While tools like Everything revolutionized local file search with their near-instant NTFS indexing, network storage presents unique challenges:

• Protocol overhead (SMB/NFS latency)
• Permission constraints across domains
• Distributed storage architectures

For enterprise SAN environments, consider these technical approaches:

// Sample Python pseudocode for distributed indexing
class NetworkIndexer:
    def __init__(self, shares):
        self.workers = [IndexWorker(share) for share in shares]
        
    def search(self, query):
        return [result for worker in self.workers 
                for result in worker.search(query)]

class IndexWorker:
    def __init__(self, share_path):
        self.share = share_path
        self.index = self._build_index()
    
    def _build_index(self):
        # Implement walk+cache logic here
        pass

Key parameters for large-scale implementations:

• Set appropriate SMB signing requirements
• Implement tiered indexing (metadata first)
• Use persistent TCP connections

# Bash script for scheduled SAN indexing
#!/bin/bash
MOUNTS=("/san/vol1" "/san/vol2")
LOG="/var/log/san_index.log"

for mount in "${MOUNTS[@]}"; do
  find "$mount" -type f -printf "%f\t%h\t%s\t%T@\n" >> temp.idx
done

mv temp.idx /search_db/active.index
echo "$(date) - Index updated" >> "$LOG"

When accessing network shares programmatically:

1. Always use service accounts with minimum privileges
2. Implement connection timeouts
3. Encrypt index files containing paths

While tools like Everything provide lightning-fast file indexing for local NTFS volumes, searching across network shares and SAN storage presents unique challenges. The fundamental limitation comes from the way these tools rely on NTFS journaling for real-time updates, which isn't available for network-mounted drives.

For enterprise environments with terabytes of data across SANs, we need different strategies:

// Example Python script for basic network share indexing
import os
from datetime import datetime

def index_network_share(root_path):
    file_index = []
    for root, dirs, files in os.walk(root_path):
        for file in files:
            full_path = os.path.join(root, file)
            stats = os.stat(full_path)
            file_index.append({
                'path': full_path,
                'size': stats.st_size,
                'modified': datetime.fromtimestamp(stats.st_mtime)
            })
    return file_index

Several commercial and open-source tools attempt to solve this:

• DocFetcher: Open-source desktop search application
• FileLocator Pro
• Windows Search Service: Can be configured for network shares

// Distributed indexing with Python and Redis
import redis
import multiprocessing

def worker(queue, share_path):
    r = redis.Redis()
    for item in index_network_share(share_path):
        r.hset('file_index', item['path'], json.dumps(item))

if __name__ == '__main__':
    shares = ['//san/volume1', '//nas/share2']
    queue = multiprocessing.Queue()
    processes = []
    
    for share in shares:
        p = multiprocessing.Process(target=worker, args=(queue, share))
        processes.append(p)
        p.start()

-- SQLite schema for efficient file search
CREATE TABLE file_index (
    path TEXT PRIMARY KEY,
    filename TEXT,
    extension TEXT,
    size INTEGER,
    modified INTEGER
);

CREATE INDEX idx_filename ON file_index(filename);
CREATE INDEX idx_extension ON file_index(extension);