Redis Improves Performance of Vector Semantic Search with Multi-Threaded Query Engine

As the demand for semantic search continues to grow, vector-based search techniques are increasingly being adopted due to their ability to handle complex queries, understand context, and return highly relevant results. Redis, known for its low-latency data store capabilities, has taken a significant step forward in this area by introducing a multi-threaded query engine. This innovation dramatically enhances the performance of vector semantic search.

In this blog post, we'll dive into how Redis leverages multi-threading to boost search performance, explore key benefits, and provide code examples to demonstrate how you can implement this in your projects.

1. Why Multi-Threading Matters for Semantic Search

Semantic search relies on the ability to compare high-dimensional vectors representing the meaning of words, sentences, or even entire documents. The complexity of these operations can lead to high computational costs, especially when dealing with large datasets and real-time search requirements.

Redis has traditionally been single-threaded, meaning it could only process one command at a time. While this was sufficient for many use cases, the increasing demand for high-performance vector searches necessitated a more efficient approach. Multi-threading allows Redis to handle multiple queries concurrently, significantly reducing the time it takes to return search results.

2. Key Benefits of Redis Multi-Threaded Query Engine

1. Improved Throughput: With the ability to execute multiple queries in parallel, Redis can process more requests in the same amount of time, enhancing overall throughput.

2. Lower Latency: By distributing the workload across multiple threads, Redis reduces the time each query spends waiting for execution, leading to faster response times.

3. Scalability: As the volume of data and the number of queries grow, Redis can scale more effectively by utilizing multi-threading to maintain high performance.

3. Implementing Multi-Threaded Vector Search in Redis

To take advantage of Redis's multi-threaded query engine for vector semantic search, you'll need to configure your Redis instance appropriately and use the vector search commands. Here's how you can do it:

Step 1: Configure Redis for Multi-Threading

First, ensure you have a Redis version that supports multi-threading. You can enable multi-threading by adjusting the following configuration in your redis.conf file:

# Enable multi-threading
threads 4

This configuration sets Redis to use 4 threads for processing queries. You can adjust the number of threads based on your system's capabilities and the workload.

Step 2: Storing Vectors in Redis

Let's assume you're storing 128-dimensional vectors representing text data. You can store these vectors using the HSET command, where each vector is stored as a field in a hash.

import redis
 
# Connect to Redis
client = redis.Redis(host='localhost', port=6379, db=0)
 
# Example vector (128-dimensional)
vector = [0.1, 0.2, 0.3, ..., 0.128]
 
# Store the vector in Redis
client.hset("vector:1", mapping={f"dim_{i}": value for i, value in enumerate(vector)})

Step 3: Implementing a Multi-Threaded Search

Now, let's perform a vector search using the multi-threaded query engine. We'll use a basic cosine similarity function to compare vectors.

import numpy as np
 
def cosine_similarity(v1, v2):
    return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
 
# Retrieve vectors from Redis
vector1 = np.array(
  [
    float(client.hget("vector:1", f"dim_{i}")) for i in range(128)
  ]
)
vector2 = np.array(
  [
    float(client.hget("vector:2", f"dim_{i}")) for i in range(128)
  ]
)
 
# Calculate cosine similarity
similarity = cosine_similarity(vector1, vector2)
print(f"Cosine Similarity: {similarity}")

Step 4: Benchmarking Performance

To see the performance benefits of multi-threading, you can benchmark your Redis instance by running multiple concurrent queries and comparing the response times with and without multi-threading enabled.

import time
from concurrent.futures import ThreadPoolExecutor
 
def query_redis():
    vector = np.array(
      [
        float(client.hget("vector:1", f"dim_{i}")) for i in range(128)
      ]
    )
    return vector
 
# Benchmark without multi-threading
start_time = time.time()
for _ in range(100):
    query_redis()
print(f"Single-threaded time: {time.time() - start_time}")
 
# Benchmark with multi-threading
start_time = time.time()
with ThreadPoolExecutor(max_workers=4) as executor:
    executor.map(query_redis, range(100))
print(f"Multi-threaded time: {time.time() - start_time}")

Conclusion

Redis's multi-threaded query engine is a game-changer for vector semantic search, offering improved throughput, lower latency, and better scalability. By leveraging multi-threading, you can ensure your search applications remain performant even as data and query volumes grow. With the code examples provided, you can start implementing multi-threaded vector search in your Redis-powered applications today.

Stay tuned for more updates and optimizations as Redis continues to innovate in the realm of high-performance search solutions!