The index field was serialized as a boolean instead of showing the
'analyed', 'not_analzyed', 'no' options. Fixed by calling
indexTokenizeOptionToString() in the builder.
Closes#3174
This decision helps people who want to rollout the oracle java without having an openjdk java installed.
* Removed any hard dependency on Java in the debian package
* The debian init script does not check for an existing JAVA_HOME anymore
* Debian and RedHat initscripts now exit if they do not find a java binary (instead of starting elasticsearch in the background and swallowing the error as there is no way to log it in that case)
* Changed the debian init script to rely on the pid file instead of the argument name of process
* Added a useful error message in case no java binary is available (in elasticsearch shell script)
Closes#3304Closes#3311
now that we have the concept of a shardIndex as part of our search execution, we can simply move to use ScoreDoc and FieldDoc instead of having our own wrappers that held the info
Also, rename shardRequestId where needed to be called shardIndex to conform with the variable name in Lucene
The previous loading of term vectors from the top level reader did not use the
correct docId. The docId in Versions.DocIdAndVersion is relative to the segment
reader in Versions.DocIdAndVersion and not to the top level reader.
Consequently the term vectors for the wrong document were returned if the
document was not on the first segment of the shard.
move away from maps to correlate between responses from different shards to unique incremental integer representing a shardRequestId (unique for the specific search request)
this allows to no longer require using maps (or CHM), and simply use atomic reference arrays, which rely on volatiles. it also removes the need to use a cache for heavy data structures since we don't really have them around anymore...
When using PlainHighlighter, TokenStreams are resetted both before highlighting
and at the beginning of highlighting, causing issues with analyzers that read
in reset() such as PatternAnalyzer. This commit removes the call to reset which
was performed before passing the TokenStream to the highlighter.
Close#3200
don't wrap in AnalysisService the indices analyzers we have with a NamedAnalyzer, since its effectively creates a new instance of an analyzer (with per field reuse strategy) and we don't benefit as much from reusing analyzers on the indices / node level
Now, the indices level analyzers return a NamedAnalyzer, also NamedAnalyzer will use the non per field reuse strategy since thats really the common case for it (no need for per field reuse there).
Also, try and reuse numeric analyzers globally instead of creating them per numeric mapper. Although those analyzers are not used during indexing (we have a custom numeric field for it), they can be used sometimes when searching in a query string for example without specific query implemenation in the mappers
in guice, we always use eager loaded singletons for all modules we create, thus, we can actually optimize the memory used by injectors by reduced the construction information they store per binding resulting in extensive reduction in memory usage for many indices/shards case on a node
also because all are eager singletons (and effectively, read only), we can not go through trying to create just in time bindings in the parent injector before trying to craete it in the current injector, resulting in improvement of object creations time and the time it takes to create an index or a shard on a node
The currently used maven shade plugin still keeps references to the
original classes in their constant pools around. This is never a problem
at runtime, but for dependency tools which try to use the constant pool
for determining dependencies will get confused (OSGI for example). This
patch simply bumps the version and will implicetely fix
fix http://jira.codehaus.org/browse/MSHADE-105Closes#3254Closes#3255
This has two advantages in the case term filter is *not* cached:
* We iterate only once over the matching docs. Before this fix we iterated once to create the FBS and another time the consume the matching docs from the FBS.
* The DocIdSetIterator#cost method of a DocIdSetIterator from the DocsEnum is accurate, because it based on the document frequency whereas the cost method of the FBS' iterator impl is based on the total number of bits (which is based on maxDoc). This will make this filter execute faster when it is included in a filtered query, because the filtered query can base its decision on what strategy to pick on an accurate heuristic.
This change doesn't have any negative implications in the case a filter is cached (which is the default). The FBS is now created lazily in the DocIdSets#toCacheable method, which is always invoked when the term filter needs to be cached.
===============
The code handling geo-shapes is not centralized and creating points takes
place at different places. Also the collection of supported geo_shapes is
not complete regarding to the GEOJSon specification. This commit
centralizes the code related to GEO calculations and extends the old API by
a set of new shapes.
Null-Shapes
===========
The latest implementation of geo-shapes allows to index null-shapes. This
means a field that is defined to hold a geo-shape can be set to null. In
example:
{
"shape": null
}
New Shapes
==========
The geo-shapes multipoint and multilinestring have been added to the
geo_shape types. Also geo_circle is introduced by this commit.
Dateline wrapping
=================
A major issue of geo-shapes is the spherical geometry. Since ElasticSearch
works on the Geo-Coordinates by wrapping the Earths surface to a plane,
some shapes are hard to define if it’s crossing the +180°, -180 longitude.
To solve this issue ElasticSearch offers the possibility to define geo
shapes crossing this borders and decompose these shapes and automatically
re-compose them in a spherical manner. This feature may change the indexed
shape-type. If for example a polygon is defined, that crosses the dateline,
it will be re-assembled to a set of polygons. This causes indexing a
multipolygon. Also linestrings crossing the dateline might be re-assembled
to multilinestrings.
Builders
========
The API has been refactored to use builders instead of using shapes. So
parsing geo-shapes will result in builder objects. These builders can be
parsed and serialized without generating any shapes. this causes shape
generation only on the nodes executing the actual operation. Also the
baseclass ShapeBuilder implements the ToXContent interface which allows to
set fields of XContent directly.
TODO’s
======
- The geo-circle will not work, if it’s crossing the dateline
- The envelope also needs to wrapped
Closes#1997#2708
Alexander Reelsen
Luca Cavanna
Simon Willnauer
Benjamin Devèze
Martijn van Groningen
Shay Banon
Britta Weber
Adrien Grand
Boaz Leskes
Andrew Raines
Florian Schilling