ELK Stack

The Elastic Stack, formerly known as the ELK Stack, is a collection of open-source software tools developed by Elastic for searching, analyzing, and visualizing data in real-time. The Elastic Stack is commonly used for various purposes including log and event data analysis, application monitoring, security analytics, and business intelligence. Its flexibility, scalability, and ease of use make it a popular choice for organizations of all sizes across different industries.

In simple terms, we use the Elastic Stack because it helps us gather, store, analyze, and visualize data from various sources quickly and efficiently.

Imagine you have a lot of information coming in from different places, like logs from your computer systems, data from your website, or metrics from your applications. Elastic Stack provides tools to gather all this data, organize it neatly, search through it easily, and create useful visuals to understand what's going on.

So whether you're troubleshooting issues in your systems, monitoring how your applications are performing, keeping an eye on security threats, or just trying to make sense of your data to make better decisions, the Elastic Stack helps you do all that in a straightforward and effective way.

Use-case of Elastic Stack

• On-Demand Services Platforms: Similar to Uber, apps providing on-demand services such as food delivery, grocery delivery, home services, etc., could benefit from the Elastic Stack for managing various aspects of their operations including real-time tracking, logistics optimization, user feedback analysis, etc.

• Social Networking Platforms: While not exactly like Tinder, social networking apps focusing on connections, recommendations, or niche communities could use the Elastic Stack for managing user data, performing personalized recommendations, analyzing user interactions, and implementing search functionalities.

• E-Commerce Platforms: E-commerce applications dealing with large volumes of products, transactions, and user interactions can leverage the Elastic Stack for search, personalized recommendations, real-time monitoring of user activities, fraud detection, and analyzing purchasing patterns for marketing purposes.

• Travel and Accommodation Services: Applications in the travel and accommodation industry, including hotel booking platforms, flight booking apps, vacation rental services, etc., could use the Elastic Stack for managing listings, search functionalities, pricing optimization, and analyzing user reviews and preferences.

• Gaming Platforms: Multiplayer gaming platforms, gaming communities, or game analytics services could utilize the Elastic Stack for managing player data, analyzing game metrics, monitoring server performance, and providing personalized gaming experiences.

Elastic Stack Architecture

The stack comprises several components that work together to enable various use cases. Here's an overview of the architecture of the Elastic Stack:

Elasticsearch:

• Elasticsearch is the core component of the stack, serving as a distributed, RESTful search and analytics engine.

• It is responsible for indexing, storing, and searching data in real-time.

• Elasticsearch uses Apache Lucene under the hood for full-text search capabilities.

• It is horizontally scalable, allowing you to add more nodes to a cluster to handle increased data volume and search load.

Logstash:

• Logstash is a data processing pipeline that ingests, transforms, and enriches data before indexing it into Elasticsearch.

• It supports a wide range of input sources, including log files, syslog, Beats, Kafka, and more.

• Logstash pipelines consist of input, filter, and output plugins, allowing you to customize data processing workflows according to your requirements.

• Common use cases include log aggregation, parsing, normalization, and data enrichment.

Beats:

• Beats are lightweight data shippers that collect and send various types of data to Elasticsearch or Logstash.

• Different Beats are available for specific use cases: Filebeat: Collects log files and sends them to Elasticsearch or Logstash.

• Metricbeat: Collects system and service metrics and ships them to Elasticsearch or Logstash.

• Packetbeat: Monitors network traffic and captures application-level protocols.

• Heartbeat: Checks the uptime and response time of services.

• Auditbeat: Collects Linux audit framework data. Beats are designed for minimal resource consumption and can be deployed on servers, containers, or edge devices.

Kibana:

• Kibana is a web-based user interface for visualizing and exploring data stored in Elasticsearch.

• It provides various tools for creating dashboards, charts, graphs, and maps to gain insights into your data.

• Kibana supports ad-hoc queries, aggregations, and filtering, allowing users to interactively explore data.

• It also offers features like Canvas for creating custom visualizations, Timelion for time series analysis, and Machine Learning for anomaly detection and forecasting.

Kafka

Apache Kafka is an open-source distributed event streaming platform. It is designed to handle high-throughput, real-time data feeds and provides capabilities for publishing, subscribing to, storing, and processing streams of records in a fault-tolerant and scalable manner. Kafka is commonly used for building real-time data pipelines and streaming applications.

Kafka follows the publish-subscribe messaging pattern, where producers publish messages to topics, and consumers subscribe to topics to receive messages.

Topics are logical channels or feeds to which messages are published.

Brokers:

• Kafka is a distributed system, and its functionality is achieved through a cluster of one or more Kafka brokers.

• Brokers are servers that store and manage topic partitions, handle client requests, and replicate data across the cluster for fault tolerance.

Topics and Partitions:

• Topics are divided into partitions, which are individual ordered logs of messages.

• Partitions allow Kafka to scale horizontally by distributing data across multiple brokers.

• Each partition can be replicated across multiple brokers for fault tolerance and high availability.

Producers:

• Producers are applications or processes that publish messages to Kafka topics.

• They send messages to Kafka brokers, specifying the target topic and optionally the partition to which the message should be sent.

Consumers:

• Consumers are applications or processes that subscribe to Kafka topics to consume messages.

• Consumers can read messages from one or more partitions of a topic, and Kafka ensures that each message is delivered to at least one consumer in each consumer group.