The Log Lifecycle

Effective log management relies not only on collecting log data but also on efficiently storing and retrieving it for analysis. In Graylog, log processing follows a structured process from ingestion to storage and restoring. Whether deploying Graylog for security monitoring, troubleshooting, or operational insights, understanding the log lifecycle is essential for optimizing log retention, search performance, and system scalability.

In this article, we’ll explore Graylog’s storage tiers, data organization, and log storage, along with the complete log flow from ingestion and processing to indexing and retention.

Highlights

The following highlights provide a summary of the key takeaways from this article:

Graylog uses data tiers for log storage to balance performance, accessibility, and cost.
Logs are organized using fields, streams, and index sets to enhance searchability and management.
Graylog supports listener-based (Syslog, GELF, CEF, HTTP) and pull-based inputs, allowing flexible data collection.
Pipelines and pipeline rules enable message parsing, transformation, and enrichment before storage.
The Graylog web interface provides search, dashboards, widgets, alerts, and reporting for log insights.

Log Lifecycle at a Glance

Understanding Graylog's log lifecycle is crucial for effectively managing and analyzing log data within the platform. This process encompasses several stages, each playing a vital role in ensuring that log messages are accurately ingested, processed, stored, and made available for analysis.

Logs are created by various sources, including servers, applications, network devices, and security tools, and are collected using Graylog Forwarders, Sidecar-managed collectors, or directly via inputs.
Logs are received by Graylog via inputs (e.g. Syslog, GELF, Beats, JSON, raw TCP/UDP) and are parsed for structured processing.
Processing pipelines apply transformations, filtering, enrichment (via lookup tables), and routing rules before indexing.
Processed logs are stored in the Data Node for fast retrieval and searchability, allowing you to query logs via Graylog’s search interface, using full-text search, dashboards, and visualization tools for analysis.
Logs are retained based on index rotation policies, with older or lower-value logs stored in the Graylog Data Warehouse for compliance and historical analysis.

Log Ingestion

Graylog utilizes inputs to receive log messages from various sources. An input is a log ingestion entry point that receives log messages from various sources and protocols. It acts as the first step in the log processing pipeline, allowing Graylog to collect, parse, and route incoming log data.There are two main types of inputs: listener and pull-based inputs.

Some of the most common input types supported include:

Syslog Inputs: Graylog can accept and parse RFC 5424 and RFC 3164 compliant syslog messages over TCP or UDP. It is important to note that many devices, especially routers and firewalls, may not send RFC-compliant syslog messages, which can result in parsing issues. Forwarding messages through rsyslog or syslog-ng can help ensure proper parsing.

GELF Inputs: The Graylog Extended Log Format (GELF) is designed to overcome the limitations of traditional syslog. It supports optional compression, chunking, and a clearly defined structure, making it ideal for application layer logging. Graylog can receive GELF messages over UDP, TCP, or HTTP.

CEF Inputs: Graylog can ingest Common Event Format (CEF) messages via TCP or UDP. CEF is a standard format for the interoperability of security-related information. When setting up CEF inputs, you can configure parameters such as ports, bind addresses, TLS settings, and threading to optimize secure log processing.

Raw HTTP Inputs: This input allows Graylog to receive arbitrary log format messages over the HTTP protocol. It is useful for ingesting logs from sources that can send HTTP POST requests. The input listens for HTTP posts on the /raw path and can be configured with various parameters, including TLS settings for secure communication.

Each input can be customized with specific parameters such as bind address, port, and receive buffer size to optimize performance and ensure reliable data collection. Additionally, Graylog supports the use of queuing systems like Apache Kafka and RabbitMQ (AMQP) as transport layers for various inputs, enhancing scalability and reliability in log ingestion.

Log Processing

After logs are ingested, Graylog can transform, enrich, and route incoming log messages before they are indexed. Processing pipelines are a flexible rule-based system used to transform, enrich, and filter log messages before they are indexed. They allow you to define custom processing logic through pipeline rules, which can modify log content, extract key data, drop irrelevant messages, or route logs to specific streams.

Key components of this process include:

Pipelines: A pipeline is a sequence of stages, each containing rules that define the processing logic. Pipelines are attached to streams, and messages that flow through these streams are processed according to the pipeline's stages and rules.

Stages: Pipelines are composed of stages, each of which can contain one or more processing rules. Stages are executed sequentially in numerical order, allowing for structured and organized processing of log messages. All stages with the same priority run concurrently across all connected pipelines.

Pipeline Rules: These are programmatic actions applied to messages within a pipeline. Rules can perform tasks such as extracting fields, renaming fields, transforming data, and routing messages to different streams. They are written using a domain-specific language that provides flexibility in defining processing logic.

Functions: Within pipeline rules, functions are predefined methods that perform specific actions on log messages. Each function can take various parameters and return outputs that influence message processing. Functions are the building blocks of pipeline rules, enabling complex processing workflows.

Hint: Message decorators can also allow for modification of message fields without altering stored data.

Additionally, Graylog Illuminate provides prebuilt log parsing, normalization, security analytics, and dashboards for common log sources like Windows, Linux, firewalls, and cloud services. It simplifies log analysis, security monitoring, and compliance by automating field extraction, correlation, and alerting, all without requiring manual pipeline rule creation.

Data Organization

It is important to note that Graylog organizes log data into a structured framework using fields, streams, and index sets to enable efficient search, filtering, and storage. This process involves several key components:

Fields: When Graylog receives a log message, it parses the message into discrete elements known as fields. Each field represents a specific piece of data within the log, such as a timestamp, source IP address, or error code. Fields are assigned data types (e.g. string, Boolean, number) upon ingestion, which dictate how the data is stored and displayed. Administrators can manage field mappings to ensure that each field is appropriately typed, facilitating accurate searches and analysis.

Streams: Streams in Graylog function as dynamic filters that route incoming messages into categories based on defined rules. By evaluating message content, streams enable the segregation of log data according to criteria such as source, severity, or specific keywords. This categorization allows for targeted processing, storage, and analysis of log subsets, enhancing the system's ability to manage large volumes of data efficiently.

Index Sets: An index set in Graylog defines how and where messages are stored within the search backend. Each index set includes configurations for parameters like index rotation strategy, retention period, and the number of shards and replicas. By assigning different streams to specific index sets, Graylog ensures that messages are stored in a manner that aligns with organizational requirements for performance, retention, and resource allocation.

Search and Analysis

The search and analysis stage in Graylog is where collected and processed log data becomes actionable. As logs move through their lifecycle—from ingestion to processing, storage, and retention—the ability to search, filter, and analyze them efficiently is crucial for security monitoring, troubleshooting, and compliance. The Graylog interface provides various tools to interact with and analyze log data:

Search: Users can perform detailed searches across log data using a search syntax that is similar to Lucene syntax. This search syntax allows for filtering and pinpointing specific log entries based on various attributes such as timestamps, log sources, and custom fields. Graylog's search query also allows advanced searches using Boolean operators, regular expressions (regex), and range queries to refine results efficiently. Additionally, users can create saved searches for commonly used queries, streamlining log analysis.
Dashboards: Graylog allows users to build custom dashboards using a combination of widgets that display log data visually. These widgets include graphs, tables, and statistical charts that provide insights into system performance, security incidents, or operational metrics. Dashboards can be shared among users and teams and configured to auto-refresh, ensuring real-time monitoring of critical events.
Widgets: Widgets are visual components used to display and interpret data sets. For example, a widget which displays the number of failed login attempts in the past 24 hours. Additionally, widgets can be used to create dashboards
Streams: Streams enable users to categorize logs dynamically based on pre-defined conditions, allowing for better organization and faster analysis. These categorized logs can then trigger alert notifications when specific conditions are met, such as security threats, system failures, or abnormal behaviors.
Alerts: Alerts are configurable notifications set up to inform you when pre-defined event conditions are triggered. For example, you can setup an alert to notify admins when there are multiple failed SSH login attempts from the same IP. Alerts can be configured to send notifications via email, Slack, PagerDuty, or webhook integrations, ensuring timely responses to critical events.
Message Analysis: The web interface provides message details, allowing users to inspect individual log entries in-depth. This includes viewing parsed fields, associated metadata, and extracted values.
Log Exporting and Reporting: Graylog enables users to export search results in formats such as CSV, JSON, or XML for further offline analysis. Organizations can also configure automated reports, summarizing key metrics and trends over specified time ranges, making compliance audits and operational reviews more efficient.

Data Tiering

Data tiering is a storage optimization strategy that categorizes log data into different tiers based on access frequency, retention needs, and cost efficiency. This approach balances performance and cost for long-term log retention and analysis. Graylog offers three storage tiers:

Hot Tier: Data in the hot tier is easy to access and search, but operating costs are generally higher because of the resources that must be allocated to maintain it.

Warm Tier: Data in the warm tier is searchable, but search performance is lower compared to the hot tier. Warm data is stored in searchable snapshots and not directly in index sets.
Data Warehouse: The Data Warehouse is a scalable, high-performance storage solution that enables long-term log retention and efficient historical data analysis by offloading logs from the primary search index to cost-effective storage.

Hint: Currently, you can utilize both Data Warehouses and archives to preserve your log data long term as both features perform similar functions; however, there are some benefits to utilizing a Data Warehouse for less immediately valuable data. Retrieving logs from a Data Warehouse is a faster process as log retrieval is granular. Additionally, the data in a Data Warehouse is compressed, so it is generally a lower cost option for data storage.