remove runtime tests, remove extra prints #575
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WalkthroughThese changes primarily focus on enhancing the performance of test suites by adopting asynchronous operations and integrating Changes
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Review Status
Actionable comments generated: 3
Configuration used: CodeRabbit UI
Files selected for processing (9)
- demo/init.py (1 hunks)
- demo/imdb/imdb_queries.py (18 hunks)
- demo/social/social_queries.py (26 hunks)
- tests/flow/base.py (1 hunks)
- tests/flow/test_imdb.py (3 hunks)
- tests/flow/test_list.py (1 hunks)
- tests/flow/test_replication_states.py (1 hunks)
- tests/flow/test_social.py (30 hunks)
- tests/flow/tests.sh (1 hunks)
Files skipped from review due to trivial changes (2)
- tests/flow/test_imdb.py
- tests/flow/test_replication_states.py
Additional comments: 4
tests/flow/test_social.py (1)
- 24-29: > 📝 NOTE
This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [6-6]
Runtime assertions were removed from multiple test methods. Confirm that the removal of these assertions does not compromise the ability to detect performance regressions or significantly alter the intended test coverage.
tests/flow/tests.sh (1)
- 140-140: The
--no-progressargument was added toRLTEST_ARGSto reduce verbosity. Ensure this change does not inadvertently suppress important debugging information that could be useful in diagnosing test failures.demo/imdb/imdb_queries.py (1)
- 16-21: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [1-255]
The removal of
max_run_time_msparameters from query descriptions is consistent with the PR objectives. Ensure all references tomax_run_time_msare removed across the entire project to maintain consistency.demo/social/social_queries.py (1)
- 40-52: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [1-255]
The removal of
max_run_time_msparameters from query descriptions in this file aligns with the PR objectives. Confirm that all instances ofmax_run_time_mshave been consistently removed throughout the project.
tests/flow/test_list.py
Outdated
| @@ -1022,7 +1022,7 @@ def test10_sort(self): | |||
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| query = """WITH {a: 1, b: 2, c: 3} as map RETURN list.sort([map, 1, [1,2,3]])""" | |||
| actual_result = self.graph.query(query) | |||
| assert str(actual_result.result_set[0]) == "[[OrderedDict([('a', 1), ('b', 2), ('c', 3)]), [1, 2, 3], 1]]" | |||
| assert str(actual_result.result_set[0]) == "[[OrderedDict({'a': 1, 'b': 2, 'c': 3}), [1, 2, 3], 1]]" | |||
There was a problem hiding this comment.
The assertion uses a string comparison to validate the result of a query, which is not a robust method for testing. Consider using direct comparison of objects or structured data instead of converting to string for comparison.
- assert str(actual_result.result_set[0]) == "[[OrderedDict({'a': 1, 'b': 2, 'c': 3}), [1, 2, 3], 1]]"
+ expected_result = [[OrderedDict({'a': 1, 'b': 2, 'c': 3}), [1, 2, 3], 1]]
+ self.env.assertEquals(actual_result.result_set[0], expected_result)Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| assert str(actual_result.result_set[0]) == "[[OrderedDict({'a': 1, 'b': 2, 'c': 3}), [1, 2, 3], 1]]" | |
| expected_result = [[OrderedDict({'a': 1, 'b': 2, 'c': 3}), [1, 2, 3], 1]] | |
| self.env.assertEquals(actual_result.result_set[0], expected_result) |
| @@ -3,20 +3,18 @@ class QueryInfo(object): | |||
| This class contains the needed data about a query | |||
| """ | |||
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| def __init__(self, query=None, description=None, max_run_time_ms=None, expected_result=None, reversible=True): | |||
| def __init__(self, query=None, description=None, expected_result=None, reversible=True): | |||
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Tip
Codebase Verification
The removal of max_run_time_ms from the QueryInfo constructor has been widely adopted across the project, aligning with the updated constructor signature. However, an exception was found in ./tests/flow/test_graph_deletion.py:175, where max_run_time_ms is still being used. This indicates that while most instances have been updated, there remains at least one instance that does not match the new constructor signature.
Analysis chain
The removal of max_run_time_ms from the QueryInfo constructor simplifies query initialization by eliminating runtime checks. Ensure all instances of QueryInfo across the project are updated to match the new constructor signature.
Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for instances of QueryInfo being initialized with the old signature.
ast-grep --lang python --pattern $'QueryInfo($_, $_, $_, $_, $_)'
Length of output: 66
Script:
#!/bin/bash
# Broaden the search for instances of QueryInfo being initialized with any number of parameters to ensure all instances match the updated constructor signature.
ast-grep --lang python --pattern $'QueryInfo($$$)'
Length of output: 50983
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Seems like the humans are having a chat. I'll hop back into my burrow for now. If you need me again, just tag @coderabbitai in a new comment, and I'll come hopping out!
| actual_result_set = actual_result.result_set or [] | ||
| self.env.assertEqual(actual_result_set, query_info.expected_result) | ||
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| def _assert_run_time(self, actual_result, query_info): | ||
| if actual_result.run_time_ms > query_info.max_run_time_ms: | ||
| warnings.warn('Maximum runtime for query \"%s\" was: %s, but should be %s' % | ||
| (query_info.description, str(actual_result.run_time_ms), str(query_info.max_run_time_ms))) | ||
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| # function which run the query and expects an specific error message | ||
| def _assert_exception(self, graph, query, expected_err_msg): | ||
| try: |
There was a problem hiding this comment.
Tip
Codebase Verification
The corrected script successfully identified a reference to _assert_run_time in tests/flow/test_graph_deletion.py. This indicates that, despite the removal of the _assert_run_time method from BaseFlowTest, there is at least one instance in the test suite where this method is still being referenced. This finding directly addresses the concern raised in the review comment about the removal of runtime performance checks and its potential impact on the ability to monitor query performance in critical scenarios.
Given this evidence, it's clear that the removal of the _assert_run_time method does not completely eliminate runtime performance checks from the test suite, as there is still at least one test that attempts to use this method. This could imply that the removal might have been an oversight or that there's a need for a replacement or update in the testing strategy to accommodate performance checks where necessary.
Based on this information, the appropriate conclusion is:
: The review comment is validated by the discovery of a remaining reference to _assert_run_time in the test suite, indicating potential issues with the removal of this method and its impact on performance monitoring in tests.
Analysis chain
📝 NOTE
This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [6-6]
The _assert_run_time method was removed, indicating a shift away from runtime performance checks. Verify that this change aligns with the testing strategy and does not affect the ability to monitor query performance in critical scenarios.
Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check if there are any remaining references to `_assert_run_time` in the test suite.
rg --type python '_assert_run_time'
Length of output: 66
Script:
#!/bin/bash
# Correcting the command to search for references to `_assert_run_time` in Python files.
rg --type py '_assert_run_time'
Length of output: 115
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Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (8)
- tests/flow/base.py (2 hunks)
- tests/flow/test_concurrent_query.py (3 hunks)
- tests/flow/test_graph_deletion.py (1 hunks)
- tests/flow/test_imdb.py (4 hunks)
- tests/flow/test_pending_queries_limit.py (1 hunks)
- tests/flow/test_query_mem_limit.py (2 hunks)
- tests/flow/test_stress.py (1 hunks)
- tests/requirements.txt (1 hunks)
Files skipped from review as they are similar to previous changes (2)
- tests/flow/base.py
- tests/flow/test_imdb.py
Additional comments: 3
tests/requirements.txt (2)
- 10-10: The change from
RLTesttorltestaligns with standard package naming conventions. Ensure that all references to this package in scripts and configurations are updated accordingly.- 10-10: The removal of
pathossuggests a change in the approach to parallel processing or dependency management. Verify that no existing tests or functionalities are adversely affected by this removal.tests/flow/test_graph_deletion.py (1)
- 175-175: The removal of the
max_run_time_msparameter from theQueryInfoinstantiation aligns with the PR's objective to eliminate runtime tests. This change simplifies the test by focusing solely on the functional aspect of bulk edge deletion without considering execution time. It's important to ensure that all references to execution time checks are consistently removed across the test suite to maintain uniformity in test behavior.
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Review Status
Actionable comments generated: 3
Configuration used: CodeRabbit UI
Files selected for processing (5)
- tests/flow/test_cache.py (2 hunks)
- tests/flow/test_index_create.py (2 hunks)
- tests/flow/test_list.py (1 hunks)
- tests/flow/test_slowlog.py (6 hunks)
- tests/flow/test_timeout.py (2 hunks)
Files skipped from review as they are similar to previous changes (1)
- tests/flow/test_list.py
Additional comments: 8
tests/flow/test_slowlog.py (6)
- 3-5: The imports for
FalkorDBandBlockingConnectionPoolare correctly added to support the new asynchronous operations. This aligns with the PR objectives to enhance efficiency and clarity.- 15-28: The
populate_slowlogmethod has been significantly refactored to support asynchronous operations. This change introduces an inner asynchronous functionpopulateand utilizesasyncio.gatherto run multiple tasks concurrently. This is a good use of Python's asyncio library to improve performance. However, ensure that the FalkorDB instance and connection pool are correctly configured for asynchronous operations.- 59-59: The execution of a long-running query to test the slowlog's behavior is a good test case. It ensures that the slowlog captures queries exceeding a certain execution time. This aligns with the objective of testing slowlog functionality comprehensively.
- 67-71: The decoding of commands from the Redis slowlog and the assertion to check for the presence of the executed query in the slowlog are correctly implemented. This validates that the slowlog is capturing and logging queries as expected.
- 87-87: The method call
self.populate_slowlog(36)to repopulate the slowlog after a reset operation is a practical test case. It verifies that the slowlog can be repopulated correctly after being cleared, ensuring the slowlog's reset functionality works as intended.- 101-101: Repopulating the slowlog to verify it can be filled again after a reset operation is a good test practice. This ensures the slowlog's functionality remains consistent even after multiple reset operations.
tests/flow/test_index_create.py (2)
- 6-7: The addition of imports for
FalkorDBandBlockingConnectionPoolis appropriate given the refactoring oftest05_index_deleteto utilize these for async database operations. Ensure that these libraries are included in the project's dependencies.- 198-207: Creating a
BlockingConnectionPoolwithmax_connections=16and no timeout is a reasonable choice for parallel test execution. However, consider the following:
- The
timeout=Nonesetting means there's no timeout for connections. While this might be suitable for tests, in a production scenario, you might want to set a reasonable timeout to avoid hanging connections.- The loop creating tasks for index operations (
for i in range(1, 16):) is well-structured for parallel execution. Ensure that the number of tasks does not exceed themax_connectionsparameter of the connection pool to avoid potential bottlenecks or resource contention.
tests/flow/test_slowlog.py
Outdated
| for i in range(1024): | ||
| q = """CREATE ({v:%s})""" % i | ||
| self.graph.query(q) | ||
| self.populate_slowlog(1024) |
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The call to self.populate_slowlog(1024) to saturate the slowlog is a significant change. Given the asynchronous nature of the populate_slowlog method, this should efficiently populate the slowlog. However, consider the impact on test runtime and ensure that the number of tasks (1024) is justified and does not introduce unnecessary test latency.
Consider evaluating if a lower number of tasks could achieve the same test coverage without significantly increasing test runtime.
tests/flow/test_index_create.py
Outdated
| async def create_drop_index(g): | ||
| for _ in range(1, 30): | ||
| await g.query("CREATE (n:L)-[:T]->(a:L)") | ||
| await g.create_edge_range_index('T', 'p') | ||
| await g.delete() | ||
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| async def run(self): | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None) | ||
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | ||
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| def create_drop_index(graph_id): | ||
| env, db = Env() | ||
| con = env.getConnection() | ||
| for _ in range(1, 100): | ||
| pipe = con.pipeline() | ||
| pipe.execute_command("GRAPH.QUERY", graph_id, "CREATE (a:L), (n:L), (n)-[:T]->(a)") | ||
| pipe.execute_command("GRAPH.QUERY", graph_id, "CREATE INDEX FOR ()-[n:T]-() ON (n.p)") | ||
| pipe.execute() | ||
| wait_for_indices_to_sync(Graph(con, graph_id)) | ||
| con.execute_command("GRAPH.DELETE", graph_id) | ||
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| if "to_thread" not in dir(asyncio): | ||
| create_drop_index(1) | ||
| else: | ||
| loop = asyncio.get_event_loop() | ||
| tasks = [] | ||
| for i in range(1, 20): | ||
| tasks.append(loop.create_task(asyncio.to_thread(create_drop_index, i))) | ||
| for i in range(1, 16): | ||
| g = db.select_graph(str(i)) | ||
| tasks.append(create_drop_index(g)) | ||
|
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| await asyncio.gather(*tasks) |
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Refactoring test05_index_delete to use async functions and FalkorDB for database operations is a significant improvement, especially for tests involving multiple concurrent index operations. However, there are a few points to consider:
- Ensure that the
FalkorDBinstance and theBlockingConnectionPoolare properly configured for the test environment. - The loop in
create_drop_indexcreates and immediately drops indices in a tight loop, which might not allow enough time for the index to be fully created or utilized before deletion. Consider adding a brief sleep or validation step to ensure the index is created and operational before deletion. - The use of
await g.delete()seems incorrect or misplaced. Typically,deleteoperations target specific database objects like indices or nodes, not the graph object itself. Verify the intention and correct the operation if necessary.
- await g.delete()
+ # Ensure the correct deletion operation is performed, e.g., delete an index or nodeCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| async def create_drop_index(g): | |
| for _ in range(1, 30): | |
| await g.query("CREATE (n:L)-[:T]->(a:L)") | |
| await g.create_edge_range_index('T', 'p') | |
| await g.delete() | |
| async def run(self): | |
| pool = BlockingConnectionPool(max_connections=16, timeout=None) | |
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | |
| def create_drop_index(graph_id): | |
| env, db = Env() | |
| con = env.getConnection() | |
| for _ in range(1, 100): | |
| pipe = con.pipeline() | |
| pipe.execute_command("GRAPH.QUERY", graph_id, "CREATE (a:L), (n:L), (n)-[:T]->(a)") | |
| pipe.execute_command("GRAPH.QUERY", graph_id, "CREATE INDEX FOR ()-[n:T]-() ON (n.p)") | |
| pipe.execute() | |
| wait_for_indices_to_sync(Graph(con, graph_id)) | |
| con.execute_command("GRAPH.DELETE", graph_id) | |
| if "to_thread" not in dir(asyncio): | |
| create_drop_index(1) | |
| else: | |
| loop = asyncio.get_event_loop() | |
| tasks = [] | |
| for i in range(1, 20): | |
| tasks.append(loop.create_task(asyncio.to_thread(create_drop_index, i))) | |
| for i in range(1, 16): | |
| g = db.select_graph(str(i)) | |
| tasks.append(create_drop_index(g)) | |
| await asyncio.gather(*tasks) | |
| async def create_drop_index(g): | |
| for _ in range(1, 30): | |
| await g.query("CREATE (n:L)-[:T]->(a:L)") | |
| await g.create_edge_range_index('T', 'p') | |
| # Ensure the correct deletion operation is performed, e.g., delete an index or node | |
| async def run(self): | |
| pool = BlockingConnectionPool(max_connections=16, timeout=None) | |
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | |
| tasks = [] | |
| for i in range(1, 16): | |
| g = db.select_graph(str(i)) | |
| tasks.append(create_drop_index(g)) | |
| await asyncio.gather(*tasks) |
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Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (1)
- build/docker/Dockerfile.compiler (1 hunks)
Additional comments: 1
build/docker/Dockerfile.compiler (1)
- 3-3: The update of the base image from
falkordb/falkordb-build:latesttofalkordb/falkordb-build:ubuntuis a good practice for ensuring build consistency. However, it's crucial to verify that theubuntutag is fully compatible with the FalkorDB build requirements and does not introduce any performance regressions.
tests/Dockerfile
Outdated
| RUN curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg ;\ | ||
| echo "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/redis.list ;\ | ||
| sudo apt-get update ;\ | ||
| sudo apt-get install redis |
There was a problem hiding this comment.
The commands to install Redis introduce a few concerns and potential improvements:
- Use of
sudo: Typically, Docker builds run as the root user by default, making the use ofsudounnecessary. Unless there's a specific reason for a different user context, consider removingsudoto simplify the commands. - Security: While adding the Redis GPG key and repository is done securely, it's important to ensure that the base image (
falkordb/falkordb-compiler) includescurl,gpg, andlsb_releaseutilities. If not, these utilities should be installed beforehand. - Efficiency: The
apt-get updatecommand is called here and again in a subsequentRUNinstruction. To optimize build times and layer sizes, consider combiningapt-get updateandapt-get installcommands into a singleRUNinstruction where possible. This approach reduces the number of layers and ensures that the package index is fresh only when needed. - Version Pinning: The command
sudo apt-get install redisinstalls the latest version of Redis available in the repository. For reproducible builds, consider pinning the Redis version to ensure consistency across builds.
-RUN curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg ;\
- echo "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/redis.list ;\
- sudo apt-get update ;\
- sudo apt-get install redis
+RUN curl -fsSL https://packages.redis.io/gpg | gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg ;\
+ echo "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main" > /etc/apt/sources.list.d/redis.list ;\
+ apt-get update ;\
+ apt-get install -y redis=<version>Consider removing sudo, combining apt-get update and apt-get install commands, and pinning the Redis version for reproducibility.
Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| RUN curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg ;\ | |
| echo "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/redis.list ;\ | |
| sudo apt-get update ;\ | |
| sudo apt-get install redis | |
| RUN curl -fsSL https://packages.redis.io/gpg | gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg ;\ | |
| echo "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main" > /etc/apt/sources.list.d/redis.list ;\ | |
| apt-get update ;\ | |
| apt-get install -y redis=<version> |
| graph = self.db.select_graph('cache_eviction') | ||
|
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| # populate graph | ||
| graph.query("UNWIND range(0, 10000) as x CREATE ({v:'/'})") |
| @@ -93,9 +93,9 @@ def _choose_random(self, iter, k): | |||
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| def test_replication_permutations(self): | |||
| for scenario in self._choose_random(permutations(keys.keys()), 2): | |||
| print(f"scenario: {scenario}") | |||
| #print(f"scenario: {scenario}") | |||
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log this if there is a failure
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Review Status
Actionable comments generated: 19
Configuration used: CodeRabbit UI
Files selected for processing (8)
- tests/flow/test_cache.py (2 hunks)
- tests/flow/test_concurrent_query.py (3 hunks)
- tests/flow/test_index_create.py (2 hunks)
- tests/flow/test_pending_queries_limit.py (1 hunks)
- tests/flow/test_query_mem_limit.py (2 hunks)
- tests/flow/test_slowlog.py (6 hunks)
- tests/flow/test_stress.py (1 hunks)
- tests/flow/test_timeout.py (2 hunks)
Files skipped from review as they are similar to previous changes (2)
- tests/flow/test_slowlog.py
- tests/flow/test_timeout.py
Additional comments: 6
tests/flow/test_cache.py (1)
- 265-265: Regarding the previous comment by AviAvni about a potential omission on line 265, it seems the concern might have been addressed in the refactoring process. However, it's crucial to ensure that all necessary operations are included in the asynchronous tasks.
The concern appears to have been addressed through the refactoring to use
asyncioandFalkorDB. Further clarification from AviAvni would be helpful if there are specific omissions still of concern.tests/flow/test_concurrent_query.py (1)
- 33-52: The method
run_queries_concurrentlyuses an inner asynchronous functionrunand then callsasyncio.run(run(self, queries))to execute it. This approach is generally correct for running async code from a synchronous context. However, it's important to note thatasyncio.runcannot be called when another asyncio event loop is running in the same thread. Ensure that this method is not called from within another asyncio event loop to avoid runtime errors.tests/flow/test_index_create.py (4)
- 6-7: The addition of imports for
FalkorDBandBlockingConnectionPoolis aligned with the PR's objective to integrateFalkorDBand asynchronous operations. Ensure that these libraries are included in the project's dependencies, and their versions are compatible with the rest of the project.- 198-210: The setup for the
BlockingConnectionPooland the instantiation ofFalkorDBwithin therunmethod are correctly implemented for asynchronous database operations. However, ensure that themax_connectionsandtimeoutparameters of theBlockingConnectionPoolare appropriately configured for your test environment to avoid potential bottlenecks or resource issues.- 203-207: Creating multiple tasks for index operations and using
asyncio.gatherto run them concurrently is a good practice for improving test performance. However, consider the potential impact on the database and the testing environment when running a large number of concurrent operations. Ensure that the database is configured to handle this level of concurrency without adverse effects.- 212-212: Using
asyncio.run(run(self))to execute the asyncrunmethod is correct. However, ensure that this call is properly managed, especially in a testing context where the event loop might be managed differently (e.g., in a test suite setup or teardown). In some cases, directly callingasyncio.runcan lead to issues if an event loop is already running or if cleanup is needed between tests. Consider verifying the event loop's state before invokingasyncio.runor using alternative approaches to manage the event loop in test environments.
tests/flow/test_concurrent_query.py
Outdated
| import random | ||
| import asyncio | ||
| from common import * | ||
| from pathos.pools import ProcessPool as Pool | ||
| from pathos.helpers import mp as pathos_multiprocess | ||
| from falkordb.asyncio import FalkorDB | ||
| from redis.asyncio import BlockingConnectionPool | ||
| from redis.asyncio import Redis as AsyncRedis | ||
|
|
||
| import asyncio |
There was a problem hiding this comment.
The import of asyncio is duplicated on lines 2 and 8. Consider removing the duplicate to clean up the code.
- import asyncioCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| import random | |
| import asyncio | |
| from common import * | |
| from pathos.pools import ProcessPool as Pool | |
| from pathos.helpers import mp as pathos_multiprocess | |
| from falkordb.asyncio import FalkorDB | |
| from redis.asyncio import BlockingConnectionPool | |
| from redis.asyncio import Redis as AsyncRedis | |
| import asyncio | |
| import random | |
| import asyncio | |
| from common import * | |
| from falkordb.asyncio import FalkorDB | |
| from redis.asyncio import BlockingConnectionPool | |
| from redis.asyncio import Redis as AsyncRedis |
tests/flow/test_concurrent_query.py
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| class testConcurrentQueryFlow(FlowTestsBase): | ||
| def __init__(self): | ||
| self.env, self.db = Env() | ||
| self.conn = redis.Redis("localhost", self.env.port) | ||
| self.async_conn = AsyncRedis() | ||
| self.graph = self.db.select_graph(GRAPH_ID) |
There was a problem hiding this comment.
The __init__ method of testConcurrentQueryFlow class initializes self.async_conn but does not specify connection details like host and port, which might lead to connection issues if the default values do not match the test environment.
Consider initializing self.async_conn with the appropriate connection details to ensure consistent behavior across different environments.
tests/flow/test_concurrent_query.py
Outdated
| async def run(self): | ||
| self.graph.query("RETURN 1") | ||
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| pool = BlockingConnectionPool(max_connections=16, timeout=None) | ||
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| tasks = [] | ||
| for i in range(0, CLIENT_COUNT): | ||
| tasks.append(asyncio.create_task(g.delete())) | ||
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| # invoke queries | ||
| assertions = pool.map(delete_graph, [GRAPH_ID] * CLIENT_COUNT) | ||
| results = await asyncio.gather(*tasks, return_exceptions=True) | ||
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| # Exactly one thread should have successfully deleted the graph. | ||
| self.env.assertEquals(assertions.count(True), 1) | ||
| # Exactly one thread should have successfully deleted the graph. | ||
| self.env.assertEquals(len(results) - sum(isinstance(res, ResponseError) for res in results), 1) | ||
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| pool.clear() | ||
| # close the connection pool | ||
| await pool.aclose() | ||
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| asyncio.run(run(self)) |
There was a problem hiding this comment.
In the method test_04_concurrent_delete, the use of asyncio.gather with return_exceptions=True is a good practice for handling multiple asynchronous tasks that may raise exceptions. However, the assertion on line 126 assumes that exactly one task successfully deletes the graph, which might not always hold true in a highly concurrent environment. Consider adding more robust error handling or checks to ensure the test's assumptions always hold.
tests/flow/test_concurrent_query.py
Outdated
| self.env.assertEquals(resultset[0][0], 0) | ||
| ############################################################################################## | ||
| # Delete graph via Redis FLUSHALL. | ||
| ############################################################################################## | ||
| self.populate_graph() | ||
| q = """UNWIND (range(0, 10000)) AS x WITH x AS x WHERE (x / 900) = 1 RETURN x""" | ||
| queries = [q] * CLIENT_COUNT | ||
| barrier = manager.Barrier(CLIENT_COUNT) | ||
| barriers = [barrier] * CLIENT_COUNT | ||
| # invoke queries | ||
| m = pool.amap(thread_run_query, queries, barriers) | ||
| # validate result. | ||
| self.env.assertTrue(all(r.result_set[0][0] == 900 for r in results)) | ||
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| self.conn.flushall() | ||
| # Make sure Graph is empty, e.g. graph was deleted. | ||
| resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set | ||
| self.env.assertEquals(resultset[0][0], 0) | ||
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| # wait for processes to return | ||
| m.wait() | ||
| #------------------------------------------------------------------- | ||
| # Delete graph via Redis FLUSHALL. | ||
| #------------------------------------------------------------------- | ||
|
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| # get the results | ||
| results = m.get() | ||
| self.populate_graph() | ||
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| # validate result. | ||
| self.env.assertTrue(all([r["result_set"][0][0] == 900 for r in results])) | ||
| # invoke queries | ||
| tasks = [] | ||
| for i in range (CLIENT_COUNT): | ||
| tasks.append(asyncio.create_task(g.query(q))) | ||
| if i == CLIENT_COUNT / 2: | ||
| del_task = asyncio.create_task(self.async_conn.flushall()) | ||
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| # Make sure Graph is empty, e.g. graph was deleted. | ||
| resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set | ||
| self.env.assertEquals(resultset[0][0], 0) | ||
| ############################################################################################## | ||
| # Delete graph via GRAPH.DELETE. | ||
| ############################################################################################## | ||
| self.populate_graph() | ||
| q = """UNWIND (range(0, 10000)) AS x WITH x AS x WHERE (x / 900) = 1 RETURN x""" | ||
| queries = [q] * CLIENT_COUNT | ||
| barrier = manager.Barrier(CLIENT_COUNT) | ||
| barriers = [barrier] * CLIENT_COUNT | ||
| # invoke queries | ||
| m = pool.amap(thread_run_query, queries, barriers) | ||
| # wait for all async tasks | ||
| results = await asyncio.gather(*tasks) | ||
| await del_task | ||
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| self.graph.delete() | ||
| # validate result. | ||
| self.env.assertTrue(all(r.result_set[0][0] == 900 for r in results)) | ||
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| # wait for processes to return | ||
| m.wait() | ||
| # Make sure Graph is empty, e.g. graph was deleted. | ||
| resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set | ||
| self.env.assertEquals(resultset[0][0], 0) | ||
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| # get the results | ||
| results = m.get() | ||
| #------------------------------------------------------------------- | ||
| # Delete graph via GRAPH.DELETE. | ||
| #------------------------------------------------------------------- | ||
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| # validate result. | ||
| self.env.assertTrue(all([r["result_set"][0][0] == 900 for r in results])) | ||
| self.populate_graph() | ||
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| # Make sure Graph is empty, e.g. graph was deleted. | ||
| resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set | ||
| self.env.assertEquals(resultset[0][0], 0) | ||
| # invoke queries | ||
| tasks = [] | ||
| for i in range (CLIENT_COUNT): | ||
| tasks.append(asyncio.create_task(g.query(q))) | ||
| if i == CLIENT_COUNT / 2: | ||
| del_task = asyncio.create_task(g.delete()) | ||
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| pool.clear() | ||
| # wait for all async tasks | ||
| results = await asyncio.gather(*tasks) | ||
| await del_task | ||
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| # validate result. | ||
| self.env.assertTrue(all(r.result_set[0][0] == 900 for r in results)) | ||
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| # Make sure Graph is empty, e.g. graph was deleted. | ||
| resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set | ||
| self.env.assertEquals(resultset[0][0], 0) | ||
|
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| # close the connection pool | ||
| await pool.aclose() | ||
|
|
There was a problem hiding this comment.
The method test_05_concurrent_read_delete demonstrates an interesting approach to testing concurrent reads and deletes on a graph. However, it's crucial to ensure that the Redis and graph operations are correctly synchronized to accurately reflect the intended test scenario. Specifically, the use of self.async_conn.delete(GRAPH_ID) and self.async_conn.flushall() to delete the graph while other queries are running may not always guarantee the graph is deleted before the queries complete, depending on task scheduling. Consider adding explicit synchronization points or checks to ensure the operations occur in the intended order.
| async def run(self): | ||
| # connection pool with 16 connections | ||
| # blocking when there's no connections available | ||
| n = self.db.config_get("THREAD_COUNT") * 5 | ||
| pool = BlockingConnectionPool(max_connections=n, timeout=None) | ||
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| tasks = [] | ||
| for i in range(0, n): | ||
| tasks.append(asyncio.create_task(issue_query(self, g, SLOW_QUERY))) | ||
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| results = await asyncio.gather(*tasks) | ||
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| # close the connection pool | ||
| await pool.aclose() | ||
|
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| # return if error encountered | ||
| return any(results) | ||
|
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| return asyncio.run(run(self)) |
There was a problem hiding this comment.
The stress_server method has been refactored to use asyncio for concurrent query execution. This approach is efficient for simulating stress on the server. However, ensure that the BlockingConnectionPool is appropriately configured for your use case, as it might introduce bottlenecks if not sized correctly.
Review the configuration of BlockingConnectionPool to ensure it matches the expected load and concurrency level.
tests/flow/test_query_mem_limit.py
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| async def run(self, queries): | ||
| qs = [] # queries | ||
| should_fails = [] # should query i fail | ||
| thread_count = self.db.config_get("THREAD_COUNT") | ||
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| # connection pool blocking when there's no available connections | ||
| pool = BlockingConnectionPool(max_connections=thread_count, timeout=None) | ||
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| for q in queries: | ||
| qs.append(q[0]) | ||
| should_fails.append(q[1]) | ||
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| tasks = [] | ||
| for q in qs: | ||
| tasks.append(asyncio.create_task(g.query(q))) | ||
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| results = await asyncio.gather(*tasks, return_exceptions=True) | ||
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| # validate results | ||
| for i, res in enumerate(results): | ||
| if should_fails[i]: | ||
| # query should have failed | ||
| self.env.assertIn("Query's mem consumption exceeded capacity", str(res)) | ||
| else: | ||
| # make sure query did not throw an exception | ||
| self.env.assertNotEqual(type(res), redis.exceptions.ResponseError) | ||
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| # close the connection pool | ||
| await pool.aclose() | ||
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| asyncio.run(run(self, queries)) |
There was a problem hiding this comment.
The stress_server method in testQueryMemoryLimit class has been refactored to use asyncio for concurrent query execution. This is a good use of asynchronous programming to simulate stress testing with memory limits. However, the handling of should_fails could be improved for clarity and maintainability.
Consider refactoring the handling of expected failures to make the logic clearer and more maintainable. For example, pairing queries with their expected outcomes in a single data structure might simplify the logic.
tests/flow/test_stress.py
Outdated
| async def query_create(g, i): | ||
| param = {'v': i} | ||
| create_query = "CREATE (:Node {v:$v})<-[:HAVE]-(:Node {v:$v})-[:HAVE]->(:Node {v:$v})" | ||
| await g.query(create_query, param) | ||
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| conn = self.env.getConnection() | ||
| create_node_range_index(graphs[0], 'Node', 'v', sync=True) | ||
| async def query_read(g): | ||
| read_query = "MATCH (n:Node)-[:HAVE]->(m:Node) RETURN n.v, m.v LIMIT 1" | ||
| await g.ro_query(read_query) | ||
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| pool = Pool(nodes=5) | ||
| async def query_update(g, i): | ||
| param = {'v': i} | ||
| update_query = "MATCH (n:Node) WITH n LIMIT 1 SET n.x = $v" | ||
| await g.query(update_query, param) | ||
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| t1 = pool.apipe(create_nodes, graphs[0], n_creations) | ||
| async def query_delete(g): | ||
| delete_query = "MATCH (n:Node)-[:HAVE*]->(m:Node) WITH n, m LIMIT 1 DELETE n, m" | ||
| await g.query(delete_query) | ||
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| t2 = pool.apipe(delete_nodes, graphs[1], n_deletions) | ||
| async def create_nodes(g, i): | ||
| params = {'v': i} | ||
| await g.query("CREATE (:Node {v: $v})-[:R]->()", params) | ||
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| t3 = pool.apipe(read_nodes, graphs[2], n_reads) | ||
| async def delete_nodes(g): | ||
| await g.query("MATCH (n:Node) WITH n LIMIT 1 DELETE n") | ||
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| t4 = pool.apipe(update_nodes, graphs[3], n_updates) | ||
| async def delete_edges(g): | ||
| await g.query("MATCH (:Node)-[r]->() WITH r LIMIT 1 DELETE r") | ||
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| t5 = pool.apipe(BGSAVE_loop, self.env, conn, 10000) | ||
| async def update_nodes(g): | ||
| await g.query("MATCH (n:Node) WITH n LIMIT 1 SET n.v = 1") | ||
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| # wait for processes to join | ||
| t1.wait() | ||
| t2.wait() | ||
| t3.wait() | ||
| t4.wait() | ||
| t5.wait() | ||
| async def read_nodes(g): | ||
| await g.ro_query("MATCH (n:Node)-[:R]->() RETURN n LIMIT 1") | ||
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| # make sure we did not crashed | ||
| conn.ping() | ||
| conn.close() | ||
| async def merge_nodes_and_edges(g, i): | ||
| params = {'a': i, 'b': i * 10} | ||
| await g.query("MERGE (a:Node {v: $a}) MERGE (b:Node {v: $b}) MERGE (a)-[:R]->(b)", params) |
There was a problem hiding this comment.
The introduction of asynchronous query functions (query_create, query_read, etc.) is a good practice for handling database operations in a non-blocking manner. However, ensure that all async functions properly handle exceptions to avoid uncaught exceptions during runtime.
Consider adding exception handling within each async query function to ensure robustness and error resilience.
tests/flow/test_stress.py
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| async def run(self): | ||
| # connection pool with 16 connections | ||
| # blocking when there's no connections available | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None) | ||
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| n_tasks = 10000 # number of tasks to run | ||
| n_creations = 0.3 # create ratio | ||
| n_deletions = 0.7 # delete ratio | ||
| n_reads = 0.735 # read ratio | ||
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| tasks = [] | ||
| for i in range(0, n_tasks): | ||
| r = random.random() | ||
| if r < n_creations: | ||
| tasks.append(asyncio.create_task(query_create(g, i))) | ||
| elif r < n_deletions: | ||
| tasks.append(asyncio.create_task(query_delete(g))) | ||
| elif r < n_reads: | ||
| tasks.append(asyncio.create_task(query_read(g))) | ||
| else: | ||
| tasks.append(asyncio.create_task(query_update(g, i))) | ||
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| # wait for all tasks to complete | ||
| await asyncio.gather(*tasks) | ||
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| # close the connection pool | ||
| await pool.aclose() | ||
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| asyncio.run(run(self)) |
There was a problem hiding this comment.
The test00_stress method demonstrates an effective use of asyncio for concurrent database operations. However, the distribution of tasks (creation, deletion, read, update) could be made more explicit for readability and maintainability.
Consider refactoring the task distribution logic to use a more structured approach, such as defining task ratios outside the loop and using a more descriptive method for task selection.
tests/flow/test_stress.py
Outdated
| def test01_bgsave_stress(self): | ||
| async def run(self): | ||
| # connection pool with 16 connections | ||
| # blocking when there's no connections available | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None) | ||
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| n_tasks = 10000 # number of tasks to run | ||
| n_creations = 0.35 # create ratio | ||
| n_deletions = 0.7 # delete ratio | ||
| n_reads = 0.735 # read ratio | ||
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| # async tasks | ||
| conn = self.env.getConnection() | ||
| bgsave_task = asyncio.create_task(BGSAVE_loop(self.env, conn)) | ||
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| tasks = [] | ||
| for i in range(0, n_tasks): | ||
| r = random.random() | ||
| if r < n_creations: | ||
| tasks.append(asyncio.create_task(create_nodes(g, i))) | ||
| elif r < n_deletions: | ||
| tasks.append(asyncio.create_task(delete_nodes(g))) | ||
| elif r < n_reads: | ||
| tasks.append(asyncio.create_task(read_nodes(g))) | ||
| else: | ||
| tasks.append(asyncio.create_task(update_nodes(g))) | ||
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| # wait for all tasks to complete | ||
| await asyncio.gather(*tasks) | ||
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| # cancel BGSAVE task | ||
| bgsave_task.cancel() | ||
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| # close the connection pool | ||
| await pool.aclose() | ||
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| asyncio.run(run(self)) |
There was a problem hiding this comment.
The test01_bgsave_stress method includes an asynchronous BGSAVE operation alongside other database tasks. This is a complex scenario that is well-handled with asyncio. However, the cancellation of the bgsave_task without checking if it completed successfully or not might lead to unhandled exceptions or incomplete operations.
Ensure proper handling of the bgsave_task cancellation, including checking for completion and handling any exceptions that may have occurred.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 20
Configuration used: CodeRabbit UI
Files selected for processing (35)
- tests/flow/test_access_del_entity.py (7 hunks)
- tests/flow/test_bound_variables.py (2 hunks)
- tests/flow/test_cache.py (12 hunks)
- tests/flow/test_call_subquery.py (11 hunks)
- tests/flow/test_comprehension_functions.py (2 hunks)
- tests/flow/test_concurrent_query.py (3 hunks)
- tests/flow/test_config.py (4 hunks)
- tests/flow/test_constraint.py (4 hunks)
- tests/flow/test_edge_index_scans.py (5 hunks)
- tests/flow/test_encode_decode.py (2 hunks)
- tests/flow/test_foreach.py (13 hunks)
- tests/flow/test_function_calls.py (2 hunks)
- tests/flow/test_graph_deletion.py (13 hunks)
- tests/flow/test_graph_info.py (1 hunks)
- tests/flow/test_graph_merge.py (21 hunks)
- tests/flow/test_imdb.py (5 hunks)
- tests/flow/test_index_create.py (15 hunks)
- tests/flow/test_index_delete.py (1 hunks)
- tests/flow/test_index_scans.py (9 hunks)
- tests/flow/test_optimizations_plan.py (4 hunks)
- tests/flow/test_optional_match.py (1 hunks)
- tests/flow/test_order_by.py (1 hunks)
- tests/flow/test_pagerank.py (5 hunks)
- tests/flow/test_params.py (1 hunks)
- tests/flow/test_path.py (1 hunks)
- tests/flow/test_path_filter.py (1 hunks)
- tests/flow/test_pending_queries_limit.py (3 hunks)
- tests/flow/test_point.py (1 hunks)
- tests/flow/test_prev_rdb_decode.py (2 hunks)
- tests/flow/test_rdb_load.py (2 hunks)
- tests/flow/test_star_projections.py (1 hunks)
- tests/flow/test_stress.py (1 hunks)
- tests/flow/test_timeout.py (6 hunks)
- tests/flow/test_undo_log.py (1 hunks)
- tests/flow/test_variable_length_traversals.py (4 hunks)
Files skipped from review as they are similar to previous changes (5)
- tests/flow/test_graph_deletion.py
- tests/flow/test_imdb.py
- tests/flow/test_index_create.py
- tests/flow/test_stress.py
- tests/flow/test_timeout.py
Additional comments: 79
tests/flow/test_prev_rdb_decode.py (2)
- 3-3: Introducing the
GRAPH_IDconstant is a good practice for maintainability and readability. It centralizes the graph name, making future changes easier and reducing the risk of typos or inconsistencies.- 19-21: Using
GRAPH_IDin therestoreandselect_graphcalls enhances consistency and maintainability. This change ensures that the graph name is defined in a single place, reducing the risk of errors if the graph name changes in the future.tests/flow/test_pending_queries_limit.py (1)
- 56-56: The previous concern about the use of
asyncio.runin a class method that could be called multiple times is still valid. Usingasyncio.runcan lead to issues if there's already a running event loop.tests/flow/test_rdb_load.py (1)
- 34-39: > 📝 NOTE
This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [1-1]
The removal of key restoration, flushing, and an unnecessary assertion in the
test_rdb_loadmethod could potentially streamline the test flow. However, it's important to ensure that these changes do not compromise the test's ability to validate the functionality it's designed to test.tests/flow/test_bound_variables.py (2)
- 6-6: Updating the
GRAPH_IDvalue to "bound_variables" is a straightforward change that likely aligns with the test's specific focus on bound variables. This change helps in maintaining clarity and relevance in the test suite.- 96-96: Replacing
self.env.flush()withself.graph.delete()in thetest06_override_bound_with_labelfunction is a good practice for targeted cleanup. This change ensures that only the relevant graph data is cleared, potentially reducing the impact on other tests and improving test isolation.tests/flow/test_pagerank.py (3)
- 9-9: Adding
self.conn = self.env.getConnection()in the__init__method is a good practice, as it ensures that a Redis connection is available for use in setup or teardown methods, as well as in the tests themselves. This change improves the structure and maintainability of the test class.- 13-13: Adding
self.conn.delete(GRAPH_ID)in thesetUpmethod is an excellent way to ensure a clean state before each test runs. This targeted deletion is more efficient and safer than flushing the entire database, as it avoids affecting unrelated data.- 68-68: Replacing
self.env.cmd('flushall')withself.conn.delete(GRAPH_ID)in the test methods is a positive change that enhances test isolation by ensuring that only relevant data is cleared before each test. This approach minimizes the risk of test pollution and makes the tests more reliable.Also applies to: 91-91
tests/flow/test_params.py (1)
- 14-15: Switching from
setUptotearDownfor graph deletion is a significant change. This ensures that the graph is deleted after each test runs, rather than before. This change is beneficial for ensuring that each test starts with a clean state, as it prevents potential interference from leftover data from previous tests. However, it's important to ensure that the first test method or an initial setup method creates the necessary graph structure, as the graph will not exist at the start of each test.tests/flow/test_star_projections.py (1)
- 169-169: Replacing
self.env.flush()withself.graph.delete()changes the behavior of how data is cleared between tests. Whileflush()might have cleared the entire database,delete()specifically removes the graph and its associated data. This change ensures that only the data related to this specific graph is removed, which can be more precise and potentially avoid affecting other tests or data unintentionally. However, it's important to ensure that this change aligns with the intended test setup and cleanup logic.tests/flow/test_point.py (1)
- 8-13: > 📝 NOTE
This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [1-1]
The removal of the
setUpmethod, which was previously used to flush the environment, implies that each test withintest_point.pynow starts without a predefined environment cleanup. This change requires careful consideration to ensure that each test method either cleans up after itself or is designed to work correctly regardless of the state left by previous tests. It's crucial to verify that the absence of asetUpmethod does not lead to flaky tests due to leftover data from previous test runs.tests/flow/test_config.py (3)
- 5-5: Adding the
GRAPH_IDconstant is a good practice for maintaining consistency and readability in the test code. It centralizes the definition of the graph ID used throughout the tests, making it easier to update or reference in multiple places.- 11-11: Changing
self.redis_graphtoself.graphimproves consistency and readability within the test class. This change aligns the variable name with its purpose more clearly, indicating that it represents a graph object rather than a RedisGraph-specific connection or client.- 266-266: Replacing
flush()withdelete()in the context oftest11_set_get_node_creation_bufferensures that only the specific graph's data is cleared, rather than flushing the entire database. This targeted approach is beneficial for test isolation and prevents unintended side effects on other tests or data. However, it's important to ensure that this change aligns with the intended test setup and cleanup logic.tests/flow/test_path_filter.py (2)
- 15-15: Adding
self.conn = self.env.getConnection()in the__init__method is a good practice as it initializes the connection once for all tests, potentially improving performance by reusing the connection. However, ensure thatself.envis properly initialized before this call to avoid anyNoneTypeerrors.- 18-18: Replacing
self.env.flush()withself.conn.delete(GRAPH_ID)in thesetUpmethod is a targeted approach to clean up the test environment before each test. This change ensures that only data related toGRAPH_IDis cleared, preventing unintended side effects on other tests or data. However, ensure thatGRAPH_IDis correctly defined and unique across tests to avoid conflicts.tests/flow/test_encode_decode.py (3)
- 6-6: Introducing the
GRAPH_IDconstant is a good practice for maintainability and consistency. It centralizes the graph ID definition, making it easier to update or reference in multiple places. Ensure that this constant is used consistently throughout the test file.- 23-23: Initializing
self.graphin the__init__method to select a graph based onGRAPH_IDis a positive change. It ensures that all test methods operate on the same graph instance, which can improve test consistency and reduce boilerplate code.- 26-26: The addition of the
tearDownmethod to delete the graph after each test is a good practice for test isolation. It ensures that each test starts with a clean state, reducing the risk of tests affecting each other. However, ensure that this method is called correctly by the testing framework after each test.tests/flow/test_cache.py (1)
- 4-5: The addition of imports for
FalkorDBandBlockingConnectionPoolaligns with the PR's objective to optimize the testing framework by utilizing asynchronous tasks for database interactions. This change should enhance performance and maintainability.tests/flow/test_path.py (2)
- 12-12: Initializing the
connattribute in the__init__method by obtaining a connection from the environment is a good practice. It ensures that each test instance has a dedicated connection to the database, which can improve test isolation and reliability.- 20-20: Modifying the
setUpmethod to delete a specific graph using the connection instead of flushing the entire environment is a more targeted approach. This change can lead to faster test execution times and less interference between tests, as it only affects the relevant graph.tests/flow/test_optional_match.py (1)
- 268-268: Replacing
self.env.flush()withself.graph.delete()intest23_optional_after_applychanges the behavior of the test setup by clearing the entire graph instead of just flushing the environment. This is a significant change that could affect the test's intention and outcome.Ensure that this change aligns with the test's objectives. If the goal is to reset the graph to a clean state before the test,
self.graph.delete()is appropriate. However, if the original intention was to perform a lighter cleanup or reset specific aspects of the environment, this change might be too aggressive.tests/flow/test_graph_info.py (1)
- 327-328: The change from flushing the entire database to deleting a specific graph by its ID (
self.conn.delete(GRAPH_ID)) is a positive improvement in terms of both performance and precision. This targeted approach minimizes the impact on unrelated data and tests, ensuring a cleaner and more efficient test environment.tests/flow/test_variable_length_traversals.py (4)
- 227-227: Replacing direct Redis connections with
self.graph.delete()for clearing previous data is a significant improvement in terms of code clarity and maintainability. This approach leverages the graph abstraction layer, making the intention behind the code clearer and reducing the risk of unintended side effects on the database state.- 266-266: The use of
self.graph.delete()intest12_close_cyclefor clearing the graph before creating a new structure is consistent with best practices for test setup. This ensures that each test starts with a clean state, improving test reliability.- 298-298: Applying
self.graph.delete()intest13_fanoutto clear the graph before constructing a new tree structure demonstrates a good understanding of test isolation principles. This practice helps in avoiding test interdependencies and ensures that tests can be run in any order without affecting each other.- 339-339: The adoption of
self.graph.delete()intest14_no_hopsfor clearing the graph data before each test case is commendable. It aligns with the principle of ensuring a clean slate for each test, which is crucial for the accuracy and reliability of test outcomes.tests/flow/test_concurrent_query.py (8)
- 15-18: The refactoring of
thread_run_queryto an asynchronous methoddelete_graphis a positive change that aligns with the PR's objectives. This method simplifies the deletion of graphs in an asynchronous context, improving test efficiency and readability.- 114-133: The previous comment regarding the use of
asyncio.gatherwithreturn_exceptions=Trueintest_04_concurrent_deleteand the assumption about exactly one task successfully deleting the graph is still valid. The test's logic might not always hold true in a highly concurrent environment, suggesting a need for more robust error handling or checks.- 137-198: The method
test_05_concurrent_read_deletedemonstrates a complex test scenario involving concurrent reads and a delete operation on the graph. The use of asynchronous operations here is appropriate and aligns with the PR's objectives. However, the separation of deletion via Redis DEL key and GRAPH.DELETE could be more clearly documented to explain the rationale behind testing both methods. Additionally, ensure thatasync_conn.delete(GRAPH_ID)andg.delete()are correctly awaited to prevent race conditions.- 203-234: In
test_06_concurrent_write_delete, the combination of a heavy write operation and a concurrent delete operation on the graph is a good stress test for concurrency. The use ofasyncio.gatherwithreturn_exceptions=Trueis correctly applied here. However, ensure that the connection pool used is appropriate for asynchronous operations, as mentioned earlier regarding the use ofBlockingConnectionPool.- 239-286: The
test_07_concurrent_write_renamemethod correctly tests the scenario of concurrently writing to a graph and renaming it. This test is crucial for ensuring atomicity and consistency in concurrent operations. The use of asynchronous operations is consistent with the PR's objectives. However, as with previous comments, ensure the use of an appropriate asynchronous connection pool.- 291-324:
test_08_concurrent_write_replaceintroduces a scenario where a heavy write operation is concurrently executed with a replace operation on the graph ID. This test is well-designed to assess the robustness of concurrent operations handling in FalkorDB. Ensure that the handling ofResponseErroris comprehensive and correctly interprets the possible outcomes of the concurrent operations.- 356-406: The
test_10_write_starvationmethod addresses an important aspect of concurrency: ensuring that write operations are not excessively delayed by read operations. This test is well-constructed and aligns with the PR's objectives to enhance the testing framework's efficiency and robustness. The methodology for measuring the delay of the write operation is innovative and should provide valuable insights into the database's concurrency handling.- 410-432:
test_11_concurrent_resize_zero_matrixtests the database's ability to handle concurrent read and write operations that resize a matrix to zero. This test is relevant for assessing the database's handling of complex concurrent operations. Ensure that the asynchronous operations are correctly managed and that the connection pool used is suitable for asynchronous tasks.tests/flow/test_undo_log.py (3)
- 9-9: Renaming
redis_contoconnimproves readability by providing a more concise and standard naming convention for database connections. This change aligns with common practices in Python database interaction.- 13-13: Replacing
flushall()withdelete()in thetearDownmethod is a significant change. Whileflushall()clears the entire database,delete()is more targeted. However, it's crucial to ensure thatdelete()is called with the appropriate keys to avoid leaving data from other tests. Ifgraph.delete()is designed to clear all data related to thegraphobject, this change is appropriate. Otherwise, there might be a risk of test data pollution.# Ensure graph.delete() effectively clears all relevant data
- 6-16: > 📝 NOTE
This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [1-1]
It's good practice to ensure that test classes inherit from
unittest.TestCaseor a similar testing framework class. This inheritance provides a structured format for writing tests and utilizing a wide range of assertions and setup/teardown methods. IftestUndoLogis intended to be used with a specific testing framework that does not require inheriting fromunittest.TestCase, please ensure that it's compatible and follows the framework's guidelines.# Verify if testUndoLog is compatible with the intended testing frameworktests/flow/test_comprehension_functions.py (2)
- 374-374: The use of
self.graph.delete()to clear data before setting up new test conditions is a good practice as it ensures a clean state for each test. This change enhances the clarity and maintainability of the test suite.- 421-421: Replacing direct Redis connection operations with graph object methods like
self.graph.delete()for data cleanup is consistent with the previous change and improves the test code's readability and maintainability. It's important to ensure that such cleanup methods are called at appropriate times to avoid unintended side effects on other tests.tests/flow/test_foreach.py (2)
- 4-4: The change to use a specific
GRAPH_ID("foreach") instead of a generic one ("G") is a good practice for clarity and avoiding potential conflicts with other tests that might be running concurrently.- 94-94: Replacing environment flushing with graph deletion (
self.graph.delete()) before each test case is a significant improvement. This ensures a clean state for each test, reducing the risk of side effects from previous tests. However, it's crucial to verify that this change does not inadvertently increase the test suite's execution time significantly.Also applies to: 181-181, 206-206, 235-235, 257-257, 274-274, 374-374, 452-452, 501-501, 580-580, 644-644
tests/flow/test_optimizations_plan.py (3)
- 456-456: The replacement of
self.env.flush()withself.graph.delete()intest29_optimize_label_scan_cached_label_idis a significant change. This adjustment seems to be aimed at clearing the graph state more specifically rather than flushing the entire environment, which could be more efficient and targeted. However, it's crucial to ensure that this change does not affect the test's ability to accurately assess the caching and retrieval of label IDs after they are created.- 485-485: Similar to the previous comment, the replacement of
self.env.flush()withself.graph.delete()intest30_optimize_mandatory_labels_order_onlyis observed. This change is consistent with the aim of making test state management more precise. As long asself.graph.delete()effectively resets the graph to a clean state without removing any necessary setup beyond the scope of the test, this change is beneficial for test clarity and efficiency.- 456-456: The removal of
self.env.flush()calls intest28_optimize_label_scan_switch_labelsandtest31_optimize_optional_labelssuggests an effort to streamline the test setup by eliminating potentially unnecessary operations. It's important to verify that the removal of these flush operations does not leave residual data that could interfere with the tests' expected outcomes. Assuming the tests' setup and teardown are correctly managed to ensure a clean state, this optimization could improve test performance.Verification successful
Given the limitations of the provided script, let's proceed with a more targeted approach to verify the impact of removing
self.env.flush()calls from the tests. We'll look into the test file for any setup or teardown methods, search for comments or documentation related toself.env.flush(), and check the commit history for relevant changes to these tests.
The analysis of the commit history and the lack of explicit setup or teardown methods or comments related to
self.env.flush()suggest that the removal ofself.env.flush()calls and the introduction ofself.graph.delete()in some cases were deliberate decisions aimed at optimizing the test setup. The changes appear to be a considered approach to maintaining a clean test environment, likely after evaluating the effectiveness ofself.graph.delete()as an alternative toself.env.flush(). Without direct evidence indicating negative impacts on test outcomes, it's reasonable to conclude that these optimizations do not compromise test integrity.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that the removal of self.env.flush() does not affect test outcomes negatively. # This script is a placeholder for manual verification steps that should be taken to ensure test integrity. echo "Manual verification needed to ensure test integrity after removal of self.env.flush() calls."Length of output: 194
Script:
#!/bin/bash # Search for setup or teardown methods in the test file echo "Searching for setup or teardown methods:" rg "def (setup|teardown)" tests/flow/test_optimizations_plan.py # Search for comments or documentation related to self.env.flush() within the test file echo -e "\nSearching for comments or documentation related to self.env.flush():" rg "self.env.flush" tests/flow/test_optimizations_plan.py -C 3 # Check the commit history for relevant changes to these tests echo -e "\nChecking commit history for relevant changes:" git log -p -- tests/flow/test_optimizations_plan.py | grep -C 10 "self.env.flush"Length of output: 8874
tests/flow/test_edge_index_scans.py (8)
- 4-5: Adding a
GRAPH_IDconstant and using it in thesetUpmethod is a good practice for maintainability and readability. It centralizes the graph ID definition, making future changes easier.- 13-13: Using the
GRAPH_IDconstant in thesetUpmethod to select the graph enhances consistency and maintainability. This change ensures that the graph ID is defined in a single place, reducing the risk of errors in case of future modifications.- 18-18: Replacing
self.graph.delete()in thetearDownmethod without any additional context seems appropriate. However, it's crucial to ensure that this does not lead to resource leaks or leftover data that could affect subsequent tests. Assuming the graph is correctly initialized for each test, this change should be safe.- 521-521: The explicit call to
self.graph.delete()intest14_self_referencing_edgebefore reinitializing the graph for a specific test scenario is a good practice. It ensures that the test environment is clean and that the test's outcome is not influenced by leftover data from previous tests. This is particularly important for tests that rely on the database's state, such as those involving indices and graph scans.- 535-535: Creating an index on the edge property
vwithintest14_self_referencing_edgeis a critical operation for testing index scans. This change aligns with the objective of optimizing and refining the testing framework by ensuring that index-related functionalities are thoroughly tested. It's important to verify that the index creation is successful and that it does not introduce performance issues or interfere with other tests.- 541-541: Validating the utilization of the index through the execution plan is a best practice for testing database optimizations. This ensures that the index is not only created but also effectively used by the query optimizer. This kind of validation is crucial for performance-related changes and enhancements.
- 545-545: Executing a query to test the index scan and comparing the results with a non-indexed query is a solid approach to validate the index's functionality. This method ensures that the index provides the expected benefits in terms of query performance and accuracy. It's important to ensure that the test covers various scenarios to fully validate the index's effectiveness.
- 550-550: Retrieving results without using the index and comparing them with indexed results is a good practice to ensure consistency and correctness of the index implementation. This step is crucial for validating that the index does not alter the expected results, only the performance characteristics of the query.
tests/flow/test_graph_merge.py (3)
- 5-5: The introduction of the
GRAPH_IDconstant is a good practice for maintainability and readability. It centralizes the graph ID definition, making it easier to update or reference in future tests.- 10-10: Refactoring global variables
redis_graphandgraph_2to an instance variableself.graphwithin thetestGraphMergeFlowclass improves encapsulation and test isolation. This change ensures that each test instance operates on its own graph instance, reducing the risk of side effects between tests.- 2-148: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [15-637]
The consistent use of
self.graphacross all test methods to execute queries instead of previously used global variables is a significant improvement. It leverages the instance variable setup in the constructor, ensuring that all tests are clearly scoped and isolated to their graph instance. This change enhances the tests' readability and maintainability by making the data flow within each test more explicit.tests/flow/test_index_scans.py (7)
- 7-8: The introduction of
GRAPH_IDas a constant to replacesocial_utils.graph_nameis a good practice for maintainability and readability. It centralizes the graph name definition, making future changes easier.- 16-16: Refactoring the index creation methods to be called on the graph object directly simplifies the code and improves readability. This change aligns with the principle of encapsulation, allowing the graph object to manage its indices.
- 21-21: Removing redundant graph selection calls and replacing them with graph deletion in the
tearDownmethod is a logical improvement. It ensures that each test case starts with a clean state, enhancing test reliability.- 24-25: Directly using the graph object for index creation within the
build_indicesmethod is a clean and efficient approach. It makes the code more concise and focused on the task at hand.- 225-225: The deletion of the graph at the beginning of
test07_index_scan_and_idensures a clean state for the test. This is a good practice for test isolation and reliability.- 693-693: Creating an index in
test_23_do_not_utilize_index_after issuing a query that does not utilize an index is a good way to test the behavior of the system before and after index creation. This ensures that the presence of an index does not inadvertently affect queries that should not use it.- 698-698: Re-issuing the same query after index creation in
test_23_do_not_utilize_index_and expecting no index scan operation is a valid test case. It verifies that the system correctly decides when not to use an index, based on the query structure and conditions.tests/flow/test_constraint.py (3)
- 12-12: Replacing
self.con.flushall()withself.con.delete(GRAPH_ID)is a targeted approach to clearing the database, focusing on removing only the data related to the specificGRAPH_ID. This change should improve test performance by avoiding the complete flush of the database, which might contain data unrelated to the tests being run. However, ensure thatGRAPH_IDis uniquely defined for each test case or test suite to prevent unintended data deletion across different tests.- 556-556: The same improvement applied to the
testConstraintEdgesclass as in thetestConstraintNodesclass, by replacingself.con.flushall()withself.con.delete(GRAPH_ID), is consistent and maintains the targeted approach to clearing the database. This consistency across different test classes is good for maintainability and readability.- 984-984: The replacement of
self.source.flushall()withself.source.delete(GRAPH_ID)in thetestConstraintReplicationclass further extends the targeted database clearing approach to replication tests. This change is consistent with the previous improvements and ensures that the database clearing is focused and efficient. It's important to verify that the replication tests are correctly isolating their data byGRAPH_IDto avoid any cross-test data interference.tests/flow/test_call_subquery.py (10)
- 817-817: Replacing the database clearing operations with
self.graph.delete()is a significant change that streamlines the setup process for tests by ensuring a consistent approach to graph cleanup. This change should improve the clarity of the test setup and potentially reduce the time taken for test execution by focusing on graph-level operations rather than broader database clearing operations.- 918-918: The introduction of
self.graph.delete()before certain tests is a good practice, ensuring that each test starts with a clean state. This is particularly important for tests that rely on the absence or presence of specific data within the graph to validate the test conditions accurately.- 1320-1320: The use of
self.graph.delete()before setting up the graph for the testtest22_indexesis a good practice. It ensures that the test environment is clean and that the test results are reliable. This change aligns with the objective of optimizing the testing framework by making the setup process more efficient and consistent.- 1355-1355: Clearing the graph at the beginning of
test25_named_pathswithself.graph.delete()is a prudent approach to ensure that the test environment is in a known state before executing the test logic. This practice enhances the reliability of the test outcomes by eliminating potential interference from leftover data from previous tests.- 1450-1450: The use of
self.graph.delete()at the start oftest26_eager_returningensures that the graph is in a clean state before the test runs. This approach is consistent with the overall objective of the PR to optimize the testing framework by removing redundant operations and focusing on necessary setup steps for each test.- 1685-1685: Initiating
test27_read_no_with_after_writing_subquerywithself.graph.delete()is a good practice, ensuring that the test operates on a clean slate. This methodical approach to test setup is crucial for maintaining the integrity and reliability of the test suite, especially when testing the behavior of subqueries in various scenarios.- 1750-1750: The decision to start
test28_resetwithself.graph.delete()is in line with best practices for test setup, ensuring that each test case begins with a clean graph. This practice is essential for tests that involve complex operations and manipulations within the graph, as it helps to avoid unintended side effects from residual data.- 1784-1784: Clearing the graph at the beginning of
test29_rewrite_star_projectionswithself.graph.delete()is a prudent measure to ensure the accuracy and reliability of the test results. This step is particularly important in tests that involve projections and data manipulation, as it ensures that the test conditions are controlled and predictable.- 1975-1975: The use of
self.graph.delete()at the start oftest30_surrounding_matchesis a good practice, ensuring that the graph is in a clean state before the test runs. This approach is consistent with the overall objective of the PR to optimize the testing framework by removing redundant operations and focusing on necessary setup steps for each test.- 2018-2018: Initiating
test31_following_scanswithself.graph.delete()ensures that the test operates on a clean slate. This methodical approach to test setup is crucial for maintaining the integrity and reliability of the test suite, especially when testing the behavior of subqueries in various scenarios.tests/flow/test_function_calls.py (1)
- 2246-2252: > 📝 NOTE
This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [1-1]
The file
test_function_calls.pyhas been reviewed in its entirety. The change fromflush()todelete()for thegraphobject, as mentioned in the PR summary, aligns with the objective of updating graph manipulation logic within the test cases. No issues related to this change were found.
| class testUndoLog(): | ||
| def __init__(self): | ||
| self.env, self.db = Env() | ||
| self.redis_con = self.env.getConnection() | ||
| self.conn = self.env.getConnection() | ||
| self.graph = self.db.select_graph(GRAPH_ID) | ||
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| def tearDown(self): | ||
| self.redis_con.flushall() | ||
| self.graph.delete() | ||
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| def test01_undo_create_node(self): | ||
| try: |
There was a problem hiding this comment.
📝 NOTE
This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [1-1]
The file lacks a module docstring that provides a high-level overview of its purpose and functionality. Adding a docstring at the beginning of the file would improve code documentation and maintainability.
"""Module docstring: This module contains tests for undo log functionality in FalkorDB."""📝 NOTE
This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [1-1]
The use of try blocks without specifying exception types in test methods (e.g., test01_undo_create_node, test02_undo_create_edge, etc.) is not a best practice. It's recommended to catch specific exceptions or use the testing framework's assertion methods designed for expecting exceptions. This approach improves the readability and robustness of the tests.
# Example refinement for catching specific exceptions or using assertion methods📝 NOTE
This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [1-1]
The tests do not include any comments or docstrings explaining their purpose or the specific conditions they are testing. Adding comments or docstrings to each test method would significantly improve the readability and maintainability of the test suite.
# Example comment or docstring for a test method| # create graph | ||
| g.query("RETURN 1") | ||
| self.graph.query("RETURN 1") |
There was a problem hiding this comment.
The query "RETURN 1" in test_23_do_not_utilize_index_ serves as a placeholder operation. While it might seem unnecessary, it's important to ensure that the test setup is consistent across different test cases. However, it's worth considering if there's a more meaningful setup operation that could be performed here to better align with the test's objectives.
Consider replacing the placeholder query with a setup operation that more directly relates to the test's focus, enhancing the test's clarity and relevance.
tests/flow/test_cache.py
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| async def run(self): | ||
| # connection pool with 16 connections | ||
| # blocking when there's no connections available | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None) | ||
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | ||
| g = db.select_graph('cache_eviction') | ||
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| tasks = [] | ||
| for i in range(1, 50): | ||
| # random param name | ||
| param_name = 'p_' + str(i) | ||
| q = f"UNWIND range(0, 50000) as x WITH x WHERE x >= ${param_name} RETURN count(x)" | ||
| params = {param_name : 0} | ||
| tasks.append(asyncio.create_task(g.query(q,params))) | ||
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| results = await asyncio.gather(*tasks) | ||
| for r in results: | ||
| self.env.assertEqual(r.result_set[0][0], 50001) | ||
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| await pool.aclose() | ||
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| asyncio.run(run(self)) |
There was a problem hiding this comment.
The refactoring of test_14_cache_eviction to use asyncio for concurrent query execution is a significant improvement. However, as previously mentioned, the use of asyncio.run in a potentially repeatedly called method could lead to issues with existing event loops. It's crucial to ensure that this method is compatible with existing event loops, possibly by using a different approach to running asynchronous tasks if the method is expected to be called multiple times in the same runtime.
Consider refactoring to ensure compatibility with existing event loops, similar to the suggestion made for test_pending_queries_limit.py.
tests/flow/test_concurrent_query.py
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| def run_queries_concurrently(self, queries): | ||
| async def run(self, queries): | ||
| connection_kwargs = { 'decode_responses': True } | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None, **connection_kwargs) | ||
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| return results | ||
| tasks = [] | ||
| for q in queries: | ||
| tasks.append(asyncio.create_task(g.query(q))) | ||
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| class testConcurrentQueryFlow(FlowTestsBase): | ||
| def __init__(self): | ||
| self.env, self.db = Env() | ||
| # skip test if we're running under Valgrind | ||
| if VALGRIND: | ||
| self.env.skip() # valgrind is not working correctly with multi processing | ||
| # wait for all tasks to complete | ||
| results = await asyncio.gather(*tasks) | ||
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| self.conn = redis.Redis("localhost", self.env.port) | ||
| self.graph = self.db.select_graph(GRAPH_ID) | ||
| # close the connection pool | ||
| await pool.aclose() | ||
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| self.populate_graph() | ||
| return results | ||
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| return asyncio.run(run(self, queries)) |
There was a problem hiding this comment.
The method run_queries_concurrently has been significantly refactored to support asynchronous operations, which is in line with the PR's objectives. However, nesting the run function within run_queries_concurrently and then calling asyncio.run(run(self, queries)) is an unconventional pattern that could be simplified. Consider refactoring to directly make run_queries_concurrently an asynchronous method and invoking it with asyncio.run where needed, to improve readability and maintainability.
- def run_queries_concurrently(self, queries):
- async def run(self, queries):
+ async def run_queries_concurrently(self, queries):Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def run_queries_concurrently(self, queries): | |
| async def run(self, queries): | |
| connection_kwargs = { 'decode_responses': True } | |
| pool = BlockingConnectionPool(max_connections=16, timeout=None, **connection_kwargs) | |
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | |
| g = db.select_graph(GRAPH_ID) | |
| return results | |
| tasks = [] | |
| for q in queries: | |
| tasks.append(asyncio.create_task(g.query(q))) | |
| class testConcurrentQueryFlow(FlowTestsBase): | |
| def __init__(self): | |
| self.env, self.db = Env() | |
| # skip test if we're running under Valgrind | |
| if VALGRIND: | |
| self.env.skip() # valgrind is not working correctly with multi processing | |
| # wait for all tasks to complete | |
| results = await asyncio.gather(*tasks) | |
| self.conn = redis.Redis("localhost", self.env.port) | |
| self.graph = self.db.select_graph(GRAPH_ID) | |
| # close the connection pool | |
| await pool.aclose() | |
| self.populate_graph() | |
| return results | |
| return asyncio.run(run(self, queries)) | |
| async def run_queries_concurrently(self, queries): | |
| connection_kwargs = { 'decode_responses': True } | |
| pool = BlockingConnectionPool(max_connections=16, timeout=None, **connection_kwargs) | |
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | |
| g = db.select_graph(GRAPH_ID) | |
| tasks = [] | |
| for q in queries: | |
| tasks.append(asyncio.create_task(g.query(q))) | |
| # wait for all tasks to complete | |
| results = await asyncio.gather(*tasks) | |
| # close the connection pool | |
| await pool.aclose() | |
| return results |
| self.env.flush() | ||
| # Make a new graph object with no cache (problematic label info) | ||
| self.graph = self.db.select_graph(GRAPH_ID) | ||
| self.graph.delete() |
There was a problem hiding this comment.
The repeated use of self.graph.delete() in multiple test methods suggests a potential for refactoring. Consider implementing a setup or teardown method that runs this cleanup automatically before or after each test. This would adhere to the DRY principle and simplify future test modifications.
+ def setUp(self):
+ self.graph.delete()And then remove the individual self.graph.delete() calls from each test method.
Also applies to: 181-181, 206-206, 235-235, 257-257, 274-274, 374-374, 452-452, 501-501, 580-580, 644-644
Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| self.graph.delete() | |
| def setUp(self): | |
| self.graph.delete() |
tests/flow/test_concurrent_query.py
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| pool = BlockingConnectionPool(max_connections=16, timeout=None, **connection_kwargs) | ||
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| return results | ||
| tasks = [] | ||
| for q in queries: | ||
| tasks.append(asyncio.create_task(g.query(q))) | ||
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| class testConcurrentQueryFlow(FlowTestsBase): | ||
| def __init__(self): | ||
| self.env, self.db = Env() | ||
| # skip test if we're running under Valgrind | ||
| if VALGRIND: | ||
| self.env.skip() # valgrind is not working correctly with multi processing | ||
| # wait for all tasks to complete | ||
| results = await asyncio.gather(*tasks) | ||
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| self.conn = redis.Redis("localhost", self.env.port) | ||
| self.graph = self.db.select_graph(GRAPH_ID) | ||
| # close the connection pool | ||
| await pool.aclose() |
There was a problem hiding this comment.
Using a BlockingConnectionPool within an asynchronous context is contradictory. The purpose of using asyncio and asynchronous operations is to avoid blocking, yet BlockingConnectionPool is designed for synchronous code. Consider using AsyncConnectionPool or equivalent asynchronous connection pool options provided by the redis.asyncio module to ensure non-blocking behavior.
- pool = BlockingConnectionPool(max_connections=16, timeout=None, **connection_kwargs)
+ pool = AsyncConnectionPool(max_connections=16, timeout=None, **connection_kwargs)Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| pool = BlockingConnectionPool(max_connections=16, timeout=None, **connection_kwargs) | |
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | |
| g = db.select_graph(GRAPH_ID) | |
| return results | |
| tasks = [] | |
| for q in queries: | |
| tasks.append(asyncio.create_task(g.query(q))) | |
| class testConcurrentQueryFlow(FlowTestsBase): | |
| def __init__(self): | |
| self.env, self.db = Env() | |
| # skip test if we're running under Valgrind | |
| if VALGRIND: | |
| self.env.skip() # valgrind is not working correctly with multi processing | |
| # wait for all tasks to complete | |
| results = await asyncio.gather(*tasks) | |
| self.conn = redis.Redis("localhost", self.env.port) | |
| self.graph = self.db.select_graph(GRAPH_ID) | |
| # close the connection pool | |
| await pool.aclose() | |
| pool = AsyncConnectionPool(max_connections=16, timeout=None, **connection_kwargs) | |
| db = FalkorDB(host='localhost', port=self.env.port, connection_pool=pool) | |
| g = db.select_graph(GRAPH_ID) | |
| tasks = [] | |
| for q in queries: | |
| tasks.append(asyncio.create_task(g.query(q))) | |
| # wait for all tasks to complete | |
| results = await asyncio.gather(*tasks) | |
| # close the connection pool | |
| await pool.aclose() |
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Review Status
Actionable comments generated: 16
Configuration used: CodeRabbit UI
Files selected for processing (10)
- tests/flow/test_cache.py (12 hunks)
- tests/flow/test_concurrent_query.py (3 hunks)
- tests/flow/test_index_create.py (15 hunks)
- tests/flow/test_list.py (3 hunks)
- tests/flow/test_pending_queries_limit.py (3 hunks)
- tests/flow/test_query_mem_limit.py (2 hunks)
- tests/flow/test_slowlog.py (6 hunks)
- tests/flow/test_stress.py (1 hunks)
- tests/flow/test_timeout.py (6 hunks)
- tests/flow/tests.sh (2 hunks)
Files skipped from review as they are similar to previous changes (6)
- tests/flow/test_index_create.py
- tests/flow/test_list.py
- tests/flow/test_slowlog.py
- tests/flow/test_stress.py
- tests/flow/test_timeout.py
- tests/flow/tests.sh
Additional comments: 2
tests/flow/test_pending_queries_limit.py (1)
- 18-24: The
issue_queryfunction correctly handles query execution and exceptions. However, it's important to ensure that the specific exception caught ("Max pending queries exceeded") is the only one expected here, as catching a broadExceptioncould obscure other issues.tests/flow/test_concurrent_query.py (1)
- 24-28: The
__init__method oftestConcurrentQueryFlowclass seems to be initializingself.envandself.dbincorrectly. Typically,Env()would not return two values directly. This might be a mistake or an oversight. Please verify the correct way to initialize these variables.
| @@ -69,7 +76,7 @@ def test_02_overflow_no_limit(self): | |||
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| self.env.assertFalse(error_encountered) | |||
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| def test_03_overflow_with_limit(self): | |||
| def _test_03_overflow_with_limit(self): | |||
There was a problem hiding this comment.
The method _test_03_overflow_with_limit seems to be intended as a test case but is prefixed with an underscore, which typically indicates a non-public method in Python. If this method is meant to be a test case, consider removing the underscore prefix to ensure it's recognized and executed by the test framework.
- def _test_03_overflow_with_limit(self):
+ def test_03_overflow_with_limit(self):Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def _test_03_overflow_with_limit(self): | |
| def test_03_overflow_with_limit(self): |
| async def run(self, queries): | ||
| qs = [] # queries | ||
| should_fails = [] # should query i fail | ||
| thread_count = self.db.config_get("THREAD_COUNT") | ||
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| # connection pool blocking when there's no available connections | ||
| pool = BlockingConnectionPool(max_connections=thread_count, timeout=None, port=self.env.port, decode_responses=True) | ||
| db = FalkorDB(connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| for q in queries: | ||
| qs.append(q[0]) | ||
| should_fails.append(q[1]) | ||
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| tasks = [] | ||
| for q in qs: | ||
| tasks.append(asyncio.create_task(g.query(q))) | ||
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| results = await asyncio.gather(*tasks, return_exceptions=True) | ||
|
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| # validate results | ||
| for i, res in enumerate(results): | ||
| if should_fails[i]: | ||
| # query should have failed | ||
| self.env.assertIn("Query's mem consumption exceeded capacity", str(res)) | ||
| else: | ||
| # make sure query did not throw an exception | ||
| self.env.assertNotEqual(type(res), redis.exceptions.ResponseError) | ||
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| # close the connection pool | ||
| await pool.aclose() | ||
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| asyncio.run(run(self, queries)) |
There was a problem hiding this comment.
The stress_server method effectively uses asyncio for concurrent query execution. However, similar to the previous file, the use of asyncio.run in a potentially reusable context (line 62) could lead to runtime issues. Consider refactoring to use an existing event loop or ensure this method is only called once.
| results = await asyncio.gather(*tasks, return_exceptions=True) | ||
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| # validate results | ||
| for i, res in enumerate(results): | ||
| if should_fails[i]: | ||
| # query should have failed | ||
| self.env.assertIn("Query's mem consumption exceeded capacity", str(res)) | ||
| else: | ||
| # make sure query did not throw an exception | ||
| self.env.assertNotEqual(type(res), redis.exceptions.ResponseError) | ||
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There was a problem hiding this comment.
The validation of query results based on memory consumption (lines 54 and 57) relies on string matching within exceptions, which is fragile and may break if the error message changes. It's recommended to catch a specific exception related to memory limits, if available, instead of relying on error message strings, as previously noted in the comments.
tests/flow/test_concurrent_query.py
Outdated
| import random | ||
| import asyncio | ||
| from common import * | ||
| from pathos.pools import ProcessPool as Pool | ||
| from pathos.helpers import mp as pathos_multiprocess | ||
| from falkordb.asyncio import FalkorDB | ||
| from redis.asyncio import BlockingConnectionPool | ||
| from redis.asyncio import Redis as AsyncRedis | ||
|
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| GRAPH_ID = "G" # Graph identifier. | ||
| import asyncio |
There was a problem hiding this comment.
Duplicate import of asyncio on lines 1 and 8. This redundancy can be removed to maintain code cleanliness and avoid confusion.
- import asyncioCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| import random | |
| import asyncio | |
| from common import * | |
| from pathos.pools import ProcessPool as Pool | |
| from pathos.helpers import mp as pathos_multiprocess | |
| from falkordb.asyncio import FalkorDB | |
| from redis.asyncio import BlockingConnectionPool | |
| from redis.asyncio import Redis as AsyncRedis | |
| GRAPH_ID = "G" # Graph identifier. | |
| import asyncio | |
| import random | |
| import asyncio | |
| from common import * | |
| from falkordb.asyncio import FalkorDB | |
| from redis.asyncio import BlockingConnectionPool | |
| from redis.asyncio import Redis as AsyncRedis |
tests/flow/test_concurrent_query.py
Outdated
| GRAPH_ID = "concurrent_query" # Graph identifier. | ||
| SECONDERY_GRAPH_ID = GRAPH_ID + "2" # Secondery graph identifier. |
There was a problem hiding this comment.
Typo in the variable name SECONDERY_GRAPH_ID. It should be corrected to SECONDARY_GRAPH_ID for clarity and to avoid potential confusion.
- SECONDERY_GRAPH_ID = GRAPH_ID + "2" # Secondery graph identifier.
+ SECONDARY_GRAPH_ID = GRAPH_ID + "2" # Secondary graph identifier.Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| GRAPH_ID = "concurrent_query" # Graph identifier. | |
| SECONDERY_GRAPH_ID = GRAPH_ID + "2" # Secondery graph identifier. | |
| GRAPH_ID = "concurrent_query" # Graph identifier. | |
| SECONDARY_GRAPH_ID = GRAPH_ID + "2" # Secondary graph identifier. |
tests/flow/test_concurrent_query.py
Outdated
| self.graph.query("RETURN 1") | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None, port=self.env.port, decode_responses=True) | ||
| db = FalkorDB(connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| tasks = [] | ||
| for i in range(0, CLIENT_COUNT): | ||
| tasks.append(asyncio.create_task(g.delete())) | ||
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| results = await asyncio.gather(*tasks, return_exceptions=True) | ||
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| # invoke queries | ||
| assertions = pool.map(delete_graph, [GRAPH_ID] * CLIENT_COUNT) | ||
| # Exactly one thread should have successfully deleted the graph. | ||
| self.env.assertEquals(len(results) - sum(isinstance(res, ResponseError) for res in results), 1) | ||
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| # Exactly one thread should have successfully deleted the graph. | ||
| self.env.assertEquals(assertions.count(True), 1) | ||
| # close the connection pool | ||
| await pool.aclose() | ||
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| pool.clear() | ||
| asyncio.run(run(self)) |
There was a problem hiding this comment.
In test_04_concurrent_delete, the use of asyncio.run inside an instance method is unconventional and might lead to issues with the event loop, especially in a testing context where multiple tests are run sequentially. Consider refactoring this to use a different approach for managing asynchronous operations within test methods.
tests/flow/test_concurrent_query.py
Outdated
| # get the results | ||
| results = m.get() | ||
| # invoke queries | ||
| q = "UNWIND (range(0, 10000)) AS x WITH x AS x WHERE (x / 900) = 1 RETURN x" | ||
| tasks = [] | ||
| del_task = None | ||
|
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| # validate result. | ||
| self.env.assertTrue(all([r["result_set"][0][0] == 900 for r in results])) | ||
| for i in range(CLIENT_COUNT): | ||
| tasks.append(asyncio.create_task(g.query(q))) | ||
| if i == CLIENT_COUNT / 2: | ||
| del_task = asyncio.create_task(async_conn.delete(GRAPH_ID)) | ||
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| # Make sure Graph is empty, e.g. graph was deleted. | ||
| resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set | ||
| self.env.assertEquals(resultset[0][0], 0) | ||
| ############################################################################################## | ||
| # Delete graph via Redis FLUSHALL. | ||
| ############################################################################################## | ||
| self.populate_graph() | ||
| q = """UNWIND (range(0, 10000)) AS x WITH x AS x WHERE (x / 900) = 1 RETURN x""" | ||
| queries = [q] * CLIENT_COUNT | ||
| barrier = manager.Barrier(CLIENT_COUNT) | ||
| barriers = [barrier] * CLIENT_COUNT | ||
| # invoke queries | ||
| m = pool.amap(thread_run_query, queries, barriers) | ||
| # wait for all async tasks | ||
| results = await asyncio.gather(*tasks) | ||
| await del_task | ||
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| self.conn.flushall() | ||
| # validate result. | ||
| self.env.assertTrue(all(r.result_set[0][0] == 900 for r in results)) | ||
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| # wait for processes to return | ||
| m.wait() | ||
| # Make sure Graph is empty, e.g. graph was deleted. | ||
| resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set | ||
| self.env.assertEquals(resultset[0][0], 0) | ||
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| # get the results | ||
| results = m.get() | ||
| #------------------------------------------------------------------- | ||
| # Delete graph via GRAPH.DELETE. | ||
| #------------------------------------------------------------------- | ||
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| # validate result. | ||
| self.env.assertTrue(all([r["result_set"][0][0] == 900 for r in results])) | ||
| self.populate_graph() | ||
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| # Make sure Graph is empty, e.g. graph was deleted. | ||
| resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set | ||
| self.env.assertEquals(resultset[0][0], 0) | ||
| ############################################################################################## | ||
| # Delete graph via GRAPH.DELETE. | ||
| ############################################################################################## | ||
| self.populate_graph() | ||
| q = """UNWIND (range(0, 10000)) AS x WITH x AS x WHERE (x / 900) = 1 RETURN x""" | ||
| queries = [q] * CLIENT_COUNT | ||
| barrier = manager.Barrier(CLIENT_COUNT) | ||
| barriers = [barrier] * CLIENT_COUNT | ||
| # invoke queries | ||
| m = pool.amap(thread_run_query, queries, barriers) | ||
| # invoke queries | ||
| tasks = [] | ||
| for i in range (CLIENT_COUNT): | ||
| tasks.append(asyncio.create_task(g.query(q))) | ||
| if i == CLIENT_COUNT / 2: | ||
| del_task = asyncio.create_task(g.delete()) | ||
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| self.graph.delete() | ||
| # wait for all async tasks | ||
| results = await asyncio.gather(*tasks) | ||
| await del_task | ||
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| # wait for processes to return | ||
| m.wait() | ||
| # validate result. | ||
| self.env.assertTrue(all(r.result_set[0][0] == 900 for r in results)) | ||
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| # get the results | ||
| results = m.get() | ||
| # Make sure Graph is empty, e.g. graph was deleted. | ||
| resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set | ||
| self.env.assertEquals(resultset[0][0], 0) | ||
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| # validate result. | ||
| self.env.assertTrue(all([r["result_set"][0][0] == 900 for r in results])) | ||
| # Close the connection | ||
| await async_conn.close() | ||
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| # Make sure Graph is empty, e.g. graph was deleted. | ||
| resultset = self.graph.query("MATCH (n) RETURN count(n)").result_set | ||
| self.env.assertEquals(resultset[0][0], 0) | ||
| # close the connection pool | ||
| await pool.aclose() | ||
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| pool.clear() | ||
| asyncio.run(run(self)) |
There was a problem hiding this comment.
The method test_05_concurrent_read_delete demonstrates a complex asynchronous flow involving concurrent reads and a delete operation. While the logic seems correct, ensure that the use of asyncio.run for running the run method is appropriate in this context. It's generally recommended to use asyncio.run at the highest level possible, not within individual test methods, to avoid issues with the event loop.
tests/flow/test_concurrent_query.py
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| async def run(self): | ||
| # connect to async graph via a connection pool | ||
| # which will block if there are no available connections | ||
| connection_kwargs = { 'decode_responses': True } | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None, port=self.env.port, decode_responses=True) | ||
| db = FalkorDB(connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| # single async connection | ||
| async_conn = AsyncRedis(port=self.env.port) | ||
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| # Test setup - validate that graph exists and possible results are None | ||
| # Create new empty graph with ID SECONDERY_GRAPH_ID | ||
| new_graph_id = SECONDERY_GRAPH_ID | ||
| graph2 = self.db.select_graph(new_graph_id) | ||
| graph2.query("RETURN 1") | ||
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| self.graph.query("MATCH (n) RETURN n") | ||
| heavy_write_query = "UNWIND(range(0, 999999)) as x CREATE(n) RETURN count(1)" | ||
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| tasks = [] | ||
| tasks.append(asyncio.create_task(g.query(heavy_write_query))) | ||
| tasks.append(asyncio.create_task(async_conn.rename(GRAPH_ID, new_graph_id))) | ||
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| results = await asyncio.gather(*tasks, return_exceptions=True) | ||
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| # Possible scenarios: | ||
| # 1. Rename is done before query is sent. The name in the graph context is new_graph, | ||
| # so when upon commit, when trying to open new_graph key, | ||
| # it will encounter an empty key since new_graph is not a valid key. | ||
| # Note: As from https://github.com/RedisGraph/RedisGraph/pull/820 | ||
| # this may not be valid since the rename event handler might actually rename the graph key, before the query execution. | ||
| # 2. Rename is done during query executing, so when commiting and comparing stored graph context name (GRAPH_ID) to the retrived value graph context name (new_graph), | ||
| # the identifiers are not the same, since new_graph value is now stored at GRAPH_ID value. | ||
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| result = results[0] | ||
| if type(result) is ResponseError: | ||
| possible_exceptions = ["Encountered different graph value when opened key " + GRAPH_ID, | ||
| "Encountered an empty key when opened key " + new_graph] | ||
| self.env.assertIn(str(result), possible_exceptions) | ||
| else: | ||
| self.env.assertEquals(1000000, result.result_set[0][0]) | ||
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| # close the connection pool | ||
| await pool.aclose() | ||
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| # close async connection | ||
| await async_conn.close() | ||
|
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There was a problem hiding this comment.
The method test_07_concurrent_write_rename uses asyncio.run within an instance method, which might lead to issues with the event loop. Consider refactoring this to use a different approach for managing asynchronous operations within test methods.
tests/flow/test_concurrent_query.py
Outdated
| async def run(self): | ||
| # connect to async graph via a connection pool | ||
| # which will block if there are no available connections | ||
| connection_kwargs = { 'decode_responses': True } | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None, port=self.env.port, decode_responses=True) | ||
| db = FalkorDB(connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| # single async connection | ||
| async_conn = AsyncRedis(port=self.env.port) | ||
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| # Test setup - validate that graph exists and possible results are None | ||
| self.graph.query("MATCH (n) RETURN n") | ||
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| heavy_write_query = "UNWIND(range(0, 999999)) as x CREATE(n) RETURN count(1)" | ||
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| tasks = [] | ||
| tasks.append(asyncio.create_task(g.query(heavy_write_query))) | ||
| tasks.append(asyncio.create_task(async_conn.set(GRAPH_ID, 1))) | ||
| results = await asyncio.gather(*tasks, return_exceptions=True) | ||
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| result = results[0] | ||
| if type(result) is ResponseError: | ||
| possible_exceptions = ["Encountered a non-graph value type when opened key " + GRAPH_ID, | ||
| "WRONGTYPE Operation against a key holding the wrong kind of value"] | ||
| self.env.assertIn(str(result), possible_exceptions) | ||
| else: | ||
| self.env.assertEquals(1000000, result.result_set[0][0]) | ||
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| pool.clear() | ||
| # close the connection pool | ||
| await pool.aclose() | ||
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| # close async connection | ||
| await async_conn.close() | ||
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There was a problem hiding this comment.
The method test_08_concurrent_write_replace also uses asyncio.run within an instance method. This pattern is repeated across several test methods and should be reconsidered for the reasons mentioned in previous comments.
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Review Status
Actionable comments generated: 3
Configuration used: CodeRabbit UI
Files selected for processing (4)
- tests/flow/test_concurrent_query.py (3 hunks)
- tests/flow/test_graph_deletion.py (13 hunks)
- tests/flow/test_pending_queries_limit.py (2 hunks)
- tests/flow/test_slowlog.py (6 hunks)
Files skipped from review as they are similar to previous changes (2)
- tests/flow/test_graph_deletion.py
- tests/flow/test_slowlog.py
Additional comments: 3
tests/flow/test_pending_queries_limit.py (3)
- 1-4: The imports are correctly updated to reflect the use of asynchronous operations and the new database interaction classes (
FalkorDBandBlockingConnectionPool). This aligns with the PR's objectives to enhance performance through asynchronous operations and optimize database interactions.- 18-24: The
issue_queryfunction has been correctly updated to be asynchronous, which is a key part of the PR's objective to utilize asynchronous tasks for improved performance. The exception handling within this function is appropriate for the context of testing query limits.- 29-31: The constructor of
testPendingQueryLimitinitializes the environment and database connection correctly. However, it's important to ensure that theEnvclass andFalkorDBare fully compatible with asynchronous operations and that any necessary cleanup is handled properly to prevent resource leaks.
|
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| # return if error encountered | ||
| return any(result) | ||
| return asyncio.run(run(self)) |
There was a problem hiding this comment.
Given the previous comments regarding the use of asyncio.run, it's crucial to apply the suggested refactoring across all instances where asyncio.run is used within this file to ensure consistency and avoid potential issues with the event loop.
Please apply the suggested refactoring to use asyncio.get_event_loop() where appropriate, as mentioned in the previous comment.
| self.db.config_set("MAX_QUEUED_QUERIES", 10) | ||
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| # re-read configuration | ||
| result = self.conn.execute_command("GRAPH.CONFIG", "GET", "MAX_QUEUED_QUERIES") | ||
| max_queued_queries = self.db.config_get("MAX_QUEUED_QUERIES") | ||
| self.env.assertEquals(max_queued_queries, 10) | ||
|
|
There was a problem hiding this comment.
📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [57-89]
The test methods (test_01_query_limit_config, test_02_overflow_no_limit, and test_03_overflow_with_limit) are logically structured and correctly test the intended functionality. However, it's important to ensure that the configuration changes made during these tests are reverted after each test to maintain test isolation and prevent side effects on subsequent tests.
Consider adding a teardown method or using a context manager to ensure configuration changes are reverted after each test.
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Review Status
Actionable comments generated: 1
Configuration used: CodeRabbit UI
Files selected for processing (1)
- tests/flow/test_cache.py (12 hunks)
Additional comments: 1
tests/flow/test_cache.py (1)
- 4-5: The addition of imports for
FalkorDBandBlockingConnectionPoolaligns with the PR's objective to optimize database interactions through more efficient connection handling and database operations. This change is a positive step towards enhancing test performance and reliability.
| async def run(self): | ||
| # connection pool with 16 connections | ||
| # blocking when there's no connections available | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None, port=self.env.port, decode_responses=True) | ||
| db = FalkorDB(connection_pool=pool) | ||
| g = db.select_graph('cache_eviction') | ||
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| tasks = [] | ||
| for i in range(1, 50): | ||
| # random param name | ||
| param_name = 'p_' + str(i) | ||
| q = f"UNWIND range(0, 50000) as x WITH x WHERE x >= ${param_name} RETURN count(x)" | ||
| params = {param_name : 0} | ||
| tasks.append(asyncio.create_task(g.query(q,params))) | ||
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| results = await asyncio.gather(*tasks) | ||
| for r in results: | ||
| self.env.assertEqual(r.result_set[0][0], 50001) | ||
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| await pool.aclose() | ||
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| asyncio.run(run(self)) |
There was a problem hiding this comment.
The implementation of the run method within test_14_cache_eviction effectively utilizes asynchronous tasks to perform concurrent query execution, which is a significant improvement in terms of performance. However, there are a few considerations and potential improvements:
-
Event Loop Management: The use of
asyncio.run(run(self))at the end of the method (line 280) is generally not recommended within a library or a framework that might be integrated into larger applications, as it forcibly creates and closes new event loops, which can lead to issues if the surrounding code also manages the event loop. Consider refactoring to use an existing event loop or ensure this method is only called once in an environment where event loop management is predictable. -
Error Handling: While the asynchronous execution and gathering of tasks are handled well, there's a lack of explicit error handling for the asynchronous tasks. Consider adding try-except blocks around the task execution or utilizing
asyncio.gather(*tasks, return_exceptions=True)to gracefully handle potential exceptions from the tasks. -
Resource Management: The
BlockingConnectionPoolis correctly closed withawait pool.aclose()(line 278), which is good practice for resource management. However, ensure that this cleanup step is guaranteed even if exceptions occur during task execution or gathering. This can be achieved by using atry-finallyblock or context managers if supported by the library. -
Performance Consideration: While the use of asynchronous operations is aimed at improving performance, the actual impact should be measured to ensure that the overhead of managing asynchronous tasks and the connection pool does not outweigh the benefits, especially considering the relatively high number of tasks being created (49 tasks in this case).
Consider addressing the event loop management and error handling concerns to ensure robustness and reliability of the asynchronous execution. Additionally, validate the performance improvements through benchmarking.
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Files selected for processing (1)
- tests/flow/test_concurrent_query.py (3 hunks)
Additional comments: 4
tests/flow/test_concurrent_query.py (4)
- 1-6: The imports are well-organized and relevant to the changes made in this file. However, ensure there's no duplicate import of
asyncioelsewhere in the file, as mentioned in previous comments.- 22-26: The
__init__method oftestConcurrentQueryFlowclass correctly initializes the environment and database connections. It's good practice to encapsulate setup logic within the constructor or setup methods to keep tests clean and focused.- 28-30: The
setUpmethod effectively flushes the connections by deleting the graph identifiers. This ensures a clean state for each test, which is crucial for avoiding flaky tests due to state pollution.- 74-82: The
test_01_concurrent_aggregationmethod correctly utilizes the refactoredrun_queries_concurrentlyto perform concurrent aggregation queries. The method ensures that the count of persons matches the expected value, which is a good practice for validating test outcomes.
tests/flow/test_concurrent_query.py
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| def run_queries_concurrently(self, queries): | ||
| async def run(self, queries): | ||
| connection_kwargs = { 'decode_responses': True } | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None, port=self.env.port, decode_responses=True) | ||
| db = FalkorDB(connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| return results | ||
| tasks = [] | ||
| for q in queries: | ||
| tasks.append(asyncio.create_task(g.query(q))) | ||
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| class testConcurrentQueryFlow(FlowTestsBase): | ||
| def __init__(self): | ||
| self.env, self.db = Env() | ||
| # skip test if we're running under Valgrind | ||
| if VALGRIND: | ||
| self.env.skip() # valgrind is not working correctly with multi processing | ||
| # wait for all tasks to complete | ||
| results = await asyncio.gather(*tasks) | ||
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| self.conn = redis.Redis("localhost", self.env.port) | ||
| self.graph = self.db.select_graph(GRAPH_ID) | ||
| # close the connection pool | ||
| await pool.aclose() | ||
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| self.populate_graph() | ||
| return results | ||
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| return asyncio.run(run(self, queries)) |
There was a problem hiding this comment.
The run_queries_concurrently method has been significantly refactored to support asynchronous operations. The inner run function and its immediate execution with asyncio.run are unconventional. Consider making run_queries_concurrently directly asynchronous and calling it with await from the test methods, as previously suggested.
- def run_queries_concurrently(self, queries):
- async def run(self, queries):
+ async def run_queries_concurrently(self, queries):And then update the calls in the test methods accordingly.
Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def run_queries_concurrently(self, queries): | |
| async def run(self, queries): | |
| connection_kwargs = { 'decode_responses': True } | |
| pool = BlockingConnectionPool(max_connections=16, timeout=None, port=self.env.port, decode_responses=True) | |
| db = FalkorDB(connection_pool=pool) | |
| g = db.select_graph(GRAPH_ID) | |
| return results | |
| tasks = [] | |
| for q in queries: | |
| tasks.append(asyncio.create_task(g.query(q))) | |
| class testConcurrentQueryFlow(FlowTestsBase): | |
| def __init__(self): | |
| self.env, self.db = Env() | |
| # skip test if we're running under Valgrind | |
| if VALGRIND: | |
| self.env.skip() # valgrind is not working correctly with multi processing | |
| # wait for all tasks to complete | |
| results = await asyncio.gather(*tasks) | |
| self.conn = redis.Redis("localhost", self.env.port) | |
| self.graph = self.db.select_graph(GRAPH_ID) | |
| # close the connection pool | |
| await pool.aclose() | |
| self.populate_graph() | |
| return results | |
| return asyncio.run(run(self, queries)) | |
| async def run_queries_concurrently(self, queries): | |
| connection_kwargs = { 'decode_responses': True } | |
| pool = BlockingConnectionPool(max_connections=16, timeout=None, port=self.env.port, decode_responses=True) | |
| db = FalkorDB(connection_pool=pool) | |
| g = db.select_graph(GRAPH_ID) | |
| tasks = [] | |
| for q in queries: | |
| tasks.append(asyncio.create_task(g.query(q))) | |
| # wait for all tasks to complete | |
| results = await asyncio.gather(*tasks) | |
| # close the connection pool | |
| await pool.aclose() | |
| return results |
tests/flow/test_concurrent_query.py
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| async def run(self): | ||
| # connect to async graph via a connection pool | ||
| # which will block if there are no available connections | ||
| connection_kwargs = { 'decode_responses': True } | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None, port=self.env.port, decode_responses=True) | ||
| db = FalkorDB(connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
| async_conn = AsyncRedis(port=self.env.port) | ||
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| # Test setup - validate that graph exists and possible results are None | ||
| self.graph.query("RETURN 1") | ||
| heavy_write_query = "UNWIND(range(0, 999999)) as x CREATE(n) RETURN count(1)" | ||
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| tasks = [] | ||
| tasks.append(asyncio.create_task(g.query(heavy_write_query))) | ||
| tasks.append(asyncio.create_task(async_conn.delete(GRAPH_ID))) | ||
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| results = await asyncio.gather(*tasks, return_exceptions=True) | ||
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| result = results[0] | ||
| if type(result) is ResponseError: | ||
| possible_exceptions = ["Encountered different graph value when opened key " + GRAPH_ID, | ||
| "Encountered an empty key when opened key " + GRAPH_ID] | ||
| self.env.assertIn(str(result), possible_exceptions) | ||
| else: | ||
| self.env.assertEquals(1000000, result.result_set[0][0]) | ||
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| pool = Pool(nodes=1) | ||
| heavy_write_query = """UNWIND(range(0,999999)) as x CREATE(n) RETURN count(n)""" | ||
| writer = pool.apipe(thread_run_query, heavy_write_query, None) | ||
| self.conn.delete(GRAPH_ID) | ||
| writer.wait() | ||
| possible_exceptions = ["Encountered different graph value when opened key " + GRAPH_ID, | ||
| "Encountered an empty key when opened key " + GRAPH_ID] | ||
| result = writer.get() | ||
| if isinstance(result, str): | ||
| self.env.assertContains(result, possible_exceptions) | ||
| else: | ||
| self.env.assertEquals(1000000, result["result_set"][0][0]) | ||
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| pool.clear() | ||
| # close the connection pool | ||
| await pool.aclose() | ||
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| # close async connection | ||
| await async_conn.close() | ||
|
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There was a problem hiding this comment.
Similar to previous comments, the test_06_concurrent_write_delete method's use of asyncio.run within an instance method is unconventional. This pattern is repeated across several test methods, suggesting a need for a broader refactoring to align with best practices for asynchronous testing.
As suggested earlier, consider making the test methods themselves asynchronous and using await for calling helper methods, thereby avoiding direct calls to asyncio.run within test methods.
tests/flow/test_concurrent_query.py
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| async def run(self): | ||
| self.graph.query("RETURN 1") | ||
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| Rq = "UNWIND range(0, 10000) AS x WITH x WHERE x = 9999 RETURN 'R', timestamp()" | ||
| Wq = "UNWIND range(0, 1000) AS x WITH x WHERE x = 27 CREATE ({v:1}) RETURN 'W', timestamp()" | ||
| Slowq = "UNWIND range(0, 100000) AS x WITH x WHERE (x % 73) = 0 RETURN count(1)" | ||
| connection_kwargs = { 'decode_responses': True } | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None, port=self.env.port, decode_responses=True) | ||
| db = FalkorDB(connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| # issue a number of slow queries, this will give us time to fill up | ||
| # RedisGraph internal threadpool queue | ||
| queries = [Slowq] * CLIENT_COUNT * 5 | ||
| nulls = [None] * CLIENT_COUNT * 5 | ||
| Rq = "UNWIND range(0, 10000) AS x WITH x WHERE x = 9999 RETURN 'R', timestamp()" | ||
| Wq = "UNWIND range(0, 1000) AS x WITH x WHERE x = 27 CREATE ({v:1}) RETURN 'W', timestamp()" | ||
| Slowq = "UNWIND range(0, 100000) AS x WITH x WHERE x % 73 = 0 RETURN count(1)" | ||
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| # issue queries asynchronously | ||
| pool.imap(thread_run_query, queries, nulls) | ||
| # issue a number of slow queries, this will give us time to fill up | ||
| # FalkorDB's internal threadpool queue | ||
| slow_queries = [] | ||
| for i in range(0, CLIENT_COUNT * 5): | ||
| slow_queries.append(asyncio.create_task(g.ro_query(Slowq))) | ||
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| # create a long sequence of read queries | ||
| queries = [Rq] * CLIENT_COUNT * 10 | ||
| nulls = [None] * CLIENT_COUNT * 10 | ||
| # create a long sequence of read queries | ||
| read_tasks = [] | ||
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| # inject a single write query close to the begining on the sequence | ||
| queries[CLIENT_COUNT] = Wq | ||
| # N Read queries | ||
| for i in range(0, CLIENT_COUNT): | ||
| read_tasks.append(asyncio.create_task(g.ro_query(Rq))) | ||
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| # invoke queries | ||
| # execute queries in parallel | ||
| results = pool.map(thread_run_query, queries, nulls) | ||
| # Single Write query | ||
| write_task = asyncio.create_task(g.query(Wq)) | ||
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| # count how many queries completed before the write query | ||
| count = 0 | ||
| write_ts = results[CLIENT_COUNT]["result_set"][0][1] | ||
| for result in results: | ||
| row = result["result_set"][0] | ||
| ts = row[1] | ||
| if ts < write_ts: | ||
| count += 1 | ||
| # 9N Read queries | ||
| for i in range(0, CLIENT_COUNT * 9): | ||
| read_tasks.append(asyncio.create_task(g.ro_query(Rq))) | ||
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| # make sure write query wasn't starved | ||
| self.env.assertLessEqual(count, len(queries) * 0.3) | ||
| # wait for all queries to return | ||
| await asyncio.gather(*slow_queries) | ||
| results = await asyncio.gather(*read_tasks) | ||
| w_res = await write_task | ||
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| # delete the key | ||
| self.conn.delete(GRAPH_ID) | ||
| # count how many queries completed before the write query | ||
| write_ts = w_res.result_set[0][1] | ||
| count = 0 | ||
| count = sum(1 for res in results if res.result_set[0][1] < write_ts) | ||
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| # make sure write query wasn't starved | ||
| self.env.assertLessEqual(count, len(read_tasks) * 0.3) | ||
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| pool.clear() | ||
| # close the connection pool | ||
| await pool.aclose() | ||
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| # delete the key | ||
| self.conn.delete(GRAPH_ID) | ||
|
|
There was a problem hiding this comment.
The test_10_write_starvation method is well-structured to test that write queries are not excessively delayed by a large number of read queries. This is an important aspect to test in a concurrent environment. However, the use of asyncio.run within the method should be addressed as part of the broader refactoring effort to align with asynchronous testing best practices.
Again, consider refactoring to make the test methods themselves asynchronous and use await for calling helper methods, avoiding direct calls to asyncio.run.
tests/flow/test_concurrent_query.py
Outdated
| async def run(self): | ||
| connection_kwargs = { 'decode_responses': True } | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None, port=self.env.port, decode_responses=True) | ||
| db = FalkorDB(connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| self.graph = self.db.select_graph(GRAPH_ID) | ||
| # make sure graph exists | ||
| self.graph.query("RETURN 1") | ||
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| self.graph.query("CREATE (:N)") | ||
| tasks = [] | ||
| read_q = "MATCH (n:N)-[r:R]->() RETURN r" | ||
| write_q = "UNWIND range(1, 10000) AS x CREATE (:M)" | ||
| for i in range(1, 10): | ||
| tasks.append(asyncio.create_task(g.query(write_q))) | ||
| for j in range(1, 10): | ||
| tasks.append(asyncio.create_task(g.ro_query(read_q))) | ||
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| def resize_and_query(): | ||
| g = Graph(self.env.getConnection(), GRAPH_ID) | ||
| await asyncio.gather(*tasks) | ||
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| for j in range(1, 10): | ||
| g.query("UNWIND range(1, 10000) AS x CREATE (:M)") | ||
| for i in range(1, 10): | ||
| g.query("MATCH (n:N)-[r:R]->() RETURN r") | ||
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| loop = asyncio.get_event_loop() | ||
| tasks = [] | ||
| for i in range(1, 10): | ||
| tasks.append(loop.create_task(asyncio.to_thread(resize_and_query))) | ||
| # close the connection pool | ||
| await pool.aclose() | ||
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| loop.run_until_complete(asyncio.wait(tasks)) | ||
| asyncio.run(run(self)) |
There was a problem hiding this comment.
The test_11_concurrent_resize_zero_matrix method follows the pattern of using asynchronous operations to test concurrent behavior. The method's structure is consistent with the rest of the test suite's approach to asynchronous testing. However, the repeated use of asyncio.run within instance methods should be addressed.
As with previous methods, consider making the test methods themselves asynchronous and using await for calling helper methods, thereby avoiding direct calls to asyncio.run.
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Review Status
Actionable comments generated: 1
Configuration used: CodeRabbit UI
Files selected for processing (4)
- tests/flow/test_concurrent_query.py (3 hunks)
- tests/flow/test_edge_index_scans.py (5 hunks)
- tests/flow/test_encode_decode.py (3 hunks)
- tests/flow/test_graph_deletion.py (13 hunks)
Files skipped from review as they are similar to previous changes (2)
- tests/flow/test_edge_index_scans.py
- tests/flow/test_graph_deletion.py
Additional comments: 28
tests/flow/test_encode_decode.py (15)
- 6-6: The addition of the
GRAPH_IDconstant is a good practice for maintainability and consistency across tests. It ensures that all tests refer to the same graph identifier, reducing the likelihood of errors due to identifier mismatches.- 23-23: Initializing
self.graphin the__init__method of the test class is a significant improvement. It centralizes the graph selection process, making the test code cleaner and reducing redundancy across test methods.- 25-26: Implementing a
tearDownmethod to delete the graph after each test ensures that tests do not interfere with each other by leaving residual data. This is a good practice for test isolation and reliability.- 30-36: Refactoring test methods to use
self.graphfor queries and operations instead of creating new graph instances enhances test performance and reduces unnecessary overhead. This change aligns with the PR's objectives of optimizing test executions.- 41-60: The tests for node creation, deletion, and validation of no compaction on nodes delete are well-structured and make efficient use of
self.graph. The logic for testing compaction and ID reuse is clear and correctly implemented.- 66-96: Similar to the node tests, the edge tests are well-organized and effectively utilize
self.graphfor creating, deleting, and validating edge operations. The approach to testing no compaction on edges delete and validating ID reuse is logically sound.- 102-109: The test for multiple edges over multiple keys demonstrates a good understanding of graph operations and the importance of testing edge cases in graph databases. Utilizing
self.graphconsistently across these operations is commendable.- 114-132: The methodical approach to testing no compaction on multiple edges delete and validating ID reuse is consistent and well-executed. The use of
self.graphfor these operations contributes to the overall efficiency and clarity of the test suite.- 137-144: Ensuring index functionality after encode-decode operations in RedisGraph version 7 is crucial for database integrity. The tests are well-designed to verify the persistence of indices, and the use of
self.graphstreamlines the process.- 149-167: Testing multiple graphs with indices and ensuring index persistence after RDB reloads is an important aspect of database functionality. The tests are clear, and the use of
self.graphfor operations on the primary graph is effective.- 172-188: The tests for multiple relationship types and their persistence after RDB reloads are well-implemented. These tests cover important functionality and edge cases in graph databases, and the consistent use of
self.graphenhances test clarity.- 20-212: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [199-223]
Testing changes to the
VKEY_MAX_ENTITY_COUNTconfiguration and its impact on virtual keys creation and deletion is critical for understanding database behavior under different configurations. The tests are thorough and well-documented.
- 229-247: The test for decoding single edge relations with deleted nodes is well-structured and addresses an important aspect of graph database functionality. The methodology and use of
self.graphfor operations are appropriate.- 253-271: Similar to the previous test, the test for decoding multi-edge relations with deleted nodes is well-conceived and executed. It addresses complex scenarios in graph databases, and the use of
self.graphis consistent and effective.- 277-298: The tests for random graph operations and their persistence after RDB reloads are comprehensive and cover a wide range of scenarios. The use of
self.graphand the methodology for comparing result sets before and after reloads are commendable.tests/flow/test_concurrent_query.py (13)
- 1-6: The addition of imports for
random,FalkorDB,BlockingConnectionPool, andAsyncRedisis appropriate for the refactoring towards asynchronous operations. However, ensure there are no duplicate imports or unused modules to maintain code cleanliness.- 13-16: Refactoring
delete_graphto an asynchronous function is a good practice for handling potentially blocking operations in a non-blocking manner. This change aligns with the asynchronous programming paradigm adopted in this PR.- 22-50: The refactoring within
testConcurrentQueryFlowto support asynchronous operations, including the setup and teardown of the test environment, is well-executed. The use ofasyncioand asynchronous Redis connections is appropriate for the intended concurrency improvements.- 73-81: The test method
test_01_concurrent_aggregationdemonstrates effective use of asynchronous operations to perform concurrent queries. This approach is expected to improve test execution speed and is consistent with the PR's objectives.- 85-95: The test method
test_02_retrieve_neighborsfollows the asynchronous pattern established in the PR, effectively querying the graph concurrently. This method is a good example of leveraging asynchronous programming to enhance test performance.- 99-105: The
test_03_concurrent_writemethod effectively demonstrates concurrent write operations using asynchronous tasks. This method aligns with the PR's goal of optimizing test executions through concurrency.- 131-192: The
test_05_concurrent_read_deletemethod's refactoring to use asynchronous operations for concurrent read and delete operations is a significant improvement. The detailed implementation, including handling Redis commands asynchronously, demonstrates a thorough understanding of asynchronous programming.- 197-228: The
test_06_concurrent_write_deletemethod's update to incorporate asynchronous operations for concurrent write and delete operations is well-executed. The use of asynchronous Redis connections and the handling of potential exceptions are appropriate for the test's objectives.- 232-279: The
test_07_concurrent_write_renamemethod's asynchronous implementation for testing concurrent write and rename operations is consistent with the PR's goals. The approach to handling potential exceptions and ensuring the graph's state post-operation is commendable.- 283-316: The
test_08_concurrent_write_replacemethod effectively uses asynchronous operations to test concurrent write and replace operations. The handling of potential exceptions and the validation of operation outcomes are well-considered.- 322-331: The
test_09_concurrent_multiple_readers_after_big_writemethod demonstrates the use of concurrent reads following a large write operation. The method effectively utilizes asynchronous operations to improve test execution speed and reliability.- 347-396: The
test_10_write_starvationmethod addresses an important aspect of database performance under concurrent operations. The asynchronous implementation and the detailed validation of the write query's execution time relative to read queries are well-executed.- 400-421: The
test_11_concurrent_resize_zero_matrixmethod's asynchronous implementation for testing concurrent operations on a graph is consistent with the PR's objectives. The comprehensive approach to testing and validating the outcomes demonstrates a good understanding of the database's behavior under concurrent operations.
| async def run(self): | ||
| self.graph.query("RETURN 1") | ||
| pool = BlockingConnectionPool(max_connections=16, timeout=None, port=self.env.port, decode_responses=True) | ||
| db = FalkorDB(connection_pool=pool) | ||
| g = db.select_graph(GRAPH_ID) | ||
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| # invoke queries | ||
| assertions = pool.map(delete_graph, [GRAPH_ID] * CLIENT_COUNT) | ||
| tasks = [] | ||
| for i in range(0, CLIENT_COUNT): | ||
| tasks.append(asyncio.create_task(g.delete())) | ||
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| # Exactly one thread should have successfully deleted the graph. | ||
| self.env.assertEquals(assertions.count(True), 1) | ||
| results = await asyncio.gather(*tasks, return_exceptions=True) | ||
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| pool.clear() | ||
| # Exactly one thread should have successfully deleted the graph. | ||
| self.env.assertEquals(len(results) - sum(isinstance(res, ResponseError) for res in results), 1) | ||
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| # close the connection pool | ||
| await pool.aclose() | ||
|
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There was a problem hiding this comment.
The test_04_concurrent_delete method has been updated to use asynchronous operations for concurrent graph deletions. This change is in line with the PR's objectives, but consider refactoring to avoid direct calls to asyncio.run within test methods for better practice.
Consider making the test methods themselves asynchronous and use await for calling helper methods to align with best practices for asynchronous testing.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (4)
- tests/flow/test_graph_deletion.py (14 hunks)
- tests/flow/test_path_filter.py (1 hunks)
- tests/flow/test_rdb_load.py (1 hunks)
- tests/flow/test_replication_states.py (4 hunks)
Files skipped from review as they are similar to previous changes (4)
- tests/flow/test_graph_deletion.py
- tests/flow/test_path_filter.py
- tests/flow/test_rdb_load.py
- tests/flow/test_replication_states.py
* Add self hosted (#584) * add self hosted runner * change self hosted label * Revert "change self hosted label" This reverts commit 9c13255. * re-add arm64 for flow tests * remove runtime tests, remove extra prints (#575) * remove runtime tests, remove extra prints * update tests requirements * remove pathos requierment, switch to python asyncio * switch to asyncio.run * install RLTest directly from github repo * update dockerfile compiler * install redis * remove sudu * remove sudo * fix setup redis * fix install redis * use venv * set venv in path * fix sanitizer * fix sanitizer build * fix sanitier build * fix sanitizer build * fix sanitizer build * add clang to sanitizer * add libc6-dbg * . * add libomp * fix sanitizer * fix redis install * fix redis * reduce flow test parallelism * create connection pool inside async func and make sure to close it * close async connection * remove flushall * specify port for connection pool * address PR comments * Update build.yml * Update test_cache.py * Update test_concurrent_query.py * Update test_concurrent_query.py * Update test_edge_index_scans.py * Update test_encode_decode.py * Update test_graph_deletion.py * Update test_graph_deletion.py * Update test_path_filter.py * address PR comments --------- Co-authored-by: Avi Avni <avi.avni@gmail.com> * update deps (#587) * integrate falkordbrs (#544) * integrate falkordbrs * fix submodule * fix * fix * separate build for debug * build rust with sanitizer flags when needed * add workspace and update * fix leak * update * address review * use alpine image in sanitizer * move to ubuntu * enable cargo * fix * fix * fix * fix * update * use target dir * addres review * use nightly rust in sanitizer * address review * fix * fix * update build * fix * address review * build * update * update * fix build * update * fix codeql and address review * address review * add alpine * update for alpine * update * fix build * remove debian * update * update * use current headers instead of generated one * clean * fix for mac * document alloc funtion * move to ubuntu image * change docker tag * update to latest * update format * revert * fix leak * always compile rust * fix makefile * review * address review * address review * GRAPH.COPY (#585) * initial work graph.copy * pipe serializer * abstract serializer * graph encoder switch to abstract serializer * switch decoder to abstract serializer * copy command wip * first copy * early flow test * additional tests * transition to worker thread execution * switch to worker thread * retry when fork failed * skip graph copy replication test when running under sanitizer * import SANITIZER * plant cloned key only after decoding is done * switch to CRON job * fork writes to file * add logs * fix leak * replicate via RESTORE, switch to FILE* * add graph.restore * fix unit-test wrong stream acquisition * fork requires gil * rename encoder/decoder v14 to latest * version bump --------- Co-authored-by: Dudi <16744955+dudizimber@users.noreply.github.com> Co-authored-by: Avi Avni <avi.avni@gmail.com>
Cleaning up flow-test output
Summary by CodeRabbit
FalkorDBandBlockingConnectionPoolacross multiple test cases.FalkorDBandBlockingConnectionPool.asyncio.gather()for enhanced performance.