diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index be9984fc153..e06a3b9df21 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -271,6 +271,14 @@ This allows:
 * the reviewer to be very confident that the feature does what it is supposed to do before merging it into the code base.
 * the contributors to be sure that they don't break already-merged features when refactoring or modifying the code base.
 
+<a name="-enable-logging"></a>
+## Enable logging
+If you need to monitor and debug your code, you can enable docarray logging:
+```python
+import logging
+logging.getLogger('docarray').setLevel(logging.DEBUG)
+```
+
 <a name="-compiling-protobuf"></a>
 ## Compiling protobuf
 
diff --git a/README.md b/README.md
index 0f11bafe950..7a2b56b59b3 100644
--- a/README.md
+++ b/README.md
@@ -444,6 +444,38 @@ match = store.find(
 )
 ```
 
+## Enable logging
+You can see more logs by setting the log level to `DEBUG` or `INFO`.
+
+Example:
+```python
+from pydantic import Field
+from docarray import BaseDocument
+from docarray.doc_index.backends.hnswlib_doc_index import HnswDocumentIndex
+from docarray.typing import NdArray
+# import logging and set the level to DEBUG
+import logging
+logging.getLogger('docarray').setLevel(logging.DEBUG)
+
+
+# define a simple document and create a document index
+class SimpleDoc(BaseDocument):
+    vector: NdArray = Field(dim=10)
+
+doc_store = HnswDocumentIndex[SimpleDoc](work_dir='temp_path/')
+```
+
+```console
+INFO - docarray - DB config created
+INFO - docarray - Runtime config created
+DEBUG - docarray - Working directory set to temp_path/
+WARNING - docarray - No index was created for `id` as it does not have a config
+INFO - docarray - Created a new index for column `vector`
+DEBUG - docarray - DB path set to temp_path/docs_sqlite.db
+INFO - docarray - Connection to DB has been established
+INFO - docarray - HnswDocumentIndex[SimpleDoc] has been initialized
+```
+
 ## Install the alpha
 
 to try out the alpha you can install it via git:
diff --git a/docarray/__init__.py b/docarray/__init__.py
index 5e3e700a61f..03e65750c14 100644
--- a/docarray/__init__.py
+++ b/docarray/__init__.py
@@ -2,5 +2,13 @@
 
 from docarray.array import DocumentArray, DocumentArrayStacked
 from docarray.base_document.document import BaseDocument
+import logging
 
 __all__ = ['BaseDocument', 'DocumentArray', 'DocumentArrayStacked']
+
+logger = logging.getLogger('docarray')
+
+handler = logging.StreamHandler()
+formatter = logging.Formatter("%(levelname)s - %(name)s - %(message)s")
+handler.setFormatter(formatter)
+logger.addHandler(handler)
diff --git a/docarray/doc_index/abstract_doc_index.py b/docarray/doc_index/abstract_doc_index.py
index 6a527b43bbb..e1fd847c841 100644
--- a/docarray/doc_index/abstract_doc_index.py
+++ b/docarray/doc_index/abstract_doc_index.py
@@ -27,6 +27,7 @@
 from docarray.typing import AnyTensor
 from docarray.utils.find import FindResult, _FindResult
 from docarray.utils.misc import torch_imported
+import logging
 
 if TYPE_CHECKING:
     from pydantic.fields import ModelField
@@ -87,10 +88,13 @@ def __init__(self, db_config=None, **kwargs):
                 'A DocumentIndex must be typed with a Document type.'
                 'To do so, use the syntax: DocumentIndex[DocumentType]'
             )
+        self._logger = logging.getLogger('docarray')
         self._db_config = db_config or self.DBConfig(**kwargs)
         if not isinstance(self._db_config, self.DBConfig):
             raise ValueError(f'db_config must be of type {self.DBConfig}')
+        self._logger.info('DB config created')
         self._runtime_config = self.RuntimeConfig()
+        self._logger.info('Runtime config created')
         self._column_infos: Dict[str, _ColumnInfo] = self._create_column_infos(
             self._schema
         )
@@ -346,6 +350,7 @@ def __delitem__(self, key: Union[str, Sequence[str]]):
 
         :param key: id or ids to delete from the Document index
         """
+        self._logger.info(f'Deleting documents with id(s) {key} from the index')
         if isinstance(key, str):
             key = [key]
         self._del_items(key)
@@ -371,10 +376,15 @@ def configure(self, runtime_config=None, **kwargs):
     def index(self, docs: Union[BaseDocument, Sequence[BaseDocument]], **kwargs):
         """index Documents into the index.
 
-        :param docs: Documents to index. NOTE: passing a Sequence of Documents that is
-            not a DocumentArray comes at a performance penalty, since compatibility
-            with the Index's schema need to be checked for every Document individually.
+        :param docs: Documents to index.
         """
+        if not isinstance(docs, (BaseDocument, DocumentArray)):
+            self._logger.warning(
+                'Passing a sequence of Documents that is not a DocumentArray comes at '
+                'a performance penalty, since compatibility with the schema of Index '
+                'needs to be checked for every Document individually.'
+            )
+        self._logger.debug(f'Indexing {len(docs)} documents')
         docs_validated = self._validate_docs(docs)
         data_by_columns = self._get_col_value_dict(docs_validated)
         self._index(data_by_columns, **kwargs)
@@ -397,6 +407,7 @@ def find(
         :param limit: maximum number of documents to return
         :return: a named tuple containing `documents` and `scores`
         """
+        self._logger.debug(f'Executing `find` for search field {search_field}')
         if isinstance(query, BaseDocument):
             query_vec = self._get_values_by_column([query], search_field)[0]
         else:
@@ -430,6 +441,7 @@ def find_batched(
         :param limit: maximum number of documents to return per query
         :return: a named tuple containing `documents` and `scores`
         """
+        self._logger.debug(f'Executing `find_batched` for search field {search_field}')
         if isinstance(queries, Sequence):
             query_vec_list = self._get_values_by_column(queries, search_field)
             query_vec_np = np.stack(
@@ -459,6 +471,7 @@ def filter(
         :param limit: maximum number of documents to return
         :return: a DocumentArray containing the documents that match the filter query
         """
+        self._logger.debug(f'Executing `filter` for the query {filter_query}')
         docs = self._filter(filter_query, limit=limit, **kwargs)
 
         if isinstance(docs, List):
@@ -478,6 +491,9 @@ def filter_batched(
         :param limit: maximum number of documents to return
         :return: a DocumentArray containing the documents that match the filter query
         """
+        self._logger.debug(
+            f'Executing `filter_batched` for the queries {filter_queries}'
+        )
         da_list = self._filter_batched(filter_queries, limit=limit, **kwargs)
 
         if len(da_list) > 0 and isinstance(da_list[0], List):
@@ -499,6 +515,7 @@ def text_search(
         :param limit: maximum number of documents to return
         :return: a named tuple containing `documents` and `scores`
         """
+        self._logger.debug(f'Executing `text_search` for search field {search_field}')
         if isinstance(query, BaseDocument):
             query_text = self._get_values_by_column([query], search_field)[0]
         else:
@@ -526,6 +543,9 @@ def text_search_batched(
         :param limit: maximum number of documents to return
         :return: a named tuple containing `documents` and `scores`
         """
+        self._logger.debug(
+            f'Executing `text_search_batched` for search field {search_field}'
+        )
         if isinstance(queries[0], BaseDocument):
             query_docs: Sequence[BaseDocument] = cast(Sequence[BaseDocument], queries)
             query_texts: Sequence[str] = self._get_values_by_column(
diff --git a/docarray/doc_index/backends/hnswlib_doc_index.py b/docarray/doc_index/backends/hnswlib_doc_index.py
index 3c8c2df14e3..3d6923fff80 100644
--- a/docarray/doc_index/backends/hnswlib_doc_index.py
+++ b/docarray/doc_index/backends/hnswlib_doc_index.py
@@ -63,6 +63,7 @@ def __init__(self, db_config=None, **kwargs):
         super().__init__(db_config=db_config, **kwargs)
         self._db_config = cast(HnswDocumentIndex.DBConfig, self._db_config)
         self._work_dir = self._db_config.work_dir
+        self._logger.debug(f'Working directory set to {self._work_dir}')
         load_existing = os.path.exists(self._work_dir) and os.listdir(self._work_dir)
         Path(self._work_dir).mkdir(parents=True, exist_ok=True)
 
@@ -83,18 +84,26 @@ def __init__(self, db_config=None, **kwargs):
         self._hnsw_indices = {}
         for col_name, col in self._column_infos.items():
             if not col.config:
-                continue  # do not create column index if no config is given
+                self._logger.warning(
+                    f'No index was created for `{col_name}` as it does not have a config'
+                )
+                continue
             if load_existing:
                 self._hnsw_indices[col_name] = self._load_index(col_name, col)
+                self._logger.info(f'Loading an existing index for column `{col_name}`')
             else:
                 self._hnsw_indices[col_name] = self._create_index(col)
+                self._logger.info(f'Created a new index for column `{col_name}`')
 
         # SQLite setup
         self._sqlite_db_path = os.path.join(self._work_dir, 'docs_sqlite.db')
+        self._logger.debug(f'DB path set to {self._sqlite_db_path}')
         self._sqlite_conn = sqlite3.connect(self._sqlite_db_path)
+        self._logger.info('Connection to DB has been established')
         self._sqlite_cursor = self._sqlite_conn.cursor()
         self._create_docs_table()
         self._sqlite_conn.commit()
+        self._logger.info(f'{self.__class__.__name__} has been initialized')
 
     ###############################################
     # Inner classes for query builder and configs #
@@ -161,10 +170,11 @@ def index(self, docs: Union[BaseDocument, Sequence[BaseDocument]], **kwargs):
         """index a document into the store"""
         if kwargs:
             raise ValueError(f'{list(kwargs.keys())} are not valid keyword arguments')
+
+        self._logger.debug(f'Indexing {len(docs)} documents')
         docs_validated = self._validate_docs(docs)
         data_by_columns = self._get_col_value_dict(docs_validated)
         hashed_ids = tuple(self._to_hashed_id(doc.id) for doc in docs_validated)
-
         # indexing into HNSWLib and SQLite sequentially
         # could be improved by processing in parallel
         for col_name, index in self._hnsw_indices.items():
@@ -196,6 +206,7 @@ def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any:
             elif op == 'filter':
                 filter_conditions.append(op_kwargs['filter_query'])
 
+        self._logger.debug(f'Executing query {query}')
         docs_filtered = ann_docs
         for cond in filter_conditions:
             da_cls = DocumentArray.__class_getitem__(
@@ -203,6 +214,7 @@ def execute_query(self, query: List[Tuple[str, Dict]], *args, **kwargs) -> Any:
             )
             docs_filtered = da_cls(filter_docs(docs_filtered, cond))
 
+        self._logger.debug(f'{len(docs_filtered)} results found')
         docs_and_scores = zip(
             docs_filtered, (doc_to_score[doc.id] for doc in docs_filtered)
         )
@@ -240,7 +252,6 @@ def _filter(
         filter_query: Any,
         limit: int,
     ) -> DocumentArray:
-
         raise NotImplementedError(
             f'{type(self)} does not support filter-only queries.'
             f' To perform post-filtering on a query, use'
diff --git a/tests/doc_index/conftest.py b/tests/doc_index/conftest.py
new file mode 100644
index 00000000000..497a740ae43
--- /dev/null
+++ b/tests/doc_index/conftest.py
@@ -0,0 +1,8 @@
+import pytest
+import logging
+
+
+@pytest.fixture(autouse=True)
+def set_logger_level():
+    logger = logging.getLogger('docarray')
+    logger.setLevel(logging.INFO)