Ref Guide: really minor typos/grammar fixes

solr/solr-ref-guide/src/a-quick-overview.adoc

@@ -18,12 +18,12 @@
 
 Solr is a search server built on top of Apache Lucene, an open source, Java-based, information retrieval library. It is designed to drive powerful document retrieval applications - wherever you need to serve data to users based on their queries, Solr can work for you.
 
-Here is a example of how Solr might be integrated into an application:
+Here is a example of how Solr could integrate with an application:
 
 .Solr integration with applications
 image::images/a-quick-overview/sample-client-app-arch.png[image,width=500,height=379]
 
-In the scenario above, Solr runs along side other server applications. For example, an online store application would provide a user interface, a shopping cart, and a way to make purchases for end users; while an inventory management application would allow store employees to edit product information. The product metadata would be kept in some kind of database, as well as in Solr.
+In the scenario above, Solr runs alongside other server applications. For example, an online store application would provide a user interface, a shopping cart, and a way to make purchases for end users; while an inventory management application would allow store employees to edit product information. The product metadata would be kept in some kind of database, as well as in Solr.
 
 Solr makes it easy to add the capability to search through the online store through the following steps:
 
@@ -37,8 +37,8 @@ Solr offers support for the simplest keyword searching through to complex querie
 
 If Solr's capabilities are not impressive enough, its ability to handle very high-volume applications should do the trick.
 
-A relatively common scenario is that you have so much data, or so many queries, that a single Solr server is unable to handle your entire workload. In this case, you can scale up the capabilities of your application using <<solrcloud.adoc#solrcloud,SolrCloud>> to better distribute the data, and the processing of requests, across many servers. Multiple options can be mixed and matched depending on the type of scalability you need.
+A relatively common scenario is that you have so much data, or so many queries, that a single Solr server is unable to handle your entire workload. In this case, you can scale up the capabilities of your application using <<solrcloud.adoc#solrcloud,SolrCloud>> to better distribute the data, and the processing of requests, across many servers. Multiple options can be mixed and matched depending on the scalability you need.
 
-For example: "Sharding" is a scaling technique in which a collection is split into multiple logical pieces called "shards" in order to scale up the number of documents in a collection beyond what could physically fit on a single server. Incoming queries are distributed to every shard in the collection, which respond with merged results. Another technique available is to increase the "Replication Factor" of your collection, which allows you to add servers with additional copies of your collection to handle higher concurrent query load by spreading the requests around to multiple machines. Sharding and Replication are not mutually exclusive, and together make Solr an extremely powerful and scalable platform.
+For example: "Sharding" is a scaling technique in which a collection is split into multiple logical pieces called "shards" in order to scale up the number of documents in a collection beyond what could physically fit on a single server. Incoming queries are distributed to every shard in the collection, which respond with merged results. Another technique available is to increase the "Replication Factor" of your collection, which allows you to add servers with additional copies of your collection to handle higher concurrent query load by spreading the requests around to multiple machines. Sharding and replication are not mutually exclusive, and together make Solr an extremely powerful and scalable platform.
 
 Best of all, this talk about high-volume applications is not just hypothetical: some of the famous Internet sites that use Solr today are Macy's, EBay, and Zappo's. For more examples, take a look at https://wiki.apache.org/solr/PublicServers.

solr/solr-ref-guide/src/distributed-requests.adoc

@@ -118,7 +118,7 @@ Chooses the JVM specifics dealing with fair policy queuing, if enabled distribut
 
 Document and term statistics are needed in order to calculate relevancy. Solr provides four implementations out of the box when it comes to document stats calculation:
 
-* `LocalStatsCache`: This only uses local term and document statistics to compute relevance. In cases with uniform term distribution across shards, this works reasonably well.This option is the default if no `<statsCache>` is configured.
+* `LocalStatsCache`: This only uses local term and document statistics to compute relevance. In cases with uniform term distribution across shards, this works reasonably well. This option is the default if no `<statsCache>` is configured.
 * `ExactStatsCache`: This implementation uses global values (across the collection) for document frequency.
 * `ExactSharedStatsCache`: This is exactly like the exact stats cache in its functionality but the global stats are reused for subsequent requests with the same terms.
 * `LRUStatsCache`: This implementation uses an LRU cache to hold global stats, which are shared between requests.

solr/solr-ref-guide/src/solr-tutorial.adoc

@@ -2,10 +2,10 @@
 :page-tocclass: right
 :experimental:
 
-This tutorial covers getting Solr up and running, ingesting a variety of data sources into multiple collections,
+This tutorial covers getting Solr up and running, ingesting a variety of data sources into Solr collections,
 and getting a feel for the Solr administrative and search interfaces.
 
-It is organized into three sections that each build on the one before it. The <<exercise-1,first exercise>> will ask you to start Solr, create a collection, index some basic documents, and then perform a few searches.
+The tutorial is organized into three sections that each build on the one before it. The <<exercise-1,first exercise>> will ask you to start Solr, create a collection, index some basic documents, and then perform some searches.
 
 The <<exercise-2,second exercise>> works with a different set of data, and explores requesting facets with the dataset.