org.apache.lucene.util.packed.MonotonicBlockPackedWriter Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of oak-lucene Show documentation
Oak Lucene integration subproject
There is a newer version: 1.9.8
/*
 * COPIED FROM APACHE LUCENE 4.7.2
 *
 * Git URL: [email protected]:apache/lucene.git, tag: releases/lucene-solr/4.7.2, path: lucene/core/src/java
 *
 * (see https://issues.apache.org/jira/browse/OAK-10786 for details)
 */

package org.apache.lucene.util.packed;

/*
 * 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 org.apache.lucene.store.DataOutput;

/**
 * A writer for large monotonically increasing sequences of positive longs.
 * 
 * The sequence is divided into fixed-size blocks and for each block, values
 * are modeled after a linear function f: x → A × x + B. The block
 * encodes deltas from the expected values computed from this function using as
 * few bits as possible. Each block has an overhead between 6 and 14 bytes.
 * 

 * Format:
 * 

 * <BLock>^BlockCount
 * 
BlockCount: ⌈ ValueCount / BlockSize ⌉
 * 
Block: <Header, (Ints)>
 * 
Header: <B, A, BitsPerValue>
 * 
B: the B from f: x → A × x + B using a
 *     {@link DataOutput#writeVLong(long) variable-length long}
 * 
A: the A from f: x → A × x + B encoded using
 *     {@link Float#floatToIntBits(float)} on
 *     {@link DataOutput#writeInt(int) 4 bytes}
 * 
BitsPerValue: a {@link DataOutput#writeVInt(int) variable-length int}
 * 
Ints: if BitsPerValue is 0, then there is nothing to read and
 *     all values perfectly match the result of the function. Otherwise, these
 *     are the
 *     zigzag-encoded
 *     {@link PackedInts packed} deltas from the expected value (computed from
 *     the function) using exaclty BitsPerValue bits per value
 * 
 * @see MonotonicBlockPackedReader
 * @lucene.internal
 */
public final class MonotonicBlockPackedWriter extends AbstractBlockPackedWriter {

  /**
   * Sole constructor.
   * @param blockSize the number of values of a single block, must be a power of 2
   */
  public MonotonicBlockPackedWriter(DataOutput out, int blockSize) {
    super(out, blockSize);
  }

  @Override
  public void add(long l) throws IOException {
    assert l >= 0;
    super.add(l);
  }

  protected void flush() throws IOException {
    assert off > 0;

    // TODO: perform a true linear regression?
    final long min = values[0];
    final float avg = off == 1 ? 0f : (float) (values[off - 1] - min) / (off - 1);

    long maxZigZagDelta = 0;
    for (int i = 0; i < off; ++i) {
      values[i] = zigZagEncode(values[i] - min - (long) (avg * i));
      maxZigZagDelta = Math.max(maxZigZagDelta, values[i]);
    }

    out.writeVLong(min);
    out.writeInt(Float.floatToIntBits(avg));
    if (maxZigZagDelta == 0) {
      out.writeVInt(0);
    } else {
      final int bitsRequired = PackedInts.bitsRequired(maxZigZagDelta);
      out.writeVInt(bitsRequired);
      writeValues(bitsRequired);
    }

    off = 0;
  }

}