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superset2/superset/db_engine_specs.py
Maxime Beauchemin b839608c32 [sql lab] a better approach at limiting queries (#4947) 
* [sql lab] a better approach at limiting queries

Currently there are two mechanisms that we use to enforce the row
limiting constraints, depending on the database engine:
1. use dbapi's `cursor.fetchmany()`
2. wrap the SQL into a limiting subquery

Method 1 isn't great as it can result in the database server storing
larger than required result sets in memory expecting another fetch
command while we know we don't need that.

Method 2 has a positive side of working with all database engines,
whether they use LIMIT, ROWNUM, TOP or whatever else since sqlalchemy
does the work as specified for the dialect. On the downside though
the query optimizer might not be able to optimize this as much as an
approach that doesn't use a subquery.

Since most modern DBs use the LIMIT syntax, this adds a regex approach
to modify the query and force a LIMIT clause without using a subquery
for the database that support this syntax and uses method 2 for all
others.

* Fixing build

* Fix lint

* Added more tests

* Fix tests
2018-05-14 14:44:05 -05:00

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# -*- coding: utf-8 -*-
# pylint: disable=C,R,W
"""Compatibility layer for different database engines
This modules stores logic specific to different database engines. Things
like time-related functions that are similar but not identical, or
information as to expose certain features or not and how to expose them.
For instance, Hive/Presto supports partitions and have a specific API to
list partitions. Other databases like Vertica also support partitions but
have different API to get to them. Other databases don't support partitions
at all. The classes here will use a common interface to specify all this.
The general idea is to use static classes and an inheritance scheme.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from collections import defaultdict, namedtuple
import inspect
import logging
import os
import re
import textwrap
import time
import boto3
from flask import g
from flask_babel import lazy_gettext as _
import pandas
import sqlalchemy as sqla
from sqlalchemy import select
from sqlalchemy.engine import create_engine
from sqlalchemy.engine.url import make_url
from sqlalchemy.sql import text
from sqlalchemy.sql.expression import TextAsFrom
import sqlparse
import unicodecsv
from werkzeug.utils import secure_filename
from superset import app, cache_util, conf, db, utils
from superset.exceptions import SupersetTemplateException
from superset.utils import QueryStatus
config = app.config
tracking_url_trans = conf.get('TRACKING_URL_TRANSFORMER')
hive_poll_interval = conf.get('HIVE_POLL_INTERVAL')
Grain = namedtuple('Grain', 'name label function duration')
class LimitMethod(object):
"""Enum the ways that limits can be applied"""
FETCH_MANY = 'fetch_many'
WRAP_SQL = 'wrap_sql'
FORCE_LIMIT = 'force_limit'
class BaseEngineSpec(object):
"""Abstract class for database engine specific configurations"""
engine = 'base' # str as defined in sqlalchemy.engine.engine
cursor_execute_kwargs = {}
time_grains = tuple()
time_groupby_inline = False
limit_method = LimitMethod.FORCE_LIMIT
time_secondary_columns = False
inner_joins = True
@classmethod
def fetch_data(cls, cursor, limit):
if cls.limit_method == LimitMethod.FETCH_MANY:
return cursor.fetchmany(limit)
return cursor.fetchall()
@classmethod
def epoch_to_dttm(cls):
raise NotImplementedError()
@classmethod
def epoch_ms_to_dttm(cls):
return cls.epoch_to_dttm().replace('{col}', '({col}/1000.0)')
@classmethod
def extra_table_metadata(cls, database, table_name, schema_name):
"""Returns engine-specific table metadata"""
return {}
@classmethod
def apply_limit_to_sql(cls, sql, limit, database):
"""Alters the SQL statement to apply a LIMIT clause"""
if cls.limit_method == LimitMethod.WRAP_SQL:
sql = sql.strip('\t\n ;')
qry = (
select('*')
.select_from(
TextAsFrom(text(sql), ['*']).alias('inner_qry'),
)
.limit(limit)
)
return database.compile_sqla_query(qry)
elif LimitMethod.FORCE_LIMIT:
no_limit = re.sub(r"""
(?ix) # case insensitive, verbose
\s+ # whitespace
LIMIT\s+\d+ # LIMIT $ROWS
;? # optional semi-colon
(\s|;)*$ # remove trailing spaces tabs or semicolons
""", '', sql)
return '{no_limit} LIMIT {limit}'.format(**locals())
return sql
@staticmethod
def csv_to_df(**kwargs):
kwargs['filepath_or_buffer'] = \
app.config['UPLOAD_FOLDER'] + kwargs['filepath_or_buffer']
kwargs['encoding'] = 'utf-8'
kwargs['iterator'] = True
chunks = pandas.read_csv(**kwargs)
df = pandas.DataFrame()
df = pandas.concat(chunk for chunk in chunks)
return df
@staticmethod
def df_to_db(df, table, **kwargs):
df.to_sql(**kwargs)
table.user_id = g.user.id
table.schema = kwargs['schema']
table.fetch_metadata()
db.session.add(table)
db.session.commit()
@staticmethod
def create_table_from_csv(form, table):
def _allowed_file(filename):
# Only allow specific file extensions as specified in the config
extension = os.path.splitext(filename)[1]
return extension and extension[1:] in app.config['ALLOWED_EXTENSIONS']
filename = secure_filename(form.csv_file.data.filename)
if not _allowed_file(filename):
raise Exception('Invalid file type selected')
kwargs = {
'filepath_or_buffer': filename,
'sep': form.sep.data,
'header': form.header.data if form.header.data else 0,
'index_col': form.index_col.data,
'mangle_dupe_cols': form.mangle_dupe_cols.data,
'skipinitialspace': form.skipinitialspace.data,
'skiprows': form.skiprows.data,
'nrows': form.nrows.data,
'skip_blank_lines': form.skip_blank_lines.data,
'parse_dates': form.parse_dates.data,
'infer_datetime_format': form.infer_datetime_format.data,
'chunksize': 10000,
}
df = BaseEngineSpec.csv_to_df(**kwargs)
df_to_db_kwargs = {
'table': table,
'df': df,
'name': form.name.data,
'con': create_engine(form.con.data.sqlalchemy_uri_decrypted, echo=False),
'schema': form.schema.data,
'if_exists': form.if_exists.data,
'index': form.index.data,
'index_label': form.index_label.data,
'chunksize': 10000,
}
BaseEngineSpec.df_to_db(**df_to_db_kwargs)
@classmethod
def convert_dttm(cls, target_type, dttm):
return "'{}'".format(dttm.strftime('%Y-%m-%d %H:%M:%S'))
@classmethod
@cache_util.memoized_func(
timeout=600,
key=lambda *args, **kwargs: 'db:{}:{}'.format(args[0].id, args[1]))
def fetch_result_sets(cls, db, datasource_type, force=False):
"""Returns the dictionary {schema : [result_set_name]}.
Datasource_type can be 'table' or 'view'.
Empty schema corresponds to the list of full names of the all
tables or views: <schema>.<result_set_name>.
"""
schemas = db.inspector.get_schema_names()
result_sets = {}
all_result_sets = []
for schema in schemas:
if datasource_type == 'table':
result_sets[schema] = sorted(
db.inspector.get_table_names(schema))
elif datasource_type == 'view':
result_sets[schema] = sorted(
db.inspector.get_view_names(schema))
all_result_sets += [
'{}.{}'.format(schema, t) for t in result_sets[schema]]
if all_result_sets:
result_sets[''] = all_result_sets
return result_sets
@classmethod
def handle_cursor(cls, cursor, query, session):
"""Handle a live cursor between the execute and fetchall calls
The flow works without this method doing anything, but it allows
for handling the cursor and updating progress information in the
query object"""
pass
@classmethod
def extract_error_message(cls, e):
"""Extract error message for queries"""
return utils.error_msg_from_exception(e)
@classmethod
def adjust_database_uri(cls, uri, selected_schema):
"""Based on a URI and selected schema, return a new URI
The URI here represents the URI as entered when saving the database,
``selected_schema`` is the schema currently active presumably in
the SQL Lab dropdown. Based on that, for some database engine,
we can return a new altered URI that connects straight to the
active schema, meaning the users won't have to prefix the object
names by the schema name.
Some databases engines have 2 level of namespacing: database and
schema (postgres, oracle, mssql, ...)
For those it's probably better to not alter the database
component of the URI with the schema name, it won't work.
Some database drivers like presto accept '{catalog}/{schema}' in
the database component of the URL, that can be handled here.
"""
return uri
@classmethod
def patch(cls):
pass
@classmethod
def get_schema_names(cls, inspector):
return inspector.get_schema_names()
@classmethod
def get_table_names(cls, schema, inspector):
return sorted(inspector.get_table_names(schema))
@classmethod
def where_latest_partition(
cls, table_name, schema, database, qry, columns=None):
return False
@classmethod
def select_star(cls, my_db, table_name, schema=None, limit=100,
show_cols=False, indent=True, latest_partition=True,
cols=None):
fields = '*'
cols = cols or []
if (show_cols or latest_partition) and not cols:
cols = my_db.get_columns(table_name, schema)
if show_cols:
fields = [sqla.column(c.get('name')) for c in cols]
full_table_name = table_name
if schema:
full_table_name = schema + '.' + table_name
qry = select(fields).select_from(text(full_table_name))
if limit:
qry = qry.limit(limit)
if latest_partition:
partition_query = cls.where_latest_partition(
table_name, schema, my_db, qry, columns=cols)
if partition_query != False: # noqa
qry = partition_query
sql = my_db.compile_sqla_query(qry)
if indent:
sql = sqlparse.format(sql, reindent=True)
return sql
@classmethod
def modify_url_for_impersonation(cls, url, impersonate_user, username):
"""
Modify the SQL Alchemy URL object with the user to impersonate if applicable.
:param url: SQLAlchemy URL object
:param impersonate_user: Bool indicating if impersonation is enabled
:param username: Effective username
"""
if impersonate_user is not None and username is not None:
url.username = username
@classmethod
def get_configuration_for_impersonation(cls, uri, impersonate_user, username):
"""
Return a configuration dictionary that can be merged with other configs
that can set the correct properties for impersonating users
:param uri: URI string
:param impersonate_user: Bool indicating if impersonation is enabled
:param username: Effective username
:return: Dictionary with configs required for impersonation
"""
return {}
@classmethod
def get_normalized_column_names(cls, cursor_description):
columns = cursor_description if cursor_description else []
return [cls.normalize_column_name(col[0]) for col in columns]
@staticmethod
def normalize_column_name(column_name):
return column_name
class PostgresBaseEngineSpec(BaseEngineSpec):
""" Abstract class for Postgres 'like' databases """
engine = ''
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('second', _('second'),
"DATE_TRUNC('second', {col}) AT TIME ZONE 'UTC'", 'PT1S'),
Grain('minute', _('minute'),
"DATE_TRUNC('minute', {col}) AT TIME ZONE 'UTC'", 'PT1M'),
Grain('hour', _('hour'),
"DATE_TRUNC('hour', {col}) AT TIME ZONE 'UTC'", 'PT1H'),
Grain('day', _('day'),
"DATE_TRUNC('day', {col}) AT TIME ZONE 'UTC'", 'P1D'),
Grain('week', _('week'),
"DATE_TRUNC('week', {col}) AT TIME ZONE 'UTC'", 'P1W'),
Grain('month', _('month'),
"DATE_TRUNC('month', {col}) AT TIME ZONE 'UTC'", 'P1M'),
Grain('quarter', _('quarter'),
"DATE_TRUNC('quarter', {col}) AT TIME ZONE 'UTC'", 'P0.25Y'),
Grain('year', _('year'),
"DATE_TRUNC('year', {col}) AT TIME ZONE 'UTC'", 'P1Y'),
)
@classmethod
def fetch_data(cls, cursor, limit):
if not cursor.description:
return []
if cls.limit_method == LimitMethod.FETCH_MANY:
return cursor.fetchmany(limit)
return cursor.fetchall()
@classmethod
def epoch_to_dttm(cls):
return "(timestamp 'epoch' + {col} * interval '1 second')"
@classmethod
def convert_dttm(cls, target_type, dttm):
return "'{}'".format(dttm.strftime('%Y-%m-%d %H:%M:%S'))
class PostgresEngineSpec(PostgresBaseEngineSpec):
engine = 'postgresql'
@classmethod
def get_table_names(cls, schema, inspector):
"""Need to consider foreign tables for PostgreSQL"""
tables = inspector.get_table_names(schema)
tables.extend(inspector.get_foreign_table_names(schema))
return sorted(tables)
class SnowflakeEngineSpec(PostgresBaseEngineSpec):
engine = 'snowflake'
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('second', _('second'), "DATE_TRUNC('SECOND', {col})", 'PT1S'),
Grain('minute', _('minute'), "DATE_TRUNC('MINUTE', {col})", 'PT1M'),
Grain('hour', _('hour'), "DATE_TRUNC('HOUR', {col})", 'PT1H'),
Grain('day', _('day'), "DATE_TRUNC('DAY', {col})", 'P1D'),
Grain('week', _('week'), "DATE_TRUNC('WEEK', {col})", 'P1W'),
Grain('month', _('month'), "DATE_TRUNC('MONTH', {col})", 'P1M'),
Grain('quarter', _('quarter'), "DATE_TRUNC('QUARTER', {col})", 'P0.25Y'),
Grain('year', _('year'), "DATE_TRUNC('YEAR', {col})", 'P1Y'),
)
@staticmethod
def normalize_column_name(column_name):
return column_name.lower()
class VerticaEngineSpec(PostgresBaseEngineSpec):
engine = 'vertica'
class RedshiftEngineSpec(PostgresBaseEngineSpec):
engine = 'redshift'
class OracleEngineSpec(PostgresBaseEngineSpec):
engine = 'oracle'
limit_method = LimitMethod.WRAP_SQL
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('minute', _('minute'), "TRUNC(TO_DATE({col}), 'MI')", 'PT1M'),
Grain('hour', _('hour'), "TRUNC(TO_DATE({col}), 'HH')", 'PT1H'),
Grain('day', _('day'), "TRUNC(TO_DATE({col}), 'DDD')", 'P1D'),
Grain('week', _('week'), "TRUNC(TO_DATE({col}), 'WW')", 'P1W'),
Grain('month', _('month'), "TRUNC(TO_DATE({col}), 'MONTH')", 'P1M'),
Grain('quarter', _('quarter'), "TRUNC(TO_DATE({col}), 'Q')", 'P0.25Y'),
Grain('year', _('year'), "TRUNC(TO_DATE({col}), 'YEAR')", 'P1Y'),
)
@classmethod
def convert_dttm(cls, target_type, dttm):
return (
"""TO_TIMESTAMP('{}', 'YYYY-MM-DD"T"HH24:MI:SS.ff6')"""
).format(dttm.isoformat())
@staticmethod
def normalize_column_name(column_name):
return column_name.lower()
class Db2EngineSpec(BaseEngineSpec):
engine = 'ibm_db_sa'
limit_method = LimitMethod.WRAP_SQL
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('second', _('second'),
'CAST({col} as TIMESTAMP)'
' - MICROSECOND({col}) MICROSECONDS',
'PT1S'),
Grain('minute', _('minute'),
'CAST({col} as TIMESTAMP)'
' - SECOND({col}) SECONDS'
' - MICROSECOND({col}) MICROSECONDS',
'PT1M'),
Grain('hour', _('hour'),
'CAST({col} as TIMESTAMP)'
' - MINUTE({col}) MINUTES'
' - SECOND({col}) SECONDS'
' - MICROSECOND({col}) MICROSECONDS ',
'PT1H'),
Grain('day', _('day'),
'CAST({col} as TIMESTAMP)'
' - HOUR({col}) HOURS'
' - MINUTE({col}) MINUTES'
' - SECOND({col}) SECONDS'
' - MICROSECOND({col}) MICROSECONDS ',
'P1D'),
Grain('week', _('week'),
'{col} - (DAYOFWEEK({col})) DAYS',
'P1W'),
Grain('month', _('month'),
'{col} - (DAY({col})-1) DAYS',
'P1M'),
Grain('quarter', _('quarter'),
'{col} - (DAY({col})-1) DAYS'
' - (MONTH({col})-1) MONTHS'
' + ((QUARTER({col})-1) * 3) MONTHS',
'P0.25Y'),
Grain('year', _('year'),
'{col} - (DAY({col})-1) DAYS'
' - (MONTH({col})-1) MONTHS',
'P1Y'),
)
@classmethod
def epoch_to_dttm(cls):
return "(TIMESTAMP('1970-01-01', '00:00:00') + {col} SECONDS)"
@classmethod
def convert_dttm(cls, target_type, dttm):
return "'{}'".format(dttm.strftime('%Y-%m-%d-%H.%M.%S'))
class SqliteEngineSpec(BaseEngineSpec):
engine = 'sqlite'
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('hour', _('hour'),
"DATETIME(STRFTIME('%Y-%m-%dT%H:00:00', {col}))",
'PT1H'),
Grain('day', _('day'), 'DATE({col})', 'P1D'),
Grain('week', _('week'),
"DATE({col}, -strftime('%W', {col}) || ' days')",
'P1W'),
Grain('month', _('month'),
"DATE({col}, -strftime('%d', {col}) || ' days', '+1 day')",
'P1M'),
Grain('week_ending_saturday', _('week_ending_saturday'),
"DATE({col}, 'weekday 6')",
'P1W/1970-01-03T00:00:00Z'),
Grain('week_start_sunday', _('week_start_sunday'),
"DATE({col}, 'weekday 0', '-7 days')",
'1969-12-28T00:00:00Z/P1W'),
)
@classmethod
def epoch_to_dttm(cls):
return "datetime({col}, 'unixepoch')"
@classmethod
@cache_util.memoized_func(
timeout=600,
key=lambda *args, **kwargs: 'db:{}:{}'.format(args[0].id, args[1]))
def fetch_result_sets(cls, db, datasource_type, force=False):
schemas = db.inspector.get_schema_names()
result_sets = {}
all_result_sets = []
schema = schemas[0]
if datasource_type == 'table':
result_sets[schema] = sorted(db.inspector.get_table_names())
elif datasource_type == 'view':
result_sets[schema] = sorted(db.inspector.get_view_names())
all_result_sets += [
'{}.{}'.format(schema, t) for t in result_sets[schema]]
if all_result_sets:
result_sets[''] = all_result_sets
return result_sets
@classmethod
def convert_dttm(cls, target_type, dttm):
iso = dttm.isoformat().replace('T', ' ')
if '.' not in iso:
iso += '.000000'
return "'{}'".format(iso)
@classmethod
def get_table_names(cls, schema, inspector):
"""Need to disregard the schema for Sqlite"""
return sorted(inspector.get_table_names())
class MySQLEngineSpec(BaseEngineSpec):
engine = 'mysql'
cursor_execute_kwargs = {'args': {}}
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('second', _('second'), 'DATE_ADD(DATE({col}), '
'INTERVAL (HOUR({col})*60*60 + MINUTE({col})*60'
' + SECOND({col})) SECOND)',
'PT1S'),
Grain('minute', _('minute'), 'DATE_ADD(DATE({col}), '
'INTERVAL (HOUR({col})*60 + MINUTE({col})) MINUTE)',
'PT1M'),
Grain('hour', _('hour'), 'DATE_ADD(DATE({col}), '
'INTERVAL HOUR({col}) HOUR)',
'PT1H'),
Grain('day', _('day'), 'DATE({col})', 'P1D'),
Grain('week', _('week'), 'DATE(DATE_SUB({col}, '
'INTERVAL DAYOFWEEK({col}) - 1 DAY))',
'P1W'),
Grain('month', _('month'), 'DATE(DATE_SUB({col}, '
'INTERVAL DAYOFMONTH({col}) - 1 DAY))',
'P1M'),
Grain('quarter', _('quarter'), 'MAKEDATE(YEAR({col}), 1) '
'+ INTERVAL QUARTER({col}) QUARTER - INTERVAL 1 QUARTER',
'P0.25Y'),
Grain('year', _('year'), 'DATE(DATE_SUB({col}, '
'INTERVAL DAYOFYEAR({col}) - 1 DAY))',
'P1Y'),
Grain('week_start_monday', _('week_start_monday'),
'DATE(DATE_SUB({col}, '
'INTERVAL DAYOFWEEK(DATE_SUB({col}, INTERVAL 1 DAY)) - 1 DAY))',
'P1W'),
)
@classmethod
def convert_dttm(cls, target_type, dttm):
if target_type.upper() in ('DATETIME', 'DATE'):
return "STR_TO_DATE('{}', '%Y-%m-%d %H:%i:%s')".format(
dttm.strftime('%Y-%m-%d %H:%M:%S'))
return "'{}'".format(dttm.strftime('%Y-%m-%d %H:%M:%S'))
@classmethod
def adjust_database_uri(cls, uri, selected_schema=None):
if selected_schema:
uri.database = selected_schema
return uri
@classmethod
def epoch_to_dttm(cls):
return 'from_unixtime({col})'
@classmethod
def extract_error_message(cls, e):
"""Extract error message for queries"""
message = str(e)
try:
if isinstance(e.args, tuple) and len(e.args) > 1:
message = e.args[1]
except Exception:
pass
return message
class PrestoEngineSpec(BaseEngineSpec):
engine = 'presto'
cursor_execute_kwargs = {'parameters': None}
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('second', _('second'),
"date_trunc('second', CAST({col} AS TIMESTAMP))",
'PT1S'),
Grain('minute', _('minute'),
"date_trunc('minute', CAST({col} AS TIMESTAMP))",
'PT1M'),
Grain('hour', _('hour'),
"date_trunc('hour', CAST({col} AS TIMESTAMP))",
'PT1H'),
Grain('day', _('day'),
"date_trunc('day', CAST({col} AS TIMESTAMP))",
'P1D'),
Grain('week', _('week'),
"date_trunc('week', CAST({col} AS TIMESTAMP))",
'P1W'),
Grain('month', _('month'),
"date_trunc('month', CAST({col} AS TIMESTAMP))",
'P1M'),
Grain('quarter', _('quarter'),
"date_trunc('quarter', CAST({col} AS TIMESTAMP))",
'P0.25Y'),
Grain('week_ending_saturday', _('week_ending_saturday'),
"date_add('day', 5, date_trunc('week', date_add('day', 1, "
'CAST({col} AS TIMESTAMP))))',
'P1W/1970-01-03T00:00:00Z'),
Grain('week_start_sunday', _('week_start_sunday'),
"date_add('day', -1, date_trunc('week', "
"date_add('day', 1, CAST({col} AS TIMESTAMP))))",
'1969-12-28T00:00:00Z/P1W'),
Grain('year', _('year'),
"date_trunc('year', CAST({col} AS TIMESTAMP))",
'P1Y'),
)
@classmethod
def adjust_database_uri(cls, uri, selected_schema=None):
database = uri.database
if selected_schema and database:
if '/' in database:
database = database.split('/')[0] + '/' + selected_schema
else:
database += '/' + selected_schema
uri.database = database
return uri
@classmethod
def convert_dttm(cls, target_type, dttm):
tt = target_type.upper()
if tt == 'DATE':
return "from_iso8601_date('{}')".format(dttm.isoformat()[:10])
if tt == 'TIMESTAMP':
return "from_iso8601_timestamp('{}')".format(dttm.isoformat())
return "'{}'".format(dttm.strftime('%Y-%m-%d %H:%M:%S'))
@classmethod
def epoch_to_dttm(cls):
return 'from_unixtime({col})'
@classmethod
@cache_util.memoized_func(
timeout=600,
key=lambda *args, **kwargs: 'db:{}:{}'.format(args[0].id, args[1]))
def fetch_result_sets(cls, db, datasource_type, force=False):
"""Returns the dictionary {schema : [result_set_name]}.
Datasource_type can be 'table' or 'view'.
Empty schema corresponds to the list of full names of the all
tables or views: <schema>.<result_set_name>.
"""
result_set_df = db.get_df(
"""SELECT table_schema, table_name FROM INFORMATION_SCHEMA.{}S
ORDER BY concat(table_schema, '.', table_name)""".format(
datasource_type.upper(),
),
None)
result_sets = defaultdict(list)
for unused, row in result_set_df.iterrows():
result_sets[row['table_schema']].append(row['table_name'])
result_sets[''].append('{}.{}'.format(
row['table_schema'], row['table_name']))
return result_sets
@classmethod
def extra_table_metadata(cls, database, table_name, schema_name):
indexes = database.get_indexes(table_name, schema_name)
if not indexes:
return {}
cols = indexes[0].get('column_names', [])
full_table_name = table_name
if schema_name and '.' not in table_name:
full_table_name = '{}.{}'.format(schema_name, table_name)
pql = cls._partition_query(full_table_name)
col_name, latest_part = cls.latest_partition(
table_name, schema_name, database, show_first=True)
return {
'partitions': {
'cols': cols,
'latest': {col_name: latest_part},
'partitionQuery': pql,
},
}
@classmethod
def handle_cursor(cls, cursor, query, session):
"""Updates progress information"""
logging.info('Polling the cursor for progress')
polled = cursor.poll()
# poll returns dict -- JSON status information or ``None``
# if the query is done
# https://github.com/dropbox/PyHive/blob/
# b34bdbf51378b3979eaf5eca9e956f06ddc36ca0/pyhive/presto.py#L178
while polled:
# Update the object and wait for the kill signal.
stats = polled.get('stats', {})
query = session.query(type(query)).filter_by(id=query.id).one()
if query.status in [QueryStatus.STOPPED, QueryStatus.TIMED_OUT]:
cursor.cancel()
break
if stats:
completed_splits = float(stats.get('completedSplits'))
total_splits = float(stats.get('totalSplits'))
if total_splits and completed_splits:
progress = 100 * (completed_splits / total_splits)
logging.info(
'Query progress: {} / {} '
'splits'.format(completed_splits, total_splits))
if progress > query.progress:
query.progress = progress
session.commit()
time.sleep(1)
logging.info('Polling the cursor for progress')
polled = cursor.poll()
@classmethod
def extract_error_message(cls, e):
if (
hasattr(e, 'orig') and
type(e.orig).__name__ == 'DatabaseError' and
isinstance(e.orig[0], dict)):
error_dict = e.orig[0]
return '{} at {}: {}'.format(
error_dict.get('errorName'),
error_dict.get('errorLocation'),
error_dict.get('message'),
)
if (
type(e).__name__ == 'DatabaseError' and
hasattr(e, 'args') and
len(e.args) > 0
):
error_dict = e.args[0]
return error_dict.get('message')
return utils.error_msg_from_exception(e)
@classmethod
def _partition_query(
cls, table_name, limit=0, order_by=None, filters=None):
"""Returns a partition query
:param table_name: the name of the table to get partitions from
:type table_name: str
:param limit: the number of partitions to be returned
:type limit: int
:param order_by: a list of tuples of field name and a boolean
that determines if that field should be sorted in descending
order
:type order_by: list of (str, bool) tuples
:param filters: a list of filters to apply
:param filters: dict of field name and filter value combinations
"""
limit_clause = 'LIMIT {}'.format(limit) if limit else ''
order_by_clause = ''
if order_by:
l = [] # noqa: E741
for field, desc in order_by:
l.append(field + ' DESC' if desc else '')
order_by_clause = 'ORDER BY ' + ', '.join(l)
where_clause = ''
if filters:
l = [] # noqa: E741
for field, value in filters.items():
l.append("{field} = '{value}'".format(**locals()))
where_clause = 'WHERE ' + ' AND '.join(l)
sql = textwrap.dedent("""\
SHOW PARTITIONS FROM {table_name}
{where_clause}
{order_by_clause}
{limit_clause}
""").format(**locals())
return sql
@classmethod
def _latest_partition_from_df(cls, df):
recs = df.to_records(index=False)
if recs:
return recs[0][0]
@classmethod
def latest_partition(cls, table_name, schema, database, show_first=False):
"""Returns col name and the latest (max) partition value for a table
:param table_name: the name of the table
:type table_name: str
:param schema: schema / database / namespace
:type schema: str
:param database: database query will be run against
:type database: models.Database
:param show_first: displays the value for the first partitioning key
if there are many partitioning keys
:type show_first: bool
>>> latest_partition('foo_table')
'2018-01-01'
"""
indexes = database.get_indexes(table_name, schema)
if len(indexes[0]['column_names']) < 1:
raise SupersetTemplateException(
'The table should have one partitioned field')
elif not show_first and len(indexes[0]['column_names']) > 1:
raise SupersetTemplateException(
'The table should have a single partitioned field '
'to use this function. You may want to use '
'`presto.latest_sub_partition`')
part_field = indexes[0]['column_names'][0]
sql = cls._partition_query(table_name, 1, [(part_field, True)])
df = database.get_df(sql, schema)
return part_field, cls._latest_partition_from_df(df)
@classmethod
def latest_sub_partition(cls, table_name, schema, database, **kwargs):
"""Returns the latest (max) partition value for a table
A filtering criteria should be passed for all fields that are
partitioned except for the field to be returned. For example,
if a table is partitioned by (``ds``, ``event_type`` and
``event_category``) and you want the latest ``ds``, you'll want
to provide a filter as keyword arguments for both
``event_type`` and ``event_category`` as in
``latest_sub_partition('my_table',
event_category='page', event_type='click')``
:param table_name: the name of the table, can be just the table
name or a fully qualified table name as ``schema_name.table_name``
:type table_name: str
:param schema: schema / database / namespace
:type schema: str
:param database: database query will be run against
:type database: models.Database
:param kwargs: keyword arguments define the filtering criteria
on the partition list. There can be many of these.
:type kwargs: str
>>> latest_sub_partition('sub_partition_table', event_type='click')
'2018-01-01'
"""
indexes = database.get_indexes(table_name, schema)
part_fields = indexes[0]['column_names']
for k in kwargs.keys():
if k not in k in part_fields:
msg = 'Field [{k}] is not part of the portioning key'
raise SupersetTemplateException(msg)
if len(kwargs.keys()) != len(part_fields) - 1:
msg = (
'A filter needs to be specified for {} out of the '
'{} fields.'
).format(len(part_fields) - 1, len(part_fields))
raise SupersetTemplateException(msg)
for field in part_fields:
if field not in kwargs.keys():
field_to_return = field
sql = cls._partition_query(
table_name, 1, [(field_to_return, True)], kwargs)
df = database.get_df(sql, schema)
if df.empty:
return ''
return df.to_dict()[field_to_return][0]
class HiveEngineSpec(PrestoEngineSpec):
"""Reuses PrestoEngineSpec functionality."""
engine = 'hive'
cursor_execute_kwargs = {'async': True}
# Scoping regex at class level to avoid recompiling
# 17/02/07 19:36:38 INFO ql.Driver: Total jobs = 5
jobs_stats_r = re.compile(
r'.*INFO.*Total jobs = (?P<max_jobs>[0-9]+)')
# 17/02/07 19:37:08 INFO ql.Driver: Launching Job 2 out of 5
launching_job_r = re.compile(
'.*INFO.*Launching Job (?P<job_number>[0-9]+) out of '
'(?P<max_jobs>[0-9]+)')
# 17/02/07 19:36:58 INFO exec.Task: 2017-02-07 19:36:58,152 Stage-18
# map = 0%, reduce = 0%
stage_progress_r = re.compile(
r'.*INFO.*Stage-(?P<stage_number>[0-9]+).*'
r'map = (?P<map_progress>[0-9]+)%.*'
r'reduce = (?P<reduce_progress>[0-9]+)%.*')
@classmethod
def patch(cls):
from pyhive import hive
from superset.db_engines import hive as patched_hive
from TCLIService import (
constants as patched_constants,
ttypes as patched_ttypes,
TCLIService as patched_TCLIService)
hive.TCLIService = patched_TCLIService
hive.constants = patched_constants
hive.ttypes = patched_ttypes
hive.Cursor.fetch_logs = patched_hive.fetch_logs
@classmethod
@cache_util.memoized_func(
timeout=600,
key=lambda *args, **kwargs: 'db:{}:{}'.format(args[0].id, args[1]))
def fetch_result_sets(cls, db, datasource_type, force=False):
return BaseEngineSpec.fetch_result_sets(
db, datasource_type, force=force)
@staticmethod
def create_table_from_csv(form, table):
"""Uploads a csv file and creates a superset datasource in Hive."""
def get_column_names(filepath):
with open(filepath, 'rb') as f:
return next(unicodecsv.reader(f, encoding='utf-8-sig'))
table_name = form.name.data
filename = form.csv_file.data.filename
bucket_path = app.config['CSV_TO_HIVE_UPLOAD_S3_BUCKET']
if not bucket_path:
logging.info('No upload bucket specified')
raise Exception(
'No upload bucket specified. You can specify one in the config file.')
upload_prefix = app.config['CSV_TO_HIVE_UPLOAD_DIRECTORY']
dest_path = os.path.join(table_name, filename)
upload_path = app.config['UPLOAD_FOLDER'] + \
secure_filename(form.csv_file.data.filename)
column_names = get_column_names(upload_path)
schema_definition = ', '.join(
[s + ' STRING ' for s in column_names])
s3 = boto3.client('s3')
location = os.path.join('s3a://', bucket_path, upload_prefix, table_name)
s3.upload_file(
upload_path, bucket_path,
os.path.join(upload_prefix, table_name, filename))
sql = """CREATE EXTERNAL TABLE {table_name} ( {schema_definition} )
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS
TEXTFILE LOCATION '{location}'
tblproperties ('skip.header.line.count'='1')""".format(**locals())
logging.info(form.con.data)
engine = create_engine(form.con.data.sqlalchemy_uri_decrypted)
engine.execute(sql)
@classmethod
def convert_dttm(cls, target_type, dttm):
tt = target_type.upper()
if tt == 'DATE':
return "CAST('{}' AS DATE)".format(dttm.isoformat()[:10])
elif tt == 'TIMESTAMP':
return "CAST('{}' AS TIMESTAMP)".format(
dttm.strftime('%Y-%m-%d %H:%M:%S'))
return "'{}'".format(dttm.strftime('%Y-%m-%d %H:%M:%S'))
@classmethod
def adjust_database_uri(cls, uri, selected_schema=None):
if selected_schema:
uri.database = selected_schema
return uri
@classmethod
def extract_error_message(cls, e):
try:
msg = e.message.status.errorMessage
except Exception:
msg = str(e)
return msg
@classmethod
def progress(cls, log_lines):
total_jobs = 1 # assuming there's at least 1 job
current_job = 1
stages = {}
for line in log_lines:
match = cls.jobs_stats_r.match(line)
if match:
total_jobs = int(match.groupdict()['max_jobs']) or 1
match = cls.launching_job_r.match(line)
if match:
current_job = int(match.groupdict()['job_number'])
total_jobs = int(match.groupdict()['max_jobs']) or 1
stages = {}
match = cls.stage_progress_r.match(line)
if match:
stage_number = int(match.groupdict()['stage_number'])
map_progress = int(match.groupdict()['map_progress'])
reduce_progress = int(match.groupdict()['reduce_progress'])
stages[stage_number] = (map_progress + reduce_progress) / 2
logging.info(
'Progress detail: {}, '
'current job {}, '
'total jobs: {}'.format(stages, current_job, total_jobs))
stage_progress = sum(
stages.values()) / len(stages.values()) if stages else 0
progress = (
100 * (current_job - 1) / total_jobs + stage_progress / total_jobs
)
return int(progress)
@classmethod
def get_tracking_url(cls, log_lines):
lkp = 'Tracking URL = '
for line in log_lines:
if lkp in line:
return line.split(lkp)[1]
@classmethod
def handle_cursor(cls, cursor, query, session):
"""Updates progress information"""
from pyhive import hive
unfinished_states = (
hive.ttypes.TOperationState.INITIALIZED_STATE,
hive.ttypes.TOperationState.RUNNING_STATE,
)
polled = cursor.poll()
last_log_line = 0
tracking_url = None
job_id = None
while polled.operationState in unfinished_states:
query = session.query(type(query)).filter_by(id=query.id).one()
if query.status == QueryStatus.STOPPED:
cursor.cancel()
break
log = cursor.fetch_logs() or ''
if log:
log_lines = log.splitlines()
progress = cls.progress(log_lines)
logging.info('Progress total: {}'.format(progress))
needs_commit = False
if progress > query.progress:
query.progress = progress
needs_commit = True
if not tracking_url:
tracking_url = cls.get_tracking_url(log_lines)
if tracking_url:
job_id = tracking_url.split('/')[-2]
logging.info(
'Found the tracking url: {}'.format(tracking_url))
tracking_url = tracking_url_trans(tracking_url)
logging.info(
'Transformation applied: {}'.format(tracking_url))
query.tracking_url = tracking_url
logging.info('Job id: {}'.format(job_id))
needs_commit = True
if job_id and len(log_lines) > last_log_line:
# Wait for job id before logging things out
# this allows for prefixing all log lines and becoming
# searchable in something like Kibana
for l in log_lines[last_log_line:]:
logging.info('[{}] {}'.format(job_id, l))
last_log_line = len(log_lines)
if needs_commit:
session.commit()
time.sleep(hive_poll_interval)
polled = cursor.poll()
@classmethod
def where_latest_partition(
cls, table_name, schema, database, qry, columns=None):
try:
col_name, value = cls.latest_partition(
table_name, schema, database)
except Exception:
# table is not partitioned
return False
for c in columns:
if str(c.name) == str(col_name):
return qry.where(c == str(value))
return False
@classmethod
def latest_sub_partition(cls, table_name, schema, database, **kwargs):
# TODO(bogdan): implement`
pass
@classmethod
def _latest_partition_from_df(cls, df):
"""Hive partitions look like ds={partition name}"""
return df.ix[:, 0].max().split('=')[1]
@classmethod
def _partition_query(
cls, table_name, limit=0, order_by=None, filters=None):
return 'SHOW PARTITIONS {table_name}'.format(**locals())
@classmethod
def modify_url_for_impersonation(cls, url, impersonate_user, username):
"""
Modify the SQL Alchemy URL object with the user to impersonate if applicable.
:param url: SQLAlchemy URL object
:param impersonate_user: Bool indicating if impersonation is enabled
:param username: Effective username
"""
# Do nothing in the URL object since instead this should modify
# the configuraiton dictionary. See get_configuration_for_impersonation
pass
@classmethod
def get_configuration_for_impersonation(cls, uri, impersonate_user, username):
"""
Return a configuration dictionary that can be merged with other configs
that can set the correct properties for impersonating users
:param uri: URI string
:param impersonate_user: Bool indicating if impersonation is enabled
:param username: Effective username
:return: Dictionary with configs required for impersonation
"""
configuration = {}
url = make_url(uri)
backend_name = url.get_backend_name()
# Must be Hive connection, enable impersonation, and set param auth=LDAP|KERBEROS
if (backend_name == 'hive' and 'auth' in url.query.keys() and
impersonate_user is True and username is not None):
configuration['hive.server2.proxy.user'] = username
return configuration
class MssqlEngineSpec(BaseEngineSpec):
engine = 'mssql'
epoch_to_dttm = "dateadd(S, {col}, '1970-01-01')"
limit_method = LimitMethod.WRAP_SQL
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('second', _('second'), 'DATEADD(second, '
"DATEDIFF(second, '2000-01-01', {col}), '2000-01-01')",
'PT1S'),
Grain('minute', _('minute'), 'DATEADD(minute, '
'DATEDIFF(minute, 0, {col}), 0)',
'PT1M'),
Grain('5 minute', _('5 minute'), 'DATEADD(minute, '
'DATEDIFF(minute, 0, {col}) / 5 * 5, 0)',
'PT5M'),
Grain('half hour', _('half hour'), 'DATEADD(minute, '
'DATEDIFF(minute, 0, {col}) / 30 * 30, 0)',
'PT0.5H'),
Grain('hour', _('hour'), 'DATEADD(hour, '
'DATEDIFF(hour, 0, {col}), 0)',
'PT1H'),
Grain('day', _('day'), 'DATEADD(day, '
'DATEDIFF(day, 0, {col}), 0)',
'P1D'),
Grain('week', _('week'), 'DATEADD(week, '
'DATEDIFF(week, 0, {col}), 0)',
'P1W'),
Grain('month', _('month'), 'DATEADD(month, '
'DATEDIFF(month, 0, {col}), 0)',
'P1M'),
Grain('quarter', _('quarter'), 'DATEADD(quarter, '
'DATEDIFF(quarter, 0, {col}), 0)',
'P0.25Y'),
Grain('year', _('year'), 'DATEADD(year, '
'DATEDIFF(year, 0, {col}), 0)',
'P1Y'),
)
@classmethod
def convert_dttm(cls, target_type, dttm):
return "CONVERT(DATETIME, '{}', 126)".format(dttm.isoformat())
class AthenaEngineSpec(BaseEngineSpec):
engine = 'awsathena'
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('second', _('second'),
"date_trunc('second', CAST({col} AS TIMESTAMP))",
'PT1S'),
Grain('minute', _('minute'),
"date_trunc('minute', CAST({col} AS TIMESTAMP))",
'PT1M'),
Grain('hour', _('hour'),
"date_trunc('hour', CAST({col} AS TIMESTAMP))",
'PT1H'),
Grain('day', _('day'),
"date_trunc('day', CAST({col} AS TIMESTAMP))",
'P1D'),
Grain('week', _('week'),
"date_trunc('week', CAST({col} AS TIMESTAMP))",
'P1W'),
Grain('month', _('month'),
"date_trunc('month', CAST({col} AS TIMESTAMP))",
'P1M'),
Grain('quarter', _('quarter'),
"date_trunc('quarter', CAST({col} AS TIMESTAMP))",
'P0.25Y'),
Grain('week_ending_saturday', _('week_ending_saturday'),
"date_add('day', 5, date_trunc('week', date_add('day', 1, "
'CAST({col} AS TIMESTAMP))))',
'P1W/1970-01-03T00:00:00Z'),
Grain('week_start_sunday', _('week_start_sunday'),
"date_add('day', -1, date_trunc('week', "
"date_add('day', 1, CAST({col} AS TIMESTAMP))))",
'1969-12-28T00:00:00Z/P1W'),
)
@classmethod
def convert_dttm(cls, target_type, dttm):
tt = target_type.upper()
if tt == 'DATE':
return "from_iso8601_date('{}')".format(dttm.isoformat()[:10])
if tt == 'TIMESTAMP':
return "from_iso8601_timestamp('{}')".format(dttm.isoformat())
return ("CAST ('{}' AS TIMESTAMP)"
.format(dttm.strftime('%Y-%m-%d %H:%M:%S')))
@classmethod
def epoch_to_dttm(cls):
return 'from_unixtime({col})'
class ClickHouseEngineSpec(BaseEngineSpec):
"""Dialect for ClickHouse analytical DB."""
engine = 'clickhouse'
time_secondary_columns = True
time_groupby_inline = True
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('minute', _('minute'),
'toStartOfMinute(toDateTime({col}))',
'PT1M'),
Grain('5 minute', _('5 minute'),
'toDateTime(intDiv(toUInt32(toDateTime({col})), 300)*300)',
'PT5M'),
Grain('10 minute', _('10 minute'),
'toDateTime(intDiv(toUInt32(toDateTime({col})), 600)*600)',
'PT10M'),
Grain('hour', _('hour'),
'toStartOfHour(toDateTime({col}))',
'PT1H'),
Grain('day', _('day'),
'toStartOfDay(toDateTime({col}))',
'P1D'),
Grain('month', _('month'),
'toStartOfMonth(toDateTime({col}))',
'P1M'),
Grain('quarter', _('quarter'),
'toStartOfQuarter(toDateTime({col}))',
'P0.25Y'),
Grain('year', _('year'),
'toStartOfYear(toDateTime({col}))',
'P1Y'),
)
@classmethod
def convert_dttm(cls, target_type, dttm):
tt = target_type.upper()
if tt == 'DATE':
return "toDate('{}')".format(dttm.strftime('%Y-%m-%d'))
if tt == 'DATETIME':
return "toDateTime('{}')".format(
dttm.strftime('%Y-%m-%d %H:%M:%S'))
return "'{}'".format(dttm.strftime('%Y-%m-%d %H:%M:%S'))
class BQEngineSpec(BaseEngineSpec):
"""Engine spec for Google's BigQuery
As contributed by @mxmzdlv on issue #945"""
engine = 'bigquery'
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('second', _('second'), 'TIMESTAMP_TRUNC({col}, SECOND)', 'PT1S'),
Grain('minute', _('minute'), 'TIMESTAMP_TRUNC({col}, MINUTE)', 'PT1M'),
Grain('hour', _('hour'), 'TIMESTAMP_TRUNC({col}, HOUR)', 'PT1H'),
Grain('day', _('day'), 'TIMESTAMP_TRUNC({col}, DAY)', 'P1D'),
Grain('week', _('week'), 'TIMESTAMP_TRUNC({col}, WEEK)', 'P1W'),
Grain('month', _('month'), 'TIMESTAMP_TRUNC({col}, MONTH)', 'P1M'),
Grain('quarter', _('quarter'),
'TIMESTAMP_TRUNC({col}, QUARTER)', 'P0.25Y'),
Grain('year', _('year'), 'TIMESTAMP_TRUNC({col}, YEAR)', 'P1Y'),
)
@classmethod
def convert_dttm(cls, target_type, dttm):
tt = target_type.upper()
if tt == 'DATE':
return "'{}'".format(dttm.strftime('%Y-%m-%d'))
return "'{}'".format(dttm.strftime('%Y-%m-%d %H:%M:%S'))
@classmethod
def fetch_data(cls, cursor, limit):
data = super(BQEngineSpec, cls).fetch_data(cursor, limit)
if len(data) != 0 and type(data[0]).__name__ == 'Row':
data = [r.values() for r in data]
return data
class ImpalaEngineSpec(BaseEngineSpec):
"""Engine spec for Cloudera's Impala"""
engine = 'impala'
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('minute', _('minute'), "TRUNC({col}, 'MI')", 'PT1M'),
Grain('hour', _('hour'), "TRUNC({col}, 'HH')", 'PT1H'),
Grain('day', _('day'), "TRUNC({col}, 'DD')", 'P1D'),
Grain('week', _('week'), "TRUNC({col}, 'WW')", 'P1W'),
Grain('month', _('month'), "TRUNC({col}, 'MONTH')", 'P1M'),
Grain('quarter', _('quarter'), "TRUNC({col}, 'Q')", 'P0.25Y'),
Grain('year', _('year'), "TRUNC({col}, 'YYYY')", 'P1Y'),
)
@classmethod
def convert_dttm(cls, target_type, dttm):
tt = target_type.upper()
if tt == 'DATE':
return "'{}'".format(dttm.strftime('%Y-%m-%d'))
return "'{}'".format(dttm.strftime('%Y-%m-%d %H:%M:%S'))
@classmethod
def get_schema_names(cls, inspector):
schemas = [row[0] for row in inspector.engine.execute('SHOW SCHEMAS')
if not row[0].startswith('_')]
return schemas
class DruidEngineSpec(BaseEngineSpec):
"""Engine spec for Druid.io"""
engine = 'druid'
inner_joins = False
class KylinEngineSpec(BaseEngineSpec):
"""Dialect for Apache Kylin"""
engine = 'kylin'
time_grains = (
Grain('Time Column', _('Time Column'), '{col}', None),
Grain('second', _('second'),
'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO SECOND) AS TIMESTAMP)',
'PT1S'),
Grain('minute', _('minute'),
'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO MINUTE) AS TIMESTAMP)',
'PT1M'),
Grain('hour', _('hour'),
'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO HOUR) AS TIMESTAMP)',
'PT1H'),
Grain('day', _('day'),
'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO DAY) AS DATE)',
'P1D'),
Grain('week', _('week'),
'CAST(TIMESTAMPADD(WEEK, WEEK(CAST({col} AS DATE)) - 1, \
FLOOR(CAST({col} AS TIMESTAMP) TO YEAR)) AS DATE)',
'P1W'),
Grain('month', _('month'),
'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO MONTH) AS DATE)',
'P1M'),
Grain('quarter', _('quarter'),
'CAST(TIMESTAMPADD(QUARTER, QUARTER(CAST({col} AS DATE)) - 1, \
FLOOR(CAST({col} AS TIMESTAMP) TO YEAR)) AS DATE)',
'P0.25Y'),
Grain('year', _('year'),
'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO YEAR) AS DATE)',
'P1Y'),
)
@classmethod
def convert_dttm(cls, target_type, dttm):
tt = target_type.upper()
if tt == 'DATE':
return "CAST('{}' AS DATE)".format(dttm.isoformat()[:10])
if tt == 'TIMESTAMP':
return "CAST('{}' AS TIMESTAMP)".format(
dttm.strftime('%Y-%m-%d %H:%M:%S'))
return "'{}'".format(dttm.strftime('%Y-%m-%d %H:%M:%S'))
engines = {
o.engine: o for o in globals().values()
if inspect.isclass(o) and issubclass(o, BaseEngineSpec)}
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