informatica real time scenarios

building a good and robust real-time data processing system is not an article that can be made clear. Before reading this article, assume that you have a basic understanding of the Apache Kafka distributed messaging system and that you can use the Spark streaming API for simple programming. Next, let's take a look at how to build a simple real-

similar to Bucketsync.I am often asked a question, the other side of the lag will not affect my game experience, from the above our synchronization principle, perhaps you can find the answer.Using UDP instead of TCPFrame synchronization and the protocol layer is TCP or UDP is not required, but we do not consider the start of TCP directly embrace UDP, the reason, if the characteristics of the TCP slightly understand, will probably back that Zi Jing: "Slow Start", "Fast retransmission", "congesti

achieve real-time scoring, the Kijiscoring module provides an inert computing system that allows applications to generate the latest recommendations for active users who often interact with them. With lazy computing, Kiji applications do not have to generate recommendations for users who do not often patronize or return. There are additional benefits that Kiji can consider when recommending

1. Overview　　Video tutorial for this course Address: Application Overview of the Kafka Combat projectThis course is carried out by a user in real-time escalation of the log, through the introduction of Kafka business and application scenarios, and with everyone to build the Kafka project of the actual development environment. Let's take a look at the lessons of t

Real-time push technology in the web domain is also called realtime technology. The purpose of this technology is to allow users to get real-time updates without having to refresh their browsers. It has a wide range of application scenarios, such as online chat rooms, online

RTP (Real Time Transport Protocol) RTP is a transmission protocol for multimedia data streams over the Internet.Rfc1889Release. RTP is defined to work during one-to-one or one-to-many transmission. It aims to provide time information and implement stream synchronization. Typical RTP applications are built on UDP, but can also work on TCP, ATM, and other protocol

http://blog.csdn.net/heanyu/article/details/6202500 RTP (Real time Transport Protocol) RTP is a transport protocol for multimedia traffic on the Internet that is published by the IETF (Internet Engineering Task Force) as RFC1889 . RTP is defined as a one-to-many transmission, with the purpose of providing time information and implementing stream synchronization

The process of user access to the Web site is based on the HTTP protocol, while the HTTP protocol works by: request-response, the client makes an access request, and the server responds to the request with resource data. That is, the server is always passive, even if the server-side resource data changes, if there is no request from the client, the user will not see these changes. This pattern is not suitable for certain scenarios, such as the need fo

batch size is selected between 0 and 5-2 seconds (Storm's current minimum delay is around 100ms), so spark The streaming is capable of satisfying all streaming quasi-real-time computing scenarios except for very high real-time requirements such as high-frequency

the default display is an empty string;%T: Use the custom time format in the--TIMEFMT option;For example, to monitor the Create,delete,modify,close_write events that occur on the/tmp/test directory and all of its internal files, use the following command:# inotify-r--timefmt '%d/%m/%y%h:%m '--format '%T%w%f '-e create,delete,modify,close_write/tmp/testThis command exits after monitoring the first event on a file, and if you want to monitor it all the

Label:SQL Server2016 new features real-time query statistics Many times there are scenarios where development complains that DBAs do not tune good databases, DBAs complain about poorly developed program code, so DBAs and developers become enemies and cannot really troubleshoot problems. DBAs can use tools such as Windows Performance Monitor, SQL Server built-in A

between 0 and 5-2 seconds (Storm's current minimum delay is around 100ms), so spark The streaming is capable of satisfying all streaming quasi-real-time computing scenarios except for very high real-time requirements such as high-frequency

-time computing? This is not suitable. It is a relative concept. If the business has low latency requirements, this problem will not exist. However, in fact, some business requirements in the enterprise have high latency requirements. Let me talk about it as follows:2.1 latency Storm's network direct transmission and memory computing have a much lower latency than Hadoop's HDFS transmission. When the computing model is more suitable for streaming, Sto

smallest batch size is selected between 0 and 5-2 seconds (Storm's current minimum delay is around 100ms), so spark The streaming is capable of satisfying all streaming quasi-real-time computing scenarios except for very high real-time requirements such as high-frequency

a way as possible. Given these two issues, polling is not the ideal approach for enterprise applications that require critical, real-time server-side updates. In this article, I'll introduce a variety of ways to override polling. Each workaround has its own prominence in some scenarios. I'll explain these scenarios a

support), EXEC (command execution) The ability to collect data on a data source is currently used by exec in our system for log capture. Flume data recipients, which can be console (console), text (file), DFS (HDFs file), RPC (THRIFT-RPC), and syslogtcp (TCP syslog log system), and so on. It is received by Kafka in our system. Flume version: 1.4.0 Flume Download and Documentation: http://flume.apache.org/ Flume Installation: $tar ZXVF apache-flume-1.4.0-bin.tar.gz/usr/local Flume Start command

also offers an extremely convenient, interactive shell that allows users to quickly build prototypes and exploratory data analysis mechanisms in real time using Scala or the Python API. When you use this interactive shell, you'll quickly find another big difference between spark and storm: Spark clearly shows a biased "functional" approach, where most API usage is implemented by a continuous method call or

computing, not a batch model.The model that we need must be able to handle very large data, so there is a good scale-out ability, preferably, we do not need to consider too much consistency, replication problems. Then, this model is a real-time computing model, or it can be considered as a flow model.Now, assuming we have such a model, we can happily design new business