20a9a0f9-edcc-437c-815d-bcc1a2d22b17

Committed 10 Nov 2023 04:50AM UTC coverage: 66.644% (-10.2%) from 76.812%

Build # 20a9a0f9-edcc-437c-815d-bcc1a2d22b17

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push

circleci

Committed by

americast

Commit Message

update docs

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91.43

/evadb/executor/drop_object_executor.py

# coding=utf-8
# Copyright 2018-2023 EvaDB
#
# Licensed 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.


import pandas as pd

from evadb.database import EvaDBDatabase
from evadb.executor.abstract_executor import AbstractExecutor
from evadb.executor.executor_utils import ExecutorError, handle_vector_store_params
from evadb.models.storage.batch import Batch
from evadb.parser.types import ObjectType
from evadb.plan_nodes.drop_object_plan import DropObjectPlan
from evadb.storage.storage_engine import StorageEngine
from evadb.third_party.vector_stores.utils import VectorStoreFactory
from evadb.utils.logging_manager import logger


class DropObjectExecutor(AbstractExecutor):
    def __init__(self, db: EvaDBDatabase, node: DropObjectPlan):
        super().__init__(db, node)

    def exec(self, *args, **kwargs):
        """Drop Object executor"""

        if self.node.object_type == ObjectType.TABLE:
            yield self._handle_drop_table(self.node.name, self.node.if_exists)

        elif self.node.object_type == ObjectType.INDEX:
            yield self._handle_drop_index(self.node.name, self.node.if_exists)

        elif self.node.object_type == ObjectType.FUNCTION:
            yield self._handle_drop_function(self.node.name, self.node.if_exists)

        elif self.node.object_type == ObjectType.DATABASE:
            yield self._handle_drop_database(self.node.name, self.node.if_exists)

    def _handle_drop_table(self, table_name: str, if_exists: bool):
        if not self.catalog().check_table_exists(table_name):
            err_msg = "Table: {} does not exist".format(table_name)
            if if_exists:
                return Batch(pd.DataFrame([err_msg]))
            else:
                raise ExecutorError(err_msg)

        table_obj = self.catalog().get_table_catalog_entry(table_name)
        storage_engine = StorageEngine.factory(self.db, table_obj)

        logger.debug(f"Dropping table {table_name}")
        storage_engine.drop(table=table_obj)

        for col_obj in table_obj.columns:
            for cache in col_obj.dep_caches:
                self.catalog().drop_function_cache_catalog_entry(cache)

        # todo also delete the indexes associated with the table
        assert self.catalog().delete_table_catalog_entry(
            table_obj
        ), "Failed to drop {}".format(table_name)

        return Batch(
            pd.DataFrame(
                {"Table Successfully dropped: {}".format(table_name)},
                index=[0],
            )
        )

    def _handle_drop_function(self, function_name: str, if_exists: bool):
        # check catalog if it already has this function entry
        if not self.catalog().get_function_catalog_entry_by_name(function_name):
            err_msg = (
                f"Function {function_name} does not exist, therefore cannot be dropped."
            )
            if if_exists:
                logger.warning(err_msg)
                return Batch(pd.DataFrame([err_msg]))
            else:
                raise RuntimeError(err_msg)
        else:
            function_entry = self.catalog().get_function_catalog_entry_by_name(
                function_name
            )
            for cache in function_entry.dep_caches:
                self.catalog().drop_function_cache_catalog_entry(cache)

            # todo also delete the indexes associated with the table

            self.catalog().delete_function_catalog_entry_by_name(function_name)

            return Batch(
                pd.DataFrame(
                    {f"Function {function_name} successfully dropped"},
                    index=[0],
                )
            )

    def _handle_drop_index(self, index_name: str, if_exists: bool):
        index_obj = self.catalog().get_index_catalog_entry_by_name(index_name)
        if not index_obj:
            err_msg = f"Index {index_name} does not exist, therefore cannot be dropped."
            if if_exists:
                logger.warning(err_msg)
                return Batch(pd.DataFrame([err_msg]))
            else:
                raise RuntimeError(err_msg)
        else:
            index = VectorStoreFactory.init_vector_store(
                index_obj.type,
                index_obj.name,
                **handle_vector_store_params(
                    index_obj.type, index_obj.save_file_path, self.catalog
                ),
            )
            assert (
                index is not None
            ), f"Failed to initialize the vector store handler for {index_obj.type}"

            if index:
                index.delete()

            self.catalog().drop_index_catalog_entry(index_name)

            return Batch(
                pd.DataFrame(
                    {f"Index {index_name} successfully dropped"},
                    index=[0],
                )
            )

    def _handle_drop_database(self, database_name: str, if_exists: bool):
        db_catalog_entry = self.catalog().get_database_catalog_entry(database_name)
        if not db_catalog_entry:
            err_msg = (
                f"Database {database_name} does not exist, therefore cannot be dropped."
            )
            if if_exists:
                logger.warning(err_msg)
                return Batch(pd.DataFrame([err_msg]))
            else:
                raise RuntimeError(err_msg)

        logger.debug(f"Dropping database {database_name}")

        self.catalog().drop_database_catalog_entry(db_catalog_entry)

        return Batch(
            pd.DataFrame(
                {f"Database {database_name} successfully dropped"},
                index=[0],
            )
        )

1	# coding=utf-8
2	# Copyright 2018-2023 EvaDB
3	#
4	# Licensed under the Apache License, Version 2.0 (the "License");
5	# you may not use this file except in compliance with the License.
6	# You may obtain a copy of the License at
7	#
8	# http://www.apache.org/licenses/LICENSE-2.0
9	#
10	# Unless required by applicable law or agreed to in writing, software
11	# distributed under the License is distributed on an "AS IS" BASIS,
12	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13	# See the License for the specific language governing permissions and
14	# limitations under the License.
15
16
17	import pandas as pd	1✔
18
19	from evadb.database import EvaDBDatabase	1✔
20	from evadb.executor.abstract_executor import AbstractExecutor	1✔
21	from evadb.executor.executor_utils import ExecutorError, handle_vector_store_params	1✔
22	from evadb.models.storage.batch import Batch	1✔
23	from evadb.parser.types import ObjectType	1✔
24	from evadb.plan_nodes.drop_object_plan import DropObjectPlan	1✔
25	from evadb.storage.storage_engine import StorageEngine	1✔
26	from evadb.third_party.vector_stores.utils import VectorStoreFactory	1✔
27	from evadb.utils.logging_manager import logger	1✔
28
29
30	class DropObjectExecutor(AbstractExecutor):	1✔
31	def __init__(self, db: EvaDBDatabase, node: DropObjectPlan):	1✔
32	super().__init__(db, node)	1✔
33
34	def exec(self, args, *kwargs):	1✔
35	"""Drop Object executor"""
36
37	if self.node.object_type == ObjectType.TABLE:	1✔
38	yield self._handle_drop_table(self.node.name, self.node.if_exists)	1✔
39
40	elif self.node.object_type == ObjectType.INDEX:	1✔
41	yield self._handle_drop_index(self.node.name, self.node.if_exists)	1✔
42
43	elif self.node.object_type == ObjectType.FUNCTION:	1✔
44	yield self._handle_drop_function(self.node.name, self.node.if_exists)	1✔
45
46	elif self.node.object_type == ObjectType.DATABASE:	1✔
47	yield self._handle_drop_database(self.node.name, self.node.if_exists)	1✔
48
49	def _handle_drop_table(self, table_name: str, if_exists: bool):	1✔
50	if not self.catalog().check_table_exists(table_name):	1✔
51	err_msg = "Table: {} does not exist".format(table_name)	1✔
52	if if_exists:	1✔
53	return Batch(pd.DataFrame([err_msg]))	1✔
54	else:
55	raise ExecutorError(err_msg)
56
57	table_obj = self.catalog().get_table_catalog_entry(table_name)	1✔
58	storage_engine = StorageEngine.factory(self.db, table_obj)	1✔
59
60	logger.debug(f"Dropping table {table_name}")	1✔
61	storage_engine.drop(table=table_obj)	1✔
62
63	for col_obj in table_obj.columns:	1✔
64	for cache in col_obj.dep_caches:	1✔
65	self.catalog().drop_function_cache_catalog_entry(cache)	×
66
67	# todo also delete the indexes associated with the table
68	assert self.catalog().delete_table_catalog_entry(	1✔
69	table_obj
70	), "Failed to drop {}".format(table_name)
71
72	return Batch(	1✔
73	pd.DataFrame(
74	{"Table Successfully dropped: {}".format(table_name)},
75	index=[0],
76	)
77	)
78
79	def _handle_drop_function(self, function_name: str, if_exists: bool):	1✔
80	# check catalog if it already has this function entry
81	if not self.catalog().get_function_catalog_entry_by_name(function_name):	1✔
82	err_msg = (	1✔
83	f"Function {function_name} does not exist, therefore cannot be dropped."
84	)
85	if if_exists:	1✔
UNCOV 86	logger.warning(err_msg)	×
UNCOV 87	return Batch(pd.DataFrame([err_msg]))	×
88	else:
89	raise RuntimeError(err_msg)
90	else:
91	function_entry = self.catalog().get_function_catalog_entry_by_name(	1✔
92	function_name
93	)
94	for cache in function_entry.dep_caches:	1✔
UNCOV 95	self.catalog().drop_function_cache_catalog_entry(cache)	×
96
97	# todo also delete the indexes associated with the table
98
99	self.catalog().delete_function_catalog_entry_by_name(function_name)	1✔
100
101	return Batch(	1✔
102	pd.DataFrame(
103	{f"Function {function_name} successfully dropped"},
104	index=[0],
105	)
106	)
107
108	def _handle_drop_index(self, index_name: str, if_exists: bool):	1✔
109	index_obj = self.catalog().get_index_catalog_entry_by_name(index_name)	1✔
110	if not index_obj:	1✔
111	err_msg = f"Index {index_name} does not exist, therefore cannot be dropped."	1✔
112	if if_exists:	1✔
113	logger.warning(err_msg)	×
114	return Batch(pd.DataFrame([err_msg]))	×
115	else:
116	raise RuntimeError(err_msg)
117	else:
118	index = VectorStoreFactory.init_vector_store(	1✔
119	index_obj.type,
120	index_obj.name,
121	**handle_vector_store_params(
122	index_obj.type, index_obj.save_file_path, self.catalog
123	),
124	)
125	assert (	1✔
126	index is not None
127	), f"Failed to initialize the vector store handler for {index_obj.type}"
128
129	if index:	1✔
130	index.delete()	1✔
131
132	self.catalog().drop_index_catalog_entry(index_name)	1✔
133
134	return Batch(	1✔
135	pd.DataFrame(
136	{f"Index {index_name} successfully dropped"},
137	index=[0],
138	)
139	)
140
141	def _handle_drop_database(self, database_name: str, if_exists: bool):	1✔
142	db_catalog_entry = self.catalog().get_database_catalog_entry(database_name)	1✔
143	if not db_catalog_entry:	1✔
144	err_msg = (	1✔
145	f"Database {database_name} does not exist, therefore cannot be dropped."
146	)
147	if if_exists:	1✔
148	logger.warning(err_msg)	1✔
149	return Batch(pd.DataFrame([err_msg]))	1✔
150	else:
151	raise RuntimeError(err_msg)
152
153	logger.debug(f"Dropping database {database_name}")	1✔
154
155	self.catalog().drop_database_catalog_entry(db_catalog_entry)	1✔
156
157	return Batch(	1✔
158	pd.DataFrame(
159	{f"Database {database_name} successfully dropped"},
160	index=[0],
161	)
162	)

georgia-tech-db / eva / 20a9a0f9-edcc-437c-815d-bcc1a2d22b17

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