Skip to main content

Centralize logs with logstash

Now a days logging is the essential part of the any application. Logging useful piece of information can easily help to find errors, fixing bug and much more. Modern application now scaling up in hundreds servers in cloud. Managing and monitoring logs of heterogeneous system is very challenging for any system administrator even more challenging for developers to fixing bugs. Last Friday evening we stared our testing with 3rd party products and stuck with a few bugs. As usual we first looked for the log and tried to find some hints to reproduce the bug. Here we got serious problems, our application scaled on few application servers such as Oracle GlassFish, Apache Tomcat. It was not a pleasant moments to search all over the server to find a few pieces of information. Here we have understand that we have to use any tools to manage, monitor and search logs. With my experience we have a few options:
1) Flume, Hadoop hdfs and ElasticSearch.
2) Kafka, Storm and Slor.
3) Logstash and Graylog2.
First option with Hadoop we have implemented in few cases but for the current project it seems very big gun. Second option also need some configuration and coding experience to build up the log managements tools from the scratch. My aim was to use something new and elegant which we can configured with less effort and easy to use. A few times i heard about logstash and decided to make a try. In the rest of the post i will describe how to install and configure logstash for centralizing log, i.e. collecting, aggregating and searching log. Most of the features of logstash is as follows:
1) Collecting log through agents
2) Aggregating logs
3) Shipping the logs in ElasticSearch
4) Web interface for searching logs
5) Open source
6) Everything in an one jar, nothing more.
7) Very well documented with examples

Take a look at the high level architecture of logstash:
For centralizing you have to need the followings components:
1) ElasticSearch
2) Redis
I have go through the getting started page and everything runs fines as a charm. Only one error i have got when tried to install Redis.
$ make
clang: error: no such file or directory: '../deps/hiredis/libhiredis.a'
clang: error: no such file or directory: '../deps/lua/src/liblua.a'
make[1]: *** [redis-server] Error 1
make: *** [all] Error 2
By googling in internet i have found the solution very easily as follows:
$ make
cd deps
make lua hiredis linenoise
and finalized the installation
$ make
cd $REDIS_CODE/src
make
In my cases i wanted to collect log from the Glassfish server.log and use the basic configuration for agent
input {
    file {
    type => "server"

    # Wildcards work, here :)
    path => [ "$DOMAIN_HOME/logs/*.log" ]
  }
}

output {
  #stdout { codec => rubydebug }
  redis { host => "127.93.1.11" data_type => "list" key => "crm" }
}
That's all. Happy coding and bloging.

Comments

Popular posts from this blog

Apache Ignite deep dive, SQL engine

Apache Ignite  is an open source memory-centric distributed database, caching and comput- ing platform. From the beginning, it was designed as an in-memory data grid for developing a high-performance software system. So, it’s core architecture design is slightly different from the traditional NoSQL databases, which can simplify building modern applications with a flexible data model and simpler high availability, high scalability.     Moreover, to understand how to design application with any databases or framework properly, you must understand the architecture of the database or framework itself. By getting a better idea of the system, you can solve different problems in your enterprise architecture landscape, can select a comprehensive database or framework that is appropriate for your application and get the maximum benefits from the system. In this article we are going to explore the Apache Ignite SQL engine.      Under the hood...

Book review: High Performance in-memory computing with Apache Ignite by Sadruddin Md

A new title  The Apache Ignite book  has been released including Ignite 2.6 and above. Read the full book review by Sadruddin Md .

Quick start with In memory Data Grid, Apache Ignite

UP1: For complete quick start guide, see also the sample chapter of the book "High performance in-memory computing with Apache Ignite" here . Even you can find the sample examples from the GitHub repository . IMDG or In memory data grid is not an in-memory relational database, an NoSQL database or a relational database. It is a different breed of software datastore. The data model is distributed across many servers in a single location or across multiple locations. This distribution is known as a data fabric. This distributed model is known as a ‘shared nothing’ architecture. IMDG has following characteristics: All servers can be active in each site. All data is stored in the RAM of the servers. Servers can be added or removed non-disruptively, to increase the amount of RAM available. The data model is non-relational and is object-based.  Distributed applications written on the platform independent language. The data fabric is resilient, allowing non-disruptive au...