open channel flume

I. Overview1, now has three machines, respectively: HADOOP1,HADOOP2,HADOOP3, to HADOOP1 for the log summary2, HADOOP1 Summary of the simultaneous output to multiple targets3, flume a data source corresponding to multiple channel, multiple sink, is configured in the consolidation-accepter.conf fileIi. deploy flume to collect logs and summary logs1, running on the

The client SDK of the Android log phone was completed last week and started debugging the log server this week.Use flume for log collection, and then go to Kafka. When testing, I always found out some of the event, and later learned that the use of channel and sink is wrong. When multiple sink use the same channel, the event is diverted from the common consumptio

Due to the frequent use of some of the Flume channel,source,sink, so in order to facilitate the summary of these channel,source,sink, but also a total of people to visit. Component Interface Type Alias Implementation Class *. Channel Memory *.

Definition: Channels is the repositories where the events is staged on a agent. Source adds the events and Sink removes it.According to the Flume 1.8.0 User guide provided by Flume official website, the main content of this paper is to summarize the Schannel of flume1.8.0 support, see the table below. Channel type Type Storage media D

.inputCharset=GBKagent1.sources.source1.spoolDir=/home/hadoop_admin/Movielogagent1.sources.source1.fileHeader=trueAgent1.sources.source1.deletePolicy=immediateagent1.sources.source1.batchSize= +Agent1.sources.source1.channels=channel1# each sink's type must be definedAgent1.sinks.sink1.type =Hdfsagent1.sinks.sink1.channel=Channel1agent1.sinks.sink1.hdfs.path= HDFs://master:9000/flumetestAgent1.sinks.sink1.hdfs.filePrefix = master-Agent1.sinks.sink1.hd

Flume based on channel selector can realize fan-in, fan-out.The same data source is distributed to different purposes, such as.The channel selector can be defined on source: 123456789 a1.sources=r1...a1.channels=c1 c2...a1.sources.r1.selector.type=multiplexinga1.sources.r1.selector.header=typea1.sources.r1.selector.mapping.type1=c1a1.sources.

First, overview:In a real-world production environment, you will typically encounter the need to pour logs from web servers such as Tomcat, Apache, etc. into HDFs for analysis. The way to configure this is to achieve the above requirements.Second, the configuration file:#agent1 nameagent1.sources=source1agent1.sinks=Sink1agent1.channels=channel1#spooling directory#set Source1agent1.sources.source1.type=Spooldiragent1.sources.source1.spoolDir=/opt/flumetest/Dataagent1.sources.source1.channels=Cha

source, to channel, to sink, is itself a byte array, and can carry headers (header information) information. An event represents the smallest complete unit of data, from an external data source, to an external destination.SummarizeAccording to the architecture design of these four systems, we can conclude that the system needs to have three basic components, namely the Agent(encapsulating data source, sending data from data source to collector),colle

1. Background information Many of the company's platforms generate a large number of logs (typically streaming data, such as the PV of search engines, queries, etc.), which require a specific log system, which in general requires the following characteristics: (1) Construct the bridge of application system and analysis system, and decouple the correlation between them; (2) support the near real-time on-line analysis system and the off-line analysis system similar to Hadoop; (3) with high scalabi

1. First you need to know flume HTTP monitoring if bootingPlease refer to the monitoring parameters of the blog flumeThat is, in Http://localhost:3000/metrics, you can access the following content2. Install the Flume monitor plugin in Open-falcon, refer to the official documentation http://book.open-falcon.org/zh_0_2/usage/flume.htmlOfficial documentation is very

1. Background information Many of the company's platforms generate a large number of logs per day (typically streaming data, such as search engine PV, queries, etc.), and processing these logs requires a specific logging system, in general, these systems need to have the following characteristics: (1) Construct the bridge of application system and analysis system, and decouple the correlation between them; (2) Support near real-time online analysis system and similar to the offline analysis sys

1. Background information Many of the company's platforms generate a large number of logs per day (typically streaming data, such as search engine PV, queries, etc.), and processing these logs requires a specific logging system, in general, these systems need to have the following characteristics: (1) Construct the bridge of application system and analysis system, and decouple the correlation between them; (2) Support near real-time online analysis system and similar to the offline analysis syst

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