Instead of deleting columns, we can select non-duplicate columns. val df = spark.sqlContext.read.cosmosDB(config) df.select(“id”).show() In ETL, data pipeline creation processes and business application development with Spark are normal. These processes involve transforming data to create business value by joining multiple tables/dataframes. Joins between two tables are based on the equality condition of the column junction. Most cases where columns are joined have the same name, which is why we often get an ambiguous column error. Here we will learn how to join to avoid ambiguous column issues. below is the mapping, and the error I get is pyspark.sql.utils.AnalysisException: u`Duplicate column(s) found in data schema: providercolumn;` In many Spark applications, we encounter a known and standard error, that is, an ambiguous column error. This error results from duplicate column names in a dataFrame. This problem with duplicate column names occurs because of joins between multiple dataFrames. Here we focus on solving this problem while linking to spark-scala.
To prevent corruption or data loss, duplicate column names are not allowed. Here`s the same for pyspark: Select or remove duplicate columns from the data frame Should Spark perform partition column deduplication? Or maybe thrown an exception if duplicate columns are detected? I`m trying to read the data from the Elasticsearch index and write it to a Spark data frame, but the index has the same field name with different case-sensitive (case) We see that the print results of the “inner_df” in the following figure show duplicate columns with the same name “dept_id”. The delta table contains duplicate column names. Column names that differ only in their case are considered duplicated. Your Apache Spark job processes a delta table if the job fails with an error message. org.apache.spark.sql.AnalysisException: duplicate column(s) found in data schema: fax; Two columns are duplicated if both columns contain the same data. Determine the list of duplicate columns. I recently noticed that if there are duplicate items in the write.partitionBy() argument, the same partition subdirectory is created multiple times. Duplicate columns in a DataFrame can cause the DataFrame to increase memory consumption and duplicate data. Therefore, duplicate columns in a Spark DataFrame can be removed by following these steps: Delta tables cannot contain duplicate column names. I have a 10GB CSV file in the Hadoop cluster with double columns.
I`m trying to parse it in SparkR, so I`m using the spark-csv package to parse it as a DataFrame: This recipe helps you resolve ambiguous column errors when joining in Spark Scala Last updated: Jul 2022 However, since df has duplicate email columns, there would be an error if I wanted to select this column: You can add a simple line, when you start a Spark session, After you successfully create the Spark session, add that line to set Spark Config. The drop() method can be used to delete one or more columns from a DataFrame in spark. It appears that Spark is not case-sensitive when determining field names. It`s probably a good idea to change the names of these columns if possible, or even tell Spark to ignore one of them so that it reads correctly: es.read.field.exclude = providercolumn If you look at the print results of the resulting dataFrame, we only have one column with “dept_id”. And if you also see the physical execution of this join operation, a step projection is included. When you save a schema, Delta Lake is case-sensitive, but not case-sensitive. The text was successfully updated, but the following errors occurred: We resolve ambiguous column issues caused by associating DataFrames with join conditions for columns with the same name. If you look here, we specify Seq(“dept_id”) as the join condition instead of employeeDF(“dept_id”) === dept_df(“dept_id”). If the columns are case sensitive (“Email” vs “Email”), you can enable case sensitivity: I want to keep the first occurrence of the email column and delete the second, how can I do this? We make the internal connection between “employeeDF” and “dept_df”. The following join statement is commonly used to perform joins. If the above doesn`t work, you can set the header to false, and then use the first row to rename the columns: you can select it by position instead of the selection call.
Spark is case-sensitive, but case-insensitive by default. The best way would be to change the name of the column upstream;) Note: For more information about deleting columns, see To remove multiple columns from a PySpark DataFrame. println(« employee DF ») val employee = Seq((1,””ramu »,3,”2018 »,10001,”M »,25000), (2,”raju »,1,”2010 »,20001,”M »,40000), (3,”mahesh »,1,”2010 »,10001,”M »,35000), (4,”suresh »,2,”2005 »,10001,”M »,45000), (5,”likitha »,2,”2010 »,40001,”F »,35000), (6,”lavanya »,2,”2010 »,50001,”F »,40000), (8,”madhu »,1,”2011 »,50001,” »,40000)) val emp_schema = Seq(« emp_id », « name »,”reporting_head_id »,”year_joined »,”dept_id »,”gender »,”salary ») val employeeDF = employee.toDF(emp_schema:_*) val dept = Seq((« Comptes »,10001), (« Marketing »,20001), (« Finance »,30001), (« Ingénierie »,40001)) val dept_schema = Seq(« department »,”dept_id ») val dept_df = dept.toDF(dept_schema:_*) employeeDF.show() println(« Department DF ») dept_df.show(). Inner Join println(“Inner Join”) val inner_df = employeeDF.join(dept_df,employeeDF(“dept_id”) === dept_df(“dept_id”),”inner”) inner_df.show(false) inner_df.explain() Parquet is case-sensitive when storing and returning column information. The Fax column is not included in the selection. I don`t know what the cause is here and how to fix it. any help please. Can you please help me deal with this scenario? Here we create employeeDF and dept_df, which contains employee-level information.
For employeeDF, column dept_id acts as a foreign key, and for dept_df, column dept_id serves as the primary key. And we use “dept_df” to connect these two dataFrames. If the column names are exactly the same, you must specify the schema manually and ignore the first row to avoid headers: Here we learned the methodology of the join statement to avoid ambiguous column errors due to joins. Here we understand that when the join is executed on columns with the same name, we use Seq(“join_column_name”) as the join condition instead of df1(“join_column_name”) === df2(“join_column_name”). println(“Final df query”) inner_df.select(“emp_id”,”name”,”dept_id”).show() You can also delete the column directly { “INDEXNAME”: { “mappings”: { “Type”: { “properties”: { “Providercolumn”: { “type”: “text”, “fields”: { “keyword”: { “type”: “keyword”, “ignore_above”: 256 } } }, “providercolumn”: { “type”: “text”, “fields”: { “keyword”: { “type”: “keyword”, “ignore_above”: 256 } } }. Here we perform a selection query using the selective columns “emp_id”, “Name”, “dept_id” to print employee records with their department ID. This query fails because the error AnalysisException: Reference `dept_id` is ambiguous, may be: dept_id, dept_id. This topic was automatically closed 28 days after the last response. New responses are no longer allowed. I don`t know if double partitioning a data frame through the same column makes sense in some real-world applications, but it will lead to schema inference issues in tools like AWS Glue Crawler. The above code produces an output directory with this structure: If this behavior is unexpected, I`ll work on a fix. Do you have a question about this project? Sign up for a free GitHub account to open an issue and contact managers and the community.
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