Updated April 3, 2023
Difference Between Graylog vs ELK
The following article provides an outline on Graylog vs ELK. Log files record all the relevant information and events that occur in a computing system. They are vital for any computing system as they throw light on all the changes that have occurred in a system or an environment. This helps the users to analyse and understand the situation, and if there is a problem, it can be used to backtrace to the root cause. When dealing with scalable systems, it is always required to have a protocol for log management. One such approach is centralized Log management, where the logs generated by various subsystems are sorted, parsed and stored in a central repository at the system level. This will essentially reduce the effort in identifying issues.
ELK is a collection of Elasticsearch – a highly scalable analytics search engine, logstash – a tool for parsing, analyzing data and kibana – an interactive GUI tool for visualization. It is open-source software. It is developed in Java and is basically a wrapper on Apache Lucene Library. It consists of an HTTP web API interface. It has no schema with JSON documents where all the data is stored. Similar to MongoDB, it is very easy to set it as it has no schema. Graylog is a powerful log management software developed to cater to the need for processing, analyzing and understanding terabytes of log data. It also offers an open-source package for users to get hands-on experience.
Head to Head Comparison Between Graylog vs ELK (Infographics)
Below are the top 8 differences between Graylog vs ELK:
Key Difference Between Graylog vs ELK
Let us discuss some of the major key differences between Graylog vs ELK:
- ELK is a stack; it collects, indexes and stores data using elastic search; logstash is the tool to analyze all the information that is stored in elastic search; this information could be log data. All the inferences and observations made by them are visualized by kibana using its interactive dashboard. The ELK stack is mainly focused on big data analysis, whereas Graylog is exclusive for log analysis. It processes log data only, unlike ELK.
- Visualization is done by kibana in ELK; kibana has to be set up separately along with the others. Graylog is the whole package of processing and visualization. Its GUI is far more interactive and user friendly than that of kibana. When it comes to log analysis, Graylog is very powerful.
- ELK stack and Graylog are both open-source tools to a certain extent for the users to get hands-on experience, continuous support and all the premium features are licensed.
- Graylog is used in many security applications, and it is centralized. Data in huge scales (Terabytes) can be analysed from multiple log sources as well as multiple geographic locations as all the data is centralized, and therefore it can be accessed anywhere.
Log analysis process in ELK:
- In most cases interested in log analysis, the ELK stack uses a filebeat (A lightweight tool used to centralize all the logs) to direct all the logs to a specific server.
- All the data that is being pushed by filebeat is pushed to logstash for processing. Logstash is pretty lightweight and flexible. It can be integrated with multiple plugins but at the cost of performance.
- Although logstash is very easy to work with, it gets difficult to process high traffic data because of its limitations.
- All the data that logstash processed will be sent to kibana for visualization. It is quite interactive and lets the users define the visualization type of their choice. Along with data representation, it also presents some stats on how the applications would behave in a production environment.
Log analysis process in Graylog:
- Graylog mainly consists of three components, namely MongoDB, Graylog main server and Graylog web interface.
- Graylog clients are set up with a specific configuration that enables server-client communication. Clients push the log data to the server, and it analyzes and stores in mongoDB.
- Graylog web UI is very user friendly; it provides control over user permissions. It uses RESTful APIs.
- The web UI can support a wide range of data types, whereas Graylog doesn’t support syslogs. Data should be sent directly to Graylog. This makes it difficult for log management in the dashboard.
Graylog vs ELK Comparison Table
Let’s discuss the top comparison between Graylog vs ELK:
| Graylog | ELK |
| Does not accept Syslog files. Data should be sent directly. | ELK supports all the majority of data types like json etc. Third-party plugins can be used for data conversion. |
| Exclusively developed for log management and log analysis. | ELK stack supports log management and logs analysis along with its other functionalities. It is a multipurpose stack. |
| Graylog is very powerful with respect to processing logs. | Logstash, part of the ELK stack that does the processing is not as fast as Graylog and has issues when there is high traffic data. |
| It is developed in java and supports GLEF (Graylog extended log format) | ELK stack is also developed in java, and it supports json format. |
| Supports real-time UDP logging / GLEF logging along with intuitive search. | Supports full-text query search analysis with elastic search. |
| Uses Lucene syntax as its search language. | Uses Query DSL, based on Lucene as its search language. |
| Builtin alerting is available. And the alerts are based on stream data. Custom alert filters can be configured in the Graylog web interface. | Builtin alerting is not available, but third party plugins like X-pack could be used to send alerts to the users. |
| Some organizations that use ELK stack are Appbrain, Hotjar, stockopedia etc. | Some organizations that use Graylog are Netflix, Cisco, Verizon and Linkedin. |
Conclusion
Overall, both the tools have their own pros and cons, as we have seen earlier. Selecting a tool is completely based on the system and its requirements. Graylog is very powerful, and its GUI is very user friendly, whereas the ELK stack is modularized and flexible. It is up to the users to decide which suits them better. There are hybrid applications in which both can be used.
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