diff --git a/pyproject.toml b/pyproject.toml
index 4da9ce2cc..3f517e9e0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,6 +64,7 @@ all = [
     "psycopg-binary",
     "opensearch-dsl==2.1.0",
     "opensearch-py==2.6.0",
+    "mariadb",
 ]
 
 qdrant = [ "qdrant-client" ]
@@ -78,6 +79,7 @@ memorydb = [ "memorydb" ]
 chromadb = [ "chromadb" ]
 awsopensearch = [ "awsopensearch" ]
 zilliz_cloud = []
+mariadb = [ "mariadb" ]
 
 [project.urls]
 "repository" = "https://github.com/zilliztech/VectorDBBench"
diff --git a/vectordb_bench/backend/clients/__init__.py b/vectordb_bench/backend/clients/__init__.py
index 3e87e1fbe..c0405d94a 100644
--- a/vectordb_bench/backend/clients/__init__.py
+++ b/vectordb_bench/backend/clients/__init__.py
@@ -35,6 +35,7 @@ class DB(Enum):
     MemoryDB = "MemoryDB"
     Chroma = "Chroma"
     AWSOpenSearch = "OpenSearch"
+    MariaDB = "MariaDB"
     Test = "test"
 
 
@@ -93,6 +94,10 @@ def init_cls(self) -> Type[VectorDB]:
             from .aws_opensearch.aws_opensearch import AWSOpenSearch
             return AWSOpenSearch
 
+        if self == DB.MariaDB:
+            from .mariadb.mariadb import MariaDB
+            return MariaDB
+
     @property
     def config_cls(self) -> Type[DBConfig]:
         """Import while in use"""
@@ -148,6 +153,10 @@ def config_cls(self) -> Type[DBConfig]:
             from .aws_opensearch.config import AWSOpenSearchConfig
             return AWSOpenSearchConfig
 
+        if self == DB.MariaDB:
+            from .mariadb.config import MariaDBConfig
+            return MariaDBConfig
+
     def case_config_cls(self, index_type: IndexType | None = None) -> Type[DBCaseConfig]:
         if self == DB.Milvus:
             from .milvus.config import _milvus_case_config
@@ -185,6 +194,10 @@ def case_config_cls(self, index_type: IndexType | None = None) -> Type[DBCaseCon
             from .pgvectorscale.config import _pgvectorscale_case_config
             return _pgvectorscale_case_config.get(index_type)
 
+        if self == DB.MariaDB:
+            from .mariadb.config import _mariadb_case_config
+            return _mariadb_case_config.get(index_type)
+
         # DB.Pinecone, DB.Chroma, DB.Redis
         return EmptyDBCaseConfig
 
diff --git a/vectordb_bench/backend/clients/mariadb/cli.py b/vectordb_bench/backend/clients/mariadb/cli.py
new file mode 100644
index 000000000..3c333b724
--- /dev/null
+++ b/vectordb_bench/backend/clients/mariadb/cli.py
@@ -0,0 +1,107 @@
+from typing import Annotated, Optional, Unpack
+
+import click
+import os
+from pydantic import SecretStr
+
+from ....cli.cli import (
+    CommonTypedDict,
+    HNSWFlavor1,
+    cli,
+    click_parameter_decorators_from_typed_dict,
+    run,
+)
+from vectordb_bench.backend.clients import DB
+
+
+class MariaDBTypedDict(CommonTypedDict):
+    user_name: Annotated[
+        str, click.option("--username",
+                          type=str,
+                          help="Username",
+                          required=True,
+                          ),
+    ]
+    password: Annotated[
+        str, click.option("--password",
+                     type=str,
+                     help="Password",
+                     required=True,
+                     ),
+    ]
+
+    host: Annotated[
+        str, click.option("--host",
+                          type=str,
+                          help="Db host",
+                          default="127.0.0.1",
+                          ),
+    ]
+
+    port: Annotated[
+        int, click.option("--port",
+                          type=int,
+                          default=3306,
+                          help="Db Port",
+                          ),
+    ]
+
+    storage_engine: Annotated[
+        int, click.option("--storage-engine",
+                          type=click.Choice(["InnoDB", "MyISAM"]),
+                          help="DB storage engine",
+                          required=True,
+                          ),
+    ]
+
+class MariaDBHNSWTypedDict(MariaDBTypedDict):
+    ...
+    m: Annotated[
+        Optional[int], click.option("--m",
+                                    type=int,
+                                    help="MariaDB system variable mhnsw_max_edges_per_node",
+                                    required=False,
+                                    ),
+    ]
+
+    ef_search: Annotated[
+        Optional[int], click.option("--ef-search",
+                                    type=int,
+                                    help="MariaDB system variable mhnsw_min_limit",
+                                    required=False,
+                                    ),
+    ]
+
+    cache_size: Annotated[
+        Optional[int], click.option("--cache-size",
+                                    type=int,
+                                    help="MariaDB system variable mhnsw_cache_size",
+                                    required=False,
+                                    ),
+    ]
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(MariaDBHNSWTypedDict)
+def MariaDBHNSW(
+    **parameters: Unpack[MariaDBHNSWTypedDict],
+):
+    from .config import MariaDBConfig, MariaDBHNSWConfig
+
+    run(
+        db=DB.MariaDB,
+        db_config=MariaDBConfig(
+            db_label=parameters["db_label"],
+            user_name=parameters["username"],
+            password=SecretStr(parameters["password"]),
+            host=parameters["host"],
+            port=parameters["port"],
+        ),
+        db_case_config=MariaDBHNSWConfig(
+            M=parameters["m"],
+            ef_search=parameters["ef_search"],
+            storage_engine=parameters["storage_engine"],
+            cache_size=parameters["cache_size"],
+        ),
+        **parameters,
+    )
diff --git a/vectordb_bench/backend/clients/mariadb/config.py b/vectordb_bench/backend/clients/mariadb/config.py
new file mode 100644
index 000000000..50b665a96
--- /dev/null
+++ b/vectordb_bench/backend/clients/mariadb/config.py
@@ -0,0 +1,71 @@
+from pydantic import SecretStr, BaseModel
+from typing import TypedDict
+from ..api import DBConfig, DBCaseConfig, MetricType, IndexType
+
+class MariaDBConfigDict(TypedDict):
+    """These keys will be directly used as kwargs in mariadb connection string,
+        so the names must match exactly mariadb API"""
+
+    user: str
+    password: str
+    host: str
+    port: int
+
+
+class MariaDBConfig(DBConfig):
+    user_name: str = "root"
+    password: SecretStr
+    host: str = "127.0.0.1"
+    port: int = 3306
+
+    def to_dict(self) -> MariaDBConfigDict:
+        pwd_str = self.password.get_secret_value()
+        return {
+            "host": self.host,
+            "port": self.port,
+            "user": self.user_name,
+            "password": pwd_str,
+        }
+
+
+class MariaDBIndexConfig(BaseModel):
+    """Base config for milvus"""
+
+    metric_type: MetricType | None = None
+
+    def parse_metric(self) -> str:
+        if self.metric_type == MetricType.L2:
+            return "euclidean"
+        elif self.metric_type == MetricType.COSINE:
+            return "cosine"
+        else:
+            raise ValueError(f"Metric type {self.metric_type} is not supported!")
+
+class MariaDBHNSWConfig(MariaDBIndexConfig, DBCaseConfig):
+    M: int | None
+    ef_search: int | None
+    index: IndexType = IndexType.HNSW
+    storage_engine: str = "InnoDB"
+    cache_size: int | None
+
+    def index_param(self) -> dict:
+        return {
+            "storage_engine": self.storage_engine,
+            "metric_type": self.parse_metric(),
+            "index_type": self.index.value,
+            "M": self.M,
+            "cache_size": self.cache_size,
+        }
+
+    def search_param(self) -> dict:
+        return {
+            "metric_type": self.parse_metric(),
+            "ef_search": self.ef_search,
+        }
+
+
+_mariadb_case_config = {
+        IndexType.HNSW: MariaDBHNSWConfig,
+}
+
+
diff --git a/vectordb_bench/backend/clients/mariadb/mariadb.py b/vectordb_bench/backend/clients/mariadb/mariadb.py
new file mode 100644
index 000000000..e70ced1f6
--- /dev/null
+++ b/vectordb_bench/backend/clients/mariadb/mariadb.py
@@ -0,0 +1,194 @@
+from ..api import VectorDB
+
+import logging
+from contextlib import contextmanager
+from typing import Any, Optional, Tuple
+from ..api import VectorDB
+from .config import MariaDBConfigDict, MariaDBIndexConfig
+import numpy as np
+
+import mariadb
+
+log = logging.getLogger(__name__)
+
+class MariaDB(VectorDB):
+    def __init__(
+            self,
+            dim: int,
+            db_config: MariaDBConfigDict,
+            db_case_config: MariaDBIndexConfig,
+            collection_name: str = "vec_collection",
+            drop_old: bool = False,
+            **kwargs,
+        ):
+
+        self.name = "MariaDB"
+        self.db_config = db_config
+        self.case_config = db_case_config
+        self.db_name = "vectordbbench"
+        self.table_name = collection_name
+        self.dim = dim
+
+        # construct basic units
+        self.conn, self.cursor = self._create_connection(**self.db_config)
+        
+        if drop_old:
+            self._drop_db()
+            self._create_db_table(dim)
+
+        self.cursor.close()
+        self.conn.close()
+        self.cursor = None
+        self.conn = None
+
+
+    @staticmethod
+    def _create_connection(**kwargs) -> Tuple[mariadb.Connection, mariadb.Cursor]:
+        conn = mariadb.connect(**kwargs)
+        cursor = conn.cursor()
+
+        assert conn is not None, "Connection is not initialized"
+        assert cursor is not None, "Cursor is not initialized"
+
+        return conn, cursor
+
+
+    def _drop_db(self):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+        log.info(f"{self.name} client drop db : {self.db_name}")
+
+        # flush tables before dropping database to avoid some locking issue
+        self.cursor.execute("FLUSH TABLES")
+        self.cursor.execute(f"DROP DATABASE IF EXISTS {self.db_name}")
+        self.cursor.execute("COMMIT")
+        self.cursor.execute("FLUSH TABLES")
+
+    def _create_db_table(self, dim: int):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        index_param = self.case_config.index_param()
+
+        try:
+            log.info(f"{self.name} client create database : {self.db_name}")
+            self.cursor.execute(f"CREATE DATABASE {self.db_name}")
+
+            log.info(f"{self.name} client create table : {self.table_name}")
+            self.cursor.execute(f"USE {self.db_name}")
+            self.cursor.execute(f"""
+              CREATE TABLE {self.table_name} (
+                id INT PRIMARY KEY,
+                v BLOB NOT NULL,
+                VECTOR INDEX (v)
+                distance_function={index_param["metric_type"]}
+              ) ENGINE={index_param["storage_engine"]}
+            """)
+            self.cursor.execute("COMMIT")
+
+        except Exception as e:
+            log.warning(
+                f"Failed to create table: {self.table_name} error: {e}"
+            )
+            raise e from None
+
+
+    @contextmanager
+    def init(self) -> None:
+        """ create and destory connections to database.
+
+        Examples:
+            >>> with self.init():
+            >>>     self.insert_embeddings()
+        """
+        self.conn, self.cursor = self._create_connection(**self.db_config)
+
+        index_param = self.case_config.index_param()
+        search_param = self.case_config.search_param()
+
+        # maximize allowed package size
+        self.cursor.execute("SET GLOBAL max_allowed_packet = 1073741824")
+
+        if index_param["index_type"] == "HNSW":
+            if index_param["M"] != None:
+                self.cursor.execute(f"SET mhnsw_max_edges_per_node = {index_param["M"]}")
+                self.cursor.execute(f"SET GLOBAL mhnsw_max_edges_per_node = {index_param["M"]}")
+            if index_param["cache_size"] != None:
+                self.cursor.execute(f"SET GLOBAL mhnsw_cache_size = {index_param["cache_size"]}")
+            if search_param["ef_search"] != None:
+                self.cursor.execute(f"SET mhnsw_min_limit = {search_param["ef_search"]}")
+                self.cursor.execute(f"SET GLOBAL mhnsw_min_limit = {search_param["ef_search"]}")
+            self.cursor.execute("COMMIT")
+
+        self.insert_sql = f"INSERT INTO {self.db_name}.{self.table_name} (id, v) VALUES (%s, %s)"
+        self.select_sql = f"SELECT id FROM {self.db_name}.{self.table_name} ORDER by vec_distance_{search_param["metric_type"]}(v, %s) LIMIT %d"
+
+        try:
+            yield
+        finally:
+            self.cursor.close()
+            self.conn.close()
+            self.cursor = None
+            self.conn = None
+    
+
+    def ready_to_load(self) -> bool:
+        pass
+
+    def optimize(self) -> None:
+        # TODO: Create index after loading data once MariaDB supports alter table to add Vector index
+        pass
+
+    @staticmethod
+    def vector_to_hex(v):
+        return np.array(v, 'float32').tobytes()
+
+    def insert_embeddings(
+        self,
+        embeddings: list[list[float]],
+        metadata: list[int],
+        **kwargs: Any,
+    ) -> Tuple[int, Optional[Exception]]:
+        """Insert embeddings into the database.
+        Should call self.init() first.
+        """
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        try:
+            metadata_arr = np.array(metadata)
+            embeddings_arr = np.array(embeddings)
+
+            batch_data = []
+            for i, row in enumerate(metadata_arr):
+                batch_data.append((int(row), self.vector_to_hex(embeddings_arr[i])));
+
+            self.cursor.executemany(self.insert_sql, batch_data)
+            self.cursor.execute("COMMIT")
+            self.cursor.execute("FLUSH TABLES")
+
+            return len(metadata), None
+        except Exception as e:
+            log.warning(
+                f"Failed to insert data into Vector table ({self.table_name}), error: {e}"
+            )
+            return 0, e
+    
+
+    def search_embedding(
+        self,
+        query: list[float],
+        k: int = 100,
+        filters: dict | None = None,
+        timeout: int | None = None,
+        **kwargs: Any,
+    ) -> (list[int]):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        search_param = self.case_config.search_param()
+
+        self.cursor.execute(self.select_sql, (self.vector_to_hex(query), k))
+
+        return [id for id, in self.cursor.fetchall()]
+
diff --git a/vectordb_bench/cli/vectordbbench.py b/vectordb_bench/cli/vectordbbench.py
index e62c25a3d..a763bb399 100644
--- a/vectordb_bench/cli/vectordbbench.py
+++ b/vectordb_bench/cli/vectordbbench.py
@@ -8,6 +8,7 @@
 from ..backend.clients.zilliz_cloud.cli import ZillizAutoIndex
 from ..backend.clients.milvus.cli import MilvusAutoIndex
 from ..backend.clients.aws_opensearch.cli import AWSOpenSearch
+from ..backend.clients.mariadb.cli import MariaDBHNSW
 
 from .cli import cli
 
@@ -22,6 +23,7 @@
 cli.add_command(MilvusAutoIndex)
 cli.add_command(AWSOpenSearch)
 cli.add_command(PgVectorScaleDiskAnn)
+cli.add_command(MariaDBHNSW)
 
 
 if __name__ == "__main__":
diff --git a/vectordb_bench/frontend/config/dbCaseConfigs.py b/vectordb_bench/frontend/config/dbCaseConfigs.py
index 78d1936d7..54cf6b489 100644
--- a/vectordb_bench/frontend/config/dbCaseConfigs.py
+++ b/vectordb_bench/frontend/config/dbCaseConfigs.py
@@ -788,6 +788,65 @@ class CaseConfigInput(BaseModel):
     },
 )
 
+CaseConfigParamInput_IndexType_MariaDB = CaseConfigInput(
+    label=CaseConfigParamType.IndexType,
+    inputHelp="Select Index Type",
+    inputType=InputType.Option,
+    inputConfig={
+        "options": [
+            IndexType.HNSW.value,
+        ],
+    },
+)
+
+CaseConfigParamInput_StorageEngine_MariaDB = CaseConfigInput(
+    label=CaseConfigParamType.storage_engine,
+    inputHelp="Select Storage Engine",
+    inputType=InputType.Option,
+    inputConfig={
+        "options": ["InnoDB", "MyISAM"],
+    },
+)
+
+CaseConfigParamInput_M_MariaDB = CaseConfigInput(
+    label=CaseConfigParamType.M,
+    inputHelp="mhnsw_max_edges_per_node",
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 2,
+        "max": 200,
+        "value": 6,
+    },
+    isDisplayed=lambda config: config.get(CaseConfigParamType.IndexType, None)
+    == IndexType.HNSW.value,
+)
+
+CaseConfigParamInput_EFSearch_MariaDB = CaseConfigInput(
+    label=CaseConfigParamType.ef_search,
+    inputHelp="mhnsw_min_limit",
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1,
+        "max": 100,
+        "value": 20,
+    },
+    isDisplayed=lambda config: config.get(CaseConfigParamType.IndexType, None)
+    == IndexType.HNSW.value,
+)
+
+CaseConfigParamInput_CacheSize_MariaDB = CaseConfigInput(
+    label=CaseConfigParamType.cache_size,
+    inputHelp="mhnsw_cache_size",
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1048576,
+        "max": (1 << 53) - 1,
+        "value": 16 * 1024 ** 3,
+    },
+    isDisplayed=lambda config: config.get(CaseConfigParamType.IndexType, None)
+    == IndexType.HNSW.value,
+)
+
 MilvusLoadConfig = [
     CaseConfigParamInput_IndexType,
     CaseConfigParamInput_M,
@@ -904,6 +963,21 @@ class CaseConfigInput(BaseModel):
     CaseConfigParamInput_query_search_list_size,
 ]
 
+MariaDBLoadingConfig = [
+    CaseConfigParamInput_IndexType_MariaDB,
+    CaseConfigParamInput_StorageEngine_MariaDB,
+    CaseConfigParamInput_M_MariaDB,
+    CaseConfigParamInput_CacheSize_MariaDB,
+]
+
+MariaDBPerformanceConfig = [
+    CaseConfigParamInput_IndexType_MariaDB,
+    CaseConfigParamInput_StorageEngine_MariaDB,
+    CaseConfigParamInput_M_MariaDB,
+    CaseConfigParamInput_CacheSize_MariaDB,
+    CaseConfigParamInput_EFSearch_MariaDB,
+]
+
 CASE_CONFIG_MAP = {
     DB.Milvus: {
         CaseLabel.Load: MilvusLoadConfig,
@@ -932,4 +1006,8 @@ class CaseConfigInput(BaseModel):
         CaseLabel.Load: PgVectorScaleLoadingConfig,
         CaseLabel.Performance: PgVectorScalePerformanceConfig,
     },
+    DB.MariaDB: {
+        CaseLabel.Load: MariaDBLoadingConfig,
+        CaseLabel.Performance: MariaDBPerformanceConfig,
+    },
 }
diff --git a/vectordb_bench/models.py b/vectordb_bench/models.py
index 75b427091..0574e3c29 100644
--- a/vectordb_bench/models.py
+++ b/vectordb_bench/models.py
@@ -70,6 +70,8 @@ class CaseConfigParamType(Enum):
     num_bits_per_dimension = "num_bits_per_dimension"
     query_search_list_size = "query_search_list_size"
     query_rescore = "query_rescore"
+    storage_engine = "storage_engine"
+    cache_size = "cache_size"
 
 
 class CustomizedCase(BaseModel):