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.