You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 chicago_crime
【Schema】
# Table: IUCR
[
(iucr_no:TEXT, Primary Key, Examples: [031A, 031B, 033A]),
(primary_description:TEXT, Examples: [HOMICIDE, CRIM SEXUAL ASSAULT, BATTERY]),
(secondary_description:TEXT, Examples: [FIRST DEGREE MURDER]),
(index_code:TEXT, Examples: [I, N])
]
# Table: Community_Area
[
(community_area_no:INTEGER, Primary Key, Examples: [1, 2, 3]),
(community_area_name:TEXT, Examples: [Rogers Park, West Ridge, Uptown]),
(side:TEXT, Examples: [Far North , North , Central]),
(population:TEXT, Examples: [54,991, 71,942, 56,362])
]
# Table: FBI_Code
[
(fbi_code_no:TEXT, Primary Key, Examples: [01A, 01B, 04A]),
(title:TEXT, Examples: [Homicide 1st & 2nd Degree]),
(description:TEXT),
(crime_against:TEXT, Examples: [Persons, Property, Society])
]
# Table: Ward
[
(ward_no:INTEGER, Primary Key, Examples: [1, 2, 3]),
(alderman_first_name:TEXT, Examples: [Daniel, Brian, Pat]),
(alderman_last_name:TEXT, Examples: [La Spata, Hopkins, Dowell]),
(alderman_name_suffix:TEXT, Examples: [Jr.]),
(ward_office_address:TEXT, Examples: [1958 N. Milwaukee Ave.]),
(ward_office_zip:TEXT, Examples: [60647, 60622, 60609]),
(ward_email:TEXT),
(ward_office_phone:TEXT, Examples: [872.206.2685, 312.643.2299, 773.373.9273]),
(ward_office_fax:TEXT, Examples: [312.448.8829, 773.536.7296, 773.324.1585]),
(city_hall_office_room:INTEGER, Examples: [200, 305, 300]),
(city_hall_office_phone:TEXT, Examples: [312.744.6836, 312.744.8734, 312.744.2690]),
(city_hall_office_fax:TEXT, Examples: [312.744.6712, 312.744.7738, 312.744.3195]),
(Population:INTEGER, Examples: [56149, 55805, 53039])
]
# Table: Crime
[
(report_no:INTEGER, Primary Key, Examples: [23757, 23758, 23759]),
(case_number:TEXT, Examples: [JB100159, JB100522, JB100839]),
(date:TEXT, Examples: [1/1/2018 2:46, 1/1/2018 11:33, 1/1/2018 18:27]),
(block:TEXT, Examples: [039XX W CORNELIA AVE]),
(iucr_no:TEXT, Examples: [110, 143B, 1310]),
(location_description:TEXT, Examples: [AUTO, APARTMENT, ALLEY]),
(arrest:TEXT, Examples: [FALSE, TRUE]),
(domestic:TEXT, Examples: [FALSE, TRUE]),
(beat:INTEGER, Examples: [1732, 1935, 935]),
(district_no:INTEGER, Examples: [17, 19, 9]),
(ward_no:INTEGER, Examples: [30, 43, 3]),
(community_area_no:INTEGER, Examples: [21, 7, 37]),
(fbi_code_no:TEXT, Examples: [01A, 15, 14]),
(latitude:TEXT, Examples: [41.94456125, 41.92972657, 41.808168]),
(longitude:TEXT, Examples: [-87.72668181, -87.64092074, -87.63333646])
]
# Table: Neighborhood
[
(neighborhood_name:TEXT, Primary Key, Examples: [Albany Park, Altgeld Gardens, Andersonville]),
(community_area_no:INTEGER, Examples: [14, 54, 77])
]
# Table: District
[
(district_no:INTEGER, Primary Key, Examples: [1, 2, 3]),
(district_name:TEXT, Examples: [Central, Wentworth, Grand Crossing]),
(address:TEXT, Examples: [1718 South State Street]),
(zip_code:INTEGER, Examples: [60616, 60609, 60637]),
(commander:TEXT, Examples: [Jake M. Alderden, Joshua Wallace, Eve T. Quarterman]),
(email:TEXT, Examples: [ CAPS001District@chicagopolice.org]),
(phone:TEXT, Examples: [312-745-4290, 312-747-8366, 312-747-8201]),
(fax:TEXT, Examples: [312-745-3694, 312-747-5396, 312-747-5479]),
(tty:TEXT, Examples: [312-745-3693, 312-747-6656, 312-747-9168]),
(twitter:TEXT, Examples: [ ChicagoCAPS01,  ChicagoCAPS02,  ChicagoCAPS03])
]
【Foreign keys】
Crime.ward_no=Ward.ward_no
Crime.iucr_no=IUCR.iucr_no
Crime.district_no=District.district_no
Crime.community_area_no=Community_Area.community_area_no
Crime.fbi_code_no=FBI_Code.fbi_code_no
Neighborhood.community_area_no=Community_Area.community_area_no

    【Question】
    In drug abuse crimes, what percentage is related to cannabis? drug abuse crime refer to title = 'Drug Abuse'; percentage = Divide (Count (secondary_description LIKE '%CANNABIS%'), Count (secondary_description)) * 100

    【Evidence】
    drug abuse crime refer to title = 'Drug Abuse'; percentage = Divide (Count (secondary_description LIKE '%CANNABIS%'), Count (secondary_description)) * 100

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(COUNT(CASE WHEN T1.secondary_description LIKE '%CANNABIS%' THEN T1.secondary_description END) AS REAL) * 100 / COUNT(T1.secondary_description) FROM IUCR AS T1 INNER JOIN Crime AS T2 ON T2.iucr_no = T1.iucr_no INNER JOIN FBI_Code AS T3 ON T3.fbi_code_no = T2.fbi_code_no WHERE T3.title = 'Drug Abuse'