Open qiulingdong opened 1 week ago
yanliang567
commented
1 week ago @qiulingdong I guess there are duplicated doc_id when you insert into the collection. you can check it by
collection.query(expr="doc_id=='xxxxx'", output_fields=["count(*)"]
/assign @qiulingdong /unassign
qiulingdong
commented
1 week ago @qiulingdong I guess there are duplicated doc_id when you insert into the collection. you can check it by
collection.query(expr="doc_id=='xxxxx'", output_fields=["count(*)"]/assign @qiulingdong /unassign
I have tried to look up, and there is no duplicate doc_id appearing. Additionally, I attempted to use attu to randomly generate 1000 data entries, and the same issue persists.
result = collection.query(expr="doc_id=='FILE593c68a689aa4797bf59f44a3a2e8c9a_CHUNK_1'", output_fields=["count(*)"])
print(f"1.count：{result}")
result = collection.query(expr="doc_id=='FILE593c68a689aa4797bf59f44a3a2e8c9a_CHUNK_2'", output_fields=["count(*)"])
print(f"2.count：{result}")
result = collection.query(expr="doc_id=='FILE593c68a689aa4797bf59f44a3a2e8c9a_CHUNK_4'", output_fields=["count(*)"])
print(f"3.count：{result}")
result = collection.query(expr="doc_id=='FILE593c68a689aa4797bf59f44a3a2e8c9a_CHUNK_6'", output_fields=["count(*)"])
print(f"4.count：{result}")1.count：data: ["{'count()': 1}"] 2.count：data: ["{'count()': 1}"] 3.count：data: ["{'count()': 1}"] 4.count：data: ["{'count()': 1}"]
xiaofan-luan
commented
1 week ago could you try 2.4.15?
rabbor
commented
5 days ago I found a similar issue on version v2.4.15, but the phenomenon is not exactly the same
When the collection is loaded, I delete the entity and release the collection after the deletion returns, but when I reconnect the data to query, the entity reappears
The steps are as follows:
1. load_collection
2. delete
3. release_collection
4. reconnect milvus db
5. get the deleted entity when queryI believe this is caused by the default consistency level is bound. So if you add consistency_level=ConsistencyLevel.Strong in the query() after delete, you will won't see the deleted entities immediately. @qiulingdong @rabbor /assign @qiulingdong /unassign
xiaofan-luan
commented
5 days ago I believe this is caused by the default consistency level is bound. So if you add consistency_level=ConsistencyLevel.Strong in the query() after delete, you will won't see the deleted entities immediately. @qiulingdong @rabbor /assign @qiulingdong /unassign
I thought there is a bug on remote loading.
@yanliang567 for some reason, L0 segment is not processed as expect.
that's why I talked about the test on delete and right now the test itself is not very solid
xiaofan-luan
commented
5 days ago especially under different failure recovery test
yanliang567
commented
4 days ago remote load is only avaliable on 2.4.16 or later, I don't think that is the issue here. @qiulingdong @rabbor please try as my comments above and keep us posted. thx.
rabbor
commented
3 days ago I was not previously aware of the "consistency_level" parameter in the documentation. Following your suggestion, I added the parameter ”consistency_level=ConsistencyLevel.Strong“ to the interface "create_collection", "delete", "search", but the same issue still occurs after testing. The steps to reproduce are as follows:
- create collection
- insert 3 user data
- delete 1 user
- search (The immediate retrieval results are as expected, but when reconnecting to the database for retrieval, deleted data reappears.)@yanliang567
yanliang567
commented
3 days ago I cannot reproduce the issue, could you please share your code sample to reproduce the issue? here is my code:
import time
from pymilvus import (
utility,
FieldSchema, CollectionSchema, DataType, MilvusClient,
Collection, AnnSearchRequest, RRFRanker, connections,
)
from pymilvus.client.constants import ConsistencyLevel
import random
import json
def create_collection(collection_name, dimension):
schema = MilvusClient.create_schema(
auto_id=False,
enable_dynamic_field=False,
)
# 2. Add fields to schema
schema.add_field(field_name="id", datatype=DataType.VARCHAR, is_primary=True, max_length=256)
schema.add_field(field_name="embeddings", datatype=DataType.FLOAT_VECTOR, dim=dimension)
schema.add_field(field_name="title", datatype=DataType.VARCHAR, max_length=256)
schema.add_field(field_name="doc_type", datatype=DataType.VARCHAR, max_length=256)
schema.add_field(field_name="uri", datatype=DataType.VARCHAR, max_length=256)
index_params = {"index_type": "HNSW", "metric_type": "COSINE", "params":{"M": 32, "efConstruction": 360}}
collection = Collection(name=collection_name, schema=schema, auto_id=False)
collection.create_index(field_name="embeddings", index_params=index_params)
collection.load()
return collection
# 1. Set up a Milvus client
host = '10.xx.xx.xx'
connections.connect(host=host)
# insert
c_name = "issue_37725_7"
dim = 128
if not utility.has_collection(c_name):
# prepare data
data = []
colors = ["green", "blue", "yellow", "red", "black", "white", "purple", "pink", "orange", "brown", "grey"]
for i in range(4000):
current_color = random.choice(colors)
doc = { "id": str(i),
"embeddings": [random.uniform(-1, 1) for _ in range(dim)],
"title": current_color,
"doc_type": "type_1"}
doc["uri"] = "uri" + str(int(i / 1000))
data.append(doc)
c = create_collection(c_name, dim)
for i in range(0, len(data), 1000):
print(f"indexing docs from {i} to {i+1000}")
c.insert(data=data[i:i+1000])
c=Collection(c_name)
c.flush()
c.load()
query_results = c.query(
expr=f"id in ['11','12','13','14','15','16','17','18','19','20']",
output_fields=["id", "title"])
print(f"{c_name} query result before delete: {query_results}")
c.delete(expr=f"id in ['11','13','15','17','19']")
query_results = c.query(
expr=f"id in ['11','12','13','14','15','16','17','18','19','20']",
output_fields=["id", "title"],
consistency_level=ConsistencyLevel.Strong
)
print(f"{c_name} query result after delete: {query_results}")
c.release()
connections.disconnect("default")
connections.connect(host=host)
c=Collection(c_name)
c.load()
query_results = c.query(
expr=f"id in ['11','12','13','14','15','16','17','18','19','20']",
output_fields=["id", "title"])
print(f"{c_name} query result after reconnect: {query_results}")
print('completed')
The code is relatively complex, and I extracted the core process for testing but couldn't reproduce it. Nevertheless, I really appreciate your help, thank you!
code ：
import json
import time
from pymilvus import MilvusClient, DataType
from pymilvus.client.types import LoadState
from pymilvus.grpc_gen import common_pb2
ConsistencyLevel = common_pb2.ConsistencyLevel
#change this
milvus_ip_port = "http://172.16.4.34:19530"
client = MilvusClient(milvus_ip_port)
collection_name = "ccc"
# create collection
schema = client.create_schema(auto_id=False,enable_dynamic_field=True)
# Add fields to schema
schema.add_field(field_name="user_id", datatype=DataType.VARCHAR, max_length=256, is_primary=True)
schema.add_field(field_name="face_feature", datatype=DataType.FLOAT_VECTOR, dim=512)
schema.add_field(field_name="user_attr", datatype=DataType.JSON)
client.create_collection(collection_name=collection_name, schema=schema)
# create index for collection
index_params = client.prepare_index_params()
index_params.add_index(
field_name="user_id",
index_type="Trie",
index_name="user_id_index"
)
index_params.add_index(
field_name="face_feature",
index_type="IVF_FLAT",
metric_type="COSINE",
params={ "nlist": 128 }
)
client.create_index(collection_name=collection_name,index_params=index_params)
# insert data
feature_1 = [0.0062522911466658115,
0.0369855880737305,
0.005088048055768013,
0.031010277569293976,
0.03752732276916504,
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insert_data_list = []
insert_data_list.append({"user_id" : "63",
"face_feature" : feature_1,
"user_attr" : {"age": 28, "name": "mery"}})
insert_data_list.append({"user_id" : "92",
"face_feature" : feature_2,
"user_attr" : {"age": 25, "name": "jck"}})
insert_result = client.insert(collection_name=collection_name, data=insert_data_list)
print(insert_result)
# delete one user
delete_result = client.delete(collection_name=collection_name, ids=["63"])
print(delete_result)
# search
# load before search
state_dict = client.get_load_state(collection_name=collection_name)
state = state_dict["state"]
if state != LoadState.Loaded :
client.load_collection(collection_name=collection_name)
search_params = {
"metric_type": "COSINE",
"params": {
"radius": 0.6,
"range_filter": 1.0
}
}
search_vector = [ feature_1, feature_2]
search_result = client.search(collection_name=collection_name,
data=search_vector,
limit=1,
search_params=search_params,
output_fields=["user_attr"],
consistency_level=ConsistencyLevel.Strong)
print(search_result)
# release collection
client.release_collection(collection_name=collection_name)
# load again
client.load_collection(collection_name=collection_name)
# search again
search_result = client.search(collection_name=collection_name,
data=search_vector,
limit=1,
search_params=search_params,
output_fields=["user_attr"],
consistency_level=ConsistencyLevel.Strong)
print(search_result)
Is there an existing issue for this?
Environment
Current Behavior
I created a strongly consistent collection and inserted multiple records. Frequently, when deleting data, I encounter situations where the data cannot be deleted properly. There are several phenomena:
Using code
print log
Expected Behavior
I expect the data in the Collection to be successfully deleted as it .
Steps To Reproduce
Milvus Log
No response
Anything else?
No response