i want to list all the tables in every database in Azure Databricks.

so i want the output to look somewhat like this:

Database | Table_name
Database1 | Table_1
Database1 | Table_2
Database1 | Table_3
Database2 | Table_1
etc..

This is what i have at the moment:

from pyspark.sql.types import *DatabaseDF = spark.sql(f"show databases")
df = spark.sql(f"show Tables FROM {DatabaseDF}")
#df = df.select("databaseName")
#list = [x["databaseName"] for x in df.collect()]print(DatabaseDF)
display(DatabaseDF)df = spark.sql(f"show Tables FROM {schemaName}")
df = df.select("TableName")
list = [x["TableName"] for x in df.collect()]## Iterate through list of schema
for x in list:
###  INPUT Required: Change for target tabletempTable = xdf2 = spark.sql(f"SELECT COUNT(*) FROM {schemaName}.{tempTable}").collect()for x in df2:rowCount = x[0]if rowCount == 0:print(schemaName + "." + tempTable + " has 0 rows")

but i'm not quite getting the results.

There is a catalog property to spark session, probably what you are looking for :

spark.catalog.listDatabases()
spark.catalog.listTables("database_name")

listDatabases returns the list of database you have.
listTables returns for a certain database name, the list of tables.

You can do something like this for example :

[(table.database, table.name)for database in spark.catalog.listDatabases()for table in spark.catalog.listTables(database.name)
]

to get the list of database and tables.

EDIT: (thx @Alex Ott) even if this solution works fine, it is quite slow. Using directly some sql commands like show databases or show tables in ... should do the work faster.