Traffic Server 是一种高性能的 web 代理缓存,用于加速互联网访问和增强网站性能。它通过将频繁访问的信息缓存于网络边缘,以提高效率并减少带宽消耗。Traffic Server 支持多种部署选项,包括作为 web 代理缓存、反向代理和多级缓存,配备了完善的管理和监控工具,以及流量分析和安全选项。
The document discusses various aspects of managing web applications using JavaScript frameworks, highlighting tools and techniques such as Angular, unit testing, and build tools like Grunt. It covers the importance of managing dependencies, and offers insights into structure and deployment. Additionally, it emphasizes the necessity of boilerplate and scaffolding to streamline development processes.
This document discusses front-end performance optimization. It notes that 80-90% of end-user response time is spent on the frontend. It provides tips for optimizing load time such as minimizing files, gzipping, concatenating scripts, and loading scripts last. It also discusses optimizing runtime performance by reducing DOM interactions and designing single page applications carefully. Finally, it emphasizes the importance of perception and not blocking the user interface.
Traffic Server 是一种高性能的 web 代理缓存,用于加速互联网访问和增强网站性能。它通过将频繁访问的信息缓存于网络边缘,以提高效率并减少带宽消耗。Traffic Server 支持多种部署选项,包括作为 web 代理缓存、反向代理和多级缓存,配备了完善的管理和监控工具,以及流量分析和安全选项。
The document discusses various aspects of managing web applications using JavaScript frameworks, highlighting tools and techniques such as Angular, unit testing, and build tools like Grunt. It covers the importance of managing dependencies, and offers insights into structure and deployment. Additionally, it emphasizes the necessity of boilerplate and scaffolding to streamline development processes.
This document discusses front-end performance optimization. It notes that 80-90% of end-user response time is spent on the frontend. It provides tips for optimizing load time such as minimizing files, gzipping, concatenating scripts, and loading scripts last. It also discusses optimizing runtime performance by reducing DOM interactions and designing single page applications carefully. Finally, it emphasizes the importance of perception and not blocking the user interface.
Lithuania has significant technical potential for renewable energy, especially biomass and wind. Renewable energy projects under 10kW receive feed-in tariffs for 12 years, while larger projects must participate in quarterly tenders. The feed-in tariff led to rapid growth of wind power capacity. Lithuania aims to source 25% of final energy from renewables by 2020 through various support policies and incentive schemes.
This document describes how to build a real-time collaborative drawing application using Node.js, Express.js, Socket.io, and Paper.js. It explains that Express serves the HTML canvas, Paper.js intercepts draw events and draws locally, Socket.io passes draw data to Express which broadcasts it to other users, and Paper.js uses sessionIds to draw other users' paths and maintain separate drawings. It also provides instructions for deploying the application to Nodejitsu.
The document describes how to create a real-time collaborative drawing application using Node.js, Express, Socket.io, and Paper.js. It explains how to install and set up the necessary technologies, handle mouse events to draw on a canvas, send drawing data to other users via Socket.io events, and receive and render drawings from other users in real-time. Key aspects covered include setting up an Express server, connecting Socket.io for real-time functionality, serializing drawing paths to JSON, and processing incoming events to update drawings across clients.
F?r b?rsbolagen ?verensst?mde utvecklingen av l?nsamhet och oms?ttning under ?ren
2006-2009 v?l med utvecklingen f?r hela det privata n?ringslivet. Prognoserna f?r de ca
250 b?rsbolagen f?r ?ren 2011-2012 antas d?rf?r fungera som goda indikatorer f?r hela
n?ringslivets utveckling dessa ?r.
Utvecklingen med stigande utdelningar och sjunkande investeringar ?r oroande. Den
l?ngsiktiga utvecklingskraften och f?rm?gan att skapa fler och b?ttre jobb i n?ringslivet ?r
beroende av investeringarna. Att prioritera utdelningar till ?garna framf?r h?llbar
l?nsamhet gynnar p? sikt varken ?gare eller anst?llda.
詹剑锋:Big databench—benchmarking big data systemshdhappy001
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This document discusses BigDataBench, an open source project for big data benchmarking. BigDataBench includes six real-world data sets and 19 workloads that cover common big data applications and preserve the four V's of big data. The workloads were chosen to represent typical application domains like search engines, social networks, and e-commerce. BigDataBench aims to provide a standardized benchmark for evaluating big data systems, architectures, and software stacks. It has been used in several case studies for workload characterization and performance evaluation of different hardware platforms for big data workloads.
The document discusses big data visualization and visual analysis, focusing on the challenges and opportunities. It begins with an overview of visualization and then discusses several challenges in big data visualization, including integrating heterogeneous data from different sources and scales, dealing with data and task complexity, limited interaction capabilities for large data, scalability for both data and users, and the need for domain and development libraries/tools. It then provides examples of visualizing taxi GPS data and traffic patterns in Beijing to identify traffic jams.
Spark is an open source cluster computing framework originally developed at UC Berkeley. Intel has made many contributions to Spark's development through code commits, patches, and collaborating with the Spark community. Spark is widely used by companies like Alibaba, Baidu, and Youku for large-scale data analytics and machine learning tasks. It allows for faster iterative jobs than Hadoop through its in-memory computing model and supports multiple workloads including streaming, SQL, and graph processing.
This document describes an interactive batch query system for game analytics based on Apache Drill. It addresses the problem of answering common ad-hoc queries over large volumes of log data by using a columnar data model and optimizing query plans. The system utilizes Drill's schema-free data model and vectorized query processing. It further improves performance by merging similar queries, reusing intermediate results, and pushing execution downwards to utilize multi-core CPUs. This provides a unified solution for both ad-hoc and scheduled batch analytics workloads at large scale.
刘诚忠:Running cloudera impala on postgre sqlhdhappy001
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This document summarizes a presentation about running Cloudera Impala on PostgreSQL to enable SQL queries on large datasets. Key points:
- The company processes 3 billion daily ad impressions and 20TB of daily report data, requiring a scalable SQL solution.
- Impala was chosen for its fast performance from in-memory processing and code generation. The architecture runs Impala coordinators and executors across clusters.
- The author hacked Impala to also scan data from PostgreSQL for mixed workloads. This involved adding new scan node types and metadata.
- Tests on a 150 million row dataset showed Impala with PostgreSQL achieving 20 million rows scanned per second per core.
7. Stripe compactions (HBASE-7667)
? 有点像LevelDB, 在每个region/store划分键
? 但是, 只有1级 (加上可选的L0)
? 相对于regions, 分区是更灵活的
–默认情况下是几个大小大致相等的stripes
? 要读取, 只是阅读有关的stripes加上L0, 如果存在
L0
HFile
HFile
get
'hbase'
HFile
HFile
HFile
HFile
H
Region start key: ccc
Architecting the Future of Big Data
? Hortonworks Inc. 2011
Row-key axis
eee
ggg
iii: region end key
8. Stripe compactions – 写入
? 数据从MEMSTORE刷新成几个文件
? 每一个stripe 大部分的时间分开Compact
MemStore
HFile
HFile
HFile
H
HDFS
H
Architecting the Future of Big Data
? Hortonworks Inc. 2011
HFile
HFile
H
HFile
H
9. Stripe compactions – 其他
? 为什么要 Level0?
–Bulk loaded 文件转至L0
–刷新也可以进入单个L0文件 (避免小文件)
–然后几个L0的文件,压缩成striped文件
? 如果压缩一个完整的stripe +L0, 可以去掉deletes
–无需major compactions, 永远?
? 2个stripes一起Compact – 如果非平衡, 重新调整
–然而非常罕见 - 非平衡的stripes 不是一个大问题
? 边界可以被用来改善将来的区域分割
Architecting the Future of Big Data
? Hortonworks Inc. 2011
10. Stripe compactions - 性能
? EC2, c1.xlarge, preload; 然后测量随机读取性能
–LoadTestTool + deletes + overwrites;
Random gets per second
2000
1500
Default gets-per-second, 30sec. MA
Stripe gets-per-second, 30sec. MA
1000
500
0
2500
3500
4500
5500
Test time, sec.
Architecting the Future of Big Data
? Hortonworks Inc. 2011
6500
7500
8500
18. Hoya AM 用 YARN部署 HBase
YARN Resource Manager
YARN Node Manager
Hoya Client
Hoya AM
HDFS
HBase Master
HDFS
YARN Node Manager
YARN Node Manager
HBase Region Server
HBase Region Server
HDFS
HDFS
19. HBase和客户端通过Zookeeper结合
YARN Resource Manager
YARN Node Manager
Hoya Client
Hoya AM
HDFS
HBase Master
HBase Client
YARN Node Manager
HDFS
YARN Node Manager
HBase Region Server
HBase Region Server
HDFS
HDFS
20. YARN 把故障通知给AM
YARN Resource Manager
YARN Node Manager
Hoya Client
Hoya AM
HDFS
HBase Master
HDFS
YARN Node Manager
YARN Node Manager
HBase Region Server
HBase Region Server
HBase Region Server
HDFS
HDFS
26. 保护YARN AM RPC:代码
// set up secret manager
secretManager = new
ClientToAMTokenSecretManager(appAttemptID, null);
Server server = RpcBinder.createProtobufServer(
new InetSocketAddress("0.0.0.0", 0), getConfig(), secretManager,
NUM_RPC_HANDLERS, blockingService, null);
server.start();
RegisterApplicationMasterResponse response = asyncRMClient
.registerApplicationMaster(appMasterHostname,
appMasterRpcPort,
null);
if (UserGroupInformation.isSecurityEnabled()) {
secretManager.setMasterKey(
response.getClientToAMTokenMasterKey().array());