feat: Support CMEK for BQ tables

bigframes/_config/bigquery_options.py

@@ -39,13 +39,15 @@ def __init__(
         bq_connection: Optional[str] = None,
         use_regional_endpoints: bool = False,
         application_name: Optional[str] = None,
+        kms_key_name: Optional[str] = None,
     ):
         self._credentials = credentials
         self._project = project
         self._location = location
         self._bq_connection = bq_connection
         self._use_regional_endpoints = use_regional_endpoints
         self._application_name = application_name
+        self._kms_key_name = kms_key_name
         self._session_started = False
 
     @property
@@ -148,3 +150,26 @@ def use_regional_endpoints(self, value: bool):
             )
 
         self._use_regional_endpoints = value
+
+    @property
+    def kms_key_name(self) -> Optional[str]:
+        """Customer managed encryption key used to control encryption of the
+        data-at-rest in BigQuery. This is of the format
+        projects/PROJECT_ID/locations/LOCATION/keyRings/KEYRING/cryptoKeys/KEY
+
+        See https://cloud.google.com/bigquery/docs/customer-managed-encryption
+        for more details.
+
+        Please make sure the project used for Bigquery DataFrames has "Cloud KMS
+        CryptoKey Encrypter/Decrypter" role in the key's project, See
+        https://cloud.google.com/bigquery/docs/customer-managed-encryption#assign_role
+        for steps on how to ensure that.
+        """
+        return self._kms_key_name
+
+    @kms_key_name.setter
+    def kms_key_name(self, value: str):
+        if self._session_started and self._kms_key_name != value:
+            raise ValueError(SESSION_STARTED_MESSAGE.format(attribute="kms_key_name"))
+
+        self._kms_key_name = value

bigframes/ml/core.py

@@ -212,7 +212,8 @@ def principal_component_info(self) -> bpd.DataFrame:
         return self._session.read_gbq(sql)
 
     def copy(self, new_model_name: str, replace: bool = False) -> BqmlModel:
-        job_config = bigquery.job.CopyJobConfig()
+        job_config = self._session._prepare_copy_job_config()
+
         if replace:
             job_config.write_disposition = "WRITE_TRUNCATE"
 
@@ -236,7 +237,7 @@ def register(self, vertex_ai_model_id: Optional[str] = None) -> BqmlModel:
             options={"vertex_ai_model_id": vertex_ai_model_id}
         )
         # Register the model and wait it to finish
-        self._session._start_query(sql)
+        self._session._start_query_bqml(sql)
 
         self._model = self._session.bqclient.get_model(self.model_name)
         return self
@@ -255,7 +256,7 @@ def _create_model_ref(
 
     def _create_model_with_sql(self, session: bigframes.Session, sql: str) -> BqmlModel:
         # fit the model, synchronously
-        _, job = session._start_query(sql)
+        _, job = session._start_query_bqml(sql)
 
         # real model path in the session specific hidden dataset and table prefix
         model_name_full = f"{job.destination.project}.{job.destination.dataset_id}.{job.destination.table_id}"
@@ -298,6 +299,9 @@ def create_model(
             options.update({"INPUT_LABEL_COLS": y_train.columns.tolist()})
 
         session = X_train._session
+        if session._bq_kms_key_name:
+            options.update({"kms_key_name": session._bq_kms_key_name})
+
         model_ref = self._create_model_ref(session._anonymous_dataset)
 
         sql = self._model_creation_sql_generator.create_model(

bigframes/pandas/__init__.py

@@ -383,6 +383,7 @@ def _set_default_session_location_if_possible(query):
         use_regional_endpoints=options.bigquery.use_regional_endpoints,
         credentials=options.bigquery.credentials,
         application_name=options.bigquery.application_name,
+        bq_kms_key_name=options.bigquery.kms_key_name,
     )
 
     bqclient = clients_provider.bqclient

bigframes/session/__init__.py

@@ -65,7 +65,6 @@
 
 import bigframes._config.bigquery_options as bigquery_options
 import bigframes.constants as constants
-from bigframes.core import log_adapter
 import bigframes.core as core
 import bigframes.core.blocks as blocks
 import bigframes.core.compile
@@ -84,7 +83,6 @@
 
 # Even though the ibis.backends.bigquery import is unused, it's needed
 # to register new and replacement ops with the Ibis BigQuery backend.
-import third_party.bigframes_vendored.ibis.backends.bigquery  # noqa
 import third_party.bigframes_vendored.ibis.expr.operations as vendored_ibis_ops
 import third_party.bigframes_vendored.pandas.io.gbq as third_party_pandas_gbq
 import third_party.bigframes_vendored.pandas.io.parquet as third_party_pandas_parquet
@@ -161,6 +159,8 @@ def __init__(
         else:
             self._location = context.location
 
+        self._bq_kms_key_name = context.kms_key_name
+
         # Instantiate a clients provider to help with cloud clients that will be
         # used in the future operations in the session
         if clients_provider:
@@ -172,9 +172,17 @@ def __init__(
                 use_regional_endpoints=context.use_regional_endpoints,
                 credentials=context.credentials,
                 application_name=context.application_name,
+                bq_kms_key_name=self._bq_kms_key_name,
             )
 
         self._create_bq_datasets()
+
+        # TODO(shobs): Remove this logic after https://github.com/ibis-project/ibis/issues/8494
+        # has been fixed. The ibis client changes the default query job config
+        # so we are going to remember the current config and restore it after
+        # the ibis client has been created
+        original_default_query_job_config = self.bqclient.default_query_job_config
+
         self.ibis_client = typing.cast(
             ibis_bigquery.Backend,
             ibis.bigquery.connect(
@@ -184,6 +192,9 @@ def __init__(
             ),
         )
 
+        self.bqclient.default_query_job_config = original_default_query_job_config
+
+        # Resolve the BQ connection for remote function and Vertex AI integration
         self._bq_connection = context.bq_connection or _BIGFRAMES_DEFAULT_CONNECTION_ID
 
         # Now that we're starting the session, don't allow the options to be
@@ -929,19 +940,21 @@ def _read_pandas_load_job(
         pandas_dataframe_copy.columns = pandas.Index(new_col_ids)
         pandas_dataframe_copy[ordering_col] = np.arange(pandas_dataframe_copy.shape[0])
 
+        job_config = self._prepare_load_job_config()
+
         # Specify the datetime dtypes, which is auto-detected as timestamp types.
         schema: list[bigquery.SchemaField] = []
         for column, dtype in zip(pandas_dataframe.columns, pandas_dataframe.dtypes):
             if dtype == "timestamp[us][pyarrow]":
                 schema.append(
                     bigquery.SchemaField(column, bigquery.enums.SqlTypeNames.DATETIME)
                 )
+        job_config.schema = schema
 
         # Clustering probably not needed anyways as pandas tables are small
         cluster_cols = [ordering_col]
-
-        job_config = bigquery.LoadJobConfig(schema=schema)
         job_config.clustering_fields = cluster_cols
+
         job_config.labels = {"bigframes-api": api_name}
 
         load_table_destination = bigframes_io.random_table(self._anonymous_dataset)
@@ -1061,7 +1074,7 @@ def read_csv(
                     f"{constants.FEEDBACK_LINK}"
                 )
 
-            job_config = bigquery.LoadJobConfig()
+            job_config = self._prepare_load_job_config()
             job_config.create_disposition = bigquery.CreateDisposition.CREATE_IF_NEEDED
             job_config.source_format = bigquery.SourceFormat.CSV
             job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY
@@ -1136,7 +1149,7 @@ def read_parquet(
         table = bigframes_io.random_table(self._anonymous_dataset)
 
         if engine == "bigquery":
-            job_config = bigquery.LoadJobConfig()
+            job_config = self._prepare_load_job_config()
             job_config.create_disposition = bigquery.CreateDisposition.CREATE_IF_NEEDED
             job_config.source_format = bigquery.SourceFormat.PARQUET
             job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY
@@ -1194,7 +1207,7 @@ def read_json(
                     "'lines' keyword is only valid when 'orient' is 'records'."
                 )
 
-            job_config = bigquery.LoadJobConfig()
+            job_config = self._prepare_load_job_config()
             job_config.create_disposition = bigquery.CreateDisposition.CREATE_IF_NEEDED
             job_config.source_format = bigquery.SourceFormat.NEWLINE_DELIMITED_JSON
             job_config.write_disposition = bigquery.WriteDisposition.WRITE_EMPTY
@@ -1518,36 +1531,83 @@ def read_gbq_function(
             session=self,
         )
 
+    def _prepare_query_job_config(
+        self,
+        job_config: Optional[bigquery.QueryJobConfig] = None,
+    ) -> bigquery.QueryJobConfig:
+        if job_config is None:
+            job_config = bigquery.QueryJobConfig()
+        else:
+            # Create a copy so that we don't mutate the original config passed
+            job_config = typing.cast(
+                bigquery.QueryJobConfig,
+                bigquery.QueryJobConfig.from_api_repr(job_config.to_api_repr()),
+            )
+
+        if bigframes.options.compute.maximum_bytes_billed is not None:
+            job_config.maximum_bytes_billed = (
+                bigframes.options.compute.maximum_bytes_billed
+            )
+
+        if self._bq_kms_key_name:
+            job_config.destination_encryption_configuration = (
+                bigquery.EncryptionConfiguration(kms_key_name=self._bq_kms_key_name)
+            )
+
+        return job_config
+
+    def _prepare_load_job_config(self) -> bigquery.LoadJobConfig:
+        # Create a copy so that we don't mutate the original config passed
+        job_config = bigquery.LoadJobConfig()
+
+        if self._bq_kms_key_name:
+            job_config.destination_encryption_configuration = (
+                bigquery.EncryptionConfiguration(kms_key_name=self._bq_kms_key_name)
+            )
+
+        return job_config
+
+    def _prepare_copy_job_config(self) -> bigquery.CopyJobConfig:
+        # Create a copy so that we don't mutate the original config passed
+        job_config = bigquery.CopyJobConfig()
+
+        if self._bq_kms_key_name:
+            job_config.destination_encryption_configuration = (
+                bigquery.EncryptionConfiguration(kms_key_name=self._bq_kms_key_name)
+            )
+
+        return job_config
+
     def _start_query(
         self,
         sql: str,
         job_config: Optional[bigquery.job.QueryJobConfig] = None,
         max_results: Optional[int] = None,
     ) -> Tuple[bigquery.table.RowIterator, bigquery.QueryJob]:
         """
-        Starts query job and waits for results.
+        Starts BigQuery query job and waits for results.
         """
-        job_config = self._prepare_job_config(job_config)
-        api_methods = log_adapter.get_and_reset_api_methods()
-        job_config.labels = bigframes_io.create_job_configs_labels(
-            job_configs_labels=job_config.labels, api_methods=api_methods
+        job_config = self._prepare_query_job_config(job_config)
+        return bigframes.session._io.bigquery.start_query_with_client(
+            self.bqclient, sql, job_config, max_results
         )
 
-        try:
-            query_job = self.bqclient.query(sql, job_config=job_config)
-        except google.api_core.exceptions.Forbidden as ex:
-            if "Drive credentials" in ex.message:
-                ex.message += "\nCheck https://cloud.google.com/bigquery/docs/query-drive-data#Google_Drive_permissions."
-            raise
+    def _start_query_bqml(
+        self,
+        sql: str,
+    ) -> Tuple[bigquery.table.RowIterator, bigquery.QueryJob]:
+        """
+        Starts BigQuery ML query job and waits for results.
+        """
+        job_config = self._prepare_query_job_config()
 
-        opts = bigframes.options.display
-        if opts.progress_bar is not None and not query_job.configuration.dry_run:
-            results_iterator = formatting_helpers.wait_for_query_job(
-                query_job, max_results, opts.progress_bar
-            )
-        else:
-            results_iterator = query_job.result(max_results=max_results)
-        return results_iterator, query_job
+        # BQML expects kms_key_name through OPTIONS and not through job config,
+        # so we must reset any encryption set in the job config
+        job_config.destination_encryption_configuration = None
+
+        return bigframes.session._io.bigquery.start_query_with_client(
+            self.bqclient, sql, job_config
+        )
 
     def _cache_with_cluster_cols(
         self, array_value: core.ArrayValue, cluster_cols: typing.Sequence[str]
@@ -1690,19 +1750,6 @@ def _start_generic_job(self, job: formatting_helpers.GenericJob):
         else:
             job.result()
 
-    def _prepare_job_config(
-        self, job_config: Optional[bigquery.QueryJobConfig] = None
-    ) -> bigquery.QueryJobConfig:
-        if job_config is None:
-            job_config = self.bqclient.default_query_job_config
-        if job_config is None:
-            job_config = bigquery.QueryJobConfig()
-        if bigframes.options.compute.maximum_bytes_billed is not None:
-            job_config.maximum_bytes_billed = (
-                bigframes.options.compute.maximum_bytes_billed
-            )
-        return job_config
-
 
 def connect(context: Optional[bigquery_options.BigQueryOptions] = None) -> Session:
     return Session(context)

bigframes/session/_io/bigquery.py

@@ -20,11 +20,17 @@
 import itertools
 import textwrap
 import types
-from typing import Dict, Iterable, Optional, Sequence, Union
+from typing import Dict, Iterable, Optional, Sequence, Tuple, Union
 import uuid
 
+import google.api_core.exceptions
 import google.cloud.bigquery as bigquery
 
+import bigframes
+from bigframes.core import log_adapter
+import bigframes.formatting_helpers as formatting_helpers
+import bigframes.session._io.bigquery as bigframes_io
+
 IO_ORDERING_ID = "bqdf_row_nums"
 MAX_LABELS_COUNT = 64
 TEMP_TABLE_PREFIX = "bqdf{date}_{random_id}"
@@ -207,3 +213,34 @@ def format_option(key: str, value: Union[bool, str]) -> str:
     if isinstance(value, bool):
         return f"{key}=true" if value else f"{key}=false"
     return f"{key}={repr(value)}"
+
+
+def start_query_with_client(
+    bq_client: bigquery.Client,
+    sql: str,
+    job_config: bigquery.job.QueryJobConfig,
+    max_results: Optional[int] = None,
+) -> Tuple[bigquery.table.RowIterator, bigquery.QueryJob]:
+    """
+    Starts query job and waits for results.
+    """
+    api_methods = log_adapter.get_and_reset_api_methods()
+    job_config.labels = bigframes_io.create_job_configs_labels(
+        job_configs_labels=job_config.labels, api_methods=api_methods
+    )
+
+    try:
+        query_job = bq_client.query(sql, job_config=job_config)
+    except google.api_core.exceptions.Forbidden as ex:
+        if "Drive credentials" in ex.message:
+            ex.message += "\nCheck https://cloud.google.com/bigquery/docs/query-drive-data#Google_Drive_permissions."
+        raise
+
+    opts = bigframes.options.display
+    if opts.progress_bar is not None and not query_job.configuration.dry_run:
+        results_iterator = formatting_helpers.wait_for_query_job(
+            query_job, max_results, opts.progress_bar
+        )
+    else:
+        results_iterator = query_job.result(max_results=max_results)
+    return results_iterator, query_job