前言
在平时的 Spark 处理中常常会有把一个如 2012-12-12 这样的 date 类型转换成一个 long 的 Unix time 然后进行计算的需求.下面是一段示例代码:
val schema = StructType( Array( StructField("id", IntegerType, nullable = true), StructField("birth", DateType, nullable = true), StructField("time", TimestampType, nullable = true) ))val data = Seq( Row(1, Date.valueOf("2012-12-12"), Timestamp.valueOf("2016-09-30 03:03:00")), Row(2, Date.valueOf("2016-12-14"), Timestamp.valueOf("2016-12-14 03:03:00")))val df = spark.createDataFrame(spark.sparkContext.parallelize(data),schema) 问题 & 解决
首先很直观的是直接把DateType cast 成 LongType, 如下:
df.select(df.col("birth").cast(LongType))
但是这样出来都是 null, 这是为什么? 答案就在 org.apache.spark.sql.catalyst.expressions.Cast 中, 先看 canCast 方法, 可以看到 DateType 其实是可以转成 NumericType 的, 然后再看下面castToLong的方法, 可以看到case DateType => buildCast[Int](_, d => null)居然直接是个 null, 看提交记录其实这边有过反复, 然后为了和 hive 统一, 所以返回最后还是返回 null 了.
虽然 DateType 不能直接 castToLong, 但是TimestampType可以, 所以这里的解决方案就是先把 DateType cast 成 TimestampType. 但是这里又会有一个非常坑爹的问题: 时区问题.
首先明确一个问题, 就是这个放到了 spark 中的 2012-12-12 到底 UTC 还是我们当前时区? 答案是如果没有经过特殊配置, 这个2012-12-12代表的是 当前时区的 2012-12-12 00:00:00., 对应 UTC 其实是: 2012-12-11 16:00:00, 少了8小时. 这里还顺便说明了Spark 入库 Date 数据的时候是带着时区的.
然后再看DateType cast toTimestampType 的代码, 可以看到buildCast[Int](_, d => DateTimeUtils.daysToMillis(d, timeZone) * 1000), 这里是带着时区的, 但是 Spark SQL 默认会用当前机器的时区. 但是大家一般底层数据比如这个2016-09-30, 都是代表的 UTC 时间, 在用 Spark 处理数据的时候, 这个时间还是 UTC 时间, 只有通过 JDBC 出去的时间才会变成带目标时区的结果. 经过摸索, 这里有两种解决方案:
- 配置 Spark 的默认时区
config("spark.sql.session.timeZone", "UTC"), 最直观. 这样直接写df.select(df.col("birth").cast(TimestampType).cast(LongType))就可以了. - 不配置 conf, 正面刚:
df.select(from_utc_timestamp(to_utc_timestamp(df.col("birth"), TimeZone.getTimeZone("UTC").getID), TimeZone.getDefault.getID).cast(LongType)), 可以看到各种 cast, 这是区别:
- 没有配置 UTC:
from_utc_timestamp(to_utc_timestamp(lit("2012-12-11 16:00:00"), TimeZone.getTimeZone("UTC").getID), TimeZone.getDefault.getID) - 配置了 UTC:
from_utc_timestamp(to_utc_timestamp(lit("2012-12-12 00:00:00"), TimeZone.getTimeZone("UTC").getID), TimeZone.getDefault.getID)多了8小时
/** * Returns true iff we can cast `from` type to `to` type. */ def canCast(from: DataType, to: DataType): Boolean = (from, to) match { case (fromType, toType) if fromType == toType => true case (NullType, _) => true case (_, StringType) => true case (StringType, BinaryType) => true case (StringType, BooleanType) => true case (DateType, BooleanType) => true case (TimestampType, BooleanType) => true case (_: NumericType, BooleanType) => true case (StringType, TimestampType) => true case (BooleanType, TimestampType) => true case (DateType, TimestampType) => true case (_: NumericType, TimestampType) => true case (StringType, DateType) => true case (TimestampType, DateType) => true case (StringType, CalendarIntervalType) => true case (StringType, _: NumericType) => true case (BooleanType, _: NumericType) => true case (DateType, _: NumericType) => true case (TimestampType, _: NumericType) => true case (_: NumericType, _: NumericType) => true ... } private[this] def castToLong(from: DataType): Any => Any = from match { case StringType => val result = new LongWrapper() buildCast[UTF8String](_, s => if (s.toLong(result)) result.value else null) case BooleanType => buildCast[Boolean](_, b => if (b) 1L else 0L) case DateType => buildCast[Int](_, d => null) case TimestampType => buildCast[Long](_, t => timestampToLong(t)) case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b) } // TimestampConverter private[this] def castToTimestamp(from: DataType): Any => Any = from match { ... case DateType => buildCast[Int](_, d => DateTimeUtils.daysToMillis(d, timeZone) * 1000) // TimestampWritable.decimalToTimestamp ... } /** * Given a timestamp, which corresponds to a certain time of day in the given timezone, returns * another timestamp that corresponds to the same time of day in UTC. * @group datetime_funcs * @since 1.5.0 */ def to_utc_timestamp(ts: Column, tz: String): Column = withExpr { ToUTCTimestamp(ts.expr, Literal(tz)) } /** * Given a timestamp, which corresponds to a certain time of day in UTC, returns another timestamp * that corresponds to the same time of day in the given timezone. * @group datetime_funcs * @since 1.5.0 */ def from_utc_timestamp(ts: Column, tz: String): Column = withExpr { FromUTCTimestamp(ts.expr, Literal(tz)) } Deep dive
配置源码解读:
val SESSION_LOCAL_TIMEZONE = buildConf("spark.sql.session.timeZone").stringConf.createWithDefaultFunction(() => TimeZone.getDefault.getID) def sessionLocalTimeZone: String = getConf(SQLConf.SESSION_LOCAL_TIMEZONE)
/** * Replace [[TimeZoneAwareExpression]] without timezone id by its copy with session local * time zone. */case class ResolveTimeZone(conf: SQLConf) extends Rule[LogicalPlan] { private val transformTimeZoneExprs: PartialFunction[Expression, Expression] = { case e: TimeZoneAwareExpression if e.timeZoneId.isEmpty => e.withTimeZone(conf.sessionLocalTimeZone) // Casts could be added in the subquery plan through the rule TypeCoercion while coercing // the types between the value expression and list query expression of IN expression. // We need to subject the subquery plan through ResolveTimeZone again to setup timezone // information for time zone aware expressions. case e: ListQuery => e.withNewPlan(apply(e.plan)) } override def apply(plan: LogicalPlan): LogicalPlan = plan.transformAllExpressions(transformTimeZoneExprs) def resolveTimeZones(e: Expression): Expression = e.transform(transformTimeZoneExprs)}/** * Mix-in trait for constructing valid [[Cast]] expressions. */trait CastSupport { /** * Configuration used to create a valid cast expression. */ def conf: SQLConf /** * Create a Cast expression with the session local time zone. */ def cast(child: Expression, dataType: DataType): Cast = { Cast(child, dataType, Option(conf.sessionLocalTimeZone)) }} org.apache.spark.sql.catalyst.analysis.Analyzer#batches 可以看到有ResolveTimeZone
lazy val batches: Seq[Batch] = Seq( Batch("Resolution", fixedPoint, ResolveTableValuedFunctions :: ResolveRelations :: ResolveReferences :: ... ResolveTimeZone(conf) :: ResolvedUuidExpressions :: TypeCoercion.typeCoercionRules(conf) ++ extendedResolutionRules : _*), Batch("Post-Hoc Resolution", Once, postHocResolutionRules: _*), Batch("View", Once, AliasViewChild(conf)), Batch("Nondeterministic", Once, PullOutNondeterministic), Batch("UDF", Once, HandleNullInputsForUDF), Batch("FixNullability", Once, FixNullability), Batch("Subquery", Once, UpdateOuterReferences), Batch("Cleanup", fixedPoint, CleanupAliases) ) Test Example
对于时区理解
在不同的时区下 sql.Timestamp 对象的表现:
这里是 GMT+8:
Timestamp "2014-06-24 07:22:15.0" - fastTime = 1403565735000 - "2014-06-24T07:22:15.000+0700"
如果是 GMT+7, 会显示如下,可以看到是同一个毫秒数
Timestamp "2014-06-24 06:22:15.0" - fastTime = 1403565735000 - "2014-06-24T06:22:15.000+0700"
test("ColumnBatch") { val schema = StructType( Array( StructField("id", IntegerType, nullable = true), StructField("birth", DateType, nullable = true), StructField("time", TimestampType, nullable = true) )) val columnarBatch = ColumnarBatch.allocate(schema, MemoryMode.ON_HEAP, 1024) val c0 = columnarBatch.column(0) val c1 = columnarBatch.column(1) val c2 = columnarBatch.column(2) c0.putInt(0, 0) // 1355241600, /3600/24 s to days c1.putInt(0, 1355241600 / 3600 / 24) // microsecond c2.putLong(0, 1355285532000000L) val internal0 = columnarBatch.getRow(0) //a way converting internal row to unsafe row. //val convert = UnsafeProjection.create(schema) //val internal = convert.apply(internal0) val enc = RowEncoder.apply(schema).resolveAndBind() val row = enc.fromRow(internal0) val df = spark.createDataFrame(Lists.newArrayList(row), schema) TimeZone.setDefault(TimeZone.getTimeZone("UTC")) val tsStr0 = df.select(col("time")).head().getTimestamp(0).toString val ts0 = df.select(col("time").cast(LongType)).head().getLong(0) TimeZone.setDefault(TimeZone.getTimeZone("GMT+8")) val tsStr1 = df.select(col("time")).head().getTimestamp(0).toString val ts1 = df.select(col("time").cast(LongType)).head().getLong(0) assert(true, "2012-12-12 04:12:12.0".equals(tsStr0)) assert(true, "2012-12-12 12:12:12.0".equals(tsStr1)) // to long 之后毫秒数都是一样的 assert(true, ts0 == ts1) } 番外 : ImplicitCastInputTypes
我们自己定义了一个Expr, 要求接受两个 input 为 DateType 的参数.
case class MockExpr(d0: Expression, d1: Expression) extends BinaryExpression with ImplicitCastInputTypes { override def left: Expression = d0 override def right: Expression = d1 override def inputTypes: Seq[AbstractDataType] = Seq(DateType, DateType) override def dataType: DataType = IntegerType override def nullSafeEval(date0: Any, date1: Any): Any = { ... }} 假设我们有如下调用, 请问这个调用符合预期吗? 结论是符合的, 因为有ImplicitCastInputTypes.
lit("2012-11-12 12:12:12.0").cast(TimestampType)lit("2012-12-12 12:12:12.0").cast(TimestampType)Column(MockExpr(tsc1.expr, tsc2.expr)) org.apache.spark.sql.catalyst.analysis.TypeCoercion.ImplicitTypeCasts
case e: ImplicitCastInputTypes if e.inputTypes.nonEmpty =>val children: Seq[Expression] = e.children.zip(e.inputTypes).map { case (in, expected) => // If we cannot do the implicit cast, just use the original input. implicitCast(in, expected).getOrElse(in)}e.withNewChildren(children)def implicitCast(e: Expression, expectedType: AbstractDataType): Option[Expression] = { implicitCast(e.dataType, expectedType).map { dt => if (dt == e.dataType) e else Cast(e, dt) }} org.apache.spark.sql.catalyst.expressions.Cast#castToDate #DateConverter
private[this] def castToDate(from: DataType): Any => Any = from match { case StringType => buildCast[UTF8String](_, s => DateTimeUtils.stringToDate(s).orNull) case TimestampType => // throw valid precision more than seconds, according to Hive. // Timestamp.nanos is in 0 to 999,999,999, no more than a second. buildCast[Long](_, t => DateTimeUtils.millisToDays(t / 1000L, timeZone))}