Logs serve as the chat history of your application, failures, and everything in between.
Initially, these logs were simple plain text, often cumbersome and difficult to decode. However, as applications have grown in complexity, a more efficient way to interpret these logs has become necessary.
This is where structured logs come into play. Unlike chaotic plain text logs, structured logs organize information systematically, often using JSON format.
Now, let’s understand logs better, especially how to decode JSON logs. We’ll utilize a tool called the JSON Loki pipeline stage to simplify the process.
{
"@timestamp": "2024-02-27T14:13:13.714+00:00",
"@version": "1",
"message": "{\"access-time\":\"2024-02-25T14:13:13.697+0000\",\"account-code\":\"viewsynergy.abc.com\",\"remote-address\":\"49.207.192.81\",\"host\":\"gt-cougar-547c68c464-vz2qp\",\"guid\":\"f53a7273-3e58-4d38-bc7a-0d95edbbfbce\",\"thread\":\"http-nio-8080-exec-12\",\"user\":-2,\"is-static-resource\":false,\"url\":\"/v2/payroll-config/income-tax-agent/calculate/7/100\",\"session-id\":\"cce324dfae80d3cfdcb63766b004dba4\"}",
"logger_name": "com.abc",
"thread_name": "pool-1-thread-1",
"level": "INFO",
"level_value": 20000,
"consumer-id": "0"
}
Exploring the JSON Loki Pipeline Stage for Parsing JSON Logs
As we navigate the complex landscape of log management and observability, parsing and understanding log data become paramount. In this context, the JSON Loki pipeline stage emerges as a powerful tool for handling logs formatted in JSON (JavaScript Object Notation). Let’s delve into the significance of the JSON Loki pipeline stage and how it aids in unraveling the intricacies of JSON-structured logs.
Understanding JSON-Structured Logs:
In modern application development, JSON has become a prevalent format for structuring log data. JSON-structured logs provide a standardized way to organize information, presenting log entries as key-value pairs within a well-defined structure. Each log entry encapsulates essential details, facilitating easier interpretation and analysis.
The Role of the JSON Loki Pipeline Stage:
The JSON Loki pipeline stage serves as a key component in the Loki log aggregation system. Its primary function is to parse JSON-formatted log entries and extract specific fields for further indexing and querying. This parsing step is crucial for converting raw log data into a format that is amenable to advanced search, filtering, and visualization.
Expressions for Log Field Extraction:
Within the JSON Loki pipeline stage, expressions are employed to define the fields that need extraction from JSON logs. These expressions target key elements within the log structure, such as
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-
-
- log levels
- timestamps
- host information
- custom attributes
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- json:
expressions:
level: level
timestamp: timestamp
host: host
message: message
In this snippet, expressions like level, timestamp, host, and message are mapped to their corresponding JSON keys. This mapping ensures that these fields are extracted and made available for further analysis.
Handling Custom Attributes:
Beyond standard log fields, applications often include custom attributes specific to their domain. The JSON Loki pipeline stage allows for the extraction of these custom attributes, enabling a more comprehensive understanding of log entries. For instance:
- json:
expressions:
url: url
account_code: "account-code"
time_taken: "time-taken"
remote_address: "remote-address"
source: message
Here, custom attributes like url, account_code, time_taken, and remote_address are extracted from the message field within the log entry.
Enabling Effective Analysis:
By employing the JSON Loki pipeline stage, organizations can transform JSON logs into a format that aligns with their observability goals. The extracted fields become key components for constructing insightful dashboards, conducting detailed searches, and gaining a holistic view of application behavior.
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