From 59e67ca849d8e1abf72f3d46aa22eadd32ba4024 Mon Sep 17 00:00:00 2001 From: yangxuan Date: Wed, 21 Jun 2023 17:44:22 +0800 Subject: [PATCH] Seperate case_id with description - Change case.get()() to case.case_cls() - Add case.case_name property - Rename cases Signed-off-by: yangxuan --- tests/test_models.py | 2 +- vectordb_bench/backend/assembler.py | 4 +- vectordb_bench/backend/cases.py | 106 ++++--- .../frontend/components/check_results/data.py | 2 +- .../components/check_results/filters.py | 2 +- .../components/run_test/caseSelector.py | 2 +- .../frontend/const/dbCaseConfigs.py | 64 ++-- .../results/result_20230609_standard.json | 282 +++++++++--------- 8 files changed, 231 insertions(+), 233 deletions(-) diff --git a/tests/test_models.py b/tests/test_models.py index 53513bc1f..555b0dbab 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -24,7 +24,7 @@ def test_test_result(self): db=DB.Milvus, db_config=DB.Milvus.config(), db_case_config=DB.Milvus.case_config_cls(index=IndexType.Flat)(), - case_config=CaseConfig(case_id=CaseType.PerformanceLZero), + case_config=CaseConfig(case_id=CaseType.Performance10M), ), metrics=Metric(), ) diff --git a/vectordb_bench/backend/assembler.py b/vectordb_bench/backend/assembler.py index c3c33e4ec..2ec8fa953 100644 --- a/vectordb_bench/backend/assembler.py +++ b/vectordb_bench/backend/assembler.py @@ -1,4 +1,4 @@ -from .cases import type2case, CaseLabel +from .cases import CaseLabel from .task_runner import CaseRunner, RunningStatus, TaskRunner from ..models import TaskConfig from ..backend.clients import EmptyDBCaseConfig @@ -11,7 +11,7 @@ class Assembler: @classmethod def assemble(cls, run_id , task: TaskConfig) -> CaseRunner: - c_cls = type2case.get(task.case_config.case_id) + c_cls = task.case_config.case_id.case_cls c = c_cls() if type(task.db_case_config) != EmptyDBCaseConfig: diff --git a/vectordb_bench/backend/cases.py b/vectordb_bench/backend/cases.py index 306f6523d..09c91852f 100644 --- a/vectordb_bench/backend/cases.py +++ b/vectordb_bench/backend/cases.py @@ -13,40 +13,38 @@ class CaseType(Enum): """ - Value will be displayed in UI - Example: - >>> c = CaseType.CapacitySDim.get()() + >>> case_cls = CaseType.CapacityDim128.case_cls >>> assert c is not None - >>> c.name + >>> CaseType.CapacityDim128.case_name "Capacity Test (128 Dim Repeated)" - >>> c.description - "" """ - CapacitySDim = "Capacity Test (128 Dim Repeated)" - CapacityLDim = "Capacity Test (960 Dim Repeated)" - - Performance100M = "Search Performance Test (100M Dataset, 768 Dim)" - PerformanceLZero = "Search Performance Test (10M Dataset, 768 Dim)" - PerformanceMZero = "Search Performance Test (1M Dataset, 768 Dim)" - - PerformanceLLow = ( - "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)" - ) - PerformanceMLow = ( - "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)" - ) - PerformanceLHigh = ( - "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)" - ) - PerformanceMHigh = ( - "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)" - ) - - def get(self) -> Case: + CapacityDim128 = 1 + CapacityDim960 = 2 + + Performance100M = 3 + Performance10M = 4 + Performance1M = 5 + + Performance10M1P = 6 + Performance1M1P = 7 + Performance10M99P = 8 + Performance1M99P = 9 + + Custom = 100 + + @property + def case_cls(self, custom_configs: dict | None = None) -> Case: return type2case.get(self) + @property + def case_name(self) -> str: + c = self.case_cls + if c is not None: + return c().name + raise ValueError("Case unsupported") + class CaseLabel(Enum): Load = auto() @@ -57,7 +55,7 @@ class Case(BaseModel): """Undifined case Fields: - case_id(CaseType): default 11 case type plus one custom cases. + case_id(CaseType): default 9 case type plus one custom cases. label(CaseLabel): performance or load. dataset(DataSet): dataset for this case runner. filter_rate(float | None): one of 99% | 1% | None @@ -86,48 +84,48 @@ def filters(self) -> dict | None: class CapacityCase(Case, BaseModel): label: CaseLabel = CaseLabel.Load - filter_rate: float | int | None = None + filter_rate: float | None = None class PerformanceCase(Case, BaseModel): label: CaseLabel = CaseLabel.Performance - filter_rate: float | int | None = None + filter_rate: float | None = None -class CapacityLDimCase(CapacityCase): - case_id: CaseType = CaseType.CapacityLDim +class CapacityDim960(CapacityCase): + case_id: CaseType = CaseType.CapacityDim960 dataset: ds.DataSet = ds.get(ds.Name.GIST, ds.Label.SMALL) name: str = "Capacity Test (960 Dim Repeated)" description: str = """This case tests the vector database's loading capacity by repeatedly inserting large-dimension vectors (GIST 100K vectors, 960 dimensions) until it is fully loaded. Number of inserted vectors will be reported.""" -class CapacitySDimCase(CapacityCase): - case_id: CaseType = CaseType.CapacitySDim +class CapacityDim128(CapacityCase): + case_id: CaseType = CaseType.CapacityDim128 dataset: ds.DataSet = ds.get(ds.Name.SIFT, ds.Label.SMALL) name: str = "Capacity Test (128 Dim Repeated)" description: str = """This case tests the vector database's loading capacity by repeatedly inserting small-dimension vectors (SIFT 100K vectors, 128 dimensions) until it is fully loaded. Number of inserted vectors will be reported.""" -class PerformanceLZero(PerformanceCase): - case_id: CaseType = CaseType.PerformanceLZero +class Performance10M(PerformanceCase): + case_id: CaseType = CaseType.Performance10M dataset: ds.DataSet = ds.get(ds.Name.Cohere, ds.Label.LARGE) name: str = "Search Performance Test (10M Dataset, 768 Dim)" description: str = """This case tests the search performance of a vector database with a large dataset (Cohere 10M vectors, 768 dimensions) at varying parallel levels. Results will show index building time, recall, and maximum QPS.""" -class PerformanceMZero(PerformanceCase): - case_id: CaseType = CaseType.PerformanceMZero +class Performance1M(PerformanceCase): + case_id: CaseType = CaseType.Performance1M dataset: ds.DataSet = ds.get(ds.Name.Cohere, ds.Label.MEDIUM) name: str = "Search Performance Test (1M Dataset, 768 Dim)" description: str = """This case tests the search performance of a vector database with a medium dataset (Cohere 1M vectors, 768 dimensions) at varying parallel levels. Results will show index building time, recall, and maximum QPS.""" -class PerformanceLLow(PerformanceCase): - case_id: CaseType = CaseType.PerformanceLLow +class Performance10M1P(PerformanceCase): + case_id: CaseType = CaseType.Performance10M1P filter_rate: float | int | None = 0.01 dataset: ds.DataSet = ds.get(ds.Name.Cohere, ds.Label.LARGE) name: str = "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)" @@ -135,8 +133,8 @@ class PerformanceLLow(PerformanceCase): Results will show index building time, recall, and maximum QPS.""" -class PerformanceMLow(PerformanceCase): - case_id: CaseType = CaseType.PerformanceMLow +class Performance1M1P(PerformanceCase): + case_id: CaseType = CaseType.Performance1M1P filter_rate: float | int | None = 0.01 dataset: ds.DataSet = ds.get(ds.Name.Cohere, ds.Label.MEDIUM) name: str = "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)" @@ -144,8 +142,8 @@ class PerformanceMLow(PerformanceCase): Results will show index building time, recall, and maximum QPS.""" -class PerformanceLHigh(PerformanceCase): - case_id: CaseType = CaseType.PerformanceLHigh +class Performance10M99P(PerformanceCase): + case_id: CaseType = CaseType.Performance10M99P filter_rate: float | int | None = 0.99 dataset: ds.DataSet = ds.get(ds.Name.Cohere, ds.Label.LARGE) name: str = "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)" @@ -153,8 +151,8 @@ class PerformanceLHigh(PerformanceCase): Results will show index building time, recall, and maximum QPS.""" -class PerformanceMHigh(PerformanceCase): - case_id: CaseType = CaseType.PerformanceMHigh +class Performance1M99P(PerformanceCase): + case_id: CaseType = CaseType.Performance1M99P filter_rate: float | int | None = 0.99 dataset: ds.DataSet = ds.get(ds.Name.Cohere, ds.Label.MEDIUM) name: str = "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)" @@ -173,15 +171,15 @@ class Performance100M(PerformanceCase): type2case = { - CaseType.CapacityLDim: CapacityLDimCase, - CaseType.CapacitySDim: CapacitySDimCase, + CaseType.CapacityDim960: CapacityDim960, + CaseType.CapacityDim128: CapacityDim128, CaseType.Performance100M: Performance100M, - CaseType.PerformanceLZero: PerformanceLZero, - CaseType.PerformanceMZero: PerformanceMZero, + CaseType.Performance10M: Performance10M, + CaseType.Performance1M: Performance1M, - CaseType.PerformanceLLow: PerformanceLLow, - CaseType.PerformanceMLow: PerformanceMLow, - CaseType.PerformanceLHigh: PerformanceLHigh, - CaseType.PerformanceMHigh: PerformanceMHigh, + CaseType.Performance10M1P: Performance10M1P, + CaseType.Performance1M1P: Performance1M1P, + CaseType.Performance10M99P: Performance10M99P, + CaseType.Performance1M99P: Performance1M99P, } diff --git a/vectordb_bench/frontend/components/check_results/data.py b/vectordb_bench/frontend/components/check_results/data.py index e7ef5b193..10fa3f459 100644 --- a/vectordb_bench/frontend/components/check_results/data.py +++ b/vectordb_bench/frontend/components/check_results/data.py @@ -58,7 +58,7 @@ def mergeTasks(tasks: list[CaseResult]): db = metricInfo["db"] db_label = metricInfo["db_label"] label = metricInfo["label"] - case_name = case_id.get()().name + case_name = case_id.case_name if label == ResultLabel.NORMAL: mergedTasks.append( { diff --git a/vectordb_bench/frontend/components/check_results/filters.py b/vectordb_bench/frontend/components/check_results/filters.py index 7ec1ca8f6..7cdcb6090 100644 --- a/vectordb_bench/frontend/components/check_results/filters.py +++ b/vectordb_bench/frontend/components/check_results/filters.py @@ -57,7 +57,7 @@ def getShowDbsAndCases(result: list[CaseResult], st) -> tuple[list[str], list[Ca allDbNames = list(set({res.task_config.db_name for res in result})) allDbNames.sort() allCasesSet = set({res.task_config.case_config.case_id for res in result}) - allCases: list[Case] = [case.get()() for case in CASE_LIST if case in allCasesSet] + allCases: list[Case] = [case.case_cls() for case in CASE_LIST if case in allCasesSet] # DB Filter dbFilterContainer = st.container() diff --git a/vectordb_bench/frontend/components/run_test/caseSelector.py b/vectordb_bench/frontend/components/run_test/caseSelector.py index 657de345c..26de62828 100644 --- a/vectordb_bench/frontend/components/run_test/caseSelector.py +++ b/vectordb_bench/frontend/components/run_test/caseSelector.py @@ -33,7 +33,7 @@ def caseSelector(st, activedDbList): def caseItem(st, allCaseConfigs, case: CaseType, activedDbList): - selected = st.checkbox(case.get()().name) + selected = st.checkbox(case.casename) st.markdown( f"
{case.get()().description}
", unsafe_allow_html=True, diff --git a/vectordb_bench/frontend/const/dbCaseConfigs.py b/vectordb_bench/frontend/const/dbCaseConfigs.py index f6609c0b9..b5eacb0ea 100644 --- a/vectordb_bench/frontend/const/dbCaseConfigs.py +++ b/vectordb_bench/frontend/const/dbCaseConfigs.py @@ -14,17 +14,17 @@ DIVIDER = "DIVIDER" CASE_LIST_WITH_DIVIDER = [ CaseType.Performance100M, - CaseType.PerformanceLZero, - CaseType.PerformanceMZero, + CaseType.Performance10M, + CaseType.Performance1M, DIVIDER, - CaseType.PerformanceLLow, - CaseType.PerformanceMLow, + CaseType.Performance10M1P, + CaseType.Performance1M1P, DIVIDER, - CaseType.PerformanceLHigh, - CaseType.PerformanceMHigh, + CaseType.Performance10M99P, + CaseType.Performance1M99P, DIVIDER, - CaseType.CapacityLDim, - CaseType.CapacitySDim, + CaseType.CapacityDim960, + CaseType.CapacityDim128, ] CASE_LIST = [item for item in CASE_LIST_WITH_DIVIDER if isinstance(item, CaseType)] @@ -225,36 +225,36 @@ class CaseConfigInput(BaseModel): CASE_CONFIG_MAP = { DB.Milvus: { - CaseType.CapacityLDim: MilvusLoadConfig, - CaseType.CapacitySDim: MilvusLoadConfig, + CaseType.CapacityDim960: MilvusLoadConfig, + CaseType.CapacityDim128: MilvusLoadConfig, CaseType.Performance100M: MilvusPerformanceConfig, - CaseType.PerformanceLZero: MilvusPerformanceConfig, - CaseType.PerformanceMZero: MilvusPerformanceConfig, - CaseType.PerformanceLLow: MilvusPerformanceConfig, - CaseType.PerformanceMLow: MilvusPerformanceConfig, - CaseType.PerformanceLHigh: MilvusPerformanceConfig, - CaseType.PerformanceMHigh: MilvusPerformanceConfig, + CaseType.Performance10M: MilvusPerformanceConfig, + CaseType.Performance1M: MilvusPerformanceConfig, + CaseType.Performance10M1P: MilvusPerformanceConfig, + CaseType.Performance1M1P: MilvusPerformanceConfig, + CaseType.Performance10M99P: MilvusPerformanceConfig, + CaseType.Performance1M99P: MilvusPerformanceConfig, }, DB.WeaviateCloud: { - CaseType.CapacityLDim: WeaviateLoadConfig, - CaseType.CapacitySDim: WeaviateLoadConfig, + CaseType.CapacityDim960: WeaviateLoadConfig, + CaseType.CapacityDim128: WeaviateLoadConfig, CaseType.Performance100M: WeaviatePerformanceConfig, - CaseType.PerformanceLZero: WeaviatePerformanceConfig, - CaseType.PerformanceMZero: WeaviatePerformanceConfig, - CaseType.PerformanceLLow: WeaviatePerformanceConfig, - CaseType.PerformanceMLow: WeaviatePerformanceConfig, - CaseType.PerformanceLHigh: WeaviatePerformanceConfig, - CaseType.PerformanceMHigh: WeaviatePerformanceConfig, + CaseType.Performance10M: WeaviatePerformanceConfig, + CaseType.Performance1M: WeaviatePerformanceConfig, + CaseType.Performance10M1P: WeaviatePerformanceConfig, + CaseType.Performance1M1P: WeaviatePerformanceConfig, + CaseType.Performance10M99P: WeaviatePerformanceConfig, + CaseType.Performance1M99P: WeaviatePerformanceConfig, }, DB.ElasticCloud: { - CaseType.CapacityLDim: ESLoadingConfig, - CaseType.CapacitySDim: ESLoadingConfig, + CaseType.CapacityDim960: ESLoadingConfig, + CaseType.CapacityDim128: ESLoadingConfig, CaseType.Performance100M: ESPerformanceConfig, - CaseType.PerformanceLZero: ESPerformanceConfig, - CaseType.PerformanceMZero: ESPerformanceConfig, - CaseType.PerformanceLLow: ESPerformanceConfig, - CaseType.PerformanceMLow: ESPerformanceConfig, - CaseType.PerformanceLHigh: ESPerformanceConfig, - CaseType.PerformanceMHigh: ESPerformanceConfig, + CaseType.Performance10M: ESPerformanceConfig, + CaseType.Performance1M: ESPerformanceConfig, + CaseType.Performance10M1P: ESPerformanceConfig, + CaseType.Performance1M1P: ESPerformanceConfig, + CaseType.Performance10M99P: ESPerformanceConfig, + CaseType.Performance1M99P: ESPerformanceConfig, }, } diff --git a/vectordb_bench/results/result_20230609_standard.json b/vectordb_bench/results/result_20230609_standard.json index 4ebce8edc..de9d7f457 100644 --- a/vectordb_bench/results/result_20230609_standard.json +++ b/vectordb_bench/results/result_20230609_standard.json @@ -26,7 +26,7 @@ "num_candidates": null }, "case_config": { - "case_id": "Capacity Test (960 Dim Repeated)", + "case_id": 2, "custom_case": {} } }, @@ -56,7 +56,7 @@ "num_candidates": null }, "case_config": { - "case_id": "Capacity Test (128 Dim Repeated)", + "case_id": 1, "custom_case": {} } }, @@ -86,7 +86,7 @@ "num_candidates": 100 }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -116,7 +116,7 @@ "num_candidates": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -146,7 +146,7 @@ "num_candidates": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -176,7 +176,7 @@ "num_candidates": 100 }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -206,7 +206,7 @@ "num_candidates": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -236,7 +236,7 @@ "num_candidates": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -262,7 +262,7 @@ "search_list": 100 }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -288,7 +288,7 @@ "search_list": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -314,7 +314,7 @@ "search_list": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -340,7 +340,7 @@ "search_list": 100 }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -366,7 +366,7 @@ "search_list": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -392,7 +392,7 @@ "search_list": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -418,7 +418,7 @@ "search_list": 100 }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -444,7 +444,7 @@ "search_list": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -470,7 +470,7 @@ "search_list": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -498,7 +498,7 @@ "ef": 100 }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -526,7 +526,7 @@ "ef": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -554,7 +554,7 @@ "ef": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -581,7 +581,7 @@ "metric_type": "L2" }, "case_config": { - "case_id": "Capacity Test (960 Dim Repeated)", + "case_id": 2, "custom_case": {} } }, @@ -608,7 +608,7 @@ "metric_type": "L2" }, "case_config": { - "case_id": "Capacity Test (128 Dim Repeated)", + "case_id": 1, "custom_case": {} } }, @@ -635,7 +635,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -662,7 +662,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -689,7 +689,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -716,7 +716,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -743,7 +743,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -770,7 +770,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -797,7 +797,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -824,7 +824,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -851,7 +851,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -878,7 +878,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -905,7 +905,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -932,7 +932,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -960,7 +960,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Capacity Test (128 Dim Repeated)", + "case_id": 1, "custom_case": null } }, @@ -988,7 +988,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Capacity Test (960 Dim Repeated)", + "case_id": 2, "custom_case": null } }, @@ -1016,7 +1016,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": null } }, @@ -1044,7 +1044,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": null } }, @@ -1072,7 +1072,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": null } }, @@ -1100,7 +1100,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": null } }, @@ -1128,7 +1128,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": null } }, @@ -1156,7 +1156,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": null } }, @@ -1184,7 +1184,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Capacity Test (128 Dim Repeated)", + "case_id": 1, "custom_case": null } }, @@ -1212,7 +1212,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Capacity Test (960 Dim Repeated)", + "case_id": 2, "custom_case": null } }, @@ -1240,7 +1240,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": null } }, @@ -1268,7 +1268,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": null } }, @@ -1296,7 +1296,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": null } }, @@ -1324,7 +1324,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": null } }, @@ -1352,7 +1352,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": null } }, @@ -1380,7 +1380,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": null } }, @@ -1408,7 +1408,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Capacity Test (128 Dim Repeated)", + "case_id": 1, "custom_case": null } }, @@ -1436,7 +1436,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Capacity Test (960 Dim Repeated)", + "case_id": 2, "custom_case": null } }, @@ -1464,7 +1464,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": null } }, @@ -1492,7 +1492,7 @@ "maxConnections": 64 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -1520,7 +1520,7 @@ "maxConnections": 64 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -1548,7 +1548,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": null } }, @@ -1576,7 +1576,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": null } }, @@ -1604,7 +1604,7 @@ "maxConnections": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": null } }, @@ -1631,7 +1631,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Capacity Test (128 Dim Repeated)", + "case_id": 1, "custom_case": {} } }, @@ -1658,7 +1658,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Capacity Test (960 Dim Repeated)", + "case_id": 2, "custom_case": {} } }, @@ -1685,7 +1685,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -1712,7 +1712,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -1739,7 +1739,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -1766,7 +1766,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -1793,7 +1793,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -1820,7 +1820,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -1847,7 +1847,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -1874,7 +1874,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -1901,7 +1901,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -1928,7 +1928,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -1955,7 +1955,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -1982,7 +1982,7 @@ "metric_type": "COSINE" }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -2010,7 +2010,7 @@ "ef": 100 }, "case_config": { - "case_id": "Capacity Test (128 Dim Repeated)", + "case_id": 1, "custom_case": {} } }, @@ -2038,7 +2038,7 @@ "ef": 100 }, "case_config": { - "case_id": "Capacity Test (960 Dim Repeated)", + "case_id": 2, "custom_case": {} } }, @@ -2066,7 +2066,7 @@ "ef": 100 }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -2094,7 +2094,7 @@ "ef": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -2122,7 +2122,7 @@ "ef": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -2150,7 +2150,7 @@ "ef": 100 }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -2178,7 +2178,7 @@ "ef": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -2206,7 +2206,7 @@ "ef": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -2234,7 +2234,7 @@ "ef": 100 }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -2262,7 +2262,7 @@ "ef": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -2290,7 +2290,7 @@ "ef": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -2318,7 +2318,7 @@ "ef": 100 }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -2346,7 +2346,7 @@ "ef": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -2374,7 +2374,7 @@ "ef": 100 }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -2400,7 +2400,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -2426,7 +2426,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -2452,7 +2452,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -2478,7 +2478,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -2504,7 +2504,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -2530,7 +2530,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -2555,7 +2555,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -2580,7 +2580,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -2605,7 +2605,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -2631,7 +2631,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -2657,7 +2657,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -2683,7 +2683,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -2709,7 +2709,7 @@ "null": null }, "case_config": { - "case_id": "Capacity Test (960 Dim Repeated)", + "case_id": 2, "custom_case": {} } }, @@ -2735,7 +2735,7 @@ "null": null }, "case_config": { - "case_id": "Capacity Test (128 Dim Repeated)", + "case_id": 1, "custom_case": {} } }, @@ -2761,7 +2761,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -2787,7 +2787,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -2813,7 +2813,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -2839,7 +2839,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -2865,7 +2865,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -2891,7 +2891,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -2917,7 +2917,7 @@ "null": null }, "case_config": { - "case_id": "Capacity Test (960 Dim Repeated)", + "case_id": 2, "custom_case": {} } }, @@ -2943,7 +2943,7 @@ "null": null }, "case_config": { - "case_id": "Capacity Test (128 Dim Repeated)", + "case_id": 1, "custom_case": {} } }, @@ -2969,7 +2969,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -2995,7 +2995,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -3021,7 +3021,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -3047,7 +3047,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -3073,7 +3073,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -3099,7 +3099,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -3125,7 +3125,7 @@ "null": null }, "case_config": { - "case_id": "Capacity Test (960 Dim Repeated)", + "case_id": 2, "custom_case": {} } }, @@ -3151,7 +3151,7 @@ "null": null }, "case_config": { - "case_id": "Capacity Test (128 Dim Repeated)", + "case_id": 1, "custom_case": {} } }, @@ -3177,7 +3177,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -3203,7 +3203,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -3229,7 +3229,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -3255,7 +3255,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -3281,7 +3281,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -3307,7 +3307,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -3333,7 +3333,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -3359,7 +3359,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -3385,7 +3385,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -3411,7 +3411,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -3437,7 +3437,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -3463,7 +3463,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -3489,7 +3489,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -3515,7 +3515,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -3541,7 +3541,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -3567,7 +3567,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -3593,7 +3593,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -3619,7 +3619,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, @@ -3645,7 +3645,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (1M Dataset, 768 Dim)", + "case_id": 5, "custom_case": {} } }, @@ -3671,7 +3671,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 1%)", + "case_id": 7, "custom_case": {} } }, @@ -3697,7 +3697,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (1M Dataset, 768 Dim, Filter 99%)", + "case_id": 9, "custom_case": {} } }, @@ -3723,7 +3723,7 @@ "null": null }, "case_config": { - "case_id": "Search Performance Test (10M Dataset, 768 Dim)", + "case_id": 4, "custom_case": {} } }, @@ -3749,7 +3749,7 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 1%)", + "case_id": 6, "custom_case": {} } }, @@ -3775,11 +3775,11 @@ "null": null }, "case_config": { - "case_id": "Filtering Search Performance Test (10M Dataset, 768 Dim, Filter 99%)", + "case_id": 8, "custom_case": {} } }, "label": "x" } ] -} \ No newline at end of file +}