Introduction

Search functionality is a critical feature in modern web applications. Elasticsearch, a powerful full-text search engine, enables fast and scalable search capabilities. In this guide, we will integrate Flask with Elasticsearch to build an advanced search system with indexing, querying, and filtering.

Setting Up Elasticsearch

Installing Elasticsearch

First, install Elasticsearch using Docker:

docker run -d --name elasticsearch -p 9200:9200 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:8.4.0

Verify the installation:

curl http://localhost:9200

If Elasticsearch is running, it will return a JSON response with version details.

Integrating Flask with Elasticsearch

Installing Dependencies

In your Flask project, install the required packages:

pip install Flask elasticsearch requests

Configuring Elasticsearch in Flask

Create a Flask app and connect it to Elasticsearch:

from flask import Flask, request, jsonify
from elasticsearch import Elasticsearch

app = Flask(__name__)
es = Elasticsearch("http://localhost:9200")

@app.route("/")
def home():
return "Flask & Elasticsearch API is running!"

if __name__ == "__main__":
app.run(debug=True)

Indexing Data in Elasticsearch

Creating an Index

An index in Elasticsearch stores and organizes documents for search.

index_name = "products"

# Define index mapping
index_mapping = {
"settings": {"number_of_shards": 1},
"mappings": {
"properties": {
"name": {"type": "text"},
"description": {"type": "text"},
"price": {"type": "float"},
"category": {"type": "keyword"}
}
}
}

# Create the index
es.indices.create(index=index_name, body=index_mapping, ignore=400)

Adding Data to the Index

Insert sample data into Elasticsearch:

def add_product(product_id, name, description, price, category):
doc = {
"name": name,
"description": description,
"price": price,
"category": category
}
es.index(index=index_name, id=product_id, body=doc)

# Example data
add_product(1, "Laptop", "High-performance laptop", 1200, "Electronics")
add_product(2, "Smartphone", "Latest model smartphone", 800, "Electronics")

Basic Full-Text Search

A simple search query to find products by name or description:

@app.route("/search", methods=["GET"])
def search():
query = request.args.get("q")
search_query = {
"query": {
"multi_match": {
"query": query,
"fields": ["name", "description"]
}
}
}
results = es.search(index=index_name, body=search_query)
return jsonify(results["hits"]["hits"])

Access the API with:

curl "http://127.0.0.1:5000/search?q=laptop"

Filtering Results

To filter by category and price range:

@app.route("/filter", methods=["GET"])
def filter_results():
category = request.args.get("category")
min_price = float(request.args.get("min_price", 0))
max_price = float(request.args.get("max_price", 10000))

    filter_query = {
        "query": {
            "bool": {
                "must": [
                    {"term": {"category": category}}
                ],
                "filter": [
                    {"range": {"price": {"gte": min_price, "lte": max_price}}}
                ]
            }
        }
    }
    results = es.search(index=index_name, body=filter_query)
    return jsonify(results["hits"]["hits"])

Access the API with:

curl "http://127.0.0.1:5000/filter?category=Electronics&min_price=500&max_price=1500"

Optimizing Search Performance

Using Analyzers for Better Text Search

Define a custom analyzer for improved search accuracy:

index_mapping["settings"]["analysis"] = {
"analyzer": {
"custom_analyzer": {
"type": "standard",
"stopwords": "_english_"
}
}
}

Implementing Pagination

To handle large search results, add pagination:

@app.route("/search_paginated", methods=["GET"])
def search_paginated():
query = request.args.get("q")
page = int(request.args.get("page", 1))
size = int(request.args.get("size", 10))
search_query = {
"query": {
"multi_match": {
"query": query,
"fields": ["name", "description"]
}
},
"from": (page - 1) * size,
"size": size
}
results = es.search(index=index_name, body=search_query)
return jsonify(results["hits"]["hits"])

Access paginated results with:

curl "http://127.0.0.1:5000/search_paginated?q=laptop&page=1&size=5"

Conclusion

By integrating Elasticsearch with Flask, you can build powerful search features that handle full-text search, filtering, and pagination efficiently. Optimizing queries and indexes ensures fast performance and scalability.

Start building advanced search functionality in your Flask applications today! 🚀