Fusion 5.9.11

• 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.

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Key highlights

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KNN performance enhancements

• 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.

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Java 17 for connectors

ingress:
  enabled: false
  pathtype: "Prefix"
  path: "/"
  #host: "ingress.example.com"
  ingressClassName: "nginx"   # Fusion 5.9.6 only
  tls:
    enabled: false
    certificateArn: ""
    # Enable the annotations field to override the default annotations
    #annotations: ""

kafka-bridge:
  target: mynamespace-connectors-backend.lucidworkstest.com:443 # mandatory
  plain-text: false # optional, false by default.  
    proxy-server: # optional - needed when a forward proxy server is used to provide outbound access to the standalone connector
    host: host
    port: some-port
    user: user # optional
    password: password # optional
  trust: # optional - needed when the client's system doesn't trust the server's certificate
    cert-collection-filepath: path1

proxy: # mandatory fusion-proxy
  user: admin
  password: password123
  url: https://fusiontest.com/ # needed only when the connector plugin requires blob store access

plugin: # mandatory
  path: ./fs.zip
  type: #optional - the suffix is added to the connector id
    suffix: remote

kafka-bridge:
  target: mynamespace-connectors-backend.lucidworkstest.com:443

proxy:
  user: admin
  password: "password123"

plugin:
  path: ./testplugin.zip

<configuration>
    <appender name="CONSOLE" class="ch.qos.logback.core.ConsoleAppender">
        <encoder class="com.lucidworks.logging.logback.classic.LucidworksPatternLayoutEncoder">
            <pattern>%d - %-5p [%t:%C{3.}@%L] - %m{nolookups}%n</pattern>
            <charset>utf8</charset>
        </encoder>
    </appender>

    <appender name="FILE" class="ch.qos.logback.core.rolling.RollingFileAppender">
        <file>${LOGDIR:-.}/connector.log</file>
        <rollingPolicy class="ch.qos.logback.core.rolling.SizeAndTimeBasedRollingPolicy">
            <!-- rollover daily -->
            <fileNamePattern>${LOGDIR:-.}/connector-%d{yyyy-MM-dd}.%i.log.gz</fileNamePattern>
            <maxFileSize>50MB</maxFileSize>
            <totalSizeCap>10GB</totalSizeCap>
        </rollingPolicy>
        <encoder class="com.lucidworks.logging.logback.classic.LucidworksPatternLayoutEncoder">
            <pattern>%d - %-5p [%t:%C{3.}@%L] - %m{nolookups}%n</pattern>
            <charset>utf8</charset>
        </encoder>
    </appender>

    <root level="INFO">
        <appender-ref ref="CONSOLE"/>
        <appender-ref ref="FILE"/>
    </root>
</configuration>

java [-Dlogging.config=[LOGBACK_XML_FILE]] \
  -jar connector-plugin-client-standalone.jar [YAML_CONFIG_FILE]

java -Dstandalone.connector.connectivity.test=true -jar connector-plugin-client-standalone.jar [YAML_CONFIG_FILE]

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Asynchronous parsing service

LucidAcademyLucidworks offers free training to help you get started.The Course for Asynchronous Parsing Service focuses on how to use asynchronous parsing to index your data more efficiently:

Visit the LucidAcademy to see the full training catalog.

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Job Config service

• 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.

/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

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Security updates

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API path changes

http://HOSTNAME/api/apps/APP_NAME/query/QUERY_PROFILE?q=hello

http://HOSTNAME/api/apps/APP_NAME/query/QUERY_PROFILE/?q=hello

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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.

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Known issues

• Saving large query pipelines may cause OOM failures under high load.
  In Fusion 5.9.x versions through 5.9.13, saving a large query pipeline during high query volume can result in thread lock, excessive memory use, and eventual OOM errors in the Query service.
  This issue is fixed in Fusion 5.9.14.
• Scheduled jobs that perform Solr operations may fail to run or save due to permission-related issues in the job-config service.
  In some cases, users without full permissions can create or modify scheduled tasks that execute actions they aren’t authorized to run directly, specifically tasks that invoked Solr using solr:// URIs.
  These jobs may fail silently or prevent schedule changes from being saved. This issue can also affect the execution of jobs after upgrade. It is fixed in Fusion 5.9.13.
• Deploying Fusion with TLS flags enabled fails in ArgoCD due to Helm chart rendering limitations.
  When using ArgoCD to deploy Fusion 5.9.10 or 5.9.11 with TLS options enabled, Helm chart rendering fails due to the use of the lookup function, which is unsupported by ArgoCD. This prevents ArgoCD from generating manifests, blocking deployment workflows that rely on TLS configuration.
  This issue is fixed in Fusion 5.9.12.
  As a workaround, deploy Fusion without enabling TLS in ArgoCD-managed environments, or perform the deployment using Helm directly.
• Incorrect Solr image repository in Helm charts.
  Fusion 5.9.11’s Helm chart incorrectly references an internal Lucidworks Artifactory repository for the Solr image. This can prevent successful deployment in environments without access to internal infrastructure.
  This issue is fixed in Fusion 5.9.12.
  As a workaround, override the Solr image repository in your custom values.yaml file.
• Jobs API returns incorrect timestamps for jobs scheduled after 12:00 UTC.
  In Fusion 5.9.11, job timestamps returned by the API may be off by 12 hours if the time is after noon UTC. This affects scheduling accuracy and may cause the UI to misinterpret whether a change has been made.
  This issue is fixed in Fusion 5.9.12.
• Cannot update existing job triggers in the Schedulers view.
  In Fusion 5.9.11, scheduled job triggers cannot be modified due to incorrect timestamp comparison logic in the Admin UI. You must delete and recreate the trigger instead.
  This issue is fixed in Fusion 5.9.12.
• Scheduler may stop working if ZooKeeper becomes briefly unavailable.
  If all job-config pods lose connection to ZooKeeper in Fusion 5.9.11, they may fail to re-elect a leader, halting all scheduling until one of the pods is manually restarted.
  This issue is fixed in Fusion 5.9.12.
• Datasource jobs show incorrect “started by” user.
  In Fusion 5.9.11, datasource jobs started in the UI incorrectly show default-subject as the initiating user in job history instead of the actual user.
  This issue is fixed in Fusion 5.9.12.
• Schema API incompatibility with nonstandard schema file names.
  In Fusion 5.9.11, new apps save Solr schemas with the name managed-schema.xml instead of managed-schema, while older apps may include only a managed-schema file.
  This mismatch can cause errors when editing or previewing schemas using the Schema API, particularly when the expected file extension is missing.
  For existing config sets, it’s best to copy the contents from managed-schema to a new managed-schema.xml, then delete managed-schema.
  Fusion 5.9.12 resolves this by supporting both file names, ensuring backward compatibility with older apps and consistent behavior for new ones.
• Job scheduling may stop working if all job-config pods lose connection to ZooKeeper.
  In rare cases, when a ZooKeeper connection is lost, the leader latch mechanism used by the job-config service might fail to recover. If no node can re-establish leadership, the job scheduler stops until at least one job-config pod is restarted.
  This issue is fixed in Fusion 5.9.13.
• Scheduled job triggers can’t be updated in the Schedulers view.
  While the Admin UI displays the current trigger time correctly, changes to the time are not saved.
  This is due to a mismatch in how the API formats UTC timestamps, especially for times after 12:00 UTC, which prevents the UI from detecting that a change has been made.
  To work around this, delete the existing scheduler entry and create a new one with the desired time.
  This issue is resolved in Fusion 5.9.13.
• Dependent job scheduling may fail.
  Jobs configured to run based on the success or failure of another job may not trigger as expected. This includes configurations using “on_success_or_failure” and “Start + Interval” options. This issue is resolved in Fusion 5.9.13.
• Aborted jobs in Fusion are listed twice in the Job History.
  This issue is 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.
  This issue is fixed in Fusion 5.9.12.
  To work around this issue, 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.
• 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.
• An issue prevents segment-based rule filtering from working correctly in Commerce Studio. This issue is fixed in Fusion 5.9.12.

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Deprecations

• 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

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Removals

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Bitnami removal

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Platform support and component versions

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Kubernetes platform support

• 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

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Component versions