From 0dd445ab5b8edaad22cd6986b432f1917274791f Mon Sep 17 00:00:00 2001 From: Dooyong Kim Date: Wed, 4 Dec 2024 15:39:22 -0800 Subject: [PATCH 1/9] Publish a blog regarding to the pluggable storage in vector db. Signed-off-by: Dooyong Kim --- _community_members/kdooyong.md | 25 +++ ...e-pluggable-storage-in-opensearch-vectordb | 211 ++++++++++++++++++ .../loading_layer_high_level.png | Bin 0 -> 77831 bytes .../searchable_snapshots_overview.png | Bin 0 -> 90058 bytes assets/media/community/members/kdooyong.jpg | Bin 0 -> 397770 bytes 5 files changed, 236 insertions(+) create mode 100644 _community_members/kdooyong.md create mode 100644 _posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb create mode 100644 assets/media/blog-images/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb/loading_layer_high_level.png create mode 100644 assets/media/blog-images/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb/searchable_snapshots_overview.png create mode 100644 assets/media/community/members/kdooyong.jpg diff --git a/_community_members/kdooyong.md b/_community_members/kdooyong.md new file mode 100644 index 000000000..f0c3aacc5 --- /dev/null +++ b/_community_members/kdooyong.md @@ -0,0 +1,25 @@ +--- +name: Dooyong Kim +short_name: kdooyong +title: 'OpenSearch Community Member: Dooyong Kim' +primary_title: Dooyong Kim +breadcrumbs: + icon: community + items: + - title: Community + url: /community/index.html + - title: Members + url: /community/members/index.html + - title: 'Dooyong Kim's Profile' + url: '/community/members/kdooyong.html' +photo: '/assets/media/community/members/kdooyong.jpg' +github: 0ctopus13prime +linkedin: doo-yong-kim-a8a97b126 +job_title_and_company: 'Software engineer at AWS' +personas: + - author +permalink: '/community/members/kdooyong.html' +redirect_from: '/authors/kdooyong/' +--- + +**Dooyong Kim** is a software engineer at AWS working on the OpenSearch Project and Amazon OpenSearch Service. His primary interests include machine learning, vector search. Outside of work, he enjoys reading and taking a nap in sofa afternoon. diff --git a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb new file mode 100644 index 000000000..3af35fb90 --- /dev/null +++ b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb @@ -0,0 +1,211 @@ +--- +layout: post +title: "Deep dive: Enable Pluggable Storage in OpenSearch VectorDB" +authors: + - kdooyong + - navneev + - vamshin +date: 2024-12-04 +categories: + - community-updates + - technical-posts +meta_keywords: OpenSearch strategic enhancements, OpenSearch core engine, vector search engine +meta_description: Showcase how a pluggable storage was enabled in OpenSearch vector db with a practical example of searchable snapshot which was not working in the past. +has_math: false +has_science_table: false +--- + +# Deep dive: Enable Pluggable Storage in OpenSearch VectorDB + +## Introduction + +In 2019, OpenSearch officially introduced the Vector Engine, which now supports three native engines: Non-Metric Space Library (NMSLIB), Facebook AI Similarity Search (Faiss), and Lucene. Unlike Lucene, which is Java-based, FAISS and NMSLIB are C++ libraries that OpenSearch accesses via a lightweight JNI layer. A limitation of these native engines is that they handle I/O through File APIs, with FAISS relying on `FILE` pointers and NMSLIB using `std::fstream` to save and load graph indexes. +This post will first provide a brief overview of KNN, then dive into the challenges we aim to address. Specifically, we’ll discuss attempts to introduce an abstract layer for loading in both native engines without performance degradation. Finally, we’ll demonstrate how after this change we can do Approximate k-NN Search with native engines on remote snapshots aka Searchable Snapshot feature is now available for vector indexes. + +## What is k-NN search? + +The k-NN (k-nearest neighbor) search algorithm identifies the k closest vectors to a given query vector. It relies on a distance metric, such as cosine similarity, to measure the similarity between two vector points - with closer points considered more similar. +One popular algorithm for approximate nearest neighbor (ANN) search in high-dimensional spaces is HNSW (Hierarchical Navigable Small World). HNSW organizes data points into a multi-layer graph structure, where each layer contains connections that enable efficient navigation through the data. Inspired by skip-lists, HNSW has layers with varying densities that increase proportionally with depth. This allows the search to effectively narrow down the search space to find vectors similar to a query vector, similar to how one might locate an address by moving from larger regions to finer details (e.g. country → state → street). +In the OpenSearch vector database, users can choose different algorithms for vector search, with HNSW being the most commonly used. In this post, any reference to the "graph index" will specifically refer to the HNSW index. + +For more information on building a k-NN similarity search engine with OpenSearch, please refer to our [documentation](https://opensearch.org/docs/latest/search-plugins/knn/index/). + +## Dependency on FILE API + +Native vector engines, such as FAISS and NMSLIB, are highly performant and provide predictable latencies. However, they come with limitations when it comes to integrating with storage systems that are not based on the file system. Unlike Lucene, which is Java-based and can leverage the Directory abstraction to read and write files, the native engines are tightly coupled with file system APIs. +The `Directory` class in Lucene offers a storage abstraction layer that enables users to read and write files, providing APIs for opening input and output streams along with utilities for tasks like getting file lengths and other file-related operations. This abstraction is the key factor for the Lucene Vector Search Engine, allowing it to store files independently of the underlying storage system. +We applied similar principles in native engines, abstracting the I/O layer to eliminate tight coupling with specific file APIs. Resolving this limitation allowed us to integrate with any directory implementation of Opensearch and make vector search compatible with them. + +## Solution — Loading Layer + +Both FAISS and NMSLIB load a graph-based vector index from storage into physical memory. During this loading phase, they rely on `fread` to fetch the bytes needed to restructure the graph index. +We can replaced `fread` and make the native engines rely on a read interface to fetch the bytes they need. FAISS already provides the `IOReader` interface, but we had to add a similar read interface in NMSLIB, called `NmslibIOReader`. +KNN search will be conducted after the graph is loaded into memory. Therefore, it should not impact average search performance. (For the benchmark result, please refer to the next section) + +![Loading layer in native engine high level overview +](/assets/media/blog-images/2024-12-04-enable-pluggable-storageoin-opensearch-vectordb/loading_layer_high_level.png){: .img-fluid} + +### Performance Benchmark Results + +#### Benchmark Environment + +|Key |Value | +|--- |--- | +|Opensearch Verison |2.18 | +|vCPUs |48 | +|Physical Memory |128gb | +|Storage type |Elastic Block Storage | +|JVM |63gb | +| | | +|total number of vectors |1M | +|dimensions |128 | + +#### Benchmark Results + +In the benchmark, we achieved identical search performance results after introducing the loading layer, compared to the baseline. Also, we did not find any evidence of disparity in system metrics or JVM GC metrics between the two. +From this, we concluded that we successfully replaced the tight coupling of the File API with Lucene's IndexInput while maintaining the same search performance, allowing users to plug in a custom Directory in OpenSearch to save a vector index in their desired storage. + +|Engine |Metric |Baseline |Candidate |Explanation | +|--- |--- |--- |--- |--- | +|Faiss |Average query latency |3.5832 ms |3.83349 ms |Average time taken to process a vector search query. | +|Faiss |P99 query latency |22.1628 ms |23.8439 ms |P99 search latency for processing a vector search query. | +|Faiss |Total Young Gen JVM GC time |0.338 sec |0.342 sec |Total time spent on Young GC in the JVM. | +| | | | | | + +## Searchable Snapshot + +With the loading layer now in place, we can now use a searchable snapshot to perform Vector search directly on it! Below is the high level flow on how does Searchable Snapshots work. + +![searchable snapshots overview](/assets/media/blog-images/2024-12-04-enable-pluggable-storageoin-opensearch-vectordb/searchable_snapshots_overview.png){: .img-fluid} + +### Perquisites + +1. Please ensure that you cluster is correctly setup for searchable snapshot feature. ([Ref](https://opensearch.org/docs/latest/tuning-your-cluster/availability-and-recovery/snapshots/searchable_snapshot/#configuring-a-node-to-use-searchable-snapshots)) + +### Setting up Vector index + +``` +PUT /knn-index/ +{ + "settings": { + "index": { + "knn": true, + } + }, + "mappings": { + "properties": { + "my_vector": { + "type": "knn_vector", + "dimension": 2 + } + } + } +} +``` + +### Ingest Data + +``` +POST _bulk?refresh +{ "index": { "_index": "knn-index", "_id": "1" } } +{ "my_vector": [1.5, 2.5], "price": 12.2 } +{ "index": { "_index": "knn-index", "_id": "2" } } +{ "my_vector": [2.5, 3.5], "price": 7.1 } +{ "index": { "_index": "knn-index", "_id": "3" } } +{ "my_vector": [3.5, 4.5], "price": 12.9 } +{ "index": { "_index": "knn-index", "_id": "4" } } +{ "my_vector": [5.5, 6.5], "price": 1.2 } +{ "index": { "_index": "knn-index", "_id": "5" } } +{ "my_vector": [4.5, 5.5], "price": 3.7 } + +``` + +### Run Query on the Local index to ensure index is correctly setup + +``` +POST knn-index/_search +{ + "query": { + "knn": { + "my_vector": { + "vector": [2, 3], + "k": 2 + } + } + } +} +``` + +### Take Snapshot + +Go ahead and take the snapshot of the above created index using [this](https://opensearch.org/docs/latest/tuning-your-cluster/availability-and-recovery/snapshots/snapshot-restore/) documentation. Once the snapshot is completed go ahead and delete the `knn-index` so that it is no longer available locally. + +### Create a Searchable snapshot index from the snapshot + +``` +POST _snapshot///_restore +{ + "storage_type": "remote_snapshot", + "indices": "knn-index" +} + +curl -X GET http://localhost:9200/_cat/indices + +``` + +For more details ref [this](https://opensearch.org/docs/latest/tuning-your-cluster/availability-and-recovery/snapshots/searchable_snapshot/#create-a-searchable-snapshot-index) documentation. + +### Run Vector Search Query + +``` +POST knn-index/_search +{ + "query": { + "knn": { + "my_vector": { + "vector": [2, 3], + "k": 2 + } + } + } +} +``` + +The above query should give you same results as the query running on the index available locally. + + +## Conclusion + +We replaced the native engines' tight coupling with file system APIs by introducing an abstract I/O layer that leverages Lucene's Directory interfaces. This approach allows the vector engine to read the graph data structures from any storage system supported by OpenSearch's Directory implementation, rather than being limited to local file storage. To ensure this change did not impact performance, we conducted extensive benchmarking. The results showed that we were able to match the search performance of the original file API-based approach. Importantly, we found no regression in search times once the graphs were loaded into memory, as this is a one-time operation for a properly scaled cluster. + +With the new read interface in place, users can now utilize any Directory implementation in OpenSearch, and it will be compatible with vector indices. This flexibility enables the use of remote storage options, such as S3, for vector data. + +## Next Steps + +In the 2.18 release, we added the capability to enable vector search queries to use ~~to load vector files at the segment level using~~ Lucene's Directory and IndexInput interfaces. Building on this, in the 2.19 release, we plan to further extend this capability during the native index creation process. Specifically, the KNN plugin will start using the IndexOutput interface to write the graph file directly to a segment([github issue](https://github.com/opensearch-project/k-NN/issues/2033)). + +Furthermore, since the KNN plugin now has the ability to stream the vector data structure files, this opens up the possibility for partial loading of these files. 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z;CEXGKjpP!d{aLT(R+B<5hU{UHf1vD42H>aS@1sire=vPs~kPK_%!pKDKcfDx)*tA zKk89<`L9)vD9yJhX?eK?2D*U+ByC0Izyv6Vf$)l~XTOGWeYHGI|7lYp!xtw|@@zR2 zvTCuPM%*x_)Q6*&-^4Pgg~3UeH!cM3G~+wid7!EWdBo-U!J&B6(>!agyTM^yv$!OM z$hUV+qw$P{T+h}MA|d6 z(d^VHBJ%-BD>F}Z6@zVn+%fWgGUACe-RE-F9}v+J3m@*mGultKzD6}GIHDwke$~na`jeod9$syA_DSK* zr@Shvo8yz@<(`aCLdL}{#u}v!OY5(=;2hdo2u4Ix!tPA7S6T~5Z`frgTz&h=GAY+9 z_Dl8(!o`uZZSEZO%H%7?a?say}wxRIf8^`Jq=VQD`< zw|gB($$Hd%aQH}|@n_&ti+(Jvp@F}PnrpPe;&dF+S8sQ=#)GOpN3Li=UOMbmC3=@X z^IDy&V7y4DN60!lF1Rk2UkzonB@au~9Z?ERaBI!~>fPx2(T7EM-JK Date: Mon, 16 Dec 2024 16:37:09 -0500 Subject: [PATCH 2/9] Doc review Signed-off-by: Fanit Kolchina --- _community_members/kdooyong.md | 2 +- ...e-pluggable-storage-in-opensearch-vectordb | 211 --------------- ...luggable-storage-in-opensearch-vectordb.md | 255 ++++++++++++++++++ 3 files changed, 256 insertions(+), 212 deletions(-) delete mode 100644 _posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb create mode 100644 _posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md diff --git a/_community_members/kdooyong.md b/_community_members/kdooyong.md index f0c3aacc5..db71a011d 100644 --- a/_community_members/kdooyong.md +++ b/_community_members/kdooyong.md @@ -22,4 +22,4 @@ permalink: '/community/members/kdooyong.html' redirect_from: '/authors/kdooyong/' --- -**Dooyong Kim** is a software engineer at AWS working on the OpenSearch Project and Amazon OpenSearch Service. His primary interests include machine learning, vector search. Outside of work, he enjoys reading and taking a nap in sofa afternoon. +**Dooyong Kim** is a software engineer at AWS working on the OpenSearch Project and Amazon OpenSearch Service. His primary interests include machine learning and vector search. Outside of work, he enjoys reading and taking a nap on a sofa in the afternoon. diff --git a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb deleted file mode 100644 index 3af35fb90..000000000 --- a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb +++ /dev/null @@ -1,211 +0,0 @@ ---- -layout: post -title: "Deep dive: Enable Pluggable Storage in OpenSearch VectorDB" -authors: - - kdooyong - - navneev - - vamshin -date: 2024-12-04 -categories: - - community-updates - - technical-posts -meta_keywords: OpenSearch strategic enhancements, OpenSearch core engine, vector search engine -meta_description: Showcase how a pluggable storage was enabled in OpenSearch vector db with a practical example of searchable snapshot which was not working in the past. -has_math: false -has_science_table: false ---- - -# Deep dive: Enable Pluggable Storage in OpenSearch VectorDB - -## Introduction - -In 2019, OpenSearch officially introduced the Vector Engine, which now supports three native engines: Non-Metric Space Library (NMSLIB), Facebook AI Similarity Search (Faiss), and Lucene. Unlike Lucene, which is Java-based, FAISS and NMSLIB are C++ libraries that OpenSearch accesses via a lightweight JNI layer. A limitation of these native engines is that they handle I/O through File APIs, with FAISS relying on `FILE` pointers and NMSLIB using `std::fstream` to save and load graph indexes. -This post will first provide a brief overview of KNN, then dive into the challenges we aim to address. Specifically, we’ll discuss attempts to introduce an abstract layer for loading in both native engines without performance degradation. Finally, we’ll demonstrate how after this change we can do Approximate k-NN Search with native engines on remote snapshots aka Searchable Snapshot feature is now available for vector indexes. - -## What is k-NN search? - -The k-NN (k-nearest neighbor) search algorithm identifies the k closest vectors to a given query vector. It relies on a distance metric, such as cosine similarity, to measure the similarity between two vector points - with closer points considered more similar. -One popular algorithm for approximate nearest neighbor (ANN) search in high-dimensional spaces is HNSW (Hierarchical Navigable Small World). HNSW organizes data points into a multi-layer graph structure, where each layer contains connections that enable efficient navigation through the data. Inspired by skip-lists, HNSW has layers with varying densities that increase proportionally with depth. This allows the search to effectively narrow down the search space to find vectors similar to a query vector, similar to how one might locate an address by moving from larger regions to finer details (e.g. country → state → street). -In the OpenSearch vector database, users can choose different algorithms for vector search, with HNSW being the most commonly used. In this post, any reference to the "graph index" will specifically refer to the HNSW index. - -For more information on building a k-NN similarity search engine with OpenSearch, please refer to our [documentation](https://opensearch.org/docs/latest/search-plugins/knn/index/). - -## Dependency on FILE API - -Native vector engines, such as FAISS and NMSLIB, are highly performant and provide predictable latencies. However, they come with limitations when it comes to integrating with storage systems that are not based on the file system. Unlike Lucene, which is Java-based and can leverage the Directory abstraction to read and write files, the native engines are tightly coupled with file system APIs. -The `Directory` class in Lucene offers a storage abstraction layer that enables users to read and write files, providing APIs for opening input and output streams along with utilities for tasks like getting file lengths and other file-related operations. This abstraction is the key factor for the Lucene Vector Search Engine, allowing it to store files independently of the underlying storage system. -We applied similar principles in native engines, abstracting the I/O layer to eliminate tight coupling with specific file APIs. Resolving this limitation allowed us to integrate with any directory implementation of Opensearch and make vector search compatible with them. - -## Solution — Loading Layer - -Both FAISS and NMSLIB load a graph-based vector index from storage into physical memory. During this loading phase, they rely on `fread` to fetch the bytes needed to restructure the graph index. -We can replaced `fread` and make the native engines rely on a read interface to fetch the bytes they need. FAISS already provides the `IOReader` interface, but we had to add a similar read interface in NMSLIB, called `NmslibIOReader`. -KNN search will be conducted after the graph is loaded into memory. Therefore, it should not impact average search performance. (For the benchmark result, please refer to the next section) - -![Loading layer in native engine high level overview -](/assets/media/blog-images/2024-12-04-enable-pluggable-storageoin-opensearch-vectordb/loading_layer_high_level.png){: .img-fluid} - -### Performance Benchmark Results - -#### Benchmark Environment - -|Key |Value | -|--- |--- | -|Opensearch Verison |2.18 | -|vCPUs |48 | -|Physical Memory |128gb | -|Storage type |Elastic Block Storage | -|JVM |63gb | -| | | -|total number of vectors |1M | -|dimensions |128 | - -#### Benchmark Results - -In the benchmark, we achieved identical search performance results after introducing the loading layer, compared to the baseline. Also, we did not find any evidence of disparity in system metrics or JVM GC metrics between the two. -From this, we concluded that we successfully replaced the tight coupling of the File API with Lucene's IndexInput while maintaining the same search performance, allowing users to plug in a custom Directory in OpenSearch to save a vector index in their desired storage. - -|Engine |Metric |Baseline |Candidate |Explanation | -|--- |--- |--- |--- |--- | -|Faiss |Average query latency |3.5832 ms |3.83349 ms |Average time taken to process a vector search query. | -|Faiss |P99 query latency |22.1628 ms |23.8439 ms |P99 search latency for processing a vector search query. | -|Faiss |Total Young Gen JVM GC time |0.338 sec |0.342 sec |Total time spent on Young GC in the JVM. | -| | | | | | - -## Searchable Snapshot - -With the loading layer now in place, we can now use a searchable snapshot to perform Vector search directly on it! Below is the high level flow on how does Searchable Snapshots work. - -![searchable snapshots overview](/assets/media/blog-images/2024-12-04-enable-pluggable-storageoin-opensearch-vectordb/searchable_snapshots_overview.png){: .img-fluid} - -### Perquisites - -1. Please ensure that you cluster is correctly setup for searchable snapshot feature. ([Ref](https://opensearch.org/docs/latest/tuning-your-cluster/availability-and-recovery/snapshots/searchable_snapshot/#configuring-a-node-to-use-searchable-snapshots)) - -### Setting up Vector index - -``` -PUT /knn-index/ -{ - "settings": { - "index": { - "knn": true, - } - }, - "mappings": { - "properties": { - "my_vector": { - "type": "knn_vector", - "dimension": 2 - } - } - } -} -``` - -### Ingest Data - -``` -POST _bulk?refresh -{ "index": { "_index": "knn-index", "_id": "1" } } -{ "my_vector": [1.5, 2.5], "price": 12.2 } -{ "index": { "_index": "knn-index", "_id": "2" } } -{ "my_vector": [2.5, 3.5], "price": 7.1 } -{ "index": { "_index": "knn-index", "_id": "3" } } -{ "my_vector": [3.5, 4.5], "price": 12.9 } -{ "index": { "_index": "knn-index", "_id": "4" } } -{ "my_vector": [5.5, 6.5], "price": 1.2 } -{ "index": { "_index": "knn-index", "_id": "5" } } -{ "my_vector": [4.5, 5.5], "price": 3.7 } - -``` - -### Run Query on the Local index to ensure index is correctly setup - -``` -POST knn-index/_search -{ - "query": { - "knn": { - "my_vector": { - "vector": [2, 3], - "k": 2 - } - } - } -} -``` - -### Take Snapshot - -Go ahead and take the snapshot of the above created index using [this](https://opensearch.org/docs/latest/tuning-your-cluster/availability-and-recovery/snapshots/snapshot-restore/) documentation. Once the snapshot is completed go ahead and delete the `knn-index` so that it is no longer available locally. - -### Create a Searchable snapshot index from the snapshot - -``` -POST _snapshot///_restore -{ - "storage_type": "remote_snapshot", - "indices": "knn-index" -} - -curl -X GET http://localhost:9200/_cat/indices - -``` - -For more details ref [this](https://opensearch.org/docs/latest/tuning-your-cluster/availability-and-recovery/snapshots/searchable_snapshot/#create-a-searchable-snapshot-index) documentation. - -### Run Vector Search Query - -``` -POST knn-index/_search -{ - "query": { - "knn": { - "my_vector": { - "vector": [2, 3], - "k": 2 - } - } - } -} -``` - -The above query should give you same results as the query running on the index available locally. - - -## Conclusion - -We replaced the native engines' tight coupling with file system APIs by introducing an abstract I/O layer that leverages Lucene's Directory interfaces. This approach allows the vector engine to read the graph data structures from any storage system supported by OpenSearch's Directory implementation, rather than being limited to local file storage. To ensure this change did not impact performance, we conducted extensive benchmarking. The results showed that we were able to match the search performance of the original file API-based approach. Importantly, we found no regression in search times once the graphs were loaded into memory, as this is a one-time operation for a properly scaled cluster. - -With the new read interface in place, users can now utilize any Directory implementation in OpenSearch, and it will be compatible with vector indices. This flexibility enables the use of remote storage options, such as S3, for vector data. - -## Next Steps - -In the 2.18 release, we added the capability to enable vector search queries to use ~~to load vector files at the segment level using~~ Lucene's Directory and IndexInput interfaces. Building on this, in the 2.19 release, we plan to further extend this capability during the native index creation process. Specifically, the KNN plugin will start using the IndexOutput interface to write the graph file directly to a segment([github issue](https://github.com/opensearch-project/k-NN/issues/2033)). - -Furthermore, since the KNN plugin now has the ability to stream the vector data structure files, this opens up the possibility for partial loading of these files. This will help reduce the memory pressure on the cluster and provide a better price-performance experience, especially when the cluster is under stress.([github issue](https://github.com/opensearch-project/k-NN/issues/1693)) - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md new file mode 100644 index 000000000..84c101bac --- /dev/null +++ b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md @@ -0,0 +1,255 @@ +--- +layout: post +title: "Deep dive: Enabling pluggable storage in the OpenSearch vector engine" +authors: + - kdooyong + - navneev + - vamshin + - kolchfa +date: 2024-12-16 +categories: + - technical-posts + - community +meta_keywords: OpenSearch strategic enhancements, OpenSearch core engine, vector search engine +meta_description: Showcase how a pluggable storage was enabled in OpenSearch vector db with a practical example of searchable snapshot which was not working in the past. +has_math: false +has_science_table: true +--- + +In 2019, OpenSearch introduced the vector engine, which supports three native engines: Non-Metric Space Library (NMSLIB), Facebook AI Similarity Search (Faiss), and Lucene. Unlike Lucene, which is Java-based, Faiss and NMSLIB are C++ libraries that OpenSearch accesses through a lightweight JNI layer. However, these native engines handle I/O using file-based APIs, with Faiss relying on `FILE` pointers and NMSLIB using `std::fstream` to manage graph indexes. + +This blog post explains how we addressed these limitations by introducing an abstraction layer for loading data into native engines without compromising performance. We'll start with an overview of k-NN search, discuss the challenges of file API dependencies, and explain the solution we implemented. Finally, we'll explore how these changes support searchable snapshots on vector indexes, which involves running approximate k-NN search on remote snapshots using native engines. + +## What is k-NN search? + +The k-nearest neighbor (k-NN) search algorithm identifies the k closest vectors to a given query vector. It uses a distance metric, such as cosine similarity, to measure similarity between vectors, with closer points considered more similar. + +In the OpenSearch vector database, you can choose different vector search algorithms. A popular algorithm for approximate nearest neighbor (ANN) search in high-dimensional spaces is Hierarchical Navigable Small World (HNSW). HNSW organizes data points into a multi-layer graph, in which each layer contains connections for efficient navigation through data. Inspired by skip lists, layers in the HNSW graph have varying densities that increase proportionally with depth. This helps narrow the search space from broader to more specific regions—--similar to locating an address by starting with a country and then narrowing down to a state, town, and street. + +For more information about building a k-NN similarity search engine with OpenSearch, see our [documentation](https://opensearch.org/docs/latest/search-plugins/knn/index/). + +## Challenges with file-based APIs + +Native vector engines, such ass Faiss and NMSLIB, offer high performance and predictable latencies. However, their reliance on file-based APIs makes it difficult to integrate them with storage that is not based on file systems. + +Lucene uses the Java-based **Directory** abstraction for reading and writing files, native engines tightly couple their operations to file-based I/O. The **Directory** class abstracts file storage, enabling operations like reading, writing, and managing file metadata across diverse storage systems. This abstraction allows the Lucene vector engine to store files independently of the underlying storage in OpenSearch. + +Unlike Lucene, native engines tightly couple their operations to file-based I/O. To address these limitations, we applied Lucene's principles to native engines. By abstracting the I/O layer, we eliminated the engines' tight coupling with specific file APIs. This enhancement enables integration with any OpenSearch directory implementation, making vector search compatible with a broader range of storage systems. + +## Introducing the loading layer + +Both Faiss and NMSLIB load graph-based vector indexes from storage into memory. During this process, they use `fread` to fetch the bytes needed to reconstruct the graph. + +To improve flexibility, we replaced `fread` with a read interface. Faiss provides an **IOReader** interface for reading index data from various storage systems. For NMSLIB, we introduced a similar read interface called **NmslibIOReader**. These interfaces allow the native engines to read data through an abstraction layer, making it possible to integrate with OpenSearch's directory implementations. + +Because k-NN search is conducted after the graph is loaded into memory, this change does not impact average search performance. + +The following diagram provides a high-level overview of the loading layer in native engines. + +![High-level overview of the loading layer in native engines](/assets/media/blog-images/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb/loading_layer_high_level.png){:style="width: 100%; max-width: 800px;"} + +## Performance benchmarks + +The following sections present performance benchmark results. + +### Benchmarking environment + +We ran benchmarking tests in an environment with the following configuration. + +| OpenSearch version | 2.18 | +| vCPUs | 48 | +| Physical memory | 128 GB | +| Storage type | Elastic Block Storage | +| JVM | 63 GB | +| Total number of vectors | 1M | +| Dimensions | 128 | + +### Benchmarking results + +During benchmarking, we observed that introducing the loading layer resulted in identical search performance compared to the baseline. Additionally, there were no differences in system metrics or JVM GC metrics when the loading layer was introduced. + +Based on these findings, we concluded that we successfully replaced the tight coupling of the File API with Lucene's **IndexInput**. This change maintained the same search performance. Additionally, with this change, you can integrate a custom **Directory** in OpenSearch and save a vector index in your preferred storage system. + +The following table presents our benchmarking results, comparing query latency with the loading layer (candidate) to the baseline. + +| Engine | Metric | Description | Baseline | Candidate | +|--------|---|---|---|---| +| Faiss | Average query latency | The time taken to process a vector search query. | 3.5832 ms | 3.83349 ms | +| Faiss | p99 query latency | The p99 latency for processing a vector search query. | 22.1628 ms | 23.8439 ms | +| Faiss | Total Young Gen JVM GC time | The time spent on Young GC in the JVM. |0.338 sec | 0.342 sec | + +The results demonstrate that replacing the file-based API with Lucene's **IndexInput** maintains search performance while enabling broader storage compatibility. + +## Configuring searchable snapshots for vector search + +With the loading layer in place, you can now perform vector searches directly on remote snapshots. At a high level, you'll create a vector index, take a snapshot of the index, and run a vector search on the snapshot. The following diagram illustrates these steps. + +![Overview of searchable snapshots](/assets/media/blog-images/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb/searchable_snapshots_overview.png){:style="width: 100%; max-width: 600px;"} + +Follow these steps to configure searchable snapshots. + +### Prerequisites + +Configure your cluster for searchable snapshots. For detailed steps, see [Configuring a node to use searchable snapshots](https://opensearch.org/docs/latest/tuning-your-cluster/availability-and-recovery/snapshots/searchable_snapshot/#configuring-a-node-to-use-searchable-snapshots). + +### Step 1: Create a local index + +Use the following request to create a local vector index: + +```json +PUT /knn-index/ +{ + "settings": { + "index": { + "knn": true + } + }, + "mappings": { + "properties": { + "my_vector": { + "type": "knn_vector", + "dimension": 2 + } + } + } +} +``` + +### Step 2: Ingest data + +Ingest some data into the index: + +```json +POST _bulk?refresh +{ "index": { "_index": "knn-index", "_id": "1" } } +{ "my_vector": [1.5, 2.5], "price": 12.2 } +{ "index": { "_index": "knn-index", "_id": "2" } } +{ "my_vector": [2.5, 3.5], "price": 7.1 } +{ "index": { "_index": "knn-index", "_id": "3" } } +{ "my_vector": [3.5, 4.5], "price": 12.9 } +{ "index": { "_index": "knn-index", "_id": "4" } } +{ "my_vector": [5.5, 6.5], "price": 1.2 } +{ "index": { "_index": "knn-index", "_id": "5" } } +{ "my_vector": [4.5, 5.5], "price": 3.7 } +``` + +### Step 3: Query the local index + +Query the local index to ensure it's configured correctly: + +```json +POST knn-index/_search +{ + "query": { + "knn": { + "my_vector": { + "vector": [2, 3], + "k": 2 + } + } + } +} +``` + +The response returns vectors closest to the query vector: + +```json +{ + "took": 16, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 2, + "relation": "eq" + }, + "max_score": 0.6666667, + "hits": [ + { + "_index": "knn-index", + "_id": "1", + "_score": 0.6666667, + "_source": { + "my_vector": [ + 1.5, + 2.5 + ], + "price": 12.2 + } + }, + { + "_index": "knn-index", + "_id": "2", + "_score": 0.6666667, + "_source": { + "my_vector": [ + 2.5, + 3.5 + ], + "price": 7.1 + } + } + ] + } +} +``` + +### Step 4: Take a snapshot + +Take a snapshot of the index. For detailed steps, see [Take and restore snapshots](https://opensearch.org/docs/latest/tuning-your-cluster/availability-and-recovery/snapshots/snapshot-restore/). After taking the snapshot, delete the `knn-index` so it is no longer available locally. + +### Step 5: Create a searchable snapshot index from the snapshot + +Use the following request to restore the original index from the snapshot as a searchable snapshot: + +```json +POST _snapshot///_restore +{ + "storage_type": "remote_snapshot", + "indices": "knn-index" +} +``` + +To verify the successful creation of the searchable snapshot index, use the following request: + +```json +GET /_cat/indices +``` +For more information, see [Create a searchable snapshot index](https://opensearch.org/docs/latest/tuning-your-cluster/availability-and-recovery/snapshots/searchable_snapshot/#create-a-searchable-snapshot-index). + +### Step 6: Run a vector search query + +Run a vector search query on the searchable snapshot index: + +```json +POST knn-index/_search +{ + "query": { + "knn": { + "my_vector": { + "vector": [2, 3], + "k": 2 + } + } + } +} +``` + +The query returns the same results as the query on the local index in [Step 3](#step-3-query-the-local-index). + +## Conclusion + +By introducing an abstract I/O layer that uses Lucene's **Directory** abstraction, we eliminated the native engines' dependency on file-based APIs that limited storage to local file systems. This change allows the vector engine to read graph data structures from any storage system supported by OpenSearch's **Directory** implementation. Our extensive benchmark testing confirmed that this change maintained the original file API-based approach's search performance. Notably, we observed no regression in search times after the graphs were loaded into memory (graph loading is a one-time operation for a properly scaled cluster). + +With this new read interface, you can now use any **Directory** implementation in OpenSearch with vector indexes. This added flexibility makes it possible to store vector data in remote storage solutions like S3. + +## Next steps + +In the 2.18 release, we introduced the capability to enable vector search queries using Lucene's **Directory** and **IndexInput** classes. Looking ahead, the 2.19 release will expand this functionality to the native index creation process. Specifically, the k-NN plugin will start using the **IndexOutput** class to write graph files directly to segments. For more information, see [this GitHub issue](https://github.com/opensearch-project/k-NN/issues/2033). + +Additionally, because the k-NN plugin now has the ability to stream vector data structure files, this presents an opportunity for partial loading of these files. This enhancement will reduce memory pressure on the cluster and deliver better price-performance, especially under high-stress conditions. For more information, see [this GitHub issue](https://github.com/opensearch-project/k-NN/issues/1693). From f9f82f3cd4fbd3811cc6a69a27f962fa5aae4e08 Mon Sep 17 00:00:00 2001 From: Dooyong Kim Date: Mon, 16 Dec 2024 18:19:22 -0800 Subject: [PATCH 3/9] Update meta info in the blog. Signed-off-by: Dooyong Kim --- ...024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md index 84c101bac..b4ba09e78 100644 --- a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md +++ b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md @@ -10,7 +10,7 @@ date: 2024-12-16 categories: - technical-posts - community -meta_keywords: OpenSearch strategic enhancements, OpenSearch core engine, vector search engine +meta_keywords: OpenSearch vector engine, vector search, pluggable storage, Faiss, NMSLIB, k-NN search, searchable snapshots, performance optimization meta_description: Showcase how a pluggable storage was enabled in OpenSearch vector db with a practical example of searchable snapshot which was not working in the past. has_math: false has_science_table: true From 63c2839787e009c24cae71631c3589eb8000827b Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Tue, 17 Dec 2024 08:22:30 -0500 Subject: [PATCH 4/9] Apply suggestions from code review Co-authored-by: Nathan Bower Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --- ...luggable-storage-in-opensearch-vectordb.md | 36 +++++++++---------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md index b4ba09e78..20dfe36ea 100644 --- a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md +++ b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md @@ -16,31 +16,31 @@ has_math: false has_science_table: true --- -In 2019, OpenSearch introduced the vector engine, which supports three native engines: Non-Metric Space Library (NMSLIB), Facebook AI Similarity Search (Faiss), and Lucene. Unlike Lucene, which is Java-based, Faiss and NMSLIB are C++ libraries that OpenSearch accesses through a lightweight JNI layer. However, these native engines handle I/O using file-based APIs, with Faiss relying on `FILE` pointers and NMSLIB using `std::fstream` to manage graph indexes. +In 2019, OpenSearch introduced the Vector Engine, which supports three native engines: Non-Metric Space Library (NMSLIB), Facebook AI Similarity Search (Faiss), and Lucene. Unlike Lucene, which is Java based, Faiss and NMSLIB are C++ libraries that OpenSearch accesses through a lightweight JNI layer. However, these native engines handle I/O using file-based APIs, with Faiss relying on `FILE` pointers and NMSLIB using `std::fstream` to manage graph indexes. -This blog post explains how we addressed these limitations by introducing an abstraction layer for loading data into native engines without compromising performance. We'll start with an overview of k-NN search, discuss the challenges of file API dependencies, and explain the solution we implemented. Finally, we'll explore how these changes support searchable snapshots on vector indexes, which involves running approximate k-NN search on remote snapshots using native engines. +This blog post explains how we addressed these limitations by introducing an abstraction layer for loading data into native engines without compromising performance. We'll start with an overview of k-NN search, discuss the challenges of file API dependencies, and explain the solution we implemented. Finally, we'll explore how these changes support searchable snapshots of vector indexes, which involves running approximate k-NN search on remote snapshots using native engines. ## What is k-NN search? -The k-nearest neighbor (k-NN) search algorithm identifies the k closest vectors to a given query vector. It uses a distance metric, such as cosine similarity, to measure similarity between vectors, with closer points considered more similar. +The k-nearest neighbors (k-NN) search algorithm identifies the k closest vectors to a given query vector. It uses a distance metric, such as cosine similarity, to measure similarity between vectors, with closer points considered to be more similar. -In the OpenSearch vector database, you can choose different vector search algorithms. A popular algorithm for approximate nearest neighbor (ANN) search in high-dimensional spaces is Hierarchical Navigable Small World (HNSW). HNSW organizes data points into a multi-layer graph, in which each layer contains connections for efficient navigation through data. Inspired by skip lists, layers in the HNSW graph have varying densities that increase proportionally with depth. This helps narrow the search space from broader to more specific regions—--similar to locating an address by starting with a country and then narrowing down to a state, town, and street. +In the OpenSearch vector database, you can choose from different vector search algorithms. A popular algorithm for approximate nearest neighbor (ANN) search in high-dimensional spaces is Hierarchical Navigable Small World (HNSW). HNSW organizes data points into a multi-layer graph in which each layer contains connections for efficient data navigation. Inspired by skip lists, HNSW graph layers have varying densities that increase proportionally with depth. This helps narrow the search space from broader to more specific regions---similar to locating an address by starting with a country and then narrowing down to a state, city, and street. For more information about building a k-NN similarity search engine with OpenSearch, see our [documentation](https://opensearch.org/docs/latest/search-plugins/knn/index/). ## Challenges with file-based APIs -Native vector engines, such ass Faiss and NMSLIB, offer high performance and predictable latencies. However, their reliance on file-based APIs makes it difficult to integrate them with storage that is not based on file systems. +Native vector engines, such as Faiss and NMSLIB, offer high performance and predictable latencies. However, their reliance on file-based APIs makes them difficult to integrate with storage that is not file system based. -Lucene uses the Java-based **Directory** abstraction for reading and writing files, native engines tightly couple their operations to file-based I/O. The **Directory** class abstracts file storage, enabling operations like reading, writing, and managing file metadata across diverse storage systems. This abstraction allows the Lucene vector engine to store files independently of the underlying storage in OpenSearch. +Lucene uses the Java-based **Directory** abstraction for reading and writing files. The **Directory** class abstracts file storage, enabling operations like reading, writing, and managing file metadata across diverse storage systems. This abstraction allows the Lucene vector engine to store files independently of the underlying OpenSearch storage. -Unlike Lucene, native engines tightly couple their operations to file-based I/O. To address these limitations, we applied Lucene's principles to native engines. By abstracting the I/O layer, we eliminated the engines' tight coupling with specific file APIs. This enhancement enables integration with any OpenSearch directory implementation, making vector search compatible with a broader range of storage systems. +Unlike Lucene, native engines tightly couple their operations to file-based I/O. To address these limitations, we applied Lucene's principles to native engines. By abstracting the I/O layer, we eliminated the engines' tight coupling to specific file APIs. This enhancement enables integration with any OpenSearch directory implementation, making vector search compatible with a broader range of storage systems. ## Introducing the loading layer Both Faiss and NMSLIB load graph-based vector indexes from storage into memory. During this process, they use `fread` to fetch the bytes needed to reconstruct the graph. -To improve flexibility, we replaced `fread` with a read interface. Faiss provides an **IOReader** interface for reading index data from various storage systems. For NMSLIB, we introduced a similar read interface called **NmslibIOReader**. These interfaces allow the native engines to read data through an abstraction layer, making it possible to integrate with OpenSearch's directory implementations. +To improve flexibility, we replaced `fread` with a read interface. Faiss provides an **IOReader** interface for reading index data from various storage systems. For NMSLIB, we introduced a similar read interface called **NmslibIOReader**. These interfaces allow the native engines to read data through an abstraction layer, making integration possible with OpenSearch's directory implementations. Because k-NN search is conducted after the graph is loaded into memory, this change does not impact average search performance. @@ -66,9 +66,9 @@ We ran benchmarking tests in an environment with the following configuration. ### Benchmarking results -During benchmarking, we observed that introducing the loading layer resulted in identical search performance compared to the baseline. Additionally, there were no differences in system metrics or JVM GC metrics when the loading layer was introduced. +During benchmarking, we observed that introducing the loading layer resulted in identical search performance compared to the baseline. Additionally, there were no differences in system metrics or JVM GC metrics when introducing the loading layer. -Based on these findings, we concluded that we successfully replaced the tight coupling of the File API with Lucene's **IndexInput**. This change maintained the same search performance. Additionally, with this change, you can integrate a custom **Directory** in OpenSearch and save a vector index in your preferred storage system. +Based on these findings, we concluded that we successfully replaced the tight coupling of the File API with Lucene's **IndexInput**. This change maintained the same search performance. Additionally, with this change you can integrate a custom **Directory** in OpenSearch and save a vector index in your preferred storage system. The following table presents our benchmarking results, comparing query latency with the loading layer (candidate) to the baseline. @@ -135,7 +135,7 @@ POST _bulk?refresh ### Step 3: Query the local index -Query the local index to ensure it's configured correctly: +Query the local index to ensure that it's configured correctly: ```json POST knn-index/_search @@ -151,7 +151,7 @@ POST knn-index/_search } ``` -The response returns vectors closest to the query vector: +The response returns the vectors closest to the query vector: ```json { @@ -201,7 +201,7 @@ The response returns vectors closest to the query vector: ### Step 4: Take a snapshot -Take a snapshot of the index. For detailed steps, see [Take and restore snapshots](https://opensearch.org/docs/latest/tuning-your-cluster/availability-and-recovery/snapshots/snapshot-restore/). After taking the snapshot, delete the `knn-index` so it is no longer available locally. +Take a snapshot of the index. For detailed steps, see [Take and restore snapshots](https://opensearch.org/docs/latest/tuning-your-cluster/availability-and-recovery/snapshots/snapshot-restore/). After taking the snapshot, delete the `knn-index` so that it is no longer available locally. ### Step 5: Create a searchable snapshot index from the snapshot @@ -240,16 +240,16 @@ POST knn-index/_search } ``` -The query returns the same results as the query on the local index in [Step 3](#step-3-query-the-local-index). +The query returns the same results as the local index query in [Step 3](#step-3-query-the-local-index). ## Conclusion -By introducing an abstract I/O layer that uses Lucene's **Directory** abstraction, we eliminated the native engines' dependency on file-based APIs that limited storage to local file systems. This change allows the vector engine to read graph data structures from any storage system supported by OpenSearch's **Directory** implementation. Our extensive benchmark testing confirmed that this change maintained the original file API-based approach's search performance. Notably, we observed no regression in search times after the graphs were loaded into memory (graph loading is a one-time operation for a properly scaled cluster). +By introducing an I/O layer that uses Lucene's **Directory** abstraction, we eliminated the native engines' dependency on file-based APIs that limit storage to local file systems. This change allows the vector engine to read graph data structures from any storage system supported by OpenSearch's **Directory** implementation. Our extensive benchmarking tests confirmed that this change maintains the search performance of the original file-API-based approach. Notably, we observed no regression in search times after the graphs were loaded into memory (graph loading is a one-time operation for a properly scaled cluster). -With this new read interface, you can now use any **Directory** implementation in OpenSearch with vector indexes. This added flexibility makes it possible to store vector data in remote storage solutions like S3. +With this new read interface, you can now use vector indexes with any OpenSearch **Directory** implementation. This added flexibility makes it possible to store vector data in remote storage solutions like Amazon Simple Storage Service (Amazon S3). ## Next steps -In the 2.18 release, we introduced the capability to enable vector search queries using Lucene's **Directory** and **IndexInput** classes. Looking ahead, the 2.19 release will expand this functionality to the native index creation process. Specifically, the k-NN plugin will start using the **IndexOutput** class to write graph files directly to segments. For more information, see [this GitHub issue](https://github.com/opensearch-project/k-NN/issues/2033). +In version 2.18, we introduced the ability to enable vector search queries using Lucene's **Directory** and **IndexInput** classes. Looking ahead, version 2.19 will expand this functionality to the native index creation process. Specifically, the k-NN plugin will begin using the **IndexOutput** class to write graph files directly to segments. For more information, see [this GitHub issue](https://github.com/opensearch-project/k-NN/issues/2033). -Additionally, because the k-NN plugin now has the ability to stream vector data structure files, this presents an opportunity for partial loading of these files. This enhancement will reduce memory pressure on the cluster and deliver better price-performance, especially under high-stress conditions. For more information, see [this GitHub issue](https://github.com/opensearch-project/k-NN/issues/1693). +Additionally, the k-NN plugin now having the ability to stream vector data structure files presents an opportunity for partial loading of these files. This enhancement will reduce memory pressure on the cluster and deliver better price-performance, especially under high-stress conditions. For more information, see [this GitHub issue](https://github.com/opensearch-project/k-NN/issues/1693). From d97ea0a34220aca8557e96c92dc8d3a263fc04ca Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Tue, 17 Dec 2024 08:56:10 -0500 Subject: [PATCH 5/9] Update _posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --- ...024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md index 20dfe36ea..a41f6ad67 100644 --- a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md +++ b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md @@ -205,7 +205,7 @@ Take a snapshot of the index. For detailed steps, see [Take and restore snapshot ### Step 5: Create a searchable snapshot index from the snapshot -Use the following request to restore the original index from the snapshot as a searchable snapshot: +Use the following request to restore the original index from the snapshot in order to create a searchable snapshot index ```json POST _snapshot///_restore From b24c8b6e94fc2ed9d45b80ad778602bb690cbcf2 Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Tue, 17 Dec 2024 08:57:50 -0500 Subject: [PATCH 6/9] Apply suggestions from code review Co-authored-by: Nathan Bower Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --- ...4-12-04-enable-pluggable-storage-in-opensearch-vectordb.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md index a41f6ad67..08b468949 100644 --- a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md +++ b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md @@ -16,7 +16,7 @@ has_math: false has_science_table: true --- -In 2019, OpenSearch introduced the Vector Engine, which supports three native engines: Non-Metric Space Library (NMSLIB), Facebook AI Similarity Search (Faiss), and Lucene. Unlike Lucene, which is Java based, Faiss and NMSLIB are C++ libraries that OpenSearch accesses through a lightweight JNI layer. However, these native engines handle I/O using file-based APIs, with Faiss relying on `FILE` pointers and NMSLIB using `std::fstream` to manage graph indexes. +In 2019, OpenSearch introduced the Vector Engine, which supports three native engines: Non-Metric Space Library (NMSLIB), Facebook AI Similarity Search (Faiss), and Lucene. Unlike Lucene, which is Java based, Faiss and NMSLIB are C++ libraries that OpenSearch accesses through a lightweight Java Native Interface (JNI) layer. However, these native engines handle I/O using file-based APIs, with Faiss relying on `FILE` pointers and NMSLIB using `std::fstream` to manage graph indexes. This blog post explains how we addressed these limitations by introducing an abstraction layer for loading data into native engines without compromising performance. We'll start with an overview of k-NN search, discuss the challenges of file API dependencies, and explain the solution we implemented. Finally, we'll explore how these changes support searchable snapshots of vector indexes, which involves running approximate k-NN search on remote snapshots using native engines. @@ -250,6 +250,6 @@ With this new read interface, you can now use vector indexes with any OpenSearch ## Next steps -In version 2.18, we introduced the ability to enable vector search queries using Lucene's **Directory** and **IndexInput** classes. Looking ahead, version 2.19 will expand this functionality to the native index creation process. Specifically, the k-NN plugin will begin using the **IndexOutput** class to write graph files directly to segments. For more information, see [this GitHub issue](https://github.com/opensearch-project/k-NN/issues/2033). +In version 2.18, we introduced the ability to use vector search queries with Lucene's **Directory** and **IndexInput** classes. Looking ahead, version 2.19 will expand this functionality to the native index creation process. Specifically, the k-NN plugin will begin using the **IndexOutput** class to write graph files directly to segments. For more information, see [this GitHub issue](https://github.com/opensearch-project/k-NN/issues/2033). Additionally, the k-NN plugin now having the ability to stream vector data structure files presents an opportunity for partial loading of these files. This enhancement will reduce memory pressure on the cluster and deliver better price-performance, especially under high-stress conditions. For more information, see [this GitHub issue](https://github.com/opensearch-project/k-NN/issues/1693). From 4a42ca1f6ce57bd2e0b329df899b6b40d9210662 Mon Sep 17 00:00:00 2001 From: Andrew Ross Date: Tue, 17 Dec 2024 08:36:36 -0800 Subject: [PATCH 7/9] Update release cadence to eight weeks Per the recommendation vote of the TSC and after getting feedback on [this issue][1], we are increasing the release cadence to every eight weeks. [1]: https://github.com/opensearch-project/.github/issues/234 Signed-off-by: Andrew Ross --- releases.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/releases.md b/releases.md index 2ab144b13..3b2bde01b 100644 --- a/releases.md +++ b/releases.md @@ -47,7 +47,7 @@ Note: We have not added a major release to the 2024 schedule yet. If/when we a OpenSearch [follows semver](https://opensearch.org/blog/technical-post/2021/08/what-is-semver/), which means we will only release breaking changes in major versions. All minor versions are compatible with every other minor version for that major. For example, 1.2.0 will work with 1.3.2, 1.4.1, etc, but may not work with 2.0. -For minor version releases, OpenSearch follows "release window" model as described in our [releasing documentation](https://github.com/opensearch-project/.github/blob/main/RELEASING.md). The goal is to release a new minor version approximately every six weeks which includes all the new features and fixes that are ready to go. Once we enter a release window, every day we will generate a release candidate. When the exit criteria are met by a release candidate, we will make an announcement, update the release date column above and publish new artifacts on that date. +For minor version releases, OpenSearch follows "release window" model as described in our [releasing documentation](https://github.com/opensearch-project/.github/blob/main/RELEASING.md). The goal is to release a new minor version approximately every eight weeks which includes all the new features and fixes that are ready to go. Once we enter a release window, every day we will generate a release candidate. When the exit criteria are met by a release candidate, we will make an announcement, update the release date column above and publish new artifacts on that date. **Note:** If we cannot pass the exit criteria by 2 weeks after the start of the release cycle window (1 week for 1.x patch releases), we will cancel the minor release and hold changes until the next release window. From 737a1d95a4565402fa38ecd41187cd3cb9297498 Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Tue, 17 Dec 2024 12:13:52 -0500 Subject: [PATCH 8/9] Update _posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --- ...024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md index 08b468949..7cbbbd451 100644 --- a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md +++ b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md @@ -59,7 +59,7 @@ We ran benchmarking tests in an environment with the following configuration. | OpenSearch version | 2.18 | | vCPUs | 48 | | Physical memory | 128 GB | -| Storage type | Elastic Block Storage | +| Storage type | Amazon Elastic Block Store (Amazon EBS)| | JVM | 63 GB | | Total number of vectors | 1M | | Dimensions | 128 | From f474756b8bd14120b36408d3988d1768e550d2d2 Mon Sep 17 00:00:00 2001 From: Dooyong Kim Date: Tue, 17 Dec 2024 17:47:06 -0800 Subject: [PATCH 9/9] Update date in the blog article. Signed-off-by: Dooyong Kim --- ...024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md index 7cbbbd451..14b8f6488 100644 --- a/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md +++ b/_posts/2024-12-04-enable-pluggable-storage-in-opensearch-vectordb.md @@ -6,7 +6,7 @@ authors: - navneev - vamshin - kolchfa -date: 2024-12-16 +date: 2024-12-18 categories: - technical-posts - community