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milvus-io--milvus/tests/python_client/cdc/testcases/test_collection.py
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chore: import upstream snapshot with attribution
2026-07-13 12:31:17 +08:00

908 lines
34 KiB
Python

"""
CDC sync tests for collection DDL operations.
"""
import time
import pytest
from pymilvus import DataType, Collection
from common.common_type import CaseLabel
from .base import TestCDCSyncBase, logger
@pytest.mark.tags(CaseLabel.CDC)
class TestCDCSyncCollectionDDL(TestCDCSyncBase):
"""Test CDC sync for collection DDL operations."""
def setup_method(self):
"""Setup for each test method."""
self.resources_to_cleanup = []
def teardown_method(self):
"""Cleanup after each test method - only cleanup upstream, downstream will sync."""
upstream_client = getattr(self, "_upstream_client", None)
if upstream_client:
for resource_type, resource_name in self.resources_to_cleanup:
if resource_type == "collection":
self.cleanup_collection(upstream_client, resource_name)
time.sleep(1) # Allow cleanup to sync to downstream
def test_create_collection(self, upstream_client, downstream_client, sync_timeout):
"""Test CREATE_COLLECTION operation sync."""
start_time = time.time()
collection_name = self.gen_unique_name("test_col_create")
# Log test start
self.log_test_start(
"test_create_collection", "CREATE_COLLECTION", collection_name
)
# Store upstream client for teardown
self._upstream_client = upstream_client
self.resources_to_cleanup.append(("collection", collection_name))
try:
# Initial cleanup
self.cleanup_collection(upstream_client, collection_name)
# Log operation
self.log_operation(
"CREATE_COLLECTION", "collection", collection_name, "upstream"
)
# Create collection in upstream
schema = self.create_default_schema(upstream_client)
logger.info(f"[SCHEMA] Collection schema: {schema}")
upstream_client.create_collection(
collection_name=collection_name, schema=schema
)
# Verify upstream creation
upstream_exists = upstream_client.has_collection(collection_name)
self.log_resource_state(
"collection",
collection_name,
"exists" if upstream_exists else "missing",
"upstream",
)
assert upstream_exists, (
f"Collection {collection_name} not created in upstream"
)
# Log sync verification start
self.log_sync_verification(
"CREATE_COLLECTION", collection_name, "exists in downstream"
)
# Wait for sync to downstream
def check_sync():
exists = downstream_client.has_collection(collection_name)
if exists:
self.log_resource_state(
"collection",
collection_name,
"exists",
"downstream",
"Sync confirmed",
)
return exists
sync_success = self.wait_for_sync(
check_sync, sync_timeout, f"create collection {collection_name}"
)
assert sync_success, (
f"Collection {collection_name} failed to sync to downstream"
)
# Log test success
duration = time.time() - start_time
self.log_test_end("test_create_collection", True, duration)
except Exception as e:
duration = time.time() - start_time
logger.error(f"[ERROR] Test failed with error: {e}")
self.log_test_end("test_create_collection", False, duration)
raise
def test_drop_collection(self, upstream_client, downstream_client, sync_timeout):
"""Test DROP_COLLECTION operation sync."""
start_time = time.time()
collection_name = self.gen_unique_name("test_col_drop")
# Log test start
self.log_test_start("test_drop_collection", "DROP_COLLECTION", collection_name)
# Store upstream client for teardown
self._upstream_client = upstream_client
self.resources_to_cleanup.append(("collection", collection_name))
try:
# Initial cleanup
self.cleanup_collection(upstream_client, collection_name)
# Create collection first
self.log_operation(
"CREATE_COLLECTION", "collection", collection_name, "upstream"
)
upstream_client.create_collection(
collection_name=collection_name,
schema=self.create_default_schema(upstream_client),
)
# Wait for creation to sync
def check_create():
return downstream_client.has_collection(collection_name)
assert self.wait_for_sync(
check_create, sync_timeout, f"create collection {collection_name}"
)
# Drop collection in upstream
self.log_operation(
"DROP_COLLECTION", "collection", collection_name, "upstream"
)
upstream_client.drop_collection(collection_name)
# Verify upstream drop
upstream_exists = upstream_client.has_collection(collection_name)
self.log_resource_state(
"collection",
collection_name,
"missing" if not upstream_exists else "exists",
"upstream",
)
assert not upstream_exists, (
f"Collection {collection_name} still exists in upstream after drop"
)
# Log sync verification start
self.log_sync_verification(
"DROP_COLLECTION", collection_name, "missing from downstream"
)
# Wait for drop to sync
def check_drop():
exists = downstream_client.has_collection(collection_name)
if not exists:
self.log_resource_state(
"collection",
collection_name,
"missing",
"downstream",
"Drop synced",
)
return not exists
sync_success = self.wait_for_sync(
check_drop, sync_timeout, f"drop collection {collection_name}"
)
assert sync_success, (
f"Collection {collection_name} drop failed to sync to downstream"
)
# Log test success
duration = time.time() - start_time
self.log_test_end("test_drop_collection", True, duration)
except Exception as e:
duration = time.time() - start_time
logger.error(f"[ERROR] Test failed with error: {e}")
self.log_test_end("test_drop_collection", False, duration)
raise
def test_rename_collection(self, upstream_client, downstream_client, sync_timeout):
"""Test RENAME_COLLECTION operation sync."""
start_time = time.time()
old_name = self.gen_unique_name("test_col_rename_old")
new_name = self.gen_unique_name("test_col_rename_new")
# Log test start
self.log_test_start(
"test_rename_collection", "RENAME_COLLECTION", f"{old_name} -> {new_name}"
)
# Store upstream client for teardown
self._upstream_client = upstream_client
self.resources_to_cleanup.append(("collection", old_name))
self.resources_to_cleanup.append(("collection", new_name))
try:
# Initial cleanup
self.cleanup_collection(upstream_client, old_name)
self.cleanup_collection(upstream_client, new_name)
# Create collection first
self.log_operation("CREATE_COLLECTION", "collection", old_name, "upstream")
upstream_client.create_collection(
collection_name=old_name,
schema=self.create_default_schema(upstream_client),
)
# Wait for creation to sync
def check_create():
return downstream_client.has_collection(old_name)
assert self.wait_for_sync(
check_create, sync_timeout, f"create collection {old_name}"
)
# Rename collection
rename_start_time = time.time()
self.log_operation(
"RENAME_COLLECTION",
"collection",
f"{old_name} -> {new_name}",
"upstream",
)
try:
upstream_client.rename_collection(old_name, new_name)
rename_duration = time.time() - rename_start_time
logger.info(
f"[SUCCESS] Rename operation completed in {rename_duration:.2f}s"
)
except Exception as e:
rename_duration = time.time() - rename_start_time
logger.error(
f"[FAILED] Rename operation failed after {rename_duration:.2f}s: {e}"
)
raise
# Verify rename in upstream
old_exists = upstream_client.has_collection(old_name)
new_exists = upstream_client.has_collection(new_name)
self.log_resource_state(
"collection",
old_name,
"missing" if not old_exists else "exists",
"upstream",
)
self.log_resource_state(
"collection",
new_name,
"exists" if new_exists else "missing",
"upstream",
)
assert not old_exists, (
f"Old collection {old_name} still exists after rename"
)
assert new_exists, f"New collection {new_name} not found after rename"
# Log sync verification start
self.log_sync_verification(
"RENAME_COLLECTION",
f"{old_name} -> {new_name}",
"completed in downstream",
)
# Wait for rename to sync
def check_rename():
return not downstream_client.has_collection(
old_name
) and downstream_client.has_collection(new_name)
sync_success = self.wait_for_sync(
check_rename,
sync_timeout,
f"rename collection {old_name} to {new_name}",
)
assert sync_success, (
f"Collection rename from {old_name} to {new_name} failed to sync to downstream"
)
# Log test success
duration = time.time() - start_time
self.log_test_end("test_rename_collection", True, duration)
except Exception as e:
duration = time.time() - start_time
logger.error(f"[ERROR] Test failed with error: {e}")
self.log_test_end("test_rename_collection", False, duration)
raise
@pytest.mark.tags(CaseLabel.CDC)
class TestCDCSyncCollectionManagement(TestCDCSyncBase):
"""Test CDC sync for collection management operations."""
def setup_method(self):
"""Setup for each test method."""
self.resources_to_cleanup = []
def teardown_method(self):
"""Cleanup after each test method - only cleanup upstream, downstream will sync."""
upstream_client = getattr(self, "_upstream_client", None)
if upstream_client:
for resource_type, resource_name in self.resources_to_cleanup:
if resource_type == "collection":
self.cleanup_collection(upstream_client, resource_name)
time.sleep(1) # Allow cleanup to sync to downstream
def test_add_collection_field(
self, upstream_client, downstream_client, sync_timeout
):
"""Test ADD_FIELD operation sync."""
# Store upstream client for teardown
self._upstream_client = upstream_client
collection_name = self.gen_unique_name("test_col_add_field")
self.resources_to_cleanup.append(("collection", collection_name))
# Initial cleanup
self.cleanup_collection(upstream_client, collection_name)
# Create collection
schema = self.create_default_schema(upstream_client)
upstream_client.create_collection(
collection_name=collection_name, schema=schema, consistency_level="Strong"
)
assert self.wait_for_sync(
lambda: upstream_client.has_collection(collection_name),
sync_timeout,
f"create collection {collection_name}",
)
# Add field
upstream_client.add_collection_field(
collection_name,
field_name="new_field",
data_type=DataType.INT64,
nullable=True,
)
print(f"DEBUG: add field {collection_name}")
res = upstream_client.describe_collection(collection_name)
print(f"DEBUG: describe collection {collection_name}: {res}")
# Wait for addition to sync
def check_add():
res = downstream_client.describe_collection(collection_name)
logger.info(
f"DEBUG: describe collection in downstream {collection_name}: {res}"
)
return "new_field" in [field["name"] for field in res["fields"]]
assert self.wait_for_sync(
check_add, sync_timeout, f"add field {collection_name}"
)
def test_load_collection(self, upstream_client, downstream_client, sync_timeout):
"""Test LOAD_COLLECTION operation sync."""
# Store upstream client for teardown
self._upstream_client = upstream_client
collection_name = self.gen_unique_name("test_col_load")
self.resources_to_cleanup.append(("collection", collection_name))
# Initial cleanup
self.cleanup_collection(upstream_client, collection_name)
# Create collection with proper schema
schema = self.create_default_schema(upstream_client)
upstream_client.create_collection(
collection_name=collection_name, schema=schema, consistency_level="Strong"
)
# Create index (required for loading)
index_params = upstream_client.prepare_index_params()
index_params.add_index(
field_name="vector", index_type="AUTOINDEX", metric_type="L2"
)
upstream_client.create_index(collection_name, index_params)
# Wait for creation to sync
def check_create():
return downstream_client.has_collection(collection_name)
assert self.wait_for_sync(
check_create, sync_timeout, f"create collection {collection_name}"
)
# Load collection
upstream_client.load_collection(collection_name)
# Wait for load to sync
def check_load():
try:
# Try to perform a search to verify the collection is loaded
query_vector = [[0.1] * 128] # dummy vector
downstream_client.search(
collection_name=collection_name,
data=query_vector,
limit=1,
output_fields=[],
)
return True
except:
return False
assert self.wait_for_sync(
check_load, sync_timeout, f"load collection {collection_name}"
)
@pytest.mark.skip(reason="skip multi-replica test")
def test_load_collection_multi_replicas(
self, upstream_client, downstream_client, sync_timeout
):
"""Test LOAD_COLLECTION operation with multiple replicas sync."""
# Store upstream client for teardown
self._upstream_client = upstream_client
collection_name = self.gen_unique_name("test_col_multi_replicas")
self.resources_to_cleanup.append(("collection", collection_name))
# Initial cleanup
self.cleanup_collection(upstream_client, collection_name)
# Create collection with proper schema
schema = self.create_default_schema(upstream_client)
upstream_client.create_collection(
collection_name=collection_name, schema=schema, consistency_level="Strong"
)
# Create index (required for loading)
index_params = upstream_client.prepare_index_params()
index_params.add_index(
field_name="vector", index_type="AUTOINDEX", metric_type="L2"
)
upstream_client.create_index(collection_name, index_params)
# Wait for creation to sync
def check_create():
return downstream_client.has_collection(collection_name)
assert self.wait_for_sync(
check_create, sync_timeout, f"create collection {collection_name}"
)
# Load collection with 2 replicas
replica_number = 2
logger.info(
f"Loading collection {collection_name} with {replica_number} replicas"
)
upstream_client.load_collection(collection_name, replica_number=replica_number)
# Verify upstream load with replicas and check replica count
def verify_upstream_replicas():
try:
# Create Collection object to get replica information
upstream_collection = Collection(
name=collection_name, using=upstream_client._using
)
# Get replicas information
replicas = upstream_collection.get_replicas()
actual_replica_count = len(replicas.groups)
logger.info(
f"Upstream collection {collection_name} has {actual_replica_count} replicas"
)
logger.info(f"Replica details: {replicas}")
# Verify replica count matches expected
if actual_replica_count != replica_number:
logger.warning(
f"Expected {replica_number} replicas, but found {actual_replica_count}"
)
return False
# Try to perform a search to verify the collection is loaded
query_vector = [[0.1] * 128]
upstream_client.search(
collection_name=collection_name,
data=query_vector,
limit=1,
output_fields=[],
)
logger.info(
f"Upstream collection {collection_name} loaded successfully with {actual_replica_count} replicas"
)
return True
except Exception as e:
logger.warning(f"Upstream load verification failed: {e}")
return False
assert self.wait_for_sync(
verify_upstream_replicas,
sync_timeout,
f"load collection {collection_name} with {replica_number} replicas in upstream",
)
# Wait for load with replicas to sync to downstream
def check_downstream_replicas():
try:
# Create Collection object to get replica information
downstream_collection = Collection(
name=collection_name, using=downstream_client._using
)
# Get replicas information
replicas = downstream_collection.get_replicas()
actual_replica_count = len(replicas.groups)
logger.info(
f"Downstream collection {collection_name} has {actual_replica_count} replicas"
)
logger.info(f"Replica details: {replicas}")
# Verify replica count matches expected
if actual_replica_count != replica_number:
logger.warning(
f"Expected {replica_number} replicas in downstream, but found {actual_replica_count}"
)
return False
# Try to perform a search to verify the collection is loaded in downstream
query_vector = [[0.1] * 128]
downstream_client.search(
collection_name=collection_name,
data=query_vector,
limit=1,
output_fields=[],
)
logger.info(
f"Downstream collection {collection_name} loaded successfully with {actual_replica_count} replicas"
)
return True
except Exception as e:
logger.warning(f"Downstream load check failed: {e}")
return False
assert self.wait_for_sync(
check_downstream_replicas,
sync_timeout,
f"load collection {collection_name} with {replica_number} replicas in downstream",
)
logger.info(
f"Successfully verified multi-replica load sync for collection {collection_name}"
)
logger.info(f"Both upstream and downstream have {replica_number} replicas")
# Now test release with multiple replicas
logger.info(
f"Testing release operation for multi-replica collection {collection_name}"
)
upstream_client.release_collection(collection_name)
# Verify upstream release
def verify_upstream_release():
try:
# Try to search - should fail if released
query_vector = [[0.1] * 128]
upstream_client.search(
collection_name=collection_name,
data=query_vector,
limit=1,
output_fields=[],
)
logger.warning(
f"Upstream collection {collection_name} is still loaded (search succeeded)"
)
return False # If search succeeds, collection is still loaded
except Exception as e:
logger.info(
f"Upstream collection {collection_name} released successfully (search failed as expected): {e}"
)
return True # If search fails, collection is released
assert self.wait_for_sync(
verify_upstream_release,
sync_timeout,
f"release collection {collection_name} in upstream",
)
# Wait for release to sync to downstream
def check_downstream_release():
try:
# Try to search - should fail if released
query_vector = [[0.1] * 128]
downstream_client.search(
collection_name=collection_name,
data=query_vector,
limit=1,
output_fields=[],
)
logger.warning(
f"Downstream collection {collection_name} is still loaded (search succeeded)"
)
return False # If search succeeds, collection is still loaded
except Exception as e:
logger.info(
f"Downstream collection {collection_name} released successfully (search failed as expected): {e}"
)
return True # If search fails, collection is released
assert self.wait_for_sync(
check_downstream_release,
sync_timeout,
f"release collection {collection_name} in downstream",
)
logger.info(
f"Successfully verified multi-replica release sync for collection {collection_name}"
)
logger.info(
f"Both upstream and downstream released the collection with {replica_number} replicas"
)
def test_release_collection(self, upstream_client, downstream_client, sync_timeout):
"""Test RELEASE_COLLECTION operation sync."""
# Store upstream client for teardown
self._upstream_client = upstream_client
collection_name = self.gen_unique_name("test_col_release")
self.resources_to_cleanup.append(("collection", collection_name))
# Initial cleanup
self.cleanup_collection(upstream_client, collection_name)
# Create collection with proper schema
schema = self.create_default_schema(upstream_client)
upstream_client.create_collection(
collection_name=collection_name, schema=schema, consistency_level="Strong"
)
index_params = upstream_client.prepare_index_params()
index_params.add_index(
field_name="vector", index_type="AUTOINDEX", metric_type="L2"
)
upstream_client.create_index(collection_name, index_params)
upstream_client.load_collection(collection_name)
# Wait for setup to sync
def check_setup():
try:
query_vector = [[0.1] * 128]
downstream_client.search(
collection_name=collection_name,
data=query_vector,
limit=1,
output_fields=[],
)
return True
except:
return False
assert self.wait_for_sync(
check_setup, sync_timeout, f"setup and load collection {collection_name}"
)
# Release collection
upstream_client.release_collection(collection_name)
# Wait for release to sync
def check_release():
try:
# Try to search - should fail if released
query_vector = [[0.1] * 128]
downstream_client.search(
collection_name=collection_name,
data=query_vector,
limit=1,
output_fields=[],
)
return False # If search succeeds, collection is still loaded
except:
return True # If search fails, collection is released
assert self.wait_for_sync(
check_release, sync_timeout, f"release collection {collection_name}"
)
def test_flush(self, upstream_client, downstream_client, sync_timeout):
"""Test FLUSH operation sync."""
# Store upstream client for teardown
self._upstream_client = upstream_client
collection_name = self.gen_unique_name("test_col_flush")
self.resources_to_cleanup.append(("collection", collection_name))
# Initial cleanup
self.cleanup_collection(upstream_client, collection_name)
# Create collection with proper schema
schema = self.create_default_schema(upstream_client)
upstream_client.create_collection(
collection_name=collection_name, schema=schema, consistency_level="Strong"
)
# Wait for creation to sync
def check_create():
return downstream_client.has_collection(collection_name)
assert self.wait_for_sync(
check_create, sync_timeout, f"create collection {collection_name}"
)
# Insert data (without immediate flush)
test_data = self.generate_test_data(5000)
insert_result = upstream_client.insert(collection_name, test_data)
# Verify data is not visible before flush
stats_before = upstream_client.get_collection_stats(collection_name)
logger.info(f"Stats before flush: {stats_before}")
# Flush collection
upstream_client.flush(collection_name)
# Wait for flush data to be visible with timeout
expected_count = (
insert_result.get("insert_count", 100) if insert_result else 100
)
def check_flush_stats():
try:
stats = upstream_client.get_collection_stats(collection_name)
logger.info(
f"DEBUG: get collection stats in upstream {collection_name}: {stats}"
)
row_count = stats.get("row_count", 0)
logger.info(
f"Current row count: {row_count}, expected: {expected_count}"
)
return row_count >= expected_count
except Exception as e:
logger.warning(f"Error checking stats: {e}")
return False
# Use timeout for waiting flush stats to update
timeout = 30 # 30 seconds timeout
assert self.wait_for_sync(
check_flush_stats,
timeout,
f"flush data visible in stats (expected: {expected_count})",
)
# Get final stats after flush
stats_after = upstream_client.get_collection_stats(collection_name)
logger.info(f"Stats after flush: {stats_after}")
# Wait for flush to sync downstream
def check_flush():
try:
downstream_stats = downstream_client.get_collection_stats(
collection_name
)
logger.info(
f"DEBUG: get collection stats in downstream {collection_name}: {downstream_stats}"
)
return downstream_stats.get("row_count", 0) >= 100
except:
return False
assert self.wait_for_sync(
check_flush, sync_timeout, f"flush collection {collection_name}"
)
def test_load_collection_with_load_fields(
self, upstream_client, downstream_client, sync_timeout
):
"""Test LOAD_COLLECTION operation with load_fields parameter sync."""
# Store upstream client for teardown
self._upstream_client = upstream_client
collection_name = self.gen_unique_name("test_col_load_fields")
self.resources_to_cleanup.append(("collection", collection_name))
# Initial cleanup
self.cleanup_collection(upstream_client, collection_name)
# Create collection with comprehensive schema (has multiple fields)
schema = self.create_comprehensive_schema(upstream_client)
upstream_client.create_collection(
collection_name=collection_name, schema=schema, consistency_level="Strong"
)
# Create index for float_vector field (required for loading)
index_params = upstream_client.prepare_index_params()
index_params.add_index(
field_name="float_vector", index_type="AUTOINDEX", metric_type="L2"
)
upstream_client.create_index(collection_name, index_params)
# Wait for creation to sync
def check_create():
return downstream_client.has_collection(collection_name)
assert self.wait_for_sync(
check_create, sync_timeout, f"create collection {collection_name}"
)
# Insert some test data
test_data = self.generate_comprehensive_test_data(100)
upstream_client.insert(collection_name, test_data)
upstream_client.flush(collection_name)
# Load collection with specific fields only (float_vector + id + varchar_field)
load_fields = ["float_vector", "id", "varchar_field"]
upstream_client.load_collection(collection_name, load_fields=load_fields)
# Verify upstream load operation succeeded
def verify_upstream_load():
try:
# Try to search to verify collection is loaded
query_vector = [[0.1] * 128]
upstream_client.search(
collection_name=collection_name,
data=query_vector,
limit=1,
output_fields=["varchar_field"],
anns_field="float_vector",
)
return True
except Exception as e:
logger.warning(f"Upstream load verification failed: {e}")
return False
assert self.wait_for_sync(
verify_upstream_load,
sync_timeout,
f"verify upstream load with load_fields in {collection_name}",
)
# Verify downstream sync - collection should be loaded and searchable
def check_downstream_load_sync():
try:
query_vector = [[0.1] * 128]
result = downstream_client.search(
collection_name=collection_name,
data=query_vector,
limit=1,
output_fields=["varchar_field"],
anns_field="float_vector",
)
return len(result) > 0 and len(result[0]) >= 0
except Exception as e:
logger.warning(f"Downstream load sync check failed: {e}")
return False
assert self.wait_for_sync(
check_downstream_load_sync,
sync_timeout,
f"verify downstream load sync for {collection_name}",
)
# Additional verification: test that both loaded and unloaded fields can be output
# (since load_fields only affects memory usage, not field accessibility)
def verify_all_fields_accessible():
try:
query_vector = [[0.1] * 128]
# Test accessing both loaded and unloaded fields
result1 = downstream_client.search(
collection_name=collection_name,
data=query_vector,
limit=1,
output_fields=["varchar_field"], # loaded field
anns_field="float_vector",
)
result2 = downstream_client.search(
collection_name=collection_name,
data=query_vector,
limit=1,
output_fields=[
"float_field"
], # unloaded field (but should still be accessible)
anns_field="float_vector",
)
return len(result1) > 0 and len(result2) > 0
except Exception as e:
logger.warning(f"Field accessibility verification failed: {e}")
return False
assert self.wait_for_sync(
verify_all_fields_accessible,
sync_timeout,
f"verify all fields accessible in {collection_name}",
)
logger.info(
f"Successfully tested load_collection with load_fields: {load_fields}"
)
logger.info("Verified CDC sync of load operation with load_fields parameter")