Thank you again
For
val r = df.filter(col("paid") > "").map(x =>
(x.getString(0),x.getString(1).....)
Can you give an example of column expression please like
df.filter(col("paid") > "").col("firstcolumn").getString ?....
On Thursday, 24 March 2016, 0:45, Michael Armbrust <[email protected]>
wrote:
You can only use as on a Column expression, not inside of a lambda function.
The reason is the lambda function is compiled into opaque bytecode that Spark
SQL is not able to see. We just blindly execute it.
However, there are a couple of ways to name the columns that come out of a map.
Either use a case class instead of a tuple. Or use .toDF("name1",
"name2"....) after the map.
>From a performance perspective, its even better though if you can avoid maps
>and stick to Column expressions. The reason is that for maps, we have to
>actually materialize and object to pass to your function. However, if you
>stick to column expression we can actually work directly on serialized data.
On Wed, Mar 23, 2016 at 5:27 PM, Ashok Kumar <[email protected]> wrote:
thank you sir
sql("select `_1` as firstcolumn from items")
is there anyway one can keep the csv column names using databricks when mapping
val r = df.filter(col("paid") > "").map(x =>
(x.getString(0),x.getString(1).....)
can I call example x.getString(0).as.(firstcolumn) in above when mapping if
possible so columns will have labels
On Thursday, 24 March 2016, 0:18, Michael Armbrust <[email protected]>
wrote:
You probably need to use `backticks` to escape `_1` since I don't think that
its a valid SQL identifier.
On Wed, Mar 23, 2016 at 5:10 PM, Ashok Kumar <[email protected]>
wrote:
Gurus,
If I register a temporary table as below
r.toDFres58: org.apache.spark.sql.DataFrame = [_1: string, _2: string, _3:
double, _4: double, _5: double]
r.toDF.registerTempTable("items")
sql("select * from items")res60: org.apache.spark.sql.DataFrame = [_1: string,
_2: string, _3: double, _4: double, _5: double]
Is there anyway I can do a select on the first column only
sql("select _1 from items" throws error
Thanking you
