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From ctarg...@apache.org
Subject [1/2] lucene-solr:branch_7x: Ref Guide: really minor typos/grammar fixes
Date Thu, 08 Mar 2018 18:11:21 GMT
Repository: lucene-solr
Updated Branches:
  refs/heads/branch_7x 567f25344 -> 1c504c974


Ref Guide: really minor typos/grammar fixes


Project: http://git-wip-us.apache.org/repos/asf/lucene-solr/repo
Commit: http://git-wip-us.apache.org/repos/asf/lucene-solr/commit/a0d21e06
Tree: http://git-wip-us.apache.org/repos/asf/lucene-solr/tree/a0d21e06
Diff: http://git-wip-us.apache.org/repos/asf/lucene-solr/diff/a0d21e06

Branch: refs/heads/branch_7x
Commit: a0d21e06bdcbc8ac694c3bbae5d8344acb8a5947
Parents: 567f253
Author: Cassandra Targett <ctargett@apache.org>
Authored: Thu Mar 8 12:03:18 2018 -0600
Committer: Cassandra Targett <ctargett@apache.org>
Committed: Thu Mar 8 12:10:58 2018 -0600

----------------------------------------------------------------------
 solr/solr-ref-guide/src/a-quick-overview.adoc     | 8 ++++----
 solr/solr-ref-guide/src/distributed-requests.adoc | 2 +-
 solr/solr-ref-guide/src/solr-tutorial.adoc        | 4 ++--
 3 files changed, 7 insertions(+), 7 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/lucene-solr/blob/a0d21e06/solr/solr-ref-guide/src/a-quick-overview.adoc
----------------------------------------------------------------------
diff --git a/solr/solr-ref-guide/src/a-quick-overview.adoc b/solr/solr-ref-guide/src/a-quick-overview.adoc
index bf9fbe8..b1f66b0 100644
--- a/solr/solr-ref-guide/src/a-quick-overview.adoc
+++ b/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.

http://git-wip-us.apache.org/repos/asf/lucene-solr/blob/a0d21e06/solr/solr-ref-guide/src/distributed-requests.adoc
----------------------------------------------------------------------
diff --git a/solr/solr-ref-guide/src/distributed-requests.adoc b/solr/solr-ref-guide/src/distributed-requests.adoc
index f5aaff4..096f632 100644
--- a/solr/solr-ref-guide/src/distributed-requests.adoc
+++ b/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.

http://git-wip-us.apache.org/repos/asf/lucene-solr/blob/a0d21e06/solr/solr-ref-guide/src/solr-tutorial.adoc
----------------------------------------------------------------------
diff --git a/solr/solr-ref-guide/src/solr-tutorial.adoc b/solr/solr-ref-guide/src/solr-tutorial.adoc
index 4abb130..81d3375 100644
--- a/solr/solr-ref-guide/src/solr-tutorial.adoc
+++ b/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.
 


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