Elastic Stack, commonly known as ELK Stack, is a powerful collection of open source tools designed to help businesses search, analyze and visualize large volumes of data in real time. ELK stands for Elasticsearch, Logstash and Kibana. In recent years, the stack has expanded to include a fourth component called Beats, a lightweight data sender. Together, these tools provide a comprehensive solution for managing and making sense of the ever-growing amount of data generated by modern IT systems.
Elasticsearch, the heart of the Elastic Stack, is a distributed RESTful search and analytics engine built on Apache Lucene. It is designed to handle large volumes of structured and unstructured data, making it an ideal choice for big data applications. Elasticsearch is highly scalable, allowing businesses to easily grow their infrastructure as their data needs increase. It also offers real-time search capabilities so users can quickly find the information they need.
Logstash is the data processing component of Elastic Stack. It is responsible for collecting, parsing and transforming data from various sources before sending it to Elasticsearch for indexing. Logstash supports a wide variety of input sources, including log files, message queues, and network data, making it a versatile tool for ingesting data into the stack. It also offers a rich set of filters and plugins that can be used to clean, enrich and transform data as it passes through the pipeline.
Kibana is the visualization layer of Elastic Stack and provides a user-friendly interface for exploring and analyzing data stored in Elasticsearch. With Kibana, users can create custom dashboards, visualizations and reports to gain insight into their data and make informed decisions. Kibana also includes features for managing Elasticsearch indexes, such as index creation and deletion, as well as monitoring the health and performance of the Elasticsearch cluster.
Beats, the newest member of Elastic Stack, is a family of lightweight data senders designed to collect and transmit various types of data to Logstash or Elasticsearch. Beats simplifies the data ingestion process by providing pre-built modules to collect specific types of data such as system logs, network traffic, and application metrics. Using Beats, businesses can easily extend the capabilities of Elastic Stack deployments to collect and analyze data from a wide variety of sources.
One of the key benefits of using Elastic Stack is its flexibility and extensibility. The modular architecture of the stack allows businesses to tailor their deployments to their specific needs, adding or removing components as needed. This flexibility extends to the way data is processed and analyzed within the stack.
Another advantage of Elastic Stack is that it is open source, meaning that it is constantly being improved and updated by a large community of developers. This ensures that the stack stays on the cutting edge of big data technology, providing users with the latest features and performance enhancements.
How ELK Stack Works
ELK Stack is a triple tool. It consists of Elasticsearch, Logstash and Kibana. They work together to process data effectively.
Logstash: Collects data from various sources, processes it and makes it ready for analysis.
Elasticsearch: Where data is stored and indexed for quick search
Kibana: Takes data from Elasticsearch and transforms it into easy-to-understand visualizations such as graphs and charts.
ELK Stack Architecture
A simple ELK stack architecture includes the following:
- Logs
The ELK stack starts by identifying the server logs that need to be analyzed. These logs contain valuable information about what is happening on the server.
- Logstash
Logstash is an open source and server-side data processing pipeline that can ingest data from many sources simultaneously, analyze the data, filter, transform and enrich the data, and then forward it to a subsystem.
Data flows through the Logstash pipeline in three phases: input phase, filter phase and output phase.
Input phase: Data is received into Logstash from a source. Logstash does not access and aggregate the data, it uses input plugins to get the data from various sources.
Filter phase: Once the data is received, one or more filter plugins take care of the processing part in the filter phase. In this stage, the required data elements are extracted from the input stream.
Output phase: The processed data is sent to a receiver and output. Output plugins can be used for many different endpoints such as Elasticsearch, HTTP, email, S3 file, PagerDuty alert or Syslog.
Logstash’s processed data is saved in a high-performance, searchable storage engine and can be easily viewed from a user interface layer.
- Elasticsearch
Elasticsearch is an open source analytics and full-text search engine. It is typically used to store, search and analyze large volumes of data quickly and in near real-time. Because it searches an index instead of searching text directly, it can achieve fast search responses.
For example, you may have a web page where you want users to be able to search for keywords or various types of data. With Elasticsearch you can create complex search functions. This includes autocomplete, correcting typos, highlighting matches, etc. Elasticsearch does everything you need to build a powerful search engine.
- Kibana
Kibana is a data visualization and management tool for Elasticsearch. It provides real-time histograms, line graphs, pie charts and maps and allows you to visualize your data and navigate the Elastic Stack.
Kibana is the official interface of Elasticsearch. Users find Kibana the most effective interface for discovering data insights and performing active management of the health of their Elastic Stack.
- Beats
Beats is a collection of lightweight and single-purpose data sending tools. It is used to push data from hundreds or thousands of machines and systems to Logstash or Elasticsearch. Beats is the perfect solution for aggregating data, which can be integrated into your servers or run in containers and then centralize the data in Elasticsearch.
There are two Beats you should take a closer look at: Filebeat & Winlogbeat.
Filebeat: It is a lightweight sender for transmitting and centralizing log data. Filebeat monitors log files or locations you specify, collects log events and forwards them to Elasticsearch or Logstash for indexing.
Winlogbeat: Winlogbeat sends Windows event logs to Elasticsearch or Logstash. It reads from one or more event logs using Windows APIs (application programming interface). Winlogbeat filters events according to user-configured criteria and sends event data to configured outputs.
Winlogbeat monitors event logs and sends new event data in a timely manner. Winlogbeat can capture event data from any event log running on your system. In this way, you can capture events such as the following:
- Application events
- Hardware events
- Security incidents
- System events
Why is ELK Stack Popular?
The reason why ELK stack is so popular is that it fulfills the need for log management and analytics tools. It enables engineers to easily manage the challenging task of monitoring applications and the IT environment.
It also provides users with a centralized platform for collecting and processing data from multiple sources. This data is stored in a single database that is scalable to store additional data. It also has analytical tools for analyzing the data.
Another important reason for ELK Stack’s popularity is that it is open source. It provides cost benefits to businesses by avoiding vendor lock-in. Open source also allows you to be part of an innovative community that is constantly developing new features.
ELK Stack also competes with market leaders like Splunk and is extremely popular among smaller companies. Splunk is known for its advanced features, which is expensive for smaller companies. ELK stack is a simple tool with powerful log management and analytics features offered at a much lower cost.
Why Logging is so Important?
With the growth of microservices and server data, logging is becoming increasingly important. It is critical to diagnose and troubleshoot problems for optimal application performance. In addition, many tools make it possible to retrieve critical business metrics and data from logs.
Logging is no longer just for finding problems. It is also used to monitor your systems.
What are the Advantages and Disadvantages of ELK ELK Stack?
The advantages of ELK Stack include the following:
- ELK works best when logs from all applications are sent together to a single ELK instance. The information from this instance reduces dependency on multiple log data sources.
- Enables fast on-premises deployment.
- There are various language clients offered by Elastic. Ruby, Python, PHP, Perl and .NET are a few examples. This is useful for users who have different languages in their code base and want to use Elasticsearch from all these languages.
- Libraries for various programming and scripting languages are available.
- Available as a free open source tool.
- It provides centralized logging. This allows users to collect logs from even the most complex cloud environment into a single searchable directory. This makes it possible to correlate and compare logs and data on events from multiple sources.
- Data analysis and visualization is a real-time process. When data is visualized in real time, agility and fast decision-making benefit.
The disadvantages of ELK Stack include the following:
- It can be difficult to manage the different components of ELK stack in a complex setup or for large enterprises.
- Although ELK stack is an open source tool, the only simple part of the whole installation process is downloading the tool. The deployment and configuration process is long and tedious. It also becomes more complicated for businesses that do not have the resources and skills for deployment. Such businesses will have to incur the additional costs of a training program or hire an ELK stack expert who can manage the deployment process.
- Some ELK Stack users have reported issues with stability and uptime, which can worsen with increasing data volumes.
The above-mentioned disadvantages clearly explain that an enterprise can undertake ELK stack deployment and management on its own, but it would still be a preferable option to use the services of specialized developers or DevOps engineers.
This specialized team not only develops innovative solutions and applications, but also seamlessly manages tedious tasks from deployment to monitoring activities.
In most cases, if a business manages the ELK stack on its own, it can be difficult for it to achieve its goals of maintaining security and compliance, as well as scaling up and down to meet the dynamic needs of the business.
ELK Stack Use Cases
Below are some examples where the ELK stack has been used successfully:
- Accenture
Accenture is one of the world’s leading IT companies and has pioneered projects related to ELK implementation. As an organization, they stated that they prefer ELK Stack, an open source software, to Splunk. Other factors cited by the organization for preferring ELK stack are the simplicity of the interface and the use of add-ons that help extend functionality.
- Netflix
Netflix, a popular movie and content streaming service, relies heavily on ELK to monitor and analyze customer service operations and security-related logs.
Netflix is another company that depends on ELK Stack to monitor and analyze customer operations and security-related logs. Elasticsearch is used for automated sharding and replication. The company also benefits from features such as flexible schema, extension models and multiple plugins.
Netflix’s extensive use of Elasticsearch has expanded from a handful of isolated deployments to over 15 clusters with nearly 800 nodes.
LinkedIn uses ELK to monitor performance and security. The IT team integrates ELK with Kafka to support their loads in real time. ELK operations include more than twenty teams and more than 100 clusters across six data centers.
- Tripwire
Tripwire is the world leader in Security Information Event Management (SIEM). This company uses ELK to support the analysis of an information packet log.
- GitHub
It is the world’s largest repository for developers to store and manage their code. GitHub uses Elasticsearch to index new code as soon as users submit it to a repository on GitHub. The data is searchable after a very short time and search results are returned for both public and private repositories.
Find out more about Kubernetes Logging Solutions: https://devopstipstricks.com/the-best-kubernetes-logging-solutions-in-2023/
