cursor type\loop question

[JD2]

Hello:
In my Hive Metastore, I have 35 tables in database that I want to export in excel. I need help on query that can loop one table at a time export one table to excel.

Any help is appreciated.

Thanking in advance for your kind help.

[Kaniz]

Hi @JD2 , 

1. Retrieve Database and Table Metadata:

   • Use spark.catalog.listDatabases() and spark.catalog.listTables(databaseName) to obtain a list of databases and tables in your Hive Metastore.

2. Iterate and Export:

   • Write a loop to process each database and table. For each table, perform the following steps:

   • from pyspark.sql import SparkSession
     
     # Initialize SparkSession
     spark = SparkSession.builder.appName("ExportTablesToExcel").getOrCreate()
     
     # Get the list of databases
     dbs = spark.catalog.listDatabases()
     
     # Loop over each database
     for db in dbs:
         # Get the list of tables in the database
         tables = spark.catalog.listTables(db.name)
     
         # Loop over each table
         for table in tables:
             # Query the table
             data = spark.table(f"{db.name}.{table.name}")
     
             # Export the data to Excel
             data.write.format("com.crealytics.spark.excel") \
                 .option("header", "true") \
                 .mode("overwrite") \
                 .save(f"/path/to/excel/{table.name}.xlsx")
     

   • This code exports each table to a separate Excel file, where the file name corresponds to the table name. You can customize the code to export the data in a different format or to a different location based on your needs.

     Please make sure to replace /path/to/excel/ with the actual path where you want to save the Excel files. Additionally, you might need to adjust the format and export options according to your preferences and Excel library of choice.

[Kaniz]

Hi @JD2, Certainly, when merging large Delta tables:

1. Use Latest Databricks Runtime:

   • Ensure you're on the latest Databricks Runtime for performance improvements and fixes.

2. Leverage Bucketing and Partitioning:

   • Implement bucketing to improve join and grouping performance.
   • Use partitioning to reduce data scanned during queries.

3. Choose the Right Join Type:

   • Select the appropriate join type based on data size and conditions.
   • Consider broadcast joins for smaller datasets and hash/sort-merge joins for larger ones.

4. Optimize Memory:

   • Increase executor memory if you encounter memory errors.
   • Adjust spark.driver.memory and spark.executor.memory settings in your code.

5. Partition Wisely:

   • Use a suitable number of partitions based on cluster resources and data size.
   • Evenly distributed partitions enhance performance and reduce memory issues.

These practices enhance the performance of your code when merging large Delta tables. Use query plans to identify bottlenecks and fine-tune your code for optimal results.