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Table of Contents

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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

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  • 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 re-index process for existing inventory DB
  • 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

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  • 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 other 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

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*Back-end only

POC evaluation results

The evaluation 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:

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Almost entire evaluation was done trough UI and 75% of those who participateparticipated, found the POC successful.   Alll All participants, however,   saw suggested some room for improvements, in front- and  back-end . The team addressed the following reported issues that were reported:

Issue

Solution

Noisy search results

Implemented searches supporting keyword “all” or “any” limiting the number of matches: MSEARCH-91

Expected results not found

All provided examples were related to the special characters in the Title that were searched using ASCII representation.  The problem will be addressed in scope of  MSEARCH-67

Bug in sorting by title

MSEARCH-99

Support phrase search

MSEARCH-92

Ranking refinement

Refinement of the default ranking system will require further analysis to be in the scope of a separate feature

Discrepancy in saving UUIDs from Action menu

MSEARCH-93 and UISEES-58

UI enhancements and bug fixes

UISEES-47, UISEES-57, UISEES-61, UISEES-62, UISEES-48, UISEES-49


Those Two evaluators who deemed determined that the POC a failure, provided did not meet their expectations and provided the following reasons:

  • 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:

Querymod-inventory (postgresPostgresSQL), sResults Countmod-search (elsasticsearchElsasticsearch), sResults Count

keyword all "April" sortby title&limit=100&offset=0

437408141268

*keyword all "April" sortby title&limit=100&offset=1001

537408141268

keyword

all

"agency"

and

source=FOLIO

sortby

title&limit=100&offset=0

3.510000.83536
keyword all "bill" sortby title&limit=100&offset=05501490.660992
keyword all "set" sortby title&limit=100&offset=0 73070.8156751

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

Examples are taken from

Jira Legacy
serverSystem JiraJIRA
serverId01505d01-b853-3c2e-90f1-ee9b165564fc
keyPERF-44

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