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Milvus Connectors

Milvus Connectors, such as the Spark-Milvus Connector, enable seamless integration of Milvus vector databases with third-party tools like Apache Spark for machine learning and data processing workflows.

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About this tool

Milvus Connectors

Milvus Connectors, such as the Spark-Milvus Connector, enable integration between Milvus vector databases and third-party tools like Apache Spark for enhanced machine learning and data processing workflows.

Source: https://github.com/zilliztech/spark-milvus

Features

  • Seamless Integration: Connects Apache Spark with Milvus, combining Spark's data processing with Milvus's vector storage and query capabilities.
  • Data Formats:
    • milvus: Write Spark DataFrame data into Milvus collections, with automatic collection creation based on the DataFrame schema.
    • milvusbinlog: Read Milvus's built-in binlog data (parquet-based, not compatible with standard parquet readers).
    • mjson: Generates JSON data in the format required by Milvus's bulk insert feature, improving write performance.
  • Milvus Options: Configurable connection and management options for Milvus within Spark jobs.
  • MilvusUtils (Scala): Utility functions to simplify code, including:
    • readMilvusCollection: Loads an entire Milvus collection into a Spark DataFrame by wrapping necessary SDK calls and logic.
    • bulkInsertFromSpark: Imports Spark output files into Milvus using bulk insert operations.
  • Multi-language Support: Examples and utilities are available for both Python and Scala.
  • Databricks Integration: Demos and usage examples for Databricks notebooks.
  • Open Source: Licensed under the Apache License 2.0.

Pricing

  • The Spark-Milvus Connector is open source and free to use under the Apache License 2.0.

Category

  • Data Integration & Migration

Tags

  • milvus, integration, machine-learning, apache-spark
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Information

Websitegithub.com
PublishedMay 13, 2025

Categories

1 Item
Data Integration & Migration

Tags

4 Items
#Milvus
#integration
#machine learning
#Apache Spark

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