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superset2/superset/db_engine_specs.py
agrawaldevesh ff9506fec2 Adding support for Pinot (#6719) 
Summary: Added limited support for visualizations with Pinot via
Sqlalchemy.

Pinot QL (PQL) is a bit weird and limited, and this patch hacks superset to
deal with that weirdness:

1. Pinot's grouping by time is best done as a long epoch. Grouping by a
time string is really slow and times out.

2. Pinot's response does not respect column aliases. So columns are not
named what they are expected to. So we remember the given column aliases
and then stamp them back onto the dataframe

3. Pinot's Json rest call does not return the output types. Instead
everything is cast to string. So when grouping by time, the group key
is integral and has to be treated specially when casting back to the
dataframe __timestamp column.

4. Finally, pinot does support grouping by on expressions. But those
expressions cannot then appear on the select clause. They are returned
regardless in the response. ie, 'select foo, count(*) from bar group by
foo' is okay, but 'select expr(foo), count(*) from bar group by
expr(foo)' ain't. One must use 'select count(*) from bar group by
expr(foo)'.

I also fixed a couple of things that looked like bugs to me: for
example, the row-ordering-limit should come at the end always.

Test Plan: Tested with the modified pinotdb sqlalchemy driver and an
internal pinot cluster. The pinotdb driver changes are in
https://github.com/agrawaldevesh/pinot-dbapi.

Pinot does not support orderby-limit for aggregated queries. To annotate
a query as an aggregate query, this patch adds a hint to the prepared
select statement that the pinotdb sqlalchemy driver then heeds.
2019-02-05 17:04:19 -08:00

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# 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.
# 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 collections import namedtuple
import hashlib
import inspect
import logging
import os
import re
import textwrap
import time
from flask import g
from flask_babel import lazy_gettext as _
import pandas
import sqlalchemy as sqla
from sqlalchemy import Column, select
from sqlalchemy.engine import create_engine
from sqlalchemy.engine.url import make_url
from sqlalchemy.sql import quoted_name, text
from sqlalchemy.sql.expression import TextAsFrom
from sqlalchemy.types import UnicodeText
import sqlparse
from werkzeug.utils import secure_filename
from superset import app, conf, db, sql_parse
from superset.exceptions import SupersetTemplateException
from superset.utils import core as utils
QueryStatus = utils.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')
builtin_time_grains = {
None: 'Time Column',
'PT1S': 'second',
'PT1M': 'minute',
'PT5M': '5 minute',
'PT10M': '10 minute',
'PT15M': '15 minute',
'PT0.5H': 'half hour',
'PT1H': 'hour',
'P1D': 'day',
'P1W': 'week',
'P1M': 'month',
'P0.25Y': 'quarter',
'P1Y': 'year',
'1969-12-28T00:00:00Z/P1W': 'week_start_sunday',
'1969-12-29T00:00:00Z/P1W': 'week_start_monday',
'P1W/1970-01-03T00:00:00Z': 'week_ending_saturday',
'P1W/1970-01-04T00:00:00Z': 'week_ending_sunday',
}
def _create_time_grains_tuple(time_grains, time_grain_functions, blacklist):
ret_list = []
blacklist = blacklist if blacklist else []
for duration, func in time_grain_functions.items():
if duration not in blacklist:
name = time_grains.get(duration)
ret_list.append(Grain(name, _(name), func, duration))
return tuple(ret_list)
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
time_grain_functions = {}
time_groupby_inline = False
limit_method = LimitMethod.FORCE_LIMIT
time_secondary_columns = False
inner_joins = True
allows_subquery = True
force_column_alias_quotes = False
arraysize = None
@classmethod
def get_time_expr(cls, expr, pdf, time_grain, grain):
# if epoch, translate to DATE using db specific conf
if pdf == 'epoch_s':
expr = cls.epoch_to_dttm().format(col=expr)
elif pdf == 'epoch_ms':
expr = cls.epoch_ms_to_dttm().format(col=expr)
if grain:
expr = grain.function.format(col=expr)
return expr
@classmethod
def get_time_grains(cls):
blacklist = config.get('TIME_GRAIN_BLACKLIST', [])
grains = builtin_time_grains.copy()
grains.update(config.get('TIME_GRAIN_ADDONS', {}))
grain_functions = cls.time_grain_functions.copy()
grain_addon_functions = config.get('TIME_GRAIN_ADDON_FUNCTIONS', {})
grain_functions.update(grain_addon_functions.get(cls.engine, {}))
return _create_time_grains_tuple(grains, grain_functions, blacklist)
@classmethod
def make_select_compatible(cls, groupby_exprs, select_exprs):
# Some databases will just return the group-by field into the select, but don't
# allow the group-by field to be put into the select list.
return select_exprs
@classmethod
def mutate_df_columns(cls, df, sql, labels_expected):
pass
@classmethod
def fetch_data(cls, cursor, limit):
if cls.arraysize:
cursor.arraysize = cls.arraysize
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)')
@classmethod
def get_datatype(cls, type_code):
if isinstance(type_code, str) and len(type_code):
return type_code.upper()
@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:
parsed_query = sql_parse.ParsedQuery(sql)
sql = parsed_query.get_query_with_new_limit(limit)
return sql
@classmethod
def get_limit_from_sql(cls, sql):
parsed_query = sql_parse.ParsedQuery(sql)
return parsed_query.limit
@classmethod
def get_query_with_new_limit(cls, sql, limit):
parsed_query = sql_parse.ParsedQuery(sql)
return parsed_query.get_query_with_new_limit(limit)
@staticmethod
def csv_to_df(**kwargs):
kwargs['filepath_or_buffer'] = \
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 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
def fetch_result_sets(cls, db, datasource_type):
"""Returns a list of tables [schema1.table1, schema2.table2, ...]
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.all_schema_names(cache=db.schema_cache_enabled,
cache_timeout=db.schema_cache_timeout,
force=True)
all_result_sets = []
for schema in schemas:
if datasource_type == 'table':
all_datasource_names = db.all_table_names_in_schema(
schema=schema, force=True,
cache=db.table_cache_enabled,
cache_timeout=db.table_cache_timeout)
elif datasource_type == 'view':
all_datasource_names = db.all_view_names_in_schema(
schema=schema, force=True,
cache=db.table_cache_enabled,
cache_timeout=db.table_cache_timeout)
all_result_sets += [
'{}.{}'.format(schema, t) for t in all_datasource_names]
return all_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 sorted(inspector.get_schema_names())
@classmethod
def get_table_names(cls, inspector, schema):
return sorted(inspector.get_table_names(schema))
@classmethod
def get_view_names(cls, inspector, schema):
return sorted(inspector.get_view_names(schema))
@classmethod
def where_latest_partition(
cls, table_name, schema, database, qry, columns=None):
return False
@classmethod
def _get_fields(cls, cols):
return [sqla.column(c.get('name')) for c in cols]
@classmethod
def select_star(cls, my_db, table_name, engine, 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 = cls._get_fields(cols)
quote = engine.dialect.identifier_preparer.quote
if schema:
full_table_name = quote(schema) + '.' + quote(table_name)
else:
full_table_name = quote(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 execute(cls, cursor, query, **kwargs):
if cls.arraysize:
cursor.arraysize = cls.arraysize
cursor.execute(query)
@classmethod
def make_label_compatible(cls, label):
"""
Conditionally mutate and/or quote a sql column/expression label. If
force_column_alias_quotes is set to True, return the label as a
sqlalchemy.sql.elements.quoted_name object to ensure that the select query
and query results have same case. Otherwise return the mutated label as a
regular string.
"""
label = cls.mutate_label(label)
return quoted_name(label, True) if cls.force_column_alias_quotes else label
@classmethod
def get_sqla_column_type(cls, type_):
"""
Return a sqlalchemy native column type that corresponds to the column type
defined in the data source (optional). Needs to be overridden if column requires
special handling (see MSSQL for example of NCHAR/NVARCHAR handling).
"""
return None
@staticmethod
def mutate_label(label):
"""
Most engines support mixed case aliases that can include numbers
and special characters, like commas, parentheses etc. For engines that
have restrictions on what types of aliases are supported, this method
can be overridden to ensure that labels conform to the engine's
limitations. Mutated labels should be deterministic (input label A always
yields output label X) and unique (input labels A and B don't yield the same
output label X).
"""
return label
class PostgresBaseEngineSpec(BaseEngineSpec):
""" Abstract class for Postgres 'like' databases """
engine = ''
time_grain_functions = {
None: '{col}',
'PT1S': "DATE_TRUNC('second', {col}) AT TIME ZONE 'UTC'",
'PT1M': "DATE_TRUNC('minute', {col}) AT TIME ZONE 'UTC'",
'PT1H': "DATE_TRUNC('hour', {col}) AT TIME ZONE 'UTC'",
'P1D': "DATE_TRUNC('day', {col}) AT TIME ZONE 'UTC'",
'P1W': "DATE_TRUNC('week', {col}) AT TIME ZONE 'UTC'",
'P1M': "DATE_TRUNC('month', {col}) AT TIME ZONE 'UTC'",
'P0.25Y': "DATE_TRUNC('quarter', {col}) AT TIME ZONE 'UTC'",
'P1Y': "DATE_TRUNC('year', {col}) AT TIME ZONE 'UTC'",
}
@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, inspector, schema):
"""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'
force_column_alias_quotes = True
time_grain_functions = {
None: '{col}',
'PT1S': "DATE_TRUNC('SECOND', {col})",
'PT1M': "DATE_TRUNC('MINUTE', {col})",
'PT5M': "DATEADD(MINUTE, FLOOR(DATE_PART(MINUTE, {col}) / 5) * 5, \
DATE_TRUNC('HOUR', {col}))",
'PT10M': "DATEADD(MINUTE, FLOOR(DATE_PART(MINUTE, {col}) / 10) * 10, \
DATE_TRUNC('HOUR', {col}))",
'PT15M': "DATEADD(MINUTE, FLOOR(DATE_PART(MINUTE, {col}) / 15) * 15, \
DATE_TRUNC('HOUR', {col}))",
'PT0.5H': "DATEADD(MINUTE, FLOOR(DATE_PART(MINUTE, {col}) / 30) * 30, \
DATE_TRUNC('HOUR', {col}))",
'PT1H': "DATE_TRUNC('HOUR', {col})",
'P1D': "DATE_TRUNC('DAY', {col})",
'P1W': "DATE_TRUNC('WEEK', {col})",
'P1M': "DATE_TRUNC('MONTH', {col})",
'P0.25Y': "DATE_TRUNC('QUARTER', {col})",
'P1Y': "DATE_TRUNC('YEAR', {col})",
}
@classmethod
def adjust_database_uri(cls, uri, selected_schema=None):
database = uri.database
if '/' in uri.database:
database = uri.database.split('/')[0]
if selected_schema:
uri.database = database + '/' + selected_schema
return uri
class VerticaEngineSpec(PostgresBaseEngineSpec):
engine = 'vertica'
class RedshiftEngineSpec(PostgresBaseEngineSpec):
engine = 'redshift'
@staticmethod
def mutate_label(label):
"""
Redshift only supports lowercase column names and aliases.
:param str label: Original label which might include uppercase letters
:return: String that is supported by the database
"""
return label.lower()
class OracleEngineSpec(PostgresBaseEngineSpec):
engine = 'oracle'
limit_method = LimitMethod.WRAP_SQL
force_column_alias_quotes = True
time_grain_functions = {
None: '{col}',
'PT1S': 'CAST({col} as DATE)',
'PT1M': "TRUNC(CAST({col} as DATE), 'MI')",
'PT1H': "TRUNC(CAST({col} as DATE), 'HH')",
'P1D': "TRUNC(CAST({col} as DATE), 'DDD')",
'P1W': "TRUNC(CAST({col} as DATE), 'WW')",
'P1M': "TRUNC(CAST({col} as DATE), 'MONTH')",
'P0.25Y': "TRUNC(CAST({col} as DATE), 'Q')",
'P1Y': "TRUNC(CAST({col} as DATE), 'YEAR')",
}
@classmethod
def convert_dttm(cls, target_type, dttm):
return (
"""TO_TIMESTAMP('{}', 'YYYY-MM-DD"T"HH24:MI:SS.ff6')"""
).format(dttm.isoformat())
@staticmethod
def mutate_label(label):
"""
Oracle 12.1 and earlier support a maximum of 30 byte length object names, which
usually means 30 characters.
:param str label: Original label which might include unsupported characters
:return: String that is supported by the database
"""
if len(label) > 30:
hashed_label = hashlib.md5(label.encode('utf-8')).hexdigest()
# truncate the hash to first 30 characters
return hashed_label[:30]
return label
class Db2EngineSpec(BaseEngineSpec):
engine = 'ibm_db_sa'
limit_method = LimitMethod.WRAP_SQL
force_column_alias_quotes = True
time_grain_functions = {
None: '{col}',
'PT1S': 'CAST({col} as TIMESTAMP)'
' - MICROSECOND({col}) MICROSECONDS',
'PT1M': 'CAST({col} as TIMESTAMP)'
' - SECOND({col}) SECONDS'
' - MICROSECOND({col}) MICROSECONDS',
'PT1H': 'CAST({col} as TIMESTAMP)'
' - MINUTE({col}) MINUTES'
' - SECOND({col}) SECONDS'
' - MICROSECOND({col}) MICROSECONDS ',
'P1D': 'CAST({col} as TIMESTAMP)'
' - HOUR({col}) HOURS'
' - MINUTE({col}) MINUTES'
' - SECOND({col}) SECONDS'
' - MICROSECOND({col}) MICROSECONDS',
'P1W': '{col} - (DAYOFWEEK({col})) DAYS',
'P1M': '{col} - (DAY({col})-1) DAYS',
'P0.25Y': '{col} - (DAY({col})-1) DAYS'
' - (MONTH({col})-1) MONTHS'
' + ((QUARTER({col})-1) * 3) MONTHS',
'P1Y': '{col} - (DAY({col})-1) DAYS'
' - (MONTH({col})-1) MONTHS',
}
@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'))
@staticmethod
def mutate_label(label):
"""
Db2 for z/OS supports a maximum of 30 byte length object names, which usually
means 30 characters.
:param str label: Original label which might include unsupported characters
:return: String that is supported by the database
"""
if len(label) > 30:
hashed_label = hashlib.md5(label.encode('utf-8')).hexdigest()
# truncate the hash to first 30 characters
return hashed_label[:30]
return label
class SqliteEngineSpec(BaseEngineSpec):
engine = 'sqlite'
time_grain_functions = {
None: '{col}',
'PT1H': "DATETIME(STRFTIME('%Y-%m-%dT%H:00:00', {col}))",
'P1D': 'DATE({col})',
'P1W': "DATE({col}, -strftime('%W', {col}) || ' days')",
'P1M': "DATE({col}, -strftime('%d', {col}) || ' days', '+1 day')",
'P1Y': "DATETIME(STRFTIME('%Y-01-01T00:00:00', {col}))",
'P1W/1970-01-03T00:00:00Z': "DATE({col}, 'weekday 6')",
'1969-12-28T00:00:00Z/P1W': "DATE({col}, 'weekday 0', '-7 days')",
}
@classmethod
def epoch_to_dttm(cls):
return "datetime({col}, 'unixepoch')"
@classmethod
def fetch_result_sets(cls, db, datasource_type):
schemas = db.all_schema_names(cache=db.schema_cache_enabled,
cache_timeout=db.schema_cache_timeout,
force=True)
all_result_sets = []
schema = schemas[0]
if datasource_type == 'table':
all_datasource_names = db.all_table_names_in_schema(
schema=schema, force=True,
cache=db.table_cache_enabled,
cache_timeout=db.table_cache_timeout)
elif datasource_type == 'view':
all_datasource_names = db.all_view_names_in_schema(
schema=schema, force=True,
cache=db.table_cache_enabled,
cache_timeout=db.table_cache_timeout)
all_result_sets += [
'{}.{}'.format(schema, t) for t in all_datasource_names]
return all_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, inspector, schema):
"""Need to disregard the schema for Sqlite"""
return sorted(inspector.get_table_names())
class MySQLEngineSpec(BaseEngineSpec):
engine = 'mysql'
time_grain_functions = {
None: '{col}',
'PT1S': 'DATE_ADD(DATE({col}), '
'INTERVAL (HOUR({col})*60*60 + MINUTE({col})*60'
' + SECOND({col})) SECOND)',
'PT1M': 'DATE_ADD(DATE({col}), '
'INTERVAL (HOUR({col})*60 + MINUTE({col})) MINUTE)',
'PT1H': 'DATE_ADD(DATE({col}), '
'INTERVAL HOUR({col}) HOUR)',
'P1D': 'DATE({col})',
'P1W': 'DATE(DATE_SUB({col}, '
'INTERVAL DAYOFWEEK({col}) - 1 DAY))',
'P1M': 'DATE(DATE_SUB({col}, '
'INTERVAL DAYOFMONTH({col}) - 1 DAY))',
'P0.25Y': 'MAKEDATE(YEAR({col}), 1) '
'+ INTERVAL QUARTER({col}) QUARTER - INTERVAL 1 QUARTER',
'P1Y': 'DATE(DATE_SUB({col}, '
'INTERVAL DAYOFYEAR({col}) - 1 DAY))',
'1969-12-29T00:00:00Z/P1W': 'DATE(DATE_SUB({col}, '
'INTERVAL DAYOFWEEK(DATE_SUB({col}, INTERVAL 1 DAY)) - 1 DAY))',
}
type_code_map = {} # loaded from get_datatype only if needed
@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 get_datatype(cls, type_code):
if not cls.type_code_map:
# only import and store if needed at least once
import MySQLdb
ft = MySQLdb.constants.FIELD_TYPE
cls.type_code_map = {
getattr(ft, k): k
for k in dir(ft)
if not k.startswith('_')
}
datatype = type_code
if isinstance(type_code, int):
datatype = cls.type_code_map.get(type_code)
if datatype and isinstance(datatype, str) and len(datatype):
return datatype
@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'
time_grain_functions = {
None: '{col}',
'PT1S': "date_trunc('second', CAST({col} AS TIMESTAMP))",
'PT1M': "date_trunc('minute', CAST({col} AS TIMESTAMP))",
'PT1H': "date_trunc('hour', CAST({col} AS TIMESTAMP))",
'P1D': "date_trunc('day', CAST({col} AS TIMESTAMP))",
'P1W': "date_trunc('week', CAST({col} AS TIMESTAMP))",
'P1M': "date_trunc('month', CAST({col} AS TIMESTAMP))",
'P0.25Y': "date_trunc('quarter', CAST({col} AS TIMESTAMP))",
'P1Y': "date_trunc('year', CAST({col} AS TIMESTAMP))",
'P1W/1970-01-03T00:00:00Z':
"date_add('day', 5, date_trunc('week', date_add('day', 1, \
CAST({col} AS TIMESTAMP))))",
'1969-12-28T00:00:00Z/P1W':
"date_add('day', -1, date_trunc('week', \
date_add('day', 1, CAST({col} AS TIMESTAMP))))",
}
@classmethod
def get_view_names(cls, inspector, schema):
"""Returns an empty list
get_table_names() function returns all table names and view names,
and get_view_names() is not implemented in sqlalchemy_presto.py
https://github.com/dropbox/PyHive/blob/e25fc8440a0686bbb7a5db5de7cb1a77bdb4167a/pyhive/sqlalchemy_presto.py
"""
return []
@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
def fetch_result_sets(cls, db, datasource_type):
"""Returns a list of tables [schema1.table1, schema2.table2, ...]
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 = []
for unused, row in result_set_df.iterrows():
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:
state = stats.get('state')
# if already finished, then stop polling
if state == 'FINISHED':
break
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(f"{field} = '{value}'")
where_clause = 'WHERE ' + ' AND '.join(l)
sql = textwrap.dedent(f"""\
SHOW PARTITIONS FROM {table_name}
{where_clause}
{order_by_clause}
{limit_clause}
""")
return sql
@classmethod
def where_latest_partition(
cls, table_name, schema, database, qry, columns=None):
try:
col_name, value = cls.latest_partition(
table_name, schema, database, show_first=True)
except Exception:
# table is not partitioned
return False
for c in columns:
if c.get('name') == col_name:
return qry.where(Column(col_name) == value)
return False
@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'
# 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 # pylint: disable=no-name-in-module
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
def fetch_result_sets(cls, db, datasource_type):
return BaseEngineSpec.fetch_result_sets(
db, datasource_type)
@classmethod
def fetch_data(cls, cursor, limit):
import pyhive
from TCLIService import ttypes
state = cursor.poll()
if state.operationState == ttypes.TOperationState.ERROR_STATE:
raise Exception('Query error', state.errorMessage)
try:
return super(HiveEngineSpec, cls).fetch_data(cursor, limit)
except pyhive.exc.ProgrammingError:
return []
@staticmethod
def create_table_from_csv(form, table):
"""Uploads a csv file and creates a superset datasource in Hive."""
def convert_to_hive_type(col_type):
"""maps tableschema's types to hive types"""
tableschema_to_hive_types = {
'boolean': 'BOOLEAN',
'integer': 'INT',
'number': 'DOUBLE',
'string': 'STRING',
}
return tableschema_to_hive_types.get(col_type, 'STRING')
bucket_path = 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.')
table_name = form.name.data
schema_name = form.schema.data
if config.get('UPLOADED_CSV_HIVE_NAMESPACE'):
if '.' in table_name or schema_name:
raise Exception(
"You can't specify a namespace. "
'All tables will be uploaded to the `{}` namespace'.format(
config.get('HIVE_NAMESPACE')))
full_table_name = '{}.{}'.format(
config.get('UPLOADED_CSV_HIVE_NAMESPACE'), table_name)
else:
if '.' in table_name and schema_name:
raise Exception(
"You can't specify a namespace both in the name of the table "
'and in the schema field. Please remove one')
full_table_name = '{}.{}'.format(
schema_name, table_name) if schema_name else table_name
filename = form.csv_file.data.filename
upload_prefix = config['CSV_TO_HIVE_UPLOAD_DIRECTORY']
upload_path = config['UPLOAD_FOLDER'] + \
secure_filename(filename)
# Optional dependency
from tableschema import Table # pylint: disable=import-error
hive_table_schema = Table(upload_path).infer()
column_name_and_type = []
for column_info in hive_table_schema['fields']:
column_name_and_type.append(
'`{}` {}'.format(
column_info['name'],
convert_to_hive_type(column_info['type'])))
schema_definition = ', '.join(column_name_and_type)
# Optional dependency
import boto3 # pylint: disable=import-error
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 = f"""CREATE TABLE {full_table_name} ( {schema_definition} )
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS
TEXTFILE LOCATION '{location}'
tblproperties ('skip.header.line.count'='1')"""
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):
msg = str(e)
match = re.search(r'errorMessage="(.*?)(?<!\\)"', msg)
if match:
msg = match.group(1)
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 # pylint: disable=no-name-in-module
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, show_first=True)
except Exception:
# table is not partitioned
return False
for c in columns:
if c.get('name') == col_name:
return qry.where(Column(col_name) == 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 f'SHOW PARTITIONS {table_name}'
@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
@staticmethod
def execute(cursor, query, async_=False):
kwargs = {'async': async_}
cursor.execute(query, **kwargs)
class MssqlEngineSpec(BaseEngineSpec):
engine = 'mssql'
epoch_to_dttm = "dateadd(S, {col}, '1970-01-01')"
limit_method = LimitMethod.WRAP_SQL
time_grain_functions = {
None: '{col}',
'PT1S': "DATEADD(second, DATEDIFF(second, '2000-01-01', {col}), '2000-01-01')",
'PT1M': 'DATEADD(minute, DATEDIFF(minute, 0, {col}), 0)',
'PT5M': 'DATEADD(minute, DATEDIFF(minute, 0, {col}) / 5 * 5, 0)',
'PT10M': 'DATEADD(minute, DATEDIFF(minute, 0, {col}) / 10 * 10, 0)',
'PT15M': 'DATEADD(minute, DATEDIFF(minute, 0, {col}) / 15 * 15, 0)',
'PT0.5H': 'DATEADD(minute, DATEDIFF(minute, 0, {col}) / 30 * 30, 0)',
'PT1H': 'DATEADD(hour, DATEDIFF(hour, 0, {col}), 0)',
'P1D': 'DATEADD(day, DATEDIFF(day, 0, {col}), 0)',
'P1W': 'DATEADD(week, DATEDIFF(week, 0, {col}), 0)',
'P1M': 'DATEADD(month, DATEDIFF(month, 0, {col}), 0)',
'P0.25Y': 'DATEADD(quarter, DATEDIFF(quarter, 0, {col}), 0)',
'P1Y': 'DATEADD(year, DATEDIFF(year, 0, {col}), 0)',
}
@classmethod
def convert_dttm(cls, target_type, dttm):
return "CONVERT(DATETIME, '{}', 126)".format(dttm.isoformat())
@classmethod
def fetch_data(cls, cursor, limit):
data = super(MssqlEngineSpec, cls).fetch_data(cursor, limit)
if len(data) != 0 and type(data[0]).__name__ == 'Row':
data = [[elem for elem in r] for r in data]
return data
@classmethod
def get_sqla_column_type(cls, type_):
if isinstance(type_, str) and re.match(r'^N(VAR){0-1}CHAR', type_):
return UnicodeText()
return None
class AthenaEngineSpec(BaseEngineSpec):
engine = 'awsathena'
time_grain_functions = {
None: '{col}',
'PT1S': "date_trunc('second', CAST({col} AS TIMESTAMP))",
'PT1M': "date_trunc('minute', CAST({col} AS TIMESTAMP))",
'PT1H': "date_trunc('hour', CAST({col} AS TIMESTAMP))",
'P1D': "date_trunc('day', CAST({col} AS TIMESTAMP))",
'P1W': "date_trunc('week', CAST({col} AS TIMESTAMP))",
'P1M': "date_trunc('month', CAST({col} AS TIMESTAMP))",
'P0.25Y': "date_trunc('quarter', CAST({col} AS TIMESTAMP))",
'P1Y': "date_trunc('year', CAST({col} AS TIMESTAMP))",
'P1W/1970-01-03T00:00:00Z': "date_add('day', 5, date_trunc('week', \
date_add('day', 1, CAST({col} AS TIMESTAMP))))",
'1969-12-28T00:00:00Z/P1W': "date_add('day', -1, date_trunc('week', \
date_add('day', 1, CAST({col} AS TIMESTAMP))))",
}
@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 PinotEngineSpec(BaseEngineSpec):
engine = 'pinot'
allows_subquery = False
inner_joins = False
_time_grain_to_datetimeconvert = {
'PT1S': '1:SECONDS',
'PT1M': '1:MINUTES',
'PT1H': '1:HOURS',
'P1D': '1:DAYS',
'P1Y': '1:YEARS',
'P1M': '1:MONTHS',
}
# Pinot does its own conversion below
time_grain_functions = {k: None for k in _time_grain_to_datetimeconvert.keys()}
@classmethod
def get_time_expr(cls, expr, pdf, time_grain, grain):
is_epoch = pdf in ('epoch_s', 'epoch_ms')
if not is_epoch:
raise NotImplementedError('Pinot currently only supports epochs')
# The DATETIMECONVERT pinot udf is documented at
# Per https://github.com/apache/incubator-pinot/wiki/dateTimeConvert-UDF
# We are not really converting any time units, just bucketing them.
seconds_or_ms = 'MILLISECONDS' if pdf == 'epoch_ms' else 'SECONDS'
tf = f'1:{seconds_or_ms}:EPOCH'
granularity = cls._time_grain_to_datetimeconvert.get(time_grain)
if not granularity:
raise NotImplementedError('No pinot grain spec for ' + str(time_grain))
# In pinot the output is a string since there is no timestamp column like pg
return f'DATETIMECONVERT({expr}, "{tf}", "{tf}", "{granularity}")'
@classmethod
def make_select_compatible(cls, groupby_exprs, select_exprs):
# Pinot does not want the group by expr's to appear in the select clause
select_sans_groupby = []
# We want identity and not equality, so doing the filtering manually
for s in select_exprs:
for gr in groupby_exprs:
if s is gr:
break
else:
select_sans_groupby.append(s)
return select_sans_groupby
@classmethod
def mutate_df_columns(cls, df, sql, labels_expected):
if df is not None and \
not df.empty and \
labels_expected is not None:
if len(df.columns) != len(labels_expected):
raise Exception(f'For {sql}, df.columns: {df.columns}'
f' differs from {labels_expected}')
else:
df.columns = labels_expected
class ClickHouseEngineSpec(BaseEngineSpec):
"""Dialect for ClickHouse analytical DB."""
engine = 'clickhouse'
time_secondary_columns = True
time_groupby_inline = True
time_grain_functions = {
None: '{col}',
'PT1M': 'toStartOfMinute(toDateTime({col}))',
'PT5M': 'toDateTime(intDiv(toUInt32(toDateTime({col})), 300)*300)',
'PT10M': 'toDateTime(intDiv(toUInt32(toDateTime({col})), 600)*600)',
'PT15M': 'toDateTime(intDiv(toUInt32(toDateTime({col})), 900)*900)',
'PT0.5H': 'toDateTime(intDiv(toUInt32(toDateTime({col})), 1800)*1800)',
'PT1H': 'toStartOfHour(toDateTime({col}))',
'P1D': 'toStartOfDay(toDateTime({col}))',
'P1W': 'toMonday(toDateTime({col}))',
'P1M': 'toStartOfMonth(toDateTime({col}))',
'P0.25Y': 'toStartOfQuarter(toDateTime({col}))',
'P1Y': 'toStartOfYear(toDateTime({col}))',
}
@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'
"""
https://www.python.org/dev/peps/pep-0249/#arraysize
raw_connections bypass the pybigquery query execution context and deal with
raw dbapi connection directly.
If this value is not set, the default value is set to 1, as described here,
https://googlecloudplatform.github.io/google-cloud-python/latest/_modules/google/cloud/bigquery/dbapi/cursor.html#Cursor
The default value of 5000 is derived from the pybigquery.
https://github.com/mxmzdlv/pybigquery/blob/d214bb089ca0807ca9aaa6ce4d5a01172d40264e/pybigquery/sqlalchemy_bigquery.py#L102
"""
arraysize = 5000
time_grain_functions = {
None: '{col}',
'PT1S': 'TIMESTAMP_TRUNC({col}, SECOND)',
'PT1M': 'TIMESTAMP_TRUNC({col}, MINUTE)',
'PT1H': 'TIMESTAMP_TRUNC({col}, HOUR)',
'P1D': 'TIMESTAMP_TRUNC({col}, DAY)',
'P1W': 'TIMESTAMP_TRUNC({col}, WEEK)',
'P1M': 'TIMESTAMP_TRUNC({col}, MONTH)',
'P0.25Y': 'TIMESTAMP_TRUNC({col}, QUARTER)',
'P1Y': 'TIMESTAMP_TRUNC({col}, YEAR)',
}
@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
@staticmethod
def mutate_label(label):
"""
BigQuery field_name should start with a letter or underscore, contain only
alphanumeric characters and be at most 128 characters long. Labels that start
with a number are prefixed with an underscore. Any unsupported characters are
replaced with underscores and an md5 hash is added to the end of the label to
avoid possible collisions. If the resulting label exceeds 128 characters, only
the md5 sum is returned.
:param str label: the original label which might include unsupported characters
:return: String that is supported by the database
"""
hashed_label = '_' + hashlib.md5(label.encode('utf-8')).hexdigest()
# if label starts with number, add underscore as first character
mutated_label = '_' + label if re.match(r'^\d', label) else label
# replace non-alphanumeric characters with underscores
mutated_label = re.sub(r'[^\w]+', '_', mutated_label)
if mutated_label != label:
# add md5 hash to label to avoid possible collisions
mutated_label += hashed_label
# return only hash if length of final label exceeds 128 chars
return mutated_label if len(mutated_label) <= 128 else hashed_label
@classmethod
def extra_table_metadata(cls, database, table_name, schema_name):
indexes = database.get_indexes(table_name, schema_name)
if not indexes:
return {}
partitions_columns = [
index.get('column_names', []) for index in indexes
if index.get('name') == 'partition'
]
cluster_columns = [
index.get('column_names', []) for index in indexes
if index.get('name') == 'clustering'
]
return {
'partitions': {
'cols': partitions_columns,
},
'clustering': {
'cols': cluster_columns,
},
}
@classmethod
def _get_fields(cls, cols):
"""
BigQuery dialect requires us to not use backtick in the fieldname which are
nested.
Using literal_column handles that issue.
http://docs.sqlalchemy.org/en/latest/core/tutorial.html#using-more-specific-text-with-table-literal-column-and-column
Also explicility specifying column names so we don't encounter duplicate
column names in the result.
"""
return [sqla.literal_column(c.get('name')).label(c.get('name').replace('.', '__'))
for c in cols]
class ImpalaEngineSpec(BaseEngineSpec):
"""Engine spec for Cloudera's Impala"""
engine = 'impala'
time_grain_functions = {
None: '{col}',
'PT1M': "TRUNC({col}, 'MI')",
'PT1H': "TRUNC({col}, 'HH')",
'P1D': "TRUNC({col}, 'DD')",
'P1W': "TRUNC({col}, 'WW')",
'P1M': "TRUNC({col}, 'MONTH')",
'P0.25Y': "TRUNC({col}, 'Q')",
'P1Y': "TRUNC({col}, 'YYYY')",
}
@classmethod
def epoch_to_dttm(cls):
return 'from_unixtime({col})'
@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
allows_subquery = False
time_grain_functions = {
None: '{col}',
'PT1S': 'FLOOR({col} TO SECOND)',
'PT1M': 'FLOOR({col} TO MINUTE)',
'PT1H': 'FLOOR({col} TO HOUR)',
'P1D': 'FLOOR({col} TO DAY)',
'P1W': 'FLOOR({col} TO WEEK)',
'P1M': 'FLOOR({col} TO MONTH)',
'P0.25Y': 'FLOOR({col} TO QUARTER)',
'P1Y': 'FLOOR({col} TO YEAR)',
}
class GSheetsEngineSpec(SqliteEngineSpec):
"""Engine for Google spreadsheets"""
engine = 'gsheets'
inner_joins = False
allows_subquery = False
class KylinEngineSpec(BaseEngineSpec):
"""Dialect for Apache Kylin"""
engine = 'kylin'
time_grain_functions = {
None: '{col}',
'PT1S': 'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO SECOND) AS TIMESTAMP)',
'PT1M': 'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO MINUTE) AS TIMESTAMP)',
'PT1H': 'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO HOUR) AS TIMESTAMP)',
'P1D': 'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO DAY) AS DATE)',
'P1W': 'CAST(TIMESTAMPADD(WEEK, WEEK(CAST({col} AS DATE)) - 1, \
FLOOR(CAST({col} AS TIMESTAMP) TO YEAR)) AS DATE)',
'P1M': 'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO MONTH) AS DATE)',
'P0.25Y': 'CAST(TIMESTAMPADD(QUARTER, QUARTER(CAST({col} AS DATE)) - 1, \
FLOOR(CAST({col} AS TIMESTAMP) TO YEAR)) AS DATE)',
'P1Y': 'CAST(FLOOR(CAST({col} AS TIMESTAMP) TO YEAR) AS DATE)',
}
@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'))
class TeradataEngineSpec(BaseEngineSpec):
"""Dialect for Teradata DB."""
engine = 'teradata'
limit_method = LimitMethod.WRAP_SQL
time_grain_functions = {
None: '{col}',
'PT1M': "TRUNC(CAST({col} as DATE), 'MI')",
'PT1H': "TRUNC(CAST({col} as DATE), 'HH')",
'P1D': "TRUNC(CAST({col} as DATE), 'DDD')",
'P1W': "TRUNC(CAST({col} as DATE), 'WW')",
'P1M': "TRUNC(CAST({col} as DATE), 'MONTH')",
'P0.25Y': "TRUNC(CAST({col} as DATE), 'Q')",
'P1Y': "TRUNC(CAST({col} as DATE), 'YEAR')",
}
engines = {
o.engine: o for o in globals().values()
if inspect.isclass(o) and issubclass(o, BaseEngineSpec)}
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