From 4e985a28227c596928ab48b9bc85d456afcb52a3 Mon Sep 17 00:00:00 2001 From: AnthonyTsu1984 <115786031+AnthonyTsu1984@users.noreply.github.com> Date: Wed, 28 Aug 2024 19:44:19 +0800 Subject: [PATCH 1/3] update docs Signed-off-by: AnthonyTsu1984 <115786031+AnthonyTsu1984@users.noreply.github.com> --- assets/highly-decoupled-architecture.png | Bin 0 -> 150222 bytes ...unstructured-data-embedding-and-milvus.png | Bin 0 -> 604341 bytes site/en/about/overview.md | 175 +++++++-------- site/en/menuStructure/en.json | 7 + site/en/userGuide/clustering-compaction.md | 211 ++++++++++++++++++ 5 files changed, 304 insertions(+), 89 deletions(-) create mode 100644 assets/highly-decoupled-architecture.png create mode 100644 assets/unstructured-data-embedding-and-milvus.png create mode 100644 site/en/userGuide/clustering-compaction.md diff --git a/assets/highly-decoupled-architecture.png b/assets/highly-decoupled-architecture.png new file mode 100644 index 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-2,143 +2,140 @@ id: overview.md title: What is Milvus related_key: Milvus Overview -summary: Milvus is an open-source vector database designed specifically for AI application development, embeddings similarity search, and MLOps. +summary: Milvus is a high-performance, highly scalable vector database that runs efficiently across a wide range of environments, from a laptop to large-scale distributed systems. It is available as both open-source software and a cloud service. --- -# Introduction +Milvus is a high-performance, highly scalable vector database that runs efficiently across a wide range of environments, from a laptop to large-scale distributed systems. It is available as both open-source software and a cloud service. -This page aims to give you an overview of Milvus by answering several questions. After reading this page, you will learn what Milvus is and how it works, as well as the key concepts, why use Milvus, supported indexes and metrics, example applications, the architecture, and relevant tools. +Milvus is an open-source project under LF AI & Data Foundation distributed under the Apache 2.0 license. Most contributors are experts from the high-performance computing (HPC) community, specializing in building large-scale systems and optimizing hardware-aware code. Core contributors include professionals from Zilliz, ARM, NVIDIA, AMD, Intel, Meta, IBM, Salesforce, Alibaba, and Microsoft. -## What is Milvus vector database? +## Unstructured Data, Embeddings, and Milvus -Milvus was created in 2019 with a singular goal: store, index, and manage massive [embedding vectors](#Embedding-vectors) generated by deep neural networks and other machine learning (ML) models. +Unstructured data, such as text, images, and audio, varies in format and carries rich underlying semantics, making it challenging to analyze. To manage this complexity, embeddings are used to convert unstructured data into numerical vectors that capture its essential characteristics. These vectors are then stored in a vector database, enabling fast and scalable searches and analytics. -As a database specifically designed to handle queries over input vectors, it is capable of indexing vectors on a trillion scale. Unlike existing relational databases which mainly deal with structured data following a pre-defined pattern, Milvus is designed from the bottom-up to handle embedding vectors converted from [unstructured data](#Unstructured-data). +Milvus offers robust data modeling capabilities, enabling you to organize your unstructured or multi-modal data into structured collections. It supports a wide range of data types for different attribute modeling, including common numerical and character types, various vector types, arrays, sets, and JSON, saving you from the effort of maintaining multiple database systems. -As the Internet grew and evolved, unstructured data became more and more common, including emails, papers, IoT sensor data, Facebook photos, protein structures, and much more. In order for computers to understand and process unstructured data, these are converted into vectors using embedding techniques. Milvus stores and indexes these vectors. Milvus is able to analyze the correlation between two vectors by calculating their similarity distance. If the two embedding vectors are very similar, it means that the original data sources are similar as well. +![Untructured data, embeddings, and Milvus](../../../assets/unstructured-data-embedding-and-milvus.png) +Milvus offers three deployment modes, covering a wide range of data scales—from local prototyping in Jupyter Notebooks to massive Kubernetes clusters managing tens of billions of vectors: -![Workflow](../../../assets/milvus_workflow.jpeg "Milvus workflow.") +- Milvus Lite is a Python library that can be easily integrated into your applications. As a lightweight version of Milvus, it’s ideal for quick prototyping in Jupyter Notebooks or running on edge devices with limited resources. [Learn more](milvus_lite.md). +- Milvus Standalone is a single-machine server deployment, with all components bundled into a single Docker image for convenient deployment. [Learn more](install_standalone-docker.md). +- Milvus Distributed can be deployed on Kubernetes clusters, featuring a cloud-native architecture designed for billion-scale or even larger scenarios. This architecture ensures redundancy in critical components. [Learn more](install_cluster-milvusoperator.md). -## Key concepts +## What Makes Milvus so Fast? -In case you are new to the world of vector database and similarity search, read the following explanation of key concepts to gain a better understanding. +Milvus was designed from day one to be a highly efficient vector database system. In most cases, Milvus outperforms other vector databases by 2-5x (see the VectorDBBench results). This high performance is the result of several key design decisions: -Learn more about [Milvus glossary](glossary.md). +**Hardware-aware Optimization**: To accommodate Milvus in various hardware environments, we have optimized its performance specifically for many hardware architectures and platforms, including AVX512, SIMD, GPUs, and NVMe SSD. -### Unstructured data +**Advanced Search Algorithms**: Milvus supports a wide range of in-memory and on-disk indexing/search algorithms, including IVF, HNSW, DiskANN, and more, all of which have been deeply optimized. Compared to popular implementations like FAISS and HNSWLib, Milvus delivers 30%-70% better performance. -Unstructured data, including images, video, audio, and natural language, is information that doesn't follow a pre-defined model or manner of organization. This data type accounts for around 80% of the world's data, and can be converted into vectors using various artificial intelligence (AI) and machine learning (ML) models. +**Search Engine in C++**: Over 80% of a vector database's performance is determined by its search engine. Milvus uses C++ for this critical component due to the language's high performance, low-level optimization, and efficient resource management. Most importantly, Milvus integrates numerous hardware-aware code optimizations, ranging from assembly-level vectorization to multi-thread parallelization and scheduling, to fully leverage hardware capabilities. -### Embedding vectors +**Column-Oriented**: Milvus is a column-oriented vector database system. The primary advantages come from the data access patterns. When performing queries, a column-oriented database reads only the specific fields involved in the query, rather than entire rows, which greatly reduces the amount of data accessed. Additionally, operations on column-based data can be easily vectorized, allowing for operations to be applied in the entire columns at once, further enhancing performance. -An embedding vector is a feature abstraction of unstructured data, such as emails, IoT sensor data, Instagram photos, protein structures, and much more. Mathematically speaking, an embedding vector is an array of floating-point numbers or binaries. Modern embedding techniques are used to convert unstructured data to embedding vectors. +## What Makes Milvus so Scalable -### Vector similarity search +In 2022, Milvus supported billion-scale vectors, and in 2023, it scaled up to tens of billions with consistent stability, powering large-scale scenarios for over 300 major enterprises, including Salesforce, PayPal, Shopee, Airbnb, eBay, NVIDIA, IBM, AT&T, LINE, ROBLOX, Inflection, etc. -Vector similarity search is the process of comparing a vector to a database to find vectors that are most similar to the query vector. Approximate nearest neighbor (ANN) search algorithms are used to accelerate the searching process. If the two embedding vectors are very similar, it means that the original data sources are similar as well. +Milvus's cloud-native and highly decoupled system architecture ensures that the system can continuously expand as data grows: -## Why Milvus? +![Highly decoupled system architecture of Milvus](../../../assets/highly-decoupled-architecture.png) -- High performance when conducting vector search on massive datasets. -- A developer-first community that offers multi-language support and toolchain. -- Cloud scalability and high reliability even in the event of a disruption. -- Hybrid search achieved by pairing scalar filtering with vector similarity search. +Milvus itself is fully stateless so it can be easily scaled with the help of Kubernetes or public clouds. In addition, Milvus components are well decoupled, with the three most critical tasks—search, data insertion, and indexing/compaction—designed as easily parallelized processes, with complex logic separated out. This ensures that the corresponding query node, data node, and index node can scale both up and out independently, optimizing performance and cost efficiency. -## What indexes and metrics are supported? +## Types of Searches Supported by Milvus -Indexes are an organization unit of data. You must declare the index type and similarity metric before you can search or query inserted entities. **If you do not specify an index type, Milvus will operate brute-force search by default.** +Milvus supports various types of search functions to meet the demands of different use cases: -### Index types +- [ANN Search](single-vector-search.md#Basic-search): Finds the top K vectors closest to your query vector. +- [Filtering Search](single-vector-search.md#Filtered-search): Performs ANN search under specified filtering conditions. +- [Range Search](single-vector-search.md#Range-search): Finds vectors within a specified radius from your query vector. +- [Hybrid Search](multi-vector-search.md): Conducts ANN search based on multiple vector fields. +- Keyword Search: Keyword search based on BM25. +- [Reranking](reranking.md): Adjusts the order of search results based on additional criteria or a secondary algorithm, refining the initial ANN search results. +- [Fetch](get-and-scalar-query.md#Get-Entities-by-ID): Retrieves data by their primary keys. +- [Query](get-and-scalar-query.md#Use-Basic-Operators): Retrieves data using specific expressions. -Most of the vector index types supported by Milvus use approximate nearest neighbors search (ANNS), including: +## Comprehensive Feature Set -- **HNSW**: HNSW is a graph-based index and is best suited for scenarios that have a high demand for search efficiency. There is also a GPU version **GPU_CAGRA**, thanks to Nvidia's contribution. -- **FLAT**: FLAT is best suited for scenarios that seek perfectly accurate and exact search results on a small, million-scale dataset. There is also a GPU version **GPU_BRUTE_FORCE**. -- **IVF_FLAT**: IVF_FLAT is a quantization-based index and is best suited for scenarios that seek an ideal balance between accuracy and query speed. There is also a GPU version **GPU_IVF_FLAT**. -- **IVF_SQ8**: IVF_SQ8 is a quantization-based index and is best suited for scenarios that seek a significant reduction on disk, CPU, and GPU memory consumption as these resources are very limited. -- **IVF_PQ**: IVF_PQ is a quantization-based index and is best suited for scenarios that seek high query speed even at the cost of accuracy. There is also a GPU version **GPU_IVF_PQ**. -- **SCANN**: SCANN is similar to IVF_PQ in terms of vector clustering and product quantization. What makes them different lies in the implementation details of product quantization and the use of SIMD (Single-Instruction / Multi-data) for efficient calculation. -- **DiskANN**: Based on Vamana graphs, DiskANN powers efficient searches within large datasets. +In addition to the key search features mentioned above, Milvus also provides a set of features implemented around ANN searches so that you can fully utilize its capabilities. -See [Vector Index](index.md) for more details. +### API and SDK -### Similarity metrics +- [RESTful API](https://milvus.io/api-reference/restful/v2.4.x/About.md) (official) +- [PyMilvus](https://milvus.io/api-reference/pymilvus/v2.4.x/About.md) (Python SDK) (official) +- [Go SDK](https://milvus.io/api-reference/go/v2.4.x/About.md) (official) +- [Java SDK](https://milvus.io/api-reference/java/v2.4.x/About.md) (official) +- [Node.js](https://milvus.io/api-reference/node/v2.4.x/About.md) (JavaScript) SDK (official) +- [C#](https://milvus.io/api-reference/csharp/v2.2.x/About.md) (contributed by Microsoft) -In Milvus, similarity metrics are used to measure similarities among vectors. Choosing a good distance metric helps improve classification and clustering performance significantly. Depending on the input data forms, specific similarity metric is selected for optimal performance. +### Advanced Data Types -The metrics that are widely used for floating point embeddings include: +In addition to primitive data types, Milvus supports various advanced data types and their respective applicable distance metrics. -- **Cosine**: This metric is normalized IP, generally used in text similarity search (NLP). -- **Euclidean distance (L2)**: This metric is generally used in the field of computer vision (CV). -- **Inner product (IP)**: This metric is generally used in the field of natural language processing (NLP). -The metrics that are widely used for binary embeddings include: -- **Hamming**: This metric is generally used in the field of natural language processing (NLP). -- **Jaccard**: This metric is generally used in the field of molecular similarity search. +- [Sparse Vectors](sparse_vector.md) +- [Binary Vectors](index-vector-fields.md) +- [JSON Support](use-json-fields.md) +- [Array Support](array_data_type.md) +- [Distance Metrics](metric.md) -See [Similarity Metrics](metric.md#floating) for more information. +### Acceleration -## Example applications +- Search Algorithms + Milvus supports a set of tunable indexing and search algorithms. For details, see [In-memory Index](index.md), [On-disk Index](disk_index.md), and [GPU Index](gpu_index.md). -Milvus makes it easy to add similarity search to your applications. Example applications of Milvus include: +- Partitions and Partition Keys + Partitions are sub-divisions of a Milvus collection. You can choose a scalar field as the partition key for better search performance. For details, see [Manage Partitions](manage-partitions.md) and [Use Partition Key](use-partition-key.md). -- [Image similarity search](image_similarity_search.md): Images made searchable and instantaneously return the most similar images from a massive database. -- [Video similarity search](video_similarity_search.md): By converting key frames into vectors and then feeding the results into Milvus, billions of videos can be searched and recommended in near real-time. -- [Audio similarity search](audio_similarity_search.md): Quickly query massive volumes of audio data such as speech, music, sound effects, and surface similar sounds. -- [Recommender system](recommendation_system.md): Recommend information or products based on user behaviors and needs. -- [Question answering system](question_answering_system.md): Interactive digital QA chatbot that automatically answers user questions. -- [DNA sequence classification](dna_sequence_classification.md): Accurately sort out the classification of a gene in milliseconds by comparing similar DNA sequences. -- [Text search engine](text_search_engine.md): Help users find the information they are looking for by comparing keywords against a database of texts. +- Tunable Consistency Model + Consistency ensures every Milvus node or replica has the same view of data when writing or reading data at a given time. You can easily tune the consistency level when conducting ANN searches in Milvus. For details, see [Consistency](consistency.md). -See [Milvus tutorials](https://github.com/milvus-io/bootcamp/tree/master/bootcamp) and [Milvus Adopters](milvus_adopters.md) for more Milvus application scenarios. +- High-throughput Data Import + To import a large volume of data into Milvus instead of inserting them one after another, consider using our high-throughput data import tools. For details, refer to [Prepare Source Data](prepare-source-data.md) and [Import Data](import_data.md). -## How is Milvus designed? +- Multi-tenancy Support + Milvus has implemented a lot of features oriented to multi-tenancy scenarios, including Partition Key, Clustering Key, and more. For details, see [Multi-tenancy Strategies](multi_tenancy.md). -As a cloud-native vector database, Milvus separates storage and computation by design. To enhance elasticity and flexibility, all components in Milvus are stateless. +### Security and Authorization -The system breaks down into four levels: +- Tunable Consistency Model + Consistency ensures every Milvus node or replica has the same view of data when writing or reading data at a given time. You can easily tune the consistency level when conducting ANN searches in Milvus. For details, see [Consistency](consistency.md). -- Access layer: The access layer is composed of a group of stateless proxies and serves as the front layer of the system and endpoint to users. -- Coordinator service: The coordinator service assigns tasks to the worker nodes and functions as the system's brain. -- Worker nodes: The worker nodes function as arms and legs and are dumb executors that follow instructions from the coordinator service and execute user-triggered DML/DDL commands. -- Storage: Storage is the bone of the system, and is responsible for data persistence. It comprises meta storage, log broker, and object storage. +- Data Isolation and Resource Control + For multi-tenancy scenarios, data isolation is the basic security requirement. Milvus implements several features to resolve your security concerns. For details, see [Manage Resource Groups](resource_group.md) and [Clustering Compaction](clustering-compaction.md). -For more information, see [Architecture Overview](architecture_overview.md). +### AI Integrations +- Embedding Model Integrations + Embedding Models convert unstructured data to their numeric representation in high-dimensional data space so that you can store them in Milvus. Currently, PyMilvus, the Python SDK, integrates several embedding models so that you can quickly prepare your data into vector embeddings. For details, see [Embedding Overview](embeddings.md). -![Architecture](../../../assets/milvus_architecture.png "Milvus architecure.") +- Reranking Model Integrations + In the realm of information retrieval and generative AI, a reranker is an essential tool that optimizes the order of results from initial searches. PyMilvus also integrates several reranking models to optimize the order of results returned from initial searches. For details, refer to [Rerankers Overview](rerankers-overview.md). -## Developer tools +- LangChain and other AI Tool Integrations + In the GenAI era, tools, such as LangChain, gain much attentions from application developers. As a core component, Milvus usually serves as the vector stores in such tools. To learn how to integrate Milvus in your favorite AI tools, refer to our [Integrations](integrate_with_openai.md) and [Tutorials](build-rag-with-milvus.md). -Milvus is supported by rich APIs and tools to facilitate DevOps. +### Tools and Ecosystem -### API access +- Attu + Attu is an all-in-one intuitive GUI that helps you manage Milvus and the data it stores. For details, refer to the [Attu](https://github.com/zilliztech/attu) repository. -Milvus has client libraries wrapped on top of the Milvus API that can be used to insert, delete, and query data programmatically from application code: +- Birdwatcher + Birdwatcher is a debugging tool for Milvus. Using it to connect to etcd, you can check the state of your Milvus system or configure it on the fly. For details, refer to [BirdWatcher](birdwatcher_overview.md). -- [PyMilvus](https://github.com/milvus-io/pymilvus) -- [Node.js SDK](https://github.com/milvus-io/milvus-sdk-node) -- [Go SDK](https://github.com/milvus-io/milvus-sdk-go) -- [Java SDK](https://github.com/milvus-io/milvus-sdk-java) -- [Restful API](https://milvus.io/api-reference/restful/v2.4.x/About.md) +- Promethus & Grafana integrations + Prometheus is an open-source system monitoring and alerting toolkit for Kubernetes. Grafana is an open-source visualizing stack that can connect with all data sources. You can use Promethus & Grafana as the monitoring service provider to visually monitor the performance of Milvus distributed. For details, see [Deploying Monitoring Services](monitor.md). -We are working on enabling more new client libraries. If you would like to contribute, go to the corresponding repository of [the Milvus Project](https://github.com/milvus-io). +- Milvus Backup + Milvus Backup is a tool that allows users to back up and restore Milvus data. It provides both CLI and API to fit itself into different application scenarios. For details, refer to [Milvus Backup](milvus_backup_overview.md). -### Milvus ecosystem tools +- Milvus Capture Data Change (CDC) + Milvus-CDC can capture and synchronize incremental data in Milvus instances and ensures the reliability of business data by seamlessly transferring it between source and target instances, allowing for easy incremental backup and disaster recovery. For details, refer to [Milvus CDC](milvus-cdc-overview.md). -The Milvus ecosystem provides helpful tools including: - -- [Attu](https://github.com/zilliztech/attu), a graphical management system for Milvus. -- [Milvus sizing tool](https://milvus.io/tools/sizing/), which helps you estimate the raw file size, memory size, and stable disk size needed for a specified number of vectors with various index types. -- [Milvus CLI](https://github.com/zilliztech/milvus_cli#overview) - -## What's next - -- Get started with a 3-minute tutorial: - - [Hello Milvus](quickstart.md) -- Install Milvus for your testing or production environment: - - [Installation Prerequisites](prerequisite-docker.md) - - [Install Milvus Standalone](install_standalone-docker.md) -- If you're interested in diving deep into the design details of Milvus: - - Read about [Milvus architecture](architecture_overview.md) +- Milvus Connectors + Milvus has planned a set of connectors for you to seamlessly integrate Milvus with third-party tools, such as Apache Spark. Currently, you can use our Spark Connector to feed your Milvus data to Apache Spark for machine-learning processing. For details, refer to [Spark-Milvus Connector](integrate_with_spark.md). +- Vector Transmission Services (VTS) + Milvus provides a set of tools for you to transfer your data between a Milvus instance and a bunch of data sources, including Zilliz clusters, Elasticsearch, Postgres (PgVector), and another Milvus instance. For details, refer to [Migrations](migrate_overview.md). diff --git a/site/en/menuStructure/en.json b/site/en/menuStructure/en.json index 2be67953f..d63b1da81 100644 --- a/site/en/menuStructure/en.json +++ b/site/en/menuStructure/en.json @@ -491,6 +491,13 @@ "id": "resource_group.md", "order": 7, "children": [] + }, + { + "label": "Clustering Compaction", + "id": "clustering-compaction.md", + "order": 8, + "children": [] + }, } ] } diff --git a/site/en/userGuide/clustering-compaction.md b/site/en/userGuide/clustering-compaction.md new file mode 100644 index 000000000..53dab53a7 --- /dev/null +++ b/site/en/userGuide/clustering-compaction.md @@ -0,0 +1,211 @@ +--- +id: clustering-compaction.md +title: Clustering Compaction +related_key: clustering, compaction +summary: Clustering compaction is designed to improve search performance and reduce costs in large collections. This guide will help you understand clustering compaction and how this feature can improve search performance. +--- + +Clustering compaction is designed to improve search performance and reduce costs in large collections. This guide will help you understand clustering compaction and how this feature can improve search performance. + +## Overview + +Milvus stores incoming entities in segments within a collection and seals a segment when it is full. If this happens, a new segment is created to accommodate additional entities. As a result, entities are arbitrarily distributed across segments. This distribution requires Milvus to search multiple segments to find the nearest neighbors to a given query vector. + +If Milvus can distribute entities among segments based on the values in a specific field, the search scope can be restricted within one segment, thus improving search performance. + +**Clustering Compaction** is a feature in Milvus that redistributes entities among segments in a collection based on the values in a scalar field. To enable this feature, you first need to select a scalar field as the **clustering key**. This allows Milvus to redistribute entities into a segment when their clustering key values fall within a specific range. When you trigger a clustering compaction, Milvus generates/updates a global index called **PartitionStats**, which records the mapping relationship between segments and clustering key values. + +Using **PartitionStats** as a reference, Milvus can prune irrelevant data upon receiving a search/query request that carries a clustering key value and restricting the search scope within the segments mapping to the value, thus improving search performance. For details on performance improvement, refer to Benchmark tests. + +## Use Clustering Compaction + +The Clustering Compaction feature in Milvus is highly configurable. You can choose to trigger it manually or set it to be triggered automatically at intervals by Milvus. To enable clustering compaction, do as follows: + +### Global Configuration + +You need to modify your Milvus configuration file as shown below. + +```yaml +dataCoord: + compaction: + clustering: + enable: true + autoEnable: false + triggerInterval: 600 + minInterval: 3600 + maxInterval: 259200 + newDataSizeThreshold: 512m + timeout: 7200 + +queryNode: + enableSegmentPrune: true + +datanode: + clusteringCompaction: + memoryBufferRatio: 0.1 + workPoolSize: 8 +common: + usePartitionKeyAsClusteringKey: true +``` + +- `dataCoord.compaction.clustering` + + | Configuration Item | Description | Default Value | + | ------------------ | ----------- | - | + | `enable` | Specifies whether to enable clustering compaction.
Setting this to `true` if you need to enable this feature for every collection having a clustering key. | `false` | + | `autoEnable` | Specifies whether to enable automatically triggered compaction.
Setting this to `true` indicates that Milvus compacts the collections having a clustering key at the specified intervals. | `false` | + | `triggerInterval` | Specifies the interval in milliseconds at which Milvus starts clustering compaction.
This parameter is valid only when `autoEnable` is set to `true`. | - | + | `minInterval` | Specifies the minimum interval in milliseconds.
This parameter is valid only when `autoEnable` is set to `true`.
Setting this to an integer greater than triggerInterval helps avoid repeated compactions within a short period. | - | + | `maxInterval` | Specifies the maximum interval in milliseconds.
This parameter is valid only when `autoEnable` is set to `true`.
Once Milvus detects that a collection has not been clustering-compacted for a duration longer than this value, it forces a clustering compaction. | - | + | `newDataSizeThreshold` | Specifies the upper threshold to trigger a clustering compaction.
This parameter is valid only when `autoEnable` is set to `true`.
Once Milvus detects that the data volume in a collection exceeds this value, it initiates a clustering compaction process. | - | + | `timeout` | Specifies the timeout duration for a clustering compaction.
A clustering compaction fails if its execution time exceeds this value. | - | + +- `queryNode` + + | Configuration Item | Description | Default Value | + | ------------------ | ----------- | - | + | `enableSegmentPrune` | Specifies whether Milvus prunes data by referring to PartitionStats upon receiving search/query requests.
Setting this to `true` enables Milvus to prune irrelevant data from segments during a search/query request. | `false` | + +- `dataNode.clusteringCompaction` + + | Configuration Item | Description | Default Value | + | ------------------ | ----------- | - | + | `memoryBufferRatio` | Specifies the memory buffer ratio for clustering compaction tasks.
Milvus flushes data when the data size exceeds the allocated buffer size calculated using this ratio. | - | + | `workPoolSize` | Specifies the worker pool size for a clustering compaction task. | - | + +- `common` + + | Configuration Item | Description | Default Value | + | ------------------ | ----------- | - | + | `usePartitionKeyAsClusteringKey` | Specifies whether to use the partition key in collections as the clustering key.
Setting this to `true` indicates that the partition key is used as the clustering key.
You can always override this setting in a collection by explicitly setting a clustering key. | `false` | + +To apply the above changes to your Milvus cluster, please follow the steps in [Configure Milvus with Helm](configure-helm.md#Configure-Milvus-via-configuration-file) and [Configure Milvus with Milvus Operators](configure_operator.md?tab=component). + +## Collection Configuration + +For clustering compacting in a specific collection, you should select a scalar field from the collection as the clustering key. + +```python +default_fields = [ + FieldSchema(name="id", dtype=DataType.INT64, is_primary=True), + FieldSchema(name="key", dtype=DataType.INT64, is_clustering_key=True), + FieldSchema(name="var", dtype=DataType.VARCHAR, max_length=1000, is_primary=False), + FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=dim) +] + +default_schema = CollectionSchema( + fields=default_fields, + description="test clustering-key collection" +) + +coll1 = Collection(name="clustering_test", schema=default_schema) +``` + +

+ +## Trigger Clustering Compaction + +If you have enabled automatic clustering compaction, Milvus automatically triggers the compaction at the specified interval. Alternatively, you can manually trigger the compaction as follows: + +```python +coll1.compact(is_clustering=True) +coll1.get_compaction_state(is_clustering=True) +coll1.wait_for_compaction_completed(is_clustering=True) +``` + +### Benchmark Test + +Data volume and query patterns combined determine the performance improvement clustering compaction can bring. An internal benchmark test demonstrates that clustering compaction yields up to a 25-fold improvement in queries per second (QPS). + +The benchmark test is on a collection containing entities from a 20-million, 768-dimensional LAION dataset with the key field designated as the clustering key. After clustering compaction is triggered in the collection, concurrent searches are sent until the CPU usage reaches a high water level. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Search FilterPrune RatioLatency (ms)QPS (reqs/s)
AvgMinMaxMedianTP99
None0%168567222941710229117.75
key > 200 and key < 80040.2%10454718281085161728.38
key > 200 and key < 60059.8%829451483882130335.78
key > 200 and key < 40079.5%55010098558489854.00
key == 100099%6824127370246431.41
+ +As the search range narrows in the search filters, the prune ratio increases. This means that more entities are skipped during the search process. When comparing the statistics in the first and last rows, you can see that searches without clustering compaction require scanning the entire collection. On the other hand, searches with clustering compaction using a specific key can achieve up to a 25-fold improvement. + +## Best practices + +Here are some tips for you to use clustering compaction efficiently: + +- Enable this for collections with large data volumes. + Search performance improves with larger data volumes in a collection. It is a good choice to enable this feature for collections with over 1 million entities. + +- Choose a proper clustering key. + You can use scalar fields commonly employed as filtering conditions as the clustering key. For a collection that holds data from multiple tenants, you can utilize the field that distinguishes one tenant from another as the clustering key. + +- Use the partition key as the clustering key. + You can set `common.usePartitionKeyAsClusteringKey` to true if you want to enable this feature for all collections in your Milvus instance or if you still face performance issues in a large collection with a partition key. By doing so, you will have a clustering key and a partition key when you choose a scalar field in a collection as the partition key. + + Note that this setting does not prevent you from choosing another scalar field as the clustering key. The explicitly designated clustering key always takes precedence. \ No newline at end of file From 9f74d913415632b62435cf03c92b443a0cb0cebd Mon Sep 17 00:00:00 2001 From: AnthonyTsu1984 <115786031+AnthonyTsu1984@users.noreply.github.com> Date: Wed, 28 Aug 2024 19:46:05 +0800 Subject: [PATCH 2/3] update docs Signed-off-by: AnthonyTsu1984 <115786031+AnthonyTsu1984@users.noreply.github.com> --- site/en/menuStructure/en.json | 1 - 1 file changed, 1 deletion(-) diff --git a/site/en/menuStructure/en.json b/site/en/menuStructure/en.json index d63b1da81..4a84d0087 100644 --- a/site/en/menuStructure/en.json +++ b/site/en/menuStructure/en.json @@ -497,7 +497,6 @@ "id": "clustering-compaction.md", "order": 8, "children": [] - }, } ] } From e37de61459527bb64f9f29e991384db05834b8d6 Mon Sep 17 00:00:00 2001 From: AnthonyTsu1984 <115786031+AnthonyTsu1984@users.noreply.github.com> Date: Wed, 28 Aug 2024 19:48:40 +0800 Subject: [PATCH 3/3] update docs Signed-off-by: AnthonyTsu1984 <115786031+AnthonyTsu1984@users.noreply.github.com> --- site/en/about/overview.md | 2 +- site/en/userGuide/clustering-compaction.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/site/en/about/overview.md b/site/en/about/overview.md index 7b9120d5f..80e0f8e73 100644 --- a/site/en/about/overview.md +++ b/site/en/about/overview.md @@ -93,7 +93,7 @@ In addition to primitive data types, Milvus supports various advanced data types Consistency ensures every Milvus node or replica has the same view of data when writing or reading data at a given time. You can easily tune the consistency level when conducting ANN searches in Milvus. For details, see [Consistency](consistency.md). - High-throughput Data Import - To import a large volume of data into Milvus instead of inserting them one after another, consider using our high-throughput data import tools. For details, refer to [Prepare Source Data](prepare-source-data.md) and [Import Data](import_data.md). + To import a large volume of data into Milvus instead of inserting them one after another, consider using our high-throughput data import tools. For details, refer to [Prepare Source Data](prepare-source-data.md) and [Import Data](import-data.md). - Multi-tenancy Support Milvus has implemented a lot of features oriented to multi-tenancy scenarios, including Partition Key, Clustering Key, and more. For details, see [Multi-tenancy Strategies](multi_tenancy.md). diff --git a/site/en/userGuide/clustering-compaction.md b/site/en/userGuide/clustering-compaction.md index 53dab53a7..9dde701ef 100644 --- a/site/en/userGuide/clustering-compaction.md +++ b/site/en/userGuide/clustering-compaction.md @@ -79,7 +79,7 @@ common: | ------------------ | ----------- | - | | `usePartitionKeyAsClusteringKey` | Specifies whether to use the partition key in collections as the clustering key.
Setting this to `true` indicates that the partition key is used as the clustering key.
You can always override this setting in a collection by explicitly setting a clustering key. | `false` | -To apply the above changes to your Milvus cluster, please follow the steps in [Configure Milvus with Helm](configure-helm.md#Configure-Milvus-via-configuration-file) and [Configure Milvus with Milvus Operators](configure_operator.md?tab=component). +To apply the above changes to your Milvus cluster, please follow the steps in [Configure Milvus with Helm](configure-helm.md) and [Configure Milvus with Milvus Operators](configure_operator.md). ## Collection Configuration