elastic kibana

After installation Kibana directly can access, this is not conducive to security, next we use Apache Password Authentication for security configurationThe Apache configuration file is as follows:authuserfile/data/kibana/.htpasswdThis is the file we want to store the password in.Next generate the password#htpasswd-C/data/kibana/.htpasswd user#new password: #Re-typ

In the Kibana display page, we click on the left column of table and find that the data in Elasticsearch is the correct data in the display, such as: Agent Www.baidu.com/test, the interface will be displayed correctly as Www.baidu.com /test, but if we show this field in term, will be divided into www.baidu.com and test two groups, by looking at Curl did not find any problems, and finally found the reason for elasticsearch to separate the results of

"]}#对ua进行解析useragent {Source = "UA"# type = "Linux-syslog"Add_tag = ["useragent"]}}output{#入eselasticsearch{hosts = ["10.130.2.53:9200", "10.130.2.46:9200", "10.130.2.54:9200"]flush_size=>50000Workers = 5Index=> "Logstash-tracklog"}} Need to note:1. The logsdate is replaced because: for example, the 2016-01-01 form of the field, into the ES, will be considered a time format, auto-completion is: 2016-01-01 08:00:00, resulting in kibana need to

This is my entire process of log analysis for haproxy in the unit.We have been in the maintenance ES cluster configuration, and did not put a set of processes including the collection end of the code, all their own once, and the online collection of logs when we generally use the logstash, but the industry many people say logstash whether it is performance and stability is not very good, The advantage of Logstash is the simple configuration, this time I chose the RsyslogToday this haproxy log, I

ObjectiveJMeter is an open source tool for performance testing, stress testing, and is being tested by a large number of testers to test product performance, load, and more. JMeter In addition to the powerful presets of various plugins, various visual charting tools, there are some inherent flaws, such as: We often can only analyze the performance of the same deployment in the report, it is inconvenient to make a vertical comparison, for example, each build will run a one-time test, but

First, open the Kibana discover interface, and we'll find that the default entry in the search box at the top of the page is "*", which also means that the default query is all information.Now, suppose our import kibana information is divided into two categories: trace and statistic, and the two types of information are differentiated in info-type.Then, when we enter Info-type:trace in the search box above

-http_ssl_module -- With-OpenSSL =/opt/openssl-1.0.1i -- With-PCRE =/opt/pcre-8.33 -- With-zlib =/opt/zlib-1.2.8 Nginx command Start:/usr/local/nginx/sbin/nginx Restart:/usr/local/nginx/sbin/nginx-s reload Stop:/usr/local/nginx/sbin/nginx-s stop View the main process: netstat-ntlp Check whether the startup is successful: netstat-ano | grep 80 Required to install Ruby to run kibana Sudo apt-Get update Wget http://cache.ruby-lang.org/pub/ruby/2.1/ruby

Elk Architecture: Elasticsearch+kibana+filebeatVersion information:Elasticsearch 5.2.1Kibana 5.2.1Filebeat 6.0.0 (preview)Today in the Elk Test, the Kibana above the discover regardless of the index, found that will be error:[Request] Data too large, data for [And in the Elasticsearch log you can see:Org.elasticsearch.common.breaker.CircuitBreakingException: [Request] data too large, data for [According to

Elasticsearch+logstash+kibana ConfigurationThere are a lot of articles about the installation of Elasticsearch+logstash+kibana, which is not repeated here, only some of the more detailed content. Considerations for installing in AWS EC2 9200,9300,5601 Port to remember to open Elasticsearch address do not write external IP, otherwise it will be a waste of data, write internal IP"ip-10-1

In the use of the Kibana plug-in Discover feature, there are two shortcuts for "filter in" (finding data that matches this value) and "filter out" (excluding data that matches this value), and when working with date data, you are prompted with the following error: Discover:failed to parse Date field [975542400000] with format [Year_month_day] failed to parse date field [975542400 with format [Year_month_day] As a hint, it should be an error that th

you to collect, analyze, and store your logs for later use (e.g., search). Kibana is also an open source and free tool, and he kibana a friendly Web interface for Logstash and Elasticsearch, which can help you summarize, analyze, and search important data logs. Elk work flow is as follows: Deploy Logstash on all services that need to collect logs, as Logstash agent (Logstash shipper) to monitor and filter

little too hard.Open source real-time log analysis Elk platform can perfectly solve our problems above, elk by Elasticsearch, Logstash and Kiabana three open source tools. Official website: https://www.elastic.coElasticsearch is an open source distributed search engine, it features: distributed, 0 configuration, automatic discovery, Index auto-shard, index copy mechanism, RESTful style interface, multi-data source, automatic search load, etc.Logstash is a fully open source tool that collects, a

This article is written to record the Logstash+elasticsearch+kibana+redis building process. All programs are running under the Windows platform.1. Download1.1 Logstash, Elasticsearch, Kinana download from official site: https://www.elastic.co/1.2 Redis official without the Windows platform. You can download Windows platform version from GitHub: https://github.com/MSOpenTech/redis/releases2. Start each part of the component2.1 Redis Boot: Still relativ

This article is a reference to the practice of logstash official documentation. The environment and required components are as follows: RedHat 5.7 64bit/centos 5.x JDK 1.6.0 _ 45 Logstash 1.3.2 (with kibana) Elasticsearch 0.90.10 Redis 2.8.4 The process of building a centralized log analysis platform is as follows: Elasticsearch 1. Download elasticsearch. wget https://download.elasticsearch.org/elasticsearch/elasticsearch/elasticsearch-0.90.10.

Elasticsearch + Logstash + Kibana ConfigurationElasticsearch + Logstash + Kibana Configuration There are many articles about the installation of Elasticsearch + Logstash + Kibana. I will not repeat them here, but I will only record some details here. Precautions for installing AWS EC2Remember to open the elasticsearch address on ports 9200,9300 and 5601. Do not w

Kubernetes Release:stac Kdriver Logging for use with Google Cloud Platform, and Elasticsearch. You can find more information and instructions in the dedicated documents. Both use FLUENTD with custom configuration as a agent on the node.Okay, here's our pits guide.1. Preparatory work The Kubernetes code in GitHub is planted down to master local. git clone https://github.com/kubernetes/kubernetes Configure ServiceAccount, this is because after the download of FLUENTD images

This is a creation in Article, where the information may have evolved or changed. Golang because of its simple syntax, quick and easy deployment is being favored by more and more developers, a Golang program developed, it is bound to care about its operation, today here to introduce you if you use the Elastic Stack to analyze Golang Memory usage of the program, convenient for the Golang program to do long-term monitoring and then tuning and diagnosis,

Kibana problem occurred, 5601 port is not connected, but the process exists, view log found the following error "Elasticsearch is still initializing the Kibana index ... Trying again in 2.5 second. " PS: View log can be used kibana-l Xxx.log {' name ': ' Kibana ', ' hostname ': ' kt52 ', ' pid ': 3607, ' Level ': "M

Benefits of the unified collection of real-time logs:1. Quickly locate the problem machine in the cluster2, no need to download the entire log file (often relatively large, download time is much)3, the log can be countedA, to find the most frequently occurring anomalies, for tuning processingB, Statistics crawler IPC, Statistical user behavior, do cluster analysis, etc.Based on the above requirements, I adopted the ELK (Elasticsearch + Logstash + kibana