How to compare the schema of two dataframes in SQL statement? - scala

Question

There are many ways to verify the schema of two data frames in spark like here. But I want to verify the schema of two data frames only in SQL, I mean SparkSQL.
Sample query 1:
SELECT DISTINCT target_person FROM INFORMATION_SCHEMA.COLUMNS WHERE COLUMN_NAME IN ('columnA','ColumnB') AND TABLE_SCHEMA='ad_facebook'
Sample query 2:
SELECT count(*) FROM information_schema.columns WHERE table_name = 'ad_facebook'
I know that there is no concept of a database (schema) in spark, but I read about metastore that it contains schema information etc.
Can we write SQL queries like above in SparkSQL?
EDIT:
I am just checking why show create table is not working on spark sql, is it because it's a temp table?
scala> val df1=spark.sql("SHOW SCHEMAS")
df1: org.apache.spark.sql.DataFrame = [databaseName: string]
scala> df1.show
+------------+
|databaseName|
+------------+
| default|
+------------+
scala> val df2=spark.sql("SHOW TABLES in default")
df2: org.apache.spark.sql.DataFrame = [database: string, tableName: string ... 1 more field]
scala> df2.show
+--------+---------+-----------+
|database|tableName|isTemporary|
+--------+---------+-----------+
| | df| true|
+--------+---------+-----------+
scala> val df3=spark.sql("SHOW CREATE TABLE default.df")
org.apache.spark.sql.catalyst.analysis.NoSuchTableException: Table or view 'df' not found in database 'default';
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.requireTableExists(SessionCatalog.scala:180)
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.getTableMetadata(SessionCatalog.scala:398)
at org.apache.spark.sql.execution.command.ShowCreateTableCommand.run(tables.scala:834)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:67)
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:182)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:67)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:623)
... 48 elided

Answer 1

Schema can be queried using DESCRIBE [EXTENDED] [db_name.]table_name
See https://docs.databricks.com/spark/latest/spark-sql/index.html#spark-sql-language-manual

Answer 2

Try this code of extracting each schema and compare. This compares name of column, datatype of column, nullable or not column.
val x = df1.schema.sortBy(x => x.name) // get dataframe 1 schema and sort it base on column name.
val y = df2.schema.sortBy(x => x.name) // // get dataframe 2 schema and sort it base on column name.
val out = x.zip(y).filter(x => x._1 != x._2) // zipping 1st column of df1, df2 ...2nd column of df1,df2 and so on for all columns and their datatypes. And filtering if any mismatch is there
if(out.size == 0) { // size of `out` should be 0 if matching
println("matching")
}
else println("not matching")

Answer 3

We can get the schema in 2 ways in SparkSQL.
Method 1:
spark.sql("desc db_name table_name").show()
This will display only top 20 rows which is exactly similar to the dataframe concept of df.show()
(meaning, any table with more than 20 columns - schema will be shown
only for first 20 columns)
For Ex:
+--------------------+---------+-------+
| col_name|data_type|comment|
+--------------------+---------+-------+
| col1| bigint| null|
| col2| string| null|
| col3| string| null|
+--------------------+---------+-------+
Method 2:
spark.sql("desc db_name table_name").collect().foreach(println)
This will display the complete schema of all the columns.
For Ex:
[col1,bigint,null]
[col2,string,null]
[col3,string,null]