我打电话给以下人员:
propertiesDF.select(
col("timestamp"), col("coordinates")(0) as "lon",
col("coordinates")(1) as "lat",
col("properties.tide (above mllw)") as "tideAboveMllw",
col("properties.wind speed") as "windSpeed")
这给了我以下错误:
org.apache.spark.sql.AnalysisException:没有这样的struct field潮流 (高于mllw)在气温,大气压,露点, 主波周期,平均波浪方向,名称,程序名称, 显着的波浪高度,潮汐(高于mllw):,能见度,水 温度,风向,风速;
现在确实存在这样的结构域。 (错误消息本身就是这样说的。)
这是架构:
root
|-- timestamp: long (nullable = true)
|-- coordinates: array (nullable = true)
| |-- element: double (containsNull = true)
|-- properties: struct (nullable = true)
| |-- air temperature: double (nullable = true)
| |-- atmospheric pressure: double (nullable = true)
| |-- dew point: double (nullable = true)
.
.
.
| |-- tide (above mllw):: string (nullable = true)
.
.
.
输入读作JSON,如下所示:
val df = sqlContext.read.json(dirName)
如何处理列名中的括号?
答案 0 :(得分:2)
首先应避免使用此类名称,但可以拆分访问路径:
val df = spark.range(1).select(struct(
lit(123).as("tide (above mllw)"),
lit(1).as("wind speed")
).as("properties"))
df.select(col("properties").getItem("tide (above mllw)"))
// or
df.select(col("properties")("tide (above mllw)"))
或用反引号括起问题字段:
df.select(col("properties.`tide (above mllw)`"))
两种解决方案都假定数据包含以下结构(基于您用于查询的访问路径):
df.printSchema
// root
// |-- properties: struct (nullable = false)
// | |-- tide (above mllw): integer (nullable = false)
// | |-- wind speed: integer (nullable = false)
答案 1 :(得分:0)
根据the documentation,您可以尝试使用单引号。像这样:
propertiesDF.select(
col("timestamp"), col("coordinates")(0) as "lon",
col("coordinates")(1) as "lat",
col("'properties.tide (above mllw)'") as "tideAboveMllw",
col("properties.wind speed") as "windSpeed")