Snowpark Migration Accelerator : Codes de problème pour Spark - Scala¶

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Vous trouverez ci-dessous un exemple d’élément Spark qui n’est pas pris en charge par Snowpark et qui génère donc cet EWI.

val df = session.range(10)
val result = df.isLocal

Sortie

The SMA adds the EWI SPRKSCL1112 to the output code to let you know that this element is not supported by Snowpark.

val df = session.range(10)
/*EWI: SPRKSCL1112 => org.apache.spark.sql.Dataset.isLocal is not supported*/
val result = df.isLocal

Correction recommandée

Comme il s’agit d’un code d’erreur générique qui s’applique à une série de fonctions non prises en charge, il n’existe pas de correction unique et spécifique. L’action appropriée dépend de l’élément utilisé.

Veuillez noter que même si l’élément n’est pas pris en charge, cela ne signifie pas nécessairement qu’il est impossible de trouver une solution ou une solution de contournement. Cela signifie seulement que l’outil SMA lui-même ne peut pas trouver la solution.

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Recommandations supplémentaires¶

• This is unlikely to be an error in the source code itself, but rather is an error in how the SMA processes the source code. The best resolution would be to post an issue in the SMA.

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.max function, first used with a column name as an argument and then with a column object.

val df = Seq(10, 12, 20, 15, 18).toDF("value")
val result1 = df.select(max("value"))
val result2 = df.select(max(col("value")))

Sortie

The SMA adds the EWI SPRKSCL1153 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq(10, 12, 20, 15, 18).toDF("value")
/*EWI: SPRKSCL1153 => org.apache.spark.sql.functions.max has a workaround, see documentation for more info*/
val result1 = df.select(max("value"))
/*EWI: SPRKSCL1153 => org.apache.spark.sql.functions.max has a workaround, see documentation for more info*/
val result2 = df.select(max(col("value")))

Correction recommandée

Snowpark has an equivalent max function that receives a column object as an argument. For that reason, the Spark overload that receives a column object as an argument is directly supported by Snowpark and does not require any changes.

For the overload that receives a string argument, you can convert the string into a column object using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq(10, 12, 20, 15, 18).toDF("value")
val result1 = df.select(max(col("value")))
val result2 = df.select(max(col("value")))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.explode function used to get the consolidated information of the array fields of the dataset.

    val explodeData = Seq(
      Row("Cat", Array("Gato","Chat")),
      Row("Dog", Array("Perro","Chien")),
      Row("Bird", Array("Ave","Oiseau"))
    )

    val explodeSchema = StructType(
      List(
        StructField("Animal", StringType),
        StructField("Translation", ArrayType(StringType))
      )
    )

    val rddExplode = session.sparkContext.parallelize(explodeData)

    val dfExplode = session.createDataFrame(rddExplode, explodeSchema)

    dfExplode.select(explode(dfExplode("Translation").alias("exploded")))

Sortie

The SMA adds the EWI SPRKSCL1102 to the output code to let you know that this function is not supported by Snowpark.

    val explodeData = Seq(
      Row("Cat", Array("Gato","Chat")),
      Row("Dog", Array("Perro","Chien")),
      Row("Bird", Array("Ave","Oiseau"))
    )

    val explodeSchema = StructType(
      List(
        StructField("Animal", StringType),
        StructField("Translation", ArrayType(StringType))
      )
    )

    val rddExplode = session.sparkContext.parallelize(explodeData)

    val dfExplode = session.createDataFrame(rddExplode, explodeSchema)

    /*EWI: SPRKSCL1102 => Explode is not supported */
    dfExplode.select(explode(dfExplode("Translation").alias("exploded")))

Correction recommandée

Since explode is not supported by Snowpark, the function flatten could be used as a substitute.

La correction suivante crée un aplatissement du dataframe dfExplode, puis effectue la requête pour répliquer le résultat dans Spark.

    val explodeData = Seq(
      Row("Cat", Array("Gato","Chat")),
      Row("Dog", Array("Perro","Chien")),
      Row("Bird", Array("Ave","Oiseau"))
    )

    val explodeSchema = StructType(
      List(
        StructField("Animal", StringType),
        StructField("Translation", ArrayType(StringType))
      )
    )

    val rddExplode = session.sparkContext.parallelize(explodeData)

    val dfExplode = session.createDataFrame(rddExplode, explodeSchema)

     var dfFlatten = dfExplode.flatten(col("Translation")).alias("exploded")
                              .select(col("exploded.value").alias("Translation"))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.min function, first used with a column name as an argument and then with a column object.

val df = Seq(1, 3, 10, 1, 3).toDF("value")
val result1 = df.select(min("value"))
val result2 = df.select(min(col("value")))

Sortie

The SMA adds the EWI SPRKSCL1136 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq(1, 3, 10, 1, 3).toDF("value")
/*EWI: SPRKSCL1136 => org.apache.spark.sql.functions.min has a workaround, see documentation for more info*/
val result1 = df.select(min("value"))
/*EWI: SPRKSCL1136 => org.apache.spark.sql.functions.min has a workaround, see documentation for more info*/
val result2 = df.select(min(col("value")))

Correction recommandée

Snowpark has an equivalent min function that receives a column object as an argument. For that reason, the Spark overload that receives a column object as an argument is directly supported by Snowpark and does not require any changes.

For the overload that takes a string argument, you can convert the string into a column object using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq(1, 3, 10, 1, 3).toDF("value")
val result1 = df.select(min(col("value")))
val result2 = df.select(min(col("value")))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Recommandations supplémentaires¶

• Inclure un fichier de projet de configuration dans le dossier d’entrée.

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.tanh function, first used with a column name as an argument and then with a column object.

val df = Seq(-1.0, 0.5, 1.0, 2.0).toDF("value")
val result1 = df.withColumn("tanh_value", tanh("value"))
val result2 = df.withColumn("tanh_value", tanh(col("value")))

Sortie

The SMA adds the EWI SPRKSCL1147 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq(-1.0, 0.5, 1.0, 2.0).toDF("value")
/*EWI: SPRKSCL1147 => org.apache.spark.sql.functions.tanh has a workaround, see documentation for more info*/
val result1 = df.withColumn("tanh_value", tanh("value"))
/*EWI: SPRKSCL1147 => org.apache.spark.sql.functions.tanh has a workaround, see documentation for more info*/
val result2 = df.withColumn("tanh_value", tanh(col("value")))

Correction recommandée

Snowpark has an equivalent tanh function that receives a column object as an argument. For that reason, the Spark overload that receives a column object as an argument is directly supported by Snowpark and does not require any changes.

For the overload that receives a string argument, you can convert the string into a column object using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq(-1.0, 0.5, 1.0, 2.0).toDF("value")
val result1 = df.withColumn("tanh_value", tanh(col("value")))
val result2 = df.withColumn("tanh_value", tanh(col("value")))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.split function that generates this EWI.

val df = Seq("apple,banana,orange", "grape,lemon,lime", "cherry,blueberry,strawberry").toDF("values")
val result1 = df.withColumn("split_values", split(col("values"), ","))
val result2 = df.withColumn("split_values", split(col("values"), ",", 0))

Sortie

The SMA adds the EWI SPRKSCL1116 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq("apple,banana,orange", "grape,lemon,lime", "cherry,blueberry,strawberry").toDF("values")
/*EWI: SPRKSCL1116 => org.apache.spark.sql.functions.split has a workaround, see documentation for more info*/
val result1 = df.withColumn("split_values", split(col("values"), ","))
/*EWI: SPRKSCL1116 => org.apache.spark.sql.functions.split has a workaround, see documentation for more info*/
val result2 = df.withColumn("split_values", split(col("values"), ",", 0))

Correction recommandée

For the Spark overload that receives two arguments, you can convert the second argument into a column object using the com.snowflake.snowpark.functions.lit function as a workaround.

La surcharge qui reçoit trois arguments n’est pas encore prise en charge par Snowpark et il n’y a pas de solution de contournement.

val df = Seq("apple,banana,orange", "grape,lemon,lime", "cherry,blueberry,strawberry").toDF("values")
val result1 = df.withColumn("split_values", split(col("values"), lit(",")))
val result2 = df.withColumn("split_values", split(col("values"), ",", 0)) // This overload is not supported yet

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.corr function, first used with column names as the arguments and then with column objects.

val df = Seq(
  (10.0, 20.0),
  (20.0, 40.0),
  (30.0, 60.0)
).toDF("col1", "col2")

val result1 = df.select(corr("col1", "col2"))
val result2 = df.select(corr(col("col1"), col("col2")))

Sortie

The SMA adds the EWI SPRKSCL1122 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq(
  (10.0, 20.0),
  (20.0, 40.0),
  (30.0, 60.0)
).toDF("col1", "col2")

/*EWI: SPRKSCL1122 => org.apache.spark.sql.functions.corr has a workaround, see documentation for more info*/
val result1 = df.select(corr("col1", "col2"))
/*EWI: SPRKSCL1122 => org.apache.spark.sql.functions.corr has a workaround, see documentation for more info*/
val result2 = df.select(corr(col("col1"), col("col2")))

Correction recommandée

Snowpark has an equivalent corr function that receives two column objects as arguments. For that reason, the Spark overload that receives column objects as arguments is directly supported by Snowpark and does not require any changes.

For the overload that receives two string arguments, you can convert the strings into column objects using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq(
  (10.0, 20.0),
  (20.0, 40.0),
  (30.0, 60.0)
).toDF("col1", "col2")

val result1 = df.select(corr(col("col1"), col("col2")))
val result2 = df.select(corr(col("col1"), col("col2")))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Vous trouverez ci-dessous un exemple de code intégré SQL qui ne peut pas être traité.

spark.sql("CREATE VIEW IF EXISTS My View" + "AS Select * From my Table WHERE date < current_date()")

Sortie

The SMA adds the EWI SPRKSCL1173 to the output code to let you know that the SQL-embedded code can not be processed.

/*EWI: SPRKSCL1173 => SQL embedded code cannot be processed.*/
spark.sql("CREATE VIEW IF EXISTS My View" + "AS Select * From my Table WHERE date < current_date()")

Correction recommandée

Assurez-vous que le code intégré SQL est une chaîne sans interpolations, variables ou concaténations de chaînes.

Recommandations supplémentaires¶

• You can find more information about SQL-embedded here.

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Voici un exemple où l’élément à traiter n’est pas un littéral et ne peut pas être évalué par SMA.

val format_type = "csv"
spark.read.format(format_type).load(path)

Sortie

The SMA adds the EWI SPRKSCL1163 to the output code to let you know that format_type parameter is not a literal and it can not be evaluated by the SMA.

/*EWI: SPRKSCL1163 => format_type is not a literal and can't be evaluated*/
val format_type = "csv"
spark.read.format(format_type).load(path)

Correction recommandée

• Assurez-vous que la valeur de la variable est valide afin d’éviter des comportements inattendus.

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.grouping_id function, first used with multiple column name as arguments and then with column objects.

val df = Seq(
  ("Store1", "Product1", 100),
  ("Store1", "Product2", 150),
  ("Store2", "Product1", 200),
  ("Store2", "Product2", 250)
).toDF("store", "product", "amount")

val result1 = df.cube("store", "product").agg(sum("amount"), grouping_id("store", "product"))
val result2 = df.cube("store", "product").agg(sum("amount"), grouping_id(col("store"), col("product")))

Sortie

The SMA adds the EWI SPRKSCL1132 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq(
  ("Store1", "Product1", 100),
  ("Store1", "Product2", 150),
  ("Store2", "Product1", 200),
  ("Store2", "Product2", 250)
).toDF("store", "product", "amount")

/*EWI: SPRKSCL1132 => org.apache.spark.sql.functions.grouping_id has a workaround, see documentation for more info*/
val result1 = df.cube("store", "product").agg(sum("amount"), grouping_id("store", "product"))
/*EWI: SPRKSCL1132 => org.apache.spark.sql.functions.grouping_id has a workaround, see documentation for more info*/
val result2 = df.cube("store", "product").agg(sum("amount"), grouping_id(col("store"), col("product")))

Correction recommandée

Snowpark has an equivalent grouping_id function that receives multiple column objects as arguments. For that reason, the Spark overload that receives multiple column objects as arguments is directly supported by Snowpark and does not require any changes.

For the overload that receives multiple string arguments, you can convert the strings into column objects using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq(
  ("Store1", "Product1", 100),
  ("Store1", "Product2", 150),
  ("Store2", "Product1", 200),
  ("Store2", "Product2", 250)
).toDF("store", "product", "amount")

val result1 = df.cube("store", "product").agg(sum("amount"), grouping_id(col("store"), col("product")))
val result2 = df.cube("store", "product").agg(sum("amount"), grouping_id(col("store"), col("product")))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.DataFrameWriter.option used to add options to a writer statement.

df.write.format("net.snowflake.spark.snowflake").option("dbtable", tablename)

Sortie

The SMA adds the EWI SPRKSCL1106 to the output code to let you know that the option method is not supported by Snowpark.

df.write.saveAsTable(tablename)
/*EWI: SPRKSCL1106 => Writer option is not supported .option("dbtable", tablename)*/

Correction recommandée

Il n’y a pas de correction recommandée pour ce scénario.

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.kurtosis function that generates this EWI. In this example, the kurtosis function is used to calculate the kurtosis of selected column.

val df = Seq("1", "2", "3").toDF("elements")
val result1 = kurtosis(col("elements"))
val result2 = kurtosis("elements")

Sortie

The SMA adds the EWI SPRKSCL1157 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq("1", "2", "3").toDF("elements")
/*EWI: SPRKSCL1157 => org.apache.spark.sql.functions.kurtosis has a workaround, see documentation for more info*/
val result1 = kurtosis(col("elements"))
/*EWI: SPRKSCL1157 => org.apache.spark.sql.functions.kurtosis has a workaround, see documentation for more info*/
val result2 = kurtosis("elements")

Correction recommandée

Snowpark has an equivalent kurtosis function that receives a column object as an argument. For that reason, the Spark overload that receives a column object as an argument is directly supported by Snowpark and does not require any changes.

For the overload that receives a string argument, you can convert the string into a column object using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq("1", "2", "3").toDF("elements")
val result1 = kurtosis(col("elements"))
val result2 = kurtosis(col("elements"))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.tan function, first used with a column name as an argument and then with a column object.

val df = Seq(math.Pi / 4, math.Pi / 3, math.Pi / 6).toDF("angle")
val result1 = df.withColumn("tan_value", tan("angle"))
val result2 = df.withColumn("tan_value", tan(col("angle")))

Sortie

The SMA adds the EWI SPRKSCL1146 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq(math.Pi / 4, math.Pi / 3, math.Pi / 6).toDF("angle")
/*EWI: SPRKSCL1146 => org.apache.spark.sql.functions.tan has a workaround, see documentation for more info*/
val result1 = df.withColumn("tan_value", tan("angle"))
/*EWI: SPRKSCL1146 => org.apache.spark.sql.functions.tan has a workaround, see documentation for more info*/
val result2 = df.withColumn("tan_value", tan(col("angle")))

Correction recommandée

Snowpark has an equivalent tan function that receives a column object as an argument. For that reason, the Spark overload that receives a column object as an argument is directly supported by Snowpark and does not require any changes.

For the overload that receives a string argument, you can convert the string into a column object using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq(math.Pi / 4, math.Pi / 3, math.Pi / 6).toDF("angle")
val result1 = df.withColumn("tan_value", tan(col("angle")))
val result2 = df.withColumn("tan_value", tan(col("angle")))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.translate function that generates this EWI. In this example, the translate function is used to replace the characters “a”, “e” and “o” in each word with “1”, “2” and “3”, respectively.

val df = Seq("hello", "world", "scala").toDF("word")
val result = df.withColumn("translated_word", translate(col("word"), "aeo", "123"))

Sortie

The SMA adds the EWI SPRKSCL1117 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq("hello", "world", "scala").toDF("word")
/*EWI: SPRKSCL1117 => org.apache.spark.sql.functions.translate has a workaround, see documentation for more info*/
val result = df.withColumn("translated_word", translate(col("word"), "aeo", "123"))

Correction recommandée

As a workaround, you can convert the second and third argument into a column object using the com.snowflake.snowpark.functions.lit function.

val df = Seq("hello", "world", "scala").toDF("word")
val result = df.withColumn("translated_word", translate(col("word"), lit("aeo"), lit("123")))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.cos function, first used with a column name as an argument and then with a column object.

val df = Seq(0.0, Math.PI / 4, Math.PI / 2, Math.PI).toDF("angle_radians")
val result1 = df.withColumn("cosine_value", cos("angle_radians"))
val result2 = df.withColumn("cosine_value", cos(col("angle_radians")))

Sortie

The SMA adds the EWI SPRKSCL1123 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq(0.0, Math.PI / 4, Math.PI / 2, Math.PI).toDF("angle_radians")
/*EWI: SPRKSCL1123 => org.apache.spark.sql.functions.cos has a workaround, see documentation for more info*/
val result1 = df.withColumn("cosine_value", cos("angle_radians"))
/*EWI: SPRKSCL1123 => org.apache.spark.sql.functions.cos has a workaround, see documentation for more info*/
val result2 = df.withColumn("cosine_value", cos(col("angle_radians")))

Correction recommandée

Snowpark has an equivalent cos function that receives a column object as an argument. For that reason, the Spark overload that receives a column object as an argument is directly supported by Snowpark and does not require any changes.

For the overload that receives a string argument, you can convert the string into a column object using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq(0.0, Math.PI / 4, Math.PI / 2, Math.PI).toDF("angle_radians")
val result1 = df.withColumn("cosine_value", cos(col("angle_radians")))
val result2 = df.withColumn("cosine_value", cos(col("angle_radians")))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.types.StructField.apply function that generates this EWI. In this example, the apply function is used to generate and instance of StructField.

val result = StructField("f1", StringType(), True, metadata)

Sortie

The SMA adds the EWI SPRKSCL1172 to the output code to let you know that metadata parameter is not supported by Snowflake.

/*EWI: SPRKSCL1172 => Snowpark does not support StructFiled with metadata parameter.*/
val result = StructField("f1", StringType(), True, metadata)

Correction recommandée

Snowpark has an equivalent com.snowflake.snowpark.types.StructField.apply function that receives three parameters. Then, as workaround, you can try to remove the metadata argument.

val result = StructField("f1", StringType(), True, metadata)

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Recommandations supplémentaires¶

• En guise de solution de contournement, vous pouvez vérifier la taille du fichier s’il est trop lourd pour être traité. Analysez également si l’outil peut y accéder afin d’éviter tout problème d’accès.

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.least function, first used with multiple column name as arguments and then with column objects.

val df = Seq((10, 20, 5), (15, 25, 30), (7, 14, 3)).toDF("value1", "value2", "value3")
val result1 = df.withColumn("least", least("value1", "value2", "value3"))
val result2 = df.withColumn("least", least(col("value1"), col("value2"), col("value3")))

Sortie

The SMA adds the EWI SPRKSCL1133 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq((10, 20, 5), (15, 25, 30), (7, 14, 3)).toDF("value1", "value2", "value3")
/*EWI: SPRKSCL1133 => org.apache.spark.sql.functions.least has a workaround, see documentation for more info*/
val result1 = df.withColumn("least", least("value1", "value2", "value3"))
/*EWI: SPRKSCL1133 => org.apache.spark.sql.functions.least has a workaround, see documentation for more info*/
val result2 = df.withColumn("least", least(col("value1"), col("value2"), col("value3")))

Correction recommandée

Snowpark has an equivalent least function that receives multiple column objects as arguments. For that reason, the Spark overload that receives multiple column objects as arguments is directly supported by Snowpark and does not require any changes.

For the overload that receives multiple string arguments, you can convert the strings into column objects using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq((10, 20, 5), (15, 25, 30), (7, 14, 3)).toDF("value1", "value2", "value3")
val result1 = df.withColumn("least", least(col("value1"), col("value2"), col("value3")))
val result2 = df.withColumn("least", least(col("value1"), col("value2"), col("value3")))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.DataFrameWriter.save used to save the DataFrame content.

df.write.format("net.snowflake.spark.snowflake").save()

Sortie

The SMA adds the EWI SPRKSCL1107 to the output code to let you know that the save method is not supported by Snowpark.

df.write.saveAsTable(tablename)
/*EWI: SPRKSCL1107 => Writer method is not supported .save()*/

Correction recommandée

Il n’y a pas de correction recommandée pour ce scénario.

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.degrees function, first used with a column name as an argument and then with a column object.

val df = Seq(math.Pi, math.Pi / 2, math.Pi / 4, math.Pi / 6).toDF("radians")
val result1 = df.withColumn("degrees", degrees("radians"))
val result2 = df.withColumn("degrees", degrees(col("radians")))

Sortie

The SMA adds the EWI SPRKSCL1156 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq(math.Pi, math.Pi / 2, math.Pi / 4, math.Pi / 6).toDF("radians")
/*EWI: SPRKSCL1156 => org.apache.spark.sql.functions.degrees has a workaround, see documentation for more info*/
val result1 = df.withColumn("degrees", degrees("radians"))
/*EWI: SPRKSCL1156 => org.apache.spark.sql.functions.degrees has a workaround, see documentation for more info*/
val result2 = df.withColumn("degrees", degrees(col("radians")))

Correction recommandée

Snowpark has an equivalent degrees function that receives a column object as an argument. For that reason, the Spark overload that receives a column object as an argument is directly supported by Snowpark and does not require any changes.

For the overload that receives a string argument, you can convert the string into a column object using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq(math.Pi, math.Pi / 2, math.Pi / 4, math.Pi / 6).toDF("radians")
val result1 = df.withColumn("degrees", degrees(col("radians")))
val result2 = df.withColumn("degrees", degrees(col("radians")))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.covar_samp function, first used with column names as the arguments and then with column objects.

val df = Seq(
  (10.0, 20.0),
  (15.0, 25.0),
  (20.0, 30.0),
  (25.0, 35.0),
  (30.0, 40.0)
).toDF("value1", "value2")

val result1 = df.select(covar_samp("value1", "value2").as("sample_covariance"))
val result2 = df.select(covar_samp(col("value1"), col("value2")).as("sample_covariance"))

Sortie

The SMA adds the EWI SPRKSCL1127 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq(
  (10.0, 20.0),
  (15.0, 25.0),
  (20.0, 30.0),
  (25.0, 35.0),
  (30.0, 40.0)
).toDF("value1", "value2")

/*EWI: SPRKSCL1127 => org.apache.spark.sql.functions.covar_samp has a workaround, see documentation for more info*/
val result1 = df.select(covar_samp("value1", "value2").as("sample_covariance"))
/*EWI: SPRKSCL1127 => org.apache.spark.sql.functions.covar_samp has a workaround, see documentation for more info*/
val result2 = df.select(covar_samp(col("value1"), col("value2")).as("sample_covariance"))

Correction recommandée

Snowpark has an equivalent covar_samp function that receives two column objects as arguments. For that reason, the Spark overload that receives two column objects as arguments is directly supported by Snowpark and does not require any changes.

For the overload that receives two string arguments, you can convert the strings into column objects using the com.snowflake.snowpark.functions.col function as a workaround.

val df = Seq(
  (10.0, 20.0),
  (15.0, 25.0),
  (20.0, 30.0),
  (25.0, 35.0),
  (30.0, 40.0)
).toDF("value1", "value2")

val result1 = df.select(covar_samp(col("value1"), col("value2")).as("sample_covariance"))
val result2 = df.select(covar_samp(col("value1"), col("value2")).as("sample_covariance"))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.next_day function, first used with a string as the second argument and then with a column object.

val df = Seq("2024-11-06", "2024-11-13", "2024-11-20").toDF("date")
val result1 = df.withColumn("next_monday", next_day(col("date"), "Mon"))
val result2 = df.withColumn("next_monday", next_day(col("date"), lit("Mon")))

Sortie

The SMA adds the EWI SPRKSCL1113 to the output code to let you know that this function is not fully supported by Snowpark, but it has a workaround.

val df = Seq("2024-11-06", "2024-11-13", "2024-11-20").toDF("date")
/*EWI: SPRKSCL1113 => org.apache.spark.sql.functions.next_day has a workaround, see documentation for more info*/
val result1 = df.withColumn("next_monday", next_day(col("date"), "Mon"))
/*EWI: SPRKSCL1113 => org.apache.spark.sql.functions.next_day has a workaround, see documentation for more info*/
val result2 = df.withColumn("next_monday", next_day(col("date"), lit("Mon")))

Correction recommandée

Snowpark has an equivalent next_day function that receives two column objects as arguments. For that reason, the Spark overload that receives two column objects as arguments is directly supported by Snowpark and does not require any changes.

For the overload that receives a column object and a string, you can convert the string into a column object using the com.snowflake.snowpark.functions.lit function as a workaround.

val df = Seq("2024-11-06", "2024-11-13", "2024-11-20").toDF("date")
val result1 = df.withColumn("next_monday", next_day(col("date"), lit("Mon")))
val result2 = df.withColumn("next_monday", next_day(col("date"), lit("Mon")))

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Vous trouverez ci-dessous un exemple de code Scala non valide d’où l’outil SMA peut sortir.

Class myClass {

    def function1() & = { 1 }

    def function2() = { 2 }

    def function3() = { 3 }

}

Sortie

The SMA adds the EWI SPRKSCL1002 to the output code to let you know that the code of the file has parsing errors, however the SMA can recovery from that error and continue analyzing the code of the file.

class myClass {

    def function1();//EWI: SPRKSCL1002 => Unexpected end of declaration. Failed token: '&' @(3,21).
    & = { 1 }

    def function2() = { 2 }

    def function3() = { 3 }

}

Correction recommandée

Puisque le message identifie l’erreur dans l’instruction, vous pouvez essayer d’identifier la syntaxe non valide et de la supprimer ou de commenter l’instruction pour éviter l’erreur d’analyse.

Class myClass {

    def function1() = { 1 }

    def function2() = { 2 }

    def function3() = { 3 }

}

Class myClass {

    // def function1() & = { 1 }

    def function2() = { 2 }

    def function3() = { 3 }

}

Recommandations supplémentaires¶

• Vérifiez que le code du fichier est un code Scala valide.

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of a function for which the SMA could not determine an appropriate mapping status, and therefore it generated this EWI. In this case, you should assume that notDefinedFunction() is a valid Spark function and the code runs.

val df = session.range(10)
val result = df.notDefinedFunction()

Sortie

The SMA adds the EWI SPRKSCL1142 to the output code to let you know that this element is not defined.

val df = session.range(10)
/*EWI: SPRKSCL1142 => org.apache.spark.sql.DataFrame.notDefinedFunction is not defined*/
val result = df.notDefinedFunction()

Correction recommandée

Pour tenter d’identifier le problème, vous pouvez effectuer les validations suivantes :

• Vérifiez s’il s’agit d’un élément Spark valide.

• Vérifiez que la syntaxe de l’élément est correcte et qu’il est correctement orthographié.

• Vérifiez que vous utilisez une version de Spark prise en charge par SMA.

If this is a valid Spark element, please report that you encountered a conversion error on that particular element using the Report an Issue option of the SMA and include any additional information that you think may be helpful.

Please note that if an element is not defined by the SMA, it does not mean necessarily that it is not supported by Snowpark. You should check the Snowpark Documentation to verify if an equivalent element exist.

Recommandations supplémentaires¶

• For more support, you can email us at sma-support@snowflake.com or post an issue in the SMA.

Scénario¶

Entrée

Below is an example of the org.apache.spark.sql.functions.variance function, first used with a column name as an argument and then with a column object.