log file Structure
In front of us, we repeatedly talk about the concept of topic, partition, this article to analyze these different topic, different partition of the message, in the file, what structure is stored.
Interested friends can pay attention to the public number "the way of architecture and technique", get the latest articles.or scan the following QR code:each topic_partition corresponds to a directory
Suppose there is a topic called my_top
Spark version is 1.0Kafka version is 0.8
Let's take a look at the architecture diagram of Kafka for more information please refer to the official
I have three machines on my side. For Kafka Log CollectionA 192.168.1.1 for serverB 192.168.1.2 for ProducerC 192.168.1.3 for Consumer
First, execute the following command in the Ka
-round.
3 Implementing the Architecture
A schema implementation architecture is shown in the following figure:
Analysis of 3.1 producer layer
The service assumptions within the PAAs platform are deployed within the Docker container, so in order to meet the non-functional requirements, another process is responsible for collecting logs and therefore does not invade the service framework and processes. Using flume ng for log collection, this open s
an absolute offset of 7:
The first is to use a binary lookup to determine which logsegment it is in, naturally in the first segment.
Open the index file for this segment, and also use binary lookup to find the largest offset in the index entry with offset less than or equal to the specified offset. The index of natural offset 6 is what we're looking for, and we know from the index file that the message with offset 6 has a position of 9807 in the data file.
Open the data file an
Log storage parsing for Kafkatags (space delimited): KafkaIntroductionThe message in Kafka is organized in topic as the basic unit, and the different topic are independent of each other. Each topic can be divided into several different partition (each topic has several partition specified when the topic is created), and each partition stores part of the message. By borrowing an official picture, you can vis
Flume real-time crawl log data and upload to Kafka
1.Linux OK zookeeper is configured, start zookeeper first
sbin/zkserver.sh start
(sbin/zkserver.sh Status View startup state) JPS can check to see Le process as Quorumpeermain
2. Start Kafka,zookeeper need to start before Kafka
bin/
Using spring to consolidate Kafka only supports kafka-2.1.0_0.9.0.0 and above versions
Kafka Configuration
View Topicbin/kafka-topics.sh--list--zookeeper localhost:2181Start a producerbin/kafka-console-producer.sh--broker-list localhost:9092--topic testOpen a consumer (2183)
Transferred from: http://www.aboutyun.com/thread-9216-1-1.htmlSeveral difficulties in using storm to process transactional real-time computing requirements: http://blog.sina.com.cn/s/blog_6ff05a2c0101ficp.htmlRecent log processing, note is log processing, if the flow calculation of some financial data such as exchange market data, is not so "rude", the latter must also consider the integrity and accuracy of
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
Original link: http://www.sjsjw.com/kf_cloud/article/020376ABA013802.asp purposeReal-time monitoring of a directory of log files, such as the new file to switch to a new file, and synchronously write to Kafka, while recording the log file line location, in order to deal with the process of abnormal exit, can be read from the last file location (considering the ef
I. Overview of the project as a whole
Outline the background of the project
Background:User whereaboutsEnterprise operations
Purpose of the Analysis project
Through the analysis of the project, we can get the following objectives: • Real-time user dynamics • Based on real-time statistical results, moderate promotion and statistical analysis results, rapid and reasonable adjustment of two, Producer module analysis
Analyze production data sources
In the us
, Rolljitterms=Config.randomsegmentjitter, time=Time )if(!Hasindex) {Error ("Could not the Find index file corresponding to log file%s, rebuilding index ...". Format (Segment.log.file.getAbsolutePath)) Segment.recover (config.maxmessagesize)//The index of the corresponding log file does not exist, the Rec Over. This place is usually met Kafka index error n
the tasks are set to being the same as the number of executors, i.e. Storm would run one task per thread.both spout and bolts are initialized by each thread (you can print the log, or observe the breakpoint). The prepare method of the bolt, or the open method of the spout method, is invoked with the instantiation, which you can think of as a special constructor. Every instance of each bolt in a multithreaded environment can be executed by different m
Simple test Project: 1, the new Java project structure is as follows:
The test class Flumetest code is as follows:
Package com.demo.flume;
Import Org.apache.log4j.Logger;
public class Flumetest {
private static final Logger Logger = Logger.getlogger (flumetest.class);
public static void Main (string[] args) throws Interruptedexception {for
(int i = i
Listen Kafka receive message consumer code as follows:
Package com.demo.flu
broker. This can be consciously verified in real-time monitoring.For broker monitoring, we do this primarily through the JMS indicator. People who have used Kafka know that the Kafka community offers a particularly large number of JMS indicators, many of which are of little use. I have listed some of the more important: the first is the broker machine in and out of bytes, is similar to I can monitor the ne
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