More test cleanups
Fixed `has_parent` query and filter returning no results with multi level child docs.
Added a benchmark for parent/child queries while indexing at the same time.
report details if shard response has failed shards
Correct offsets in FVH also if stored field is used for highlighting

The SimpleFragemntsBuilder did not correct offsets if the used
analysis chais could produce broken offsets that could lead to
StringArrayIndexOutOfBounds Exceptions

Closes #3140
Fallback to extract terms if MultiPhraseQuery is large

Currently if MPQ is very large highlighing can take down a node
or cause high CPU / RAM consumption. If the query grows > 16 terms
we just extract the terms and do term by term highlighting.

Closes  #3142 #3128
Stabelize more tests

Wait for relocation before checking statistics or run refresh / optimze.
ChildrenQuery and ParentQuery now take into account documents that have been marked.

Closes #3144
Fixed minor issue in ASCT#indexExists(...)
Added shortcuts for several common commands

added simple way to add more complex mappings as well as shortcuts for flush
and status etc. all checking if requests return failed shards
Making deb installable by being lintian compatible

According to #2515 the ubuntu software center does not allow to install
debian packages which are not lintian compatible

I worked on the package and made it lintian compatible by doing

* Ignoring errors about arch dependent binaries as we will not split
  this package. The arch dependent libraries are used correctly.
* Added a copyright file pointing to the apache license in debian

Closes #2515
Closes #2320
Fix addMapping() in AbstractSharedClusterTest for more than one field
term vector request
================================

Returns information and statistics on terms in the fields of a particular document as stored in the index.

        curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true'

Tree types of values can be requested: term information, term statistics and field statistics.
By default, all term information and field statistics are returned for all fields but no term statistics.

Optionally, you can specify the fields for which the information is retrieved either with a parameter in the url

	curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?fields=text,...'

or adding by adding the requested fields in the request body (see example below).

Term information
-------------------------

- term frequency in the field (always returned)
- term positions ("positions" : true)
- start and end offsets ("offsets" : true)
- term payloads ("payloads" : true), as base64 encoded bytes

If the requested information wasn't stored in the index, it will be omitted without further warning.
See [mapping](http://www.elasticsearch.org/guide/reference/mapping/core-types/) on how to configure your index to store term vectors.

Term statistics
-------------------------

Setting "term_statistics" to "true" (default is "false") will return

- total term frequency (how often a term occurs in all documents)
- document frequency (the number of documents containing the current term)

By default these values are not returned since term statistics can have a serious performance impact.

Field statistics
-------------------------

Setting "field_statistics" to "false" (default is "true") will omit

- document count (how many documents contain this field)
- sum of document frequencies (the sum of document frequencies for all terms in this field)
- sum of total term frequencies (the sum of total term frequencies of each term in this field)

Behavior
-------------------------

The term and field statistics are not accurate. Deleted documents are not taken into account. The information is only retrieved for the shard the requested document resides in. The term and field statistics are therefore only useful as relative measures whereas the absolute numbers have no meaning in this context.

Example
-------------------------

First, we create an index that stores term vectors, payloads etc. :

    curl -s -XPUT 'http://localhost:9200/twitter/' -d '{
        "mappings": {
            "tweet": {
                "properties": {
                    "text": {
                                "type": "string",
                                "term_vector": "with_positions_offsets_payloads",
                                "store" : "yes",
                                "index_analyzer" : "fulltext_analyzer"
                         },
                     "fullname": {
                                "type": "string",
                                "term_vector": "with_positions_offsets_payloads",
                                "index_analyzer" : "fulltext_analyzer"
                         }
                 }
            }
        },
        "settings" : {
            "index" : {
                "number_of_shards" : 1,
                "number_of_replicas" : 0
            },
            "analysis": {
                    "analyzer": {
                        "fulltext_analyzer": {
                            "type": "custom",
                            "tokenizer": "whitespace",
                            "filter": [
                                "lowercase",
                                "type_as_payload"
                            ]
                        }
                    }
            }
         }
    }'

Second, we add some documents:

    curl -XPUT 'http://localhost:9200/twitter/tweet/1?pretty=true' -d '{
      "fullname" : "John Doe",
      "text" : "twitter test test test "

    }'

    curl -XPUT 'http://localhost:9200/twitter/tweet/2?pretty=true' -d '{
      "fullname" : "Jane Doe",
      "text" : "Another twitter test ..."

    }'

The following request returns all information and statistics for field "text" in document "1" (John Doe):

     curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true' -d '{
                    "fields" : ["text"],
                    "offsets" : true,
                    "payloads" : true,
                    "positions" : true,
                    "term_statistics" : true,
                    "field_statistics" : true
            }'
Equivalently, all parameters can be passed as URI parameters:
     curl -GET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true&fields=text&offsets=true&payloads=true&positions=true&term_statistics=true&field_statistics=true'

Response:

  {
    "_index" : "twitter",
    "_type" : "tweet",
    "_id" : "1",
    "_version" : 1,
    "exists" : true,
    "term_vectors" : {
      "text" : {
        "field_statistics" : {
          "sum_doc_freq" : 6,
          "doc_count" : 2,
          "sum_ttf" : 8
        },
        "terms" : {
          "test" : {
            "doc_freq" : 2,
            "ttf" : 4,
            "term_freq" : 3,
            "pos" : [ 1, 2, 3 ],
            "start" : [ 8, 13, 18 ],
            "end" : [ 12, 17, 22 ],
            "payload" : [ "d29yZA==", "d29yZA==", "d29yZA==" ]
          },
          "twitter" : {
            "doc_freq" : 2,
            "ttf" : 2,
            "term_freq" : 1,
            "pos" : [ 0 ],
            "start" : [ 0 ],
            "end" : [ 7 ],
            "payload" : [ "d29yZA==" ]
          }
        }
      }
    }
  }

Further changes:
-------------------------

XContentBuilder
new method
public XContentBuilder field(XContentBuilderString name, int offset, int length, int... value)
to put an integer array.

IndicesAnalysisService
make token filter for saving payloads available in elasticsearch

AbstractFieldMapper/TypeParser
make term vector options string available and also fix the parsing of this string:
with_positions_payloads is actually allowed as can be seen in TermVectorsConsumerPerFields.

Closes #3114