Spark sql cast to decimal

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import org.apache.spark.sql. {SaveMode, SparkSession} object ReproduceSparkDecimalBug extends App { case class SimpleDecimal (value: BigDecimal) val path = "/tmp/sparkTest" val spark = SparkSession.builder ().master ( "local" ).getOrCreate () import spark.implicits._ spark .sql ( "SELECT CAST (10.12345 AS DECIMAL (38,4)) AS value " ) .write .mode (SaveMode.Overwrite) .parquet (path) // works fine and the dataframe will have a decimal (38,4) val df = spark.read.parquet (path) df. java.lang.ClassCastException: org.apache.spark.sql.types.Decimal cannot be cast to java.lang. Number at org.apache.spark.sql.catalyst.expressions.aggregate.Percentile ... This means that in case an operation causes overflows, the result is the same with the corresponding operation in a Java/Scala program (e.g., if the sum of 2 integers is higher than the maximum value representable, the result is a negative number). On the other hand, Spark SQL returns null for decimal overflows. Spark-sql cast as decimal. SparkSQL function require type Decimal, Default data type for decimal values in Spark-SQL is, well, decimal. If you cast your literals in the query into floats, and use the same UDF, Default data type for decimal values in Spark-SQL is, well, decimal. How to use CAST between SQL and the host language. The key use of CAST is to deal with data types that are available in SQL but not in the host language that you use. The following list offers some examples of these data types: SQL has DECIMAL and NUMERIC, but FORTRAN and Pascal don’t. SQL has FLOAT and REAL, but standard COBOL doesn’t. A mutable implementation of BigDecimal that can hold a Long if values are small enough. The semantics of the fields are as follows: - _precision and _scale represent the SQL precision and scale we are looking for - If decimalVal is set, it represents the whole decimal value - Otherwise, the decimal value is longVal / (10 ** _scale) I imported a large csv file into databricks as a table. I am able to run sql queries on it in a databricks notebook. In my table, I have a column that contains date information in the mm/dd/yyyy format : However, it is specific to SQL Server. In contrast, the CAST() function is a part of ANSI-SQL functions, which is widely available in many other database products. SQL Server CONVERT() function examples. Let’s take some examples of using the CONVERT() function. A) Using the CONVERT() function to convert a decimal to an integer example A mutable implementation of BigDecimal that can hold a Long if values are small enough. The semantics of the fields are as follows: - _precision and _scale represent the SQL precision and scale we are looking for - If decimalVal is set, it represents the whole decimal value - Otherwise, the decimal value is longVal / (10 ** _scale) How to use CAST between SQL and the host language. The key use of CAST is to deal with data types that are available in SQL but not in the host language that you use. The following list offers some examples of these data types: SQL has DECIMAL and NUMERIC, but FORTRAN and Pascal don’t. SQL has FLOAT and REAL, but standard COBOL doesn’t. DataType abstract class is the base type of all built-in data types in Spark SQL, e.g. strings, longs. DataType has two main type families: Atomic Types as an internal type to represent types that are not null , UDTs, arrays, structs, and maps import org.apache.spark.sql.functions.col val df2 = empDF.withColumn("sal",col("sal").cast("Decimal")) .withColumn("hiredate",col("hiredate").cast("Date")) Jul 25, 2019 · Spark Dataframe API also provides date function to_date() which parses Date from String object and converts to Spark DateType format. when dates are in ‘yyyy-MM-dd’ format, spark function auto-cast to DateType by casting rules. When dates are not in specified format this function returns null. Jul 30, 2009 · There is a SQL config 'spark.sql.parser.escapedStringLiterals' that can be used to fallback to the Spark 1.6 behavior regarding string literal parsing. For example, if the config is enabled, the regexp that can match "\abc" is "^\abc$". Currently when using decimal type (BigDecimal in scala case class) there's no way to enforce precision and scale. This is quite critical when saving data - regarding space usage and compatibility with external systems (for example Hive table) because spark saves data as Decimal(38,18) How to cast Decimal columns of dataframe to DoubleType while moving data to Hive using spark ? ... java.math.BigDecimal is not a valid external type for schema of ... Jul 30, 2009 · There is a SQL config 'spark.sql.parser.escapedStringLiterals' that can be used to fallback to the Spark 1.6 behavior regarding string literal parsing. For example, if the config is enabled, the regexp that can match "\abc" is "^\abc$". The key thing to remember is that in Spark RDD/DF are immutable. So once created you can not change them. However there are many situation where you want the column type to be different. E.g By default Spark comes with cars.csv where year column is a String. If you want to use a datetime function you need the column as a Datetime. Since Spark version 1.4 you can apply the cast method with DataType on the column: import org.apache.spark.sql.types.IntegerType val df2 = df.withColumn("yearTmp", df.year.cast(IntegerType)) .drop("year") .withColumnRenamed("yearTmp", "year") If you are using sql expressions you can also do: By using Spark withColumn on a DataFrame and using cast function on a column, we can change datatype of a DataFrame column. The below statement changes the datatype from String to Integer for the “salary” column. You can use cast operation as below: val df = sc.parallelize(Seq(0.000043)).toDF("num") df.createOrReplaceTempView("data") spark.sql("select CAST (num as DECIMAL (8,6)) from data") adjust the precision and scale accordingly. share. DataType abstract class is the base type of all built-in data types in Spark SQL, e.g. strings, longs. DataType has two main type families: Atomic Types as an internal type to represent types that are not null , UDTs, arrays, structs, and maps You can use cast operation as below: val df = sc.parallelize(Seq(0.000043)).toDF("num") df.createOrReplaceTempView("data") spark.sql("select CAST (num as DECIMAL (8,6)) from data") adjust the precision and scale accordingly. share. You can use cast operation as below: val df = sc.parallelize(Seq(0.000043)).toDF("num") df.createOrReplaceTempView("data") spark.sql("select CAST (num as DECIMAL (8,6)) from data") adjust the precision and scale accordingly. share. What changes were proposed in this pull request? HIVE-12063 improved pad decimal numbers with trailing zeros to the scale of the column. The following description is copied from the description of HIVE-12063. [SPARK-17495] [SQL] Support Decimal type in Hive-hash #17056 tejasapatil wants to merge 7 commits into apache : master from tejasapatil : SPARK-17495_decimal Conversation 39 Commits 7 Checks 0 Files changed The key thing to remember is that in Spark RDD/DF are immutable. So once created you can not change them. However there are many situation where you want the column type to be different. E.g By default Spark comes with cars.csv where year column is a String. If you want to use a datetime function you need the column as a Datetime. Spark-sql cast as decimal. SparkSQL function require type Decimal, Default data type for decimal values in Spark-SQL is, well, decimal. If you cast your literals in the query into floats, and use the same UDF, Default data type for decimal values in Spark-SQL is, well, decimal. How to cast Decimal columns of dataframe to DoubleType while moving data to Hive using spark ? ... java.math.BigDecimal is not a valid external type for schema of ... To type cast integer to float in pyspark we will be using cast() function with FloatType() as argument. Let’s see an example of type conversion or casting of integer column to decimal column and integer column to float column in pyspark. Type cast an integer column to decimal column in pyspark; Type cast an integer column to float column in ... Jun 20, 2018 · in combination with dplyr, an R numeric value (i.e. a double value) is explicitly casted as decimal(2,1) and not double. EDIT: You can see this for instance when casting the sparklyr dataframe to a spark dataframe. It holds for plain constant values