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scala - 如何使用 saveAsTextFile 在 spark 数据框中进行自定义分区

转载 作者:行者123 更新时间:2023-12-04 16:08:55 27 4
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我在 Spark 中创建了数据,然后执行了连接操作,最后我必须将输出保存到分区文件中。

我正在将数据框转换为 RDD,然后另存为允许我使用多字符定界符的文本文件。我的问题是在这种情况下如何使用数据框列作为自定义分区。

我不能对自定义分区使用以下选项,因为它不支持多字符定界符:

dfMainOutput.write.partitionBy("DataPartiotion","StatementTypeCode")
.format("csv")
.option("delimiter", "^")
.option("nullValue", "")
.option("codec", "gzip")
.save("s3://trfsdisu/SPARK/FinancialLineItem/output")

为了使用多字符定界符,我在 RDD 中将其转换为如下代码:

dfMainOutput.rdd.map(x=>x.mkString("|^|")).saveAsTextFile("dir path to store")

但在上面的选项中,我将如何根据列“DataPartiotion”和“StatementTypeCode”进行自定义分区?

我是否必须再次从 RDD 转换回 DataFrame?

这是我试过的代码

val dfMainOutput = df1result.join(latestForEachKey, Seq("LineItem_organizationId", "LineItem_lineItemId"), "outer")
.select($"LineItem_organizationId", $"LineItem_lineItemId",
when($"DataPartition_1".isNotNull, $"DataPartition_1").otherwise($"DataPartition_1").as("DataPartition_1"),
when($"StatementTypeCode_1".isNotNull, $"StatementTypeCode_1").otherwise($"StatementTypeCode").as("StatementTypeCode"),
when($"StatementTypeCode_1".isNotNull, $"StatementTypeCode_1").otherwise($"StatementTypeCode").alias("StatementtypeCode"),
when($"LineItemName_1".isNotNull, $"LineItemName_1").otherwise($"LineItemName").as("LineItemName"),
when($"LocalLanguageLabel_1".isNotNull, $"LocalLanguageLabel_1").otherwise($"LocalLanguageLabel").as("LocalLanguageLabel"),
when($"FinancialConceptLocal_1".isNotNull, $"FinancialConceptLocal_1").otherwise($"FinancialConceptLocal").as("FinancialConceptLocal"),
when($"FinancialConceptGlobal_1".isNotNull, $"FinancialConceptGlobal_1").otherwise($"FinancialConceptGlobal").as("FinancialConceptGlobal"),
when($"IsDimensional_1".isNotNull, $"IsDimensional_1").otherwise($"IsDimensional").as("IsDimensional"),
when($"InstrumentId_1".isNotNull, $"InstrumentId_1").otherwise($"InstrumentId").as("InstrumentId"),
when($"LineItemSequence_1".isNotNull, $"LineItemSequence_1").otherwise($"LineItemSequence").as("LineItemSequence"),
when($"PhysicalMeasureId_1".isNotNull, $"PhysicalMeasureId_1").otherwise($"PhysicalMeasureId").as("PhysicalMeasureId"),
when($"FinancialConceptCodeGlobalSecondary_1".isNotNull, $"FinancialConceptCodeGlobalSecondary_1").otherwise($"FinancialConceptCodeGlobalSecondary").as("FinancialConceptCodeGlobalSecondary"),
when($"IsRangeAllowed_1".isNotNull, $"IsRangeAllowed_1").otherwise($"IsRangeAllowed".cast(DataTypes.StringType)).as("IsRangeAllowed"),
when($"IsSegmentedByOrigin_1".isNotNull, $"IsSegmentedByOrigin_1").otherwise($"IsSegmentedByOrigin".cast(DataTypes.StringType)).as("IsSegmentedByOrigin"),
when($"SegmentGroupDescription".isNotNull, $"SegmentGroupDescription").otherwise($"SegmentGroupDescription").as("SegmentGroupDescription"),
when($"SegmentChildDescription_1".isNotNull, $"SegmentChildDescription_1").otherwise($"SegmentChildDescription").as("SegmentChildDescription"),
when($"SegmentChildLocalLanguageLabel_1".isNotNull, $"SegmentChildLocalLanguageLabel_1").otherwise($"SegmentChildLocalLanguageLabel").as("SegmentChildLocalLanguageLabel"),
when($"LocalLanguageLabel_languageId_1".isNotNull, $"LocalLanguageLabel_languageId_1").otherwise($"LocalLanguageLabel_languageId").as("LocalLanguageLabel_languageId"),
when($"LineItemName_languageId_1".isNotNull, $"LineItemName_languageId_1").otherwise($"LineItemName_languageId").as("LineItemName_languageId"),
when($"SegmentChildDescription_languageId_1".isNotNull, $"SegmentChildDescription_languageId_1").otherwise($"SegmentChildDescription_languageId").as("SegmentChildDescription_languageId"),
when($"SegmentChildLocalLanguageLabel_languageId_1".isNotNull, $"SegmentChildLocalLanguageLabel_languageId_1").otherwise($"SegmentChildLocalLanguageLabel_languageId").as("SegmentChildLocalLanguageLabel_languageId"),
when($"SegmentGroupDescription_languageId_1".isNotNull, $"SegmentGroupDescription_languageId_1").otherwise($"SegmentGroupDescription_languageId").as("SegmentGroupDescription_languageId"),
when($"SegmentMultipleFundbDescription_1".isNotNull, $"SegmentMultipleFundbDescription_1").otherwise($"SegmentMultipleFundbDescription").as("SegmentMultipleFundbDescription"),
when($"SegmentMultipleFundbDescription_languageId_1".isNotNull, $"SegmentMultipleFundbDescription_languageId_1").otherwise($"SegmentMultipleFundbDescription_languageId").as("SegmentMultipleFundbDescription_languageId"),
when($"IsCredit_1".isNotNull, $"IsCredit_1").otherwise($"IsCredit".cast(DataTypes.StringType)).as("IsCredit"),
when($"FinancialConceptLocalId_1".isNotNull, $"FinancialConceptLocalId_1").otherwise($"FinancialConceptLocalId").as("FinancialConceptLocalId"),
when($"FinancialConceptGlobalId_1".isNotNull, $"FinancialConceptGlobalId_1").otherwise($"FinancialConceptGlobalId").as("FinancialConceptGlobalId"),
when($"FinancialConceptCodeGlobalSecondaryId_1".isNotNull, $"FinancialConceptCodeGlobalSecondaryId_1").otherwise($"FinancialConceptCodeGlobalSecondaryId").as("FinancialConceptCodeGlobalSecondaryId"),
when($"FFAction_1".isNotNull, $"FFAction_1").otherwise((concat(col("FFAction"), lit("|!|"))).as("FFAction")))
.filter(!$"FFAction".contains("D"))

val dfMainOutputFinal = dfMainOutput.select(concat_ws("|^|", columns.map(c => col(c)): _*).as("concatenated"))

dfMainOutputFinal.write.partitionBy("DataPartition_1","StatementTypeCode")
.format("csv")
.option("codec", "gzip")
.save("s3://trfsdisu/SPARK/FinancialLineItem/output")

最佳答案

这可以通过使用 concat_ws 来完成,此函数的工作方式与 mkString 类似,但可以直接在数据帧上执行。这使得到 rdd 的转换步骤变得多余,并且可以使用 df.write.partitionBy() 方法。一个连接所有可用列的小示例,

import org.apache.spark.sql.functions._
import spark.implicits._

val df = Seq(("01", "20000", "45.30"), ("01", "30000", "45.30"))
.toDF("col1", "col2", "col3")

val df2 = df.select($"DataPartiotion", $"StatementTypeCode",
concat_ws("|^|", df.schema.fieldNames.map(c => col(c)): _*).as("concatenated"))

这会给你一个像这样的结果数据框,

+--------------+-----------------+------------------+
|DataPartiotion|StatementTypeCode| concatenated|
+--------------+-----------------+------------------+
| 01| 20000|01|^|20000|^|45.30|
| 01| 30000|01|^|30000|^|45.30|
+--------------+-----------------+------------------+

关于scala - 如何使用 saveAsTextFile 在 spark 数据框中进行自定义分区,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/46599054/

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