merge in sql server table using dataframe

Question

how to use merge statement in 2 dataframe

df1=spark.sql("" sellect cole1,col2 from table1""")

Table2 from sql server

Answer 1

To merge two DataFrames using the SQL MERGE statement in Azure Databricks with Spark, you'll need to :

1. Load your data from SQL Server and other sources into DataFrames.
2. Register the DataFrames as temporary views to use them in SQL statements.
3. Write a MERGE statement using SQL to merge the DataFrames.
4. I am assuming you have one DataFrame loaded from a SQL Server table and another DataFrame created from Spark SQL:

# Load DataFrame from SQL Server

df2 = spark.read \

    .format("jdbc") \

    .option("url", "jdbc:sqlserver://<server_name>;databaseName=<database_name>") \

    .option("dbtable", "Table2") \

    .option("user", "<username>") \

    .option("password", "<password>") \

    .load()

# Load another DataFrame using Spark SQL

df1 = spark.sql("SELECT col1, col2 FROM table1")

Then you register your temp views :

df1.createOrReplaceTempView("df1_view")

df2.createOrReplaceTempView("df2_view")

Using Spark SQL, you can write a MERGE statement. In Spark, you might need to use Delta Lake to perform merge operations effectively.

# Ensure Delta Lake is available

spark.conf.set("spark.databricks.delta.formatCheck.enabled", False)

Now, create Delta tables and perform the merge:

# Create Delta table from df2

df2.write.format("delta").mode("overwrite").saveAsTable("delta_table2")

# Merge df1 into delta_table2

spark.sql("""

    MERGE INTO delta_table2 AS target

    USING df1_view AS source

    ON target.col1 = source.col1

    WHEN MATCHED THEN

      UPDATE SET target.col2 = source.col2

    WHEN NOT MATCHED THEN

      INSERT (col1, col2) VALUES (source.col1, source.col2)

""")