Merge branch 'master' of https://github.com/MicrosoftDocs/azure-docs-pr into front-door-wildcard-domain

Author: v-dihans

articles/openshift/howto-using-azure-redhat-openshift.md

@@ -13,7 +13,7 @@ keywords: aro, openshift, az aro, red hat, cli
 # Create, access, and manage an Azure Red Hat OpenShift 4.3 Cluster
 
 > [!IMPORTANT]
-> Please note that Azure Red Hat OpenShift 4.3 is currently only available in private preview in East US. Private preview acceptance is by invitation only. Please be sure to register your subscription before attempting to enable this feature: [Azure Red Hat OpenShift Private Preview Registration](https://aka.ms/aro-preview-register)
+> Please note that Azure Red Hat OpenShift 4.3 is currently only available in private preview in East US and East US 2. Private preview acceptance is by invitation only. Please be sure to register your subscription before attempting to enable this feature: [Azure Red Hat OpenShift Private Preview Registration](https://aka.ms/aro-preview-register)
 
 > [!NOTE]
 > Preview features are self-service and are provided as is and as available and are excluded from the service-level agreement (SLA) and limited warranty. Therefore, the features aren't meant for production use.
@@ -61,7 +61,7 @@ The `az aro` extension allows you to create, access, and delete Azure Red Hat Op
    az -v
    ...
    Extensions:
-   aro                                0.1.0
+   aro                                0.3.0
    ...
    ```
 
@@ -104,7 +104,7 @@ Follow these steps to create a virtual network containing two empty subnets.
 4. Add two empty subnets to your virtual network.
 
    ```console
-    for subnet in "$CLUSTER-master" "$CLUSTER-worker"; do
+   for subnet in "$CLUSTER-master" "$CLUSTER-worker"; do
      az network vnet subnet create \
        -g "$RESOURCEGROUP" \
        --vnet-name vnet \
@@ -137,6 +137,8 @@ az aro create \
   --vnet vnet \
   --master-subnet "$CLUSTER-master" \
   --worker-subnet "$CLUSTER-worker" \
+  --cluster-resource-group "aro-$CLUSTER" \
+  --domain "$CLUSTER" \
   --pull-secret "$PULL_SECRET"
 ```
 

articles/search/search-query-fuzzy.md

@@ -16,11 +16,11 @@ Azure Cognitive Search supports fuzzy search, a type of query that compensates f
 
 ## What is fuzzy search?
 
-It's an expansion exercise that produces a match on terms having a similar composition. When a fuzzy search is specified, the engine builds a graph of similarly composed terms, for all whole terms in the query. For example, if your query includes three terms "university of washington", a graph is created for each term (`search=university~ of~ washington~`). 
+It's an expansion exercise that produces a match on terms having a similar composition. When a fuzzy search is specified, the engine builds a graph (based on [deterministic finite automaton theory](https://en.wikipedia.org/wiki/Deterministic_finite_automaton)) of similarly composed terms, for all whole terms in the query. For example, if your query includes three terms "university of washington", a graph is created for every term  in the query `search=university~ of~ washington~` (there is no stop-word removal in fuzzy search, so "of" gets a graph).
 
 The graph consists of up to 50 expansions, or permutations, of each term, capturing both correct and incorrect variants in the process. The engine then returns the topmost relevant matches in the response. 
 
-For a term like "university", the graph might have "unversty, universty, university, universe, inverse". Any documents that match on those in the graph are included in results. In contrast with language analyzers that can handle irregularities between singular and plural forms of the same word ("mice" and "mouse"), the comparisons in a fuzzy query are taken at face value with no attempt at reconciling the semantic differences. "Universe" and "inverse" will match because the character discrepancies are small.
+For a term like "university", the graph might have "unversty, universty, university, universe, inverse". Any documents that match on those in the graph are included in results. In contrast with other queries that analyze the text to handle different forms of the same word ("mice" and "mouse"), the comparisons in a fuzzy query are taken at face value without any linguistic analysis on the text. "Universe" and "inverse", which are semantically different, will match because the syntactic discrepancies are small.
 
 A match succeeds if the discrepancies are limited to two or fewer edits, where an edit is an inserted, deleted, substituted, or transposed character. The string correction algorithm that specifies the differential is the [Damerau-Levenshtein distance](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance) metric, described as the "minimum number of operations (insertions, deletions, substitutions, or transpositions of two adjacent characters) required to change one word into the other". 
 
@@ -37,7 +37,7 @@ In Azure Cognitive Search:
 Collectively, the graphs are submitted as match criteria against tokens in the index. As you can imagine, fuzzy search is inherently slower than other query forms. The size and complexity of your index can determine whether the benefits are enough to offset the latency of the response.
 
 > [!NOTE]
-> Because fuzzy search tends to be slow, it might be worthwhile to investigate alternatives such as n-gram indexing, with its progression of short character sequences (two and three character sequences for bigram and trigram tokens). Depending on your language and query surface, n-gram might give you better performance.
+> Because fuzzy search tends to be slow, it might be worthwhile to investigate alternatives such as n-gram indexing, with its progression of short character sequences (two and three character sequences for bigram and trigram tokens). Depending on your language and query surface, n-gram might give you better performance. The trade off is that n-gram indexing is very storage intensive and generates much bigger indexes.
 >
 > Another alternative, which you could consider if you want to handle just the most egregious cases, would be a [synonym map](search-synonyms.md). For example, mapping "search" to "serach, serch, sarch", or "retrieve" to "retreive".
 
@@ -90,7 +90,7 @@ In the response, because you added hit highlighting, formatting is applied to "s
             "Test queries with <em>special</em> characters, plus strings for MSFT, SQL and Java."
         ]
 
-Try the request again, misspelling "special" by taking out letters several letters ("pe"):
+Try the request again, misspelling "special" by taking out several letters ("pe"):
 
     search=scial~&highlight=Description
 

articles/service-fabric/service-fabric-scale-up-node-type.md

@@ -29,7 +29,7 @@ Here is the process for updating the VM size and operating system of the primary
     To find the new scale set in the template, search for the "Microsoft.Compute/virtualMachineScaleSets" resource named by the *vmNodeType2Name* parameter.  The new scale set is added to the primary node type using the properties->virtualMachineProfile->extensionProfile->extensions->properties->settings->nodeTypeRef setting.
 4. Check the cluster health and verify all the nodes are healthy.
 5. Disable the nodes in the old scale set of the primary node type with the intent to remove node. You can disable all at once and the operations are queued. Wait until all nodes are disabled, which may take some time.  As the older nodes in the node type are disabled, the system services and seed nodes migrate to the VMs of the new scale set in the primary node type.
-6. Remove the older scale set from the primary node type.
+6. Remove the older scale set from the primary node type. (After the nodes are disabled as in step 5, in the virtual machine scale set blade in the Azure portal, deallocate the nodes from the old node type one by one.)
 7. Remove the load balancer associated with the old scale set. The cluster is unavailable while the new public IP address and load balancer are configured for the new scale set.  
 8. Store DNS settings of the public IP address associated with the old primary node type scale set in a variable and remove that public IP address.
 9. Replace the DNS settings of the public IP address associated with the new primary node type scale set with the DNS settings of the deleted public IP address.  The cluster is now reachable again.

includes/functions-supported-languages.md

@@ -18,7 +18,7 @@ ms.custom: include file
 |[F#](../articles/azure-functions/functions-reference-fsharp.md)|GA (.NET Framework 4.7)|GA (.NET Core 2.2)| GA (.NET Core 3.1) |
 |[Java](../articles/azure-functions/functions-reference-java.md)|N/A|GA (Java 8)| GA (Java 8)|
 |[PowerShell](../articles/azure-functions/functions-reference-powershell.md) |N/A|GA (PowerShell Core 6)| GA (PowerShell Core 6)|
-|[Python](../articles/azure-functions/functions-reference-python.md)#python-version|N/A|GA (Python 3.6 & 3.7)| GA (Python 3.6, 3.7, & 3.8)|
+|[Python](../articles/azure-functions/functions-reference-python.md#python-version)|N/A|GA (Python 3.6 & 3.7)| GA (Python 3.6, 3.7, & 3.8)|
 |[TypeScript](../articles/azure-functions/functions-reference-node.md#typescript) |N/A|GA<sup>1</sup>| GA<sup>1</sup> |