Apache OpenNLP 2.x opens the door to accessing various models via the ONNX runtime. To migrate you will need to update any deprecated OpenNLP methods that you may be using.
Snowball has folded the “German2” stemmer into their “German” stemmer, so there‘s no “German2” anymore. For Lucene APIs (TokenFilter, TokenFilterFactory) that accept String, “German2” will be mapped to “German” to avoid breaking users. If you were previously creating German2Stemmer instances, you’ll need to change your code to create GermanStemmer instances instead. For more information see https://snowballstem.org/algorithms/german2/stemmer.html
RomanianAnalyzer now works with Romanian in its modern unicode form, and normalizes cedilla forms to forms with commas. Both forms are still in use in “the wild”: you should reindex Romanian documents.
For indices newly created as of 10.0.0 onwards, IndexWriter preserves document blocks indexed via IndexWriter#addDocuments or IndexWriter#updateDocuments when index sorting is configured. Document blocks are maintained alongside their parent documents during sort and merge. The internally used parent field must be configured in IndexWriterConfig only if index sorting is used together with documents blocks. See IndexWriterConfig#setParendField for reference.
The API of interfaces for accepting highlights has changed to allow performance improvements. Look at the issue and the PR diff to get a sense of what's changed (changes are minor).
The deprecated Stored Fields and Term Vectors apis relied upon threadlocal storage and have been removed.
Instead, call storedFields()/termVectors() to return an instance which can fetch data for multiple documents, and will be garbage-collected as usual.
For example:
TopDocs hits = searcher.search(query, 10); StoredFields storedFields = reader.storedFields(); for (ScoreDoc hit : hits.scoreDocs) { Document doc = storedFields.document(hit.doc); }
Note that these StoredFields and TermVectors instances should only be consumed in the thread where they were acquired. For instance, it is illegal to share them across threads.
Lucene 9.x and earlier versions allowed to set a TokenStream on Field instances independently from a string, binary or numeric value. This is no longer allowed on the base Field class. If you need to replicate this behavior, you need to either provide two fields, one with a TokenStream and another one with a value, or create a sub-class of Field that overrides TokenStream tokenStream(Analyzer, TokenStream) to return a custom TokenStream.
PersianAnalyzer now includes PersianStemFilter, that would change analysis results. If you need the exactly same analysis behaviour as 9.x, clone PersianAnalyzer in 9.x or create custom analyzer by using CustomAnalyzer on your own.
These classes no longer take a determinizeWorkLimit and no longer determinize behind the scenes. It is the responsibility of the caller to to call Operations.determinize() for DFA execution.
These classes have been removed and consolidated into FieldExistsQuery. To migrate, caller simply replace those classes with the new one during object instantiation.
Except for a few exceptions, almost all normalizer and stemmer classes are now package private. If your code depends on constants defined in them, copy the constant values and re-define them in your code.
The behavior of LongRangeFacetCounts/DoubleRangeFacetCounts #getTopChildren actually returns the top-n ranges ordered by count from 10.0 onwards (as described in the Facets API) instead of returning all ranges ordered by constructor-specified range order. The pre-existing behavior in 9.x and earlier can be retained by migrating to the new Facets#getAllChildren API (LUCENE-10550).
SortedSetDocValues#nextOrd() no longer returns NO_MORE_ORDS when ordinals are exhausted for the currently-positioned document. Callers should instead use SortedSetDocValues#docValueCount() to determine the number of valid ordinals for the currently-positioned document up-front. It is now illegal to call SortedSetDocValues#nextOrd() more than SortedSetDocValues#docValueCount() times for the currently-positioned document (doing so will result in undefined behavior).
Directory#openChecksumInput no longer takes in IOContext as a parameter, and will always use value IOContext.READONCE for opening internally, as that's the only valid usage pattern for checksum input. Callers should remove the parameter when calling this method.
The former DaciukMihovAutomatonBuilder#build functionality is exposed through Automata#makeStringUnion. Users should be able to directly migrate to the Automata static method as a 1:1 replacement.
The deprecated getter for the Executor that was optionally provided to the IndexSearcher constructors has been removed. Users that want to execute concurrent tasks should rely instead on the TaskExecutor that the searcher holds, retrieved via IndexSearcher#getTaskExecutor.
The CheckIndex former -fast behaviour of performing checksum checks only, is now the default. Added a new parameter: -level X, to set the detail level of the index check. The higher the value, the more checks are performed. Sample -level usage: 1 (Default) - Checksum checks only, 2 - all level 1 checks as well as logical integrity checks, 3 - all level 2 checks as well as slow checks.
MethodHandle and hidden classes (GITHUB#12873)Custom functions in the expressions module must now be passed in a Map using MethodHandle as values. To convert legacy code using maps of reflective java.lang.reflect.Method, use the converter method JavascriptCompiler#convertLegacyFunctions. This should make the mapping mostly compatible. The use of MethodHandle and Dynamic Class-File Constants (JEP 309) now also allows to pass private methods or methods from different classloaders. It is also possible to adapt guards or filters using the MethodHandles class.
The new implementation of the Javascript expressions compiler no longer supports use of custom ClassLoader, because it uses the new JDK 15 feature hidden classes (JEP 371). Due to the use of MethodHandle, classloader isolation is no longer needed, because JS code can only call MHs that were resolved by the application before using the expressions module.
Expression#evaluate() declares to throw IOException (GITHUB#12878)The expressions module has changed the Expression#evaluate() method signature: It now declares that it may throw IOException. This was an oversight because compiled expressions call DoubleValues#doubleValue behind the scenes, which may throw IOException on index problems, bubbling up unexpectedly to the caller.
(Reverse)PathHierarchyTokenizer now produces sequential (instead of overlapping) tokens with accurate offsets, making positional queries and highlighters possible for fields tokenized with this tokenizer.
This method has been removed in order to enable more search-time optimizations. Use the doc ID passed to LeafCollector#collect to know which doc ID is being collected.
In order to adapt to the removal of Scorable#docID(), ScoreCachingWrappingScorer now wraps a LeafCollector rather than a Scorable.
Some classes with only final fields and no programming logic were converted to record classes. Those changes are mostly compatible with Lucene 9.x code (constructors, accessor methods), but record's fields are only available with accessor methods. Some code may need to be refactored to access the members using method calls instead of field accesses. Affected classes:
IOContext, MergeInfo, and FlushInfo (GITHUB#13205)BooleanClause (GITHUB#13261)The readOnce, load and random flags on IOContext have been replaced with a new ReadAdvice enum.
IOContext#LOAD has been removed, it should be replaced with ioContext.withReadAdvice(ReadAdvice.RANDOM_PRELOAD).
IOContext.READ has been removed, it should be replaced with IOContext.DEFAULT.
TimeLimitingCollector has been removed, use IndexSearcher#setTimeout(QueryTimeout) to time out queries instead.
IndexSearch#search(Query, Collector) is now being deprecated in favor of IndexSearcher#search(Query, CollectorManager), as CollectorManager implementation would allow taking advantage of intra-query concurrency via its map-reduce API design. To migrate, use a provided CollectorManager implementation that suits your use cases, or change your Collector implementation to follow the new API pattern. The straight forward approach would be to instantiate the single-threaded Collector in a wrapper CollectorManager.
For example
public class CustomCollectorManager implements CollectorManager<CustomCollector, List<Object>> { @Override public CustomCollector newCollector() throws IOException { return new CustomCollector(); } @Override public List<Object> reduce(Collection<CustomCollector> collectors) throws IOException { List<Object> all = new ArrayList<>(); for (CustomCollector c : collectors) { all.addAll(c.getResult()); } return all; } } List<Object> results = searcher.search(query, new CustomCollectorManager());
KnnVectorsReader objects use small heap memory, so it's not worth maintaining heap usage for them hence removed Accountable interface from KnnVectorsReader.
IntField(String name, int value). Use IntField(String, int, Field.Store) with Field.Store#NO instead.DoubleField(String name, double value). Use DoubleField(String, double, Field.Store) with Field.Store#NO instead.FloatField(String name, float value). Use FloatField(String, float, Field.Store) with Field.Store#NO instead.LongField(String name, long value). Use LongField(String, long, Field.Store) with Field.Store#NO instead.LongPoint#newDistanceFeatureQuery(String field, float weight, long origin, long pivotDistance). Use LongField#newDistanceFeatureQuery insteadBooleanQuery#TooManyClauses, BooleanQuery#getMaxClauseCount(), BooleanQuery#setMaxClauseCount(). Use IndexSearcher#TooManyClauses, IndexSearcher#getMaxClauseCount(), IndexSearcher#setMaxClauseCount() insteadByteBuffersDataInput#size(). Use ByteBuffersDataInput#length() insteadSortedSetDocValuesFacetField#label. FacetsConfig#pathToString(String[]) can be applied to path as a replacement if string path is desired.ConcurrentMergeScheduler now disables auto I/O throttling by default. There is still some throttling happening at the CPU level, since ConcurrentMergeScheduler has a maximum number of threads it can use, which is only a fraction of the total number of threads of the host by default.
The test framework is now a Java module. All the classes have been moved from org.apache.lucene.* to org.apache.lucene.tests.* to avoid package name conflicts with the core module. If you were using the Lucene test framework, the migration should be fairly automatic (package prefix).
Added interval functions and min-should-match support to StandardQueryParser. This means that interval function prefixes (fn:) and the @ character after parentheses will parse differently than before. If you need the exact previous behavior, clone the StandardSyntaxParser from the previous version of Lucene and create a custom query parser with that parser.
Lucene Core now logs certain warnings and errors using Java Util Logging (JUL). It is therefore recommended to install wrapper libraries with JUL logging handlers to feed the log events into your app's own logging system.
Under normal circumstances Lucene won't log anything, but in the case of a problem users should find the logged information in the usual log files.
Lucene also provides a JavaLoggingInfoStream implementation that logs IndexWriter events using JUL.
To feed Lucene's log events into the well-known Log4J system, we refer to the Log4j JDK Logging Adapter in combination with the corresponding system property: java.util.logging.manager=org.apache.logging.log4j.jul.LogManager.
The Kuromoji and Nori analysis modules had some way to customize the backing dictionaries by passing a path to file or classpath resources using some inconsistently implemented APIs. This was buggy from the beginning, but some users made use of it. Due to move to Java module system, especially the resource lookup on classpath stopped to work correctly. The Lucene team therefore implemented new APIs to create dictionary implementations with custom data files. Unfortunately there were some shortcomings in the 9.1 version, also when using the now deprecated ctors, so users are advised to upgrade to Lucene 9.2 or stay with 9.0.
See LUCENE-10558 for more details and workarounds.
All binary analysis packages (and corresponding Maven artifacts) have been renamed and are now consistent with repository module analysis. You will need to adjust build dependencies to the new coordinates:
| Old Artifact Coordinates | New Artifact Coordinates |
|---|---|
| org.apache.lucene:lucene-analyzers-common | org.apache.lucene:lucene-analysis-common |
| org.apache.lucene:lucene-analyzers-icu | org.apache.lucene:lucene-analysis-icu |
| org.apache.lucene:lucene-analyzers-kuromoji | org.apache.lucene:lucene-analysis-kuromoji |
| org.apache.lucene:lucene-analyzers-morfologik | org.apache.lucene:lucene-analysis-morfologik |
| org.apache.lucene:lucene-analyzers-nori | org.apache.lucene:lucene-analysis-nori |
| org.apache.lucene:lucene-analyzers-opennlp | org.apache.lucene:lucene-analysis-opennlp |
| org.apache.lucene:lucene-analyzers-phonetic | org.apache.lucene:lucene-analysis-phonetic |
| org.apache.lucene:lucene-analyzers-smartcn | org.apache.lucene:lucene-analysis-smartcn |
| org.apache.lucene:lucene-analyzers-stempel | org.apache.lucene:lucene-analysis-stempel |
LucenePackage class has been removed. The implementation string can be retrieved from Version.getPackageImplementationVersion().
DataOutput's writeShort(), writeInt(), and writeLong() methods now encode with little-endian byte order. If you have custom subclasses of DataInput/DataOutput, you will need to adjust them from big-endian byte order to little-endian byte order.
Java 11 supports to use Direct IO without native wrappers from Java code. NativeUnixDirectory in the misc module was therefore removed and replaced by DirectIODirectory. To use it, you need a JVM and operating system that supports Direct IO.
The discountOverlaps() parameter for both BM25Similarity and LegacyBM25Similarity is now set by the constructor of those classes.
These packages in the lucene-misc module are renamed:
| Old Package Name | New Package Name |
|---|---|
| org.apache.lucene.document | org.apache.lucene.misc.document |
| org.apache.lucene.index | org.apache.lucene.misc.index |
| org.apache.lucene.search | org.apache.lucene.misc.search |
| org.apache.lucene.store | org.apache.lucene.misc.store |
| org.apache.lucene.util | org.apache.lucene.misc.util |
The following classes were moved to the lucene-core module:
These packages in the lucene-sandbox module are renamed:
| Old Package Name | New Package Name |
|---|---|
| org.apache.lucene.codecs | org.apache.lucene.sandbox.codecs |
| org.apache.lucene.document | org.apache.lucene.sandbox.document |
| org.apache.lucene.search | org.apache.lucene.sandbox.search |
These packages in the lucene-backwards-codecs module are renamed:
| Old Package Name | New Package Name |
|---|---|
| org.apache.lucene.codecs | org.apache.lucene.backward_codecs |
Previously, JapanesePartOfSpeechStopFilterFactory added no filter if args didn't include “tags”. Now, it will load the default stop tags returned by JapaneseAnalyzer.getDefaultStopTags() (i.e. the tags fromstoptags.txt in the lucene-analyzers-kuromoji jar.)
These packages in the lucene-analysis-icu module are renamed:
| Old Package Name | New Package Name |
|---|---|
| org.apache.lucene.collation | org.apache.lucene.analysis.icu |
Base analysis factories are moved to lucene-core, also their package names are renamed.
| Old Class Name | New Class Name |
|---|---|
| org.apache.lucene.analysis.util.TokenizerFactory | org.apache.lucene.analysis.TokenizerFactory |
| org.apache.lucene.analysis.util.CharFilterFactory | org.apache.lucene.analysis.CharFilterFactory |
| org.apache.lucene.analysis.util.TokenFilterFactory | org.apache.lucene.analysis.TokenizerFactory |
The service provider files placed in META-INF/services for custom analysis factories should be renamed as follows:
StandardTokenizerFactory is moved to lucene-core module.
The org.apache.lucene.analysis.standard package in lucene-analysis-common module is split into org.apache.lucene.analysis.classic and org.apache.lucene.analysis.email.
We now follow the Java rules for accepting backslashes. Alphabetic characters other than s, S, w, W, d or D that are preceded by a backslash are considered illegal syntax and will throw an exception.
The commonly used regular expressions \w \W \d \D \s and \S now work the same way Java Pattern matching works. Previously these expressions were (mis)interpreted as searches for the literal characters w, d, s etc.
The factory option name to output the original term was corrected in accordance with its Javadoc.
This command-line tool no longer forceMerges to a single segment. Instead, by default it just follows (configurable) merge policy. If you really want to merge to a single segment, you can pass -max-segments 1.
Simply use FSTCompiler instead of the previous Builder. Use either the simple constructor with default settings, or the FSTCompiler.Builder to tune and tweak any parameter.
User dictionary now strictly validates if the (concatenated) segment is the same as the surface form. This change avoids unexpected runtime exceptions or behaviours. For example, these entries are not allowed at all and an exception is thrown when loading the dictionary file.
# concatenated "日本経済新聞" does not match the surface form "日経新聞" 日経新聞,日本 経済 新聞,ニホン ケイザイ シンブン,カスタム名詞 # concatenated "日経新聞" does not match the surface form "日本経済新聞" 日本経済新聞,日経 新聞,ニッケイ シンブン,カスタム名詞
JapaneseTokenizer and JapaneseAnalyzer no longer emits original tokens when discardCompoundToken option is not specified. The constructor option has been introduced since Lucene 8.5.0, and the default value is changed to true.
When given the text “株式会社”, JapaneseTokenizer (mode != NORMAL) emits decompounded tokens “株式” and “会社” only and no longer outputs the original token “株式会社” by default. To output original tokens, discardCompoundToken option should be explicitly set to false. Be aware that if this option is set to false, SynonymFilter or SynonymGraphFilter does not work correctly (see LUCENE-9173).
The SPI names for concrete subclasses of TokenizerFactory, TokenFilterFactory, and CharfilterFactory are no longer derived from their class name. Instead, each factory must have a static “NAME” field like this:
/** o.a.l.a.standard.StandardTokenizerFactory's SPI name */ public static final String NAME = "standard";
A factory can be resolved/instantiated with its NAME by using methods such as TokenizerFactory.lookupClass(String) or TokenizerFactory.forName(String, Map<String,String>).
If there are any user-defined factory classes that don't have proper NAME field, an exception will be thrown when (re)loading factories. e.g., when calling TokenizerFactory.reloadTokenizers(ClassLoader).
In addition starting all factories need to implement a public no-arg constructor, too. The reason for this change comes from the fact that Lucene now uses java.util.ServiceLoader instead its own implementation to load the factory classes to be compatible with Java Module System changes (e.g., load factories from modules). In the future, extensions to Lucene developed on the Java Module System may expose the factories from their module-info.java file instead of META-INF/services.
This constructor is never called by Lucene, so by default it throws an UnsupportedOperationException. User-defined factory classes should implement it in the following way:
/** Default ctor for compatibility with SPI */ public StandardTokenizerFactory() { throw defaultCtorException(); }
(defaultCtorException() is a protected static helper method)
TermsEnum has been changed to be fully abstract, so non-abstract subclasses must implement all its methods. Non-Performance critical TermsEnums can use BaseTermsEnum as a base class instead. The change was motivated by several performance issues with FilterTermsEnum that caused significant slowdowns and massive memory consumption due to not delegating all method from TermsEnum.
RAM-based directory implementation have been removed. ByteBuffersDirectory can be used as a RAM-resident replacement, although it is discouraged in favor of the default MMapDirectory.
SpanQuery and PhraseQuery now always calculate their slops as (1.0 / (1.0 + distance)). Payload factor calculation is performed by PayloadDecoder in the lucene-queries module.
Scorers are no longer allowed to produce negative scores. If you have custom query implementations, you should make sure their score formula may never produce negative scores.
As a side-effect of this change, negative boosts are now rejected and FunctionScoreQuery maps negative values to 0.
Instead use FunctionScoreQuery and a DoubleValuesSource implementation. BoostedQuery and BoostingQuery may be replaced by calls to FunctionScoreQuery.boostByValue() and FunctionScoreQuery.boostByQuery(). To replace more complex calculations in CustomScoreQuery, use the lucene-expressions module:
SimpleBindings bindings = new SimpleBindings(); bindings.add("score", DoubleValuesSource.SCORES); bindings.add("boost1", DoubleValuesSource.fromIntField("myboostfield")); bindings.add("boost2", DoubleValuesSource.fromIntField("myotherboostfield")); Expression expr = JavascriptCompiler.compile("score * (boost1 + ln(boost2))"); FunctionScoreQuery q = new FunctionScoreQuery(inputQuery, expr.getDoubleValuesSource(bindings));
Changing IndexOptions for a field on the fly will now result into an IllegalArgumentException. If a field is indexed (FieldType.indexOptions() != IndexOptions.NONE) then all documents must have the same index options for that field.
Instead use IndexSearcher.createWeight(), rewriting the query first, and using a boost of 1f.
Memory codecs (MemoryPostingsFormat, MemoryDocValuesFormat) have been removed from the codebase.
The Direct doc-value format has been removed from the codebase.
Caching everything is discouraged as it disables the ability to skip non-interesting documents. ALWAYS_CACHE can be replaced by a UsageTrackingQueryCachingPolicy with an appropriate config.
To retain the old behaviour, pass EnglishAnalyzer.ENGLISH_STOP_WORDS_SET as an argument to the constructor
English stop words are now defined in EnglishAnalyzer.ENGLISH_STOP_WORDS_SET in the analysis-common module.
TopDocs.maxScore is removed. IndexSearcher and TopFieldCollector no longer have an option to compute the maximum score when sorting by field. If you need to know the maximum score for a query, the recommended approach is to run a separate query:
TopDocs topHits = searcher.search(query, 1); float maxScore = topHits.scoreDocs.length == 0 ? Float.NaN : topHits.scoreDocs[0].score;
Thanks to other optimizations that were added to Lucene 8, this query will be able to efficiently select the top-scoring document without having to visit all matches.
Because filling sort values doesn't have a significant overhead, the fillFields option has been removed from TopFieldCollector factory methods. Everything behaves as if it was previously set to true.
Computing scores at collection time is less efficient than running a second request in order to only compute scores for documents that made it to the top hits. As a consequence, the trackDocScores option has been removed and can be replaced with the new TopFieldCollector.populateScores() helper method.
Lucene 8 received optimizations for collection of top-k matches by not visiting all matches. However these optimizations won't help if all matches still need to be visited in order to compute the total number of hits. As a consequence, IndexSearcher's search() and searchAfter() methods were changed to only count hits accurately up to 1,000, and Topdocs.totalHits was changed from a long to an object that says whether the hit count is accurate or a lower bound of the actual hit count.
This RAM-based directory implementation is an old piece of code that uses inefficient thread synchronization primitives and can be confused as “faster” than the NIO-based MMapDirectory. It is deprecated and scheduled for removal in future versions of Lucene.
Scorer has a number of methods that should never be called from Collectors, for example those that advance the underlying iterators. To hide these, LeafCollector.setScorer() now takes a Scorable, an abstract class that scorers can extend, with methods docId() and score().
If a custom Scorer implementation does not have an associated Weight, it can probably be replaced with a Scorable instead.
Most code should just require recompilation, though possibly requiring some added casts.
Instead of overriding TokenStreamComponents.setReader() to customise analyzer initialisation, you should now pass a Consumer<Reader> instance to the TokenStreamComponents constructor.
LowerCaseTokenizer combined tokenization and filtering in a way that broke token normalization, so they have been removed. Instead, use a LetterTokenizer followed by a LowerCaseFilter.
CharTokenizer now only performs tokenization. To perform any type of filtering use a TokenFilter chain as you would with any other Tokenizer.
Both Highlighter and FastVectorHighlighter need a custom WeightedSpanTermExtractor or FieldQuery, respectively, in order to support ToParentBlockJoinQuery/ToChildBlockJoinQuery.
Normalization is now type-safe, with CharFilterFactory.normalize() returning a Reader and TokenFilterFactory.normalize() returning a TokenFilter.
Scores computed by the BM25Similarity are lower than previously as the k1+1 constant factor was removed from the numerator of the scoring formula. Ordering of results is preserved unless scores are computed from multiple fields using different similarities. The previous behaviour is now exposed by the LegacyBM25Similarity class which can be found in the lucene-misc jar.
IndexWriter.getDocStats() should be used instead of maxDoc() / numDocs() which offers a consistent view on document stats. Previously calling two methods in order to get point in time stats was subject to concurrent changes.
IndexSearcher now performs max clause count checks on all types of queries (including BooleanQueries). This led to a logical move of the clauses count from BooleanQuery to IndexSearcher.
TopDocs.merge()'s API has been changed to stop allowing passing in a parameter to indicate if it should set shard indices for hits as they are seen during the merge process. This is done to simplify the API to be more dynamic in terms of passing in custom tie breakers. If shard indices are to be used for tie breaking docs with equal scores during TopDocs.merge(), then it is mandatory that the input ScoreDocs have their shard indices set to valid values prior to calling merge()
TopDocsCollector shall no longer return an empty TopDocs for malformed arguments. Rather, an IllegalArgumentException shall be thrown. This is introduced for better defence and to ensure that there is no bubbling up of errors when Lucene is used in multi level applications
Sorting on a numeric field that is indexed with both doc values and points may use an optimization to skip non-competitive documents. This optimization relies on the assumption that the same data is stored in these points and doc values.
The per field data-structures are implicitly defined by the first document indexed that contains a certain field. Once defined, the per field data-structures are not changeable for the whole index. For example, if you first index a document where a certain field is indexed with doc values and points, all subsequent documents containing this field must also have this field indexed with only doc values and points.
This also means that an index created in the previous version that doesn't satisfy this requirement can not be updated.
Previously IndexWriter could update doc values for a binary or numeric docValue field that was also indexed with other data structures (e.g. postings, vectors etc). This is not allowed anymore. A field must be indexed with only doc values to be allowed for doc values updates in IndexWriter.
SortedDocValues no longer extends BinaryDocValues: SortedDocValues do not have a per-document binary value, they have a per-document numeric ordValue(). The ordinal can then be dereferenced to its binary form with lookupOrd(), but it was a performance trap to implement a binaryValue() on the SortedDocValues api that does this behind-the-scenes on every document.
You can replace calls of binaryValue() with lookupOrd(ordValue()) as a “quick fix”, but it is better to use the ordinal alone (integer-based datastructures) for per-document access, and only call lookupOrd() a few times at the end (e.g. for the hits you want to display). Otherwise, if you really don't want per-document ordinals, but instead a per-document byte[], use a BinaryDocValues field.
Lucene index readers are now using so little memory with the default codec that it was decided to remove the ability to estimate their RAM usage.
LongValueFacetCounts will now automatically detect whether-or-not an indexed field is single- or multi-valued. The user no longer needs to provide this information to the ctors. Migrating should be as simple as no longer providing this boolean.
They can now be found in the org.apache.lucene.queries.spans package.
SpanBoostQuery was a no-op unless used at the top level of a SpanQuery nested structure. Use a standard BoostQuery here instead.
Rather than using setSort() to change sort values, you should instead create a new Sort instance with the new values.
The side-car taxonomy index now uses doc values for ord-to-path lookup (LUCENE-9450) and parent lookup (LUCENE-10122) instead of stored fields and positions (respectively). Document ordinals are now encoded with SortedNumericDocValues instead of using a custom (v-int) binary format. Performance gains have been observed with these encoding changes. These changes were introduced in 9.0, and 9.x releases remain backwards-compatible with 8.x indexes, but starting with 10.0, only the newer formats are supported. Users will need to create a new index with all their documents using 9.0 or later to pick up the new format and remain compatible with 10.x releases. Just re-adding documents to an existing index is not enough to pick up the changes as the format will “stick” to whatever version was used to initially create the index.
Additionally, OrdinalsReader (and sub-classes) are fully removed starting with 10.0. These classes were @Deprecated starting with 9.0. Users are encouraged to rely on the default taxonomy facet encodings where possible. If custom formats are needed, users will need to manage the indexed data on their own and create new Facet implementations to use it.