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Fusion 5.12
    Fusion 5.12


    Lucidworks Fusion 5 lets customers easily deploy AI-powered data discovery and search applications in a modern, containerized, cloud-native architecture. Data scientists interact with those applications by:

    • Leveraging existing machine learning models and workflows

    • Using popular tools (Python ML, TensorFlow, scikit-learn, and spaCy) to quickly create and deploy new models

    Fusion combines the Apache Solr open-source search engine with the distributed power of Apache Spark for artificial intelligence. Highly scalable, Fusion indexes and stores data for real-time discovery.

    • Index billions of records of any type, from any data source

    • Process thousands of queries per second from thousands of concurrent users

    • Conduct full-text search using standard SQL capabilities and powerful analytics

    To learn about the latest Fusion features and changes, see the Fusion release notes.

    Key concepts

    Fusion’s ecosystem allows you to manage and access your data in an intuitive fashion.

    Apache Solr

    Solr is the fast open-source search platform built on Apache Lucene™ that provides scalable indexing and search, as well as faceting, hit highlighting, and advanced analysis/tokenization capabilities. Solr and Lucene are managed by the Apache Software Foundation.

    For more information, see the Solr Reference Guide for your Fusion release.

    Apache Spark

    Apache Spark is an open-source cluster-computing framework that serves as a fast and general execution engine for large-scale data processing jobs that can be decomposed into stepwise tasks, which are distributed across a cluster of networked computers.

    Spark improves on previous MapReduce implementations by using resilient distributed datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner.

    See Spark Operations for more information.


    Connectors are the out-of-the-box components for pulling your data into Fusion. Lucidworks provides a wide variety of connectors, each specialized for a particular data type. When you add a datasource to a collection, you specify the connector to use for ingesting data.

    Connectors are distributed separately from Fusion. For complete information, see Fusion Connectors.

    Fusion offers dozens of connectors so you can access your data from a large variety of sources.

    To learn more about Fusion connectors, see connectors concepts or the connectors section.


    Pipelines dictate how data flows through Fusion and becomes accessible by a search application. Fusion has two types of pipelines: index pipelines and query pipelines.

    Index pipelines ingest data, indexes it, and stores it in a format that is optimized for searching.

    Query pipelines filter, transform, and augment Solr queries and responses to return all and only the most relevant search results.

    How-to information

    Want to start right away? See get started for detailed instructions.

    Interested in using Fusion 5 with Kubernetes? See Kubernetes concepts and How to Deploy Fusion on Azure Kubernetes Service. We also have guides for deploying Fusion on Amazon Elastic Kubernetes Service and Google Kubernetes Engine.

    Looking to upgrade your Fusion instance? See Fusion 5 Upgrades.

    Important reference information

    Our reference section includes information on Fusion’s API, index pipelines stages, query pipelines stages, connections, and more.

    See Reference for complete reference information.


    Lucidworks offers free training to help you get started with Fusion. Visit the LucidAcademy and select classes such as Working With Data and Query Fine-Tuning to learn how Fusion ingests, indexes, and queries data.

    Key Improvements over Fusion 4

    Fusion 5 offers the following upgrade benefits over Fusion 4:

    • Predictive Merchandiser: AI-powered visual tool including abilities to pin, boost, bury, or block products, rewrite and fix misspellings, and interpret synonyms.

    • Advanced Machine Learning models and jobs.

    • Semantic Vector Search to improve product discovery by influencing recall, relevancy, and precision.

    • Signals for automatic self-tuning relevancy.

    • Goes beyond search for personalized content discovery: Browse pages, Recommendations, Chatbots, AB Testing, Analytics, and more.