wehub-resource-sync
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732 lines
29 KiB
Python
732 lines
29 KiB
Python
"""
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CDC sync tests for index operations.
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"""
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import time
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import random
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import pytest
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from common.common_type import CaseLabel
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from .base import TestCDCSyncBase, logger
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@pytest.mark.tags(CaseLabel.CDC)
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class TestCDCSyncIndex(TestCDCSyncBase):
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"""Test CDC sync for index operations."""
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def setup_method(self):
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"""Setup for each test method."""
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self.resources_to_cleanup = []
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def teardown_method(self):
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"""Cleanup after each test method - only cleanup upstream, downstream will sync."""
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upstream_client = getattr(self, "_upstream_client", None)
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if upstream_client:
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for resource_type, resource_name in self.resources_to_cleanup:
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if resource_type == "collection":
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self.cleanup_collection(upstream_client, resource_name)
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time.sleep(1) # Allow cleanup to sync to downstream
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def test_create_index(self, upstream_client, downstream_client, sync_timeout):
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"""Test CREATE_INDEX operation sync."""
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# Store upstream client for teardown
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self._upstream_client = upstream_client
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collection_name = self.gen_unique_name("test_col_create_idx")
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self.resources_to_cleanup.append(("collection", collection_name))
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# Initial cleanup
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self.cleanup_collection(upstream_client, collection_name)
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# Create collection
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upstream_client.create_collection(
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collection_name=collection_name,
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schema=self.create_default_schema(upstream_client),
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)
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# Wait for creation to sync
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def check_create():
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return downstream_client.has_collection(collection_name)
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assert self.wait_for_sync(
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check_create, sync_timeout, f"create collection {collection_name}"
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)
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# Create index
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index_params = upstream_client.prepare_index_params()
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index_params.add_index(
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field_name="vector",
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index_type="IVF_FLAT",
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metric_type="L2",
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params={"nlist": 128},
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)
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upstream_client.create_index(collection_name, index_params)
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# Wait for index creation to sync
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def check_index():
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try:
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downstream_indexes = downstream_client.list_indexes(collection_name)
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return len(downstream_indexes) > 0
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except:
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return False
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assert self.wait_for_sync(
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check_index, sync_timeout, f"create index on {collection_name}"
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)
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def test_drop_index(self, upstream_client, downstream_client, sync_timeout):
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"""Test DROP_INDEX operation sync."""
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# Store upstream client for teardown
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self._upstream_client = upstream_client
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collection_name = self.gen_unique_name("test_col_drop_idx")
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self.resources_to_cleanup.append(("collection", collection_name))
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# Initial cleanup
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self.cleanup_collection(upstream_client, collection_name)
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# Create collection and index
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upstream_client.create_collection(
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collection_name=collection_name,
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schema=self.create_default_schema(upstream_client),
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)
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index_params = upstream_client.prepare_index_params()
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index_params.add_index(
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field_name="vector",
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index_type="IVF_FLAT",
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metric_type="L2",
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params={"nlist": 128},
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)
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upstream_client.create_index(collection_name, index_params)
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# Wait for setup to sync
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def check_setup():
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try:
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return (
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downstream_client.has_collection(collection_name)
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and len(downstream_client.list_indexes(collection_name)) > 0
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)
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except:
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return False
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assert self.wait_for_sync(
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check_setup, sync_timeout, f"setup collection and index {collection_name}"
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)
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# Drop index
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upstream_client.drop_index(collection_name, "vector")
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# Wait for index drop to sync
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def check_drop():
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try:
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downstream_indexes = downstream_client.list_indexes(collection_name)
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return len(downstream_indexes) == 0
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except:
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return True # If error, assume index is dropped
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assert self.wait_for_sync(
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check_drop, sync_timeout, f"drop index on {collection_name}"
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)
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def test_create_vector_indexes_comprehensive(
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self, upstream_client, downstream_client, sync_timeout
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):
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"""Test CREATE_INDEX operation sync for all vector index types."""
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# Store upstream client for teardown
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self._upstream_client = upstream_client
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# Test cases for different vector types and their applicable indexes
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test_cases = [
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# FLOAT_VECTOR indexes
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{
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"field_name": "float_vector",
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"field_type": "FLOAT_VECTOR",
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"index_tests": [
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{"index_type": "FLAT", "metric_type": "L2", "params": {}},
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{
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"index_type": "IVF_FLAT",
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"metric_type": "L2",
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"params": {"nlist": 128},
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},
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{
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"index_type": "IVF_SQ8",
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"metric_type": "L2",
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"params": {"nlist": 128},
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},
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{
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"index_type": "IVF_PQ",
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"metric_type": "L2",
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"params": {"nlist": 128, "m": 16, "nbits": 8},
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},
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{
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"index_type": "HNSW",
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"metric_type": "L2",
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"params": {"M": 16, "efConstruction": 200},
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},
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],
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},
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# FLOAT16_VECTOR indexes
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{
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"field_name": "float16_vector",
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"field_type": "FLOAT16_VECTOR",
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"index_tests": [
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{"index_type": "FLAT", "metric_type": "L2", "params": {}},
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{
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"index_type": "IVF_FLAT",
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"metric_type": "L2",
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"params": {"nlist": 128},
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},
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{
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"index_type": "HNSW",
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"metric_type": "L2",
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"params": {"M": 16, "efConstruction": 200},
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},
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],
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},
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# BINARY_VECTOR indexes
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{
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"field_name": "binary_vector",
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"field_type": "BINARY_VECTOR",
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"index_tests": [
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{"index_type": "BIN_FLAT", "metric_type": "HAMMING", "params": {}},
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{
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"index_type": "BIN_IVF_FLAT",
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"metric_type": "HAMMING",
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"params": {"nlist": 128},
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},
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],
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},
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# SPARSE_FLOAT_VECTOR indexes
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{
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"field_name": "sparse_vector",
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"field_type": "SPARSE_FLOAT_VECTOR",
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"index_tests": [
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{
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"index_type": "SPARSE_INVERTED_INDEX",
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"metric_type": "IP",
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"params": {},
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},
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],
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},
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]
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for test_case in test_cases:
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for index_test in test_case["index_tests"]:
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collection_name = self.gen_unique_name(
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f"test_idx_{test_case['field_type'].lower()}_{index_test['index_type'].lower()}"
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)
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self.resources_to_cleanup.append(("collection", collection_name))
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try:
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logger.info(
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f"[INDEX_TEST] Testing {test_case['field_type']} with {index_test['index_type']} index"
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)
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# Initial cleanup
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self.cleanup_collection(upstream_client, collection_name)
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# Create collection with specific vector field
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schema = self._create_vector_schema(
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upstream_client,
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test_case["field_name"],
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test_case["field_type"],
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)
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upstream_client.create_collection(
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collection_name=collection_name, schema=schema
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)
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# Wait for creation to sync
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def check_create():
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return downstream_client.has_collection(collection_name)
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assert self.wait_for_sync(
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check_create,
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sync_timeout,
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f"create collection {collection_name}",
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)
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# Insert test data before creating index (better practice)
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test_data = self._generate_test_data_for_vector_field(
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test_case["field_name"], test_case["field_type"], 100
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)
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upstream_client.insert(collection_name, test_data)
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upstream_client.flush(collection_name)
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logger.info(
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f"[DATA_INSERTED] Inserted 100 records before creating {test_case['field_type']} index"
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)
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# Create specific index
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index_params = upstream_client.prepare_index_params()
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index_params.add_index(
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field_name=test_case["field_name"],
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index_type=index_test["index_type"],
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metric_type=index_test["metric_type"],
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params=index_test["params"],
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)
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upstream_client.create_index(collection_name, index_params)
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# Wait for index creation to sync
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def check_index():
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try:
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downstream_indexes = downstream_client.list_indexes(
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collection_name
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)
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return len(downstream_indexes) > 0
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except:
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return False
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assert self.wait_for_sync(
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check_index,
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sync_timeout,
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f"create {index_test['index_type']} index on {collection_name}",
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)
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# Verify index details
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try:
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index_info = downstream_client.describe_index(
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collection_name, test_case["field_name"]
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)
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logger.info(
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f"[INDEX_VERIFICATION] {index_test['index_type']} index created successfully: {index_info}"
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)
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except Exception as e:
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logger.warning(
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f"Failed to verify {index_test['index_type']} index details: {e}"
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)
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except Exception as e:
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logger.error(
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f"[INDEX_ERROR] Failed to test {test_case['field_type']} with {index_test['index_type']}: {e}"
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)
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raise
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def test_create_scalar_indexes_comprehensive(
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self, upstream_client, downstream_client, sync_timeout
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):
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"""Test CREATE_INDEX operation sync for all scalar field index types."""
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# Store upstream client for teardown
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self._upstream_client = upstream_client
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# Test cases for different scalar types and their applicable indexes
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test_cases = [
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# VARCHAR indexes
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{
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"field_name": "varchar_field",
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"field_type": "VARCHAR",
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"index_tests": [
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{"index_type": "INVERTED", "params": {}},
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{"index_type": "Trie", "params": {}},
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],
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},
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# BOOL indexes
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{
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"field_name": "bool_field",
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"field_type": "BOOL",
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"index_tests": [
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{"index_type": "INVERTED", "params": {}},
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],
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},
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# INT32 indexes
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{
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"field_name": "int32_field",
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"field_type": "INT32",
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"index_tests": [
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{"index_type": "INVERTED", "params": {}},
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{"index_type": "STL_SORT", "params": {}},
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],
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},
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# INT64 indexes
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{
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"field_name": "int64_field",
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"field_type": "INT64",
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"index_tests": [
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{"index_type": "INVERTED", "params": {}},
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{"index_type": "STL_SORT", "params": {}},
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],
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},
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# FLOAT indexes
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{
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"field_name": "float_field",
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"field_type": "FLOAT",
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"index_tests": [
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{"index_type": "INVERTED", "params": {}},
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],
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},
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# DOUBLE indexes
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{
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"field_name": "double_field",
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"field_type": "DOUBLE",
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"index_tests": [
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{"index_type": "INVERTED", "params": {}},
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],
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},
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# ARRAY indexes
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{
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"field_name": "int32_array",
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"field_type": "ARRAY",
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"element_type": "INT32",
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"index_tests": [
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{"index_type": "INVERTED", "params": {}},
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],
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},
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# JSON indexes - use AUTOINDEX (recommended) with proper JSON path syntax
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{
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"field_name": "json_field",
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"field_type": "JSON",
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"index_tests": [
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{
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"index_type": "AUTOINDEX",
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"params": {
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"json_path": 'json_field["name"]',
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"json_cast_type": "VARCHAR",
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},
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},
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],
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},
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]
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for test_case in test_cases:
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for index_test in test_case["index_tests"]:
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collection_name = self.gen_unique_name(
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f"test_idx_{test_case['field_type'].lower()}_{index_test['index_type'].lower()}"
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)
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self.resources_to_cleanup.append(("collection", collection_name))
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try:
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logger.info(
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f"[SCALAR_INDEX_TEST] Testing {test_case['field_type']} with {index_test['index_type']} index"
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)
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# Initial cleanup
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self.cleanup_collection(upstream_client, collection_name)
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# Create collection with specific scalar field
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schema = self._create_scalar_schema(upstream_client, test_case)
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upstream_client.create_collection(
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collection_name=collection_name, schema=schema
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)
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# Wait for creation to sync
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def check_create():
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return downstream_client.has_collection(collection_name)
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assert self.wait_for_sync(
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check_create,
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sync_timeout,
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f"create collection {collection_name}",
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)
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# Insert test data before creating index (better practice)
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test_data = self._generate_test_data_for_scalar_field(
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test_case["field_name"], test_case["field_type"], 100
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)
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upstream_client.insert(collection_name, test_data)
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upstream_client.flush(collection_name)
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logger.info(
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f"[DATA_INSERTED] Inserted 100 records before creating {test_case['field_type']} index"
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)
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# Create specific index
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index_params = upstream_client.prepare_index_params()
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if test_case["field_type"] == "JSON":
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# JSON fields need special handling with index_name
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index_params.add_index(
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field_name=test_case["field_name"],
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index_type=index_test["index_type"],
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index_name=f"{test_case['field_name']}_name_index",
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params=index_test["params"],
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)
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else:
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index_params.add_index(
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field_name=test_case["field_name"],
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index_type=index_test["index_type"],
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params=index_test["params"],
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)
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upstream_client.create_index(collection_name, index_params)
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# Wait for index creation to sync
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def check_index():
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try:
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downstream_indexes = downstream_client.list_indexes(
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collection_name
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)
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return len(downstream_indexes) > 0
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except:
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return False
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assert self.wait_for_sync(
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check_index,
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sync_timeout,
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f"create {index_test['index_type']} index on {collection_name}",
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)
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# Verify index details
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try:
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index_info = downstream_client.describe_index(
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collection_name, test_case["field_name"]
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)
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logger.info(
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f"[SCALAR_INDEX_VERIFICATION] {index_test['index_type']} index created successfully: {index_info}"
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)
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except Exception as e:
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logger.warning(
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f"Failed to verify {index_test['index_type']} scalar index details: {e}"
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)
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except Exception as e:
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logger.error(
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f"[SCALAR_INDEX_ERROR] Failed to test {test_case['field_type']} with {index_test['index_type']}: {e}"
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)
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raise
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def _create_vector_schema(self, client, field_name, field_type):
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"""Create schema for vector index testing."""
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from pymilvus import DataType
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schema = client.create_schema(enable_dynamic_field=True)
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schema.add_field("id", DataType.INT64, is_primary=True, auto_id=True)
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if field_type == "FLOAT_VECTOR":
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schema.add_field(field_name, DataType.FLOAT_VECTOR, dim=128)
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elif field_type == "FLOAT16_VECTOR":
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schema.add_field(field_name, DataType.FLOAT16_VECTOR, dim=64)
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elif field_type == "BFLOAT16_VECTOR":
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schema.add_field(field_name, DataType.BFLOAT16_VECTOR, dim=64)
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elif field_type == "BINARY_VECTOR":
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schema.add_field(field_name, DataType.BINARY_VECTOR, dim=128)
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elif field_type == "SPARSE_FLOAT_VECTOR":
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schema.add_field(field_name, DataType.SPARSE_FLOAT_VECTOR)
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elif field_type == "INT8_VECTOR":
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schema.add_field(field_name, DataType.INT8_VECTOR, dim=128)
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return schema
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def _create_scalar_schema(self, client, test_case):
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"""Create schema for scalar index testing."""
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from pymilvus import DataType
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schema = client.create_schema(enable_dynamic_field=True)
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schema.add_field("id", DataType.INT64, is_primary=True, auto_id=True)
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schema.add_field(
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"vector", DataType.FLOAT_VECTOR, dim=128
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) # Required for collection
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field_name = test_case["field_name"]
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field_type = test_case["field_type"]
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|
|
if field_type == "VARCHAR":
|
|
schema.add_field(field_name, DataType.VARCHAR, max_length=1000)
|
|
elif field_type == "BOOL":
|
|
schema.add_field(field_name, DataType.BOOL)
|
|
elif field_type == "INT32":
|
|
schema.add_field(field_name, DataType.INT32)
|
|
elif field_type == "INT64":
|
|
schema.add_field(field_name, DataType.INT64)
|
|
elif field_type == "FLOAT":
|
|
schema.add_field(field_name, DataType.FLOAT)
|
|
elif field_type == "DOUBLE":
|
|
schema.add_field(field_name, DataType.DOUBLE)
|
|
elif field_type == "ARRAY" and test_case.get("element_type") == "INT32":
|
|
schema.add_field(
|
|
field_name,
|
|
DataType.ARRAY,
|
|
element_type=DataType.INT32,
|
|
max_capacity=100,
|
|
)
|
|
elif field_type == "JSON":
|
|
schema.add_field(field_name, DataType.JSON)
|
|
|
|
return schema
|
|
|
|
def _generate_test_data_for_vector_field(self, field_name, field_type, count=100):
|
|
"""Generate test data for specific vector field type."""
|
|
from pymilvus import DataType
|
|
|
|
data = []
|
|
for _ in range(count):
|
|
record = {}
|
|
|
|
if field_type == "FLOAT_VECTOR":
|
|
vectors = self._gen_vectors(1, 128, DataType.FLOAT_VECTOR)
|
|
record[field_name] = vectors[0]
|
|
elif field_type == "FLOAT16_VECTOR":
|
|
vectors = self._gen_vectors(1, 64, DataType.FLOAT16_VECTOR)
|
|
record[field_name] = vectors[0]
|
|
elif field_type == "BFLOAT16_VECTOR":
|
|
vectors = self._gen_vectors(1, 64, DataType.BFLOAT16_VECTOR)
|
|
record[field_name] = vectors[0]
|
|
elif field_type == "BINARY_VECTOR":
|
|
vectors = self._gen_vectors(1, 128, DataType.BINARY_VECTOR)
|
|
record[field_name] = vectors[0]
|
|
elif field_type == "SPARSE_FLOAT_VECTOR":
|
|
vectors = self._gen_vectors(1, 1000, DataType.SPARSE_FLOAT_VECTOR)
|
|
record[field_name] = vectors[0]
|
|
elif field_type == "INT8_VECTOR":
|
|
vectors = self._gen_vectors(1, 128, DataType.INT8_VECTOR)
|
|
record[field_name] = vectors[0]
|
|
|
|
data.append(record)
|
|
|
|
return data
|
|
|
|
def _generate_test_data_for_scalar_field(self, field_name, field_type, count=100):
|
|
"""Generate test data for specific scalar field type."""
|
|
data = []
|
|
for i in range(count):
|
|
record = {
|
|
"vector": [
|
|
random.random() for _ in range(128)
|
|
], # Required base vector field
|
|
}
|
|
|
|
if field_type == "VARCHAR":
|
|
record[field_name] = f"test_string_{i}_{random.randint(1000, 9999)}"
|
|
elif field_type == "BOOL":
|
|
record[field_name] = random.choice([True, False])
|
|
elif field_type == "INT32":
|
|
record[field_name] = random.randint(-1000000, 1000000)
|
|
elif field_type == "INT64":
|
|
record[field_name] = random.randint(-1000000000, 1000000000)
|
|
elif field_type == "FLOAT":
|
|
record[field_name] = random.uniform(-1000.0, 1000.0)
|
|
elif field_type == "DOUBLE":
|
|
record[field_name] = random.uniform(-1000.0, 1000.0)
|
|
elif field_type == "ARRAY":
|
|
record[field_name] = [
|
|
random.randint(-100, 100) for _ in range(random.randint(1, 10))
|
|
]
|
|
elif field_type == "JSON":
|
|
record[field_name] = {
|
|
"name": f"test_item_{i}",
|
|
"value": random.randint(1, 1000),
|
|
"category": random.choice(["A", "B", "C"]),
|
|
"metadata": {
|
|
"score": random.uniform(0.0, 100.0),
|
|
"created": f"2024-01-{random.randint(1, 28):02d}",
|
|
},
|
|
}
|
|
|
|
data.append(record)
|
|
|
|
return data
|
|
|
|
def test_create_bfloat16_int8_vector_indexes(
|
|
self, upstream_client, downstream_client, sync_timeout
|
|
):
|
|
"""Test CREATE_INDEX operation sync for BFLOAT16_VECTOR and INT8_VECTOR (combined test due to 4-vector limit)."""
|
|
# Store upstream client for teardown
|
|
self._upstream_client = upstream_client
|
|
|
|
# Test cases for BFLOAT16_VECTOR and INT8_VECTOR
|
|
test_cases = [
|
|
# BFLOAT16_VECTOR indexes - use AUTOINDEX for compatibility
|
|
{
|
|
"field_name": "bfloat16_vector",
|
|
"field_type": "BFLOAT16_VECTOR",
|
|
"index_tests": [
|
|
{"index_type": "AUTOINDEX", "metric_type": "L2", "params": {}},
|
|
],
|
|
},
|
|
# INT8_VECTOR indexes - use AUTOINDEX for compatibility
|
|
{
|
|
"field_name": "int8_vector",
|
|
"field_type": "INT8_VECTOR",
|
|
"index_tests": [
|
|
{"index_type": "AUTOINDEX", "metric_type": "L2", "params": {}},
|
|
],
|
|
},
|
|
]
|
|
|
|
for test_case in test_cases:
|
|
for index_test in test_case["index_tests"]:
|
|
collection_name = self.gen_unique_name(
|
|
f"test_idx_{test_case['field_type'].lower()}_{index_test['index_type'].lower()}"
|
|
)
|
|
self.resources_to_cleanup.append(("collection", collection_name))
|
|
|
|
try:
|
|
logger.info(
|
|
f"[{test_case['field_type']}_INDEX_TEST] Testing {test_case['field_type']} with {index_test['index_type']} index"
|
|
)
|
|
|
|
# Initial cleanup
|
|
self.cleanup_collection(upstream_client, collection_name)
|
|
|
|
# Create collection with specific vector field
|
|
schema = self._create_vector_schema(
|
|
upstream_client,
|
|
test_case["field_name"],
|
|
test_case["field_type"],
|
|
)
|
|
upstream_client.create_collection(
|
|
collection_name=collection_name, schema=schema
|
|
)
|
|
|
|
# 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 test data before creating index (better practice)
|
|
if test_case["field_type"] == "BFLOAT16_VECTOR":
|
|
test_data = self.generate_bfloat16_test_data(100)
|
|
else: # INT8_VECTOR
|
|
test_data = self.generate_int8_test_data(100)
|
|
upstream_client.insert(collection_name, test_data)
|
|
upstream_client.flush(collection_name)
|
|
logger.info(
|
|
f"[DATA_INSERTED] Inserted 100 records before creating {test_case['field_type']} index"
|
|
)
|
|
|
|
# Create specific index
|
|
index_params = upstream_client.prepare_index_params()
|
|
index_params.add_index(
|
|
field_name=test_case["field_name"],
|
|
index_type=index_test["index_type"],
|
|
metric_type=index_test["metric_type"],
|
|
params=index_test["params"],
|
|
)
|
|
upstream_client.create_index(collection_name, index_params)
|
|
|
|
# Wait for index creation to sync
|
|
def check_index():
|
|
try:
|
|
downstream_indexes = downstream_client.list_indexes(
|
|
collection_name
|
|
)
|
|
return len(downstream_indexes) > 0
|
|
except:
|
|
return False
|
|
|
|
assert self.wait_for_sync(
|
|
check_index,
|
|
sync_timeout,
|
|
f"create {index_test['index_type']} index on {collection_name}",
|
|
)
|
|
|
|
# Verify index details
|
|
try:
|
|
index_info = downstream_client.describe_index(
|
|
collection_name, test_case["field_name"]
|
|
)
|
|
logger.info(
|
|
f"[{test_case['field_type']}_INDEX_VERIFICATION] {index_test['index_type']} index created successfully: {index_info}"
|
|
)
|
|
except Exception as e:
|
|
logger.warning(
|
|
f"Failed to verify {index_test['index_type']} {test_case['field_type']} index details: {e}"
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(
|
|
f"[{test_case['field_type']}_INDEX_ERROR] Failed to test {test_case['field_type']} with {index_test['index_type']}: {e}"
|
|
)
|
|
raise
|