## +24 ## Multi Producer, Multi Consumer, Lock Free, Atomic Queue User API and Implementation ... ## EREZ STRAUSS ## Lockfree, MPMC Queue - Legal All Statements and representations are my own and do MPMC Queue - Agenda 1. Trading Platform, Latency 2. Queues Environment 3. Queues Requirement 4. Atomic hardware and C++ Atomic 5. Queues Classification 6. Queue creation, push, pop 7. Queue internals internals 8. Testing 9. Performance - Benchmarking ## Lockfree, MPMC Queue - Background Trading platform - every message is tracked, at microseconds resolution - Queues are essential data structure used

http://github.com/ask/celery/ Keywords task queue, job queue, asynchronous, rabbitmq, amqp, redis, Django, Python, Webhooks, queue, distributed Celery is a task queue/job queue based on distributed message passing RabbitMQ. You can set up new workers without central configuration (e.g. use your dads laptop while the queue is temporarily overloaded).| |Concurrency|Tasks are executed in parallel using the multiprocessing key used to deliver this task. It’s used by e.g. retry() to resend the task to the same destination queue. NOTE As some messaging backends doesn’t have advanced routing capabilities, you can’t trust the

version of Celery (2.2). For the latest stable version please go here. ## Celery - Distributed Task Queue Contents: • Getting Started • Introduction • Broker Installation • First steps com/ask/celery/ Keywords: task queue, job queue, asynchronous, rabbitmq, amqp, redis, python, webhooks, queue, distributed Celery is an open source asynchronous task queue/job queue based on distributed message set up new workers without central configuration (e.g. use your grandma's laptop to help if the queue is temporarily congested).| |Concurrency|Concurrency is achieved by using multiprocessing, Eventlet

Redelivery and Undelivered Messages 1.27 Message Expiry 1.28 Large Messages 1.29 Paging 1.30 Queue Attributes 1.31 Scheduled Messages 1.32 Last-Value Queues 1.33 Message Grouping 1.34 Extra asynchronous messaging: message queue messaging (also known as point-to-point messaging) and publish subscribe messaging. We'll summarise them briefly here: ## The Message Queue Pattern With this type of of messaging you send a message to a queue. The message is then typically persisted to provide a guarantee of delivery, then some time later the messaging system delivers the message to a consumer. The

Duplicate Message Detection 1.58 Clusters 1.59 Federation 1.60 Address Federation 1.60.1 Queue Federation 1.60.2 High Availability and Failover 1.61 Graceful Server Shutdown 1.62 Libaio Native auto-delete-addresses-delay Address Settings. • Support logging HTTP access. • Add a CLI command to purge a queue. - Support user and role manipulation for PropertiesLoginModule via management interfaces. • Docker Support SASL_EXTERNAL for AMQP clients. - New examples showing virtual topic mapping and exclusive queue features. #### 2.5. 0 Full release notes. Highlights: • Exclusive consumers. • Equivalent ActiveMQ

## Welcome to Django Q Django Q is a native Django task queue and worker application using Python multiprocessing. ## Features • Multiprocessing worker pool • Asynchronous tasks • Scheduled and repeated 'timeout': 60, 'compress': True, 'save_limit': 250, 'queue_limit': 500, 'cpu_affinity': 1, 'label': 'Django Q', 'redis': • Set to -1 for no success storage at all. • Defaults to 250 • Failures are always saved. ## queue_limit This does not limit the amount of tasks that can be queued overall on Redis, but rather how

Repackaged '-all' clients 104 20. Address Model ..... 106 20.1. Address ..... 106 20.2. Queue ..... 106 20.3. Routing Type ..... 106 20.4. Automatic Configuration ..... 107 20.5. Basic Manual 32. Federation ..... 183 32.1. Benefits ..... 183 32.2. Address Federation ..... 183 32.3. Queue Federation ..... 184 32.4. WAN Full Mesh ..... 185 32.5. Configuring Federation ..... 186 33 193 33.3. Configuring Downstream Federation ..... 197 34. Queue Federation ..... 199 34.1. Use Cases ..... 199 34.2. Configuring Queue Federation ..... 200 34.3. Configuring Downstream Federation

## Greenplum资源管理器 姚珂男/Pivotal kyao@pivotal.io ## Agenda • Greenplum数据库 • Resource Queue • Resource Group ## Greenplum数据库 • 基于PostgreSQL • 分布式 • OLAP • MPP(Massively Parallel Processing) ## Greenplum数据库 fe3eb6bc09e8312568f4d7a/p4_1.jpg) ## Resource Queue • SQL语句并发控制 • 基于cost的并发控制 • 基于priority的CPU控制 • 内存控制 ## Running Example • CREATE RESOURCE QUEUE rq WITH ( active_statements = 6, max_cost min_cost = 50000, priority = high, memory_limit = '1024MB' ); • CREATE ROLE r1 RESOURCE QUEUE rq; • SELECT * FROM gp_toolkit.gp_resqueue_status; ## 内存控制 • virtual memory note keeping (gp_malloc)

Duplicate Message Detection 1.58 Clusters 1.59 Federation 1.60 Address Federation 1.60.1 Queue Federation 1.60.2 High Availability and Failover 1.61 Graceful Server Shutdown 1.62 Libaio Native diverts • Ability to "disabled" a queue so that messages are not routed to it • Support JVM GC & thread metrics • Support for resetting queue properties by unsetting them in broker.xml akin to ActiveMQ 5's individual • Automatic creation of expiry resources • Improved API for queue creation • Allow users to override JAVA ARGs via environment variable • Reduce heap usage during

Duplicate Message Detection 1.58 Clusters 1.59 Federation 1.60 Address Federation 1.60.1 Queue Federation 1.60.2 High Availability and Failover 1.61 Graceful Server Shutdown 1.62 Libaio Native auto-delete-addresses-delay Address Settings. • Support logging HTTP access. • Add a CLI command to purge a queue. - Support user and role manipulation for PropertiesLoginModule via management interfaces. • Docker Support SASL_EXTERNAL for AMQP clients. - New examples showing virtual topic mapping and exclusive queue features. #### 2.5. 0 Full release notes. Highlights: • Exclusive consumers. • Equivalent ActiveMQ