Summary

The provided web content introduces the basics of log analysis and the ELK (Elasticsearch, Logstash, Kibana) stack, explaining its components, architecture, and the importance of log analysis for problem diagnosis and resolution.

Abstract

The web content delves into the significance of log analysis in troubleshooting and reducing the time to resolve issues by examining logs from operating systems, applications, and network devices. It outlines the ELK stack, which consists of Elasticsearch for storing and searching logs, Logstash for collecting and processing log data, and Kibana for visualizing and analyzing log information. The architecture of ELK is detailed, emphasizing its open-source nature and the role of Beats as lightweight data shippers that enhance the ELK stack's capabilities by collecting various types of data and sending them to Elasticsearch through Logstash. The content also explains the concept of Beats, their different types, and how they contribute to the broader Elastic Stack. Furthermore, it describes the individual functionalities of Logstash, Elasticsearch, and Kibana, highlighting their roles in handling log data efficiently. The article concludes by providing insights into the importance of ELK in the context of modern data analysis and DevOps practices.

Opinions

The author, Bruno DELB, a DevOps coach with expertise in Cloud technologies, emphasizes the importance of context when interpreting log messages.
The article suggests that the ELK stack is an essential tool for analyzing logs, identifying root causes of issues, and building countermeasures to mitigate risks.
The author expresses a preference for the ELK stack's flexibility, reliability, and scalability, which are crucial for managing large volumes of log data.
The article conveys that the ELK stack, when augmented with Beats, becomes the Elastic Stack, which offers a comprehensive solution for log analysis and monitoring.
Bruno DELB's role as an MVP Alibaba Cloud and his enthusiasm for new technologies like DevOps, Cloud, Agile, blockchains, and metaverse imply an endorsement of the ELK stack within these innovative fields.
The author encourages further exploration of ELK and related topics through his articles on Alibaba Cloud blog space, his personal blog, and his DevOpsTestLab Youtube channel.
Bruno DELB promotes his book "Alibaba Cloud Guide" and "DevOps Patterns Guide" on Amazon, indicating his commitment to sharing knowledge and best practices in the field.

Introduction to log analysis with ELK (Elasticsearch)

In this story, I introduce the basics of log analysis and the main concepts of ELK.

Log analysis helps to diagnose problems and to reduce the resolution time.

Logs come mainly from OS (Operating Systems), applications, and network devices. Many logs are unstructured data (example: system logs, CPU data, configuration files, …).

They are usually stored in a hard drive or sent to a log collector.

Interpretation of the log messages needs to take into account the context.

To analyze logs, you need identify root causes and build counter measures to reduce the risks.

Architecture

ELK (Elasticsearch Logstash Kibana) is based on three open source:

Logstash: it collects, transforms, and routes log entries;
Elasticsearch: it stores and allows to search logs;
Kibana: is allows to visualize and analyze logs.

Beats

Beats are lightweight data shippers: they capture data (logs, metrics, or network packet data) and send them to Elasticsearch via Logstash.

Main beats are:

AuditBeat: collects data of Linux audit framework and ships the logs;
FileBeat: tails and ships logs files;
FunctionBeat: reads and ships events from serverless infrastructure;
HearBeat: pings remote services (for availability);
JournalBeat: reads and ships event from Journald;
MetricBeat: fetches metrics from the OS and services;
PacketBeat: sniffes packets to monitor the network and applications;
WinlogBeat: fetches and ships Windows event logs.

If beats are used with ELK, it’s called Elastic Stack.

Logstash

Logstash collects data inputs and feeds them to Elasticsearch.

Various types of data and various sources are supported.

Logstash is based on a pipeline:

it gets data from data source (inputs),
it transforms these inputs by applying rules (filters),
it sends this result to the endpoint of Elasticsearch.

Elasticsearch

Elasticsearch is a distributed search engine and an analytics engine highly flexible. It’s based on a NoSQL database and it’s based on the Lucene search engine.

It provides simple deployment, maximum reliability, and horizontal scalability.

It uses RESTful APIs.

It provides advanced queries to perform detailed analysis. Data are stored centrally for quick search.

An index is a logical namespace. It allows to organize data. An index has one or more shards (5 by default).

A shard is a Lucene index. It’s also a search engine and have zero or more replicas (1 by default).

Kibana

Kibana allows to visualize results of complex queries on Elasticsearch documents.

The dashboard provides interactive diagrams, geospatial data, timelines, and graphs. Kibana allows to custom graphs.

There are different methods to search data:

Free text searches: search for a string;
Field-level searches: search for a string within a specific field;
Logical statements: combine searches into a logical statement;
Proximity searches: search terms within a specific character proximity.

Now you have a clear understanding of what is ELK and why a such tool is so important.

Bruno DELB: DevOps coach with Cloud skills, technical, and soft skills, I’m MVP Alibaba Cloud. I love new technologies: DevOps, Cloud, Agile, blockchains, and metaverse.

More articles on my Alibaba Cloud blog space.

More articles on my blog http://www.DevOpsTestLab.com.

My DevOpsTestLab Youtube channel.

My LinkedIn profile: https://fr.linkedin.com/in/brunodelb

Bruno DELB: DevOps coach with Cloud skills, technical, and soft skills, I’m MVP Alibaba Cloud. I love new technologies: DevOps, Cloud, Agile, blockchains, and metaverse.

More articles on my Alibaba Cloud blog space.

More articles on my blog http://www.DevOpsTestLab.com.

My DevOpsTestLab Youtube channel.

My LinkedIn profile: https://fr.linkedin.com/in/brunodelb

My book Alibaba Cloud Guide on Amazon: https://www.amazon.com/DevOps-Patterns-Guide-Bruno-Delb-ebook/dp/B08QSMX9LC

My book DevOps Patterns Guide on Amazon: https://www.amazon.com/DevOps-Patterns-Guide-Bruno-Delb-ebook/dp/B08QSMX9LC