Add Querying Functionality to OSB

benchmarks/osb/extensions/data_set.py

@@ -61,7 +61,7 @@ class HDF5DataSet(DataSet):
 
     def __init__(self, dataset_path: str, context: Context):
         file = h5py.File(dataset_path)
-        self.data = cast(h5py.Dataset, file[self._parse_context(context)])
+        self.data = cast(h5py.Dataset, file[self.parse_context(context)])
         self.current = self.BEGINNING
 
     def read(self, chunk_size: int):
@@ -93,7 +93,7 @@ def reset(self):
         self.current = self.BEGINNING
 
     @staticmethod
-    def _parse_context(context: Context) -> str:
+    def parse_context(context: Context) -> str:
         if context == Context.NEIGHBORS:
             return "neighbors"
 
@@ -176,6 +176,9 @@ def reset(self):
         self.file.seek(BigANNVectorDataSet.DATA_SET_HEADER_LENGTH)
         self.current = BigANNVectorDataSet.BEGINNING
 
+    def __del__(self):
+        self.file.close()
+
     @staticmethod
     def _get_data_size(file_name):
         ext = file_name.split('.')[-1]

benchmarks/osb/extensions/param_sources.py

@@ -4,6 +4,7 @@
 # this file be licensed under the Apache-2.0 license or a
 # compatible open source license.
 import copy
+from abc import ABC, abstractmethod
 
 from .data_set import Context, HDF5DataSet, DataSet, BigANNVectorDataSet
 from .util import bulk_transform, parse_string_parameter, parse_int_parameter, \
@@ -15,17 +16,42 @@ def register(registry):
         "bulk-from-data-set", BulkVectorsFromDataSetParamSource
     )
 
+    registry.register_param_source(
+        "knn-query-from-data-set", QueryVectorsFromDataSetParamSource
+    )
 
-class BulkVectorsFromDataSetParamSource:
-    def __init__(self, workload, params, **kwargs):
+
+class VectorsFromDataSetParamSource(ABC):
+    """ Abstract class that can read vectors from a data set and partition the
+    vectors across multiple clients.
+
+    Attributes:
+        index_name: Name of the index to generate the query for
+        field_name: Name of the field to generate the query for
+        data_set_format: Format data set is serialized with. bigann or hdf5
+        data_set_path: Path to data set
+        context: Context the data set will be used in.
+        data_set: Structure containing meta data about data and ability to read
+        num_vectors: Number of vectors to use from the data set
+        total: Number of vectors for the partition
+        current: Current vector offset in data set
+        infinite: Property of param source signalling that it can be exhausted
+        percent_completed: Progress indicator for how exhausted data set is
+        offset: Offset into the data set to start at. Relevant when there are
+                multiple partitions
+    """
+
+    def __init__(self, params, context: Context):
+        self.index_name: str = parse_string_parameter("index", params)
+        self.field_name: str = parse_string_parameter("field", params)
+
+        self.context = context
         self.data_set_format = parse_string_parameter("data_set_format", params)
         self.data_set_path = parse_string_parameter("data_set_path", params)
-        self.data_set: DataSet = self._read_data_set()
+        self.data_set: DataSet = self._read_data_set(self.data_set_format,
+                                                     self.data_set_path,
+                                                     self.context)
 
-        self.field_name: str = parse_string_parameter("field", params)
-        self.index_name: str = parse_string_parameter("index", params)
-        self.bulk_size: int = parse_int_parameter("bulk_size", params)
-        self.retries: int = parse_int_parameter("retries", params, 10)
         self.num_vectors: int = parse_int_parameter(
             "num_vectors", params, self.data_set.size()
         )
@@ -35,29 +61,143 @@ def __init__(self, workload, params, **kwargs):
         self.percent_completed = 0
         self.offset = 0
 
-    def _read_data_set(self):
-        if self.data_set_format == HDF5DataSet.FORMAT_NAME:
-            return HDF5DataSet(self.data_set_path, Context.INDEX)
-        if self.data_set_format == BigANNVectorDataSet.FORMAT_NAME:
-            return BigANNVectorDataSet(self.data_set_path)
+    def _read_data_set(self, data_set_format: str, data_set_path: str,
+                       data_set_context: Context):
+        if data_set_format == HDF5DataSet.FORMAT_NAME:
+            return HDF5DataSet(data_set_path, data_set_context)
+        if data_set_format == BigANNVectorDataSet.FORMAT_NAME:
+            return BigANNVectorDataSet(data_set_path)
         raise ConfigurationError("Invalid data set format")
 
     def partition(self, partition_index, total_partitions):
-        if self.data_set.size() % total_partitions != 0:
-            raise ValueError("Data set must be divisible by number of clients")
+        """
+        Splits up the parameters source so that multiple clients can read data
+        from it.
+        Args:
+            partition_index: index of one particular partition
+            total_partitions: total number of partitions data set is split into
+
+        Returns:
+            The parameter source for this particular partion
+        """
+        if self.num_vectors % total_partitions != 0:
+            raise ValueError("Num vectors must be divisible by number of "
+                             "partitions")
 
         partition_x = copy.copy(self)
         partition_x.num_vectors = int(self.num_vectors / total_partitions)
         partition_x.offset = int(partition_index * partition_x.num_vectors)
 
         # We need to create a new instance of the data set for each client
-        partition_x.data_set = partition_x._read_data_set()
+        partition_x.data_set = partition_x._read_data_set(
+            self.data_set_format,
+            self.data_set_path,
+            self.context
+        )
         partition_x.data_set.seek(partition_x.offset)
         partition_x.current = partition_x.offset
         return partition_x
 
+    @abstractmethod
     def params(self):
+        """
+        Returns: A single parameter from this sourc
+        """
+        pass
+
+
+class QueryVectorsFromDataSetParamSource(VectorsFromDataSetParamSource):
+    """ Query parameter source for k-NN. Queries are created from data set
+    provided.
+
+    Attributes:
+        k: The number of results to return for the search
+        vector_batch: List of vectors to be read from data set. Read are batched
+                        so that we do not need to read from disk for each query
+    """
+
+    VECTOR_READ_BATCH_SIZE = 100  # batch size to read vectors from data-set
+
+    def __init__(self, workload, params, **kwargs):
+        super().__init__(params, Context.QUERY)
+        self.k = parse_int_parameter("k", params)
+        self.vector_batch = None
 
+    def params(self):
+        """
+        Returns: A query parameter with a vector from a data set
+        """
+        if self.current >= self.num_vectors + self.offset:
+            raise StopIteration
+
+        if self.vector_batch is None or len(self.vector_batch) == 0:
+            self.vector_batch = self._batch_read(self.data_set)
+            if self.vector_batch is None:
+                raise StopIteration
+        vector = self.vector_batch.pop(0)
+        self.current += 1
+        self.percent_completed = self.current / self.total
+
+        return self._build_query_body(self.index_name, self.field_name, self.k,
+                                      vector)
+
+    def _batch_read(self, data_set: DataSet):
+        return list(data_set.read(self.VECTOR_READ_BATCH_SIZE))
+
+    def _build_query_body(self, index_name: str, field_name: str, k: int,
+                          vector) -> dict:
+        """Builds a k-NN query that can be used to execute an approximate nearest
+        neighbor search against a k-NN plugin index
+        Args:
+            index_name: name of index to search
+            field_name: name of field to search
+            k: number of results to return
+            vector: vector used for query
+        Returns:
+            A dictionary containing the body used for search, a set of request
+            parameters to attach to the search and the name of the index.
+        """
+        return {
+            "index": index_name,
+            "request-params": {
+                "_source": {
+                    "exclude": [field_name]
+                }
+            },
+            "body": {
+                "size": k,
+                "query": {
+                    "knn": {
+                        field_name: {
+                            "vector": vector,
+                            "k": k
+                        }
+                    }
+                }
+            }
+        }
+
+
+class BulkVectorsFromDataSetParamSource(VectorsFromDataSetParamSource):
+    """ Create bulk index requests from a data set of vectors.
+
+    Attributes:
+        bulk_size: number of vectors per request
+        retries: number of times to retry the request when it fails
+    """
+
+    DEFAULT_RETRIES = 10
+
+    def __init__(self, workload, params, **kwargs):
+        super().__init__(params, Context.INDEX)
+        self.bulk_size: int = parse_int_parameter("bulk_size", params)
+        self.retries: int = parse_int_parameter("retries", params,
+                                                self.DEFAULT_RETRIES)
+
+    def params(self):
+        """
+        Returns: A bulk index parameter with vectors from a data set.
+        """
         if self.current >= self.num_vectors + self.offset:
             raise StopIteration
 

benchmarks/osb/params/no-train-params.json

@@ -14,7 +14,10 @@
   "hnsw_ef_construction": 512,
   "hnsw_m": 16,
 
-
+  "query_k": 10,
+  "query_clients": 10,
+  "query_data_set_format": "hdf5",
+  "query_data_set_path": "<path to data>",
 
   "ivf_nlists": 1,
   "ivf_nprobes": 1,

benchmarks/osb/params/train-params.json

@@ -27,5 +27,10 @@
   "train_index_data_set_format": "hdf5",
   "train_index_data_set_path": "<path to data>",
   "train_index_num_vectors": 1000000,
-  "train_index_bulk_index_clients": 10
+  "train_index_bulk_index_clients": 10,
+
+  "query_k": 10,
+  "query_clients": 10,
+  "query_data_set_format": "hdf5",
+  "query_data_set_path": "<path to data>"
 }

benchmarks/osb/procedures/no-train-test.json

@@ -45,6 +45,19 @@
                 "index": "{{ target_index_name }}",
                 "retries": 100
             }
+        },
+        {
+            "operation": {
+                "name": "knn-query-from-data-set",
+                "operation-type": "search",
+                "index": "{{ target_index_name }}",
+                "param-source": "knn-query-from-data-set",
+                "k": {{ query_k }},
+                "field": "{{ target_field_name }}",
+                "data_set_format": "{{ query_data_set_format }}",
+                "data_set_path": "{{ query_data_set_path }}"
+          },
+            "clients": {{ query_clients }}
         }
     ]
 }

benchmarks/osb/procedures/train-test.json

@@ -99,6 +99,19 @@
                 "index": "{{ target_index_name }}",
                 "retries": 100
             }
+        },
+        {
+            "operation": {
+                "name": "knn-query-from-data-set",
+                "operation-type": "search",
+                "index": "{{ target_index_name }}",
+                "param-source": "knn-query-from-data-set",
+                "k": {{ query_k }},
+                "field": "{{ target_field_name }}",
+                "data_set_format": "{{ query_data_set_format }}",
+                "data_set_path": "{{ query_data_set_path }}"
+          },
+            "clients": {{ query_clients }}
         }
     ]
 }