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Centralized Logging System Using ELK Stack

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Centralized Logging System Using ELK Stack

  1. 1. Centralized Logging System By:- Rohit Sharma Email:- rohitrsh@gmail.com
  2. 2. Agenda The agenda of this session is below fields: a. Discuss about CLS b. Centralized logging tools c. ELK Stack : Introduction d. Implementation and configuration of ELK stack
  3. 3. What is CLS? • CLS stands for Centralized Logging System. The CLS is designed to collect and manage information retrieved from operating systems and/or applications. This information can then be processed by a central managing system to generate information for auditing and reporting. • Using the Central Logging System, your company is able to analysis the data quickly. The system automates control processes, giving users additional time to respond more effectively to any anomalies. Proper system configuration results in the automatic escalation of events, for example, according to predefined procedures.
  4. 4. Why CLS? – Logs are a critical part of any system, they provide vital information about the application and answer questions on what the system is doing and what has happened. Most of the processes running on the system generate logs in one form or other. For convenience, these logs are often collected in files on a local disk with the log rotation option. When the system is hosted on one machine, file logs are easy to access and analyze, but when system grows to multiple hosts, log management is becoming a nightmare. It is difficult to look up a particular error across thousands of log files on hundreds of servers without the help of specific tools. A common approach to this issue is to deploy and configure a centralized logging system, so that data from each log file of each host is pushed to a central location • Benefits for organization and IT department – Fulfillment of auditing/compliance requirements – Optimization of time and resources – Systems status information – Single point of control – Archived history of your activities – Universality and scalability of your systems – Historical log database
  5. 5. CLS Tools in Market • Splunk • Splunk, an industry-leading platform for machine data, automatically indexes all your log data, including structured, unstructured and complex multi-line application log data. Splunk aims to provide a deeper understanding of real-time data. • Loggly • A cloud-based log management service, Loggly makes the log management process much less cumbersome. With a simple set-up process and intuitive tools, Loggly doesn’t require a ton of on-ramping. Loggly provides immediate value by interpreting and making sense of data pouring in from your applications, platforms and systems instantly. • Graylog2 • An open-source data analytics system that’s been field-tested around the globe, Graylog2 collects and aggregates events from a multitude of sources and presents your data in a streamlines, simplified interface where you can drill down to important metrics, identify key relationships, generate powerful data visualizations and derive actionable insights. • Fluntd • An open-source data collector for processing data streams, fluentd offers more than 150 plugins for extended functionality, more robust log management and additional uses. It works with more than 125 types of systems and is designed for high-volume data streams. You don’t need any ad-hoc scripts to use fluentd; the functionality is built in out of the box. It’s similar to syslogd but uses JSON for log messages.
  6. 6. Introduction to ELK Stack
  7. 7. What is ELK Stack? – Elastisearch ELK Stack offers a set of applications and utilities, each serving a distinct purpose, which combine to create a powerful, end-to-end search and analytics platform. (L)ogstash captures log data in a central location,(E)lastisearch takes it a step further with real-time analysis and (K)ibana transforms data into powerful visualizations for actionable insights. This comprehensive platform is built on Apache Lucene and offered under an Apache 2 Open-Source License. • Key Features: – Stacked solution with powerful components – Powerful analytics with instant insights – Visualize data with Kibana – Resistant clusters for security and reliability – Document-oriented – No Schema; automatic interpretation – Conflict management with optimistic version control – Multi-tenancy with individual or group queries – Redundancy for data security
  8. 8. ELK Solution Architecture  The Shippers usually known as agents , it will forward all the logs to broker which is configure in syslogs to be forward. I have used logstash jumberjack shipper agent.  The Broker just like shipper agent just need to configure it as broker (collector), its store logs in local storage forwarded by shipper agent.  Elasticsearch index all the logs collected by broker agent. For indexing It converts all the logs in Json. So It can be easily stored in any non-structure database (ie mongodb, hadoop)
  9. 9. Logstash – Logstash is a tool for managing events and logs. It is written in JRuby and requires JVM to run it. Usually one client is installed per host, and can listen to multiple sources including log files, Windows events, syslog events, etc. The downside of using JVM is that memory usage can be higher than you would expect for log transportation. However, community has developed Lumberjack that is deployed on each host. It collects and ships logs to Logstash which is running centralized log hosts. Logstash itself is only a client (shipper) that can send log message to centralized storage. • Input: Input can be file, syslog, Redis, logstash-farwarder (Lumberjack) • Filers: are format the logs as per the require format. i.e. apache, syslog. Also we can create custom filer using GROK pattern. • Output: Filtered log output can be stored on Elasticsearch, File, Graphite.  Log processing Input  Filters  Codecs Output
  10. 10. Elasticsearch – ElasticSearch,built on top of Apache Lucene, is a search engine with focus on real-time analysis of the data, and is based on the RESTful architecture. It provides standard full text search functionality and powerful search based on query. ElasticSearch is document-oriented/based and you can store everything you want as JSON. This makes it powerful, simple and flexible. • Indexing: ElasticSearch is able to achieve fast search responses because, instead of searching the text directly, it searches an index instead. • DSL Query: The Query DSL is ElasticSearch's way of making Lucene's query syntax accessible to users, allowing complex queries to be composed using a JSON syntax • Visualize: It can be integrate with any frontend tool which visualize JSON data. • NoSQL Integration: Usually it index and store all the data in local disk, but in big infrastructure it can be integrate with Any NoSQL DB i.e. Cassandra, MongoDB, Hadoop.
  11. 11. Kibana – Kibana is the frontend part of the ELK stack, which will present the data stored from Logstash into ElasticSearch, in a very customizable interface with histogram and other panels which will create a big overview for you. Great for real-time analysis and search of data you have parsed into ElasticSearch, and very easy to implement • Query Dashboard: is use to fetch the data to analytical data for any request of incident on basis of custom query and time stamp. • Monitoring Dashboard: Its static dashboard need, provide various monitoring graphs such as histogram, pie chart on the basis of configured queries.
  12. 12. Enhancements? – As its open source below are the future enhancements : • Email alerting: Currently, Kabana doesn't support email alerting however there’s some plugins are available on github. From that email alerting can be integrate. • GROK Patterns: Using GROK pattern we can easily parse any log format in logstash its uses regex to read the log files print complete exception traces. There are GROK debugger available which reads the logs format and create the GROK patterns – http://grokdebug.herokuapp.com/ • PacketBeat Integration: PacketBeat another frontend solution to visualise elasticsearch index, it provides enhance capabilities to monitor and analysis the logs. – http://packetbeat.com/ • Kibana Queries: As Kibana user DSL (Distributed search language) to analyse the data need to work on it. So we can have good hands on DSL.
  13. 13. Other Solutions – All other open source solution like ELK stack : • Fluentd: Fluentd is an open source data collector, which lets you unify the data collection and consumption for a better use and understanding of data – http://www.fluentd.org/architecture • Apache Flume: Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. – http://flume.apache.org/ • Socket Appenders: For log4j can use socket appender, it directly forward logs to logstash broker node. So we can remove logstash-farwarder. – https://logging.apache.org/log4j/1.2/apidocs/org/apache/log4j/net/S ocketAppender.html • MongoDB Appenders: This is directly forward log4j logs into MongoDB database. So we can there is no requirement of logstash, we can directly configured eslasticsearch with MongoDB plugin. – https://github.com/log4mongo/log4mongo-net
  14. 14. ELK Stack Questions?
  15. 15. ELK Stack Thank You! Rohit Sharma

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