Json to yaml converter linux

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To convert JSON to YAML on Linux, here are the detailed steps, providing you with a robust “json to yaml converter linux” solution. This process is essential for streamlining configuration management, especially in DevOps and infrastructure-as-code environments. Whether you need a “json to yaml example” for a quick conversion or a deep dive into “json to yaml command line” options, this guide has you covered.

Using yq (Recommended)

yq is a lightweight and portable command-line YAML processor. It’s often referred to as “jq for YAML” because it offers similar powerful parsing capabilities.

  1. Installation:
    • Snap (recommended for quick setup): 
      sudo snap install yq
    • Homebrew (on Linux with Homebrew installed): 
      brew install yq
    • Manual Download (for specific versions or systems without package managers):
      • Visit the yq GitHub releases page: https://github.com/mikefarah/yq/releases
      • Download the appropriate binary for your Linux architecture (e.g., yq_linux_amd64).
      • Make it executable and move it to your PATH: 
        wget https://github.com/mikefarah/yq/releases/latest/download/yq_linux_amd64 -O /usr/local/bin/yq
chmod +x /usr/local/bin/yq
  2. Conversion:
    • From a JSON file: 
      yq -P -o yaml your_file.json > your_file.yaml
      • -P: Pretty print JSON (important for proper parsing).
      • -o yaml: Specify YAML as the output format.
    • From standard input (piping): 
      cat your_file.json | yq -P -o yaml > your_file.yaml

      or directly 

      echo '{"name": "John", "age": 30}' | yq -P -o yaml

      This will output:

      0.0
      0.0 out of 5 stars (based on 0 reviews)
      Excellent0%
      Very good0%
      Average0%
      Poor0%
      Terrible0%

      There are no reviews yet. Be the first one to write one.

      ​
      		Amazon.com: Check Amazon for Json to yaml
      Latest Discussions & Reviews:
      		 
      name: John
age: 30

Using jq and yaml-cli (Alternative)

This method involves using jq to process JSON and then piping its output to a YAML converter like yaml-cli.

  1. Install jq: 
    sudo apt-get install jq   # Debian/Ubuntu
sudo yum install jq       # CentOS/RHEL
sudo dnf install jq       # Fedora
  2. Install yaml-cli (requires Node.js and npm):
    • First, ensure Node.js and npm are installed. If not, follow instructions from nodejs.org or your distro’s package manager.
    • Then install yaml-cli globally: 
      sudo npm install -g yaml-cli
  3. Conversion: 
    cat your_file.json | jq '.' | yaml > your_file.yaml
    • jq '.': Simply pretty-prints the JSON. jq is used here primarily for its robust JSON parsing, ensuring valid JSON before piping.
    • yaml: The yaml-cli command which converts JSON (or JavaScript objects) to YAML.

These methods provide efficient and reliable ways to “convert json to yaml” on your Linux system, covering various scenarios from simple command-line operations to integrating with shell scripts.

Table of Contents

Understanding JSON and YAML: The Why Behind the Conversion

JSON (JavaScript Object Notation) and YAML (YAML Ain’t Markup Language) are both popular data serialization formats, widely used in web applications, configuration files, and data exchange. While they serve similar purposes, their design philosophies and readability aspects differ significantly. Understanding these differences and why you might convert between them is crucial for effective data management, especially in a Linux environment where configuration is king.

JSON: The Web’s Lingua Franca for Data Exchange

JSON rose to prominence as a lightweight data-interchange format. It’s built on two structures: a collection of name/value pairs (objects) and an ordered list of values (arrays). Its simplicity and direct mapping to JavaScript objects made it the de facto standard for APIs and web services.

  • Syntax: JSON is easily identifiable by its curly braces {} for objects, square brackets [] for arrays, and comma-separated key-value pairs. All keys must be strings enclosed in double quotes.
  • Strengths:
    • Ubiquitous: Nearly every programming language has robust JSON parsers. This makes it incredibly versatile for data exchange across diverse systems.
    • Strict and Predictable: Its rigid syntax makes it easy for machines to parse, reducing ambiguity.
    • Fast Parsing: Due to its strictness, JSON parsers are often highly optimized for speed.
  • Weaknesses:
    • Human Readability: For complex configurations, JSON can become verbose and difficult to read, especially with nested structures and extensive use of quotes and commas. Comments are not natively supported, which can hinder understanding.
    • Configuration Files: While used, it’s not always ideal for human-edited configuration files due to verbosity.

YAML: Configuration for Humans

YAML was designed with human readability in mind, making it an excellent choice for configuration files, infrastructure-as-code definitions (like Kubernetes, Ansible, Docker Compose), and data serialization where humans are likely to edit the content.

  • Syntax: YAML relies heavily on indentation to define structure, avoiding most of the punctuation found in JSON. It uses colons : for key-value pairs, hyphens - for list items, and allows for comments using #.
  • Strengths:
    • Human Readability: This is YAML’s strongest suit. Its minimalist syntax and indentation-based structure make it very easy to read and understand, even for complex hierarchies.
    • Comments: Support for comments (#) is invaluable for documenting configuration logic directly within the file.
    • Data Types: Supports various data types explicitly, including integers, floats, booleans, strings, and null.
    • Anchors & Aliases: Advanced features like anchors (&) and aliases (*) allow for reusing data chunks within a document, reducing redundancy and improving maintainability for large configurations.
  • Weaknesses:
    • Whitespace Sensitivity: Its reliance on indentation can be a double-edged sword. Incorrect indentation can lead to parsing errors that are sometimes hard to debug.
    • Parsing Complexity: Parsing YAML can be slightly more complex for machines compared to JSON due to its flexible syntax and optional features.
    • Security Concerns: Due to its flexibility and ability to represent complex data structures, improper parsing of untrusted YAML can pose security risks (e.g., arbitrary code execution if not handled carefully in certain programming languages). Always ensure your YAML source is trusted, especially in automated systems.

Why Convert JSON to YAML?

The primary reasons for converting JSON to YAML, especially on Linux, often revolve around configuration management and human interaction:

  1. Improved Readability for Configuration: Many modern tools, particularly in the DevOps and cloud native space (e.g., Kubernetes manifests, Ansible playbooks, Docker Compose files), heavily favor YAML for their configuration. Converting a JSON output from an API or a script into a YAML file makes it instantly more digestible and editable for human operators. Imagine debugging a 500-line JSON file versus a 500-line YAML file – the latter is significantly easier.
  2. Leveraging YAML-Specific Features: While JSON is a subset of YAML (meaning valid JSON is also valid YAML), converting allows you to then leverage YAML’s more advanced features like comments, anchors, and aliases. This can simplify complex configurations and add essential documentation.
  3. Tool Compatibility: When integrating data from JSON-based APIs or legacy systems into YAML-driven tools, conversion becomes a necessity. For instance, if a monitoring system exports its configuration in JSON, but your automation framework (like Ansible) expects YAML, a conversion step is vital.
  4. Templating and Automation: In automation scripts, generating configuration often starts with structured data, which might be easier to manipulate as JSON programmatically. However, the final output for deployment might need to be in YAML format for compatibility with the target system. Tools like yq are perfectly suited for these “json to yaml command line” scenarios.
  5. Documentation: Converting data from JSON to YAML can make it more suitable for documentation purposes, as the clean, indentation-based structure is easier to include in reports or READMEs.

In essence, while JSON excels at machine-to-machine communication, YAML steps in when human understanding and ease of modification become paramount. The Linux command line, with powerful utilities like yq, provides the perfect environment for these transformations. Html escape forward slash

Essential Linux Tools for JSON to YAML Conversion

When it comes to transforming data on the Linux command line, flexibility and power are paramount. For “json to yaml converter linux” tasks, several tools stand out, each with its own strengths and ideal use cases. While our online tool is convenient for quick, client-side conversions, understanding the command-line alternatives is crucial for automation, scripting, and large-scale data processing.

1. yq: The Go-to Tool for YAML Manipulation

If you’re dealing with YAML files on Linux, yq (by Mike Farah) is arguably the most powerful and versatile tool available. It’s often called “jq for YAML” because it offers a similar, flexible query language for manipulating YAML, but it also handles JSON seamlessly, making it a fantastic “json to yaml command line” utility.

  • Installation:
    • Snap (recommended): sudo snap install yq
    • Homebrew (on Linux): brew install yq
    • Manual Download: Grab the appropriate binary from the yq GitHub releases page and place it in your PATH (e.g., /usr/local/bin). 
      # Example for AMD64
wget https://github.com/mikefarah/yq/releases/latest/download/yq_linux_amd64 -O /usr/local/bin/yq
chmod +x /usr/local/bin/yq
  • Key Features for JSON to YAML Conversion:
    • Direct Conversion: yq can directly parse JSON and output YAML without any intermediate steps.
    • Format Specification: Use -o yaml or --output-format yaml to explicitly set the output to YAML.
    • Pretty Printing: The -P flag (--prettyPrint) is essential when converting JSON, as it ensures yq parses it correctly as JSON before converting.
    • In-Place Editing: While not directly for JSON to YAML, yq -i allows for in-place modifications, which can be useful for subsequent YAML operations.
    • Path Expressions: Like jq, yq supports powerful path expressions (e.g., .data.field) to extract or modify specific parts of the data, which is invaluable for complex transformations.
  • “json to yaml example” with yq: 
    # Convert a JSON file to YAML
yq -P -o yaml input.json > output.yaml

# Convert JSON from stdin
echo '{"name": "Alice", "details": {"age": 30, "city": "NYC"}}' | yq -P -o yaml

    Output: 

    name: Alice
details:
  age: 30
  city: NYC
  • Why yq is Preferred: Its single-tool approach for both JSON and YAML, powerful querying, and excellent cross-platform support make it the first choice for most command-line data transformations.

2. jq and yaml-cli (or other Node.js-based converters)

This approach leverages the strengths of two specialized tools: jq for robust JSON processing and a Node.js-based utility for the actual JSON-to-YAML conversion. This is a common pattern in shell scripting: piping the output of one command as input to another.

  • jq (JSON Query Processor):
    • Installation: sudo apt install jq (Debian/Ubuntu), sudo yum install jq (RHEL/CentOS), sudo dnf install jq (Fedora).
    • Purpose: jq is a command-line JSON processor. It can slice, filter, map, and transform structured data with ease. While it doesn’t output YAML natively, it ensures your JSON input is valid and can be formatted cleanly before being passed to a YAML converter.
  • yaml-cli (Node.js YAML Converter):
    • Installation: Requires Node.js and npm. Once installed, sudo npm install -g yaml-cli.
    • Purpose: yaml-cli is a simple command-line tool that can convert JSON (or JavaScript objects) from standard input to YAML.
  • “json to yaml example” with jq and yaml-cli: 
    # Convert a JSON file
cat input.json | jq '.' | yaml > output.yaml

# Convert JSON string
echo '{"product": {"id": 123, "item": "widget"}}' | jq '.' | yaml

    Output: Svg free online editor 

    product:
  id: 123
  item: widget
  • Considerations: This method introduces a dependency on Node.js and npm, which might be overkill if yq alone suffices. However, if you already have Node.js in your environment or need jq for complex JSON manipulations beforehand, it’s a perfectly viable “json to yaml converter linux” strategy.

3. Python with json and pyyaml Libraries

Python is a ubiquitous language in the Linux ecosystem, and its powerful libraries make it an excellent choice for scripting complex data transformations. This method is particularly useful when the conversion is part of a larger script that might involve more advanced logic, API calls, or data validation.

  • Prerequisites: Python 3 (usually pre-installed on Linux) and the pyyaml library.
  • Installation of pyyaml: 
    pip install pyyaml
  • Python Script Example (convert.py): 
    import json
import yaml
import sys

def json_to_yaml(json_input_str):
    try:
        # Load JSON data
        data = json.loads(json_input_str)
        # Dump to YAML with a 2-space indent for readability
        yaml_output = yaml.dump(data, indent=2, default_flow_style=False)
        return yaml_output
    except json.JSONDecodeError as e:
        return f"Error: Invalid JSON input. {e}"
    except Exception as e:
        return f"An unexpected error occurred: {e}"

if __name__ == "__main__":
    if len(sys.argv) > 1:
        # Read from file if filename provided
        input_file = sys.argv[1]
        try:
            with open(input_file, 'r') as f:
                json_data = f.read()
        except FileNotFoundError:
            sys.exit(f"Error: File not found at '{input_file}'")
        except Exception as e:
            sys.exit(f"Error reading file '{input_file}': {e}")
    else:
        # Read from stdin if no filename provided
        json_data = sys.stdin.read()

    yaml_output = json_to_yaml(json_data)
    print(yaml_output)
  • Running the Python Script: 
    # Convert from a file
python3 convert.py input.json > output.yaml

# Convert from stdin
echo '{"config": {"debug": true, "log_level": "info"}}' | python3 convert.py
  • Advantages:
    • Flexibility: Allows for complex pre-processing or post-processing of data within the same script.
    • Error Handling: Can include more sophisticated error handling and logging.
    • No External Binaries: Relies only on Python and its packages, often simplifying deployment in Python-heavy environments.
  • Disadvantages: Requires writing and maintaining a script for a task that yq handles in a single command. More overhead for simple, one-off conversions.

Choosing the right “json to yaml converter linux” tool depends on your specific needs: yq for general-purpose, efficient command-line operations; jq + yaml-cli for situations where jq‘s powerful JSON processing is already in play; and Python for more intricate scripting scenarios.

Advanced yq Techniques for Complex Conversions

While simple “json to yaml command line” conversions are straightforward, yq truly shines when dealing with more complex data transformations. Its powerful query language allows you to filter, modify, and restructure JSON (and YAML) data before outputting it as YAML. This is incredibly valuable for tasks like extracting specific configuration blocks, sanitizing sensitive information, or merging multiple data sources.

1. Extracting Specific Data and Converting

Often, you don’t need to convert the entire JSON document; you only need a specific section. yq‘s path expressions (similar to jq) let you zero in on what matters.

  • Scenario: You have a large JSON file containing various configurations, but you only need the database section to be converted to YAML. Empty lines in markdown

  • JSON Example (config.json): 

    {
  "app": {
    "name": "MyApp",
    "version": "1.0",
    "environment": "production"
  },
  "database": {
    "type": "PostgreSQL",
    "host": "db.example.com",
    "port": 5432,
    "username": "admin",
    "connections": 100
  },
  "logging": {
    "level": "INFO",
    "path": "/var/log/myapp.log"
  }
}

  • Command: 

    yq -P -o yaml '.database' config.json

  • Output (database.yaml): 

    type: PostgreSQL
host: db.example.com
port: 5432
username: admin
connections: 100

    This command tells yq to parse config.json (as JSON due to -P), select only the .database key, and then output it as YAML.

2. Modifying Data During Conversion

What if you need to convert to YAML but also change a value or add a new field on the fly? yq‘s in-place modification capabilities (or direct piping with modification) are perfect for this. Empty line in python

  • Scenario: You have a JSON API response, and you want to convert it to YAML for a configuration file. However, you need to set a specific enabled flag to true and change a count value.

  • JSON Example (api_response.json): 

    {
  "service": {
    "name": "AnalyticsService",
    "status": "active",
    "enabled": false,
    "metrics": {
      "interval": "60s",
      "count": 50
    }
  }
}

  • Command: 

    yq -P -o yaml '.service.enabled = true | .service.metrics.count = 100' api_response.json

  • Output: 

    service:
  name: AnalyticsService
  status: active
  enabled: true
  metrics:
    interval: 60s
    count: 100

    Here, . is the current context. We set enabled to true and count to 100 before the final YAML output. The pipe | chains operations. Empty line regex

3. Handling Arrays and Iterating Over Elements

Processing arrays is a common requirement. yq can iterate over array elements, apply transformations, and then output the result as YAML.

  • Scenario: Convert a JSON list of users to a YAML list, but only include specific fields.

  • JSON Example (users.json): 

    [
  {
    "id": 1,
    "name": "User A",
    "email": "[email protected]",
    "role": "admin"
  },
  {
    "id": 2,
    "name": "User B",
    "email": "[email protected]",
    "role": "user"
  }
]

  • Command: 

    yq -P -o yaml 'map({id: .id, username: .name})' users.json
    • map(...): This function iterates over each element in the array.
    • {id: .id, username: .name}: For each element, it constructs a new object containing only the id and name (renamed to username).

  • Output: Install zabbix sender 

    - id: 1
  username: User A
- id: 2
  username: User B

4. Merging JSON/YAML Documents

yq can also merge multiple JSON or YAML files, which is incredibly powerful for building complex configurations from modular components.

  • Scenario: You have a base JSON configuration and a JSON overlay for environment-specific settings. You want to merge them and output as YAML.

  • JSON Example (base.json): 

    {
  "api": {
    "url": "https://api.example.com/v1",
    "timeout": 30
  },
  "features": {
    "logging": true
  }
}

  • JSON Example (production_override.json): 

    {
  "api": {
    "timeout": 60
  },
  "features": {
    "monitoring": true
  }
}

  • Command (using yq eval-all for multiple documents): Json.stringify examples 

    yq eval-all 'select(fileIndex == 0) * select(fileIndex == 1)' base.json production_override.json -o yaml -P
    • eval-all: Processes multiple input files.
    • select(fileIndex == 0) * select(fileIndex == 1): This is the merge operation. * performs a deep merge, with later documents overriding earlier ones for conflicting keys.
    • -o yaml -P: Output the merged result as YAML, ensuring JSON is parsed correctly.

  • Output: 

    api:
  url: https://api.example.com/v1
  timeout: 60
features:
  logging: true
  monitoring: true

    Notice how timeout was overridden and monitoring was added.

These advanced yq techniques demonstrate why it’s such an invaluable “json to yaml converter linux” tool, extending far beyond simple format conversion to robust data manipulation and automation. Mastering these will significantly enhance your ability to manage configurations and data pipelines efficiently.

Scripting and Automation with JSON to YAML Conversion

The true power of command-line tools like yq comes to light when integrated into scripts for automation. In Linux environments, automating “json to yaml converter linux” tasks is crucial for DevOps pipelines, configuration management, and general system administration. This section will walk you through common scripting scenarios and best practices for robust automation.

1. Basic Conversion within a Shell Script

The simplest form of automation involves calling yq directly within a shell script. Text truncate not working

  • Scenario: You have a directory full of .json configuration files that need to be converted to .yaml for a deployment tool.

  • Script Example (convert_configs.sh): 

    #!/bin/bash

INPUT_DIR="./json_configs"
OUTPUT_DIR="./yaml_configs"

# Create output directory if it doesn't exist
mkdir -p "$OUTPUT_DIR"

echo "Converting JSON files in '$INPUT_DIR' to YAML in '$OUTPUT_DIR'..."

# Loop through all .json files in the input directory
for json_file in "$INPUT_DIR"/*.json; do
    # Check if any .json files were found
    if [[ -e "$json_file" ]]; then
        # Extract filename without extension
        filename=$(basename -- "$json_file")
        filename_no_ext="${filename%.*}"

        output_file="$OUTPUT_DIR/${filename_no_ext}.yaml"

        echo "Converting '$json_file' to '$output_file'..."
        if yq -P -o yaml "$json_file" > "$output_file"; then
            echo "Successfully converted."
        else
            echo "Error converting '$json_file'. Skipping."
            # Optionally, you might want to exit or log the error
            exit 1
        fi
    fi
done

echo "Conversion complete."

  • Usage:

    1. Create json_configs directory and put some .json files in it.
    2. Make the script executable: chmod +x convert_configs.sh
    3. Run the script: ./convert_configs.sh

This script demonstrates a basic loop, file manipulation, and error handling for “json to yaml converter linux” operations.

2. Converting API Responses to YAML for Further Processing

Many APIs return data in JSON. You might want to convert this to YAML for easier human review or to feed into a YAML-aware tool. Ai voice changer online free female

  • Scenario: Fetch user data from an API and save it as a YAML configuration.

  • Script Example (fetch_user_config.sh): 

    #!/bin/bash

API_URL="https://api.example.com/v1/users/config/123" # Replace with your actual API endpoint
OUTPUT_FILE="user_config.yaml"
API_KEY="your_secret_api_key" # Be cautious with hardcoding secrets! Use environment variables or a secure vault.

echo "Fetching user configuration from $API_URL..."

# Fetch JSON data using curl, ensure it's valid, then convert to YAML
if curl -s -H "Authorization: Bearer $API_KEY" "$API_URL" | yq -P -o yaml > "$OUTPUT_FILE"; then
    echo "Successfully fetched and converted user configuration to '$OUTPUT_FILE'."
    cat "$OUTPUT_FILE" # Display the converted YAML
else
    echo "Error: Failed to fetch or convert user configuration."
    exit 1
fi

  • Best Practices for API Keys: Never hardcode API keys directly in scripts that might be shared or committed to version control.

    • Use environment variables: API_KEY=$MY_API_KEY ./script.sh
    • Use a secrets management tool (e.g., HashiCorp Vault, AWS Secrets Manager).

3. Integrating with Configuration Management Tools

Tools like Ansible often require variables or facts in YAML format. You can use “json to yaml command line” tools to generate these from dynamic sources.

  • Scenario: A monitoring system exports its current status as JSON. You want to convert this into an Ansible variable file (.yml) to use in a playbook that adjusts configurations based on status. Ai voice editor online free

  • JSON Status Example (monitor_status.json): 

    {
  "system_health": "good",
  "disk_usage": "70%",
  "service_status": {
    "web_server": "running",
    "database": "running",
    "cache": "stopped"
  }
}

  • Script Segment within an Ansible Pre-Task (or a standalone script): 

    # This might be run as a local_action or shell task in Ansible
# Or as a pre-build step in a CI/CD pipeline

# Assuming monitor_status.json is available
yq -P -o yaml '. | {"monitor_facts": .}' monitor_status.json > /path/to/ansible/roles/my_role/vars/monitor_facts.yml
    • . | {"monitor_facts": .}: This yq expression wraps the entire JSON content under a top-level monitor_facts key in the YAML output. This is a common pattern for Ansible variable files.

  • Resulting monitor_facts.yml: 

    monitor_facts:
  system_health: good
  disk_usage: 70%
  service_status:
    web_server: running
    database: running
    cache: stopped

    Now, in your Ansible playbook, you can access these facts as {{ monitor_facts.system_health }}.

4. Robustness and Error Handling

When automating, anticipating errors is key. Is ipv6 hexadecimal

  • Check yq Exit Code: The if statements in the examples check the exit status of yq. A non-zero exit code usually indicates an error (e.g., malformed JSON input).
  • Input Validation: For critical automation, consider adding checks for input file existence ([[ -e "$json_file" ]]) or basic JSON validity before calling yq.
  • Logging: Redirect stderr to a log file (yq ... 2>> error.log) for debugging automated runs.
  • Temporary Files: For complex transformations, use mktemp to create secure temporary files to avoid race conditions or permission issues.

By leveraging yq (or similar tools) within shell scripts, you can build powerful and flexible automation workflows that seamlessly integrate “json to yaml converter linux” functionality into your daily operations. This approach is highly effective for managing configurations, processing data, and orchestrating complex deployments.

Common Pitfalls and Troubleshooting JSON to YAML Conversion

While “json to yaml converter linux” tools are powerful, you might encounter issues, especially with malformed input or unexpected data structures. Understanding common pitfalls and how to troubleshoot them is crucial for smooth automation and conversion.

1. Invalid JSON Input

This is by far the most common problem. If your JSON input is not well-formed, any converter will fail.

  • Signs: Error messages like Error: Invalid JSON format, jq: parse error, yq: Error: Did not find a valid JSON object or array.
  • Causes:
    • Missing or Extra Commas: JSON is strict about commas separating elements.
    • Unquoted Keys or Values: All JSON keys must be double-quoted. String values must also be double-quoted.
    • Incorrect Brackets/Braces: Mismatched {}, [] characters.
    • Trailing Commas: While some JavaScript engines tolerate them, standard JSON does not allow trailing commas (e.g., [1, 2, 3,]).
    • Comments: JSON does not support comments. If your input has // or /* */, it’s not valid JSON.
    • Single Quotes: JSON strictly requires double quotes for strings.
  • Troubleshooting:
    • Use jq to validate: Even if you’re primarily using yq, jq is excellent for JSON validation. 
      jq . your_file.json

      If it’s invalid, jq will output an error and usually point to the line number and character offset of the syntax error.

    • Use an Online JSON Validator: Copy and paste your JSON into an online validator (e.g., jsonlint.com) to quickly identify syntax errors with visual feedback.
    • Text Editor with JSON Linting: Many modern text editors (VS Code, Sublime Text, Atom) have built-in JSON formatters and linters that highlight syntax errors as you type.

2. Character Encoding Issues

Sometimes, non-ASCII characters or strange symbols can cause problems, especially if the source encoding isn’t properly handled.

  • Signs: Unexpected characters in output, parsing errors for seemingly valid JSON.
  • Causes: JSON is typically UTF-8. If your input file uses a different encoding (e.g., ISO-8859-1) and the converter expects UTF-8, you might see issues.
  • Troubleshooting:
    • Check File Encoding: Use file -i your_file.json to inspect the encoding.
    • Convert Encoding (if necessary): Use iconv to convert the file to UTF-8 before processing: 
      iconv -f ISO-8859-1 -t UTF-8 your_file_latin1.json | yq -P -o yaml > output.yaml
    • Ensure Terminal Encoding: Make sure your terminal is set to UTF-8 (most modern Linux systems default to this).

3. yq or Tool Not Found

This is a common issue when first setting up your “json to yaml converter linux” environment. Ai urdu voice generator free online download

  • Signs: command not found: yq, yq: command not found.
  • Causes:
    • The tool (e.g., yq, jq, yaml) is not installed.
    • The tool is installed, but its executable path is not in your system’s PATH environment variable.
  • Troubleshooting:
    • Verify Installation: Re-run the installation command for the specific tool.
    • Check PATH: 
      echo $PATH

      Then, check if the directory where the tool is installed (e.g., /usr/local/bin, ~/.local/bin, or the npm global bin path) is in your PATH.

    • Relocate or Link: If you manually downloaded a binary, ensure it’s in a directory listed in PATH or create a symbolic link from a PATH directory to its location. 
      sudo mv ~/Downloads/yq_linux_amd64 /usr/local/bin/yq
sudo chmod +x /usr/local/bin/yq
    • Restart Shell: Sometimes, changes to PATH require a new shell session to take effect.

4. Unexpected YAML Output Formatting

While the conversion might succeed, the YAML output might not be exactly what you expected (e.g., single-line strings, weird indentation).

  • Signs: Strings are quoted when you don’t want them, lists are on a single line, incorrect indentation.
  • Causes:
    • Missing yq -P (for JSON input): If you’re feeding JSON to yq and omit -P, yq might try to parse it as YAML, leading to incorrect parsing.
    • yq Output Options: yq has various output options that control formatting.
    • Scalar Types: YAML might quote strings if they could be misinterpreted as other data types (e.g., yes, no, numbers).
  • Troubleshooting (yq specific):
    • Always use -P for JSON input: This tells yq to parse the input as JSON first.
    • Control Indentation: yq uses 2 spaces by default. If you need 4, you’d typically configure it via yq configuration, though for basic conversion, it’s usually fine.
    • --prettyPrint (for yq output): Ensures a more readable output format.
    • Check Scalar Flow Styles: For specific string quoting or list display, you might need more advanced yq expressions or configuration if default output is insufficient. For instance, default_flow_style=False in PyYAML forces block style for collections.

5. Large File Performance Issues

For extremely large JSON files (hundreds of MBs or GBs), direct command-line processing might be slow or consume significant memory.

  • Signs: Commands take a long time, system becomes unresponsive, Out of memory errors.
  • Causes: Loading the entire file into memory before processing.
  • Troubleshooting:
    • Stream Processing (if applicable): Some tools are designed for stream processing, but yq and jq typically load into memory.
    • Break Down Files: If possible, split the large JSON into smaller, manageable chunks, process them individually, and then combine the resulting YAML files if necessary.
    • Use a Scripting Language: For very large files, a Python script with efficient I/O operations might be more memory-friendly than a pure shell command if not optimized for streaming.

By being aware of these common pitfalls and applying the recommended troubleshooting steps, you can ensure your “json to yaml converter linux” operations are robust and efficient, saving you time and frustration in your automation workflows.

Practical JSON to YAML Use Cases in DevOps and IT

The ability to “convert json to yaml” on Linux isn’t just a technical exercise; it’s a fundamental capability that underpins many modern DevOps practices and IT operations. From configuring infrastructure to managing applications, the conversion between these two data formats streamlines workflows, improves readability, and facilitates automation.

1. Kubernetes Manifest Generation

Kubernetes, the de facto standard for container orchestration, uses YAML for all its resource definitions (Deployments, Services, Pods, etc.). While you might interact with Kubernetes APIs that return JSON, or have scripts that generate JSON data, the final manifest files must be in YAML. How to rephrase sentences online

  • Scenario: A script generates dynamic application configurations (e.g., environment variables, resource limits) as JSON based on internal logic or external data sources. This JSON needs to be integrated into a Kubernetes ConfigMap or Deployment manifest.

  • Example:

    • JSON output from a script: 
      {
  "APP_NAME": "my-service",
  "API_ENDPOINT": "http://backend-api:8080/v1",
  "DEBUG_MODE": "false"
}
    • Converting and integrating into a ConfigMap: 
      # Assume dynamic_config.json contains the JSON above
# Create a ConfigMap structure in YAML, embedding the converted JSON
yq -P -o yaml '
  .data = (. | to_json) # Convert entire JSON to a single string value for 'data' key
  | .metadata.name = "my-app-config"
  | .apiVersion = "v1"
  | .kind = "ConfigMap"
' dynamic_config.json > my-app-configmap.yaml

      Correction: For ConfigMap data, each key-value pair of the JSON becomes a separate key-value pair in the YAML data section.
      A better approach would be: 

      # If your JSON keys are simple strings and values are strings:
# Example JSON: {"APP_NAME": "my-service", "API_ENDPOINT": "http://backend-api:8080/v1"}
# Command:
echo '{"APP_NAME": "my-service","API_ENDPOINT": "http://backend-api:8080/v1"}' | \
yq -P -o yaml '
  .metadata.name = "my-app-config"
  | .apiVersion = "v1"
  | .kind = "ConfigMap"
  | .data = .
' - > my-app-configmap.yaml

      This creates: 

      apiVersion: v1
kind: ConfigMap
metadata:
  name: my-app-config
data:
  APP_NAME: my-service
  API_ENDPOINT: http://backend-api:8080/v1

    This seamless conversion allows dynamic data to be injected into static YAML templates, enabling powerful GitOps workflows. Change delimiter in excel mac

2. Ansible Playbooks and Inventories

Ansible, a popular automation engine, relies heavily on YAML for its playbooks, roles, and inventory files. When integrating Ansible with other systems that expose data in JSON (e.g., cloud provider APIs, CMDBs), converting JSON to YAML is a common task.

  • Scenario: You fetch a list of EC2 instances from AWS using the AWS CLI (which outputs JSON), and you want to convert this into a dynamic Ansible inventory file (YAML format) to manage those instances.

  • Example (get_aws_inventory.sh): 

    #!/bin/bash

# Fetch EC2 instance data (simplified JSON output for example)
# In reality, this JSON would be much larger and require filtering with 'jq'
# aws ec2 describe-instances --query 'Reservations[*].Instances[*].[InstanceId, PublicIpAddress, Tags[?Key==`Name`].Value | [0]]' --output json
DUMMY_AWS_JSON='[
  {"id": "i-0123456789abcdef0", "ip": "54.1.2.3", "name": "web-server-01"},
  {"id": "i-0FEDCBA9876543210", "ip": "54.4.5.6", "name": "db-server-01"}
]'

echo "$DUMMY_AWS_JSON" | yq -P -o yaml '
  . | {
      all: {
          hosts: (
              .[] | {
                  (.name): {
                      ansible_host: .ip,
                      instance_id: .id
                  }
              }
          )
      }
  }
' - > dynamic_aws_inventory.yml

  • Resulting dynamic_aws_inventory.yml: 

    all:
  hosts:
    web-server-01:
      ansible_host: 54.1.2.3
      instance_id: i-0123456789abcdef0
    db-server-01:
      ansible_host: 54.4.5.6
      instance_id: i-0FEDCBA9876543210

    This allows Ansible to use the dynamically generated inventory, automating the management of cloud resources based on real-time data. Change delimiter in excel to pipe

3. Docker Compose File Generation

While Docker Compose files are usually written manually, in complex scenarios or microservices architectures, parts of them might be generated dynamically.

  • Scenario: A build system generates a list of service versions and image names in JSON format. This needs to be incorporated into a docker-compose.yml file.

  • Example:

    • JSON build output: 
      {
  "web_app": {
    "image": "myorg/web-app:1.2.0",
    "ports": ["80:80"]
  },
  "database": {
    "image": "postgres:14",
    "environment": {
      "POSTGRES_DB": "appdb"
    }
  }
}
    • Command to convert and embed: 
      echo '{"web_app": {"image": "myorg/web-app:1.2.0","ports": ["80:80"]},"database": {"image": "postgres:14","environment": {"POSTGRES_DB": "appdb"}}}' | \
yq -P -o yaml '
  .version = "3.8"
  | .services = .
' - > generated-docker-compose.yml

  • Resulting generated-docker-compose.yml: 

    version: "3.8"
services:
  web_app:
    image: myorg/web-app:1.2.0
    ports:
      - "80:80"
  database:
    image: postgres:14
    environment:
      POSTGRES_DB: appdb

    This enables dynamic generation of Docker Compose files, useful for CI/CD pipelines where service configurations change frequently.

4. Configuration Templating and Overlays

In many setups, you have a base configuration and then environment-specific overrides. Often, the base might be a static JSON/YAML, and the overrides might come from different sources.

  • Scenario: You have a base application configuration in JSON, and environment-specific parameters are stored in a separate JSON file. You want to merge these, creating a final YAML configuration for deployment.

  • Example (revisiting merge with yq): 

    # base.json: {"debug": false, "log_level": "INFO"}
# dev_overlay.json: {"debug": true}
yq eval-all 'select(fileIndex == 0) * select(fileIndex == 1)' base.json dev_overlay.json -o yaml -P > final_dev_config.yaml

    This generates a merged YAML file, making it easy to manage layered configurations for different environments (dev, staging, prod) without manual editing.

These practical examples demonstrate that a “json to yaml converter linux” tool is not just about changing formats; it’s a vital component in building resilient, automated, and human-friendly infrastructure and application management systems. The efficiency gained by automating these conversions contributes significantly to faster deployment cycles and reduced human error.

The Role of jq in Pre-processing JSON for YAML Conversion

While yq is a fantastic “json to yaml converter linux” tool that can handle most conversion tasks directly, there are specific scenarios where jq (JSON Query Processor) becomes an invaluable pre-processing step. jq excels at filtering, transforming, and manipulating JSON data with unparalleled power and flexibility. When your JSON input is complex, requires significant restructuring, or needs to be filtered before converting to YAML, piping jq‘s output to yq creates a robust and highly efficient workflow.

Why jq Before yq?

  1. Complex Filtering: jq‘s query language is incredibly sophisticated for selecting specific data points from deeply nested JSON structures. While yq can also filter, jq often provides more concise and powerful syntax for complex selection patterns.
  2. Data Transformation and Restructuring: jq can reshape JSON objects and arrays in ways that are cumbersome or impossible with simple yq transformations. This includes:
    • Renaming keys.
    • Creating new objects from existing fields.
    • Flattening nested structures.
    • Aggregating data.
    • Performing arithmetic or string operations on values.
  3. Validation and Error Handling: jq will fail gracefully (with an error message) if the input JSON is malformed. This can act as a crucial first-line validation step in a pipeline.
  4. Performance on Pure JSON: For very large JSON files, jq is highly optimized for JSON parsing and processing. If you have extensive JSON manipulation before conversion, jq might offer better performance for that specific step.

Common Scenarios for jq Pre-processing

Scenario 1: Filtering and Selecting Specific Attributes

Imagine you get a verbose JSON output from an API, and you only need a few fields for your YAML configuration.

  • JSON Example (api_data.json): 

    {
  "id": "12345",
  "timestamp": "2023-10-27T10:00:00Z",
  "user_details": {
    "name": "Alice Johnson",
    "email": "[email protected]",
    "roles": ["admin", "developer"],
    "last_login": "2023-10-26T18:30:00Z"
  },
  "preferences": {
    "theme": "dark",
    "notifications": true
  },
  "audit_trail": [...] # Very large and irrelevant for config
}

  • Goal: Convert only user_details and preferences to YAML, excluding the audit_trail and top-level id/timestamp.

  • Command (jq then yq): 

    jq '{user: .user_details, settings: .preferences}' api_data.json | yq -P -o yaml
    • jq '{user: .user_details, settings: .preferences}': This jq expression constructs a new JSON object. It takes the user_details and renames it to user, and takes preferences and renames it to settings. This creates a cleaner JSON structure for conversion.
    • The output of jq is then piped to yq -P -o yaml.

  • Output: 

    user:
  name: Alice Johnson
  email: [email protected]
  roles:
    - admin
    - developer
  last_login: 2023-10-26T18:30:00Z
settings:
  theme: dark
  notifications: true

    This demonstrates jq‘s ability to precisely pick and rename parts of the data, which can then be elegantly converted to YAML.

Scenario 2: Flattening and Restructuring Data

Sometimes, the JSON structure isn’t ideal for the target YAML format. You might need to flatten nested objects or combine fields.

  • JSON Example (sensor_data.json): 

    [
  {
    "sensorId": "temp-001",
    "location": {"room": "lab", "building": "A"},
    "readings": [{"type": "temperature", "value": 25.5}, {"type": "humidity", "value": 60}]
  },
  {
    "sensorId": "light-002",
    "location": {"room": "office", "building": "B"},
    "readings": [{"type": "light", "value": 700}]
  }
]

  • Goal: Create a YAML list where each item combines sensorId, room, building, and the first reading‘s type and value.

  • Command (jq then yq): 

    jq 'map({
    id: .sensorId,
    room: .location.room,
    building: .location.building,
    reading_type: .readings[0].type,
    reading_value: .readings[0].value
})' sensor_data.json | yq -P -o yaml
    • map(...): Iterates over each object in the array.
    • Inside map, a new object is constructed with flattened and selected fields.
    • readings[0].type: Accesses the type of the first reading.

  • Output: 

    - id: temp-001
  room: lab
  building: A
  reading_type: temperature
  reading_value: 25.5
- id: light-002
  room: office
  building: B
  reading_type: light
  reading_value: 700

    This complex restructuring is where jq truly shines before the “json to yaml converter linux” step.

Scenario 3: Converting JSON Lines (NDJSON)

Some systems output JSON data as newline-delimited JSON (NDJSON), where each line is a valid JSON object. To convert this to a single YAML document (often a list of objects), jq is indispensable.

  • JSON Lines Example (logs.ndjson): 

    {"event": "start", "timestamp": "..."}
{"event": "process", "data": {...}}
{"event": "end", "status": "success"}

  • Goal: Convert this into a single YAML list of events.

  • Command (jq then yq): 

    jq -s '.' logs.ndjson | yq -P -o yaml
    • jq -s '.': The -s (slurp) option reads all inputs into a single JSON array. This is perfect for NDJSON, as it consolidates each line into an element of a new array.
    • This array is then piped to yq for conversion.

  • Output: 

    - event: start
  timestamp: "..."
- event: process
  data: {}
- event: end
  status: success

    This pattern is incredibly useful for processing logs or streaming data for analysis or configuration.

In summary, while yq is powerful, consider jq as your essential partner for pre-processing complex or messy JSON data. The jq then yq pipeline allows you to meticulously prepare your JSON input, ensuring the final YAML output is precisely structured and perfectly suited for its intended use. This is a common and highly effective pattern for advanced “json to yaml command line” operations in Linux.

Choosing the Right Approach: Online vs. Command-Line Converters

When faced with the task of converting JSON to YAML, you typically have two main avenues: utilizing an online “json to yaml converter linux” tool or opting for command-line utilities. Both have their merits and drawbacks, and the best choice depends on your specific needs, the nature of the data, and your environment.

Online JSON to YAML Converters (Like Our Tool)

Online tools, accessible via web browsers, offer a quick and user-friendly way to perform conversions.

  • Advantages:

    • Ease of Use: No installation required. Simply open the browser, paste your JSON, and click convert. The interface is usually intuitive.
    • Instant Visual Feedback: You immediately see the converted YAML, which is great for debugging and understanding the transformation.
    • Accessibility: Usable from any device with a web browser, regardless of the underlying operating system.
    • No Local Dependencies: You don’t need to install yq, jq, or Node.js.
    • Quick Checks & Small Snippets: Ideal for testing small JSON snippets, verifying syntax, or getting a quick “json to yaml example” without setting up a local environment.

  • Disadvantages:

    • Security Concerns for Sensitive Data: This is the most critical drawback. When you paste data into an online converter, you are sending that data to a third-party server (unless the tool explicitly states and proves client-side processing, like our own tool). For sensitive information (API keys, personal data, confidential configurations), this is a significant security risk. Always verify if the tool processes data purely on the client side (in your browser) or sends it to a server. Our tool is designed to be client-side only, ensuring your data never leaves your browser.
    • Lack of Automation: Online tools are manual. You cannot integrate them into scripts, CI/CD pipelines, or automated workflows.
    • Limited Customization: Most online tools offer basic conversion. They typically don’t support advanced features like filtering specific fields, restructuring data, or merging multiple files, which are common requirements in DevOps.
    • File Size Limitations: There might be practical limits on how much data you can paste into a web form, making them unsuitable for large JSON files.
    • Internet Dependency: Requires an active internet connection.

Command-Line JSON to YAML Converters (e.g., yq, jq + yaml-cli, Python)

Command-line utilities are executed directly in your terminal, making them a staple for developers, system administrators, and DevOps engineers.

  • Advantages:

    • Security for Sensitive Data: Highly secure for sensitive data. All processing occurs locally on your machine. Your data never leaves your system unless you explicitly pipe it to external commands or network services. This is a paramount advantage for corporate environments and handling confidential information.
    • Automation and Scripting: This is where command-line tools truly shine. They can be seamlessly integrated into shell scripts, Makefiles, CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions), and configuration management tools (Ansible, Puppet, Chef). This enables hands-off, repeatable conversions.
    • Advanced Data Manipulation: Tools like yq and jq offer powerful query languages to filter, transform, merge, and restructure data before or during conversion. This level of control is rarely found in online tools.
    • Handling Large Files: Designed to efficiently process large files, often streaming data rather than loading it entirely into memory (though yq and jq often load into memory for their query engines, they are optimized for large inputs).
    • Offline Capability: Once installed, they work entirely offline, which is useful in restricted network environments.
    • Standardization: Using command-line tools can help standardize workflows across a team, as everyone uses the same version of the tool and the same commands.

  • Disadvantages:

    • Initial Setup: Requires installation and configuration of the tool on your system.
    • Learning Curve: While basic commands are simple, mastering the advanced query languages (especially for jq or complex yq expressions) can take time.
    • Text-Based Output: The output is text-based, which might be less visually appealing for quick inspection compared to a web interface.

When to Choose Which?

  • Choose Online Converters (like ours, ensuring client-side processing):

    • For small, non-sensitive JSON snippets that you need to quickly inspect or convert on the fly.
    • When you need a quick syntax check or a simple “json to yaml example.”
    • If you’re on a restricted system where you cannot install software.

  • Choose Command-Line Converters (yq, jq, Python):

    • For any sensitive or proprietary data.
    • When the conversion is part of an automated workflow (scripts, CI/CD, config management).
    • When you need to transform, filter, or restructure the data before or during conversion.
    • For large files or batch processing.
    • When you need robust error handling and logging.
    • If you’re working in a professional DevOps or IT environment where repeatable processes are key.

In conclusion, for critical tasks, automation, and sensitive data, command-line “json to yaml converter linux” tools are the clear choice due to their security, flexibility, and power. Online tools serve as excellent quick-look utilities, but always prioritize data security and consider their limitations before using them for anything beyond trivial, non-sensitive data. Our online tool is designed for convenience without compromising on client-side data privacy.

FAQ

Is there a JSON to YAML converter on Linux?

Yes, absolutely! Linux offers several powerful command-line tools to convert JSON to YAML, with yq being the most popular and versatile choice. Other options include piping jq‘s output to a Node.js-based yaml-cli tool, or writing a custom script using Python’s json and pyyaml libraries.

How do I install yq on Linux?

You can install yq using several methods. For a quick setup, use Snap: sudo snap install yq. If you have Homebrew installed on Linux: brew install yq. Alternatively, you can download the appropriate binary from the yq GitHub releases page, make it executable (chmod +x), and move it to a directory in your PATH (e.g., /usr/local/bin).

What is the simplest command to convert a JSON file to YAML using yq?

The simplest command is yq -P -o yaml input.json > output.yaml. The -P flag ensures yq parses the input as JSON, and -o yaml specifies YAML as the output format.

Can I convert JSON from standard input (stdin) to YAML on Linux?

Yes, you can pipe JSON directly into yq. For example: echo '{"key": "value"}' | yq -P -o yaml. This is very useful for scripting and chaining commands.

What is the jq command for JSON to YAML conversion?

jq itself does not convert JSON to YAML. It’s a JSON processor. However, you can use jq to process JSON and then pipe its output to another tool like yaml-cli. The typical command would be cat input.json | jq '.' | yaml > output.yaml, where yaml is provided by yaml-cli (a Node.js package).

Is yq better than jq for JSON to YAML conversion?

For direct JSON to YAML conversion, yq is generally preferred because it is a single tool designed to handle both JSON and YAML directly. jq excels at highly complex JSON transformations and filtering, but it requires a separate tool for YAML output. Often, jq is used before yq for advanced JSON pre-processing.

How do I convert JSON to YAML in a shell script?

You can embed the yq command directly into your shell script. For example: 

#!/bin/bash
json_data='{"name": "test", "value": 123}'
echo "$json_data" | yq -P -o yaml > config.yaml

This allows for automation and batch processing of JSON files.

Can I convert multiple JSON files to YAML in a single operation?

Yes, using a shell loop with yq. For example: 

for file in *.json; do
    yq -P -o yaml "$file" > "${file%.json}.yaml"
done

This iterates through all JSON files in the current directory and converts them to YAML.

What are the common issues when converting JSON to YAML on Linux?

The most common issues include:

  1. Invalid JSON input: Missing commas, unquoted keys/values, incorrect brackets.
  2. Tool not found: yq or jq not installed or not in your system’s PATH.
  3. Character encoding problems: Especially with non-UTF-8 characters.
    Always validate your JSON input first (e.g., using jq .).

How can I validate JSON before converting it to YAML?

You can validate JSON using jq. If jq . your_file.json runs without errors and outputs pretty-printed JSON, your file is valid. If it’s invalid, jq will print an error message indicating the location of the syntax error.

Can I use Python to convert JSON to YAML on Linux?

Yes, Python is a very robust option. You’ll need the json and pyyaml libraries. A simple Python script can read JSON, parse it, and then dump it as YAML. This is ideal for more complex programmatic conversions.

How do I handle sensitive data during JSON to YAML conversion?

Always use command-line tools like yq or local scripts when dealing with sensitive data. Online converters, unless explicitly stated and proven to be client-side only (like our tool), might send your data to a server, posing a security risk.

What’s the difference between JSON and YAML?

JSON is primarily a data interchange format, strict in syntax, and commonly used for APIs. YAML is designed for human readability and configuration files, using indentation for structure and supporting comments. JSON is a subset of YAML, meaning valid JSON is also valid YAML.

Why would I convert JSON to YAML?

Common reasons include:

  • Improved Human Readability: YAML is easier to read and edit for configuration files (e.g., Kubernetes manifests, Ansible playbooks).
  • Tool Compatibility: Many DevOps tools prefer or require YAML for their configurations.
  • Adding Comments: YAML supports comments, which are crucial for documenting configuration logic.
  • Leveraging YAML Features: Using features like anchors and aliases to reduce redundancy.

Can yq modify JSON data before converting it to YAML?

Yes, yq has powerful data manipulation capabilities similar to jq. You can use yq expressions to filter, rename, modify values, and restructure JSON data before outputting it as YAML. For example: yq -P -o yaml '.items[] | select(.status == "active")' input.json.

How can I convert JSON lines (NDJSON) to a single YAML array?

If you have a file where each line is a JSON object (NDJSON), you can use jq -s '.' to slurp all lines into a single JSON array, then pipe that to yq:
jq -s '.' input.ndjson | yq -P -o yaml

What if my JSON input has comments?

Standard JSON does not support comments. If your JSON input has comments, it’s technically invalid JSON and yq (or any strict JSON parser) will likely throw an error. You would need to strip comments first, possibly with a pre-processor, if the source is not strictly JSON.

Can yq merge multiple JSON files and output as YAML?

Yes, yq can merge multiple JSON (or YAML) files using yq eval-all. For example, yq eval-all 'select(fileIndex == 0) * select(fileIndex == 1)' file1.json file2.json -o yaml -P performs a deep merge and outputs the result in YAML.

Is there a yq equivalent for Windows?

Yes, the yq binary is cross-platform. You can download the yq_windows_amd64.exe from the GitHub releases page and add it to your system’s PATH for use in Command Prompt or PowerShell.

Does yq preserve the order of keys in JSON when converting to YAML?

yq generally tries to preserve key order where possible, as does standard YAML. However, JSON parsers are not strictly required to preserve key order, and YAML parsers also might reorder keys depending on the internal representation. For most practical configuration uses, key order is not a functional requirement.

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