feat: Add Custom Dataset support for Streaming Tests

vectordb_bench/backend/cases.py

@@ -51,6 +51,7 @@ class CaseType(Enum):
     PerformanceCustomDataset = 101
 
     StreamingPerformanceCase = 200
+    StreamingCustomDataset = 201
 
     LabelFilterPerformanceCase = 300
 
@@ -474,6 +475,85 @@ def __init__(
         )
 
 
+class StreamingCustomDataset(Case):
+    case_id: CaseType = CaseType.StreamingCustomDataset
+    label: CaseLabel = CaseLabel.Streaming
+    name: str = "Streaming Performance With Custom Dataset"
+    description: str = ""
+    dataset: DatasetManager
+    insert_rate: int
+    search_stages: list[float]
+    concurrencies: list[int]
+    optimize_after_write: bool = True
+    read_dur_after_write: int = 30
+
+    def __init__(
+        self,
+        description: str,
+        dataset_config: dict,
+        insert_rate: int = 500,
+        search_stages: list[float] | str = (0.5, 0.8),
+        concurrencies: list[int] | str = (5, 10),
+        optimize_after_write: bool = True,
+        read_dur_after_write: int = 30,
+        **kwargs,
+    ):
+        num_per_batch = config.NUM_PER_BATCH
+        if insert_rate % config.NUM_PER_BATCH != 0:
+            _insert_rate = max(
+                num_per_batch,
+                insert_rate // num_per_batch * num_per_batch,
+            )
+            log.warning(
+                f"[streaming_case init] insert_rate(={insert_rate}) should be "
+                f"divisible by NUM_PER_BATCH={num_per_batch}), reset to {_insert_rate}",
+            )
+            insert_rate = _insert_rate
+
+        dataset_config = CustomDatasetConfig(**dataset_config)
+        dataset = CustomDataset(
+            name=dataset_config.name,
+            size=dataset_config.size,
+            dim=dataset_config.dim,
+            metric_type=metric_type_map(dataset_config.metric_type),
+            use_shuffled=dataset_config.use_shuffled,
+            with_gt=dataset_config.with_gt,
+            dir=dataset_config.dir,
+            file_num=dataset_config.file_count,
+            train_file=dataset_config.train_name,
+            test_file=f"{dataset_config.test_name}.parquet",
+            train_id_field=dataset_config.train_id_name,
+            train_vector_field=dataset_config.train_col_name,
+            test_vector_field=dataset_config.test_col_name,
+            gt_neighbors_field=dataset_config.gt_col_name,
+            scalar_labels_file=f"{dataset_config.scalar_labels_name}.parquet",
+        )
+        name = f"Streaming-Perf - Custom - {dataset_config.name}, {insert_rate} rows/s"
+        description = (
+            description
+            if description
+            else f"This case tests the search performance of vector database while maintaining "
+            f"a fixed insertion speed. (dataset: Custom - {dataset_config.name})"
+        )
+
+        if isinstance(search_stages, str):
+            search_stages = json.loads(search_stages)
+        if isinstance(concurrencies, str):
+            concurrencies = json.loads(concurrencies)
+
+        super().__init__(
+            name=name,
+            description=description,
+            dataset=DatasetManager(data=dataset),
+            insert_rate=insert_rate,
+            search_stages=search_stages,
+            concurrencies=concurrencies,
+            optimize_after_write=optimize_after_write,
+            read_dur_after_write=read_dur_after_write,
+            **kwargs,
+        )
+
+
 class NewIntFilterPerformanceCase(PerformanceCase):
     case_id: CaseType = CaseType.NewIntFilterPerformanceCase
     dataset_with_size_type: DatasetWithSizeType
@@ -572,6 +652,7 @@ def filters(self) -> Filter:
     CaseType.Performance1536D50K: Performance1536D50K,
     CaseType.PerformanceCustomDataset: PerformanceCustomDataset,
     CaseType.StreamingPerformanceCase: StreamingPerformanceCase,
+    CaseType.StreamingCustomDataset: StreamingCustomDataset,
     CaseType.NewIntFilterPerformanceCase: NewIntFilterPerformanceCase,
     CaseType.LabelFilterPerformanceCase: LabelFilterPerformanceCase,
 }

vectordb_bench/custom/custom_case.json

@@ -14,5 +14,33 @@
             "use_shuffled": false,
             "with_gt": true
         }
+    },
+    {
+        "case_type": "streaming",
+        "description": "",
+        "dataset_config": {
+            "name": "My Streaming Dataset",
+            "dir": "/my_dataset_path",
+            "size": 100000,
+            "dim": 768,
+            "metric_type": "L2",
+            "file_count": 3,
+            "use_shuffled": false,
+            "with_gt": true,
+            "train_name": "shuffle_train",
+            "test_name": "test",
+            "gt_name": "neighbors",
+            "train_id_name": "id",
+            "train_col_name": "emb",
+            "test_col_name": "emb",
+            "gt_col_name": "neighbors_id",
+            "scalar_labels_name": "scalar_labels",
+            "label_percentages": [],
+            "with_label_percentages": [
+                0.001,
+                0.02,
+                0.5
+            ]
+        }
     }
 ]

vectordb_bench/frontend/components/custom/displayCustomStreamingCase.py

@@ -0,0 +1,49 @@
+from vectordb_bench.frontend.components.custom.getCustomConfig import CustomStreamingCaseConfig
+
+
+def displayCustomStreamingCase(streamingCase: CustomStreamingCaseConfig, st, key):
+
+    columns = st.columns([1, 2])
+    streamingCase.dataset_config.name = columns[0].text_input(
+        "Name", key=f"{key}_name", value=streamingCase.dataset_config.name
+    )
+    streamingCase.dataset_config.dir = columns[1].text_input(
+        "Folder Path", key=f"{key}_dir", value=streamingCase.dataset_config.dir
+    )
+
+    columns = st.columns(2)
+    streamingCase.dataset_config.dim = columns[0].number_input(
+        "dim", key=f"{key}_dim", value=streamingCase.dataset_config.dim
+    )
+    streamingCase.dataset_config.size = columns[1].number_input(
+        "size", key=f"{key}_size", value=streamingCase.dataset_config.size
+    )
+
+    columns = st.columns(3)
+    streamingCase.dataset_config.train_name = columns[0].text_input(
+        "train file name",
+        key=f"{key}_train_name",
+        value=streamingCase.dataset_config.train_name,
+    )
+    streamingCase.dataset_config.test_name = columns[1].text_input(
+        "test file name", key=f"{key}_test_name", value=streamingCase.dataset_config.test_name
+    )
+    streamingCase.dataset_config.gt_name = columns[2].text_input(
+        "ground truth file name", key=f"{key}_gt_name", value=streamingCase.dataset_config.gt_name
+    )
+
+    columns = st.columns([1, 1, 2, 2])
+    streamingCase.dataset_config.train_id_name = columns[0].text_input(
+        "train id name", key=f"{key}_train_id_name", value=streamingCase.dataset_config.train_id_name
+    )
+    streamingCase.dataset_config.train_col_name = columns[1].text_input(
+        "train emb name", key=f"{key}_train_col_name", value=streamingCase.dataset_config.train_col_name
+    )
+    streamingCase.dataset_config.test_col_name = columns[2].text_input(
+        "test emb name", key=f"{key}_test_col_name", value=streamingCase.dataset_config.test_col_name
+    )
+    streamingCase.dataset_config.gt_col_name = columns[3].text_input(
+        "ground truth emb name", key=f"{key}_gt_col_name", value=streamingCase.dataset_config.gt_col_name
+    )
+
+    streamingCase.description = st.text_area("description", key=f"{key}_description", value=streamingCase.description)

vectordb_bench/frontend/components/custom/getCustomConfig.py

@@ -34,16 +34,47 @@ class CustomCaseConfig(BaseModel):
     dataset_config: CustomDatasetConfig = CustomDatasetConfig()
 
 
+class CustomStreamingCaseConfig(BaseModel):
+    case_type: str = "streaming"
+    description: str = ""
+    dataset_config: CustomDatasetConfig = CustomDatasetConfig()
+
+
 def get_custom_configs():
     with open(config.CUSTOM_CONFIG_DIR, "r") as f:
         custom_configs = json.load(f)
-        return [CustomCaseConfig(**custom_config) for custom_config in custom_configs]
+        return [
+            CustomCaseConfig(**custom_config) 
+            for custom_config in custom_configs 
+            if custom_config.get("case_type") != "streaming"
+        ]
+
+
+def get_custom_streaming_configs():
+    with open(config.CUSTOM_CONFIG_DIR, "r") as f:
+        custom_configs = json.load(f)
+        return [
+            CustomStreamingCaseConfig(**custom_config) 
+            for custom_config in custom_configs 
+            if custom_config.get("case_type") == "streaming"
+        ]
 
 
 def save_custom_configs(custom_configs: list[CustomDatasetConfig]):
     with open(config.CUSTOM_CONFIG_DIR, "w") as f:
         json.dump([custom_config.dict() for custom_config in custom_configs], f, indent=4)
 
 
+def save_all_custom_configs(performance_configs: list[CustomCaseConfig], streaming_configs: list[CustomStreamingCaseConfig]):
+    """Save both performance and streaming configs to the same JSON file"""
+    all_configs = [config.dict() for config in performance_configs] + [config.dict() for config in streaming_configs]
+    with open(config.CUSTOM_CONFIG_DIR, "w") as f:
+        json.dump(all_configs, f, indent=4)
+
+
 def generate_custom_case():
     return CustomCaseConfig()
+
+
+def generate_custom_streaming_case():
+    return CustomStreamingCaseConfig()

vectordb_bench/frontend/components/run_test/caseSelector.py

@@ -7,6 +7,7 @@
     UICaseItemCluster,
     get_case_config_inputs,
     get_custom_case_cluter,
+    get_custom_streaming_case_cluster,
 )
 from vectordb_bench.frontend.config.styles import (
     CASE_CONFIG_SETTING_COLUMNS,
@@ -32,7 +33,7 @@ def caseSelector(st, activedDbList: list[DB]):
     activedCaseList: list[CaseConfig] = []
     dbToCaseClusterConfigs = defaultdict(lambda: defaultdict(dict))
     dbToCaseConfigs = defaultdict(lambda: defaultdict(dict))
-    caseClusters = UI_CASE_CLUSTERS + [get_custom_case_cluter()]
+    caseClusters = UI_CASE_CLUSTERS + [get_custom_case_cluter(), get_custom_streaming_case_cluster()]
     for caseCluster in caseClusters:
         activedCaseList += caseClusterExpander(st, caseCluster, dbToCaseClusterConfigs, activedDbList)
     for db in dbToCaseClusterConfigs:

vectordb_bench/frontend/config/dbCaseConfigs.py

@@ -161,6 +161,34 @@ def get_custom_case_cluter() -> UICaseItemCluster:
     return UICaseItemCluster(label="Custom Search Performance Test", uiCaseItems=get_custom_case_items())
 
 
+def get_custom_streaming_case_items() -> list[UICaseItem]:
+    from vectordb_bench.frontend.components.custom.getCustomConfig import get_custom_streaming_configs
+
+    custom_streaming_configs = get_custom_streaming_configs()
+    return [
+        UICaseItem(
+            label=f"{custom_config.dataset_config.name} - Streaming",
+            description=f"Streaming test with custom dataset: {custom_config.dataset_config.name}",
+            cases=[
+                CaseConfig(
+                    case_id=CaseType.StreamingCustomDataset,
+                    custom_case={
+                        "description": custom_config.description,
+                        "dataset_config": custom_config.dataset_config.dict(),
+                    },
+                )
+            ],
+            caseLabel=CaseLabel.Streaming,
+            extra_custom_case_config_inputs=custom_streaming_config_with_custom_dataset,
+        )
+        for custom_config in custom_streaming_configs
+    ]
+
+
+def get_custom_streaming_case_cluster() -> UICaseItemCluster:
+    return UICaseItemCluster(label="Custom Streaming Test", uiCaseItems=get_custom_streaming_case_items())
+
+
 def generate_custom_streaming_case() -> CaseConfig:
     return CaseConfig(
         case_id=CaseType.StreamingPerformanceCase,
@@ -207,6 +235,13 @@ def generate_custom_streaming_case() -> CaseConfig:
     ),
 ]
 
+# Config for custom streaming tests (with custom dataset from JSON)
+# Filter out the dataset_with_size_type from the existing config
+custom_streaming_config_with_custom_dataset: list[ConfigInput] = [
+    config for config in custom_streaming_config 
+    if config.label != CaseConfigParamType.dataset_with_size_type
+]
+
 
 def generate_label_filter_cases(dataset_with_size_type: DatasetWithSizeType) -> list[CaseConfig]:
     label_percentages = dataset_with_size_type.get_manager().data.scalar_label_percentages