Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/15959#discussion_r89258891
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/Statistics.scala
---
@@ -58,60 +61,176 @@ case class Statistics(
}
}
+
/**
- * Statistics for a column.
+ * Statistics collected for a column.
+ *
+ * 1. Supported data types are defined in `ColumnStat.supportsType`.
+ * 2. The JVM data type stored in min/max is the external data type (used
in Row) for the
+ * corresponding Catalyst data type. For example, for DateType we store
java.sql.Date, and for
+ * TimestampType we store java.sql.Timestamp.
+ * 3. For integral types, they are all upcasted to longs, i.e. shorts are
stored as longs.
+ * 4. There is no guarantee that the statistics collected are accurate.
Approximation algorithms
+ * (sketches) might have been used, and the data collected can also be
stale.
+ *
+ * @param distinctCount number of distinct values
+ * @param min minimum value
+ * @param max maximum value
+ * @param nullCount number of nulls
+ * @param avgLen average length of the values. For fixed-length types,
this should be a constant.
+ * @param maxLen maximum length of the values. For fixed-length types,
this should be a constant.
*/
-case class ColumnStat(statRow: InternalRow) {
+case class ColumnStat(
+ distinctCount: BigInt,
+ min: Option[Any],
+ max: Option[Any],
+ nullCount: BigInt,
+ avgLen: Long,
+ maxLen: Long) {
- def forNumeric[T <: AtomicType](dataType: T): NumericColumnStat[T] = {
- NumericColumnStat(statRow, dataType)
- }
- def forString: StringColumnStat = StringColumnStat(statRow)
- def forBinary: BinaryColumnStat = BinaryColumnStat(statRow)
- def forBoolean: BooleanColumnStat = BooleanColumnStat(statRow)
+ // We currently don't store min/max for byte arrays. This can change in
the future and then
+ // we need to remove this require.
+ require(min.isEmpty || !min.get.isInstanceOf[Array[Byte]])
+ require(max.isEmpty || !max.get.isInstanceOf[Array[Byte]])
- override def toString: String = {
- // use Base64 for encoding
- Base64.encodeBase64String(statRow.asInstanceOf[UnsafeRow].getBytes)
+ /**
+ * Returns a map from string to string that can be used to serialize the
column stats.
+ * The key is the name of the field (e.g. "distinctCount" or "min"), and
the value is the string
+ * representation for the value. The deserialization side is defined in
[[ColumnStat.fromMap]].
+ *
+ * As part of the protocol, the returned map always contains a key
called "version".
+ * In the case min/max values are null (None), they won't appear in the
map.
+ */
+ def toMap: Map[String, String] = {
+ val map = new scala.collection.mutable.HashMap[String, String]
+ map.put(ColumnStat.KEY_VERSION, "1")
+ map.put(ColumnStat.KEY_DISTINCT_COUNT, distinctCount.toString)
+ map.put(ColumnStat.KEY_NULL_COUNT, nullCount.toString)
+ map.put(ColumnStat.KEY_AVG_LEN, avgLen.toString)
+ map.put(ColumnStat.KEY_MAX_LEN, maxLen.toString)
+ min.foreach { v => map.put(ColumnStat.KEY_MIN_VALUE, v.toString) }
+ max.foreach { v => map.put(ColumnStat.KEY_MAX_VALUE, v.toString) }
+ map.toMap
}
}
-object ColumnStat {
- def apply(numFields: Int, str: String): ColumnStat = {
- // use Base64 for decoding
- val bytes = Base64.decodeBase64(str)
- val unsafeRow = new UnsafeRow(numFields)
- unsafeRow.pointTo(bytes, bytes.length)
- ColumnStat(unsafeRow)
+
+object ColumnStat extends Logging {
+
+ // List of string keys used to serialize ColumnStat
+ val KEY_VERSION = "version"
+ private val KEY_DISTINCT_COUNT = "distinctCount"
+ private val KEY_MIN_VALUE = "min"
+ private val KEY_MAX_VALUE = "max"
+ private val KEY_NULL_COUNT = "nullCount"
+ private val KEY_AVG_LEN = "avgLen"
+ private val KEY_MAX_LEN = "maxLen"
+
+ /** Returns true iff the we support gathering column statistics on
column of the given type. */
+ def supportsType(dataType: DataType): Boolean = dataType match {
+ case _: IntegralType => true
+ case _: DecimalType => true
+ case DoubleType | FloatType => true
+ case BooleanType => true
+ case DateType => true
+ case TimestampType => true
+ case BinaryType | StringType => true
+ case _ => false
}
-}
-case class NumericColumnStat[T <: AtomicType](statRow: InternalRow,
dataType: T) {
- // The indices here must be consistent with
`ColumnStatStruct.numericColumnStat`.
- val numNulls: Long = statRow.getLong(0)
- val max: T#InternalType = statRow.get(1,
dataType).asInstanceOf[T#InternalType]
- val min: T#InternalType = statRow.get(2,
dataType).asInstanceOf[T#InternalType]
- val ndv: Long = statRow.getLong(3)
-}
+ /**
+ * Creates a [[ColumnStat]] object from the given map. This is used to
deserialize column stats
+ * from some external storage. The serialization side is defined in
[[ColumnStat.toMap]].
+ */
+ def fromMap(table: String, field: StructField, map: Map[String, String])
+ : Option[ColumnStat] = {
+ val str2val: (String => Any) = field.dataType match {
+ case _: IntegralType => _.toLong
+ case _: DecimalType => new java.math.BigDecimal(_)
+ case DoubleType | FloatType => _.toDouble
+ case BooleanType => _.toBoolean
+ case DateType => java.sql.Date.valueOf
+ case TimestampType => java.sql.Timestamp.valueOf
+ case StringType => identity
+ // This version of Spark does not use min/max for binary columns so
we ignore it.
--- End diff --
actually it's also true for string type:
https://github.com/apache/spark/pull/15959/files#diff-a4113ed6a89e8d19a39f5c27ce95658bR213
, shall we put the `StringType` and `BinaryType` in the same case statement?
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