package org.apache.lucene.search;
   
   /*
    * Licensed to the Apache Software Foundation (ASF) under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * The ASF licenses this file to You under the Apache License, Version 2.0
    * (the "License"); you may not use this file except in compliance with
    * the License.  You may obtain a copy of the License at
   *
   *     http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  import java.io.IOException;
  import java.util.ArrayList;
  import java.util.Collection;
  import java.util.Collections;
  import java.util.IdentityHashMap;
  import java.util.Iterator;
  import java.util.LinkedHashMap;
  import java.util.List;
  import java.util.Map;
  import java.util.Set;
  
  import org.apache.lucene.index.LeafReader;
  import org.apache.lucene.index.LeafReader.CoreClosedListener;
  import org.apache.lucene.index.LeafReaderContext;
  import org.apache.lucene.util.Accountable;
  import org.apache.lucene.util.Accountables;
  import org.apache.lucene.util.Bits;
  import org.apache.lucene.util.RamUsageEstimator;
  import org.apache.lucene.util.RoaringDocIdSet;
  
  /**
   * A {@link FilterCache} that evicts filters using a LRU (least-recently-used)
   * eviction policy in order to remain under a given maximum size and number of
   * bytes used.
   *
   * This class is thread-safe.
   *
   * Note that filter eviction runs in linear time with the total number of
   * segments that have cache entries so this cache works best with
   * {@link FilterCachingPolicy caching policies} that only cache on "large"
   * segments, and it is advised to not share this cache across too many indices.
   *
   * Typical usage looks like this:
   * <pre class="prettyprint">
   *   final int maxNumberOfCachedFilters = 256;
   *   final long maxRamBytesUsed = 50 * 1024L * 1024L; // 50MB
   *   // these cache and policy instances can be shared across several filters and readers
   *   // it is fine to eg. store them into static variables
   *   final FilterCache filterCache = new LRUFilterCache(maxNumberOfCachedFilters, maxRamBytesUsed);
   *   final FilterCachingPolicy defaultCachingPolicy = new UsageTrackingFilterCachingPolicy();
   *   
   *   // ...
   *   
   *   // Then at search time
   *   Filter myFilter = ...;
   *   Filter myCacheFilter = filterCache.doCache(myFilter, defaultCachingPolicy);
   *   // myCacheFilter is now a wrapper around the original filter that will interact with the cache
   *   IndexSearcher searcher = ...;
   *   TopDocs topDocs = searcher.search(new ConstantScoreQuery(myCacheFilter), 10);
   * </pre>
   *
   * This cache exposes some global statistics ({@link #getHitCount() hit count},
   * {@link #getMissCount() miss count}, {@link #getCacheSize() number of cache
   * entries}, {@link #getCacheCount() total number of DocIdSets that have ever
   * been cached}, {@link #getEvictionCount() number of evicted entries}). In
   * case you would like to have more fine-grained statistics, such as per-index
   * or per-filter-class statistics, it is possible to override various callbacks:
   * {@link #onHit}, {@link #onMiss},
   * {@link #onFilterCache}, {@link #onFilterEviction},
   * {@link #onDocIdSetCache}, {@link #onDocIdSetEviction} and {@link #onClear}.
   * It is better to not perform heavy computations in these methods though since
   * they are called synchronously and under a lock.
   *
   * @see FilterCachingPolicy
   * @lucene.experimental
   * @deprecated Use {@link LRUQueryCache} instead
   */
  @Deprecated
  public class LRUFilterCache implements FilterCache, Accountable {
  
    // memory usage of a simple query-wrapper filter around a term query
    static final long FILTER_DEFAULT_RAM_BYTES_USED = 216;
  
    static final long HASHTABLE_RAM_BYTES_PER_ENTRY =
        2 * RamUsageEstimator.NUM_BYTES_OBJECT_REF // key + value
        * 2; // hash tables need to be oversized to avoid collisions, assume 2x capacity
  
    static final long LINKED_HASHTABLE_RAM_BYTES_PER_ENTRY =
        HASHTABLE_RAM_BYTES_PER_ENTRY
        + 2 * RamUsageEstimator.NUM_BYTES_OBJECT_REF; // previous & next references
 
   private final int maxSize;
   private final long maxRamBytesUsed;
   // maps filters that are contained in the cache to a singleton so that this
   // cache does not store several copies of the same filter
   private final Map<Filter, Filter> uniqueFilters;
   // The contract between this set and the per-leaf caches is that per-leaf caches
   // are only allowed to store sub-sets of the filters that are contained in
   // mostRecentlyUsedFilters. This is why write operations are performed under a lock
   private final Set<Filter> mostRecentlyUsedFilters;
   private final Map<Object, LeafCache> cache;
 
   // these variables are volatile so that we do not need to sync reads
   // but increments need to be performed under the lock
   private volatile long ramBytesUsed;
   private volatile long hitCount;
   private volatile long missCount;
   private volatile long cacheCount;
   private volatile long cacheSize;
 
   /**
    * Create a new instance that will cache at most <code>maxSize</code> filters
    * with at most <code>maxRamBytesUsed</code> bytes of memory.
    */
   public LRUFilterCache(int maxSize, long maxRamBytesUsed) {
     this.maxSize = maxSize;
     this.maxRamBytesUsed = maxRamBytesUsed;
     uniqueFilters = new LinkedHashMap<Filter, Filter>(16, 0.75f, true);
     mostRecentlyUsedFilters = uniqueFilters.keySet();
     cache = new IdentityHashMap<>();
     ramBytesUsed = 0;
   }
 
   /**
    * Expert: callback when there is a cache hit on a given filter.
    * Implementing this method is typically useful in order to compute more
    * fine-grained statistics about the filter cache.
    * @see #onMiss
    * @lucene.experimental
    */
   protected void onHit(Object readerCoreKey, Filter filter) {
     hitCount += 1;
   }
 
   /**
    * Expert: callback when there is a cache miss on a given filter.
    * @see #onHit
    * @lucene.experimental
    */
   protected void onMiss(Object readerCoreKey, Filter filter) {
     assert filter != null;
     missCount += 1;
   }
 
   /**
    * Expert: callback when a filter is added to this cache.
    * Implementing this method is typically useful in order to compute more
    * fine-grained statistics about the filter cache.
    * @see #onFilterEviction
    * @lucene.experimental
    */
   protected void onFilterCache(Filter filter, long ramBytesUsed) {
     this.ramBytesUsed += ramBytesUsed;
   }
 
   /**
    * Expert: callback when a filter is evicted from this cache.
    * @see #onFilterCache
    * @lucene.experimental
    */
   protected void onFilterEviction(Filter filter, long ramBytesUsed) {
     this.ramBytesUsed -= ramBytesUsed;
   }
 
   /**
    * Expert: callback when a {@link DocIdSet} is added to this cache.
    * Implementing this method is typically useful in order to compute more
    * fine-grained statistics about the filter cache.
    * @see #onDocIdSetEviction
    * @lucene.experimental
    */
   protected void onDocIdSetCache(Object readerCoreKey, long ramBytesUsed) {
     cacheSize += 1;
     cacheCount += 1;
     this.ramBytesUsed += ramBytesUsed;
   }
   
   /**
    * Expert: callback when one or more {@link DocIdSet}s are removed from this
    * cache.
    * @see #onDocIdSetCache
    * @lucene.experimental
    */
   protected void onDocIdSetEviction(Object readerCoreKey, int numEntries, long sumRamBytesUsed) {
     this.ramBytesUsed -= sumRamBytesUsed;
     cacheSize -= numEntries;
   }
 
   /**
    * Expert: callback when the cache is completely cleared.
    * @lucene.experimental
    */
   protected void onClear() {
     ramBytesUsed = 0;
     cacheSize = 0;
   }
 
   /** Whether evictions are required. */
   boolean requiresEviction() {
     final int size = mostRecentlyUsedFilters.size();
     if (size == 0) {
       return false;
     } else {
       return size > maxSize || ramBytesUsed() > maxRamBytesUsed;
     }
   }
 
   synchronized DocIdSet get(Filter filter, LeafReaderContext context) {
     final Object readerKey = context.reader().getCoreCacheKey();
     final LeafCache leafCache = cache.get(readerKey);
     if (leafCache == null) {
       onMiss(readerKey, filter);
       return null;
     }
     // this get call moves the filter to the most-recently-used position
     final Filter singleton = uniqueFilters.get(filter);
     if (singleton == null) {
       onMiss(readerKey, filter);
       return null;
     }
     final DocIdSet cached = leafCache.get(singleton);
     if (cached == null) {
       onMiss(readerKey, singleton);
     } else {
       onHit(readerKey, singleton);
     }
     return cached;
   }
 
   synchronized void putIfAbsent(Filter filter, LeafReaderContext context, DocIdSet set) {
     // under a lock to make sure that mostRecentlyUsedFilters and cache remain sync'ed
     assert set.isCacheable();
     Filter singleton = uniqueFilters.get(filter);
     if (singleton == null) {
       uniqueFilters.put(filter, filter);
       onFilterCache(singleton, LINKED_HASHTABLE_RAM_BYTES_PER_ENTRY + ramBytesUsed(filter));
     } else {
       filter = singleton;
     }
     final Object key = context.reader().getCoreCacheKey();
     LeafCache leafCache = cache.get(key);
     if (leafCache == null) {
       leafCache = new LeafCache(key);
       final LeafCache previous = cache.put(context.reader().getCoreCacheKey(), leafCache);
       ramBytesUsed += HASHTABLE_RAM_BYTES_PER_ENTRY;
       assert previous == null;
       // we just created a new leaf cache, need to register a close listener
       context.reader().addCoreClosedListener(new CoreClosedListener() {
         @Override
         public void onClose(Object ownerCoreCacheKey) {
           clearCoreCacheKey(ownerCoreCacheKey);
         }
       });
     }
     leafCache.putIfAbsent(filter, set);
     evictIfNecessary();
   }
 
   synchronized void evictIfNecessary() {
     // under a lock to make sure that mostRecentlyUsedFilters and cache keep sync'ed
     if (requiresEviction()) {
       Iterator<Filter> iterator = mostRecentlyUsedFilters.iterator();
       do {
         final Filter filter = iterator.next();
         iterator.remove();
         onEviction(filter);
       } while (iterator.hasNext() && requiresEviction());
     }
   }
 
   /**
    * Remove all cache entries for the given core cache key.
    */
   public synchronized void clearCoreCacheKey(Object coreKey) {
     final LeafCache leafCache = cache.remove(coreKey);
     if (leafCache != null) {
       ramBytesUsed -= HASHTABLE_RAM_BYTES_PER_ENTRY;
       onDocIdSetEviction(coreKey, leafCache.cache.size(), leafCache.ramBytesUsed);
     }
   }
 
   /**
    * Remove all cache entries for the given filter.
    */
   public synchronized void clearFilter(Filter filter) {
     final Filter singleton = uniqueFilters.remove(filter);
     if (singleton != null) {
       onEviction(singleton);
     }
   }
 
   private void onEviction(Filter singleton) {
     onFilterEviction(singleton, LINKED_HASHTABLE_RAM_BYTES_PER_ENTRY + ramBytesUsed(singleton));
     for (LeafCache leafCache : cache.values()) {
       leafCache.remove(singleton);
     }
   }
 
   /**
    * Clear the content of this cache.
    */
   public synchronized void clear() {
     cache.clear();
     mostRecentlyUsedFilters.clear();
     onClear();
   }
 
   // pkg-private for testing
   synchronized void assertConsistent() {
     if (requiresEviction()) {
       throw new AssertionError("requires evictions: size=" + mostRecentlyUsedFilters.size()
           + ", maxSize=" + maxSize + ", ramBytesUsed=" + ramBytesUsed() + ", maxRamBytesUsed=" + maxRamBytesUsed);
     }
     for (LeafCache leafCache : cache.values()) {
       Set<Filter> keys = Collections.newSetFromMap(new IdentityHashMap<Filter, Boolean>());
       keys.addAll(leafCache.cache.keySet());
       keys.removeAll(mostRecentlyUsedFilters);
       if (!keys.isEmpty()) {
         throw new AssertionError("One leaf cache contains more keys than the top-level cache: " + keys);
       }
     }
     long recomputedRamBytesUsed =
           HASHTABLE_RAM_BYTES_PER_ENTRY * cache.size()
         + LINKED_HASHTABLE_RAM_BYTES_PER_ENTRY * uniqueFilters.size();
     for (Filter filter : mostRecentlyUsedFilters) {
       recomputedRamBytesUsed += ramBytesUsed(filter);
     }
     for (LeafCache leafCache : cache.values()) {
       recomputedRamBytesUsed += HASHTABLE_RAM_BYTES_PER_ENTRY * leafCache.cache.size();
       for (DocIdSet set : leafCache.cache.values()) {
         recomputedRamBytesUsed += set.ramBytesUsed();
       }
     }
     if (recomputedRamBytesUsed != ramBytesUsed) {
       throw new AssertionError("ramBytesUsed mismatch : " + ramBytesUsed + " != " + recomputedRamBytesUsed);
     }
 
     long recomputedCacheSize = 0;
     for (LeafCache leafCache : cache.values()) {
       recomputedCacheSize += leafCache.cache.size();
     }
     if (recomputedCacheSize != getCacheSize()) {
       throw new AssertionError("cacheSize mismatch : " + getCacheSize() + " != " + recomputedCacheSize);
     }
   }
 
   // pkg-private for testing
   // return the list of cached filters in LRU order
   synchronized List<Filter> cachedFilters() {
     return new ArrayList<>(mostRecentlyUsedFilters);
   }
 
   @Override
   public Filter doCache(Filter filter, FilterCachingPolicy policy) {
     while (filter instanceof CachingWrapperFilter) {
       // should we throw an exception instead?
       filter = ((CachingWrapperFilter) filter).in;
     }
 
     return new CachingWrapperFilter(filter, policy);
   }
 
   /**
    *  Provide the DocIdSet to be cached, using the DocIdSet provided
    *  by the wrapped Filter. <p>This implementation returns the given {@link DocIdSet},
    *  if {@link DocIdSet#isCacheable} returns <code>true</code>, else it calls
    *  {@link #cacheImpl(DocIdSetIterator, org.apache.lucene.index.LeafReader)}
    *  <p>Note: This method returns {@linkplain DocIdSet#EMPTY} if the given docIdSet
    *  is <code>null</code> or if {@link DocIdSet#iterator()} return <code>null</code>. The empty
    *  instance is use as a placeholder in the cache instead of the <code>null</code> value.
    */
   protected DocIdSet docIdSetToCache(DocIdSet docIdSet, LeafReader reader) throws IOException {
     if (docIdSet == null || docIdSet.isCacheable()) {
       return docIdSet;
     } else {
       final DocIdSetIterator it = docIdSet.iterator();
       if (it == null) {
         return null;
       } else {
         return cacheImpl(it, reader);
       }
     }
   }
 
   @Override
   public long ramBytesUsed() {
     return ramBytesUsed;
   }
 
   @Override
   public Collection<Accountable> getChildResources() {
     synchronized (this) {
       return Accountables.namedAccountables("segment", cache);
     }
   }
 
   /**
    * Return the number of bytes used by the given filter. The default
    * implementation returns {@link Accountable#ramBytesUsed()} if the filter
    * implements {@link Accountable} and <code>1024</code> otherwise.
    */
   protected long ramBytesUsed(Filter filter) {
     if (filter instanceof Accountable) {
       return ((Accountable) filter).ramBytesUsed();
     }
     return FILTER_DEFAULT_RAM_BYTES_USED;
   }
 
   /**
    * Default cache implementation: uses {@link RoaringDocIdSet}.
    */
   protected DocIdSet cacheImpl(DocIdSetIterator iterator, LeafReader reader) throws IOException {
     return new RoaringDocIdSet.Builder(reader.maxDoc()).add(iterator).build();
   }
 
   /**
    * Return the total number of times that a {@link Filter} has been looked up
    * in this {@link FilterCache}. Note that this number is incremented once per
    * segment so running a cached filter only once will increment this counter
    * by the number of segments that are wrapped by the searcher.
    * Note that by definition, {@link #getTotalCount()} is the sum of
    * {@link #getHitCount()} and {@link #getMissCount()}.
    * @see #getHitCount()
    * @see #getMissCount()
    */
   public final long getTotalCount() {
     return getHitCount() + getMissCount();
   }
 
   /**
    * Over the {@link #getTotalCount() total} number of times that a filter has
    * been looked up, return how many times a cached {@link DocIdSet} has been
    * found and returned.
    * @see #getTotalCount()
    * @see #getMissCount()
    */
   public final long getHitCount() {
     return hitCount;
   }
 
   /**
    * Over the {@link #getTotalCount() total} number of times that a filter has
    * been looked up, return how many times this filter was not contained in the
    * cache.
    * @see #getTotalCount()
    * @see #getHitCount()
    */
   public final long getMissCount() {
     return missCount;
   }
 
   /**
    * Return the total number of {@link DocIdSet}s which are currently stored
    * in the cache.
    * @see #getCacheCount()
    * @see #getEvictionCount()
    */
   public final long getCacheSize() {
     return cacheSize;
   }
 
   /**
    * Return the total number of cache entries that have been generated and put
    * in the cache. It is highly desirable to have a {@link #getHitCount() hit
    * count} that is much higher than the {@link #getCacheCount() cache count}
    * as the opposite would indicate that the filter cache makes efforts in order
    * to cache filters but then they do not get reused.
    * @see #getCacheSize()
    * @see #getEvictionCount()
    */
   public final long getCacheCount() {
     return cacheCount;
   }
 
   /**
    * Return the number of cache entries that have been removed from the cache
    * either in order to stay under the maximum configured size/ram usage, or
    * because a segment has been closed. High numbers of evictions might mean
    * that filters are not reused or that the {@link FilterCachingPolicy
    * caching policy} caches too aggressively on NRT segments which get merged
    * early.
    * @see #getCacheCount()
    * @see #getCacheSize()
    */
   public final long getEvictionCount() {
     return getCacheCount() - getCacheSize();
   }
 
   // this class is not thread-safe, everything but ramBytesUsed needs to be called under a lock
   private class LeafCache implements Accountable {
 
     private final Object key;
     private final Map<Filter, DocIdSet> cache;
     private volatile long ramBytesUsed;
 
     LeafCache(Object key) {
       this.key = key;
       cache = new IdentityHashMap<>();
       ramBytesUsed = 0;
     }
 
     private void onDocIdSetCache(long ramBytesUsed) {
       this.ramBytesUsed += ramBytesUsed;
       LRUFilterCache.this.onDocIdSetCache(key, ramBytesUsed);
     }
 
     private void onDocIdSetEviction(long ramBytesUsed) {
       this.ramBytesUsed -= ramBytesUsed;
       LRUFilterCache.this.onDocIdSetEviction(key, 1, ramBytesUsed);
     }
 
     DocIdSet get(Filter filter) {
       return cache.get(filter);
     }
 
     void putIfAbsent(Filter filter, DocIdSet set) {
       if (cache.containsKey(filter) == false) {
         cache.put(filter, set);
         onDocIdSetCache(HASHTABLE_RAM_BYTES_PER_ENTRY + set.ramBytesUsed());
       }
     }
 
     void remove(Filter filter) {
       DocIdSet removed = cache.remove(filter);
       if (removed != null) {
         onDocIdSetEviction(HASHTABLE_RAM_BYTES_PER_ENTRY + removed.ramBytesUsed());
       }
     }
 
     @Override
     public long ramBytesUsed() {
       return ramBytesUsed;
     }
 
     @Override
     public Collection<Accountable> getChildResources() {
       return Collections.emptyList();
     }
 
   }
 
   private class CachingWrapperFilter extends Filter {
 
     private final Filter in;
     private final FilterCachingPolicy policy;
 
     CachingWrapperFilter(Filter in, FilterCachingPolicy policy) {
       this.in = in;
       this.policy = policy;
     }
 
     @Override
     public DocIdSet getDocIdSet(LeafReaderContext context, Bits acceptDocs) throws IOException {
       if (context.ord == 0) {
         policy.onUse(in);
       }
 
       DocIdSet set = get(in, context);
       if (set == null) {
         // do not apply acceptDocs yet, we want the cached filter to not take them into account
         set = in.getDocIdSet(context, null);
         if (policy.shouldCache(in, context, set)) {
           set = docIdSetToCache(set, context.reader());
           if (set == null) {
             // null values are not supported
             set = DocIdSet.EMPTY;
           }
           // it might happen that another thread computed the same set in parallel
           // although this might incur some CPU overhead, it is probably better
           // this way than trying to lock and preventing other filters to be
           // computed at the same time?
           putIfAbsent(in, context, set);
         }
       }
       return set == DocIdSet.EMPTY ? null : BitsFilteredDocIdSet.wrap(set, acceptDocs);
     }
 
     @Override
     public boolean equals(Object obj) {
       return super.equals(obj)
           && in.equals(((CachingWrapperFilter) obj).in);
     }
 
     @Override
     public int hashCode() {
       return 31 * super.hashCode() + in.hashCode();
     }
 
     @Override
     public String toString(String field) {
       return "CachingWrapperFilter(" + in.toString(field) + ")";
     }
   }
 
 }