# Package org.apache.lucene.search

Code to search indices.

See: Description

- Interface Summary
  Interface | Description
  --- | ---
  BoostAttribute | Add this `Attribute` to a `TermsEnum` returned by `MultiTermQuery.getTermsEnum(Terms,AttributeSource)` and update the boost on each returned term.
  Collector | Expert: Collectors are primarily meant to be used to gather raw results from a search, and implement sorting or custom result filtering, collation, etc.
  CollectorManager<C extends Collector,T> | A manager of collectors.
  FuzzyTermsEnum.LevenshteinAutomataAttribute | reuses compiled automata across different segments, because they are independent of the index
  LeafCollector | Collector decouples the score from the collected doc: the score computation is skipped entirely if it's not needed.
  LeafFieldComparator | Expert: comparator that gets instantiated on each leaf from a top-level `FieldComparator` instance.
  MaxNonCompetitiveBoostAttribute | Add this `Attribute` to a fresh `AttributeSource` before calling `MultiTermQuery.getTermsEnum(Terms,AttributeSource)`.
  QueryCache | A cache for queries.
  QueryCachingPolicy | A policy defining which filters should be cached.
  ReferenceManager.RefreshListener | Use to receive notification when a refresh has finished.
  SearcherLifetimeManager.Pruner | See `SearcherLifetimeManager.prune(org.apache.lucene.search.SearcherLifetimeManager.Pruner)`.

- Class Summary
  Class | Description
  --- | ---
  AutomatonQuery | A `Query` that will match terms against a finite-state machine.
  BlendedTermQuery | A `Query` that blends index statistics across multiple terms.
  BlendedTermQuery.Builder | A Builder for `BlendedTermQuery`.
  BlendedTermQuery.DisjunctionMaxRewrite | A `BlendedTermQuery.RewriteMethod` that creates a `DisjunctionMaxQuery` out of the sub queries.
  BlendedTermQuery.RewriteMethod | A `BlendedTermQuery.RewriteMethod` defines how queries for individual terms should be merged.
  BooleanClause | A clause in a BooleanQuery.
  BooleanQuery | A Query that matches documents matching boolean combinations of other queries, e.g.
  BooleanQuery.Builder | A builder for boolean queries.
  BoostAttributeImpl | Implementation class for `BoostAttribute`.
  BoostQuery | A `Query` wrapper that allows to give a boost to the wrapped query.
  BulkScorer | This class is used to score a range of documents at once, and is returned by `Weight.bulkScorer(org.apache.lucene.index.LeafReaderContext)`.
  CachingCollector | Caches all docs, and optionally also scores, coming from a search, and is then able to replay them to another collector.
  CollectionStatistics | Contains statistics for a collection (field)
  ConjunctionDISI | A conjunction of DocIdSetIterators.
  ConstantScoreQuery | A query that wraps another query and simply returns a constant score equal to 1 for every document that matches the query.
  ConstantScoreQuery.ConstantBulkScorer | We return this as our `BulkScorer` so that if the CSQ wraps a query with its own optimized top-level scorer (e.g.
  ConstantScoreScorer | A constant-scoring `Scorer`.
  ConstantScoreWeight | A Weight that has a constant score equal to the boost of the wrapped query.
  ControlledRealTimeReopenThread<T> | Utility class that runs a thread to manage periodicc reopens of a `ReferenceManager`, with methods to wait for a specific index changes to become visible.
  DisiPriorityQueue | A priority queue of DocIdSetIterators that orders by current doc ID.
  DisiWrapper | Wrapper used in `DisiPriorityQueue`.
  DisjunctionDISIApproximation | A `DocIdSetIterator` which is a disjunction of the approximations of the provided iterators.
  DisjunctionMaxQuery | A query that generates the union of documents produced by its subqueries, and that scores each document with the maximum score for that document as produced by any subquery, plus a tie breaking increment for any additional matching subqueries.
  DocIdSet | A DocIdSet contains a set of doc ids.
  DocIdSetIterator | This abstract class defines methods to iterate over a set of non-decreasing doc ids.
  DocValuesDocIdSet | Base class for DocIdSet to be used with DocValues.
  DocValuesRewriteMethod | Rewrites MultiTermQueries into a filter, using DocValues for term enumeration.
  DoubleValues | Per-segment, per-document double values, which can be calculated at search-time
  DoubleValuesSource | Base class for producing `DoubleValues` To obtain a `DoubleValues` object for a leaf reader, clients should call `DoubleValuesSource.getValues(LeafReaderContext, DoubleValues)`.
  EarlyTerminatingSortingCollector | A `Collector` that early terminates collection of documents on a per-segment basis, if the segment was sorted according to the given `Sort`.
  Explanation | Expert: Describes the score computation for document and query.
  FieldComparator<T> | Expert: a FieldComparator compares hits so as to determine their sort order when collecting the top results with `TopFieldCollector`.
  FieldComparator.DocComparator | Sorts by ascending docID
  FieldComparator.DoubleComparator | Parses field's values as double (using `LeafReader.getNumericDocValues(java.lang.String)` and sorts by ascending value
  FieldComparator.FloatComparator | Parses field's values as float (using `LeafReader.getNumericDocValues(String)` and sorts by ascending value
  FieldComparator.IntComparator | Parses field's values as int (using `LeafReader.getNumericDocValues(String)` and sorts by ascending value
  FieldComparator.LongComparator | Parses field's values as long (using `LeafReader.getNumericDocValues(String)` and sorts by ascending value
  FieldComparator.NumericComparator<T extends Number> | Base FieldComparator class for numeric types
  FieldComparator.RelevanceComparator | Sorts by descending relevance.
  FieldComparator.TermOrdValComparator | Sorts by field's natural Term sort order, using ordinals.
  FieldComparator.TermValComparator | Sorts by field's natural Term sort order.
  FieldComparatorSource | Provides a `FieldComparator` for custom field sorting.
  FieldDoc | Expert: A ScoreDoc which also contains information about how to sort the referenced document.
  FieldValueHitQueue<T extends FieldValueHitQueue.Entry> | Expert: A hit queue for sorting by hits by terms in more than one field.
  FieldValueHitQueue.Entry | Extension of ScoreDoc to also store the `FieldComparator` slot.
  FieldValueQuery | A `Query` that matches documents that have a value for a given field as reported by `LeafReader.getDocsWithField(String)`.
  FilterCollector | `Collector` delegator.
  FilteredDocIdSetIterator | Abstract decorator class of a DocIdSetIterator implementation that provides on-demand filter/validation mechanism on an underlying DocIdSetIterator.
  FilterLeafCollector | `LeafCollector` delegator.
  FilterScorer | A `FilterScorer` contains another `Scorer`, which it uses as its basic source of data, possibly transforming the data along the way or providing additional functionality.
  FilterWeight | A `FilterWeight` contains another `Weight` and implements all abstract methods by calling the contained weight's method.
  FuzzyQuery | Implements the fuzzy search query.
  FuzzyTermsEnum | Subclass of TermsEnum for enumerating all terms that are similar to the specified filter term.
  FuzzyTermsEnum.LevenshteinAutomataAttributeImpl | Stores compiled automata as a list (indexed by edit distance)
  IndexOrDocValuesQuery | A query that uses either an index structure (points or terms) or doc values in order to run a query, depending which one is more efficient.
  IndexSearcher | Implements search over a single IndexReader.
  IndexSearcher.LeafSlice | A class holding a subset of the `IndexSearcher`s leaf contexts to be executed within a single thread.
  LegacyNumericRangeQuery<T extends Number> | Deprecated Instead index with `IntPoint`, `LongPoint`, `FloatPoint`, `DoublePoint`, and create range queries with `IntPoint.newRangeQuery()`, `LongPoint.newRangeQuery()`, `FloatPoint.newRangeQuery()`, `DoublePoint.newRangeQuery()` respectively.
  LiveFieldValues<S,T> | Tracks live field values across NRT reader reopens.
  LongValues | Per-segment, per-document long values, which can be calculated at search-time
  LongValuesSource | Base class for producing `LongValues` To obtain a `LongValues` object for a leaf reader, clients should call `LongValuesSource.getValues(LeafReaderContext, DoubleValues)`.
  LRUQueryCache | A `QueryCache` that evicts queries using a LRU (least-recently-used) eviction policy in order to remain under a given maximum size and number of bytes used.
  MatchAllDocsQuery | A query that matches all documents.
  MatchNoDocsQuery | A query that matches no documents.
  MaxNonCompetitiveBoostAttributeImpl | Implementation class for `MaxNonCompetitiveBoostAttribute`.
  MultiCollector | A `Collector` which allows running a search with several `Collector`s.
  MultiCollectorManager | A `CollectorManager` implements which wrap a set of `CollectorManager` as `MultiCollector` acts for `Collector`.
  MultiPhraseQuery | A generalized version of `PhraseQuery`, with the possibility of adding more than one term at the same position that are treated as a disjunction (OR).
  MultiPhraseQuery.Builder | A builder for multi-phrase queries
  MultiTermQuery | An abstract `Query` that matches documents containing a subset of terms provided by a `FilteredTermsEnum` enumeration.
  MultiTermQuery.RewriteMethod | Abstract class that defines how the query is rewritten.
  MultiTermQuery.TopTermsBlendedFreqScoringRewrite | A rewrite method that first translates each term into `BooleanClause.Occur.SHOULD` clause in a BooleanQuery, but adjusts the frequencies used for scoring to be blended across the terms, otherwise the rarest term typically ranks highest (often not useful eg in the set of expanded terms in a FuzzyQuery).
  MultiTermQuery.TopTermsBoostOnlyBooleanQueryRewrite | A rewrite method that first translates each term into `BooleanClause.Occur.SHOULD` clause in a BooleanQuery, but the scores are only computed as the boost.
  MultiTermQuery.TopTermsScoringBooleanQueryRewrite | A rewrite method that first translates each term into `BooleanClause.Occur.SHOULD` clause in a BooleanQuery, and keeps the scores as computed by the query.
  NGramPhraseQuery | This is a `PhraseQuery` which is optimized for n-gram phrase query.
  PhraseQuery | A Query that matches documents containing a particular sequence of terms.
  PhraseQuery.Builder | A builder for phrase queries.
  PointInSetQuery | Abstract query class to find all documents whose single or multi-dimensional point values, previously indexed with e.g.
  PointInSetQuery.Stream | Iterator of encoded point values.
  PointRangeQuery | Abstract class for range queries against single or multidimensional points such as `IntPoint`.
  PositiveScoresOnlyCollector | A `Collector` implementation which wraps another `Collector` and makes sure only documents with scores > 0 are collected.
  PrefixQuery | A Query that matches documents containing terms with a specified prefix.
  Query | The abstract base class for queries.
  QueryRescorer | A `Rescorer` that uses a provided Query to assign scores to the first-pass hits.
  RandomAccessWeight | Base class to build `Weight`s that are based on random-access structures such as live docs or doc values.
  ReferenceManager<G> | Utility class to safely share instances of a certain type across multiple threads, while periodically refreshing them.
  RegexpQuery | A fast regular expression query based on the `org.apache.lucene.util.automaton` package.
  Rescorer | Re-scores the topN results (`TopDocs`) from an original query.
  ScoreCachingWrappingScorer | A `Scorer` which wraps another scorer and caches the score of the current document.
  ScoreDoc | Holds one hit in `TopDocs`.
  Scorer | Expert: Common scoring functionality for different types of queries.
  Scorer.ChildScorer | A child Scorer and its relationship to its parent.
  ScorerSupplier | A supplier of `Scorer`.
  ScoringRewrite<B> | Base rewrite method that translates each term into a query, and keeps the scores as computed by the query.
  SearcherFactory | Factory class used by `SearcherManager` to create new IndexSearchers.
  SearcherLifetimeManager | Keeps track of current plus old IndexSearchers, closing the old ones once they have timed out.
  SearcherLifetimeManager.PruneByAge | Simple pruner that drops any searcher older by more than the specified seconds, than the newest searcher.
  SearcherManager | Utility class to safely share `IndexSearcher` instances across multiple threads, while periodically reopening.
  SimpleCollector | Base `Collector` implementation that is used to collect all contexts.
  SimpleFieldComparator<T> | Base `FieldComparator` implementation that is used for all contexts.
  Sort | Encapsulates sort criteria for returned hits.
  SortedNumericSelector | Selects a value from the document's list to use as the representative value
  SortedNumericSortField | SortField for `SortedNumericDocValues`.
  SortedSetSelector | Selects a value from the document's set to use as the representative value
  SortedSetSortField | SortField for `SortedSetDocValues`.
  SortField | Stores information about how to sort documents by terms in an individual field.
  SortRescorer | A `Rescorer` that re-sorts according to a provided Sort.
  SynonymQuery | A query that treats multiple terms as synonyms.
  TermInSetQuery | Specialization for a disjunction over many terms that behaves like a `ConstantScoreQuery` over a `BooleanQuery` containing only `BooleanClause.Occur.SHOULD` clauses.
  TermQuery | A Query that matches documents containing a term.
  TermRangeQuery | A Query that matches documents within an range of terms.
  TermStatistics | Contains statistics for a specific term
  TimeLimitingCollector | The `TimeLimitingCollector` is used to timeout search requests that take longer than the maximum allowed search time limit.
  TimeLimitingCollector.TimerThread | Thread used to timeout search requests.
  TopDocs | Represents hits returned by `IndexSearcher.search(Query,int)`.
  TopDocsCollector<T extends ScoreDoc> | A base class for all collectors that return a `TopDocs` output.
  TopFieldCollector | A `Collector` that sorts by `SortField` using `FieldComparator`s.
  TopFieldDocs | Represents hits returned by `IndexSearcher.search(Query,int,Sort)`.
  TopScoreDocCollector | A `Collector` implementation that collects the top-scoring hits, returning them as a `TopDocs`.
  TopTermsRewrite<B> | Base rewrite method for collecting only the top terms via a priority queue.
  TotalHitCountCollector | Just counts the total number of hits.
  TwoPhaseIterator | Returned by `Scorer.twoPhaseIterator()` to expose an approximation of a `DocIdSetIterator`.
  UsageTrackingQueryCachingPolicy | A `QueryCachingPolicy` that tracks usage statistics of recently-used filters in order to decide on which filters are worth caching.
  Weight | Expert: Calculate query weights and build query scorers.
  Weight.DefaultBulkScorer | Just wraps a Scorer and performs top scoring using it.
  WildcardQuery | Implements the wildcard search query.

- Enum Summary
  Enum | Description
  --- | ---
  BooleanClause.Occur | Specifies how clauses are to occur in matching documents.
  SortedNumericSelector.Type | Type of selection to perform.
  SortedSetSelector.Type | Type of selection to perform.
  SortField.Type | Specifies the type of the terms to be sorted, or special types such as CUSTOM

- Exception Summary
  Exception | Description
  --- | ---
  BooleanQuery.TooManyClauses | Thrown when an attempt is made to add more than `BooleanQuery.getMaxClauseCount()` clauses.
  CollectionTerminatedException | Throw this exception in `LeafCollector.collect(int)` to prematurely terminate collection of the current leaf.
  TimeLimitingCollector.TimeExceededException | Thrown when elapsed search time exceeds allowed search time.

## Package org.apache.lucene.search Description

Code to search indices.

## Table Of Contents

1. Search Basics
2. The Query Classes
3. Scoring: Introduction
4. Scoring: Basics
5. Changing the Scoring
6. Appendix: Search Algorithm

## Search Basics

Lucene offers a wide variety of `Query` implementations, most of which are in this package, its subpackage (`spans`, or the queries module. These implementations can be combined in a wide variety of ways to provide complex querying capabilities along with information about where matches took place in the document collection. The Query Classes section below highlights some of the more important Query classes. For details on implementing your own Query class, see Custom Queries -- Expert Level below.

To perform a search, applications usually call `IndexSearcher.search(Query,int)`.

Once a Query has been created and submitted to the `IndexSearcher`, the scoring process begins. After some infrastructure setup, control finally passes to the `Weight` implementation and its `Scorer` or `BulkScore` instances. See the Algorithm section for more notes on the process.

## Query Classes

### `TermQuery`

Of the various implementations of `Query`, the `TermQuery` is the easiest to understand and the most often used in applications. A `TermQuery` matches all the documents that contain the specified `Term`, which is a word that occurs in a certain `Field`. Thus, a `TermQuery` identifies and scores all `Document`s that have a `Field` with the specified string in it. Constructing a `TermQuery` is as simple as:

             TermQuery tq = new TermQuery(new Term("fieldName", "term"));

In this example, the `Query` identifies all `Document`s that have the `Field` named `"fieldName"` containing the word `"term"`.

### `BooleanQuery`

Things start to get interesting when one combines multiple `TermQuery` instances into a `BooleanQuery`. A `BooleanQuery` contains multiple `BooleanClause`s, where each clause contains a sub-query (`Query` instance) and an operator (from `BooleanClause.Occur`) describing how that sub-query is combined with the other clauses:

1. `SHOULD` — Use this operator when a clause can occur in the result set, but is not required. If a query is made up of all SHOULD clauses, then every document in the result set matches at least one of these clauses.

2. `MUST` — Use this operator when a clause is required to occur in the result set. Every document in the result set will match all such clauses.

3. `MUST NOT` — Use this operator when a clause must not occur in the result set. No document in the result set will match any such clauses.

Boolean queries are constructed by adding two or more `BooleanClause` instances. If too many clauses are added, a `TooManyClauses` exception will be thrown during searching. This most often occurs when a `Query` is rewritten into a `BooleanQuery` with many `TermQuery` clauses, for example by `WildcardQuery`. The default setting for the maximum number of clauses 1024, but this can be changed via the static method `BooleanQuery.setMaxClauseCount(int)`.

### Phrases

Another common search is to find documents containing certain phrases. This is handled three different ways:

1. `PhraseQuery` — Matches a sequence of `Term`s. `PhraseQuery` uses a slop factor to determine how many positions may occur between any two terms in the phrase and still be considered a match. The slop is 0 by default, meaning the phrase must match exactly.

2. `MultiPhraseQuery` — A more general form of PhraseQuery that accepts multiple Terms for a position in the phrase. For example, this can be used to perform phrase queries that also incorporate synonyms.

3. `SpanNearQuery` — Matches a sequence of other `SpanQuery` instances. `SpanNearQuery` allows for much more complicated phrase queries since it is constructed from other `SpanQuery` instances, instead of only `TermQuery` instances.

### `TermRangeQuery`

The `TermRangeQuery` matches all documents that occur in the exclusive range of a lower `Term` and an upper `Term` according to `BytesRef.compareTo()`. It is not intended for numerical ranges; use `PointRangeQuery` instead. For example, one could find all documents that have terms beginning with the letters `a` through `c`.

### `PointRangeQuery`

The `PointRangeQuery` matches all documents that occur in a numeric range. For PointRangeQuery to work, you must index the values using a one of the numeric fields (`IntPoint`, `LongPoint`, `FloatPoint`, or `DoublePoint`).

### `PrefixQuery`, `WildcardQuery`, `RegexpQuery`

While the `PrefixQuery` has a different implementation, it is essentially a special case of the `WildcardQuery`. The `PrefixQuery` allows an application to identify all documents with terms that begin with a certain string. The `WildcardQuery` generalizes this by allowing for the use of `*` (matches 0 or more characters) and `?` (matches exactly one character) wildcards. Note that the `WildcardQuery` can be quite slow. Also note that `WildcardQuery` should not start with `*` and `?`, as these are extremely slow. Some QueryParsers may not allow this by default, but provide a `setAllowLeadingWildcard` method to remove that protection. The `RegexpQuery` is even more general than WildcardQuery, allowing an application to identify all documents with terms that match a regular expression pattern.

### `FuzzyQuery`

A `FuzzyQuery` matches documents that contain terms similar to the specified term. Similarity is determined using Levenshtein distance. This type of query can be useful when accounting for spelling variations in the collection.

## Scoring — Introduction

Lucene scoring is the heart of why we all love Lucene. It is blazingly fast and it hides almost all of the complexity from the user. In a nutshell, it works. At least, that is, until it doesn't work, or doesn't work as one would expect it to work. Then we are left digging into Lucene internals or asking for help on java-user@lucene.apache.org to figure out why a document with five of our query terms scores lower than a different document with only one of the query terms.

While this document won't answer your specific scoring issues, it will, hopefully, point you to the places that can help you figure out the what and why of Lucene scoring.

Lucene scoring supports a number of pluggable information retrieval models, including:

- Vector Space Model (VSM)
- Probabilistic Models such as Okapi BM25 and DFR
- Language models

These models can be plugged in via the `Similarity API`, and offer extension hooks and parameters for tuning. In general, Lucene first finds the documents that need to be scored based on boolean logic in the Query specification, and then ranks this subset of matching documents via the retrieval model. For some valuable references on VSM and IR in general refer to Lucene Wiki IR references.

The rest of this document will cover Scoring basics and explain how to change your `Similarity`. Next, it will cover ways you can customize the lucene internals in Custom Queries -- Expert Level, which gives details on implementing your own `Query` class and related functionality. Finally, we will finish up with some reference material in the Appendix.

## Scoring — Basics

Scoring is very much dependent on the way documents are indexed, so it is important to understand indexing. (see Lucene overview before continuing on with this section) Be sure to use the useful `IndexSearcher.explain(Query, doc)` to understand how the score for a certain matching document was computed.

Generally, the Query determines which documents match (a binary decision), while the Similarity determines how to assign scores to the matching documents.

### Fields and Documents

In Lucene, the objects we are scoring are `Document`s. A Document is a collection of `Field`s. Each Field has `semantics` about how it is created and stored (`tokenized`, `stored`, etc). It is important to note that Lucene scoring works on Fields and then combines the results to return Documents. This is important because two Documents with the exact same content, but one having the content in two Fields and the other in one Field may return different scores for the same query due to length normalization.

### Score Boosting

Lucene allows influencing search results by "boosting" at different times:

- Index-time boost by calling `Field.setBoost()` before a document is added to the index.
- Query-time boost by applying a boost to a query by wrapping with `BoostQuery`.

Indexing time boosts are pre-processed for storage efficiency and written to storage for a field as follows:

- All boosts of that field (i.e. all boosts under the same field name in that doc) are multiplied.
- The boost is then encoded into a normalization value by the Similarity object at index-time: `computeNorm()`. The actual encoding depends upon the Similarity implementation, but note that most use a lossy encoding (such as multiplying the boost with document length or similar, packed into a single byte!).
- Decoding of any index-time normalization values and integration into the document's score is also performed at search time by the Similarity.

## Changing Scoring — Similarity

Changing `Similarity` is an easy way to influence scoring, this is done at index-time with `IndexWriterConfig.setSimilarity(Similarity)` and at query-time with `IndexSearcher.setSimilarity(Similarity)`. Be sure to use the same Similarity at query-time as at index-time (so that norms are encoded/decoded correctly); Lucene makes no effort to verify this.

You can influence scoring by configuring a different built-in Similarity implementation, or by tweaking its parameters, subclassing it to override behavior. Some implementations also offer a modular API which you can extend by plugging in a different component (e.g. term frequency normalizer).

Finally, you can extend the low level `Similarity` directly to implement a new retrieval model, or to use external scoring factors particular to your application. For example, a custom Similarity can access per-document values via `NumericDocValues` and integrate them into the score.

See the `org.apache.lucene.search.similarities` package documentation for information on the built-in available scoring models and extending or changing Similarity.

## Custom Queries — Expert Level

Custom queries are an expert level task, so tread carefully and be prepared to share your code if you want help.

With the warning out of the way, it is possible to change a lot more than just the Similarity when it comes to matching and scoring in Lucene. Lucene's search is a complex mechanism that is grounded by three main classes:

1. `Query` — The abstract object representation of the user's information need.
2. `Weight` — The internal interface representation of the user's Query, so that Query objects may be reused. This is global (across all segments of the index) and generally will require global statistics (such as docFreq for a given term across all segments).
3. `Scorer` — An abstract class containing common functionality for scoring. Provides both scoring and explanation capabilities. This is created per-segment.
4. `BulkScorer` — An abstract class that scores a range of documents. A default implementation simply iterates through the hits from `Scorer`, but some queries such as `BooleanQuery` have more efficient implementations.

Details on each of these classes, and their children, can be found in the subsections below.

### The Query Class

In some sense, the `Query` class is where it all begins. Without a Query, there would be nothing to score. Furthermore, the Query class is the catalyst for the other scoring classes as it is often responsible for creating them or coordinating the functionality between them. The `Query` class has several methods that are important for derived classes:

1. `createWeight(IndexSearcher searcher,boolean)` — A `Weight` is the internal representation of the Query, so each Query implementation must provide an implementation of Weight. See the subsection on The Weight Interface below for details on implementing the Weight interface.
2. `rewrite(IndexReader reader)` — Rewrites queries into primitive queries. Primitive queries are: `TermQuery`, `BooleanQuery`, and other queries that implement `createWeight(IndexSearcher searcher,boolean,float)`

### The Weight Interface

The `Weight` interface provides an internal representation of the Query so that it can be reused. Any `IndexSearcher` dependent state should be stored in the Weight implementation, not in the Query class. The interface defines five methods that must be implemented:

1. `getQuery()` — Pointer to the Query that this Weight represents.
2. `getValueForNormalization()` — A weight can return a floating point value to indicate its magnitude for query normalization. Typically a weight such as TermWeight that scores via a `Similarity` will just defer to the Similarity's implementation: `SimWeight#getValueForNormalization()`. For example, with `Lucene's classic vector-space formula`, this is implemented as the sum of squared weights: `(idf * boost)2`
3. `normalize(float norm, float boost)` — Performs query normalization:
   - `boost`: A query-boost factor from any wrapping queries that should be multiplied into every document's score. For example, a TermQuery that is wrapped within a BooleanQuery with a boost of `5` would receive this value at this time. This allows the TermQuery (the leaf node in this case) to compute this up-front a single time (e.g. by multiplying into the IDF), rather than for every document.
   - `norm`: Passes in a a normalization factor which may allow for comparing scores between queries.
   Typically a weight such as TermWeight that scores via a `Similarity` will just defer to the Similarity's implementation: `SimWeight#normalize(float,float)`.
4. `scorer()` — Construct a new `Scorer` for this Weight. See The Scorer Class below for help defining a Scorer. As the name implies, the Scorer is responsible for doing the actual scoring of documents given the Query.
5. `bulkScorer()` — Construct a new `BulkScorer` for this Weight. See The BulkScorer Class below for help defining a BulkScorer. This is an optional method, and most queries do not implement it.
6. `explain(LeafReaderContext context, int doc)` — Provide a means for explaining why a given document was scored the way it was. Typically a weight such as TermWeight that scores via a `Similarity` will make use of the Similarity's implementation: `SimScorer#explain(int doc, Explanation freq)`.

### The Scorer Class

The `Scorer` abstract class provides common scoring functionality for all Scorer implementations and is the heart of the Lucene scoring process. The Scorer defines the following methods which must be implemented:

1. `iterator()` — Return a `DocIdSetIterator` that can iterate over all document that matches this Query.
2. `docID()` — Returns the id of the `Document` that contains the match.
3. `score()` — Return the score of the current document. This value can be determined in any appropriate way for an application. For instance, the `TermScorer` simply defers to the configured Similarity: `SimScorer.score(int doc, float freq)`.
4. `freq()` — Returns the number of matches for the current document. This value can be determined in any appropriate way for an application. For instance, the `TermScorer` simply defers to the term frequency from the inverted index: `PostingsEnum.freq()`.
5. `getChildren()` — Returns any child subscorers underneath this scorer. This allows for users to navigate the scorer hierarchy and receive more fine-grained details on the scoring process.

### The BulkScorer Class

The `BulkScorer` scores a range of documents. There is only one abstract method:

1. `score(LeafCollector,Bits,int,int)` — Score all documents up to but not including the specified max document.

### Why would I want to add my own Query?

In a nutshell, you want to add your own custom Query implementation when you think that Lucene's aren't appropriate for the task that you want to do. You might be doing some cutting edge research or you need more information back out of Lucene (similar to Doug adding SpanQuery functionality).

## Appendix: Search Algorithm

This section is mostly notes on stepping through the Scoring process and serves as fertilizer for the earlier sections.

In the typical search application, a `Query` is passed to the `IndexSearcher`, beginning the scoring process.

Once inside the IndexSearcher, a `Collector` is used for the scoring and sorting of the search results. These important objects are involved in a search:

1. The `Weight` object of the Query. The Weight object is an internal representation of the Query that allows the Query to be reused by the IndexSearcher.
2. The IndexSearcher that initiated the call.
3. A `Sort` object for specifying how to sort the results if the standard score-based sort method is not desired.

Assuming we are not sorting (since sorting doesn't affect the raw Lucene score), we call one of the search methods of the IndexSearcher, passing in the `Weight` object created by `IndexSearcher.createNormalizedWeight(Query,boolean)` and the number of results we want. This method returns a `TopDocs` object, which is an internal collection of search results. The IndexSearcher creates a `TopScoreDocCollector` and passes it along with the Weight, Filter to another expert search method (for more on the `Collector` mechanism, see `IndexSearcher`). The TopScoreDocCollector uses a `PriorityQueue` to collect the top results for the search.

If a Filter is being used, some initial setup is done to determine which docs to include. Otherwise, we ask the Weight for a `Scorer` for each `IndexReader` segment and proceed by calling `BulkScorer.score(LeafCollector,Bits)`.

At last, we are actually going to score some documents. The score method takes in the Collector (most likely the TopScoreDocCollector or TopFieldCollector) and does its business.Of course, here is where things get involved. The `Scorer` that is returned by the `Weight` object depends on what type of Query was submitted. In most real world applications with multiple query terms, the `Scorer` is going to be a `BooleanScorer2` created from `BooleanWeight` (see the section on custom queries for info on changing this).

Assuming a BooleanScorer2, we first initialize the Coordinator, which is used to apply the coord() factor. We then get a internal Scorer based on the required, optional and prohibited parts of the query. Using this internal Scorer, the BooleanScorer2 then proceeds into a while loop based on the `DocIdSetIterator.nextDoc()` method. The nextDoc() method advances to the next document matching the query. This is an abstract method in the Scorer class and is thus overridden by all derived implementations. If you have a simple OR query your internal Scorer is most likely a DisjunctionSumScorer, which essentially combines the scorers from the sub scorers of the OR'd terms.

Copyright © 2000-2018 Apache Software Foundation. All Rights Reserved.