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wine.co.za kept MariaDB as its primary data store and added Manticore Search as a separate search layer. In a case study published by Manticore Search on September 29, 2026, WineNet IT Director Kevin Kidson said the change made it possible to search with several descriptive terms, in flexible order, and improved autocomplete suggestions. The account describes functional gains, but reports no before-and-after latency or resource benchmarks.
What wine.co.za needed to improve
Before adding Manticore, wine.co.za relied on MariaDB full-text indexes and wildcard queries. Kidson said users needed to know what they were looking for, or at least how to begin spelling it. That made searches harder when someone could describe a wine but did not know its name.
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The case study illustrates the difference with dry chenin stellenbosch: a searcher can combine a style, grape variety and region instead of relying on one exact starting term. Kidson said users could enter those words in a different order, too.
What the archive contains
The case study reports 144,915 records in six collections:
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| Collection | Records |
|---|---|
| Wineries | 907 |
| Wines | 45,833 |
| Contacts | 4,644 |
| International agents | 3,447 |
| News articles | 41,602 |
| Classified adverts | 48,482 |
The six categories sum to 144,915. The case study says the site also has several smaller databases, so this total does not represent every database on the site. The figures come from the vendor case study and its interviewee, rather than an independently audited inventory.
How the search architecture works
Manticore did not replace MariaDB. MariaDB remains the system that stores and updates the source data; searchable data is copied from it to Manticore once a day. The website sends search requests to Manticore through a .NET library.
This divides responsibilities: MariaDB holds the application data, while Manticore serves the search experience. The daily copy is also an important operational detail: the search index is refreshed on that schedule, rather than described as receiving each source-data change immediately.
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Multiword queries with flexible term order
The central reported improvement is the ability to combine descriptive terms, such as “dry,” “chenin” and “Stellenbosch,” even when the searcher does not know a wine’s name. Kidson said this kind of combined query was not possible for the site before Manticore.
Autocomplete suggestions
Manticore also powers suggestions in the site’s search boxes. Kidson characterized both search and dropdown suggestions as “MUCH better.” These are his observations in the case study, not results from a published usability study.
Implementation effort and reliability, as reported
Kidson estimated that installing Manticore and learning how to use it took about 24 hours in total. Because he worked around other responsibilities, he recalls the work spanning roughly two days. That is one interviewee’s estimate for this installation, not a general implementation-time forecast.
He also described the system as “rock solid” and said it had run reliably for years. The case study does not provide uptime figures or independently measured reliability data, so the statement should be read as customer testimony rather than a quantified guarantee.
What the case study does—and does not—show
The account documents a practical architecture and the search features WineNet says it gained: descriptive multiword queries, flexible term order and more useful autocomplete. It does not provide a product comparison or enough evidence to rank Manticore against other search systems.
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It also publishes no measured before-and-after search latency, resource use or other detailed performance figures. Kidson’s positive comments support a qualitative account of the experience, but they do not establish a numerical speedup or savings. The source is Manticore Search’s vendor case study, published September 29, 2026; its implementation details and evaluations are attributed to Kidson.
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