Mixing
Spark 2.3 regression (vs 1.6) upon caching a dataframe as a temporary view ('createOrReplaceTempView')
It seems that there's a change of behaviour between 1.6 and 2.3 when caching a Dataframe and saving it as a temp table. In 1.6, the following code executed printUDF once. The equivalent code in 2.3 (or even same as is) executes it twice.
val rdd = context.parallelize(Seq(1, 2, 3)).map(Row(_))
val schema = StructType(StructField("num", IntegerType) :: Nil)
val df1 = sql.createDataFrame(rdd, schema)
df1.registerTempTable("data_table")
sql.udf.register("printUDF", (x:Int) => {print(x)
x
})
val df2 = sql.sql("select printUDF(num) result from data_table").cache()
df2.collect() //execute cache
df2.registerTempTable("cached_table")
val df3 = sql.sql("select result from cached_table")
df3.collect()
1.6 prints 123 while 2.3 prints 123123, thus evaluating the dataframe twice.
Managed to overcome by skipping the temporary table and selecting directly from the cached dataframe, but was wondering if that is an expected behavior / known issue.
UPDATE: According to https://issues.apache.org/jira/browse/SPARK-26510, seems like a bug.
scala apache-spark apache-spark-sql apache-spark-2.0
asked Dec 31 '18 at 11:57
HagaiHagai
1089
add a comment |
It seems that there's a change of behaviour between 1.6 and 2.3 when caching a Dataframe and saving it as a temp table. In 1.6, the following code executed printUDF once. The equivalent code in 2.3 (or even same as is) executes it twice.
val rdd = context.parallelize(Seq(1, 2, 3)).map(Row(_))
val schema = StructType(StructField("num", IntegerType) :: Nil)
val df1 = sql.createDataFrame(rdd, schema)
df1.registerTempTable("data_table")
sql.udf.register("printUDF", (x:Int) => {print(x)
x
})
val df2 = sql.sql("select printUDF(num) result from data_table").cache()
df2.collect() //execute cache
df2.registerTempTable("cached_table")
val df3 = sql.sql("select result from cached_table")
df3.collect()
1.6 prints 123 while 2.3 prints 123123, thus evaluating the dataframe twice.
Managed to overcome by skipping the temporary table and selecting directly from the cached dataframe, but was wondering if that is an expected behavior / known issue.
UPDATE: According to https://issues.apache.org/jira/browse/SPARK-26510, seems like a bug.
scala apache-spark apache-spark-sql apache-spark-2.0
asked Dec 31 '18 at 11:57
HagaiHagai
1089
add a comment |
It seems that there's a change of behaviour between 1.6 and 2.3 when caching a Dataframe and saving it as a temp table. In 1.6, the following code executed printUDF once. The equivalent code in 2.3 (or even same as is) executes it twice.
val rdd = context.parallelize(Seq(1, 2, 3)).map(Row(_))
val schema = StructType(StructField("num", IntegerType) :: Nil)
val df1 = sql.createDataFrame(rdd, schema)
df1.registerTempTable("data_table")
sql.udf.register("printUDF", (x:Int) => {print(x)
x
})
val df2 = sql.sql("select printUDF(num) result from data_table").cache()
df2.collect() //execute cache
df2.registerTempTable("cached_table")
val df3 = sql.sql("select result from cached_table")
df3.collect()
1.6 prints 123 while 2.3 prints 123123, thus evaluating the dataframe twice.
Managed to overcome by skipping the temporary table and selecting directly from the cached dataframe, but was wondering if that is an expected behavior / known issue.
UPDATE: According to https://issues.apache.org/jira/browse/SPARK-26510, seems like a bug.
scala apache-spark apache-spark-sql apache-spark-2.0
asked Dec 31 '18 at 11:57
HagaiHagai
1089
It seems that there's a change of behaviour between 1.6 and 2.3 when caching a Dataframe and saving it as a temp table. In 1.6, the following code executed printUDF once. The equivalent code in 2.3 (or even same as is) executes it twice.
val rdd = context.parallelize(Seq(1, 2, 3)).map(Row(_))
val schema = StructType(StructField("num", IntegerType) :: Nil)
val df1 = sql.createDataFrame(rdd, schema)
df1.registerTempTable("data_table")
sql.udf.register("printUDF", (x:Int) => {print(x)
x
})
val df2 = sql.sql("select printUDF(num) result from data_table").cache()
df2.collect() //execute cache
df2.registerTempTable("cached_table")
val df3 = sql.sql("select result from cached_table")
df3.collect()
1.6 prints 123 while 2.3 prints 123123, thus evaluating the dataframe twice.
Managed to overcome by skipping the temporary table and selecting directly from the cached dataframe, but was wondering if that is an expected behavior / known issue.
UPDATE: According to https://issues.apache.org/jira/browse/SPARK-26510, seems like a bug.
scala apache-spark apache-spark-sql apache-spark-2.0
scala apache-spark apache-spark-sql apache-spark-2.0
asked Dec 31 '18 at 11:57
HagaiHagai
1089
asked Dec 31 '18 at 11:57
HagaiHagai
1089
edited Jan 1 at 10:15
Hagai
asked Dec 31 '18 at 11:57
HagaiHagai
1089
asked Dec 31 '18 at 11:57
HagaiHagai
1089
asked Dec 31 '18 at 11:57
HagaiHagai
1089
1089
add a comment |
add a comment |
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