Merge pull request #173841 from HeidiSteen/heidist-fresh

PRMerger19 · web-flow · commit ee94e947eb9a · 2021-09-28T12:08:57.000-07:00
[azure search] freshness pass
diff --git a/articles/search/search-filters.md b/articles/search/search-filters.md
@@ -8,14 +8,14 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: conceptual
-ms.date: 03/02/2021
+ms.date: 09/28/2021
 ms.custom: devx-track-csharp
 ---
 # Filters in Azure Cognitive Search 
 
 A *filter* provides value-based criteria for selecting documents used in a query. A filter can be a single value or an OData [filter expression](search-query-odata-filter.md). In contrast with full text search, a filter value or expression only returns a strict match.
 
-Some search experiences, such as [faceted navigation](search-filters-facets.md), depend on filters as part of the implementation, but you can use filters anytime you want to scope a query to specific values. If instead your goal is to scope a query to specific fields, there are alternative methods, described below.
+Some search experiences, such as [faceted navigation](search-faceted-navigation.md), depend on filters as part of the implementation, but you can use filters anytime you want to scope a query to specific values. If instead your goal is to scope a query to specific fields, there are alternative methods, described below.
 
 ## When to use a filter
 
@@ -83,13 +83,13 @@ POST https://[service name].search.windows.net/indexes/hotels/docs/search?api-ve
     var results = searchIndexClient.Documents.Search("*", parameters);
 ```
 
-## Filter usage patterns
+## Filter patterns
 
 The following examples illustrate several usage patterns for filter scenarios. For more ideas, see [OData expression syntax > Examples](./search-query-odata-filter.md#examples).
 
 + Standalone **$filter**, without a query string, useful when the filter expression is able to fully qualify documents of interest. Without a query string, there is no lexical or linguistic analysis, no scoring, and no ranking. Notice the search string is just an asterisk, which means "match all documents".
 
-  ```http
+  ```json
   {
     "search": "*",
     "filter": "Rooms/any(room: room/BaseRate ge 60 and room/BaseRate lt 300) and Address/City eq 'Honolulu"
@@ -98,15 +98,15 @@ The following examples illustrate several usage patterns for filter scenarios. F
 
 + Combination of query string and **$filter**, where the filter creates the subset, and the query string provides the term inputs for full text search over the filtered subset. The addition of terms (walking distance theaters) introduces search scores in the results, where documents that best match the terms are ranked higher. Using a filter with a query string is the most common usage pattern.
 
-  ```http
+  ```json
   {
     "search": "walking distance theaters",
     "filter": "Rooms/any(room: room/BaseRate ge 60 and room/BaseRate lt 300) and Address/City eq 'Seattle'"
   }
 
 + Compound queries, separated by "or", each with its own filter criteria (for example, 'beagles' in 'dog' or 'siamese' in 'cat'). Expressions combined with `or` are evaluated individually, with the union of documents matching each expression sent back in the response. This usage pattern is achieved through the `search.ismatchscoring` function. You can also use the non-scoring version, `search.ismatch`.
 
-   ```
+   ```http
    # Match on hostels rated higher than 4 OR 5-star motels.
    $filter=search.ismatchscoring('hostel') and Rating ge 4 or search.ismatchscoring('motel') and Rating eq 5
 
@@ -116,17 +116,12 @@ The following examples illustrate several usage patterns for filter scenarios. F
 
   It is also possible to combine full-text search via `search.ismatchscoring` with filters using `and` instead of `or`, but this is functionally equivalent to using the `search` and `$filter` parameters in a search request. For example, the following two queries produce the same result:
 
-  ```
+  ```http
   $filter=search.ismatchscoring('pool') and Rating ge 4
 
   search=pool&$filter=Rating ge 4
   ```
 
-Follow up with these articles for comprehensive guidance on specific use cases:
-
-+ [Facet filters](search-filters-facets.md)
-+ [Security trimming](search-security-trimming-for-azure-search.md) 
-
 ## Field requirements for filtering
 
 In the REST API, filterable is *on* by default for simple fields. Filterable fields increase index size; be sure to set `"filterable": false` for fields that you don't plan to actually use in a filter. For more information about settings for field definitions, see [Create Index](/rest/api/searchservice/create-index).
diff --git a/articles/search/search-get-started-portal.md b/articles/search/search-get-started-portal.md
@@ -195,7 +195,7 @@ Facet filters are included in search requests. You can use the facet parameter t
 
 + Only filterable fields can be faceted. Only retrievable fields can be returned in the results.
 
-+ The *Rating* field is double-precision floating point and the grouping will be by precise value. For more information on grouping by interval (for instance, "3 star ratings," "4 star ratings," etc.), see [How to implement faceted navigation in Azure Cognitive Search](./search-faceted-navigation.md#filter-based-on-a-range).
++ The *Rating* field is double-precision floating point and the grouping will be by precise value. For more information on grouping by interval (for instance, "3 star ratings," "4 star ratings," etc.), see ["Query parameters" in the REST API](/rest/api/searchservice/search-documents#query-parameters).
 
 ### <a name="highlight-query"></a> Highlight search results
 
diff --git a/articles/search/search-lucene-query-architecture.md b/articles/search/search-lucene-query-architecture.md
@@ -1,22 +1,22 @@
 ---
 title: Full text query and indexing engine architecture (Lucene) 
 titleSuffix: Azure Cognitive Search
-description: Examines Lucene query processing and document retrieval concepts for full text search, as related to Azure Cognitive Search.
+description: Explore Lucene query processing and document retrieval concepts for full text search, as related to Azure Cognitive Search.
 
 manager: nitinme
 author: yahnoosh
 ms.author: jlembicz
 ms.service: cognitive-search
 ms.topic: conceptual
-ms.date: 11/04/2019
+ms.date: 09/28/2021
 ---
 
 # Full text search in Azure Cognitive Search
 
 This article is for developers who need a deeper understanding of how Lucene full text search works in Azure Cognitive Search. For text queries, Azure Cognitive Search will seamlessly deliver expected results in most scenarios, but occasionally you might get a result that seems "off" somehow. In these situations, having a background in the four stages of Lucene query execution (query parsing, lexical analysis, document matching, scoring) can help you identify specific changes to query parameters or index configuration that will deliver the desired outcome. 
 
 > [!Note] 
-> Azure Cognitive Search uses Lucene for full text search, but Lucene integration is not exhaustive. We selectively expose and extend Lucene functionality to enable the scenarios important to Azure Cognitive Search. 
+> Azure Cognitive Search uses [Apache Lucene](https://lucene.apache.org/) for full text search, but Lucene integration is not exhaustive. We selectively expose and extend Lucene functionality to enable the scenarios important to Azure Cognitive Search. 
 
 ## Architecture overview and diagram
 
diff --git a/articles/search/search-query-overview.md b/articles/search/search-query-overview.md
@@ -84,7 +84,7 @@ You might also need filters to invoke a specialized query form, as described in
 |-----------------|-------------|
 | Range filters | In Azure Cognitive Search, range queries are built using the filter parameter. For more information and examples, see [Range filter example](search-query-simple-examples.md#example-5-range-filters). |
 | Geo-location search | If a searchable field is of [Edm.GeographyPoint type](/rest/api/searchservice/supported-data-types), you can create a filter expression for "find near me" or map-based search controls. Fields that drive geo-search contain coordinates. For more information and an example, see [Geo-search example](search-query-simple-examples.md#example-6-geo-search). |
-| Faceted navigation | A facet navigation structure becomes instrumental in user-directed navigation when you invoke a filter in response to an `onclick` event on a facet. As such, facets and filters go hand-in-hand. If you add facet navigation, you will need filters to complete the experience. For more information, see [How to build a facet filter](search-filters-facets.md). |
+| Faceted navigation | In [faceted navigation](search-faceted-navigation.md) tree, users can select facets, narrowing the results on each click. Each facet is backed by a filter that excludes documents that no longer match the criteria. |
 
 > [!NOTE]
 > Text that's used in a filter expression is not analyzed during query processing. The text input is presumed to be a verbatim case-sensitive character pattern that either succeeds or fails on the match. Filter expressions are constructed using [OData syntax](query-odata-filter-orderby-syntax.md) and passed in a **`filter`** parameter in all *filterable* fields in your index. For more information, see [Filters in Azure Cognitive Search](search-filters.md).
diff --git a/articles/search/search-what-is-azure-search.md b/articles/search/search-what-is-azure-search.md
@@ -8,19 +8,19 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: overview
-ms.date: 07/21/2021
+ms.date: 09/28/2021
 ms.custom: contperf-fy21q1
 ---
 # What is Azure Cognitive Search?
 
 Azure Cognitive Search ([formerly known as "Azure Search"](whats-new.md#new-service-name)) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.
 
-Search is foundational to any app that surfaces text content to users, with common scenarios including catalog or document search, retail product search, or knowledge mining for data science.
+Search is foundational to any app that surfaces text content to users, with common scenarios including catalog or document search, online retail, or knowledge mining for data science.
 
 When you create a search service, you'll work with the following capabilities:
 
 + A search engine for full text search with storage for user-owned content in a search index
-+ Rich indexing, with text analysis and [optional AI enrichment](cognitive-search-concept-intro.md) for advanced content extraction and transformation
++ Rich indexing, with [text analysis](search-analyzers.md) and [optional AI enrichment](cognitive-search-concept-intro.md) for advanced content extraction and transformation
 + Rich query capabilities, including simple syntax, full Lucene syntax, and typeahead search
 + Programmability through REST APIs and client libraries in Azure SDKs for .NET, Python, Java, and JavaScript
 + Azure integration at the data layer, machine learning layer, and AI (Cognitive Services)
@@ -29,7 +29,7 @@ Architecturally, a search service sits between the external data stores that con
 
 ![Azure Cognitive Search architecture](media/search-what-is-azure-search/azure-search-diagram.svg "Azure Cognitive Search architecture")
 
-Cognitive Search can integrate with other Azure services in the form of *indexers* that automate data ingestion/retrieval from Azure data sources, and *skillsets* that incorporate consumable AI from Cognitive Services, such as image and text analysis, or custom AI that you create in Azure Machine Learning or wrap inside Azure Functions.
+Across the Azure platform, Cognitive Search can integrate with other Azure services in the form of *indexers* that automate data ingestion/retrieval from Azure data sources, and *skillsets* that incorporate consumable AI from Cognitive Services, such as image and natural language processing, or custom AI that you create in Azure Machine Learning or wrap inside Azure Functions.
 
 ## Inside a search service
 
@@ -83,7 +83,7 @@ Customers often ask how Azure Cognitive Search compares with other search-relate
 |-------------|-----------------|
 | Microsoft Search | [Microsoft Search](/microsoftsearch/overview-microsoft-search) is for Microsoft 365 authenticated users who need to query over content in SharePoint. It's offered as a ready-to-use search experience, enabled and configured by administrators, with the ability to accept external content through connectors from Microsoft and other sources. If this describes your scenario, then Microsoft Search with Microsoft 365 is an attractive option to explore.<br/><br/>In contrast, Azure Cognitive Search executes queries over an index that you define, populated with data and documents you own, often from diverse sources. Azure Cognitive Search has crawler capabilities for some Azure data sources through [indexers](search-indexer-overview.md), but you can push any JSON document that conforms to your index schema into a single, consolidated searchable resource. You can also customize the indexing pipeline to include machine learning and lexical analyzers. Because Cognitive Search is built to be a plug-in component in larger solutions, you can integrate search into almost any app, on any platform.|
 |Bing | [Bing Web Search API](../cognitive-services/bing-web-search/index.yml) searches the indexes on Bing.com for matching terms you submit. Indexes are built from HTML, XML, and other web content on public sites. Built on the same foundation, [Bing Custom Search](/azure/cognitive-services/bing-custom-search/) offers the same crawler technology for web content types, scoped to individual web sites.<br/><br/>In Cognitive Search, you can define and populate the index. You can use [indexers](search-indexer-overview.md) to crawl data on Azure data sources, or push any index-conforming JSON document to your search service. |
-|Database search | Many database platforms include a built-in search experience. SQL Server has [full text search](/sql/relational-databases/search/full-text-search). Cosmos DB and similar technologies have queryable indexes. When evaluating products that combine search and storage, it can be challenging to determine which way to go. Many solutions use both: DBMS for storage, and Azure Cognitive Search for specialized search features.<br/><br/>Compared to DBMS search, Azure Cognitive Search stores content from heterogeneous sources and offers specialized text processing features such as linguistic-aware text processing (stemming, lemmatization, word forms) in [56 languages](/rest/api/searchservice/language-support). It also supports autocorrection of misspelled words, [synonyms](/rest/api/searchservice/synonym-map-operations), [suggestions](/rest/api/searchservice/suggestions), [scoring controls](/rest/api/searchservice/add-scoring-profiles-to-a-search-index), [facets](./search-filters-facets.md),  and [custom tokenization](/rest/api/searchservice/custom-analyzers-in-azure-search). The [full text search engine](search-lucene-query-architecture.md) in Azure Cognitive Search is built on Apache Lucene, an industry standard in information retrieval. However, while Azure Cognitive Search persists data in the form of an inverted index, it is not a replacement for true data storage and we don't recommend using it in that capacity. For more information, see this [forum post](https://stackoverflow.com/questions/40101159/can-azure-search-be-used-as-a-primary-database-for-some-data). <br/><br/>Resource utilization is another inflection point in this category. Indexing and some query operations are often computationally intensive. Offloading search from the DBMS to a dedicated solution in the cloud preserves system resources for transaction processing. Furthermore, by externalizing search, you can easily adjust scale to match query volume.|
+|Database search | Many database platforms include a built-in search experience. SQL Server has [full text search](/sql/relational-databases/search/full-text-search). Cosmos DB and similar technologies have queryable indexes. When evaluating products that combine search and storage, it can be challenging to determine which way to go. Many solutions use both: DBMS for storage, and Azure Cognitive Search for specialized search features.<br/><br/>Compared to DBMS search, Azure Cognitive Search stores content from heterogeneous sources and offers specialized text processing features such as linguistic-aware text processing (stemming, lemmatization, word forms) in [56 languages](/rest/api/searchservice/language-support). It also supports autocorrection of misspelled words, [synonyms](/rest/api/searchservice/synonym-map-operations), [suggestions](/rest/api/searchservice/suggestions), [scoring controls](/rest/api/searchservice/add-scoring-profiles-to-a-search-index), [facets](search-faceted-navigation.md),  and [custom tokenization](/rest/api/searchservice/custom-analyzers-in-azure-search). The [full text search engine](search-lucene-query-architecture.md) in Azure Cognitive Search is built on Apache Lucene, an industry standard in information retrieval. However, while Azure Cognitive Search persists data in the form of an inverted index, it is not a replacement for true data storage and we don't recommend using it in that capacity. For more information, see this [forum post](https://stackoverflow.com/questions/40101159/can-azure-search-be-used-as-a-primary-database-for-some-data). <br/><br/>Resource utilization is another inflection point in this category. Indexing and some query operations are often computationally intensive. Offloading search from the DBMS to a dedicated solution in the cloud preserves system resources for transaction processing. Furthermore, by externalizing search, you can easily adjust scale to match query volume.|
 |Dedicated search solution | Assuming you have decided on dedicated search with full spectrum functionality, a final categorical comparison is between on premises solutions or a cloud service. Many search technologies offer controls over indexing and query pipelines, access to richer query and filtering syntax, control over rank and relevance, and features for self-directed and intelligent search. <br/><br/>A cloud service is the right choice if you want a turn-key solution with minimal overhead and maintenance, and adjustable scale. <br/><br/>Within the cloud paradigm, several providers offer comparable baseline features, with full-text search, geo-search, and the ability to handle a certain level of ambiguity in search inputs. Typically, it's a [specialized feature](search-features-list.md), or the ease and overall simplicity of APIs, tools, and management that determines the best fit. |
 
 Among cloud providers, Azure Cognitive Search is strongest for full text search workloads over content stores and databases on Azure, for apps that rely primarily on search for both information retrieval and content navigation. 
