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fix: Retroactively add granularity param to charts #12960

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Original file line number Diff line number Diff line change
@@ -0,0 +1,134 @@
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""add granularity to charts where missing

Revision ID: 070c043f2fdb
Revises: 41ce8799acc3
Create Date: 2021-02-04 09:34:13.608891

"""

# revision identifiers, used by Alembic.
revision = "070c043f2fdb"
down_revision = "41ce8799acc3"

import json

from alembic import op
from sqlalchemy import and_, Boolean, Column, Integer, String, Text
from sqlalchemy.ext.declarative import declarative_base

from superset import db

Base = declarative_base()


class Slice(Base):
__tablename__ = "slices"

id = Column(Integer, primary_key=True)
params = Column(Text)
datasource_id = Column(Integer)
datasource_type = Column(String(200))


class SqlaTable(Base):
__tablename__ = "tables"

id = Column(Integer, primary_key=True)
main_dttm_col = Column(String(250))


class TableColumn(Base):
__tablename__ = "table_columns"

id = Column(Integer, primary_key=True)
table_id = Column(Integer)
is_dttm = Column(Boolean)
column_name = Column(String(255))


def upgrade():
"""
Adds the granularity param to charts without it populated. This is required for
time range filtering to work properly. Uses the following approach:

- Find all charts without a granularity or granularity_sqla param.
- Get the dataset that backs the chart.
- If the dataset has the main dttm column set, use it.
- Otherwise, find all the dttm columns in the dataset and use the first one (this
matches the behavior of Explore view on the frontend)
- If no dttm columns exist in the dataset, don't change the chart.
"""
bind = op.get_bind()
session = db.Session(bind=bind)

slices_changed = 0

for slc in (
session.query(Slice)
.filter(
and_(
Slice.datasource_type == "table", Slice.params.notlike('%"granularity%')
)
)
.all()
):
try:
params = json.loads(slc.params)
if "granularity" in params or "granularity_sqla" in params:
continue

table = session.query(SqlaTable).get(slc.datasource_id)
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Is this performant? I wonder if the join should be part of the slice query.

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This ended up only altering 150 slices in our DB (and only got to this step for about 4k) so i'm not sure performance matters that much. It's a trade off between doing the join with a much larger number of slices (200k+) vs. waiting until we get to this step. idk

if not table:
continue

if table.main_dttm_col:
params["granularity"] = table.main_dttm_col
slc.params = json.dumps(params, sort_keys=True)
print(f"Set granularity for slice {slc.id} to {table.main_dttm_col}")
slices_changed += 1
continue

table_columns = (
session.query(TableColumn)
.filter(TableColumn.table_id == table.id)
.filter(TableColumn.is_dttm == True)
.all()
)
if len(table_columns):
params["granularity"] = table_columns[0].column_name
slc.params = json.dumps(params, sort_keys=True)
print(
f"Set granularity for slice {slc.id} to {table_columns[0].column_name}"
)
slices_changed += 1
except Exception as e:
print(e)
print(f"Parsing params for slice {slc.id} failed.")
pass

print(f"{slices_changed} slices altered")
session.commit()
session.close()


def downgrade():
"""
It's impossible to downgrade this migration.
"""
pass