Overview of why (and how) we have adopted a functional reactive approach to solve problems of scale @ AOL, in particular within AOL Advertising's forecasting platform.
Owning Web Performance with PhantomJS 2 - Fluent 2016Wesley Hales
Running a synthetic testing server or script to measure web performance is a great entry point into the world of automated web-page testing. We have an abundance of standardized APIs to measure every part of how the page renders in the browser. PhantomJS 2, released in 2015, gives us the ability to measure navigation timing APIs in an automated fashion.
Wesley Hales introduces the basics of creating a simple PhantomJS script that only extracts the performance data we need (from the W3C Navigation Timing API) and explains how this script can be Dockerized and run across many different nodes and regions of the Web. You’ll walk away with a new view on automated web-performance testing and the tools required to setup a simulated RUM network.
Все говорят, что для максимальной производительности работы из Java с базой данных нужно использовать PreparedStatements и Batch DML. Практика показывает, что нельзя слепо идти на поводу у прописных истин. Нужно понимать особенности конкретной базы и характера передаваемых данных.
В докладе мы рассмотрим то, как эффективное использование протокола PostgreSQL позволяет добиться высокой производительности при выборке и сохранении данных. На примерах увидим как простые изменения в коде приложения и JDBC драйвера на порядок ускоряют запросы. Мы увидим как задействовать механизм server prepared statements из клиенсткого кода и узнаем его узкие места. Обсудим средства эффективной передачи данных в базу.
Многие обсуждаемые доработки недавно вошли в состав официального JDBC драйвера. Доклад будет полезен не только Java программистам, т.к. многие подводные грабли вытекают из самого протокола общения PostgreSQL с внешним миром.
Leveraging your hadoop cluster better - running performant code at scaleMichael Kopp
Somebody once said that hadoop is a way of running highly unperformant code at scale. In this talk I want to show how we can change that and make map reduce jobs more performant. I will show how to analyze them at scale and optimize the job itself, instead of just tinkering with hadoop options. The result is a much better utilized cluster and jobs that run in a fraction of the original time running performant code at scale! Most of the time when speaking about Hadoop people only consider scale, however, when looking at it it very often runs highly unperformant jobs. By actually looking at the performance characteristics of the jobs themselves and optimizing and tuning those far better results can be achieved. Examples include small changes that cut jobs down from 15 hours to 2 hours without adding any more hardware. The concepts and techniques explained in the talk will be applicable regardless which tool is used to identify the performance characteristics, what is important is that by applying performance analysis and optimization techniques that we have used on other applications for a long time we can make hadoop jobs much more effective and performant! The attendees will be able to understand those techniques and apply them to their map/reduce/PIG/hive or other mapreduce jobs.
The shield is a plugin for Elasticsearch that enables you to easily secure an elasticsearch cluster.
Kibana is an open source analytics and visualization platform designed to work with Elasticsearch
CI and CD at Scale: Scaling Jenkins with Docker and Apache MesosCarlos Sanchez
In this presentation Carlos Sanchez will share his experience running Jenkins at scale, using Docker and Apache Mesos to create one of the biggest (if not the biggest) Jenkins clusters to date.
By taking advantage of Apache Mesos, the Jenkins platform is dynamically scaled to run jobs across hundreds of Jenkins masters, on Docker containers distributed across the Mesos cluster. Jenkins slaves are dynamically created based on load, using the Jenkins Mesos and Docker plugins, running in containers distributed across multiple hosts, and isolating job execution.
This presentation will allow a better understanding of Apache Mesos and the challenges of running Docker containerized and distributed applications, particularly JVM ones, by sharing a real world use case, including good and bad decisions and how they affected the development.
Overview of why (and how) we have adopted a functional reactive approach to solve problems of scale @ AOL, in particular within AOL Advertising's forecasting platform.
Owning Web Performance with PhantomJS 2 - Fluent 2016Wesley Hales
Running a synthetic testing server or script to measure web performance is a great entry point into the world of automated web-page testing. We have an abundance of standardized APIs to measure every part of how the page renders in the browser. PhantomJS 2, released in 2015, gives us the ability to measure navigation timing APIs in an automated fashion.
Wesley Hales introduces the basics of creating a simple PhantomJS script that only extracts the performance data we need (from the W3C Navigation Timing API) and explains how this script can be Dockerized and run across many different nodes and regions of the Web. You’ll walk away with a new view on automated web-performance testing and the tools required to setup a simulated RUM network.
Все говорят, что для максимальной производительности работы из Java с базой данных нужно использовать PreparedStatements и Batch DML. Практика показывает, что нельзя слепо идти на поводу у прописных истин. Нужно понимать особенности конкретной базы и характера передаваемых данных.
В докладе мы рассмотрим то, как эффективное использование протокола PostgreSQL позволяет добиться высокой производительности при выборке и сохранении данных. На примерах увидим как простые изменения в коде приложения и JDBC драйвера на порядок ускоряют запросы. Мы увидим как задействовать механизм server prepared statements из клиенсткого кода и узнаем его узкие места. Обсудим средства эффективной передачи данных в базу.
Многие обсуждаемые доработки недавно вошли в состав официального JDBC драйвера. Доклад будет полезен не только Java программистам, т.к. многие подводные грабли вытекают из самого протокола общения PostgreSQL с внешним миром.
Leveraging your hadoop cluster better - running performant code at scaleMichael Kopp
Somebody once said that hadoop is a way of running highly unperformant code at scale. In this talk I want to show how we can change that and make map reduce jobs more performant. I will show how to analyze them at scale and optimize the job itself, instead of just tinkering with hadoop options. The result is a much better utilized cluster and jobs that run in a fraction of the original time running performant code at scale! Most of the time when speaking about Hadoop people only consider scale, however, when looking at it it very often runs highly unperformant jobs. By actually looking at the performance characteristics of the jobs themselves and optimizing and tuning those far better results can be achieved. Examples include small changes that cut jobs down from 15 hours to 2 hours without adding any more hardware. The concepts and techniques explained in the talk will be applicable regardless which tool is used to identify the performance characteristics, what is important is that by applying performance analysis and optimization techniques that we have used on other applications for a long time we can make hadoop jobs much more effective and performant! The attendees will be able to understand those techniques and apply them to their map/reduce/PIG/hive or other mapreduce jobs.
The shield is a plugin for Elasticsearch that enables you to easily secure an elasticsearch cluster.
Kibana is an open source analytics and visualization platform designed to work with Elasticsearch
CI and CD at Scale: Scaling Jenkins with Docker and Apache MesosCarlos Sanchez
In this presentation Carlos Sanchez will share his experience running Jenkins at scale, using Docker and Apache Mesos to create one of the biggest (if not the biggest) Jenkins clusters to date.
By taking advantage of Apache Mesos, the Jenkins platform is dynamically scaled to run jobs across hundreds of Jenkins masters, on Docker containers distributed across the Mesos cluster. Jenkins slaves are dynamically created based on load, using the Jenkins Mesos and Docker plugins, running in containers distributed across multiple hosts, and isolating job execution.
This presentation will allow a better understanding of Apache Mesos and the challenges of running Docker containerized and distributed applications, particularly JVM ones, by sharing a real world use case, including good and bad decisions and how they affected the development.
