logstash log file location

Official website https://www.elastic.coSoftware version: Logstash 2.2.0 all Pluginselasticsearch 2.2.0Kibana 4.4.0Note: This environment becomes Centos6.5 64 bits, the single machine does the test, the specific configuration is simple.1.Logstash installation ConfigurationUnzip to/usr/local/logstash-2.2.0/Logstash confi

Some logs, such as Apache, do not support JSON with Grok plugins like NginxGrok using regular expressions for row-matching splitsThe predefined locations are defined in the/opt/logstash/vendor/bundle/jruby/1.9/gems/logstash-patterns-core-2.0.5/patternsApache in File Grok-patternsView official documentsHttps://www.elastic.co/guide/en/

-----Unlocking User StatementsAlter user Assp_test account unlock-----1, the DBA role of the user login, unlock, first set the specific time format, in order to see the specific timeAlter session set nls_date_format= ' Yyyy-mm-dd hh24:mi:ss ';-----2, check the specific lock timeSelect Username,lock_date from dba_users where username= ' assp_test ';-----3. Unlocking the userAlter user Assp_test account unlock-----4, check that the IP is the result of the test user is lockedThe path to view Oracle

Nodejs NPM install installation environment Logstash log analysis and graphical display Small search engines and graphical display Ruby-developed tools are encapsulated into jar packages in the Java environment. Logstash Analysis Read logs from the back to the front in real time Elastic search Storage Kibana web page Java-jar

Turn from: http://blog.c1gstudio.com/archives/1765 Logstash + Elasticsearch + kibana+redis+syslog-ng Elasticsearch is an open source, distributed, restful search engine built on Lucene. Designed for cloud computing, to achieve real-time search, stable, reliable, fast, easy to install and use. Supports the use of JSON for data indexing over HTTP. Logstash is a platform for application

PartyCase BackJingTypically, the logs are stored on different devices that are scattered. If you manage hundreds of dozens of of servers, you are also using the traditional method of logging in to each machine in turn. This is not feeling very cumbersome and inefficient. Open Source Real-time log analyticsELKthe platform can perfectly solve the problem of log collection and

-n7100", "Sign" = "e9853bb1e8bd56874b647bc08e7ba576"}For ease of understanding and testing, I used the Logstash profile configuration file to set up.Sample.confThis includes the ability to implement UrlDecode and KV plug-ins, which need to be run./plugin Install contrib installs the default plug-in for Logstash.Input {file{Path="/home/vovo/access.log"#

Write in front: In doing Elk logstash processing MySQL slow query log when the problem: 1, the test database does not have slow log, so there is no log information, resulting in ip:9200/_plugin/head/interface anomalies (suddenly appear log data, deleted the index disappeared

Grok-patterns contains log parsing rules for regular expressions with many underlying variables, including Apache log parsing (which can also be used for nginx log parsing). Based on Nginx log analysis configuration: 1. Configure the Nginx log format as follows: Log_format

Sometimes we need to analyze some server logs and alarm the wrong logs, where we use Logstash to collect these logs and send error log data using our own developed mail delivery system.For example we have several files that need to be monitored (BI logs)We can collect these file logs by configuring Logstash

1, Logstash plug-in configurationLogstash under Config folder to add the contents of the test.conf file:input{ TCP { = = "Server " = "0.0.0.0 " = 4567 = > json_lines }}output{ elasticsearch{ hosts=>["127.0.0.1:9200"] = > "user-%{+yyyy. MM.DD} " } = Rubydebug}}Start Logstash:./

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 analysi

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)bin/kafka-console-consumer.sh--zookeeper localhost:2181--topic test--from-beginningCreate a Themebin/kafka-topics.sh--create--zookeeper 10.92.1.177:2183--replication-factor 1--partitions 1--topic test

Environment: MongoDB 3.2.17 Logstash 6The MongoDB log Instance format file path is/root/mongodb.log:2018-03-06T03:11:51.338+0800NBSP;INBSP;COMMANDNBSP;NBSP;[CONN1978967]NBSP;COMMANDNBSP;TOP_FBA. $cmd command:createindexes{createindexes: "top_amazon_fba_inventory_data_2018-03-06", indexes:[{key:{sellerid:1,sku:1,updatetime:1 },name: "Sellerid_1_sku_1_updatetime_1

When using Logstash, some regular expressions are written for finer-grained cutting logs. How to use input { file { type => "billin" path => "/data/logs/product/result.log" } } filter { grok { type => "billin" pattern => "%{BILLINCENTER}" patterns_dir => "/data/

To facilitate quantitative analysis of nginxaccess logs, filter matches using logstash 1. Determine nginx log format log_format access ' $remote _addr- $remote _user[$time _local] ' ' $http _host $request _method $uri ' ' $status $body _bytes_sent ' ' $upstream _status $upstream _addr $request _time ' ' $upstream _response_time $http _user_agent '; 2. Use logstashgrok to match the

口号 -agent.sinks.k1.brokerlist=10.90.11.19:19092,10.90.11.32:19092,10.90.11.45:19092,10.90.11.47:19092,10.90.11.48:19092 - #设置Kafka的Topic -Agent.sinks.k1.topic=kafkatest + #设置序列化方式 -agent.sinks.k1.serializer.class=Kafka.serializer.StringEncoder +Agent.sinks.k1.channel=c1Create a Kafka topic1 cd/data1/kafka/kafka_2. One-0.10. 1.0/./kafka-topics. SH 3 - 10.90. 11.19:12181Start flume1 /usr/local/apache-flume-1.7. 0-bin/bin/flume-ng agent-n agent-dflume.monitoring.type=http-dflume.monitoring.port=98

#此处以收集mysql慢查询日志为准 to add different field values depending on the file nameInput {file{Path="/data/order-slave-slow.log"type="Mysql-slow-log"start_position="beginning"codec=Multiline {pattern="^# [email protected]:"negate=true What=Previous}} file{Path="/data/other-slave-slow.log"type="Mysql-slow-