More cleanup for first phase of x-plugins 986.

Author: debadair

010_Intro/05_What_is_it.asciidoc

@@ -33,14 +33,21 @@ and hides complicated search theory away from beginners. It _just works_,
 right out of the box. With minimal understanding, you can soon become
 productive.((("Elasticsearch", "installing")))
 
-Elasticsearch can be((("Apache 2 license"))) downloaded, used, and modified free of charge. It is
-available under the http://www.apache.org/licenses/LICENSE-2.0.html[Apache 2 license],
-one of the most flexible open source licenses available.
-
 As your knowledge grows, you can leverage more of Elasticsearch's advanced
 features. The entire engine is configurable and flexible. Pick and choose
 from the advanced features to tailor Elasticsearch to your problem domain.
 
+You can ((("Apache 2 license"))) download, use, and modify Elasticsearch free of charge. 
+It is available under the http://www.apache.org/licenses/LICENSE-2.0.html[Apache 2 license],
+one of the most flexible open source licenses available. The source is hosted on GitHub
+at https://github.com/elastic/elasticsearch[github.com/elastic/elasticsearch]. See
+https://github.com/elastic/elasticsearch/blob/master/CONTRIBUTING.md[Contributing to 
+Elasticsearch] if you would like to join our amazing community of contributors!
+
+If you have any questions related to Elasticsearch, including specific features, 
+language clients and plugins, join the conversation at 
+https://discuss.elastic.co[discuss.elastic.co]. 
+
 .The Mists of Time
 ***************************************
 

010_Intro/10_Installing_ES.asciidoc

@@ -7,22 +7,17 @@ The only requirement for installing Elasticsearch is a recent version of Java.
 Preferably, you should install the latest version of the((("Java", "installing"))) official Java
 from http://www.java.com[_www.java.com_].
 
-You can download the latest version of Elasticsearch from
+You can get the latest version of Elasticsearch from
 https://www.elastic.co/downloads/elasticsearch[_elasticsearch.co/downloads/elasticsearch_].
 
-[source,sh]
---------------------------------------------------
-curl -L -O http://download.elastic.co/PATH/TO/VERSION.zip <1>
-unzip elasticsearch-$VERSION.zip
-cd  elasticsearch-$VERSION
---------------------------------------------------
-<1> Fill in the URL for the latest version available on
-    http://www.elastic.co/downloads/elasticsearch[_elastic.co/downloads/elasticsearch_].
+To install Elasticsearch, download and extract the archive file for your platform. For 
+more information, see the {ref}/_installation.html[Installation] topic in the Elasticsearch 
+Reference. 
 
 [TIP]
 ====
-When installing Elasticsearch in production, you can use the method
-described previously, or the Debian or RPM packages provided on the
+When installing Elasticsearch in production, you can choose to use 
+the Debian or RPM packages provided on the
 http://www.elastic.co/downloads/elasticsearch[downloads page]. You can also use
 the officially supported
 https://github.com/elasticsearch/puppet-elasticsearch[Puppet module] or
@@ -43,8 +38,7 @@ You do not have to install Marvel, but it will make this book much more
 interactive by allowing you to  experiment with the code samples on your local
 Elasticsearch cluster.
 
-Marvel is available as a plug-in.((("Marvel", "downloading and installing"))) To download and install it, run this command
-in the Elasticsearch directory:
+Marvel is available as a plug-in.((("Marvel", "downloading and installing"))) To download and install it, run this command in the Elasticsearch directory:
 
 [source,sh]
 --------------------------------------------------
@@ -64,14 +58,15 @@ echo 'marvel.agent.enabled: false' >> ./config/elasticsearch.yml
 [[running-elasticsearch]]
 === Running Elasticsearch
 
-Elasticsearch is now ready to run. ((("Elasticsearch", "running")))You can start it up in the foreground
-with this:
+Elasticsearch is now ready to run. ((("Elasticsearch", "running"))) To start it up in the foreground:
 
 [source,sh]
 --------------------------------------------------
-./bin/elasticsearch
+cd elasticsearch-<version>
+./bin/elasticsearch <1> <2>
 --------------------------------------------------
-Add `-d` if you want to run it in the background as a daemon.
+<1> Add `-d` if you want to run it in the background as a daemon.
+<2> If you're running Elasticsearch on Windows, simply run `bin\elasticsearch.bat` instead.
 
 Test it out by opening another terminal window and running the following:
 
@@ -80,6 +75,11 @@ Test it out by opening another terminal window and running the following:
 curl 'http://localhost:9200/?pretty'
 --------------------------------------------------
 
+TIP: If you're running Elasticsearch on Windows, you can download cURL from 
+http://curl.haxx.se/download.html[`http://curl.haxx.se/download.html`]. cURL
+provides a convenient way to submit requests to Elasticsearch and 
+installing cURL enables you to copy and paste many of the examples in this
+book to try them out.
 
 You should see a response like this:
 

020_Distributed_Cluster/15_Add_an_index.asciidoc

@@ -23,9 +23,8 @@ that you have determines the maximum amount of data that your index can hold.
 
 [NOTE]
 ====
-While there is no theoretical limit to the amount of data that a primary shard
-can hold, there is a practical limit.  What constitutes the maximum shard size
-depends entirely on your use case: the hardware you have, the size and
+While a primary shard can technically contain up to Integer.MAX_VALUE - 128 documents, 
+the practical limit depends on your use case: the hardware you have, the size and
 complexity of your documents, how you index and query your documents, and your
 expected response times.
 ====

030_Data/10_Index.asciidoc

@@ -89,13 +89,13 @@ field has been generated for us:
 {
    "_index":    "website",
    "_type":     "blog",
-   "_id":       "wM0OSFhDQXGZAWDf0-drSA",
+   "_id":       "AVFgSgVHUP18jI2wRx0w",
    "_version":  1,
    "created":   true
 }
 --------------------------------------------------
 
-Autogenerated IDs are 22 character long, URL-safe, Base64-encoded string
+Autogenerated IDs are 20 character long, URL-safe, Base64-encoded string
 _universally unique identifiers_, or((("UUIDs (universally unique identifiers)"))) http://en.wikipedia.org/wiki/Uuid[UUIDs].
 
 

040_Distributed_CRUD/10_Shard_interaction.asciidoc

@@ -14,7 +14,7 @@ We can send our requests to any node in the cluster.((("nodes", "sending request
 capable of serving any request.  Every node knows the location of every
 document in the cluster and so can forward requests directly to the required
 node. In the following examples, we will send all of our requests to `Node 1`,
-which we will refer to as  the _requesting node_.
+which we will refer to as  the _coordinating node_.
 
 TIP: When sending requests, it is good practice to round-robin through all the
 nodes in the cluster, in order to spread the load.

040_Distributed_CRUD/15_Create_index_delete.asciidoc

@@ -21,7 +21,7 @@ delete a document on both the primary and any replica shards:
 3. `Node 3` executes the request on the primary shard. If it is successful,
    it forwards the request in parallel to the replica shards on `Node 1` and
    `Node 2`. Once all of the replica shards report success, `Node 3` reports
-   success to the requesting node, which reports success to the client.
+   success to the coordinating node, which reports success to the client.
 
 By the time the client receives a successful response, the document change has
 been executed on the primary shard and on all replica shards. Your change is

040_Distributed_CRUD/20_Retrieving.asciidoc

@@ -19,7 +19,7 @@ primary or replica shard:
 3. `Node 2` returns the document to `Node 1`, which returns the document
    to the client.
 
-For read requests, the requesting node will choose a different shard copy on
+For read requests, the coordinating node will choose a different shard copy on
 every request in order to balance the load; it round-robins through all
 shard copies.
 

040_Distributed_CRUD/25_Partial_updates.asciidoc

@@ -21,7 +21,7 @@ document:
 4. If `Node 3` has managed to update the document successfully, it forwards
    the new version of the document in parallel to the replica shards on `Node 1` 
    and `Node 2` to be reindexed. Once all replica shards report success,
-   `Node 3` reports success to the requesting node,  which reports success to
+   `Node 3` reports success to the coordinating node,  which reports success to
    the client.
 
 The `update` API also accepts the `routing`, `replication`, `consistency`, and

040_Distributed_CRUD/30_Bulk_requests.asciidoc

@@ -2,7 +2,7 @@
 === Multidocument Patterns
 
 The patterns for the `mget` and `bulk` APIs((("mget (multi-get) API", "retrieving multiple documents, process of")))((("documents", "retrieving multiple with mget"))) are similar to those for
-individual documents. The difference is that the requesting node knows in
+individual documents. The difference is that the coordinating node knows in
 which shard each document lives. It breaks up the multidocument request into
 a multidocument request _per shard_, and forwards these in parallel to each
 participating node.
@@ -44,7 +44,7 @@ The sequence of steps((("bulk API", "multiple document changes with")))((("docum
    action succeeds, the primary forwards the new document (or deletion) to its
    replica shards in parallel, and then moves on to the next action. Once all
    replica shards report success for all actions, the node reports success to
-   the requesting node, which collates the responses and returns them to the
+   the coordinating node, which collates the responses and returns them to the
    client.
 
 The `bulk` API also accepts((("replication request parameter", "in bulk requests")))((("consistency request parameter", "in bulk requests"))) the `replication` and `consistency` parameters

050_Search/15_Pagination.asciidoc

@@ -37,12 +37,12 @@ to be sorted centrally to ensure that the overall order is correct.
 To understand why ((("deep paging, problems with")))deep paging is problematic, let's imagine that we are
 searching within a single index with five primary shards.  When we request the
 first page of results (results 1 to 10), each shard produces its own top 10
-results and returns them to the _requesting node_, which then sorts all 50
+results and returns them to the _coordinating node_, which then sorts all 50
 results in order to select the overall top 10.
 
 Now imagine that we ask for page 1,000--results 10,001 to 10,010. Everything
 works in the same way except that each shard has to produce its top 10,010
-results. The requesting node then sorts through all 50,050 results and
+results. The coordinating node then sorts through all 50,050 results and
 discards 50,040 of them!
 
 You can see that, in a distributed system, the cost of sorting results

052_Mapping_Analysis/45_Mapping.asciidoc

@@ -202,9 +202,10 @@ for an existing type) later, using the `/_mapping` endpoint.
 
 [NOTE]
 ================================================
-Although you can _add_ to an existing mapping, you can't _change_ it.  If a field
-already exists in the mapping, the data from that
-field probably has already been indexed.  If you were to change the field mapping, the already indexed data would be wrong and would not be properly searchable.
+Although you can _add_ to an existing mapping, you can't _change_ existing
+field mappings.  If a mapping already exists for a field, data from that
+field has probably been indexed.  If you were to change the field mapping, 
+the indexed data would be wrong and would not be properly searchable.
 ================================================
 
 We can update a mapping to add a new field, but we can't change an existing

080_Structured_Search/05_term.asciidoc

@@ -143,7 +143,7 @@ GET /my_store/products/_search
 // SENSE: 080_Structured_Search/05_Term_text.json
 
 Except there is a little hiccup: we don't get any results back!  Why is
-that? The problem isn't with the the `term` query; it is with the way
+that? The problem isn't with the `term` query; it is with the way
 the data has been indexed. ((("analyze API, using to understand tokenization"))) If we use the `analyze` API (<<analyze-api>>), we
 can see that our UPC has been tokenized into smaller tokens:
 

200_Language_intro/60_Mixed_language_fields.asciidoc

@@ -128,7 +128,7 @@ PUT /movies
 When querying the catchall `general` field, you can use
 `minimum_should_match` to reduce the number of low-quality matches.  It may
 also be necessary to boost the other fields slightly more than the `general`
-field, so that matches on the the main language fields are given more weight
+field, so that matches on the main language fields are given more weight
 than those on the `general` field:
 
 [source,js]

230_Stemming/10_Algorithmic_stemmers.asciidoc

@@ -87,7 +87,6 @@ documentation, which shows the following:
 --------------------------------------------------
 <1> The `keyword_marker` token filter lists words that should not be
     stemmed.((("keyword_marker token filter")))  This defaults to the empty list.
-
 <2> The `english` analyzer uses two stemmers: the `possessive_english`
     and the `english` stemmer. The ((("english stemmer")))((("possessive_english stemmer")))possessive stemmer removes `'s`
     from any words before passing them on to the `english_stop`,

260_Synonyms/70_Symbol_synonyms.asciidoc

@@ -8,7 +8,7 @@ during tokenization.
 
 While most punctuation is seldom important for full-text search, character
 combinations like emoticons((("emoticons"))) may be very signficant, even changing the meaning
-of the the text.  Compare these:
+of the text.  Compare these:
 
 [role="pagebreak-before"]
 * I am thrilled to be at work on Sunday.

300_Aggregations/75_sigterms.asciidoc

@@ -5,11 +5,22 @@ Because the `significant_terms` aggregation((("significant_terms aggregation", "
 statistics, you need to have a certain threshold of data for it to become effective.
 That means we won't be able to index a small amount of example data for the demo.
 
-Instead, we have a pre-prepared dataset of around 80,000 documents.  This is
-saved as a snapshot (for more information about snapshots and restore, see
-<<backing-up-your-cluster>>) in our public demo repository.  You can "restore"
-this dataset into your cluster by using these commands:
+Instead, we prepared a dataset that contains about 80,000 documents and saved it
+as a snapshot in our public demo repository.  To "restore"
+this dataset into your cluster:
 
+. Add the following setting to your `elasticsearch.yml` configuration file to 
+whitelist the Elastic demo repository:
++
+[source,js]
+----
+repositories.url.allowed_urls: ["http://download.elastic.co/*"]
+----
+. Restart Elasticsearch.
+
+. Run the following snapshot commands. (For more information about using 
+snapshots, see <<backing-up-your-cluster, Backing Up Your Cluster>>.)
++
 [source,js]
 ----
 PUT /_snapshot/sigterms <1>

310_Geopoints/20_Geopoints.asciidoc

@@ -1,5 +1,5 @@
 [[geopoints]]
-== Geo-Points
+== Geo Points
 
 A _geo-point_ is a single latitude/longitude point on the Earth's surface.((("geo-points"))) Geo-points
 can be used to calculate distance from a point, to determine whether a point

310_Geopoints/30_Filter_by_geopoint.asciidoc

@@ -1,5 +1,5 @@
 [[filter-by-geopoint]]
-=== Filtering by Geo-Point
+=== Filtering by Geo Point
 
 Four geo-point filters ((("geo-points", "filtering by")))((("filtering", "by geo-points")))can be used to include or exclude documents by
 geolocation:

310_Geopoints/32_Bounding_box.asciidoc

@@ -1,5 +1,5 @@
 [[geo-bounding-box]]
-=== geo_bounding_box Filter
+=== Geo Bounding Box Filter
 
 This is by far the most efficient geo-filter because its calculation is very
 simple. ((("geo_bounding_box filter")))((("filtering", "by geo-points", "geo_bounding_box filter"))) You provide it with the `top`, `bottom`, `left`, and `right`

310_Geopoints/34_Geo_distance.asciidoc

@@ -1,5 +1,5 @@
 [[geo-distance]]
-=== geo_distance Filter
+=== Geo Distance Filter
 
 The `geo_distance` filter draws a circle around the specified location and
 finds all documents((("geo_distance filter")))((("filtering", "by geo-points", "geo_distance filter"))) that have a geo-point within that circle:

310_Geopoints/36_Caching_geofilters.asciidoc

@@ -1,5 +1,5 @@
 [[geo-caching]]
-=== Caching geo-filters
+=== Caching Geo Filters
 
 The results of geo-filters are not cached by default,((("caching", "of geo-filters")))((("filters", "caching geo-filters")))((("geo-filters, caching"))) for two reasons:
 

320_Geohashes/60_Geohash_cell_filter.asciidoc

@@ -1,5 +1,5 @@
 [[geohash-cell-filter]]
-=== geohash_cell Filter
+=== Geohash Cell Filter
 
 The `geohash_cell` filter simply translates a `lat/lon` location((("geohash_cell filter")))((("filters", "geohash_cell"))) into a
 geohash with the specified precision and finds all locations that contain

330_Geo_aggs/60_Geo_aggs.asciidoc

@@ -1,5 +1,5 @@
 [[geo-aggs]]
-== Geo-aggregations
+== Geo Aggregations
 
 Although filtering or scoring results by geolocation is useful,((("geo-aggregations")))((("aggregations", "geo"))) it is often more
 useful to be able to present information to the user on a map. A search may

330_Geo_aggs/62_Geo_distance_agg.asciidoc

@@ -1,5 +1,5 @@
 [[geo-distance-agg]]
-=== geo_distance Aggregation
+=== Geo Distance Aggregation
 
 The `geo_distance` agg is useful((("geo_distance aggregation")))((("aggregations", "geo_distance"))) for searches such as
 to "find all pizza restaurants within 1km of me." The search results

330_Geo_aggs/64_Geohash_grid_agg.asciidoc

@@ -1,5 +1,5 @@
 [[geohash-grid-agg]]
-=== geohash_grid Aggregation
+=== Geohash Grid Aggregation
 
 The number of results returned by a query may be far too many to display each
 geo-point individually on a map.((("geohash_grid aggregation")))((("aggregations", "geohash_grid"))) The `geohash_grid` aggregation buckets nearby

330_Geo_aggs/66_Geo_bounds_agg.asciidoc

@@ -1,5 +1,5 @@
 [[geo-bounds-agg]]
-=== geo_bounds Aggregation
+=== Geo Bounds Aggregation
 
 In our <<geohash-grid-agg,previous example>>, we filtered our results by using a
 bounding box that covered the greater New York area.((("aggregations", "geo_bounds")))((("geo_bounds aggregation")))  However, our results

340_Geoshapes/70_Geoshapes.asciidoc

@@ -1,5 +1,5 @@
 [[geo-shapes]]
-== Geo-shapes
+== Geo Shapes
 
 Geo-shapes use a completely different approach than geo-points.((("geo-shapes"))) A circle on a
 computer screen does not consist of a perfect continuous line. Instead it is

340_Geoshapes/72_Mapping_geo_shapes.asciidoc

@@ -1,5 +1,5 @@
 [[mapping-geo-shapes]]
-=== Mapping geo-shapes
+=== Mapping Geo Shapes
 
 Like fields of type `geo_point`, geo-shapes((("mapping (types)", "geo-shapes")))((("geo-shapes", "mapping"))) have to be mapped explicitly
 before they can be used:

340_Geoshapes/74_Indexing_geo_shapes.asciidoc

@@ -1,5 +1,5 @@
 [[indexing-geo-shapes]]
-=== Indexing geo-shapes
+=== Indexing Geo Shapes
 
 Shapes are represented using http://geojson.org/[GeoJSON], a simple open
 standard for encoding two-dimensional shapes in JSON.((("JSON", "shapes in (GeoJSON)")))((("shapes", see="geo-shapes")))((("GeoJSON")))((("geo-shapes", "indexing")))  Each shape definition

340_Geoshapes/76_Querying_geo_shapes.asciidoc

@@ -1,5 +1,5 @@
 [[querying-geo-shapes]]
-=== Querying geo-shapes
+=== Querying Geo Shapes
 
 The unusual thing ((("geo-shapes", "querying")))about the {ref}/query-dsl-geo-shape-query.html[`geo_shape` query] is that it allows us to query and filter using shapes, rather than just points.
 

340_Geoshapes/80_Caching_geo_shapes.asciidoc

@@ -1,5 +1,5 @@
 [[geo-shape-caching]]
-=== Geo-shape Filters and Caching
+=== Geo Shape Filters and Caching
 
 The `geo_shape` query and filter perform the same function.((("caching", "geo-shape filters and")))((("filters", "geo_shape")))((("geo-shapes", "geo_shape filters, caching and")))  The query simply
 acts as a filter: any matching documents receive a relevance `_score` of