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Fusion 5.9
    Fusion 5.9

    Fusion 5.9.14August 15, 2025

    Fusion 5.9.14 is a maintenance release that adds official support for Kubernetes 1.33, introduces the new Local Chunker index stage for Ray-hosted model support, expands observability and async processing for pipelines, and improves operational tooling with structured JSON logs. This release also includes numerous internal security updates and system hardening improvements.

    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

    Fusion now supports Kubernetes 1.33 and kubectl 1.33

    Fusion 5.9.14 adds official support for Kubernetes 1.33, including an upgrade to kubectl 1.33. This ensures compatibility with the latest Kubernetes platform features, security patches, and performance improvements.

    By aligning with Kubernetes 1.33:

    • Platform compatibility is expanded, allowing you to deploy or upgrade Fusion in modern, compliant Kubernetes environments.

    • Security posture is improved, as support for current Kubernetes versions ensures you benefit from upstream fixes and hardened defaults.

    • Operational reliability is enhanced, reducing friction during upgrades and simplifying lifecycle management.

    This update future-proofs your deployment infrastructure and allows DevOps teams to adopt the latest Kubernetes tooling without workarounds.

    New Local Chunker index stage

    Fusion 5.9.14 adds a new Local Chunker index pipeline stage that performs document chunking using models hosted in your own Ray infrastructure as an alternative to SaaS-based Lucidworks AI. This makes it easier to deploy RAG pipelines in fully self-hosted environments.

    This stage gives you control over chunk processing and storage, with the option to include or exclude chunk text depending on your deployment preferences. By default, it uses the vector context variable as input, aligning with the output of Ray-hosted models.

    Asynchronous processing for LWAI Vectorize Query and LWAI Prediction pipeline stages

    To help reduce query latency and improve overall performance, the LWAI Vectorize Query and LWAI Prediction stages now support asynchronous execution.

    This enhancement allows more efficient parallel processing in pipelines that use multiple AI-powered stages. As with other async-enabled stages, you can aggregate results using the Merge Async Results stage.

    For details on configuring asynchronous query pipeline stages, see Enable Asynchronous Query Pipeline Processing.

    Standardized component logging using structured JSON format

    Fusion services can now log in structured JSON format instead of plaintext (Log4j-style) output. This improves compatibility with monitoring tools and log aggregation systems by making logs easier to parse, filter, and analyze.

    JSON logging is supported for the following services:

    • admin

    • apps-manager

    • connectors

    • connectors-backend

    • connectors-classic

    • distributed-compute (job-launcher, job-rest-server)

    • indexing

    • job-config

    • ml-model-service (including kuberay-operator, seldon-webhook-service)

    • proxy / api-gateway

    • query

    • solr

    • templating

    • webapps

    JSON logging is off by default. To enable it, you can set jsonOutput: true globally or for specific services in the values.yaml configuration file.

    This update does not affect log formats in the following services:

    • admin-ui

    • auth-ui

    • insights

    • pm-ui

    • rules-ui

    • argo

    • kafka

    • spark

    • zookeeper

    Improved visibility for Solr ingestion errors

    Fusion 5.9.14 enhances logging for Solr indexing. These improvements make it easier to identify and address problematic documents without losing valid ones:

    • The system logs detailed information about each document that fails to index, including the cause of the error.

    • Logs use a structured, machine-readable format, making them easier to parse and monitor.

    • Indexing continues for valid documents even when some fail, preventing unnecessary data loss.

    These changes help you troubleshoot ingestion issues faster, trace failures to specific documents, and keep indexing pipelines running smoothly in self-hosted Fusion environments.

    Security improvements

    Fusion 5.9.14 includes a broad set of internal security fixes and hardening updates. These updates improve the overall security posture of the platform by addressing potential vulnerabilities and ensuring better alignment with secure development best practices.

    This ongoing work helps protect your deployments from evolving threats and ensures Fusion continues to meet enterprise-grade security standards.

    Bug fixes

    • Fixed an issue where certain datasource jobs could not be stopped.

      Fusion 5.9.14 resolves this issue and restores proper job control behavior.

    • Fixed excessive debug logging in Spark driver pods.

      Fusion 5.9.14 reduces logging verbosity by default and improves configurability of log level settings for Spark drivers.

    • Fixed pipeline errors in Commerce Studio caused by empty or invalid collection names.

      Fusion 5.9.14 resolves this issue by correcting the thread handling logic, validating collection name inputs, and raising logging levels to warn when invalid configurations are detected, helping you troubleshoot more effectively and avoid unnecessary pipeline failures.

    • Fixed an issue where Spark jobs would disappear from Fusion after pod deletion.

      Fusion 5.9.14 ensures Spark jobs remain visible and manageable even after pod restarts.

    • Fixed out-of-memory errors when saving large query pipelines under high load.

      Fusion 5.9.14 resolves this issue by disabling unnecessary JMX monitoring in script engine pools and improving thread synchronization, ensuring pipeline changes no longer disrupt high-RPS environments.

    • Fixed job and datasource errors when Fusion collections pointed to remapped Solr collections.

      Previously, jobs and V2 datasources failed with “Collection not found” errors when a Fusion collection pointed to a Solr collection with a different name. Fusion 5.9.14 removes this restriction, allowing jobs and datasources to work with valid Fusion collections regardless of Solr collection naming, restoring expected behavior and improving compatibility with remapped environments.

    • Fixed incorrect “down” status reporting in the job-config service.

      In some cases, the job-config service could incorrectly report a “down” status in the Fusion UI, even though the service was healthy and running. Fusion 5.9.14 corrects this reporting error so the UI reflects the true status of the service, making it easier to monitor system health and avoid unnecessary debugging.

    Known issues

    • In some asynchronous pipeline stages, the "Asynchronous Execution Config" checkbox is misaligned such that the checkbox is not directly clickable. To work around this issue, click on the checkbox title, “Asynchronous Execution Config”.

    Removals

    Bitnami removal

    Starting August 28, 2025, Bitnami will be removed from Fusion’s Helm charts due to changes in how they host images.

    Fusion 5.9.14 includes an updated Helm chart with new image references. Upgrading to this release ensures continued access to container images and prevents deployment issues. See Docker Images by Fusion Version for the complete list of container images.

    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): 1.29, 1.30, 1.31, 1.32, 1.33

    • Microsoft Azure Kubernetes Service (AKS): 1.29, 1.30, 1.31, 1.32, 1.33

    • Amazon Elastic Kubernetes Service (EKS): 1.29, 1.30, 1.31, 1.32, 1.33

    Support is also offered for Rancher Kubernetes Engine (RKE and RKE2) and OpenShift 4 versions that are based on Kubernetes 1.29, 1.30, 1.31, 1.32, 1.33; note that RKE2 may require some Helm chart modification. 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 5.9.14
    (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.

    Ray

    ray[serve] 2.46.0

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