describes a "Hello World" example using FreeRTOS on an embedded operating system. It creates two tasks, a Tx task that sends the string "Hello World" to a queue every second, and an Rx task that receives the string from the queue and prints it. A timer is also created that expires after 10 seconds, checks if the Rx task has received enough messages, and prints whether the example passed or failed before deleting the two tasks.
ok.ru is one of top 10 internet sites of the World, according to similarweb.com. Under the hood, it has several thousand servers. Each of those servers own only fraction of the data or business logic. Shared nothing architecture can be hardly applied to social network, due to its nature, so a lot of communication happens between these servers, diverse in kind and volume. This makes ok.ru one of the largest, complicated, yet highly loaded distributed systems in the world.
This talk is about our experience in building always available, resilient to failures distributed systems in Java, their basic and not so basic failure and recovery scenarios, methods of failure testing and diagnostics. We’ll also discuss on possible disasters and how to prevent or get over them.
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
The document discusses Java concurrency concepts including the Java memory model, thread synchronization, locks, and common utilities in the java.util.concurrent package. It covers thread coordination mechanisms like synchronized, volatile, locks, and wait/notify. It also summarizes data structures like ConcurrentHashMap and Executors like ThreadPoolExecutor. The document mentions parallel programming techniques including fork/join and coroutines.
Non-blocking synchronization — what is it and why we (don't?) need itAlexey Fyodorov
This document contains notes from a presentation on non-blocking synchronization. It introduces concepts like concurrency, locking, compare-and-swap (CAS) operations, and how they are implemented in Java using classes like AtomicLong. CAS allows updating shared memory without locking, but can involve retries if the expected value changes. Java provides various atomic data structures like scalars, arrays, and accumulators that have lock-free or obstruction-free guarantees. Non-blocking data structures like lock-free stacks and queues are also discussed.
The document discusses the Disruptor, a high performance inter-thread messaging library created by LMAX. It introduces key concepts like the ring buffer, producer-consumer pattern, and consumer dependency graphs. It then explains the Disruptor architecture which uses a multithreaded producer to populate a ring buffer of event data which is consumed by handler threads in a specified order without blocking. Finally, it outlines examples of unicast, multicast and pipelined consumer configurations and references additional resources to learn more about the Disruptor and related concurrency concepts.
This document discusses thread dumps, heap dumps, and best practices for generating and analyzing them. It defines a thread dump as a snapshot of all Java threads with information like name, state, and ID. A heap dump snapshots the Java heap showing object references and usage. The document recommends generating thread/heap dumps when full GC is frequent, memory isn't being reclaimed, or response times decrease. It provides commands to generate dumps and JVM options for tuning garbage collection and heap size. Finally, it includes links for further reading on Java memory management and performance tuning.
Slides from #PromCon2018 Munich.
https://promcon.io/2018-munich/talks/thanos-prometheus-at-scale/
Bartłomiej Płotka
Fabian Reinartz
The Prometheus Monitoring system has been thriving for several years. Along with its powerful data model, operational simplicity and reliability have been a key factor in its success. However, some questions were still largely unaddressed to this day. How can we store historical data at the order of petabytes in a reliable and cost-efficient way? Can we do so without sacrificing responsive query times? And what about a global view of all our metrics and transparent handling of HA setups?
Thanos takes Prometheus' strong foundations and extends it into a clustered, yet coordination free, globally scalable metric system. It retains Prometheus's simple operational model and even simplifies deployments further. Under the hood, Thanos uses highly cost-efficient object storage that's available in virtually all environments today. By building directly on top of the storage format introduced with Prometheus 2.0, Thanos achieves near real-time responsiveness even for cold queries against historical data. All while having virtually no cost overhead beyond that of the underlying object storage.
We will show the theoretical concepts behind Thanos and demonstrate how it seamlessly integrates into existing Prometheus setups.
The document discusses several high performance architectural patterns:
- Event sourcing, which models data as a sequence of immutable events and allows reconstructing past states;
- CQRS, which separates read and write operations into different models for improved performance;
- LMAX Disruptor, a high performance inter-thread messaging library that enables lock-free, wait-free, bounded queues for passing data between threads.
When combined, these patterns enable building highly scalable and available systems by allowing separate scaling of read and write workloads, easy evolution of data models, and maximizing throughput with disruption-free concurrency.
In-depth caching in Varnish - GOG Varnish Meetup, march 2019GOG.com dev team
You think Varnish can cache responses by URL only? Not even close. Learn all different caching strategies available in Varnish, their benefits and consequences of usage. Learn how to and when to queue requests for the same endpoint, how to handle requests with conditional caching headers and how to have two levels of cache by tagging the responses.
La scalabilité des applications est une préoccupation importante. Beaucoup de pertes en scalabilité proviennent de code contenant des locks qui produisent une importante contention en cas de forte charge.
Dans cette présentation nous allons aborder différentes techniques (striping, copy-on-write, ring buffer, spinning, ...) qui vont nous permettre de réduire cette contention ou d'obtenir un code sans lock. Nous expliquerons aussi les concepts de Compare-And-Swap et de barrières mémoires.
The document discusses using AMQP/MQ Light as a microservices transport. It compares using MQ Light to using REST for communication between microservices. MQ Light uses a publish-subscribe model where services subscribe to topics to receive messages, rather than services calling each other directly. This allows for looser coupling between services and avoids services needing to know about changes to other services.
Distributed system coordination by zookeeper and introduction to kazoo python...Jimmy Lai
Zookeeper is a coordination tool to let people build distributed systems easier. In this slides, the author summarizes the usage of zookeeper and provides Kazoo Python library as example.
The document discusses common concurrency problems in Java like shared mutable state, visibility issues, inconsistent synchronization, and unsafe publication and provides examples of how to properly implement threading concepts like locking, waiting and notifying with synchronization, volatile variables, atomic classes and safe initialization techniques to avoid concurrency bugs. It also cautions against unsafe practices like synchronizing on the wrong objects or misusing threading methods that can lead to deadlocks, race conditions and other concurrency problems.
The document discusses performance tuning of Java applications. It covers identifying bottlenecks in Java applications, techniques for performance engineering like defining problems, breaking down into sections, isolating issues and finding bottlenecks. It also provides examples of common bottlenecks like lock contention, deadlocks and waiting for I/O responses. Specific cases discussed include threads waiting for locks, circular waiting conditions causing hangs, and threads blocked waiting for database or network responses.
The document discusses concurrency programming in Java. It covers the scope of concurrency including multi-threading, multi-core, and distributed systems. It then discusses key aspects of concurrency programming like shared data/coordination for correctness and performance. It provides examples of thread-safety issues and how to address them using locks, volatile fields, and final fields to safely publish objects between threads.
pg / shardman: шардинг в PostgreSQL на основе postgres / fdw, pg / pathman и ...Ontico
Pg_shardman provides PostgreSQL sharding via partitioning and foreign data wrappers (FDW), with high availability through logical replication and ACID transactions. It aims for horizontal scalability of reads and writes with an OLTP workload. Key features include hash-sharding data across nodes, replication for high availability, and distributed two-phase commit with a distributed snapshot manager to provide transactions across shards. It utilizes partitioning, FDW, and logical replication capabilities in PostgreSQL to provide a sharded cluster with transactions.
Java Core | Understanding the Disruptor: a Beginner's Guide to Hardcore Concu...JAX London
2011-11-02 | 05:45 PM - 06:35 PM | Victoria
The Disruptor is new open-source concurrency framework, designed as a high performance mechanism for inter-thread messaging. It was developed at LMAX as part of our efforts to build the world's fastest financial exchange. Using the Disruptor as an example, this talk will explain of some of the more detailed and less understood areas of concurrency, such as memory barriers and cache coherency. These concepts are often regarded as scary complex magic only accessible by wizards like Doug Lea and Cliff Click. Our talk will try and demystify them and show that concurrency can be understood by us mere mortal programmers.
This document discusses refactoring C++ code to make it easier to test. It begins by describing hard-to-test code patterns like tight coupling, dependence on global states, and high complexity. It then discusses refactoring techniques like abstracting dependencies through interfaces, injecting dependencies, and avoiding hard-coded values. The document provides examples refactoring files, odometers, timers, and domain logic to be more testable. It concludes by emphasizing writing unit tests that are fast and focus on verifying code through abstraction and injection.
The document discusses slowdowns, hangs, and thread dumps in troubleshooting Java applications. It explains that thread dumps provide a snapshot of thread states and what each thread is doing at a point in time. This can help identify issues like threads getting stuck or competing for resources. The document also demonstrates example output of a thread dump with threads in different states like waiting and provides tips for analyzing thread dumps to diagnose performance problems.
This document describes a "Hello World" example using FreeRTOS on an embedded operating system. It creates two tasks, a Tx task that sends the string "Hello World" to a queue every second, and an Rx task that receives the string from the queue and prints it. A timer is also created that expires after 10 seconds, checks if the Rx task has received enough messages, and prints whether the example passed or failed before deleting the two tasks.
ok.ru is one of top 10 internet sites of the World, according to similarweb.com. Under the hood, it has several thousand servers. Each of those servers own only fraction of the data or business logic. Shared nothing architecture can be hardly applied to social network, due to its nature, so a lot of communication happens between these servers, diverse in kind and volume. This makes ok.ru one of the largest, complicated, yet highly loaded distributed systems in the world.
This talk is about our experience in building always available, resilient to failures distributed systems in Java, their basic and not so basic failure and recovery scenarios, methods of failure testing and diagnostics. We’ll also discuss on possible disasters and how to prevent or get over them.
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
The document discusses Java concurrency concepts including the Java memory model, thread synchronization, locks, and common utilities in the java.util.concurrent package. It covers thread coordination mechanisms like synchronized, volatile, locks, and wait/notify. It also summarizes data structures like ConcurrentHashMap and Executors like ThreadPoolExecutor. The document mentions parallel programming techniques including fork/join and coroutines.
Slides from tech talk about the art of non-blocking waiting in Java with LockSupport.park/unpark and AbstractQueuedSynchronizer. Presented on JPoint 2016 Conference.
String is immutable in Java for security and efficiency reasons. Immutability allows strings to be shared and cached without risk of unexpected changes from another thread. The String class is declared final to prevent extension and mutation. Immutable strings enable safe sharing across threads and applications like databases and networking without the risk of data corruption from concurrent modifications.
This document discusses concepts related to threads and concurrency in Java. It begins by defining processes and threads, explaining that processes contain threads. It then covers key threading concepts like thread pools, synchronization, and data management between threads. The document provides examples of why threads are used and best practices for writing threaded code in Java. It also discusses common threading issues like deadlocks and race conditions and how to avoid them.
The document discusses key concepts related to threads and concurrency in Java. It defines processes, threads, and the Java memory model. It then covers various concurrency utilities in Java like synchronized blocks, volatile fields, atomic classes, thread pools, blocking queues, and locking mechanisms like ReentrantLock. The last part discusses high-level concurrency constructs like semaphores, latches, barriers, and phaser.
For More information, refer to Java EE 7 performance tuning and optimization book:
The book is published by Packt Publishing:
http://www.packtpub.com/java-ee-7-performance-tuning-and-optimization/book
The document discusses Java concurrency concepts including locks, threads, atomics, and thread pools. It provides examples of using ReentrantLock for locking, AtomicLong for atomic counters, and ThreadPoolExecutor for managing threads. The document also mentions different Java concurrency implementations and creating a lock factory to choose the implementation.
The document summarizes the key new features introduced in each version of Java from JDK 1.0 to Java SE 8. Some major enhancements included strings in switch statements in Java 7, binary and underscore literals, multi-catch exceptions, try-with-resources for automatic resource management, and lambda expressions in Java 8.
This presentation is about advanced multithreading and concurrency in Java. I have tried my best to explain the concepts with code. Feel free to reach me if you have any questions or concerns.
This document describes a unit project on threading and Java threading. It explains concepts like semaphores, test-and-set operations, reentrancy, and preemption. It also reviews two sample Java classes, Msynch and Msynch1, that demonstrate multithreading concepts. Msynch is described as functioning properly while Msynch1 contains three synchronization errors that would prevent proper execution.
This document discusses memory ordering and synchronization in multithreaded programs. It begins with background on mutexes, semaphores, and their differences. It then discusses problems that can occur with locking-based synchronization methods like mutexes, such as deadlocks, priority inversion, and performance issues. Alternative lock-free programming techniques using atomic operations are presented as a way to synchronize access without locks. Finally, memory ordering, consistency models, barriers, and their implementations in compilers, Linux kernels, and ARM architectures are covered in detail.
Looming Marvelous - Virtual Threads in Java Javaland.pdfjexp
Nowadays we have 2 options for concurrency in Java:
* simple, synchronous, blocking code with limited scalability that tracks well linearly at runtime, or.
* complex, asynchronous libraries with high scalability that are harder to handle.
Project Loom aims to bring together the best aspects of these two approaches and make them available to developers.
In the talk, I'll briefly cover the history and challenges of concurrency in Java before we dive into Loom's approaches and do some behind-the-scenes implementation. To manage so many threads reasonably needs some structure - for this there are proposals for "Structured Concurrency" which we will also look at. Some examples and comparisons to test Loom will round up the talk.
Project Loom is included in Java 19 and 20 as a preview feature, it can already be tested how well it works with our applications and libraries.
Spoiler: Pretty good.
This document discusses concurrency and concurrent programming in Java. It introduces the built-in concurrency primitives like wait(), notify(), synchronized, and volatile. It then discusses higher-level concurrency utilities and data structures introduced in JDK 5.0 like Executors, ExecutorService, ThreadPools, Future, Callable, ConcurrentHashMap, CopyOnWriteArrayList that provide safer and more usable concurrency constructs. It also briefly covers topics like Java Memory Model, memory barriers, and happens-before ordering.
The document discusses various synchronization primitives in Java including ReentrantLock, Condition, Semaphore, Future, CyclicBarrier, CountDownLatch, and Exchanger. It explains what each one is used for and provides examples of how they work. The key points are that these primitives provide ways to synchronize threads, block and unblock threads under certain conditions, and coordinate work between multiple threads through techniques like waiting for barriers or counting down latches. Underlying them all is the AbstractQueuedSynchronizer which provides a framework for atomically managing shared state and enqueueing/dequeuing threads.
This document discusses techniques for writing highly scalable Java programs for multi-core systems. It begins with an overview of hardware trends showing an increasing number of cores per chip. It then discusses profiling tools that can identify lock contention issues. The document provides best practices for Java programming including reducing locking scope, splitting locks, stripping locks, using atomic variables, and lock-free algorithms. It emphasizes using concurrent containers and immutable/thread-local data where possible.
The document discusses concepts related to concurrency and multithreading in Java. It defines key terms like threads, locks, and conditions. It explains that threads allow independent tasks to run concurrently for improved performance. Synchronization ensures predictable sharing of memory between threads using monitors, volatile fields provide a lightweight synchronization. Locks provide more flexible locking than synchronized blocks. Conditions can be used to signal between threads waiting on a shared resource like a blocking queue.
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
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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
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Microsoft AI Nonprofit Use Cases and Live Demo_2025.04.30.pdfTechSoup
In this webinar we will dive into the essentials of generative AI, address key AI concerns, and demonstrate how nonprofits can benefit from using Microsoft’s AI assistant, Copilot, to achieve their goals.
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Wondershare Filmora is a video editing software and app designed for both beginners and experienced users. It's known for its user-friendly interface, drag-and-drop functionality, and a wide range of tools and features for creating and editing videos. Filmora is available on Windows, macOS, iOS (iPhone/iPad), and Android platforms.
TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora
Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.
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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.
This presentation explores code comprehension challenges in scientific programming based on a survey of 57 research scientists. It reveals that 57.9% of scientists have no formal training in writing readable code. Key findings highlight a "documentation paradox" where documentation is both the most common readability practice and the biggest challenge scientists face. The study identifies critical issues with naming conventions and code organization, noting that 100% of scientists agree readable code is essential for reproducible research. The research concludes with four key recommendations: expanding programming education for scientists, conducting targeted research on scientific code quality, developing specialized tools, and establishing clearer documentation guidelines for scientific software.
Presented at: The 33rd International Conference on Program Comprehension (ICPC '25)
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AgentExchange is Salesforce’s latest innovation, expanding upon the foundation of AppExchange by offering a centralized marketplace for AI-powered digital labor. Designed for Agentblazers, developers, and Salesforce admins, this platform enables the rapid development and deployment of AI agents across industries.
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Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...Eric D. Schabell
It's time you stopped letting your telemetry data pressure your budgets and get in the way of solving issues with agility! No more I say! Take back control of your telemetry data as we guide you through the open source project Fluent Bit. Learn how to manage your telemetry data from source to destination using the pipeline phases covering collection, parsing, aggregation, transformation, and forwarding from any source to any destination. Buckle up for a fun ride as you learn by exploring how telemetry pipelines work, how to set up your first pipeline, and exploring several common use cases that Fluent Bit helps solve. All this backed by a self-paced, hands-on workshop that attendees can pursue at home after this session (https://o11y-workshops.gitlab.io/workshop-fluentbit).
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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.
https://arxiv.org/abs/2501.16495
Designing AI-Powered APIs on Azure: Best Practices& ConsiderationsDinusha Kumarasiri
AI is transforming APIs, enabling smarter automation, enhanced decision-making, and seamless integrations. This presentation explores key design principles for AI-infused APIs on Azure, covering performance optimization, security best practices, scalability strategies, and responsible AI governance. Learn how to leverage Azure API Management, machine learning models, and cloud-native architectures to build robust, efficient, and intelligent API solutions
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
213년 10월 15일 화
3. 오늘 내용
Hotspot JVM Locking Optimization Review
java.util.concurrent
Atomic CAS
AbstractQueueSynchronizer, Lock & Condition
Concurrent Collections
Fork-Join framework
3
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4. 지난 시간 리뷰 :
Hotspot JVM Lock Optimization
volatile (JMM)
park/unpark
4
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6. Mark word (Object Header)
2bits are used for lock state
01 : unlocked
00 : light-weight locked
10 : heavy-weight locked
11 : marked for GC
1 more bit is used for biased locking (obtained via CAS)
X01 : unlocked
X is 0 for not biased, X is 1 for biased or biasable
2bit epoch is used for indicate the bias-holding thread’s lock/unlock
6
Hashcode Age 0 01
lock record address 00
monitor address 10
forward address, etc.. 11
thread id Age 1 01epoch
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7. Biased Locking
(quickly reacquirable lock)
make it cheap for a single thread to re-acquire the same
lock
when biased bit is 1
when the biased thread locks/unlocks
word에서 thread id 만을 검사하고 epoch bit 변경
when other thread locks
revoke (expensive) : 먼저 light-weight lock 상태로 되돌린
다
7
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8. volatile
단순한 메모리 변수가 아니라 barrier를 필요로 함
thread가 접근한 변수들의 visibility와 ordering 모두 만족해야
x86 등에서 실제 구현 형태
보통 volatile field에 set 후에 StoreLoad(x86에서는 mfence
등에 해당)를 호출하는 것으로 구현
CPU마다 다르긴 하나 x86, SPARC에서는 다른 instrunction
은 모두 no-op
8
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9. park/unpark 모델 (JDK 5)
9
Park Unpark
Parked
Unparked
None
Impossible None
None Unparked
Parked Unparked
현재 상태
Operation
[표] 현재 상태 + Operation => 다음 상태
park/unpark는 biased를 사용하지 않으므로 thread간 lock 경
쟁이 심할 경우 synchronized에 비해 더 효율적
synchronized는 single thread이거나 contention이 약하면 더
효율적
913년 10월 15일 화
12. AbstractQueuedSynchronizer (1)
package java.util.concurrent.locks
a small common framework of most provided synchronizers
acquire : blocks the calling thread unless/until the synchronization
state allows it to proceed
release : changes synchronization state in a way that may allow
one or more blocked threads to unblock
tryAcquire/tryRelease : non-blocking version (child class should
implement if necessary)
acquireShared/releaseShared : shared mode
12
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13. AbstractQueuedSynchronizer (2)
acquire operation
while (synchronization state does not allow acquire) {
enqueue current thread if not already queued;
possibly block current thread;
}
dequeue current thread if it was queued;
release operation
update synchronization state;
if (state may permit a blocked thread to acquire)
unblock one or more queued threads;
13
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14. AbstractQueuedSynchronizer (3)
int synchronization state (32bit)
blocking
LockSupport.park
LockSupport.unpark(Thread)
FIFO wait queue of blocked threads (CLH queue)
Condition queue of blocked threads
class AbstractQueuedSynchronizer.ConditionObject
14
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15. AbstractQueuedSynchronizer (4)
class Mutex {
class Sync extends AbstractQueuedSynchronizer {
public boolean tryAcquire(int ignore) {
return compareAndSetState(0, 1); // locked 이면 state == 1
}
public boolean tryRelease(int ignore) {
setState(0); return true; // unlocked 이면 state == 0
}
}
private final Sync sync = new Sync();
public void lock() { sync.acquire(0); }
public void unlock() { sync.release(0); }
}
15
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16. AbstractQueuedSynchronizer (5)
ReentrantLock (and Condition) : state is recursive lock count
ReentrantReadWriteLock : state 16bit writer count, 16bit reader count
Semaphore : state is current count, acquire calls acquireShared to
decrement or block (if <= 0), release calls releaseShared to increment
and unblock (if > 0)
CountDownLatch : state is count, countDown calls releaseShared
(decrements 1), await calls acquireShared (if state is 0, return
immediately)
FutureTask : state is state machine (initial|running|cancelled|done). set/
cancel calls release which eventually unblock the awaiting threads which
are trying to acquire to get the value
16
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18. ThreadPoolExecutor
Core pool size, Max pool size, blocking queue size
java.util.concurrent.Executors helper class
newFixedThreadPool (Core pool size == max pool size)
newCachedThreadPool
Core pool size = 0, Max pool size = Integer.MAX_VALUE,
SynchronousQueue
newSingleThreadExecutor (Unbounded queue)
newScheduledThreadPool (Can execute by schedule)
18
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19. Fork/Join (JDK 7)
a parallel version of divide-conquer algorithm
Result solve(Problem problem) {
if (problem is small)
directly solve problem
else {
split problem into independent parts
fork new subtasks to solve each part
join all subtasks
compose result from subresults
}
}
19
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20. Fork/Join
work-stealing
deque : array, top, base
push/pop/take(steal)
owner thread : stack(LIFO) push/pop
idle thread : take and if fail then yield or set inactive
20
base top
deque
push
pop
steal (thread 2)
(thread 1)
2013년 10월 15일 화
21. Fork/Join
ForkJoinTask methods
child classes should override exec(), setRawResult(), getRawResult()
public static void invokeAll(ForkJoinTask<?> t1, ForkJoinTask<?> t2) {
t2.fork();
t1.invoke();
t2.join();
}
ForkJoinPool : can specify parallelism (thread pool size)
RecursiveAction (result가 없는 ForkJoinTask)
RecursiveTask (result가 있는 ForkJoinTask)
21
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22. Concurrent Collections
CopyOnWriteArrayList : read-mostly, high contention
LinkedBlockingQueue : 2-lock queue algorithm
separation of takeLock and putLock
ConcurrentHashMap
lock striping
no lock when get (multiple access to volatile data)
ConcurrentSkipListMap
concurrent version of skip-list
a concurrent replacement for TreeMap
22
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23. 다음 시간 예정
some more concurrent collections
Generics
Annotation
I/O
Object Serialization
RMI
23
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