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70b311735b
related issue: #40698 1. use vector datat types instead of hard code datatpe names 2. update search pagination tests 3. remove checking distances in search results checking, for knowhere customize the distances for different metrics and indexes. Now only assert the distances are sorted correct. --------- Signed-off-by: yanliang567 <yanliang.qiao@zilliz.com>
336 lines
16 KiB
Python
336 lines
16 KiB
Python
import os
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import random
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from utils.util_log import test_log as log
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from common.common_type import CaseLabel, CheckTasks
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from common import common_type as ct
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from common import common_func as cf
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from base.client_base import TestcaseBase
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import pytest
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prefix = "query_iter_"
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class TestQueryIterator(TestcaseBase):
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@pytest.mark.tags(CaseLabel.L0)
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@pytest.mark.parametrize("primary_field", [ct.default_string_field_name, ct.default_int64_field_name])
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@pytest.mark.parametrize("with_growing", [False, True])
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def test_query_iterator_normal(self, primary_field, with_growing):
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"""
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target: test query iterator normal
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method: 1. query iterator
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2. check the result, expect pk
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verify: no pk lost in interator results
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3. query iterator with checkpoint file
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4. iterator.next() for 10 times
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5. delete some entities before calling a new query iterator
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6. call a new query iterator with the same checkpoint file, with diff batch_size and output_fields
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7. iterator.next() until the end
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verify:
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1. no pk lost in interator results for the 2 iterators
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2. no dup pk in the 2 iterators
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expected: query iterators successfully
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"""
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# 1. initialize with data
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nb = 4000
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batch_size = 200
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collection_w, _, _, insert_ids, _ = \
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self.init_collection_general(prefix, True, is_index=False, nb=nb, is_flush=True,
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auto_id=False, primary_field=primary_field)
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collection_w.create_index(ct.default_float_vec_field_name, {"metric_type": "L2"})
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collection_w.load()
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# 2. query iterator
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expr = "float >= 0"
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collection_w.query_iterator(batch_size, expr=expr,
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check_task=CheckTasks.check_query_iterator,
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check_items={"count": nb,
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"batch_size": batch_size})
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# 3. query iterator with checkpoint file
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iterator_cp_file = f"/tmp/it_{collection_w.name}_cp"
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iterator = collection_w.query_iterator(batch_size, expr=expr, iterator_cp_file=iterator_cp_file)[0]
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iter_times = 0
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first_iter_times = nb // batch_size // 2 # only iterate half of the data for the 1st time
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pk_list1 = []
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while iter_times < first_iter_times:
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iter_times += 1
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res = iterator.next()
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if len(res) == 0:
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iterator.close()
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assert False, f"The iterator ends before {first_iter_times} times iterators: iter_times: {iter_times}"
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break
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for i in range(len(res)):
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pk_list1.append(res[i][primary_field])
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file_exist = os.path.isfile(iterator_cp_file)
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assert file_exist is True, "The checkpoint file exists without iterator close"
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# 4. try to delete and insert some entities before calling a new query iterator
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delete_ids = random.sample(insert_ids[:nb//2], 101) + random.sample(insert_ids[nb//2:], 101)
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del_res, _ = collection_w.delete(expr=f"{primary_field} in {delete_ids}")
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assert del_res.delete_count == len(delete_ids)
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data = cf.gen_default_list_data(nb=333, start=nb)
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collection_w.insert(data)
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if not with_growing:
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collection_w.flush()
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# 5. call a new query iterator with the same checkpoint file to continue the first iterator
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iterator2 = collection_w.query_iterator(batch_size*2, expr=expr,
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output_fields=[primary_field, ct.default_float_field_name],
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iterator_cp_file=iterator_cp_file)[0]
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while True:
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res = iterator2.next()
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if len(res) == 0:
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iterator2.close()
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break
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for i in range(len(res)):
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pk_list1.append(res[i][primary_field])
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# 6. verify
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assert len(pk_list1) == len(set(pk_list1)) == nb
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file_exist = os.path.isfile(iterator_cp_file)
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assert file_exist is False, "The checkpoint was deleted after the iterator close"
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@pytest.mark.tags(CaseLabel.L1)
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def test_query_iterator_using_default_batch_size(self):
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"""
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target: test query iterator normal
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method: 1. query iterator
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2. check the result, expect pk
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expected: query successfully
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix, True)[0]
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# 2. query iterator
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collection_w.query_iterator(check_task=CheckTasks.check_query_iterator,
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check_items={"count": ct.default_nb,
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"batch_size": ct.default_batch_size})
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("offset", [500, 1000, 1777])
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def test_query_iterator_with_offset(self, offset):
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"""
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target: test query iterator normal
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method: 1. query iterator
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2. check the result, expect pk
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expected: query successfully
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"""
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# 1. initialize with data
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batch_size = 300
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collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
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collection_w.create_index(ct.default_float_vec_field_name, {"metric_type": "L2"})
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collection_w.load()
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# 2. search iterator
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expr = "int64 >= 0"
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collection_w.query_iterator(batch_size, expr=expr, offset=offset,
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check_task=CheckTasks.check_query_iterator,
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check_items={"count": ct.default_nb - offset,
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"batch_size": batch_size})
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("vector_data_type", ct.all_dense_vector_types)
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def test_query_iterator_output_different_vector_type(self, vector_data_type):
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"""
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target: test query iterator with output fields
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method: 1. query iterator output different vector type
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2. check the result, expect pk
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expected: query successfully
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"""
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# 1. initialize with data
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batch_size = 400
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collection_w = self.init_collection_general(prefix, True,
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vector_data_type=vector_data_type)[0]
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# 2. query iterator
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expr = "int64 >= 0"
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collection_w.query_iterator(batch_size, expr=expr,
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output_fields=[ct.default_float_vec_field_name],
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check_task=CheckTasks.check_query_iterator,
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check_items={"count": ct.default_nb,
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"batch_size": batch_size})
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("batch_size", [10, 777, 2000])
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def test_query_iterator_with_different_batch_size(self, batch_size):
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"""
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target: test query iterator normal
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method: 1. query iterator
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2. check the result, expect pk
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expected: query successfully
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"""
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# 1. initialize with data
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offset = 500
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collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
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collection_w.create_index(ct.default_float_vec_field_name, {"metric_type": "L2"})
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collection_w.load()
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# 2. search iterator
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expr = "int64 >= 0"
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collection_w.query_iterator(batch_size=batch_size, expr=expr, offset=offset,
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check_task=CheckTasks.check_query_iterator,
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check_items={"count": ct.default_nb - offset,
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"batch_size": batch_size})
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("offset", [0, 10, 1000])
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@pytest.mark.parametrize("limit", [0, 100, 10000])
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def test_query_iterator_with_different_limit(self, limit, offset):
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"""
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target: test query iterator normal
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method: 1. query iterator
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2. check the result, expect pk
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expected: query successfully
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix, True)[0]
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# 2. query iterator
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Count = limit if limit + offset <= ct.default_nb else ct.default_nb - offset
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collection_w.query_iterator(limit=limit, expr="", offset=offset,
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check_task=CheckTasks.check_query_iterator,
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check_items={"count": max(Count, 0),
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"batch_size": ct.default_batch_size})
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@pytest.mark.tags(CaseLabel.L2)
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def test_query_iterator_invalid_batch_size(self):
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"""
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target: test query iterator invalid limit and offset
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method: query iterator using invalid limit and offset
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expected: raise exception
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"""
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# 1. initialize with data
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nb = 17000 # set nb > 16384
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collection_w = self.init_collection_general(prefix, True, nb=nb)[0]
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# 2. search iterator
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expr = "int64 >= 0"
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error = {"err_code": 1, "err_msg": "batch size cannot be less than zero"}
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collection_w.query_iterator(batch_size=-1, expr=expr, check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L0)
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@pytest.mark.parametrize("batch_size", [500])
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@pytest.mark.parametrize("auto_id", [False])
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def test_query_iterator_empty_expr_with_cp_file_for_times(self, auto_id, batch_size):
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"""
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target: verify 2 query iterators with/out checkpoint file works independently
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method: 1. create a collection
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2. query the 1st iterator with empty expr and checkpoint file
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3. iterator.next() for some times
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4. call a new query iterator with the same checkpoint file
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expected: verify the 2nd iterator can get the whole results
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"""
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# 0. initialize with data
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collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, auto_id=auto_id)[0:4]
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# 1. call a new query iterator and iterator for some times
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iterator_cp_file = f"/tmp/it_{collection_w.name}_cp"
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iterator = collection_w.query_iterator(batch_size=batch_size//2, iterator_cp_file=iterator_cp_file)[0]
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iter_times = 0
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first_iter_times = ct.default_nb // batch_size // 2 // 2 # only iterate half of the data for the 1st time
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while iter_times < first_iter_times:
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iter_times += 1
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res = iterator.next()
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if len(res) == 0:
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iterator.close()
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assert False, f"The iterator ends before {first_iter_times} times iterators: iter_times: {iter_times}"
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break
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# 2. call a new query iterator to get all the results of the collection
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collection_w.query_iterator(batch_size=batch_size,
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check_task=CheckTasks.check_query_iterator,
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check_items={"batch_size": batch_size,
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"count": ct.default_nb,
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"exp_ids": insert_ids})
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file_exist = os.path.isfile(iterator_cp_file)
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assert file_exist is True, "The checkpoint exists if not iterator.close()"
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iterator.close()
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file_exist = os.path.isfile(iterator_cp_file)
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assert file_exist is False, "The checkpoint was deleted after the iterator close"
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("offset", [1000])
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@pytest.mark.parametrize("batch_size", [500, 1000])
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def test_query_iterator_expr_empty_with_random_pk_pagination(self, batch_size, offset):
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"""
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target: test query iterator with empty expression
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method: create a collection using random pk, query empty expression with a limit
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expected: return topK results by order
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"""
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# 1. initialize with data
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collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, random_primary_key=True)[0:4]
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# 2. query with empty expr and check the result
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exp_ids = sorted(insert_ids)
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collection_w.query_iterator(batch_size, output_fields=[ct.default_string_field_name],
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check_task=CheckTasks.check_query_iterator,
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check_items={"batch_size": batch_size, "count": ct.default_nb, "exp_ids": exp_ids})
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# 3. query with pagination
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exp_ids = sorted(insert_ids)[offset:]
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collection_w.query_iterator(batch_size, offset=offset, output_fields=[ct.default_string_field_name],
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check_task=CheckTasks.check_query_iterator,
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check_items={"batch_size": batch_size, "count": ct.default_nb - offset, "exp_ids": exp_ids})
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("primary_field", [ct.default_string_field_name, ct.default_int64_field_name])
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def test_query_iterator_with_dup_pk(self, primary_field):
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"""
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target: test query iterator with duplicate pk
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method: 1. insert entities with duplicate pk
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2. query iterator
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3. check the result, expect pk
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expected: query successfully
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"""
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# 1. initialize with data
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nb = 3000
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collection_w = self.init_collection_general(prefix, insert_data=False, is_index=False,
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auto_id=False, primary_field=primary_field)[0]
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# insert entities with duplicate pk
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data = cf.gen_default_list_data(nb=nb)
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for _ in range(3):
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collection_w.insert(data)
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collection_w.flush()
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# create index
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index_type = "HNSW"
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index_params = {"index_type": index_type, "metric_type": ct.default_L0_metric,
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"params": cf.get_index_params_params(index_type)}
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collection_w.create_index(ct.default_float_vec_field_name, index_params)
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collection_w.load()
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# 2. query iterator
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collection_w.query_iterator(check_task=CheckTasks.check_query_iterator,
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check_items={"count": nb,
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"batch_size": ct.default_batch_size})
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.skip("issue #37109, need debug due to the resolution of the issue")
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def test_query_iterator_on_two_collections(self):
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"""
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target: test query iterator on two collections
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method: 1. create two collections
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2. query iterator on the first collection
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3. check the result, expect pk
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expected: query successfully
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix, True)[0]
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collection_w2 = self.init_collection_general(prefix, False, primary_field=ct.default_string_field_name)[0]
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data = cf.gen_default_list_data(nb=ct.default_nb, primary_field=ct.default_string_field_name)
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string_values = [cf.gen_str_by_length(20) for _ in range(ct.default_nb)]
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data[2] = string_values
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collection_w2.insert(data)
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# 2. call a new query iterator and iterator for some times
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batch_size = 150
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iterator_cp_file = f"/tmp/it_{collection_w.name}_cp"
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iterator2 = collection_w2.query_iterator(batch_size=batch_size // 2, iterator_cp_file=iterator_cp_file)[0]
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iter_times = 0
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first_iter_times = ct.default_nb // batch_size // 2 // 2 # only iterate half of the data for the 1st time
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while iter_times < first_iter_times:
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iter_times += 1
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res = iterator2.next()
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if len(res) == 0:
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iterator2.close()
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assert False, f"The iterator ends before {first_iter_times} times iterators: iter_times: {iter_times}"
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break
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# 3. query iterator on the second collection with the same checkpoint file
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iterator = collection_w.query_iterator(batch_size=batch_size, iterator_cp_file=iterator_cp_file)[0]
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print(iterator.next())
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