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Fusion Server 5.0.2 Release NotesSubscribe

Release date: 18 December 2019

Component versions:

Solr 8.3.1

ZooKeeper 3.5.6

Spark 2.4.3

Upgrade Instructions

To upgrade from Fusion 5.0.x to 5.0.2 using the GKE installation script (setup_f5_gke.sh), run the following command:

./setup_f5_gke.sh -c <cluster> -r <release> -n <namespace> -p <project> -z <zone> -i <instance_type> --upgrade

To upgrade from Fusion 5.0.x to 5.0.2 using Helm, run the following commands:

helm repo update
helm upgrade ${RELEASE} "lucidworks/fusion" --timeout 180 --namespace "${NAMESPACE}" --values "${RELEASE}_${NAMESPACE}_fusion_values.yaml"

New features

Predictive Merchandiser

Fusion 5.0.2 integrates Predictive Merchandiser as a component of Fusion AI.

Predictive Merchandiser is an AI powered merchandising tool that provides insights and recommendations for optimizing search results and product placement.

Predictive Merchandiser Board

It provides an easy to use interface, allowing you to:

  • Connect to a Fusion Server data source and simulate the shopping experience of your customers.

  • Pin, boost, bury, and block specific products in your catalog.

  • Create business rules to display custom banners, or redirect customers to a different page on your site, for example.

  • Create search rewriting strategies to manage spelling mistakes, synonym matching, phrase matching, and poorly performing queries.

  • Review query rewriting strategies that were generated automatically using machine learning models in Fusion.

  • Deploy new business rules and search rewrites to the Fusion data source where they can be triggered by live queries.

  • View analytics reports that show, for example, the variation in site visits over time, the most clicked on products, and the most searched for terms.

If you have a Fusion AI license, you can access Predicitive Merchandiser by navigating to Relevance > Rules and selecting Merchandiser.

To learn more, see Predictive Merchandiser.

Jupyter Integration

Starting with Fusion 5.0.2, we now provide a Jupyter service that can be run from the Fusion Helm chart. Jupyter is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.

  • Run/Debug Spark code in Scala/Python (replacement for spark-shell)

  • Run SQL queries via Fusion SQL

  • Debug Scala and SQL transforms in PBL jobs

  • Everything else for which Jupyter is designed

To learn more, see Jupyter Support in Fusion.

Improvements

  • The HTML parser now parses the application/xhtml+xml media type by default.

  • The HTML parser can filter HTML content using jsoup. New HTML parser configuration options include:

    Configuration option

    Default value

    Description

    excludeFilters

    N/A

    Jsoup-formatted selectors for elements to exclude from the HTML

    filterBeforeMapping

    False

    Apply exclude filters before performing HTML field mapping

    filterBeforeExtractingLinks

    False

    Apply exclude filters before performing link extraction

  • Index pipelines can now receive nested JSON documents through the Parallel Bulk Loader.

  • The Fusion UI now supports undo for deleted rules.

  • A new boolean property, shareState, is introduced for Javascript Query Stages. When set to true, a single Javscript engine will be used for all queries on a Javascript query stage. Previously, a unique engine was used for each thread.

    Under certain conditions, this can improve performance significantly.

  • If a rule creation API is incorrectly formatted in JSON, Fusion now produces a detailed error message.

  • Improved the output from phrase extraction jobs. It is now more useful for non-signals datasets.

  • Improvement made in error propagation between services.

  • The Windows Share SMB 2/3 Connector now parses data into individual Solr fields instead of a large JSON document.

  • Various UI improvements and fixes.

Bug Fixes

  • Fixed a bug that sometimes resulted in the <app>_recommender_models collection to delete the <app>_recommender_models collection.

  • The collection_alias field is now required for time-based partitioning in the Fusion SQL service.

  • The roles selector in the New User panel now correctly displays the available role types.

  • Fixed a bug that prevented the Box Connector from correctly indexing PowerPoint files.

  • Fixed a bug that prevented the Box Connector from indexing Japanese text with UTF-8 character encoding.

  • Fixed a bug that prevented V2 connector jobs from succeeding if a fetcher was defined without a phase name.

  • Fixed a bug that produced an Out of Memory (OOM) error when indexing large JSON files with a V2 connector.

  • V1 connector stateful jobs are no longer dependent on server IPs. Re-crawling after restarting V1 connectors now works as expected.

  • Fixed a bug that removed CJK characters from file names when uploading a file.

  • Fixed a bug the prevented the Jobs API from scheduling a recently created job.

  • Fixed a bug that sometimes caused DefaultHostInfoMetricReporter to fail on Java 11.

  • Boost lists will now utilize the correct term, orig_score, when rewriting a query.

  • Corrections made to the default synonym detection job settings.

  • Fixed a bug that sometimes caused pytests to fail when using the fusion user.

  • Logstash is now enabled by default.

Known issues

  • If High performance mode is selected on the Web connector configuration, the crawl does not start.

    Note
    This bug is fixed in Fusion 5.1.0.
  • Deploying an application into the webapps service requires a non-empty, non-root context path.

    Note
    This bug is fixed in Fusion 5.1.0.