Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Besides using the col function to reference a column,
Spark/Scala DataFrame supports using $"col_name"
(based on implicit conversion and must have import spark.implicit._)
while PySpark DataFrame support using …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
http://www.legendu.net/misc/blog/use-xgboost-with-spark/
http://www.legendu.net/misc/blog/use-lightgbm-with-spark/
You can run Apache Ray on top of Spark via analytics-zoo …
You can aggregate all values in Columns of a DataFrame.
Just use aggregation functions in select without groupBy,
which is very similar to SQL syntax.
The aggregation functions all and any are available since Spark 3.0.
However,
they can be achieved using other aggregation functions such as sum
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Spark DataFrame is a great implementation of distributed DataFrame, if you don't mind having dependency on Spark. It can be used in a non-distributed way of course. Spark DataFrame …
Spark DataFrame is an alias to Dataset[Row].
Even though a Spark DataFrame is stored as Rows in a Dataset,
built-in operations/functions (in org.apache.spark.sql.functions) for Spark DataFrame are Column-based.
Sometimes,
there might be transformations on a DataFrame that is hard to express as Column expressions
but rather evey convenient to express as Row expressions.
The traditional way to resolve this issue is to wrap the row-based function into a UDF.
It is worthing knowing that Spark DataFrame supports map/flatMap APIs
which works on Rows.
They are still experimental as Spark 2.4.3.
It is suggested that you stick to Column-based operations/functions until the Row-based methods mature.