# Graylog vs Elastic/ELK Stack: The Key Differences to Know

As businesses grow and integrate distributed systems to cope with higher
demands, they frequently encounter challenges monitoring system operations and
resolving problems. A common strategy to address this involves generating logs,
metrics, and traces. Yet, effectively managing these data necessitates an
additional tool.

Graylog and Elastic Stack have become prominent options in log management and
observability platforms. Both platforms boast advanced search functionalities,
data visualization tools, and capabilities for setting alerts to detect
anomalies. While they share many features, Graylog and Elastic Stack have
distinct differences.

Graylog excels at managing and analyzing log data. Conversely, Elastic Stack is
noted for its adaptability in managing various data types, such as metrics,
audit records, and traces, in addition to its robust log management features.

This brief comparison only begins to uncover the differences between Graylog and
Elastic Stack. This article thoroughly compares their respective features,
advantages, and limitations, thereby aiding in well-informed decision-making.

## What is the Elastic Stack?

The Elastic Stack, previously known as ELK Stack, is a powerful open-source tool
suite for effective log management and observability. Developed by Elastic, it
offers a comprehensive solution for gathering, centralizing, and analyzing large
volumes of data from diverse sources. Here's an overview of its key components:

- [Elasticsearch](https://www.elastic.co/elasticsearch): a distributed, RESTful
  search engine designed to store log data in a manner that optimizes search
  speed.

- [Logstash](https://www.elastic.co/logstash): a log collector that retrieves
  logs from multiple sources, processes them, and sends them to Elasticsearch
  for storage.

- [Kibana](https://www.elastic.co/kibana): a web interface used for searching,
  visualizing, and analyzing logs.

- [Beats](https://www.elastic.co/beats): lightweight data shippers capable of
  collecting and transmitting various data to Logstash or Elasticsearch.

![Elasticsearch user interface](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/96aebf90-eab2-44ac-2998-34303babf800/public
=1121x768)

The following sections will examine the workings of each component in the
Elastic Stack, shedding light on how they effectively work together.

### 1. Gathering data using log collectors

The first step in the Elastic Stack's data processing pipeline is data
ingestion, primarily handled by Logstash. This robust log collector can collect
logs from various sources, such as application logs, database logs, and
operating systems. Logstash's role includes processing, parsing, enriching, and
transforming data before routing it to targets like Elasticsearch.

Alternatively, Beats offers specialized tools for data collection, including
logs, metrics, and network packet data, for a more streamlined approach within
the Elastic Stack. This data can be either processed further by Logstash or sent
straight to Elasticsearch. Some examples of Beats are:

- Filebeat: Responsible for log collection and shipping.
- Metricbeat: Dedicated to metric shipping.
- Packetbeat: Specializes in network packet data transmission.

### 2. Data indexing in Elasticsearch

Once Elastic or Beats collects data, it is transferred to Elasticsearch for
indexing. This step is essential for transforming raw data into a structured
format, enabling near-real-time search capabilities.

Elasticsearch excels at indexing various data types, including structured and
unstructured text, geospatial, and numerical data. The data is organized into
sophisticated data structures and serialized as JSON documents. Elasticsearch
uses a data structure called an inverted index, which catalogs each unique word
found in any document and also identifies every record in the database where
each word appears.

For data retrieval, Elasticsearch employs a JSON-style query language (Query
DSL). Here's an example of such a query:

```json
{
  "query": {
    "bool": {
      "must": [
        { "match": { "title":   "Search" }},
        { "match": { "content": "Elasticsearch" }}
      ],
      "filter": [
        { "term":  { "status": "published" }},
        { "range": { "publish_date": { "gte": "2015-01-01" }}}
      ]
    }
  }
}
```

Furthermore, Elasticsearch offers the flexibility to formulate SQL-like queries
for searching and aggregating data within its search environment.

![Example of SQL-style search in Elasticsearch](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/a372e490-6a1f-45b8-5cd7-c71d79ddd700/orig
=1000x562)

### 3. Data visualization in Kibana

After Elasticsearch indexes the data, it is presented in Kibana, an interface
tailored explicitly for visualizing logs and various data types. Kibana provides
visualization tools such as pie charts, line graphs, histograms, or heat maps.

![Example of a Kibana dashboard visualization](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/cbee2432-fae9-4085-b3e4-b18f7095bb00/public
=1357x768)

Users interact with Kibana to analyze logs indexed in Elasticsearch. They can
perform queries and apply filters to pinpoint specific data for visualization.
Using an intuitive editor, Kibana enables the selection and customization of
various visualization formats, like line graphs or pie charts. There's also the
convenience of saving these visualizations, incorporating them into dashboards,
exporting them as images or PDFs, or sharing them via links.

The advanced visualization capabilities of Kibana significantly enhance data
comprehension, spotlighting trends and patterns in an accessible and
user-friendly manner, thereby facilitating insightful data analysis and
exploration.

### Pros

- All the tools in the Elastic Stack are available for free, making it easier to
  start using them.
- Configurable for high availability
- Has managed solutions that include customer service support.
- Offers monitoring features for Elasticsearch, Logstash, and Kibana to track
  their health.
- Supported by an active and extensive community.

### Cons

- The learning curve can be steep for new users.
- Upgrades can be challenging due to the requirement for version uniformity
  across all components. This is particularly problematic when older versions
  are required for specific reasons.
- Logstash demands substantial memory resources, with a minimum requirement of
  2GB.

## What Is Graylog?

Graylog is a free and open-source centralized log management system. It was
designed for efficient aggregation, parsing, and handling of large volumes of
log data from various sources like operating systems, applications, and
databases. Key components of Graylog's architecture include:

- **OpenSearch/Elasticsearch**: storing and indexing logs, enabling fast search
  operations.
- **Graylog Server**: acts as a processing layer, handling log parsing,
  enrichment, and management.
- **MongoDB**: stores operational data and metadata, not the log data.
- **Web Interface**: provides a user-friendly interface for searching,
  analyzing, and visualizing log data.

Graylog simplifies log management, offering powerful search and analysis
capabilities through a straightforward interface.

![Screenshot of the Graylog architecture](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/78fa263a-0c5e-4b20-1022-54d08b67d200/lg1x
=439x438)

Now, let's explore how Graylog works.

### 1. Data ingestion through inputs

Graylog is good at handling various data types, including structured,
semi-structured, or unstructured logs. It supports multiple formats such as
JSON, RAW/Plaintext, Common Event Format (CEF), and RFC 5424 (Syslog).

Graylog uses 'inputs' to receive messages. These inputs are divided into two
categories: listener inputs and pull inputs. Listener inputs are set up to wait
for applications to send data over TCP or UDP. Common examples of listener
inputs are Syslog, Beats, and GELF inputs. Conversely, pull inputs actively
fetch data from a specific endpoint before forwarding it to Graylog. Examples of
pull inputs include GELF Kafka Input, Syslog AMQP Input, and AWS CloudTrail
Input.

### 2. Data indexing using Elasticsearch/OpenSearch

After receiving data, Graylog moves on to the indexing phase. For this purpose,
it uses Elasticsearch or OpenSearch (a fork of Elasticsearch with comparable
capabilities). Indexing effectively organizes the data into efficient data
structures, significantly improving retrieval speeds.

For searching data, users can access the Graylog web interface, which features a
search field with autocomplete functionality:

![Guide on searching log data in Graylog](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/c716a7c4-2467-4edd-c38b-dea8c7c00c00/lg1x
=1200x580)

The search syntax used in Graylog closely resembles the Lucene syntax. Here is
an example of a more complex search query:

```text
"ssh login" AND source:example.org
("ssh login" AND (source:example.org OR source:another.example.org)) OR _exists_:always_find_me
```

Once the indexing is complete, the next step in the Graylog process is to
visualize the data for extracting valuable insights.

### 3. Visualizing data with Graylog

Graylog offers a customizable interface for visualizing aggregated data. The
Graylog dashboard comprises widgets capable of displaying data through various
visualization methods such as bar charts, pie charts, area charts, line charts,
data tables, or scatterplots, among others:

![Screenshot of Graylog dashboard](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/d00c824c-941a-4a5d-0642-7226f31fe400/public
=1920x930)

### Graylog Pros:

- Graylog is free, making it an accessible option for users.
- It offers a more straightforward learning curve, with a single interface for
  data collection, searching, and visualization, unlike solutions like ELK,
  where each function requires a different tool.
- Includes a built-in parser for various log types, and users can easily create
  and immediately test their own parsing rules within the web interface against
  data in the database.
- A managed version is available with customer service.

### Graylog Cons:

- Primarily focused on log data, which may be limiting if you need to handle
  other kinds of data.
- The dashboard user interface is less refined than Kibana's.
- A smaller community compared to the Elastic Stack, which may result in fewer
  resources, less frequent updates, and limited support.

## Similarities between Graylog and Elastic Stack

Graylog and Elastic Stack display several similar characteristics:

- Both are accessible for free and provide managed versions. These managed
  versions are available through paid monthly plans and come with customer
  support.
- Both use Elasticsearch for indexing data. However, it's worth noting that
  Graylog is confined to using Elasticsearch version 7.x and now focuses solely
  on integrating with OpenSearch.
- Both platforms can index log data, perform searches, and offer visualization
  capabilities essential for thorough data analysis and gaining insights.

## Key things to note when choosing between Graylog and Elastic Stack

Lets now look at some of the differences between the tools:

When choosing between Graylog and Elastic Stack, it's essential to consider
their differences:

- The Elastic Stack is designed as a comprehensive big data solution capable of
  handling a wide range of data, including logs, metrics, and traces. Graylog,
  on the other hand, is primarily focused on log management, with its features
  tailored to streamline log analysis.
- Graylog offers a unified user interface for handling tasks such as data input,
  parsing, sorting, and visualization. In contrast, the Elastic Stack employs
  separate tools for each function.

- In Graylog, index sets can be created in the web UI without direct interaction
  with Elasticsearch. However, you need to engage with Elasticsearch directly
  for index management in the Elastic Stack.

- Graylog supports Elasticsearch up to version 7.10 but is restricted from using
  newer versions, such as Elasticsearch 8 or higher, due to licensing
  limitations. The Elastic Stack, however, is compatible with multiple versions
  of Elasticsearch, including the latest ones.

## Choosing between Graylog and Elastic/ELK stack

Choosing between Graylog and the Elastic (ELK) Stack largely depends on your
organization's specific needs and resources. Graylog stands out as a robust,
user-friendly option for log management, particularly suited if your focus is
primarily on log management. On the other hand, if your needs encompass more
than log management, such as handling monitoring data, including metrics, the
Elastic Stack offers a broader range of functionalities.

Setting up either Graylog or the Elastic Stack can be complex, and if you're
looking for a more straightforward solution, consider Better Stack. While Better
Stack might offer a partial range of functionalities of Graylog or the Elastic
Stack, it is a compelling alternative, especially for companies with limited
resources or those who want to avoid the complexities of setting up and managing
Elastic Stack or Graylog.

[Better Stack](https://betterstack.com/) provides features like real-time log monitoring, allowing you to
watch activities on your platform closely. This real-time display of incoming
logs can be crucial for timely responses to issues or tracking system
performance:

![Screenshot of Live Tail in Better Stack](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/58707aba-4716-4611-6da6-ea6bc9c30600/lg1x)

When you want to gain more insights, Better Stack enables you to create
customized dashboards:

<iframe src="https://telemetry.betterstack.com/dashboards/iP9roB" width="100%" height="400"></iframe>

Moreover, Better Stack offers the capability to set up custom alerts. This
feature ensures that you stay informed about critical events or anomalies in
your system. You can receive alerts through various channels like email, phone
calls, or webhooks, allowing immediate action in response to any detected
issues.

![Screenshot of Better Stack configured with the necessary options](https://imagedelivery.net/xZXo0QFi-1_4Zimer-T0XQ/a6c96c76-e31b-4e6e-849b-ca545dc3dd00/orig)

## Final thoughts

In conclusion, the choice between Graylog and the Elastic Stack depends on your
requirements. Graylog is ideal for those who need a user-friendly, efficient log
management system with an intuitive interface. It's perfect for straightforward
log management tasks.

Conversely, the Elastic Stack is better suited for broader data management
needs, including log management, data metrics, etc. It offers scalability and a
wide range of analytical tools with its Elasticsearch, Logstash, and Kibana
suite. If simplicity in log management is your priority, go for Graylog. If you
require a more versatile system with extensive capabilities, the Elastic Stack
is the more suitable choice.