Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/4421#discussion_r24247234
--- Diff: python/pyspark/sql.py ---
@@ -1469,6 +1470,44 @@ def applySchema(self, rdd, schema):
df = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(),
schema.json())
return DataFrame(df, self)
+ def applyNames(self, nameString, plainRdd):
+ """
+ Builds a DataFrame from an RDD based on column names.
+
+ Assumes RDD contains iterables of equal length.
+ >>> unparsedStrings = sc.parallelize(["1, A1, true", "2, B2,
false", "3, C3, true", "4, D4, false"])
+ >>> input = unparsedStrings.map(lambda x: x.split(",")).map(lambda
x: [int(x[0]), x[1], bool(x[2])])
+ >>> df1 = sqlCtx.applyNames("a b c", input)
+ >>> df1.registerTempTable("df1")
+ >>> sqlCtx.sql("select a from df1").collect()
+ [Row(a=1), Row(a=2), Row(a=3), Row(a=4)]
+ >>> input2 = unparsedStrings.map(lambda x:
x.split(",")).map(lambda x: [int(x[0]), x[1], bool(x[2]), {"k":int(x[0]),
"v":2*int(x[0])}, x])
+ >>> df2 = sqlCtx.applyNames("a b c d e", input2)
+ >>> df2.registerTempTable("df2")
+ >>> sqlCtx.sql("select d['k']+d['v'] from df2").collect()
+ [Row(c0=3), Row(c0=6), Row(c0=9), Row(c0=12)]
+ >>> sqlCtx.sql("select b, e[1] from df2").collect()
+ [Row(b=u' A1', c1=u' A1'), Row(b=u' B2', c1=u' B2'), Row(b=u' C3',
c1=u' C3'), Row(b=u' D4', c1=u' D4')]
+ """
+ fieldNames = [f for f in re.split("( |\\\".*?\\\"|'.*?')",
nameString) if f.strip()]
+ reservedWords = set(map(string.lower,["ABS","ALL","AND",
"APPROXIMATE", "AS", "ASC", "AVG", "BETWEEN", "BY", \
--- End diff --
I can't really speak to this patch in general, since I don't know much
about this part of Spark SQL, but to avoid duplication it probably makes sense
to keep the list of reserved words in the JVM and fetch it into Python from
there.
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