sql server – Romesh Malinga Perera

What-if Analysis with SQL server (Hypothetical Indexes) – Using python

If you are a Database administrator or a developer working with a transaction database, you might have come across this problem

“Is it worthy to build that index?”

Exact answer for that question is only known once you build it. However, luckily SQL server provides you with functionality to check the workload performance under hypothetical indexes (without actually creating them)

You can find more information about hypothetical indexes here.

I will just provide you with a simple python code that will help you with the hypothetical index creation. Example code will compose of 3 parts

Index creation
Enabling the index (unlike the normal indexes you need to enable them before using)
Executing the query under the hypothetical index

Index creation

def hyp_create_index_v2(connection, schema_name, tbl_name, col_names, idx_name, include_cols=()):
    """
    Create an hypothetical index on the given table

    :param connection: sql_connection
    :param schema_name: name of the database schema
    :param tbl_name: name of the database table
    :param col_names: string list of column names
    :param idx_name: name of the index
    :param include_cols: columns that needed to be added as includes
    """
    query = f"CREATE NONCLUSTERED INDEX {idx_name} ON {schema_name}.{tbl_name} ({', '.join(col_names)}) " \
            f"INCLUDE ({', '.join(include_cols)}) WITH STATISTICS_ONLY = -1"
    cursor = connection.cursor()
    cursor.execute(query)
    connection.commit()
    logging.info(f"Added HYP: {idx_name}")

def hyp_create_index_v2(connection, schema_name, tbl_name, col_names, idx_name, include_cols=()):

"""

Create an hypothetical index on the given table

:param connection: sql_connection

:param schema_name: name of the database schema

:param tbl_name: name of the database table

:param col_names: string list of column names

:param idx_name: name of the index

:param include_cols: columns that needed to be added as includes

"""

query = f"CREATE NONCLUSTERED INDEX {idx_name} ON {schema_name}.{tbl_name} ({', '.join(col_names)}) " \

f"INCLUDE ({', '.join(include_cols)}) WITH STATISTICS_ONLY = -1"

cursor = connection.cursor()

cursor.execute(query)

connection.commit()

logging.info(f"Added HYP: {idx_name}")

Enabling the indexes

def hyp_enable_index(connection):
    """
    This enables the hypothetical indexes for the given connection. This will be enabled for a given connection and all
    hypothetical queries must be executed via the same connection
    :param connection: connection for which hypothetical indexes will be enabled
    """
    query = f'''SELECT dbid = Db_id(),
                    objectid = object_id,
                    indid = index_id
                FROM   sys.indexes
                WHERE  is_hypothetical = 1;'''
    cursor = connection.cursor()
    cursor.execute(query)
    result_rows = cursor.fetchall()
    for result_row in result_rows:
        query_2 = f"DBCC AUTOPILOT(0, {result_row[0]}, {result_row[1]}, {result_row[2]})"
        cursor.execute(query_2)

def hyp_enable_index(connection):

"""

This enables the hypothetical indexes for the given connection. This will be enabled for a given connection and all

hypothetical queries must be executed via the same connection

:param connection: connection for which hypothetical indexes will be enabled

"""

query = f'''SELECT dbid = Db_id(),

objectid = object_id,

indid = index_id

FROM sys.indexes

WHERE is_hypothetical = 1;'''

cursor = connection.cursor()

cursor.execute(query)

result_rows = cursor.fetchall()

for result_row in result_rows:

query_2 = f"DBCC AUTOPILOT(0, {result_row[0]}, {result_row[1]}, {result_row[2]})"

cursor.execute(query_2)

Executing the query

def hyp_execute_query(connection, query):
    """
    This hypothetically executes the given query and return the estimated sub tree cost. If required we can add the
    operation cost as well. However, most of the cases operation cost at the top level is 0.

    :param connection: sql_connection
    :param query: query that need to be executed
    :return: estimated sub tree cost
    """
    hyp_enable_index(connection)
    cursor = connection.cursor()
    cursor.execute("SET AUTOPILOT ON")
    cursor.execute(query)
    stat_xml = cursor.fetchone()[0]
    cursor.execute("SET AUTOPILOT OFF")
    query_plan = QueryPlan(stat_xml)
    return query_plan.estimated_sub_tree_cost, query_plan.index_seeks

def hyp_execute_query(connection, query):

"""

This hypothetically executes the given query and return the estimated sub tree cost. If required we can add the

operation cost as well. However, most of the cases operation cost at the top level is 0.

:param connection: sql_connection

:param query: query that need to be executed

:return: estimated sub tree cost

"""

hyp_enable_index(connection)

cursor = connection.cursor()

cursor.execute("SET AUTOPILOT ON")

cursor.execute(query)

stat_xml = cursor.fetchone()[0]

cursor.execute("SET AUTOPILOT OFF")

query_plan = QueryPlan(stat_xml)

return query_plan.estimated_sub_tree_cost, query_plan.index_seeks

Tag Archives: sql server

Microsoft SQL Server 2016 Database with IMDB 2013 Dataset

What-if Analysis with SQL server (Hypothetical Indexes) – Using python

Index creation

Enabling the indexes

Executing the query