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Table of Contents

Background:

In October of 2020, during the Technical Council meeting it was decided to look into Elasticsearch as a possible alternative for the existing Inventory search due to its poor performance and inability to provide exact count of records matching search criteria.

The development team has been formed in December 2020 and a PO was assigned to the team.  The scope of the work was limited to what was limited to the back-end work due to the limited time for delivery.  The details of what was discussed are here

Scope:

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Scope

In October 2020, Technical Council recommended to investigate a possible Elasticsearch implementation to address poor performance of the inventory search. Scope was determined to include the following deliverables for the Iris release: 

Back-end:

• Send update notification messages from inventory and source record storage (SRS)
• Providing Provide Inventory and SRS APIs for fetching view for indexing by ids
• Extract common library for using it in other modules
• Build infrastructure necessary to support ElasticsearchDeliverables - front

Front-end:

• Update Stripes Components to support new Search API
• Provide “switch” to allow using Elasticsearch or existing searchDeliverables - infrastructure

Infrastructure:

• Add Elasticsearch cluster to CI/CD and setup it on environments (k8s conf)
• Check configuration of existing Kafka cluster

Sending update notification messages from source record storage (SRS) - determined as out of scope for Elasticsearch  UXPROD-2791

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In December 2020, it was determined that Elasticsearch is not the right tool for querying MARC records. SRS search has been removed from the scope of Elasticsearch POC and it has become a separate feature (UXPROD-2791).

Delivered functionality

Work delivered by the Falcon team as a part of the Iris release includes:

Back-end:

• Sending Implemented sending add/update/delete notification messages from Inventorymessage publication to Kafka in mod-inventory-storage
• Built Search APIs for searching and faceting
• Combined instances + holding + items into a single index
• Implemented reindex re-index process for existing inventory DB
• Front-end
  • Built UI for alternate Inventory search app that represents searching capabilities of Elasticsearch
• Spring-based implementation that supports:
  • Up to five language-specific analyzers configured on tenant level
  • Near real-time inserts, updates and deletions
  • Boolean operators (AND, OR, NOT)
  • Nested search using brackets
  • All or Any keyword search
  • Exact phrase search
  • Left- and right-hand truncation, wildcards searches in some fields

 Front-end:

Due to the rigid structure of the existing Inventory's Search Component, it was not possible to make any changes that would allow for switching between PostgreSQL and Elasticsearch implementation. To provide users with an easy way to evaluate the back-end work, we built an alternative UI (Inventory ES app) that allowed non-technical users compare behavior between existing search and search powered by Elasticsearch.  The new UI introduced:

• New UI components for advanced search that include:
  • Auto-resized textbox
  • Supported fields and operators auto-suggestion
  • Boolean operators support
  • Nested search using brackets
• New UI components for filters and facets
• Default results sort by ranking
• Preserved non-search related Inventory app functionality

  Infrastructure:

• Added Elasticsearch cluster to CI/CD and set it up on the reference environments
• Updated existing Kafka cluster configuration
• Introduced option of setting up performance testing environment in the community

As a result, the following search options and filters are supported:

Search options:

Filters and facets:

*Back-end only

POC evaluation results

Evaluation of the POC took place from April 5th to April 9th, 2021 and it was conducted in the the Bugfest environment (~8 millions records) by eight librarians representing:

• Chicago University (2 participants)
• Duke University
• Missouri State University (2 participants)
• Simmons University
• EBSCO
• Index Data

Almost entire evaluation was done trough UI and 75% of those who participated, found the POC successful. All participants, however, suggested some improvements. The team addressed the following reported issues:

Two evaluators determined that the POC did not meet their expectations and provided the following reasons:

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Proposed next steps:

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• Expected to perform complex queries of multiple fields and across record types (including MARC fields)
• Expected a different UI more like a catalog or discovery system advanced search
• Expected support for additional operators (not equal to, starts with, etc.)
• UI not user friendly
• Preferred a simple left-anchored search than the provided relevancy ranking

Search performance comparison

The table below represents comparison of response time for the same query executed in Inventory app and Inventory ES app in the environment with 8 million of instances:

For all Elasticsearch queries after calling a query the first time, the time for all subsequent queries is less than 250ms (due to Elasticsearch OOB caching)

Examples are taken from

Proposed next steps

• Incorporate UI components created in scope of POC into Stripes components
• Redesign Inventory UI Search component so that it can include new UI components created by POC, especially filters and facets
• Conduct usability study for advanced search textbox
• Use mod-search endpoints for searching
• Conduct analysis of ranking refinements (weights and boosts)
• Conduct analysis of further search refinements
• Define and prioritize work for cross app/cross record types searches
• Define UI for cross app/cross record types searches
• Define requirements for cross-tenant searches

Additional links

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• TC Meeting on October 14, 2020
• UXPROD-2791 SRS MARC Query API
• Search – Technical documentation
• POC Evaluation survey
• Responses Detailed responses to raised issues
• POC Overview for MM SIG (slides) and (recording)
• 110 -111 Sprints Demo
• Supported search types