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Fusion 5.9
    Fusion 5.9

    Fusion 5.9.11

    Released on March 20, 2025, this maintenance release provides KNN-based performance enhancements for Neural Hybrid Search, Java 17 support for connectors, a new asynchronous parsing service, the Apps Manager API, and critical security updates.

    Upgrading to the latest version of Fusion 5.9 offers several key benefits:

    • Access to latest features: Stay current with the latest features and functionality to ensure compatibility and optimal performance.

    • Simplified process: Fusion 5.9.5 and later use an in-place upgrade strategy, making upgrades easier than ever.

    • Extended support: Upgrading keeps you up-to-date with the latest supported Kubernetes versions, as outlined in the Lucidworks Fusion Product Lifecycle policy.

    For supported Kubernetes versions and key component versions, see Platform support and component versions.

    Key highlights

    KNN performance enhancements

    The Neural Hybrid Query stage now uses K-Nearest Neighbors (KNN) instead of vector similarity (vecSim). KNN is a more efficient method for finding the most relevant results, leading to faster and more accurate searches that help users find the most relevant information faster.

    New configuration options give you finer control over the speed and accuracy of neural hybrid queries:

    • vectorDepth (Number of Vector Results) sets the number of vector results to return from the vector portion of the hybrid query. Increasing vectorDepth retrieves more vector results but may increase query time. Lowering it speeds up search but may reduce result diversity.

    • vecPreFilterBoolean (Block pre-filtering) indicates whether to prevent pre-filtering. Pre-filtering can improve performance, while blocking it can yield more accurate facet counts and search results.

    Java 17 for connectors

    Connectors have been updated to use Java 17. If you use remote connectors, you must upgrade to JVM 17. See Configure Remote V2 Connectors for more information.

    Asynchronous parsing service

    An asynchronous parsing service for connectors has been added. While traditional synchronous parsing can create delays in indexing when handling large documents or high data volumes, asynchronous parsing processes files in the background, allowing indexing to continue without waiting for each document to be fully parsed. This new service brings more efficient data processing, improved search freshness, scalability without added complexity, and better support for diverse data types, supporting HTML, JSON, and other formats.

    A new parsing stage, Apache Tika Container, has been added to route asynchronous parsing through the new service. This stage is now required when using asynchronous parsing for connectors.

    To use asynchronous parsing for connectors, be sure that Async Parsing is checked in the datasource and the Apache Tika Container parser stage is enabled in the index pipeline. See Use Tika Asynchronous Parsing for detailed steps to set up asynchronous parsing.

    Other parsers, such as HTML and JSON, are now supported by the asynchronous parsing service. By enabling asynchronous parsing, the parser configuration linked to your datasource is used.

    To learn how to start using the new asynchronous parsing service, see the following demonstration:

    This update also includes a new Async Parsing API.

    Lucidworks offers free training to help you get started with Fusion. Check out the Asynchronous Parsing Service course, which focuses on how to use the asynchronous parsing service for connectors to improve efficiency and responsiveness:

    Asynchronous Parsing Service

    Visit the LucidAcademy to see the full training catalog.

    Job Config service

    The new Job Config service provides more accurate and reliable job status reporting. This service uses asynchronous communication through Kafka for improved efficiency over the previous synchronous calls, with these benefits:

    • More accurate job tracking. You get real-time, reliable job status updates, reducing uncertainty about whether a job is running, completed, or failed.

    • Faster troubleshooting. With detailed job histories and improved reporting, teams can quickly diagnose issues instead of chasing down incomplete or outdated job statuses.

    • Seamless transition. For most users, no action is required—API calls through api-gateway automatically reroute. Internal API users need only a simple update to continue tracking jobs accurately.

    The following API endpoints have been migrated from the admin service to the new job-config service:

    /api/tasks/{id}
/api/jobs/{resource}/schedule
/api/tasks
/api/jobs/{resource}/actions
/api/tasks/_schema
/api/jobs
/api/jobs/{resource}
/api/jobs/{resource}/history
/api/jobs/_schema

    For API calls made to the api-gateway service, you do not need to make any changes; the endpoints above are automatically rerouted to the new job-config service. See Job Config API for reference information about these endpoints.

    If you are making internal API calls to the admin service using any of the endpoints above, you must update your API calls to point to the new job-config service.

    Security updates

    Lucidworks remains committed to providing a secure and resilient platform. Fusion 5.9.11 includes critical security updates across a number of Fusion services, including the admin, connectors, distributed compute, indexing, job configuration, and query services, ensuring continued protection and reliability for your deployments.

    Bug fixes

    • Critical fix for Azure Kubernetes Service (AKS) and Amazon Elastic Kubernetes Service (EKS) compatibility on Kubernetes 1.30+

      By upgrading the Kubernetes Client library to version 6.2.0, this update prevents token refresh failures that previously caused service disruptions. Affected services—including connectors backend, indexing, job rest server, and job launcher services—now operate reliably in OIDC-enabled AKS and EKS environments, strengthening Fusion’s stability on modern Kubernetes deployments.

    Known issues

    • Scheduled jobs configured to trigger based on the success or failure of another job do not execute as expected. The root cause is that the completion event of a job is not triggering the onJobEventReceived handler. This issue is fixed in Fusion 5.9.12.

    • Aborted jobs in Fusion are listed twice in the Job History. This issue is expected to be fixed in Fusion 5.9.12.

    • When a new datasource is configured in Indexing > Index Workbench, the simulated results do not display, and the following error is generated: "Failed to simulate results from a working pipeline." As a result, the Index Workbench cannot simulate results for new datasources, preventing users from configuring the indexing process within the workbench.

      Configure each part of the indexing process separately instead of using the Index Workbench:

      • Configure datasources in Indexing > Datasources.

      • Configure parsers in Indexing > Parsers.

      • Configure index pipelines in Indexing > Index Pipelines.

      This issue is fixed in Fusion 5.9.12.

    • In Fusion 5.9.10, the Apache Spark 3.4.1 upgrade impacted jobs that use Python 3.7 behavior or compatibility, which may have automatically updated to Python 3.10.x and no longer function correctly. If you have not yet updated the code for Python 3.10.x, update your code to ensure compatibility with Python 3.10.x and then test your Spark jobs in a staging environment before deploying to production.

    Deprecations

    For full details on deprecations, see Deprecations and Removals.

    • The Parsers Indexing CRUD API, which provides CRUD operations for parsers, allowing users to create, read, update, and delete parsers, is deprecated in Fusion 5.9.11. This feature was originally deprecated in Fusion 5.12.0. It will be removed in a future release no later than September 4, 2025.

      The Async Parsing API replaces the Parsers Indexing CRUD API and is available in Fusion 5.9.11. This API provides improved functionality and aligns with Fusion’s updated architecture, ensuring consistency across versions.

    • The Word2Vec Model Training Job, which trains a shallow neural model to generate vector embeddings for text data, is deprecated in Fusion 5.9.11. It will be removed in a future release no later than September 4, 2025.

      Lucidworks offers a wide array of AI solutions, including Lucidworks AI. Lucidworks AI provides easy-to-use, AI-powered data discovery and search capabilities including:

      • Pre-trained embedding models

      • Custom embedding model training

      • Seamless integration with Fusion

    Platform support and component versions

    Kubernetes platform support

    Lucidworks has tested and validated support for the following Kubernetes platforms and versions:

    • Google Kubernetes Engine (GKE): 1.28, 1.29, 1.30, 1.31

    • Microsoft Azure Kubernetes Service (AKS): 1.28, 1.29, 1.30, 1.31

    • Amazon Elastic Kubernetes Service (EKS): 1.28, 1.29, 1.30, 1.31

    Support is also offered for Rancher Kubernetes Engine (RKE) and OpenShift 4 versions that are based on Kubernetes 1.28, 1.29, 1.30, 1.31. OpenStack and customized Kubernetes installations are not supported.

    For more information on Kubernetes version support, see the Kubernetes support policy.

    Component versions

    The following table details the versions of key components that may be critical to deployments and upgrades.

    Component Version

    Solr

    fusion-solr 5.9.11
    (based on Solr 9.6.1)

    ZooKeeper

    3.9.1

    Spark

    3.4.1

    Ingress Controllers

    Nginx, Ambassador (Envoy), GKE Ingress Controller

    Istio not supported.

    More information about support dates can be found at Lucidworks Fusion Product Lifecycle.