#754

Committed 04 Sep 2023 09:54PM UTC coverage: 74.807% (-5.5%) from 80.336%

Build # #754

Build Type

push

circle-ci

Committed by

jiashenC

Commit Message

update case

Run Details

8727 of 11666 relevant lines covered (74.81%)

0.75 hits per line

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48.28

/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)

    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),
            )
            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],
                )
            )

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:	×
41	yield self._handle_drop_index(self.node.name, self.node.if_exists)	×
42
43	elif self.node.object_type == ObjectType.FUNCTION:	×
44	yield self._handle_drop_function(self.node.name, self.node.if_exists)	×
45
46	def _handle_drop_table(self, table_name: str, if_exists: bool):	1✔
47	if not self.catalog().check_table_exists(table_name):	1✔
48	err_msg = "Table: {} does not exist".format(table_name)	×
49	if if_exists:	×
50	return Batch(pd.DataFrame([err_msg]))	×
51	else:
52	raise ExecutorError(err_msg)
53
54	table_obj = self.catalog().get_table_catalog_entry(table_name)	1✔
55	storage_engine = StorageEngine.factory(self.db, table_obj)	1✔
56
57	logger.debug(f"Dropping table {table_name}")	1✔
58	storage_engine.drop(table=table_obj)	1✔
59
60	for col_obj in table_obj.columns:	1✔
61	for cache in col_obj.dep_caches:	1✔
62	self.catalog().drop_function_cache_catalog_entry(cache)	×
63
64	# todo also delete the indexes associated with the table
65	assert self.catalog().delete_table_catalog_entry(	1✔
66	table_obj
67	), "Failed to drop {}".format(table_name)
68
69	return Batch(	1✔
70	pd.DataFrame(
71	{"Table Successfully dropped: {}".format(table_name)},
72	index=[0],
73	)
74	)
75
76	def _handle_drop_function(self, function_name: str, if_exists: bool):	1✔
77	# check catalog if it already has this function entry
78	if not self.catalog().get_function_catalog_entry_by_name(function_name):	×
79	err_msg = (	×
80	f"Function {function_name} does not exist, therefore cannot be dropped."
81	)
82	if if_exists:	×
83	logger.warning(err_msg)	×
84	return Batch(pd.DataFrame([err_msg]))	×
85	else:
86	raise RuntimeError(err_msg)
87	else:
88	function_entry = self.catalog().get_function_catalog_entry_by_name(	×
89	function_name
90	)
91	for cache in function_entry.dep_caches:	×
92	self.catalog().drop_function_cache_catalog_entry(cache)	×
93
94	# todo also delete the indexes associated with the table
95
96	self.catalog().delete_function_catalog_entry_by_name(function_name)	×
97
98	return Batch(	×
99	pd.DataFrame(
100	{f"Function {function_name} successfully dropped"},
101	index=[0],
102	)
103	)
104
105	def _handle_drop_index(self, index_name: str, if_exists: bool):	1✔
106	index_obj = self.catalog().get_index_catalog_entry_by_name(index_name)	×
107	if not index_obj:	×
108	err_msg = f"Index {index_name} does not exist, therefore cannot be dropped."	×
109	if if_exists:	×
110	logger.warning(err_msg)	×
111	return Batch(pd.DataFrame([err_msg]))	×
112	else:
113	raise RuntimeError(err_msg)
114	else:
115	index = VectorStoreFactory.init_vector_store(	×
116	index_obj.type,
117	index_obj.name,
118	**handle_vector_store_params(index_obj.type, index_obj.save_file_path),
119	)
120	assert (	×
121	index is not None
122	), f"Failed to initialize the vector store handler for {index_obj.type}"
123
124	if index:	×
125	index.delete()	×
126
127	self.catalog().drop_index_catalog_entry(index_name)	×
128
129	return Batch(	×
130	pd.DataFrame(
131	{f"Index {index_name} successfully dropped"},
132	index=[0],
133	)
134	)

georgia-tech-db / eva / #754

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