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Azure Data Explorer Blog
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Optimizing Vector Similarity Searches at Scale

Anshul_Sharma's avatar
Anshul_Sharma
Icon for Microsoft rankMicrosoft
Jul 24, 2023

This post is co-authored by @adieldar (Principal Data Scientist, Microsoft)

 

In a previous blog – Azure Data Explorer for Vector Similarity Search, we focused on how Azure Data Explorer (Kusto) is perfectly suited for storing and searching vector embeddings.  

In this blog, we will focus on performance tuning and optimizations for running vector similarity searches at scale.  

 

We will continue working on the Wikipedia scenario where we generate the embeddings of wiki pages using OpenAI and store them in kusto. We then use series_cosine_similarity_fl  kusto function to perform similarity searches. 

Demo scenario

 

Optimizing for scale

 

To optimize the cosine similarity search we need to split the vectors table to many extents that are evenly distributed among all cluster nodes. This can be done by setting Partitioning Policy for the embedding table using the .alter-merge policy partitioning command: 

 

 

 

 

.alter-merge table WikipediaEmbeddingsTitleD policy partitioning  
``` 
{ 
  "PartitionKeys": [ 
    { 
      "ColumnName": "vector_id_str", 
      "Kind": "Hash", 
      "Properties": { 
        "Function": "XxHash64", 
        "MaxPartitionCount": 2048,      //  set it to max value create smaller partitions thus more balanced spread among all cluster nodes 
        "Seed": 1, 
        "PartitionAssignmentMode": "Uniform" 
      } 
    } 
  ], 
  "EffectiveDateTime": "2000-01-01"     //  set it to old date in order to apply partitioning on existing data 
} 
``` 

 

 

In the example above we modified the partitioning policy for WikipediaEmbeddingsTitleD. This table was created from WikipediaEmbeddings by projecting the documents’ title and embeddings.

 

Notes: 

  • The partitioning process requires a string key with high cardinality, so we also projected the unique vector_id and converted it to string.  
  • The best practice is to create an empty table, modify its partition policy then ingest the data. In that case there is no need to define the old EffectiveDateTime as above. 
  • It takes some time after data ingestion until the policy is applied. 

To test the effect of partitioning we created in a similar manner multiple tables containing up to 1M embedding vectors and tested the cosine similarity performance on clusters with 1, 2, 4, 8 & 20 nodes (SKU Standard_E4d_v5).

The following table and chart compare search performance (in seconds) before and after partitioning: 

 

 

Number of Nodes 

# of vectors 

1* (no partitioning)

2 

4 

8 

20 

25,000 Vectors 

3.4 

0.95 

0.67 

0.57 

0.51 

50,000 Vectors 

6.2 

1.5 

0.92 

0.65 

0.55 

100,000 Vectors 

12.4 

2.6 

1.55 

1 

0.57 

200,000 Vectors 

24.2 

5.2 

2.8 

1.65 

0.63 

400,000 Vectors 

48.5 

10.3 

5.4 

2.95 

0.87 

800,000 Vectors 

96.5 

20.5 

10.5 

6 

1.2 

1,000,000 Vectors 

102 

26 

13.3 

7.2 

1.4 

 

* Note that the cluster has 2 nodes, but the tables are stored on a single node (this is our baseline before applying the partitioning policy) 

 

 

You can see that even on the smallest 2 nodes cluster the search speed is improved by more than x4 factor, and in general the speed is inversely proportional to the number of nodes. The number of embedding vectors that are needed for common LLM scenarios (e.g. Retrieval Augmented Generation) rarely exceeds 100K, thus by having 8 nodes searching can be done in 1 sec. 

 

How can you get started? 

If you would like to try this demo, head to the  azure_kusto_vector  GitHub repository and follow the instructions. 

The Notebook in the repo will allow you to -    

  1. Download precomputed embeddings created by OpenAI API.  
  2. Store the embeddings in ADX.  
  3. Convert raw text query to an embedding with OpenAI API.  
  4. Use ADX to perform cosine similarity search in the stored embeddings  

You can start by -

We look forward to your feedback and all the exciting things you build with kusto & vectors! 

 

Updated Nov 09, 2023
Version 4.0

4 Comments

  • vsrekha213's avatar
    vsrekha213
    Copper Contributor

    Hi,

    The provided example demonstrates using pre-computed vector data stored in a CSV file. While this works, I'm interested in a more permanent solution. Can we directly store vector data within a Fabric Warehouse table, in a dedicated column, instead of relying on a notebook to load it each time? My goal is to persistently store the vector data in a database, making it readily available for future use without requiring manual loading through notebooks.

  • AleksandraJ's avatar
    AleksandraJ
    Copper Contributor

    Hi!

    Nice article. However I have a doubts that I can follow above example and github repo with free ADX cluster. I tried it and I don't see how I can do this. Moreover it is not mentioned in article or anywhere in github repo, but you need spark session up and running and you can not run it locally (or it is quite tricky and not quick option also if you have already pyspark installed locally). Any other option how to write data to a free kusto table (free cluster)  without using spark dataframe and authentication ?

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This post is co-authored by @adieldar (Principal Data Scientist, Microsoft)

\n

 

\n

In a previous blog – Azure Data Explorer for Vector Similarity Search, we focused on how Azure Data Explorer (Kusto) is perfectly suited for storing and searching vector embeddings.  

\n

In this blog, we will focus on performance tuning and optimizations for running vector similarity searches at scale.  

\n

 

\n

We will continue working on the Wikipedia scenario where we generate the embeddings of wiki pages using OpenAI and store them in kusto. We then use series_cosine_similarity_fl  kusto function to perform similarity searches. 

\n

\n

Demo scenario

\n

 

\n

Optimizing for scale

\n

 

\n

To optimize the cosine similarity search we need to split the vectors table to many extents that are evenly distributed among all cluster nodes. This can be done by setting Partitioning Policy for the embedding table using the .alter-merge policy partitioning command: 

\n

 

\n

 

\n

 

\n

 

\n
.alter-merge table WikipediaEmbeddingsTitleD policy partitioning  \n``` \n{ \n  \"PartitionKeys\": [ \n    { \n      \"ColumnName\": \"vector_id_str\", \n      \"Kind\": \"Hash\", \n      \"Properties\": { \n        \"Function\": \"XxHash64\", \n        \"MaxPartitionCount\": 2048,      //  set it to max value create smaller partitions thus more balanced spread among all cluster nodes \n        \"Seed\": 1, \n        \"PartitionAssignmentMode\": \"Uniform\" \n      } \n    } \n  ], \n  \"EffectiveDateTime\": \"2000-01-01\"     //  set it to old date in order to apply partitioning on existing data \n} \n``` 
\n

 

\n

 

\n

In the example above we modified the partitioning policy for WikipediaEmbeddingsTitleD. This table was created from WikipediaEmbeddings by projecting the documents’ title and embeddings.

\n

 

\n

Notes: 

\n\n

To test the effect of partitioning we created in a similar manner multiple tables containing up to 1M embedding vectors and tested the cosine similarity performance on clusters with 1, 2, 4, 8 & 20 nodes (SKU Standard_E4d_v5).

\n

The following table and chart compare search performance (in seconds) before and after partitioning: 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 

\n
\n

Number of Nodes 

\n
\n

# of vectors 

\n
\n

1* (no partitioning)

\n
\n

2 

\n
\n

4 

\n
\n

8 

\n
\n

20 

\n
\n

25,000 Vectors 

\n
\n

3.4 

\n
\n

0.95 

\n
\n

0.67 

\n
\n

0.57 

\n
\n

0.51 

\n
\n

50,000 Vectors 

\n
\n

6.2 

\n
\n

1.5 

\n
\n

0.92 

\n
\n

0.65 

\n
\n

0.55 

\n
\n

100,000 Vectors 

\n
\n

12.4 

\n
\n

2.6 

\n
\n

1.55 

\n
\n

1 

\n
\n

0.57 

\n
\n

200,000 Vectors 

\n
\n

24.2 

\n
\n

5.2 

\n
\n

2.8 

\n
\n

1.65 

\n
\n

0.63 

\n
\n

400,000 Vectors 

\n
\n

48.5 

\n
\n

10.3 

\n
\n

5.4 

\n
\n

2.95 

\n
\n

0.87 

\n
\n

800,000 Vectors 

\n
\n

96.5 

\n
\n

20.5 

\n
\n

10.5 

\n
\n

6 

\n
\n

1.2 

\n
\n

1,000,000 Vectors 

\n
\n

102 

\n
\n

26 

\n
\n

13.3 

\n
\n

7.2 

\n
\n

1.4 

\n
\n

 

\n

* Note that the cluster has 2 nodes, but the tables are stored on a single node (this is our baseline before applying the partitioning policy) 

\n

 

\n

\n

 

\n

You can see that even on the smallest 2 nodes cluster the search speed is improved by more than x4 factor, and in general the speed is inversely proportional to the number of nodes. The number of embedding vectors that are needed for common LLM scenarios (e.g. Retrieval Augmented Generation) rarely exceeds 100K, thus by having 8 nodes searching can be done in 1 sec. 

\n

 

\n

How can you get started? 

\n

\n

If you would like to try this demo, head to the  azure_kusto_vector  GitHub repository and follow the instructions. 

\n

The Notebook in the repo will allow you to -    

\n
    \n
  1. Download precomputed embeddings created by OpenAI API.  
  2. \n
  3. Store the embeddings in ADX.  
  4. \n
  5. Convert raw text query to an embedding with OpenAI API.  
  6. \n
  7. Use ADX to perform cosine similarity search in the stored embeddings  
  8. \n
\n

You can start by -

\n\n

We look forward to your feedback and all the exciting things you build with kusto & vectors! 

\n

 

","body@stringLength":"32439","rawBody":"

This post is co-authored by @adieldar (Principal Data Scientist, Microsoft)

\n

 

\n

In a previous blog – Azure Data Explorer for Vector Similarity Search, we focused on how Azure Data Explorer (Kusto) is perfectly suited for storing and searching vector embeddings.  

\n

In this blog, we will focus on performance tuning and optimizations for running vector similarity searches at scale.  

\n

 

\n

We will continue working on the Wikipedia scenario where we generate the embeddings of wiki pages using OpenAI and store them in kusto. We then use series_cosine_similarity_fl  kusto function to perform similarity searches. 

\n

\n

Demo scenario

\n

 

\n

Optimizing for scale

\n

 

\n

To optimize the cosine similarity search we need to split the vectors table to many extents that are evenly distributed among all cluster nodes. This can be done by setting Partitioning Policy for the embedding table using the .alter-merge policy partitioning command: 

\n

 

\n

 

\n

 

\n

 

\n.alter-merge table WikipediaEmbeddingsTitleD policy partitioning \n``` \n{ \n \"PartitionKeys\": [ \n { \n \"ColumnName\": \"vector_id_str\", \n \"Kind\": \"Hash\", \n \"Properties\": { \n \"Function\": \"XxHash64\", \n \"MaxPartitionCount\": 2048, // set it to max value create smaller partitions thus more balanced spread among all cluster nodes \n \"Seed\": 1, \n \"PartitionAssignmentMode\": \"Uniform\" \n } \n } \n ], \n \"EffectiveDateTime\": \"2000-01-01\" // set it to old date in order to apply partitioning on existing data \n} \n``` \n

 

\n

 

\n

In the example above we modified the partitioning policy for WikipediaEmbeddingsTitleD. This table was created from WikipediaEmbeddings by projecting the documents’ title and embeddings.

\n

 

\n

Notes: 

\n\n

To test the effect of partitioning we created in a similar manner multiple tables containing up to 1M embedding vectors and tested the cosine similarity performance on clusters with 1, 2, 4, 8 & 20 nodes (SKU Standard_E4d_v5).

\n

The following table and chart compare search performance (in seconds) before and after partitioning: 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 

\n
\n

Number of Nodes 

\n
\n

# of vectors 

\n
\n

1* (no partitioning)

\n
\n

2 

\n
\n

4 

\n
\n

8 

\n
\n

20 

\n
\n

25,000 Vectors 

\n
\n

3.4 

\n
\n

0.95 

\n
\n

0.67 

\n
\n

0.57 

\n
\n

0.51 

\n
\n

50,000 Vectors 

\n
\n

6.2 

\n
\n

1.5 

\n
\n

0.92 

\n
\n

0.65 

\n
\n

0.55 

\n
\n

100,000 Vectors 

\n
\n

12.4 

\n
\n

2.6 

\n
\n

1.55 

\n
\n

1 

\n
\n

0.57 

\n
\n

200,000 Vectors 

\n
\n

24.2 

\n
\n

5.2 

\n
\n

2.8 

\n
\n

1.65 

\n
\n

0.63 

\n
\n

400,000 Vectors 

\n
\n

48.5 

\n
\n

10.3 

\n
\n

5.4 

\n
\n

2.95 

\n
\n

0.87 

\n
\n

800,000 Vectors 

\n
\n

96.5 

\n
\n

20.5 

\n
\n

10.5 

\n
\n

6 

\n
\n

1.2 

\n
\n

1,000,000 Vectors 

\n
\n

102 

\n
\n

26 

\n
\n

13.3 

\n
\n

7.2 

\n
\n

1.4 

\n
\n

 

\n

* Note that the cluster has 2 nodes, but the tables are stored on a single node (this is our baseline before applying the partitioning policy) 

\n

 

\n

\n

 

\n

You can see that even on the smallest 2 nodes cluster the search speed is improved by more than x4 factor, and in general the speed is inversely proportional to the number of nodes. The number of embedding vectors that are needed for common LLM scenarios (e.g. Retrieval Augmented Generation) rarely exceeds 100K, thus by having 8 nodes searching can be done in 1 sec. 

\n

 

\n

How can you get started? 

\n

\n

If you would like to try this demo, head to the  azure_kusto_vector  GitHub repository and follow the instructions. 

\n

The Notebook in the repo will allow you to -    

\n
    \n
  1. Download precomputed embeddings created by OpenAI API.  
  2. \n
  3. Store the embeddings in ADX.  
  4. \n
  5. Convert raw text query to an embedding with OpenAI API.  
  6. \n
  7. Use ADX to perform cosine similarity search in the stored embeddings  
  8. \n
\n

You can start by -

\n\n

We look forward to your feedback and all the exciting things you build with kusto & vectors! 

\n

 

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Optimizing Vector Similarity Searches at Scale","moderationData":{"__ref":"ModerationData:moderation_data:4109589"},"body":"

vsrekha213  absolutely, the example shows how-to load it one-time from Notebooks - the data once loaded into Kusto, stays there for future use depending on your retention settings. In Fabric, you can use the same capabilities in Eventhouse. Event house overview (preview) - Microsoft Fabric | Microsoft Learn

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Hi,

The provided example demonstrates using pre-computed vector data stored in a CSV file. While this works, I'm interested in a more permanent solution. Can we directly store vector data within a Fabric Warehouse table, in a dedicated column, instead of relying on a notebook to load it each time? My goal is to persistently store the vector data in a database, making it readily available for future use without requiring manual loading through notebooks.

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Hi AleksandraJ  - one quick option is for you to use the Notebook in repo and run it in Microsoft Fabric. You can just use the KQL Database in Fabric. 
If you want to avoid using spark, you can just download the dataset, and upload it to kusto using the Ingestion Wizard. Ingest data into Azure Data Explorer using the ingestion wizard - Azure Data Explorer | Microsoft Learn

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Hi!

Nice article. However I have a doubts that I can follow above example and github repo with free ADX cluster. I tried it and I don't see how I can do this. Moreover it is not mentioned in article or anywhere in github repo, but you need spark session up and running and you can not run it locally (or it is quite tricky and not quick option also if you have already pyspark installed locally). Any other option how to write data to a free kusto table (free cluster)  without using spark dataframe and authentication ?

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