json-data-extraction

name	json-data-extraction
description	Extract, parse, and query JSON data from large enterprise files efficiently

JSON Data Extraction from Enterprise Data

Overview

This skill covers parsing and extracting information from large JSON files containing enterprise data like employee records, Slack messages, and product metadata.

Use Cases

• Extracting specific fields from employee databases
• Searching through Slack message histories for mentions or keywords
• Finding relationships between employees and documents/reports
• Aggregating data across multiple JSON sources

Installation & Setup

# Python has built-in json module, no installation needed
python3 -c "import json; print('JSON module available')"

Code Examples

Basic JSON Loading

import json

with open('/root/DATA/metadata/employee.json', 'r') as f:
    employee_data = json.load(f)

# Access specific employee
employee = employee_data.get('eid_1e9356f5', {})

Extracting Data with Filters

import json

with open('/root/DATA/products/ContentForce.json', 'r') as f:
    product_data = json.load(f)

# Extract Slack messages mentioning specific employee
messages = product_data.get('slack', [])
for msg in messages:
    if 'Market Research Report' in msg.get('Message', {}).get('text', ''):
        print(msg)

Extracting IDs from Text

import re

def extract_employee_ids(text):
    """Extract employee IDs (format: eid_xxxxxxxx) from text"""
    pattern = r'eid_[a-f0-9]{8}'
    return re.findall(pattern, text)

# Usage
text = "@eid_1e9356f5 created this channel. @eid_06cddbb3 joined."
ids = extract_employee_ids(text)  # Returns ['eid_1e9356f5', 'eid_06cddbb3']

Finding Report Authors and Reviewers

import json
import re

def find_report_authors_and_reviewers(product_json_path, report_name):
    """Find employees who authored/reviewed a report"""
    with open(product_json_path, 'r') as f:
        data = json.load(f)

    authors = set()
    reviewers = set()

    messages = data.get('slack', [])
    for msg in messages:
        text = msg.get('Message', {}).get('text', '')
        if report_name.lower() in text.lower():
            # Author: person sharing the report
            author_id = msg.get('Message', {}).get('User', {}).get('userId')
            if author_id and author_id.startswith('eid_'):
                authors.add(author_id)

            # Reviewers: people responding in thread
            for reply in msg.get('ThreadReplies', []):
                reviewer_id = reply.get('User', {}).get('userId')
                if reviewer_id and reviewer_id.startswith('eid_'):
                    reviewers.add(reviewer_id)

    return list(authors), list(reviewers)

Best Practices

1. Memory Efficiency: For large files, process in chunks rather than loading entire file
2. Error Handling: Always check if keys exist before accessing nested values
3. ID Pattern Matching: Employee IDs follow format eid_ followed by 8 hex characters
4. Text Search: Use case-insensitive matching for report/document names
5. Deduplication: Use sets to avoid duplicate IDs before converting to lists

Common Patterns

Pattern 1: Search for Document Mentions

Look for link syntax in Slack: <https://...|Report Name>

Pattern 2: Extract User IDs from Messages

• Author: Message.User.userId
• Reviewers: Message.ThreadReplies[].User.userId
• Mentions: Look for @eid_ patterns in message text

Pattern 3: Cross-Reference Data

Load employee.json to map IDs to names if needed for verification

Token Tracking

When using this with APIs, track token consumption:

# After API calls
tokens_used = response.usage.input_tokens + response.usage.output_tokens