Version 5.3


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.

See Concepts for more information.

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.

See the Apache Solr Reference Guide for more information.

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 Apache Spark for more information.


Connectors are the built-in mechanism 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 in order 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 how to upgrade.

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.