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/*
* 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.
*/
package org.apache.datasketches.quantiles;
import static java.lang.Math.max;
import static java.lang.Math.min;
import static org.apache.datasketches.Util.ceilingPowerOf2;
import static org.apache.datasketches.quantiles.Util.checkIsCompactMemory;
import java.util.Random;
import org.apache.datasketches.Family;
import org.apache.datasketches.SketchesArgumentException;
import org.apache.datasketches.kll.KllFloatsSketch;
import org.apache.datasketches.memory.Memory;
import org.apache.datasketches.memory.WritableMemory;
/**
* This is a stochastic streaming sketch that enables near-real time analysis of the
* approximate distribution of real values from a very large stream in a single pass.
* The analysis is obtained using a getQuantiles(*) function or its inverse functions the
* Probability Mass Function from getPMF(*) and the Cumulative Distribution Function from getCDF(*).
*
* <p>Consider a large stream of one million values such as packet sizes coming into a network node.
* The absolute rank of any specific size value is simply its index in the hypothetical sorted
* array of values.
* The normalized rank (or fractional rank) is the absolute rank divided by the stream size,
* in this case one million.
* The value corresponding to the normalized rank of 0.5 represents the 50th percentile or median
* value of the distribution, or getQuantile(0.5). Similarly, the 95th percentile is obtained from
* getQuantile(0.95). Using the getQuantiles(0.0, 1.0) will return the min and max values seen by
* the sketch.</p>
*
* <p>From the min and max values, for example, 1 and 1000 bytes,
* you can obtain the PMF from getPMF(100, 500, 900) that will result in an array of
* 4 fractional values such as {.4, .3, .2, .1}, which means that
* <ul>
* <li>40% of the values were &lt; 100,</li>
* <li>30% of the values were &ge; 100 and &lt; 500,</li>
* <li>20% of the values were &ge; 500 and &lt; 900, and</li>
* <li>10% of the values were &ge; 900.</li>
* </ul>
* A frequency histogram can be obtained by simply multiplying these fractions by getN(),
* which is the total count of values received.
* The getCDF(*) works similarly, but produces the cumulative distribution instead.
*
* <p>The accuracy of this sketch is a function of the configured value <i>k</i>, which also affects
* the overall size of the sketch. Accuracy of this quantile sketch is always with respect to
* the normalized rank. A <i>k</i> of 128 produces a normalized, rank error of about 1.7%.
* For example, the median value returned from getQuantile(0.5) will be between the actual values
* from the hypothetically sorted array of input values at normalized ranks of 0.483 and 0.517, with
* a confidence of about 99%.</p>
*
* <pre>
Table Guide for DoublesSketch Size in Bytes and Approximate Error:
K =&gt; | 16 32 64 128 256 512 1,024
~ Error =&gt; | 12.145% 6.359% 3.317% 1.725% 0.894% 0.463% 0.239%
N | Size in Bytes -&gt;
------------------------------------------------------------------------
0 | 8 8 8 8 8 8 8
1 | 72 72 72 72 72 72 72
3 | 72 72 72 72 72 72 72
7 | 104 104 104 104 104 104 104
15 | 168 168 168 168 168 168 168
31 | 296 296 296 296 296 296 296
63 | 424 552 552 552 552 552 552
127 | 552 808 1,064 1,064 1,064 1,064 1,064
255 | 680 1,064 1,576 2,088 2,088 2,088 2,088
511 | 808 1,320 2,088 3,112 4,136 4,136 4,136
1,023 | 936 1,576 2,600 4,136 6,184 8,232 8,232
2,047 | 1,064 1,832 3,112 5,160 8,232 12,328 16,424
4,095 | 1,192 2,088 3,624 6,184 10,280 16,424 24,616
8,191 | 1,320 2,344 4,136 7,208 12,328 20,520 32,808
16,383 | 1,448 2,600 4,648 8,232 14,376 24,616 41,000
32,767 | 1,576 2,856 5,160 9,256 16,424 28,712 49,192
65,535 | 1,704 3,112 5,672 10,280 18,472 32,808 57,384
131,071 | 1,832 3,368 6,184 11,304 20,520 36,904 65,576
262,143 | 1,960 3,624 6,696 12,328 22,568 41,000 73,768
524,287 | 2,088 3,880 7,208 13,352 24,616 45,096 81,960
1,048,575 | 2,216 4,136 7,720 14,376 26,664 49,192 90,152
2,097,151 | 2,344 4,392 8,232 15,400 28,712 53,288 98,344
4,194,303 | 2,472 4,648 8,744 16,424 30,760 57,384 106,536
8,388,607 | 2,600 4,904 9,256 17,448 32,808 61,480 114,728
16,777,215 | 2,728 5,160 9,768 18,472 34,856 65,576 122,920
33,554,431 | 2,856 5,416 10,280 19,496 36,904 69,672 131,112
67,108,863 | 2,984 5,672 10,792 20,520 38,952 73,768 139,304
134,217,727 | 3,112 5,928 11,304 21,544 41,000 77,864 147,496
268,435,455 | 3,240 6,184 11,816 22,568 43,048 81,960 155,688
536,870,911 | 3,368 6,440 12,328 23,592 45,096 86,056 163,880
1,073,741,823 | 3,496 6,696 12,840 24,616 47,144 90,152 172,072
2,147,483,647 | 3,624 6,952 13,352 25,640 49,192 94,248 180,264
4,294,967,295 | 3,752 7,208 13,864 26,664 51,240 98,344 188,456
* </pre>
* <p>There is more documentation available on
* <a href="https://datasketches.apache.org">datasketches.apache.org</a>.</p>
*
* <p>This is an implementation of the Low Discrepancy Mergeable Quantiles Sketch, using double
* values, described in section 3.2 of the journal version of the paper "Mergeable Summaries"
* by Agarwal, Cormode, Huang, Phillips, Wei, and Yi.
* <a href="http://dblp.org/rec/html/journals/tods/AgarwalCHPWY13"></a></p>
*
* <p>This algorithm is independent of the distribution of values, which can be anywhere in the
* range of the IEEE-754 64-bit doubles.
*
* <p>This algorithm intentionally inserts randomness into the sampling process for values that
* ultimately get retained in the sketch. The results produced by this algorithm are not
* deterministic. For example, if the same stream is inserted into two different instances of this
* sketch, the answers obtained from the two sketches may not be be identical.</p>
*
* <p>Similarly, there may be directional inconsistencies. For example, the resulting array of
* values obtained from getQuantiles(fractions[]) input into the reverse directional query
* getPMF(splitPoints[]) may not result in the original fractional values.</p>
*
* @author Kevin Lang
* @author Lee Rhodes
* @author Jon Malkin
*/
public abstract class DoublesSketch {
static final int DOUBLES_SER_VER = 3;
static final int MAX_PRELONGS = Family.QUANTILES.getMaxPreLongs();
static final int MIN_K = 2;
static final int MAX_K = 1 << 15;
/**
* Setting the seed makes the results of the sketch deterministic if the input values are
* received in exactly the same order. This is only useful when performing test comparisons,
* otherwise is not recommended.
*/
static Random rand = new Random();
/**
* Parameter that controls space usage of sketch and accuracy of estimates.
*/
final int k_;
DoublesSketch(final int k) {
Util.checkK(k);
k_ = k;
}
synchronized static void setRandom(final long seed) {
DoublesSketch.rand = new Random(seed);
}
/**
* Returns a new builder
* @return a new builder
*/
public static final DoublesSketchBuilder builder() {
return new DoublesSketchBuilder();
}
/**
* Heapify takes the sketch image in Memory and instantiates an on-heap Sketch.
* The resulting sketch will not retain any link to the source Memory.
* @param srcMem a Memory image of a Sketch.
* <a href="{@docRoot}/resources/dictionary.html#mem">See Memory</a>
* @return a heap-based Sketch based on the given Memory
*/
public static DoublesSketch heapify(final Memory srcMem) {
if (checkIsCompactMemory(srcMem)) {
return CompactDoublesSketch.heapify(srcMem);
}
return UpdateDoublesSketch.heapify(srcMem);
}
/**
* Wrap this sketch around the given Memory image of a DoublesSketch, compact or non-compact.
*
* @param srcMem the given Memory image of a DoublesSketch that may have data,
* @return a sketch that wraps the given srcMem
*/
public static DoublesSketch wrap(final Memory srcMem) {
if (checkIsCompactMemory(srcMem)) {
return DirectCompactDoublesSketch.wrapInstance(srcMem);
}
return DirectUpdateDoublesSketchR.wrapInstance(srcMem);
}
/**
* This returns an approximation to the value of the data item
* that would be preceded by the given fraction of a hypothetical sorted
* version of the input stream so far.
*
* <p>We note that this method has a fairly large overhead (microseconds instead of nanoseconds)
* so it should not be called multiple times to get different quantiles from the same
* sketch. Instead use getQuantiles(), which pays the overhead only once.
*
* <p>If the sketch is empty this returns Double.NaN.
*
* @param fraction the specified fractional position in the hypothetical sorted stream.
* These are also called normalized ranks or fractional ranks.
* If fraction = 0.0, the true minimum value of the stream is returned.
* If fraction = 1.0, the true maximum value of the stream is returned.
*
* @return the approximation to the value at the above fraction
*/
public double getQuantile(final double fraction) {
if (isEmpty()) { return Double.NaN; }
if (fraction < 0.0 || fraction > 1.0) {
throw new SketchesArgumentException("Fraction cannot be less than zero or greater than 1.0");
}
if (fraction == 0.0) { return getMinValue(); }
if (fraction == 1.0) { return getMaxValue(); }
final DoublesAuxiliary aux = new DoublesAuxiliary(this);
return aux.getQuantile(fraction);
}
/**
* Gets the upper bound of the value interval in which the true quantile of the given rank
* exists with a confidence of at least 99%.
* @param fraction the given normalized rank as a fraction
* @return the upper bound of the value interval in which the true quantile of the given rank
* exists with a confidence of at least 99%. Returns NaN if the sketch is empty.
*/
public double getQuantileUpperBound(final double fraction) {
return getQuantile(min(1.0, fraction + Util.getNormalizedRankError(k_, false)));
}
/**
* Gets the lower bound of the value interval in which the true quantile of the given rank
* exists with a confidence of at least 99%.
* @param fraction the given normalized rank as a fraction
* @return the lower bound of the value interval in which the true quantile of the given rank
* exists with a confidence of at least 99%. Returns NaN if the sketch is empty.
*/
public double getQuantileLowerBound(final double fraction) {
return getQuantile(max(0, fraction - Util.getNormalizedRankError(k_, false)));
}
/**
* This is a more efficient multiple-query version of getQuantile().
*
* <p>This returns an array that could have been generated by using getQuantile() with many
* different fractional ranks, but would be very inefficient.
* This method incurs the internal set-up overhead once and obtains multiple quantile values in
* a single query. It is strongly recommend that this method be used instead of multiple calls
* to getQuantile().
*
* <p>If the sketch is empty this returns null.
*
* @param fRanks the given array of fractional (or normalized) ranks in the hypothetical
* sorted stream of all the input values seen so far.
* These fRanks must all be in the interval [0.0, 1.0] inclusively.
*
* @return array of approximate quantiles of the given fRanks in the same order as in the given
* fRanks array.
*/
public double[] getQuantiles(final double[] fRanks) {
if (isEmpty()) { return null; }
DoublesAuxiliary aux = null;
final double[] quantiles = new double[fRanks.length];
for (int i = 0; i < fRanks.length; i++) {
final double fRank = fRanks[i];
if (fRank == 0.0) { quantiles[i] = getMinValue(); }
else if (fRank == 1.0) { quantiles[i] = getMaxValue(); }
else {
if (aux == null) {
aux = new DoublesAuxiliary(this);
}
quantiles[i] = aux.getQuantile(fRank);
}
}
return quantiles;
}
/**
* This is also a more efficient multiple-query version of getQuantile() and allows the caller to
* specify the number of evenly spaced fractional ranks.
*
* <p>If the sketch is empty this returns null.
*
* @param evenlySpaced an integer that specifies the number of evenly spaced fractional ranks.
* This must be a positive integer greater than 1.
* A value of 2 will return the min and the max value. A value of 3 will return the min,
* the median and the max value, etc.
*
* @return array of approximations to the given fractions in the same order as given fractions
* array.
*/
public double[] getQuantiles(final int evenlySpaced) {
if (isEmpty()) { return null; }
return getQuantiles(org.apache.datasketches.Util.evenlySpaced(0.0, 1.0, evenlySpaced));
}
/**
* Returns an approximation to the normalized (fractional) rank of the given value from 0 to 1
* inclusive.
*
* <p>The resulting approximation has a probabilistic guarantee that be obtained from the
* getNormalizedRankError(false) function.
*
* <p>If the sketch is empty this returns NaN.</p>
*
* @param value to be ranked
* @return an approximate rank of the given value
*/
public double getRank(final double value) {
if (isEmpty()) { return Double.NaN; }
final DoublesSketchAccessor samples = DoublesSketchAccessor.wrap(this);
long total = 0;
int weight = 1;
samples.setLevel(DoublesSketchAccessor.BB_LVL_IDX);
for (int i = 0; i < samples.numItems(); i++) {
if (samples.get(i) < value) {
total += weight;
}
}
long bitPattern = getBitPattern();
for (int lvl = 0; bitPattern != 0L; lvl++, bitPattern >>>= 1) {
weight *= 2;
if ((bitPattern & 1L) > 0) { // level is not empty
samples.setLevel(lvl);
for (int i = 0; i < samples.numItems(); i++) {
if (samples.get(i) < value) {
total += weight;
} else {
break; // levels are sorted, no point comparing further
}
}
}
}
return (double) total / getN();
}
/**
* Returns an approximation to the Probability Mass Function (PMF) of the input stream
* given a set of splitPoints (values).
*
* <p>The resulting approximations have a probabilistic guarantee that be obtained from the
* getNormalizedRankError(true) function.
*
* <p>If the sketch is empty this returns null.</p>
*
* @param splitPoints an array of <i>m</i> unique, monotonically increasing double values
* that divide the real number line into <i>m+1</i> consecutive disjoint intervals.
* The definition of an "interval" is inclusive of the left splitPoint (or minimum value) and
* exclusive of the right splitPoint, with the exception that the last interval will include
* the maximum value.
* It is not necessary to include either the min or max values in these splitpoints.
*
* @return an array of m+1 doubles each of which is an approximation
* to the fraction of the input stream values (the mass) that fall into one of those intervals.
* The definition of an "interval" is inclusive of the left splitPoint and exclusive of the right
* splitPoint, with the exception that the last interval will include maximum value.
*/
public double[] getPMF(final double[] splitPoints) {
if (isEmpty()) { return null; }
return DoublesPmfCdfImpl.getPMFOrCDF(this, splitPoints, false);
}
/**
* Returns an approximation to the Cumulative Distribution Function (CDF), which is the
* cumulative analog of the PMF, of the input stream given a set of splitPoint (values).
*
* <p>The resulting approximations have a probabilistic guarantee that be obtained from the
* getNormalizedRankError(false) function.
*
* <p>If the sketch is empty this returns null.</p>
*
* @param splitPoints an array of <i>m</i> unique, monotonically increasing double values
* that divide the real number line into <i>m+1</i> consecutive disjoint intervals.
* The definition of an "interval" is inclusive of the left splitPoint (or minimum value) and
* exclusive of the right splitPoint, with the exception that the last interval will include
* the maximum value.
* It is not necessary to include either the min or max values in these splitpoints.
*
* @return an array of m+1 double values, which are a consecutive approximation to the CDF
* of the input stream given the splitPoints. The value at array position j of the returned
* CDF array is the sum of the returned values in positions 0 through j of the returned PMF
* array.
*/
public double[] getCDF(final double[] splitPoints) {
if (isEmpty()) { return null; }
return DoublesPmfCdfImpl.getPMFOrCDF(this, splitPoints, true);
}
/**
* Returns the configured value of K
* @return the configured value of K
*/
public int getK() {
return k_;
}
/**
* Returns the min value of the stream.
* If the sketch is empty this returns Double.NaN.
*
* @return the min value of the stream
*/
public abstract double getMinValue();
/**
* Returns the max value of the stream.
* If the sketch is empty this returns Double.NaN.
*
* @return the max value of the stream
*/
public abstract double getMaxValue();
/**
* Returns the length of the input stream so far.
* @return the length of the input stream so far
*/
public abstract long getN();
/**
* Get the rank error normalized as a fraction between zero and one.
* The error of this sketch is specified as a fraction of the normalized rank of the hypothetical
* sorted stream of items presented to the sketch.
*
* <p>Suppose the sketch is presented with N values. The raw rank (0 to N-1) of an item
* would be its index position in the sorted version of the input stream. If we divide the
* raw rank by N, it becomes the normalized rank, which is between 0 and 1.0.
*
* <p>For example, choosing a K of 256 yields a normalized rank error of less than 1%.
* The upper bound on the median value obtained by getQuantile(0.5) would be the value in the
* hypothetical ordered stream of values at the normalized rank of 0.51.
* The lower bound would be the value in the hypothetical ordered stream of values at the
* normalized rank of 0.49.
*
* <p>The error of this sketch cannot be translated into an error (relative or absolute) of the
* returned quantile values.
*
* @return the rank error normalized as a fraction between zero and one.
* @deprecated v2.0.0. Replaced by {@link #getNormalizedRankError(boolean)}
*/
@Deprecated
public double getNormalizedRankError() {
return Util.getNormalizedRankError(getK(), true);
}
/**
* Gets the approximate rank error of this sketch normalized as a fraction between zero and one.
* @param pmf if true, returns the "double-sided" normalized rank error for the getPMF() function.
* Otherwise, it is the "single-sided" normalized rank error for all the other queries.
* @return if pmf is true, returns the normalized rank error for the getPMF() function.
* Otherwise, it is the "single-sided" normalized rank error for all the other queries.
*/
public double getNormalizedRankError(final boolean pmf) {
return Util.getNormalizedRankError(k_, pmf);
}
/**
* Static method version of {@link #getNormalizedRankError()}
* @param k the configuration parameter of a DoublesSketch
* @return the rank error normalized as a fraction between zero and one.
* @deprecated v2.0.0. Replaced by {@link #getNormalizedRankError(int, boolean)}
*/
@Deprecated
public static double getNormalizedRankError(final int k) {
return Util.getNormalizedRankError(k, true);
}
/**
* Gets the normalized rank error given k and pmf.
* Static method version of the {@link #getNormalizedRankError(boolean)}.
* @param k the configuation parameter
* @param pmf if true, returns the "double-sided" normalized rank error for the getPMF() function.
* Otherwise, it is the "single-sided" normalized rank error for all the other queries.
* @return if pmf is true, the normalized rank error for the getPMF() function.
* Otherwise, it is the "single-sided" normalized rank error for all the other queries.
* @see KllFloatsSketch
*/
public static double getNormalizedRankError(final int k, final boolean pmf) {
return Util.getNormalizedRankError(k, pmf);
}
/**
* Gets the approximate value of <em>k</em> to use given epsilon, the normalized rank error.
* @param epsilon the normalized rank error between zero and one.
* @param pmf if true, this function returns the value of <em>k</em> assuming the input epsilon
* is the desired "double-sided" epsilon for the getPMF() function. Otherwise, this function
* returns the value of <em>k</em> assuming the input epsilon is the desired "single-sided"
* epsilon for all the other queries.
* @return the value of <i>k</i> given a value of epsilon.
* @see KllFloatsSketch
*/
public static int getKFromEpsilon(final double epsilon, final boolean pmf) {
return Util.getKFromEpsilon(epsilon, pmf);
}
/**
* Returns true if this sketch is empty
* @return true if this sketch is empty
*/
public boolean isEmpty() {
return getN() == 0;
}
/**
* Returns true if this sketch is direct
* @return true if this sketch is direct
*/
public abstract boolean isDirect();
/**
* Returns true if this sketch is in estimation mode.
* @return true if this sketch is in estimation mode.
*/
public boolean isEstimationMode() {
return getN() >= 2L * k_;
}
/**
* Returns true if the backing resource of <i>this</i> is identical with the backing resource
* of <i>that</i>. The capacities must be the same. If <i>this</i> is a region,
* the region offset must also be the same.
* @param that A different non-null object
* @return true if the backing resource of <i>this</i> is the same as the backing resource
* of <i>that</i>.
*/
public boolean isSameResource(final Memory that) { //Overridden by direct sketches
return false;
}
/**
* Serialize this sketch to a byte array. An UpdateDoublesSketch will be serialized in
* an unordered, non-compact form; a CompactDoublesSketch will be serialized in ordered,
* compact form. A DirectUpdateDoublesSketch can only wrap a non-compact array, and a
* DirectCompactDoublesSketch can only wrap a compact array.
*
* @return byte array of this sketch
*/
public byte[] toByteArray() {
if (isCompact()) {
return toByteArray(true);
}
return toByteArray(false);
}
/**
* Serialize this sketch in a byte array form.
* @param compact if true the sketch will be serialized in compact form.
* DirectCompactDoublesSketch can wrap() only a compact byte array;
* DirectUpdateDoublesSketch can wrap() only a non-compact byte array.
* @return this sketch in a byte array form.
*/
public byte[] toByteArray(final boolean compact) {
return DoublesByteArrayImpl.toByteArray(this, compact, compact);
}
/**
* Returns summary information about this sketch.
*/
@Override
public String toString() {
return toString(true, false);
}
/**
* Returns summary information about this sketch. Used for debugging.
* @param sketchSummary if true includes sketch summary
* @param dataDetail if true includes data detail
* @return summary information about the sketch.
*/
public String toString(final boolean sketchSummary, final boolean dataDetail) {
return DoublesUtil.toString(sketchSummary, dataDetail, this);
}
/**
* Returns a human readable string of the preamble of a byte array image of a DoublesSketch.
* @param byteArr the given byte array
* @return a human readable string of the preamble of a byte array image of a DoublesSketch.
*/
public static String toString(final byte[] byteArr) {
return PreambleUtil.toString(byteArr, true);
}
/**
* Returns a human readable string of the preamble of a Memory image of a DoublesSketch.
* @param mem the given Memory
* @return a human readable string of the preamble of a Memory image of a DoublesSketch.
*/
public static String toString(final Memory mem) {
return PreambleUtil.toString(mem, true);
}
/**
* From an source sketch, create a new sketch that must have a smaller value of K.
* The original sketch is not modified.
*
* @param srcSketch the sourcing sketch
* @param smallerK the new sketch's value of K that must be smaller than this value of K.
* It is required that this.getK() = smallerK * 2^(nonnegative integer).
* @param dstMem the destination Memory. It must not overlap the Memory of this sketch.
* If null, a heap sketch will be returned, otherwise it will be off-heap.
*
* @return the new sketch.
*/
public DoublesSketch downSample(final DoublesSketch srcSketch, final int smallerK,
final WritableMemory dstMem) {
return downSampleInternal(srcSketch, smallerK, dstMem);
}
/**
* Computes the number of retained items (samples) in the sketch
* @return the number of retained items (samples) in the sketch
*/
public int getRetainedItems() {
return Util.computeRetainedItems(getK(), getN());
}
/**
* Returns the number of bytes this sketch would require to store in compact form, which is not
* updatable.
* @return the number of bytes this sketch would require to store in compact form.
*/
public int getCompactStorageBytes() {
return getCompactStorageBytes(getK(), getN());
}
/**
* Returns the number of bytes a DoublesSketch would require to store in compact form
* given the values of <i>k</i> and <i>n</i>. The compact form is not updatable.
* @param k the size configuration parameter for the sketch
* @param n the number of items input into the sketch
* @return the number of bytes required to store this sketch in compact form.
*/
public static int getCompactStorageBytes(final int k, final long n) {
if (n == 0) { return 8; }
final int metaPreLongs = DoublesSketch.MAX_PRELONGS + 2; //plus min, max
return metaPreLongs + Util.computeRetainedItems(k, n) << 3;
}
/**
* Returns the number of bytes this sketch would require to store in native form: compact for
* a CompactDoublesSketch, non-compact for an UpdateDoublesSketch.
* @return the number of bytes this sketch would require to store in compact form.
*/
public int getStorageBytes() {
if (isCompact()) { return getCompactStorageBytes(); }
return getUpdatableStorageBytes();
}
/**
* Returns the number of bytes this sketch would require to store in updatable form.
* This uses roughly 2X the storage of the compact form.
* @return the number of bytes this sketch would require to store in updatable form.
*/
public int getUpdatableStorageBytes() {
return getUpdatableStorageBytes(getK(), getN());
}
/**
* Returns the number of bytes a sketch would require to store in updatable form.
* This uses roughly 2X the storage of the compact form
* given the values of <i>k</i> and <i>n</i>.
* @param k the size configuration parameter for the sketch
* @param n the number of items input into the sketch
* @return the number of bytes this sketch would require to store in updatable form.
*/
public static int getUpdatableStorageBytes(final int k, final long n) {
if (n == 0) { return 8; }
final int metaPre = DoublesSketch.MAX_PRELONGS + 2; //plus min, max
final int totLevels = Util.computeNumLevelsNeeded(k, n);
if (n <= k) {
final int ceil = Math.max(ceilingPowerOf2((int)n), DoublesSketch.MIN_K * 2);
return metaPre + ceil << 3;
}
return metaPre + (2 + totLevels) * k << 3;
}
/**
* Puts the current sketch into the given Memory in compact form if there is sufficient space,
* otherwise, it throws an error.
*
* @param dstMem the given memory.
*/
public void putMemory(final WritableMemory dstMem) {
putMemory(dstMem, true);
}
/**
* Puts the current sketch into the given Memory if there is sufficient space, otherwise,
* throws an error.
*
* @param dstMem the given memory.
* @param compact if true, compacts and sorts the base buffer, which optimizes merge
* performance at the cost of slightly increased serialization time.
*/
public void putMemory(final WritableMemory dstMem, final boolean compact) {
if (isDirect() && isCompact() == compact) {
final Memory srcMem = getMemory();
srcMem.copyTo(0, dstMem, 0, getStorageBytes());
} else {
final byte[] byteArr = toByteArray(compact);
final int arrLen = byteArr.length;
final long memCap = dstMem.getCapacity();
if (memCap < arrLen) {
throw new SketchesArgumentException(
"Destination Memory not large enough: " + memCap + " < " + arrLen);
}
dstMem.putByteArray(0, byteArr, 0, arrLen);
}
}
/**
* @return the iterator for this class
*/
public DoublesSketchIterator iterator() {
return new DoublesSketchIterator(this, getBitPattern());
}
//Restricted
/*
* DoublesMergeImpl.downSamplingMergeInto requires the target sketch to implement update(), so
* we ensure that the target is an UpdateSketch. The public API, on the other hand, just
* specifies a DoublesSketch. This lets us be more specific about the type without changing the
* public API.
*/
UpdateDoublesSketch downSampleInternal(final DoublesSketch srcSketch, final int smallerK,
final WritableMemory dstMem) {
final UpdateDoublesSketch newSketch = dstMem == null
? HeapUpdateDoublesSketch.newInstance(smallerK)
: DirectUpdateDoublesSketch.newInstance(smallerK, dstMem);
if (srcSketch.isEmpty()) { return newSketch; }
DoublesMergeImpl.downSamplingMergeInto(srcSketch, newSketch);
return newSketch;
}
//Restricted abstract
/**
* Returns true if this sketch is compact
* @return true if this sketch is compact
*/
abstract boolean isCompact();
/**
* Returns the base buffer count
* @return the base buffer count
*/
abstract int getBaseBufferCount();
/**
* Returns the bit pattern for valid log levels
* @return the bit pattern for valid log levels
*/
abstract long getBitPattern();
/**
* Returns the item capacity for the combined base buffer
* @return the item capacity for the combined base buffer
*/
abstract int getCombinedBufferItemCapacity();
/**
* Returns the combined buffer, in non-compact form.
* @return the combined buffer, in non-compact form.
*/
abstract double[] getCombinedBuffer();
/**
* Gets the Memory if it exists, otherwise returns null.
* @return the Memory if it exists, otherwise returns null.
*/
abstract WritableMemory getMemory();
}