#Vb qb sdk query filter how to
In this tutorial, I’ve explained how to filter rows from Spark DataFrame based on single or multiple conditions and SQL expression using where() function, also learned filtering rows by providing conditions on the array and struct column with Scala examples.Īlternatively, you also use filter() function to filter the rows on DataFrame. (arrayStructureData),arrayStructureSchema)Įxamples explained here are also available at GitHub project for reference. Val arrayStructureSchema = new StructType() Val spark: SparkSession = SparkSession.builder()
#Vb qb sdk query filter code
Source code of Spark DataFrame using where() If your DataFrame consists of nested struct columns, you can use any of the above syntaxes to filter the rows based on the nested column.ĭf.where(df("name.lastname") = "Williams") The below example uses array_contains() SQL function which checks if a value contains in an array if present it returns true otherwise false.ĭf.where(array_contains(df("languages"),"Java")) When you want to filter rows from DataFrame based on value present in an array collection column, you can use the first syntax.
Run the Query method and provide the QueryRequest object that you created in the preceding step. Create an instance of the QueryRequest class and provide query operation parameters. Create an instance of the AmazonDynamoDBClient class. Below is just a simple example, you can extend this with AND(&), OR(||), and NOT(!) conditional expressions as needed.ĭf.where(df("state") = "OH" & df("gender") = "M") The following are the steps to query a table using the low-level AWS SDK for.
To filter rows on DataFrame based on multiple conditions, you case use either Column with a condition or SQL expression. If you are coming from SQL background, you can use that knowledge in Spark to filter DataFrame rows with SQL expressions. Use Column with the condition to filter the rows from DataFrame, using this you can express complex condition by referring column names using col(name), $"colname" dfObject("colname"), this approach is mostly used while working with DataFrames. | |- element: string (containsNull = true) | |- middlename: string (nullable = true)
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