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Building a Full-Text Search Engine in Go

From tokenization to indexing — how I built a full-text search engine over Wikipedia abstracts in Go.

evergreen#studying#golang#search#systems

Introduction

As a developer who's been fascinated by tokenization since building a simple interpreter, I decided to take on an exciting project: creating a full-text search engine in Go. This journey not only deepened my understanding of search algorithms but also shed light on the intricate processes behind tools like grep. In this blog post, I'll walk you through the development of this search engine, discussing the key concepts, implementation details, and how it compares to other search tools.

The Inspiration: Tokenization and Interpreters

My interest in tokenization began when I built a simple interpreter. The process of breaking down complex strings into meaningful tokens fascinated me, and I saw an opportunity to apply this knowledge to a larger-scale project. Thus, the idea of building a full-text search engine was born.

The Dataset: Wikipedia Abstracts

For this project, I chose to work with a substantial dataset: the Wikipedia abstracts. You can find the dataset at: https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-abstract1.xml.gz

This XML file is about 1.1 GB when decompressed (120 MB compressed), providing a rich corpus for our search engine to index and query.

tokenization was the thing that got me into this whole rabbit hole. still hasn't let me go.

The Pre-processing Pipeline

The heart of our search engine lies in its pre-processing pipeline. Here's a breakdown of the steps:

  1. Tokenization: We break down the text into individual tokens (words or phrases).
  2. Removing Common Phrases: We filter out common words like "a" and "the" that don't add significant meaning to searches.
  3. Lowercasing: We convert all text to lowercase to ensure case-insensitive searching.
  4. Stemming: We reduce words to their root form. For example, "fighting", "fighter", and "fight" all become "fight".

This pre-processing step is crucial for creating an efficient and effective search index.

Indexing: The Foundation of Fast Searches

After pre-processing, we create an inverted index. This data structure maps each unique token to a list of documents or positions where it appears. The inverted index is the secret sauce that makes our searches lightning-fast.

Searching: Finding the Needle in the Haystack

When a user performs a search, we:

  1. Pre-process the search query using the same pipeline as our indexed documents.
  2. Look up each processed token in our inverted index.
  3. Find the intersection of the document sets for each token.

This approach allows us to quickly narrow down the relevant documents without scanning the entire corpus.

Comparing with grep: On-the-fly vs. Indexed Searching

During development, I was curious about how our approach compared to tools like grep. I asked ChatGPT:

"I want to ask a question about grep. I'm building a search engine in golang for a very large app, and in this app we first preprocess the strings into indexes, get the intersection of the disjointed set of the index that we search, so there's pre-processing in terms of tokenization and so on. Is this how grep works? Does it tokenize our entire system to find things or does it search on the fly?"

The answer illuminated some key differences:

  1. Indexing vs. On-the-fly: Our search engine pre-processes and indexes the data, while grep typically searches on-the-fly.
  2. Speed vs. Flexibility: Our indexed approach is faster for repeated searches but requires upfront processing. Grep is more flexible but can be slower for large datasets.
  3. Memory Usage: Our index requires more memory, while grep has a smaller footprint.
  4. Regular Expressions: Grep excels at regex-based searches, which our current implementation doesn't support.

Challenges and Learnings

Building this search engine came with its share of challenges:

  1. Memory Management: Indexing a large dataset required careful memory management in Go.
  2. Concurrency: Leveraging Go's goroutines to parallelize indexing and searching operations.
  3. Stemming Algorithm: Implementing an effective stemming algorithm for better search results.
  4. XML Parsing: Efficiently parsing the large Wikipedia XML dataset.

grep searches on the fly. we index first and fly later.

Future Improvements

While our search engine is functional, there's always room for improvement:

  1. Ranking Algorithm: Implement TF-IDF or BM25 for better result ranking.
  2. Phrase Searching: Add support for exact phrase matches.
  3. Fuzzy Matching: Implement algorithms like Levenshtein distance for typo-tolerant searches.
  4. Distributed Indexing: Scale the system to handle even larger datasets across multiple machines.

Building a full-text search engine in Go has been an enlightening journey. It's deepened my appreciation for the complexities of information retrieval and the elegance of well-implemented algorithms. Whether you're building your own search tool or simply curious about how search works under the hood, I hope this blog post has provided valuable insights.