Creating a Python-based indexing program to mimic Google’s search process is a good way to understand the power of search technology. While the example below is simplified, the approach shows how Google achieves rapid search speeds by leveraging techniques like full-text search, effective indexing, and structured data storage.
1. Setting Up a Virtual Environment (venv)
A virtual environment helps maintain the required packages for the project. Here’s how to set it up on both Windows and Linux.
Windows Setup:
Open Command Prompt, navigate to the project folder, and create a virtual environment:
python -m venv venv
Activate the virtual environment:
venv\Scripts\activate
Linux Setup:
Open a terminal, navigate to your project folder, and create a virtual environment:
python3 -m venv venv
Activate it:
source venv/bin/activate
With the virtual environment activated, install any required packages (e.g., SQLite3 is included in Python by default, but ensure the sqlite3
library is available).
2. Project Structure and Files
Organize the project directory with the following structure:
search_index/
│
├── venv/
├── init_db.py
├── add_document.py
├── search_documents.py
└── data/
└── search_index.db
- init_db.py: Script to create the database and initialize the necessary table.
- add_document.py: Script to add documents to the index.
- search_documents.py: Script to search the indexed documents.
- data/search_index.db: The SQLite database file storing indexed information.
3. Initializing the Database
To set up the database with full-text search capabilities, use the FTS5 module in SQLite3, which allows for fast search indexing. In init_db.py
, write code to create a database and initialize a virtual table using FTS5.
Code for init_db.py
# init_db.py
import sqlite3
import os
DB_PATH = "data/search_index.db"
def init_db():
if not os.path.exists("data"):
os.makedirs("data")
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
# Enable Full Text Search (FTS5) for efficient indexing
cursor.execute('''
CREATE VIRTUAL TABLE IF NOT EXISTS documents USING FTS5(title, content);
''')
conn.commit()
conn.close()
if __name__ == "__main__":
init_db()
print("Database and table initialized.")
Running init_db.py
will create the search_index.db
file with a virtual table called documents
, storing title
and content
fields.
python init_db.py
4. Adding Documents to the Index
Adding documents is essential to building a usable search index. In add_document.py
, create code to add document titles and content to the database. By indexing both fields, you can efficiently search for terms within document titles and content.
Code for add_document.py
# add_document.py
import sqlite3
DB_PATH = "data/search_index.db"
def add_document(title, content):
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
cursor.execute('''
INSERT INTO documents (title, content) VALUES (?, ?)
''', (title, content))
conn.commit()
conn.close()
print(f"Document '{title}' added to index.")
if __name__ == "__main__":
add_document("Python Basics", "Python is a versatile programming language.")
add_document("Learning SQLite", "SQLite is a powerful database for small applications.")
Each time add_document.py
is run, it adds a document to the documents
table in search_index.db
. You can test this by running:
python add_document.py
5. Implementing Search Functionality
Google’s speed in searching large datasets hinges on advanced indexing. While simplified here, SQLite’s MATCH
keyword provides a basic version of this by performing full-text searches within indexed columns. Use search_documents.py
to implement a search function that retrieves documents based on keywords.
Code for search_documents.py
# search_documents.py
import sqlite3
DB_PATH = "data/search_index.db"
def search_documents(query):
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
cursor.execute('''
SELECT title, content FROM documents WHERE documents MATCH ?
''', (query,))
results = cursor.fetchall()
conn.close()
if results:
for title, content in results:
print(f"Title: {title}\nContent: {content}\n")
else:
print("No matching documents found.")
if __name__ == "__main__":
search_documents("Python")
This script does the following:
- Full-Text Search with MATCH: Uses the
MATCH
keyword to perform full-text searches ontitle
andcontent
fields. - Results Display: Prints any document titles and contents matching the search query.
Run search_documents.py
to test:
python search_documents.py
If there’s a match for “Python,” it displays the title and content for each matching document.
Understanding Search Speed
Google’s search speed comes from large-scale infrastructure, optimized indexing, and distributed databases. In this example, SQLite’s FTS5 enables rapid searching by creating an index on the title
and content
fields, which is then used by the MATCH
function to scan and retrieve relevant rows.
This is how it works:
- Indexing: The FTS5 virtual table in SQLite enables indexing. Indexes act like a roadmap that helps the search engine locate relevant content quickly without scanning the entire table.
- Full-Text Search: With full-text search enabled, SQLite can locate rows matching specific keywords in milliseconds, even if there are thousands of records.
- Efficient Querying: SQL queries with
MATCH
are optimized by the FTS index, which keeps lookups fast by storing tokenized words and phrases in a searchable format.
Enhancing the System with Phrase Matching
To take this further, you can use phrase matching to locate exact phrases instead of individual keywords. Updating search_documents.py
with quotes around the query allows it to look for exact matches.
Replace the existing function in search_documents.py with this function:
# search_documents.py (updated)
def search_documents(query):
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
# Wrapping query in quotes to find exact phrase
formatted_query = f'"{query}"'
cursor.execute('''
SELECT title, content FROM documents WHERE documents MATCH ?
''', (formatted_query,))
results = cursor.fetchall()
conn.close()
if results:
for title, content in results:
print(f"Title: {title}\nContent: {content}\n")
else:
print("No matching documents found.")
Running this updated code will only return documents with exact phrase matches, an enhancement that can be valuable when searching large, specific text corpora.
Thank you for reading this article. We hope you found it helpful and informative. If you have any questions, or if you would like to suggest new Python code examples or topics for future tutorials/articles, please feel free to join and comment. Your feedback and suggestions are always welcome!
You can find the same tutorial on Medium.com.