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author | Timothy Hunter <timhunter@databricks.com> | 2016-02-23 15:31:17 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2016-02-23 15:31:17 -0800 |
commit | 15e30155631d52e35ab8522584027ab350e5acb3 (patch) | |
tree | f7678f8ba2c87de7900806fdb8531b474b3f0a2d /sql/core | |
parent | 9cdd867da978629ea2f61f94e3c346fa0bfecf0e (diff) | |
download | spark-15e30155631d52e35ab8522584027ab350e5acb3.tar.gz spark-15e30155631d52e35ab8522584027ab350e5acb3.tar.bz2 spark-15e30155631d52e35ab8522584027ab350e5acb3.zip |
[SPARK-6761][SQL][ML] Fixes to API and documentation of approximate quantiles
## What changes were proposed in this pull request?
This continues thunterdb 's work on `approxQuantile` API. It changes the signature of `approxQuantile` from `(col: String, quantile: Double, epsilon: Double): Double` to `(col: String, probabilities: Array[Double], relativeError: Double): Array[Double]` and update API doc. It also improves the error message in tests and simplifies the merge algorithm for summaries.
## How was the this patch tested?
Use the same unit tests as before.
Closes #11325
Author: Timothy Hunter <timhunter@databricks.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes #11332 from mengxr/SPARK-6761.
Diffstat (limited to 'sql/core')
4 files changed, 150 insertions, 120 deletions
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala index 7f110c4e7f..39a31ab028 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala @@ -37,13 +37,37 @@ import org.apache.spark.util.sketch.{BloomFilter, CountMinSketch} final class DataFrameStatFunctions private[sql](df: DataFrame) { /** - * Calculate the approximate quantile of numerical column of a DataFrame. - * @param col the name of the column - * @param quantile the quantile number - * @return the approximate quantile + * Calculates the approximate quantiles of a numerical column of a DataFrame. + * + * The result of this algorithm has the following deterministic bound: + * If the DataFrame has N elements and if we request the quantile at probability `p` up to error + * `err`, then the algorithm will return a sample `x` from the DataFrame so that the *exact* rank + * of `x` is close to (p * N). + * More precisely, + * + * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N). + * + * This method implements a variation of the Greenwald-Khanna algorithm (with some speed + * optimizations). + * The algorithm was first present in [[http://dx.doi.org/10.1145/375663.375670 Space-efficient + * Online Computation of Quantile Summaries]] by Greenwald and Khanna. + * + * @param col the name of the numerical column + * @param probabilities a list of quantile probabilities + * Each number must belong to [0, 1]. + * For example 0 is the minimum, 0.5 is the median, 1 is the maximum. + * @param relativeError The relative target precision to achieve (>= 0). + * If set to zero, the exact quantiles are computed, which could be very expensive. + * Note that values greater than 1 are accepted but give the same result as 1. + * @return the approximate quantiles at the given probabilities + * + * @since 2.0.0 */ - def approxQuantile(col: String, quantile: Double, epsilon: Double): Double = { - StatFunctions.approxQuantile(df, col, quantile, epsilon) + def approxQuantile( + col: String, + probabilities: Array[Double], + relativeError: Double): Array[Double] = { + StatFunctions.multipleApproxQuantiles(df, Seq(col), probabilities, relativeError).head.toArray } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala index eb056d555b..26e4eda542 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala @@ -17,7 +17,6 @@ package org.apache.spark.sql.execution.stat -import scala.annotation.tailrec import scala.collection.mutable.ArrayBuffer import org.apache.spark.Logging @@ -33,59 +32,37 @@ private[sql] object StatFunctions extends Logging { import QuantileSummaries.Stats /** - * Calculates the approximate quantile for the given column. - * - * If you need to compute multiple quantiles at once, you should use [[multipleApproxQuantiles]] - * - * Note on the target error. + * Calculates the approximate quantiles of multiple numerical columns of a DataFrame in one pass. * * The result of this algorithm has the following deterministic bound: - * if the DataFrame has N elements and if we request the quantile `phi` up to error `epsi`, - * then the algorithm will return a sample `x` from the DataFrame so that the *exact* rank - * of `x` close to (phi * N). More precisely: - * - * floor((phi - epsi) * N) <= rank(x) <= ceil((phi + epsi) * N) - * - * Note on the algorithm used. + * If the DataFrame has N elements and if we request the quantile at probability `p` up to error + * `err`, then the algorithm will return a sample `x` from the DataFrame so that the *exact* rank + * of `x` is close to (p * N). + * More precisely, * - * This method implements a variation of the Greenwald-Khanna algorithm - * (with some speed optimizations). The algorithm was first present in the following article: - * "Space-efficient Online Computation of Quantile Summaries" by Greenwald, Michael - * and Khanna, Sanjeev. (http://dl.acm.org/citation.cfm?id=375670) + * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N). * - * The performance optimizations are detailed in the comments of the implementation. - * - * @param df the dataframe to estimate quantiles on - * @param col the name of the column - * @param quantile the target quantile of interest - * @param epsilon the target error. Should be >= 0. - * */ - def approxQuantile( - df: DataFrame, - col: String, - quantile: Double, - epsilon: Double = QuantileSummaries.defaultEpsilon): Double = { - require(quantile >= 0.0 && quantile <= 1.0, "Quantile must be in the range of (0.0, 1.0).") - val Seq(Seq(res)) = multipleApproxQuantiles(df, Seq(col), Seq(quantile), epsilon) - res - } - - /** - * Runs multiple quantile computations in a single pass, with the same target error. - * - * See [[approxQuantile)]] for more details on the approximation guarantees. + * This method implements a variation of the Greenwald-Khanna algorithm (with some speed + * optimizations). + * The algorithm was first present in [[http://dx.doi.org/10.1145/375663.375670 Space-efficient + * Online Computation of Quantile Summaries]] by Greenwald and Khanna. * * @param df the dataframe - * @param cols columns of the dataframe - * @param quantiles target quantiles to compute - * @param epsilon the precision to achieve + * @param cols numerical columns of the dataframe + * @param probabilities a list of quantile probabilities + * Each number must belong to [0, 1]. + * For example 0 is the minimum, 0.5 is the median, 1 is the maximum. + * @param relativeError The relative target precision to achieve (>= 0). + * If set to zero, the exact quantiles are computed, which could be very expensive. + * Note that values greater than 1 are accepted but give the same result as 1. + * * @return for each column, returns the requested approximations */ def multipleApproxQuantiles( df: DataFrame, cols: Seq[String], - quantiles: Seq[Double], - epsilon: Double): Seq[Seq[Double]] = { + probabilities: Seq[Double], + relativeError: Double): Seq[Seq[Double]] = { val columns: Seq[Column] = cols.map { colName => val field = df.schema(colName) require(field.dataType.isInstanceOf[NumericType], @@ -94,7 +71,7 @@ private[sql] object StatFunctions extends Logging { Column(Cast(Column(colName).expr, DoubleType)) } val emptySummaries = Array.fill(cols.size)( - new QuantileSummaries(QuantileSummaries.defaultCompressThreshold, epsilon)) + new QuantileSummaries(QuantileSummaries.defaultCompressThreshold, relativeError)) // Note that it works more or less by accident as `rdd.aggregate` is not a pure function: // this function returns the same array as given in the input (because `aggregate` reuses @@ -115,40 +92,49 @@ private[sql] object StatFunctions extends Logging { } val summaries = df.select(columns: _*).rdd.aggregate(emptySummaries)(apply, merge) - summaries.map { summary => quantiles.map(summary.query) } + summaries.map { summary => probabilities.map(summary.query) } } /** * Helper class to compute approximate quantile summary. * This implementation is based on the algorithm proposed in the paper: * "Space-efficient Online Computation of Quantile Summaries" by Greenwald, Michael - * and Khanna, Sanjeev. (http://dl.acm.org/citation.cfm?id=375670) + * and Khanna, Sanjeev. (http://dx.doi.org/10.1145/375663.375670) * * In order to optimize for speed, it maintains an internal buffer of the last seen samples, * and only inserts them after crossing a certain size threshold. This guarantees a near-constant * runtime complexity compared to the original algorithm. * - * @param compressThreshold the compression threshold: after the internal buffer of statistics - * crosses this size, it attempts to compress the statistics together - * @param epsilon the target precision - * @param sampled a buffer of quantile statistics. See the G-K article for more details + * @param compressThreshold the compression threshold. + * After the internal buffer of statistics crosses this size, it attempts to compress the + * statistics together. + * @param relativeError the target relative error. + * It is uniform across the complete range of values. + * @param sampled a buffer of quantile statistics. + * See the G-K article for more details. * @param count the count of all the elements *inserted in the sampled buffer* * (excluding the head buffer) * @param headSampled a buffer of latest samples seen so far */ class QuantileSummaries( val compressThreshold: Int, - val epsilon: Double, + val relativeError: Double, val sampled: ArrayBuffer[Stats] = ArrayBuffer.empty, private[stat] var count: Long = 0L, val headSampled: ArrayBuffer[Double] = ArrayBuffer.empty) extends Serializable { import QuantileSummaries._ + /** + * Returns a summary with the given observation inserted into the summary. + * This method may either modify in place the current summary (and return the same summary, + * modified in place), or it may create a new summary from scratch it necessary. + * @param x the new observation to insert into the summary + */ def insert(x: Double): QuantileSummaries = { headSampled.append(x) if (headSampled.size >= defaultHeadSize) { - this.withHeadInserted + this.withHeadBufferInserted } else { this } @@ -162,7 +148,7 @@ private[sql] object StatFunctions extends Logging { * * @return a new quantile summary object. */ - private def withHeadInserted: QuantileSummaries = { + private def withHeadBufferInserted: QuantileSummaries = { if (headSampled.isEmpty) { return this } @@ -187,7 +173,7 @@ private[sql] object StatFunctions extends Logging { if (newSamples.isEmpty || (sampleIdx == sampled.size && opsIdx == sorted.length - 1)) { 0 } else { - math.floor(2 * epsilon * currentCount).toInt + math.floor(2 * relativeError * currentCount).toInt } val tuple = Stats(currentSample, 1, delta) @@ -200,67 +186,80 @@ private[sql] object StatFunctions extends Logging { newSamples.append(sampled(sampleIdx)) sampleIdx += 1 } - new QuantileSummaries(compressThreshold, epsilon, newSamples, currentCount) + new QuantileSummaries(compressThreshold, relativeError, newSamples, currentCount) } + /** + * Returns a new summary that compresses the summary statistics and the head buffer. + * + * This implements the COMPRESS function of the GK algorithm. It does not modify the object. + * + * @return a new summary object with compressed statistics + */ def compress(): QuantileSummaries = { // Inserts all the elements first - val inserted = this.withHeadInserted + val inserted = this.withHeadBufferInserted assert(inserted.headSampled.isEmpty) assert(inserted.count == count + headSampled.size) val compressed = - compressImmut(inserted.sampled, mergeThreshold = 2 * epsilon * inserted.count) - new QuantileSummaries(compressThreshold, epsilon, compressed, inserted.count) + compressImmut(inserted.sampled, mergeThreshold = 2 * relativeError * inserted.count) + new QuantileSummaries(compressThreshold, relativeError, compressed, inserted.count) } + private def shallowCopy: QuantileSummaries = { + new QuantileSummaries(compressThreshold, relativeError, sampled, count, headSampled) + } + + /** + * Merges two (compressed) summaries together. + * + * Returns a new summary. + */ def merge(other: QuantileSummaries): QuantileSummaries = { + require(headSampled.isEmpty, "Current buffer needs to be compressed before merge") + require(other.headSampled.isEmpty, "Other buffer needs to be compressed before merge") if (other.count == 0) { - this + this.shallowCopy } else if (count == 0) { - other + other.shallowCopy } else { - // We rely on the fact that they are ordered to efficiently interleave them. - val thisSampled = sampled.toList - val otherSampled = other.sampled.toList - val res: ArrayBuffer[Stats] = ArrayBuffer.empty - - @tailrec - def mergeCurrent( - thisList: List[Stats], - otherList: List[Stats]): Unit = (thisList, otherList) match { - case (Nil, l) => - res.appendAll(l) - case (l, Nil) => - res.appendAll(l) - case (h1 :: t1, h2 :: t2) if h1.value > h2.value => - mergeCurrent(otherList, thisList) - case (h1 :: t1, l) => - // We know that h1.value <= all values in l - // TODO(thunterdb) do we need to adjust g and delta? - res.append(h1) - mergeCurrent(t1, l) - } - - mergeCurrent(thisSampled, otherSampled) - val comp = compressImmut(res, mergeThreshold = 2 * epsilon * count) - new QuantileSummaries(other.compressThreshold, other.epsilon, comp, other.count + count) + // Merge the two buffers. + // The GK algorithm is a bit unclear about it, but it seems there is no need to adjust the + // statistics during the merging: the invariants are still respected after the merge. + // TODO: could replace full sort by ordered merge, the two lists are known to be sorted + // already. + val res = (sampled ++ other.sampled).sortBy(_.value) + val comp = compressImmut(res, mergeThreshold = 2 * relativeError * count) + new QuantileSummaries( + other.compressThreshold, other.relativeError, comp, other.count + count) } } + /** + * Runs a query for a given quantile. + * The result follows the approximation guarantees detailed above. + * The query can only be run on a compressed summary: you need to call compress() before using + * it. + * + * @param quantile the target quantile + * @return + */ def query(quantile: Double): Double = { require(quantile >= 0 && quantile <= 1.0, "quantile should be in the range [0.0, 1.0]") + require(headSampled.isEmpty, + "Cannot operate on an uncompressed summary, call compress() first") - if (quantile <= epsilon) { + if (quantile <= relativeError) { return sampled.head.value } - if (quantile >= 1 - epsilon) { + if (quantile >= 1 - relativeError) { return sampled.last.value } // Target rank val rank = math.ceil(quantile * count).toInt - val targetError = math.ceil(epsilon * count) + val targetError = math.ceil(relativeError * count) // Minimum rank at current sample var minRank = 0 var i = 1 @@ -291,9 +290,10 @@ private[sql] object StatFunctions extends Logging { val defaultHeadSize: Int = 50000 /** - * The default value for epsilon. + * The default value for the relative error (1%). + * With this value, the best extreme percentiles that can be approximated are 1% and 99%. */ - val defaultEpsilon: Double = 0.01 + val defaultRelativeError: Double = 0.01 /** * Statisttics from the Greenwald-Khanna paper. diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala index 7f9229244b..e865dbe6b5 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala @@ -126,22 +126,29 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext { } test("approximate quantile") { - val df = Seq.tabulate(1000)(i => (i, 2.0 * i)).toDF("singles", "doubles") - - val expected_1 = 500.0 - val expected_2 = 1600.0 + val n = 1000 + val df = Seq.tabulate(n)(i => (i, 2.0 * i)).toDF("singles", "doubles") + val q1 = 0.5 + val q2 = 0.8 val epsilons = List(0.1, 0.05, 0.001) for (epsilon <- epsilons) { - val result1 = df.stat.approxQuantile("singles", 0.5, epsilon) - val result2 = df.stat.approxQuantile("doubles", 0.8, epsilon) - - val error_1 = 2 * 1000 * epsilon - val error_2 = 2 * 2000 * epsilon - - assert(math.abs(result1 - expected_1) < error_1) - assert(math.abs(result2 - expected_2) < error_2) + val Array(single1) = df.stat.approxQuantile("singles", Array(q1), epsilon) + val Array(double2) = df.stat.approxQuantile("doubles", Array(q2), epsilon) + // Also make sure there is no regression by computing multiple quantiles at once. + val Array(d1, d2) = df.stat.approxQuantile("doubles", Array(q1, q2), epsilon) + val Array(s1, s2) = df.stat.approxQuantile("singles", Array(q1, q2), epsilon) + + val error_single = 2 * 1000 * epsilon + val error_double = 2 * 2000 * epsilon + + assert(math.abs(single1 - q1 * n) < error_single) + assert(math.abs(double2 - 2 * q2 * n) < error_double) + assert(math.abs(s1 - q1 * n) < error_single) + assert(math.abs(s2 - q2 * n) < error_single) + assert(math.abs(d1 - 2 * q1 * n) < error_double) + assert(math.abs(d2 - 2 * q2 * n) < error_double) } } @@ -296,9 +303,9 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext { class DataFrameStatPerfSuite extends QueryTest with SharedSQLContext with Logging { // Turn on this test if you want to test the performance of approximate quantiles. - ignore("describe() should not be slowed down too much by quantiles") { + ignore("computing quantiles should not take much longer than describe()") { val df = sqlContext.range(5000000L).toDF("col1").cache() - def millis(f: => Any): Double = { + def seconds(f: => Any): Double = { // Do some warmup logDebug("warmup...") for (i <- 1 to 10) { @@ -314,15 +321,14 @@ class DataFrameStatPerfSuite extends QueryTest with SharedSQLContext with Loggin (end - start) / 1e9 } logDebug("execute done") - times.sum.toDouble / times.length.toDouble - + times.sum / times.length.toDouble } logDebug("*** Normal describe ***") - val t1 = millis { df.describe() } + val t1 = seconds { df.describe() } logDebug(s"T1 = $t1") logDebug("*** Just quantiles ***") - val t2 = millis { + val t2 = seconds { StatFunctions.multipleApproxQuantiles(df, Seq("col1"), Seq(0.1, 0.25, 0.5, 0.75, 0.9), 0.01) } logDebug(s"T1 = $t1, T2 = $t2") diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/stat/ApproxQuantileSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/stat/ApproxQuantileSuite.scala index 6992b4c723..0a989d026c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/stat/ApproxQuantileSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/stat/ApproxQuantileSuite.scala @@ -46,12 +46,12 @@ class ApproxQuantileSuite extends SparkFunSuite { val approx = summary.query(quant) // The rank of the approximation. val rank = data.count(_ < approx) // has to be <, not <= to be exact - val lower = math.floor((quant - summary.epsilon) * data.size) - assert(rank >= lower, - s"approx_rank: $rank ! >= $lower, requested quantile = $quant") - val upper = math.ceil((quant + summary.epsilon) * data.size) - assert(rank <= upper, - s"approx_rank: $rank ! <= $upper, requested quantile = $quant") + val lower = math.floor((quant - summary.relativeError) * data.size) + val upper = math.ceil((quant + summary.relativeError) * data.size) + val msg = + s"$rank not in [$lower $upper], requested quantile: $quant, approx returned: $approx" + assert(rank >= lower, msg) + assert(rank <= upper, msg) } for { |