The more Spark code I write, the more I hit the same use cases where the
Scala APIs feel a bit awkward. I'd love to understand if there are
historical reasons for these and whether there is opportunity + interest to
improve the APIs. Here are my top two:
1. registerTempTable() returns Unit
def cachedDF(path: String, tableName: String) = { val df =
sqlContext.read.load(path).cache() df.registerTempTable(tableName) df}//
vs.def cachedDF(path: String, tableName: String) =
sqlContext.read.load(path).cache().registerTempTable(tableName)
2. No toDF() implicit for creating a DataFrame from an RDD + schema
val schema: StructType = ...val rdd = sc.textFile(...) .map(...)
.aggregate(...)val df = sqlContext.createDataFrame(rdd, schema)// vs.val
schema: StructType = ...val df = sc.textFile(...) .map(...)
.aggregate(...) .toDF(schema)
Have you encountered other examples where small, low-risk API tweaks could
make common use cases more consistent + simpler to code?
/Sim
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