---
draft: false
title: Batch vector search with Qdrant
slug: batch-vector-search-with-qdrant
short_description: Introducing efficient batch vector search capabilities,
  streamlining and optimizing large-scale searches for enhanced performance.
description: "Discover the latest feature designed to streamline and optimize
  large-scale searches. "
preview_image: /blog/from_cms/andrey.vasnetsov_career_mining_on_the_moon_with_giant_machines_813bc56a-5767-4397-9243-217bea869820.png
date: 2022-09-26T15:39:53.751Z
author: Kacper Łukawski
featured: false
tags:
  - Data Science
  - Vector Database
  - Machine Learning
  - Information Retrieval
---
The latest release of Qdrant 0.10.0 has introduced a lot of functionalities that simplify some common tasks. Those new possibilities come with some slightly modified interfaces of the client library. One of the recently introduced features is the possibility to query the collection with multiple vectors at once — a batch search mechanism.

There are a lot of scenarios in which you may need to perform multiple non-related tasks at the same time. Previously, you only could send several requests to Qdrant API on your own. But multiple parallel requests may cause significant network overhead and slow down the process, especially in case of poor connection speed.

Now, thanks to the new batch search, you don’t need to worry about that. Qdrant will handle multiple search requests in just one API call and will perform those requests in the most optimal way.

## An example of using the batch search

We’ve used the official Python client to show how the batch search might be integrated with your application. Since there have been some changes in the interfaces of Qdrant 0.10.0, we’ll go step by step.

## Creating the collection

The first step is to create a collection with a specified configuration — at least vector size and the distance function used to measure the similarity between vectors.

```python
from qdrant_client import QdrantClient
from qdrant_client.conversions.common_types import VectorParams

client = QdrantClient("localhost", 6333)
client.recreate_collection(
    collection_name="test_collection",
    vectors_config=VectorParams(size=4, distance=Distance.EUCLID),
)
```

## Loading the vectors

With the collection created, we can put some vectors into it. We’re going to have just a few examples.

```python
vectors = [
    [.1, .0, .0, .0],
    [.0, .1, .0, .0],
    [.0, .0, .1, .0],
    [.0, .0, .0, .1],
    [.1, .0, .1, .0],
    [.0, .1, .0, .1],
    [.1, .1, .0, .0],
    [.0, .0, .1, .1],
    [.1, .1, .1, .1],
]

client.upload_collection(
    collection_name="test_collection",
    vectors=vectors,
)
```

## Batch search in a single request

Now we’re ready to start looking for similar vectors, as our collection has some entries. Let’s say we want to find the distance between the selected vector and the most similar database entry and at the same time find the two most similar objects for a different vector query. Up till 0.9, we would need to call the API twice. Now, we can send both requests together:

```python
results = client.search_batch(
    collection_name="test_collection",
    requests=[
        SearchRequest(
            vector=[0., 0., 2., 0.],
            limit=1,
        ),
        SearchRequest(
            vector=[0., 0., 0., 0.01],
            with_vector=True,
            limit=2,
        )
    ]
)

# Out: [
#   [ScoredPoint(id=2, version=0, score=1.9, 
#                payload=None, vector=None)],
#   [ScoredPoint(id=3, version=0, score=0.09, 
#                payload=None, vector=[0.0, 0.0, 0.0, 0.1]),
#    ScoredPoint(id=1, version=0, score=0.10049876, 
#                payload=None, vector=[0.0, 0.1, 0.0, 0.0])]
# ]

```

Each instance of the SearchRequest class may provide its own search parameters, including vector query but also some additional filters. The response will be a list of individual results for each request. In case any of the requests is malformed, there will be an exception thrown, so either all of them pass or none of them.

And that’s it! You no longer have to handle the multiple requests on your own. Qdrant will do it under the hood.

## Benchmark

The batch search is fairly easy to be integrated into your application, but if you prefer to see some numbers before deciding to switch, then it’s worth comparing four different options:

1. Querying the database sequentially.
2. Using many threads/processes with individual requests.
3. Utilizing the batch search of Qdrant in a single request.
4. Combining parallel processing and batch search.

In order to do that, we’ll create a richer collection of points, with vectors from the *glove-25-angular* dataset, quite a common choice for ANN comparison. If you’re interested in seeing some more details of how we benchmarked Qdrant, let’s take a [look at the Gist](https://gist.github.com/kacperlukawski/2d12faa49e06a5080f4c35ebcb89a2a3).

## The results

We launched the benchmark 5 times on 10000 test vectors and averaged the results. Presented numbers are the mean values of all the attempts:

1. Sequential search: 225.9 seconds
2. Batch search: 208.0 seconds
3. Multiprocessing search (8 processes): 194.2 seconds
4. Multiprocessing batch search (8 processes, batch size 10): 148.9 seconds

The results you may achieve on a specific setup may vary depending on the hardware, however, at the first glance, it seems that batch searching may save you quite a lot of time.

Additional improvements could be achieved in the case of distributed deployment, as Qdrant won’t need to make extensive inter-cluster requests. Moreover, if your requests share the same filtering condition, the query optimizer would be able to reuse it among batch requests.

## Summary

Batch search allows packing different queries into a single API call and retrieving the results in a single response. If you ever struggled with sending several consecutive queries into Qdrant, then you can easily switch to the new batch search method and simplify your application code. As shown in the benchmarks, that may almost effortlessly speed up your interactions with Qdrant even by over 30%, even not considering the spare network overhead and possible reuse of filters!