Introduction to TextFSM in Python

TextFSM is a Python library used for parsing semi-structured text into structured data. It's particularly useful for extracting information from command-line outputs. This article will introduce you to TextFSM, explain how it works, and provide examples with code and outputs to help you get started.

What is TextFSM?

TextFSM is a Python library that uses a template-based approach to parse text. It was developed by Google and is designed to be simple yet powerful enough to handle complex parsing tasks. TextFSM uses templates written in a domain-specific language (DSL) to define the structure of the text being parsed. These templates describe the expected text patterns and the data to extract.

Advantages of Using TextFSM

Using TextFSM for text parsing offers several advantages:

• Consistency: Templates ensure consistent parsing results across different text samples.
• Reusability: Templates can be reused for similar text structures, saving development time.
• Ease of Use: The DSL is easy to learn and use, making it accessible to a wide range of users.
• Automation: TextFSM can automate the extraction of structured data from unstructured text, reducing manual effort.

Key Features of TextFSM

TextFSM offers several features that make it a valuable tool for text parsing:

• Template-Based Parsing: TextFSM uses templates to define the structure and patterns of the text to be parsed. This approach makes it easy to reuse templates for similar text structures.
• State Machine: TextFSM operates as a state machine, allowing it to handle complex text parsing scenarios with multiple states and transitions.
• Efficiency: TextFSM is efficient and can handle large volumes of text data quickly.
• Flexibility: The DSL used in TextFSM templates is flexible and powerful, allowing for the extraction of a wide range of data patterns.

Installing TextFSM

First, you need to install the TextFSM library. You can do this using pip:

pip install textfsm

Creating TextFSM Templates

To use TextFSM, you need to create templates that define the patterns and structure of the text you want to parse. A TextFSM template consists of three main sections:

1. Value Definitions: Define the variables to extract from the text.
2. Start State: The initial state of the state machine.
3. Other States: Additional states that define transitions and patterns to match.

Example Template

Here is an example of a TextFSM template for parsing the output of the show ip interface brief command from a Cisco device:

Value Filldown Interface (\S+)
Value Filldown IP_Address (\S+)
Value Filldown OK (\S+)
Value Filldown Method (\S+)
Value Filldown Status (\S+)
Value Filldown Protocol (\S+)

Start
  ^Interface\s+IP-Address\s+OK\?\s+Method\s+Status\s+Protocol -> Interfaces

Interfaces
  ^${Interface}\s+${IP_Address}\s+${OK}\s+${Method}\s+${Status}\s+${Protocol} -> Record
  ^\s*$$

Using TextFSM in Python

Once you have your template, you can use TextFSM in your Python code to parse text. Here's a step-by-step guide:

1. Import TextFSM and Load the Template

import textfsm

template_file = 'path_to_template_file'
with open(template_file) as template:
    fsm = textfsm.TextFSM(template)

2. Parse the Text Data

with open('path_to_text_file') as text:
    results = fsm.ParseText(text.read())

3. Access the Parsed Data

# Print the header
print(fsm.header)

# Print each row of parsed data
for row in results:
    print(row)

Parsing Show Commands

Let's parse the output of the show version command from a Cisco device.

Template: Save this as show_ip_interface_brief.textfsm

Value Filldown Interface (\S+)
Value Filldown IP_Address (\S+)
Value Filldown OK (\S+)
Value Filldown Method (\S+)
Value Filldown Status (\S+)
Value Filldown Protocol (\S+)

Start
  ^Interface\s+IP-Address\s+OK\?\s+Method\s+Status\s+Protocol -> Interfaces

Interfaces
  ^${Interface}\s+${IP_Address}\s+${OK}\s+${Method}\s+${Status}\s+${Protocol} -> Record
  ^\s*$$

The Below Given data is Sample Data save this file as show_ip_interface_brief.txt

Interface              IP-Address      OK? Method Status                Protocol
FastEthernet0/0        192.168.1.1     YES manual up                    up      
FastEthernet0/1        unassigned      YES unset  administratively down down    

Code Example:

import textfsm

# Load the template
template_file = 'show_ip_interface_brief.textfsm'
with open(template_file) as template:
    fsm = textfsm.TextFSM(template)

# Load the text data
with open('show_ip_interface_brief.txt') as text:
    results = fsm.ParseText(text.read())

# Print the results
print(fsm.header)
for row in results:
    print(row)

Output:

['Interface', 'IP_Address', 'OK', 'Method', 'Status', 'Protocol']
['FastEthernet0/0', '192.168.1.1', 'YES', 'manual', 'up', 'up']
['FastEthernet0/1', 'unassigned', 'YES', 'unset', 'administratively', 'down']
['FastEthernet0/1', 'unassigned', 'YES', 'unset', 'administratively', 'down']

Conclusion

TextFSM is a valuable tool for anyone who needs to parse and extract data from raw text. Its template-based approach and state machine model make it powerful and flexible enough to handle a variety of text parsing tasks. By following the steps outlined in this article, you can start using TextFSM in your Python projects and benefit from its capabilities. Whether you are a network engineer processing device logs or a developer extracting data from unstructured text, TextFSM can simplify and streamline your workflow.