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# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Sample app that uses the Data Loss Prevention API to inspect a string, a
local file or a file on Google Cloud Storage."""
from __future__ import print_function
import argparse
import json
import os
# [START dlp_inspect_string_basic]
def inspect_string_basic(
project,
content_string,
info_types=["PHONE_NUMBER"],
):
"""Uses the Data Loss Prevention API to analyze strings for protected data.
Args:
project: The Google Cloud project id to use as a parent resource.
content_string: The string to inspect.
info_types: A list of strings representing info types to look for.
A full list of info type categories can be fetched from the API.
Returns:
None; the response from the API is printed to the terminal.
"""
# Import the client library.
import google.cloud.dlp
# Instantiate a client.
dlp = google.cloud.dlp_v2.DlpServiceClient()
# Prepare info_types by converting the list of strings into a list of
# dictionaries (protos are also accepted).
info_types = [{"name": info_type} for info_type in info_types]
# Construct the configuration dictionary.
inspect_config = {
"info_types": info_types,
"include_quote": True,
}
# Construct the `item`.
item = {"value": content_string}
# Convert the project id into a full resource id.
parent = dlp.project_path(project)
# Call the API.
response = dlp.inspect_content(parent, inspect_config, item)
# Print out the results.
if response.result.findings:
for finding in response.result.findings:
print("Quote: {}".format(finding.quote))
print("Info type: {}".format(finding.info_type.name))
print("Likelihood: {}".format(finding.likelihood))
else:
print("No findings.")
# [END dlp_inspect_string_basic]
# [START dlp_inspect_string]
def inspect_string(
project,
content_string,
info_types,
custom_dictionaries=None,
custom_regexes=None,
min_likelihood=None,
max_findings=None,
include_quote=True,
):
"""Uses the Data Loss Prevention API to analyze strings for protected data.
Args:
project: The Google Cloud project id to use as a parent resource.
content_string: The string to inspect.
info_types: A list of strings representing info types to look for.
A full list of info type categories can be fetched from the API.
min_likelihood: A string representing the minimum likelihood threshold
that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
max_findings: The maximum number of findings to report; 0 = no maximum.
include_quote: Boolean for whether to display a quote of the detected
information in the results.
Returns:
None; the response from the API is printed to the terminal.
"""
# Import the client library.
import google.cloud.dlp
# Instantiate a client.
dlp = google.cloud.dlp_v2.DlpServiceClient()
# Prepare info_types by converting the list of strings into a list of
# dictionaries (protos are also accepted).
info_types = [{"name": info_type} for info_type in info_types]
# Prepare custom_info_types by parsing the dictionary word lists and
# regex patterns.
if custom_dictionaries is None:
custom_dictionaries = []
dictionaries = [
{
"info_type": {"name": "CUSTOM_DICTIONARY_{}".format(i)},
"dictionary": {"word_list": {"words": custom_dict.split(",")}},
}
for i, custom_dict in enumerate(custom_dictionaries)
]
if custom_regexes is None:
custom_regexes = []
regexes = [
{
"info_type": {"name": "CUSTOM_REGEX_{}".format(i)},
"regex": {"pattern": custom_regex},
}
for i, custom_regex in enumerate(custom_regexes)
]
custom_info_types = dictionaries + regexes
# Construct the configuration dictionary. Keys which are None may
# optionally be omitted entirely.
inspect_config = {
"info_types": info_types,
"custom_info_types": custom_info_types,
"min_likelihood": min_likelihood,
"include_quote": include_quote,
"limits": {"max_findings_per_request": max_findings},
}
# Construct the `item`.
item = {"value": content_string}
# Convert the project id into a full resource id.
parent = dlp.project_path(project)
# Call the API.
response = dlp.inspect_content(parent, inspect_config, item)
# Print out the results.
if response.result.findings:
for finding in response.result.findings:
try:
if finding.quote:
print("Quote: {}".format(finding.quote))
except AttributeError:
pass
print("Info type: {}".format(finding.info_type.name))
print("Likelihood: {}".format(finding.likelihood))
else:
print("No findings.")
# [END dlp_inspect_string]
# [START dlp_inspect_table]
def inspect_table(
project,
data,
info_types,
custom_dictionaries=None,
custom_regexes=None,
min_likelihood=None,
max_findings=None,
include_quote=True,
):
"""Uses the Data Loss Prevention API to analyze strings for protected data.
Args:
project: The Google Cloud project id to use as a parent resource.
data: Json string representing table data.
info_types: A list of strings representing info types to look for.
A full list of info type categories can be fetched from the API.
min_likelihood: A string representing the minimum likelihood threshold
that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
max_findings: The maximum number of findings to report; 0 = no maximum.
include_quote: Boolean for whether to display a quote of the detected
information in the results.
Returns:
None; the response from the API is printed to the terminal.
Example:
data = {
"header":[
"email",
"phone number"
],
"rows":[
[
"robertfrost@xyz.com",
"4232342345"
],
[
"johndoe@pqr.com",
"4253458383"
]
]
}
>> $ python inspect_content.py table \
'{"header": ["email", "phone number"],
"rows": [["robertfrost@xyz.com", "4232342345"],
["johndoe@pqr.com", "4253458383"]]}'
>> Quote: robertfrost@xyz.com
Info type: EMAIL_ADDRESS
Likelihood: 4
Quote: johndoe@pqr.com
Info type: EMAIL_ADDRESS
Likelihood: 4
"""
# Import the client library.
import google.cloud.dlp
# Instantiate a client.
dlp = google.cloud.dlp_v2.DlpServiceClient()
# Prepare info_types by converting the list of strings into a list of
# dictionaries (protos are also accepted).
info_types = [{"name": info_type} for info_type in info_types]
# Prepare custom_info_types by parsing the dictionary word lists and
# regex patterns.
if custom_dictionaries is None:
custom_dictionaries = []
dictionaries = [
{
"info_type": {"name": "CUSTOM_DICTIONARY_{}".format(i)},
"dictionary": {"word_list": {"words": custom_dict.split(",")}},
}
for i, custom_dict in enumerate(custom_dictionaries)
]
if custom_regexes is None:
custom_regexes = []
regexes = [
{
"info_type": {"name": "CUSTOM_REGEX_{}".format(i)},
"regex": {"pattern": custom_regex},
}
for i, custom_regex in enumerate(custom_regexes)
]
custom_info_types = dictionaries + regexes
# Construct the configuration dictionary. Keys which are None may
# optionally be omitted entirely.
inspect_config = {
"info_types": info_types,
"custom_info_types": custom_info_types,
"min_likelihood": min_likelihood,
"include_quote": include_quote,
"limits": {"max_findings_per_request": max_findings},
}
# Construct the `table`. For more details on the table schema, please see
# https://cloud.google.com/dlp/docs/reference/rest/v2/ContentItem#Table
headers = [{"name": val} for val in data["header"]]
rows = []
for row in data["rows"]:
rows.append(
{"values": [{"string_value": cell_val} for cell_val in row]}
)
table = {}
table["headers"] = headers
table["rows"] = rows
item = {"table": table}
# Convert the project id into a full resource id.
parent = dlp.project_path(project)
# Call the API.
response = dlp.inspect_content(parent, inspect_config, item)
# Print out the results.
if response.result.findings:
for finding in response.result.findings:
try:
if finding.quote:
print("Quote: {}".format(finding.quote))
except AttributeError:
pass
print("Info type: {}".format(finding.info_type.name))
print("Likelihood: {}".format(finding.likelihood))
else:
print("No findings.")
# [END dlp_inspect_table]
# [START dlp_inspect_file]
def inspect_file(
project,
filename,
info_types,
min_likelihood=None,
custom_dictionaries=None,
custom_regexes=None,
max_findings=None,
include_quote=True,
mime_type=None,
):
"""Uses the Data Loss Prevention API to analyze a file for protected data.
Args:
project: The Google Cloud project id to use as a parent resource.
filename: The path to the file to inspect.
info_types: A list of strings representing info types to look for.
A full list of info type categories can be fetched from the API.
min_likelihood: A string representing the minimum likelihood threshold
that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
max_findings: The maximum number of findings to report; 0 = no maximum.
include_quote: Boolean for whether to display a quote of the detected
information in the results.
mime_type: The MIME type of the file. If not specified, the type is
inferred via the Python standard library's mimetypes module.
Returns:
None; the response from the API is printed to the terminal.
"""
import mimetypes
# Import the client library.
import google.cloud.dlp
# Instantiate a client.
dlp = google.cloud.dlp_v2.DlpServiceClient()
# Prepare info_types by converting the list of strings into a list of
# dictionaries (protos are also accepted).
if not info_types:
info_types = ["FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS"]
info_types = [{"name": info_type} for info_type in info_types]
# Prepare custom_info_types by parsing the dictionary word lists and
# regex patterns.
if custom_dictionaries is None:
custom_dictionaries = []
dictionaries = [
{
"info_type": {"name": "CUSTOM_DICTIONARY_{}".format(i)},
"dictionary": {"word_list": {"words": custom_dict.split(",")}},
}
for i, custom_dict in enumerate(custom_dictionaries)
]
if custom_regexes is None:
custom_regexes = []
regexes = [
{
"info_type": {"name": "CUSTOM_REGEX_{}".format(i)},
"regex": {"pattern": custom_regex},
}
for i, custom_regex in enumerate(custom_regexes)
]
custom_info_types = dictionaries + regexes
# Construct the configuration dictionary. Keys which are None may
# optionally be omitted entirely.
inspect_config = {
"info_types": info_types,
"custom_info_types": custom_info_types,
"min_likelihood": min_likelihood,
"limits": {"max_findings_per_request": max_findings},
}
# If mime_type is not specified, guess it from the filename.
if mime_type is None:
mime_guess = mimetypes.MimeTypes().guess_type(filename)
mime_type = mime_guess[0]
# Select the content type index from the list of supported types.
supported_content_types = {
None: 0, # "Unspecified"
"image/jpeg": 1,
"image/bmp": 2,
"image/png": 3,
"image/svg": 4,
"text/plain": 5,
}
content_type_index = supported_content_types.get(mime_type, 0)
# Construct the item, containing the file's byte data.
with open(filename, mode="rb") as f:
item = {"byte_item": {"type": content_type_index, "data": f.read()}}
# Convert the project id into a full resource id.
parent = dlp.project_path(project)
# Call the API.
response = dlp.inspect_content(parent, inspect_config, item)
# Print out the results.
if response.result.findings:
for finding in response.result.findings:
try:
print("Quote: {}".format(finding.quote))
except AttributeError:
pass
print("Info type: {}".format(finding.info_type.name))
print("Likelihood: {}".format(finding.likelihood))
else:
print("No findings.")
# [END dlp_inspect_file]
# [START dlp_inspect_gcs]
def inspect_gcs_file(
project,
bucket,
filename,
topic_id,
subscription_id,
info_types,
custom_dictionaries=None,
custom_regexes=None,
min_likelihood=None,
max_findings=None,
timeout=300,
):
"""Uses the Data Loss Prevention API to analyze a file on GCS.
Args:
project: The Google Cloud project id to use as a parent resource.
bucket: The name of the GCS bucket containing the file, as a string.
filename: The name of the file in the bucket, including the path, as a
string; e.g. 'images/myfile.png'.
topic_id: The id of the Cloud Pub/Sub topic to which the API will
broadcast job completion. The topic must already exist.
subscription_id: The id of the Cloud Pub/Sub subscription to listen on
while waiting for job completion. The subscription must already
exist and be subscribed to the topic.
info_types: A list of strings representing info types to look for.
A full list of info type categories can be fetched from the API.
min_likelihood: A string representing the minimum likelihood threshold
that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
max_findings: The maximum number of findings to report; 0 = no maximum.
timeout: The number of seconds to wait for a response from the API.
Returns:
None; the response from the API is printed to the terminal.
"""
# Import the client library.
import google.cloud.dlp
# This sample additionally uses Cloud Pub/Sub to receive results from
# potentially long-running operations.
import google.cloud.pubsub
# This sample also uses threading.Event() to wait for the job to finish.
import threading
# Instantiate a client.
dlp = google.cloud.dlp_v2.DlpServiceClient()
# Prepare info_types by converting the list of strings into a list of
# dictionaries (protos are also accepted).
if not info_types:
info_types = ["FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS"]
info_types = [{"name": info_type} for info_type in info_types]
# Prepare custom_info_types by parsing the dictionary word lists and
# regex patterns.
if custom_dictionaries is None:
custom_dictionaries = []
dictionaries = [
{
"info_type": {"name": "CUSTOM_DICTIONARY_{}".format(i)},
"dictionary": {"word_list": {"words": custom_dict.split(",")}},
}
for i, custom_dict in enumerate(custom_dictionaries)
]
if custom_regexes is None:
custom_regexes = []
regexes = [
{
"info_type": {"name": "CUSTOM_REGEX_{}".format(i)},
"regex": {"pattern": custom_regex},
}
for i, custom_regex in enumerate(custom_regexes)
]
custom_info_types = dictionaries + regexes
# Construct the configuration dictionary. Keys which are None may
# optionally be omitted entirely.
inspect_config = {
"info_types": info_types,
"custom_info_types": custom_info_types,
"min_likelihood": min_likelihood,
"limits": {"max_findings_per_request": max_findings},
}
# Construct a storage_config containing the file's URL.
url = "gs://{}/{}".format(bucket, filename)
storage_config = {"cloud_storage_options": {"file_set": {"url": url}}}
# Convert the project id into full resource ids.
topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
parent = dlp.location_path(project, 'global')
# Tell the API where to send a notification when the job is complete.
actions = [{"pub_sub": {"topic": topic}}]
# Construct the inspect_job, which defines the entire inspect content task.
inspect_job = {
"inspect_config": inspect_config,
"storage_config": storage_config,
"actions": actions,
}
operation = dlp.create_dlp_job(parent, inspect_job=inspect_job)
print("Inspection operation started: {}".format(operation.name))
# Create a Pub/Sub client and find the subscription. The subscription is
# expected to already be listening to the topic.
subscriber = google.cloud.pubsub.SubscriberClient()
subscription_path = subscriber.subscription_path(project, subscription_id)
# Set up a callback to acknowledge a message. This closes around an event
# so that it can signal that it is done and the main thread can continue.
job_done = threading.Event()
def callback(message):
try:
if message.attributes["DlpJobName"] == operation.name:
# This is the message we're looking for, so acknowledge it.
message.ack()
# Now that the job is done, fetch the results and print them.
job = dlp.get_dlp_job(operation.name)
if job.inspect_details.result.info_type_stats:
for finding in job.inspect_details.result.info_type_stats:
print(
"Info type: {}; Count: {}".format(
finding.info_type.name, finding.count
)
)
else:
print("No findings.")
# Signal to the main thread that we can exit.
job_done.set()
else:
# This is not the message we're looking for.
message.drop()
except Exception as e:
# Because this is executing in a thread, an exception won't be
# noted unless we print it manually.
print(e)
raise
subscriber.subscribe(subscription_path, callback=callback)
finished = job_done.wait(timeout=timeout)
if not finished:
print(
"No event received before the timeout. Please verify that the "
"subscription provided is subscribed to the topic provided."
)
# [END dlp_inspect_gcs]
# [START dlp_inspect_datastore]
def inspect_datastore(
project,
datastore_project,
kind,
topic_id,
subscription_id,
info_types,
custom_dictionaries=None,
custom_regexes=None,
namespace_id=None,
min_likelihood=None,
max_findings=None,
timeout=300,
):
"""Uses the Data Loss Prevention API to analyze Datastore data.
Args:
project: The Google Cloud project id to use as a parent resource.
datastore_project: The Google Cloud project id of the target Datastore.
kind: The kind of the Datastore entity to inspect, e.g. 'Person'.
topic_id: The id of the Cloud Pub/Sub topic to which the API will
broadcast job completion. The topic must already exist.
subscription_id: The id of the Cloud Pub/Sub subscription to listen on
while waiting for job completion. The subscription must already
exist and be subscribed to the topic.
info_types: A list of strings representing info types to look for.
A full list of info type categories can be fetched from the API.
namespace_id: The namespace of the Datastore document, if applicable.
min_likelihood: A string representing the minimum likelihood threshold
that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
max_findings: The maximum number of findings to report; 0 = no maximum.
timeout: The number of seconds to wait for a response from the API.
Returns:
None; the response from the API is printed to the terminal.
"""
# Import the client library.
import google.cloud.dlp
# This sample additionally uses Cloud Pub/Sub to receive results from
# potentially long-running operations.
import google.cloud.pubsub
# This sample also uses threading.Event() to wait for the job to finish.
import threading
# Instantiate a client.
dlp = google.cloud.dlp_v2.DlpServiceClient()
# Prepare info_types by converting the list of strings into a list of
# dictionaries (protos are also accepted).
if not info_types:
info_types = ["FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS"]
info_types = [{"name": info_type} for info_type in info_types]
# Prepare custom_info_types by parsing the dictionary word lists and
# regex patterns.
if custom_dictionaries is None:
custom_dictionaries = []
dictionaries = [
{
"info_type": {"name": "CUSTOM_DICTIONARY_{}".format(i)},
"dictionary": {"word_list": {"words": custom_dict.split(",")}},
}
for i, custom_dict in enumerate(custom_dictionaries)
]
if custom_regexes is None:
custom_regexes = []
regexes = [
{
"info_type": {"name": "CUSTOM_REGEX_{}".format(i)},
"regex": {"pattern": custom_regex},
}
for i, custom_regex in enumerate(custom_regexes)
]
custom_info_types = dictionaries + regexes
# Construct the configuration dictionary. Keys which are None may
# optionally be omitted entirely.
inspect_config = {
"info_types": info_types,
"custom_info_types": custom_info_types,
"min_likelihood": min_likelihood,
"limits": {"max_findings_per_request": max_findings},
}
# Construct a storage_config containing the target Datastore info.
storage_config = {
"datastore_options": {
"partition_id": {
"project_id": datastore_project,
"namespace_id": namespace_id,
},
"kind": {"name": kind},
}
}
# Convert the project id into full resource ids.
topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
parent = dlp.location_path(project, 'global')
# Tell the API where to send a notification when the job is complete.
actions = [{"pub_sub": {"topic": topic}}]
# Construct the inspect_job, which defines the entire inspect content task.
inspect_job = {
"inspect_config": inspect_config,
"storage_config": storage_config,
"actions": actions,
}
operation = dlp.create_dlp_job(parent, inspect_job=inspect_job)
print("Inspection operation started: {}".format(operation.name))
# Create a Pub/Sub client and find the subscription. The subscription is
# expected to already be listening to the topic.
subscriber = google.cloud.pubsub.SubscriberClient()
subscription_path = subscriber.subscription_path(project, subscription_id)
# Set up a callback to acknowledge a message. This closes around an event
# so that it can signal that it is done and the main thread can continue.
job_done = threading.Event()
def callback(message):
try:
if message.attributes["DlpJobName"] == operation.name:
# This is the message we're looking for, so acknowledge it.
message.ack()
# Now that the job is done, fetch the results and print them.
job = dlp.get_dlp_job(operation.name)
if job.inspect_details.result.info_type_stats:
for finding in job.inspect_details.result.info_type_stats:
print(
"Info type: {}; Count: {}".format(
finding.info_type.name, finding.count
)
)
else:
print("No findings.")
# Signal to the main thread that we can exit.
job_done.set()
else:
# This is not the message we're looking for.
message.drop()
except Exception as e:
# Because this is executing in a thread, an exception won't be
# noted unless we print it manually.
print(e)
raise
# Register the callback and wait on the event.
subscriber.subscribe(subscription_path, callback=callback)
finished = job_done.wait(timeout=timeout)
if not finished:
print(
"No event received before the timeout. Please verify that the "
"subscription provided is subscribed to the topic provided."
)
# [END dlp_inspect_datastore]
# [START dlp_inspect_bigquery]
def inspect_bigquery(
project,
bigquery_project,
dataset_id,
table_id,
topic_id,
subscription_id,
info_types,
custom_dictionaries=None,
custom_regexes=None,
min_likelihood=None,
max_findings=None,
timeout=300,
):
"""Uses the Data Loss Prevention API to analyze BigQuery data.
Args:
project: The Google Cloud project id to use as a parent resource.
bigquery_project: The Google Cloud project id of the target table.
dataset_id: The id of the target BigQuery dataset.
table_id: The id of the target BigQuery table.
topic_id: The id of the Cloud Pub/Sub topic to which the API will
broadcast job completion. The topic must already exist.
subscription_id: The id of the Cloud Pub/Sub subscription to listen on
while waiting for job completion. The subscription must already
exist and be subscribed to the topic.
info_types: A list of strings representing info types to look for.
A full list of info type categories can be fetched from the API.
namespace_id: The namespace of the Datastore document, if applicable.
min_likelihood: A string representing the minimum likelihood threshold
that constitutes a match. One of: 'LIKELIHOOD_UNSPECIFIED',
'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE', 'LIKELY', 'VERY_LIKELY'.
max_findings: The maximum number of findings to report; 0 = no maximum.
timeout: The number of seconds to wait for a response from the API.
Returns:
None; the response from the API is printed to the terminal.
"""
# Import the client library.
import google.cloud.dlp
# This sample additionally uses Cloud Pub/Sub to receive results from
# potentially long-running operations.
import google.cloud.pubsub
# This sample also uses threading.Event() to wait for the job to finish.
import threading
# Instantiate a client.
dlp = google.cloud.dlp_v2.DlpServiceClient()
# Prepare info_types by converting the list of strings into a list of
# dictionaries (protos are also accepted).
if not info_types:
info_types = ["FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS"]
info_types = [{"name": info_type} for info_type in info_types]
# Prepare custom_info_types by parsing the dictionary word lists and
# regex patterns.
if custom_dictionaries is None:
custom_dictionaries = []
dictionaries = [
{
"info_type": {"name": "CUSTOM_DICTIONARY_{}".format(i)},
"dictionary": {"word_list": {"words": custom_dict.split(",")}},
}
for i, custom_dict in enumerate(custom_dictionaries)
]
if custom_regexes is None:
custom_regexes = []
regexes = [
{
"info_type": {"name": "CUSTOM_REGEX_{}".format(i)},
"regex": {"pattern": custom_regex},
}
for i, custom_regex in enumerate(custom_regexes)
]
custom_info_types = dictionaries + regexes
# Construct the configuration dictionary. Keys which are None may
# optionally be omitted entirely.
inspect_config = {
"info_types": info_types,
"custom_info_types": custom_info_types,
"min_likelihood": min_likelihood,
"limits": {"max_findings_per_request": max_findings},
}
# Construct a storage_config containing the target Bigquery info.
storage_config = {
"big_query_options": {
"table_reference": {
"project_id": bigquery_project,
"dataset_id": dataset_id,
"table_id": table_id,
}
}
}
# Convert the project id into full resource ids.
topic = google.cloud.pubsub.PublisherClient.topic_path(project, topic_id)
parent = dlp.location_path(project, 'global')
# Tell the API where to send a notification when the job is complete.
actions = [{"pub_sub": {"topic": topic}}]
# Construct the inspect_job, which defines the entire inspect content task.
inspect_job = {
"inspect_config": inspect_config,
"storage_config": storage_config,
"actions": actions,
}
operation = dlp.create_dlp_job(parent, inspect_job=inspect_job)
print("Inspection operation started: {}".format(operation.name))
# Create a Pub/Sub client and find the subscription. The subscription is
# expected to already be listening to the topic.
subscriber = google.cloud.pubsub.SubscriberClient()
subscription_path = subscriber.subscription_path(project, subscription_id)
# Set up a callback to acknowledge a message. This closes around an event
# so that it can signal that it is done and the main thread can continue.
job_done = threading.Event()
def callback(message):
try:
if message.attributes["DlpJobName"] == operation.name:
# This is the message we're looking for, so acknowledge it.
message.ack()
# Now that the job is done, fetch the results and print them.
job = dlp.get_dlp_job(operation.name)
if job.inspect_details.result.info_type_stats:
for finding in job.inspect_details.result.info_type_stats:
print(
"Info type: {}; Count: {}".format(
finding.info_type.name, finding.count
)
)
else:
print("No findings.")
# Signal to the main thread that we can exit.
job_done.set()
else:
# This is not the message we're looking for.
message.drop()
except Exception as e:
# Because this is executing in a thread, an exception won't be
# noted unless we print it manually.
print(e)
raise
# Register the callback and wait on the event.
subscriber.subscribe(subscription_path, callback=callback)
finished = job_done.wait(timeout=timeout)
if not finished:
print(
"No event received before the timeout. Please verify that the "
"subscription provided is subscribed to the topic provided."
)
# [END dlp_inspect_bigquery]
if __name__ == "__main__":
default_project = os.environ.get("GOOGLE_CLOUD_PROJECT")
parser = argparse.ArgumentParser(description=__doc__)
subparsers = parser.add_subparsers(
dest="content", help="Select how to submit content to the API."
)
subparsers.required = True
parser_string = subparsers.add_parser("string", help="Inspect a string.")
parser_string.add_argument("item", help="The string to inspect.")
parser_string.add_argument(
"--project",
help="The Google Cloud project id to use as a parent resource.",
default=default_project,
)
parser_string.add_argument(
"--info_types",
nargs="+",
help="Strings representing info types to look for. A full list of "
"info categories and types is available from the API. Examples "
'include "FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS". '
"If unspecified, the three above examples will be used.",
default=["FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS"],
)
parser_string.add_argument(
"--custom_dictionaries",
action="append",
help="Strings representing comma-delimited lists of dictionary words"
" to search for as custom info types. Each string is a comma "
"delimited list of words representing a distinct dictionary.",
default=None,
)
parser_string.add_argument(
"--custom_regexes",
action="append",
help="Strings representing regex patterns to search for as custom "
" info types.",
default=None,
)
parser_string.add_argument(
"--min_likelihood",
choices=[
"LIKELIHOOD_UNSPECIFIED",
"VERY_UNLIKELY",
"UNLIKELY",
"POSSIBLE",
"LIKELY",
"VERY_LIKELY",
],
help="A string representing the minimum likelihood threshold that "
"constitutes a match.",
)
parser_string.add_argument(
"--max_findings",
type=int,
help="The maximum number of findings to report; 0 = no maximum.",
)
parser_string.add_argument(
"--include_quote",
type=bool,
help="A boolean for whether to display a quote of the detected "
"information in the results.",
default=True,
)
parser_table = subparsers.add_parser("table", help="Inspect a table.")
parser_table.add_argument(
"data", help="Json string representing a table.", type=json.loads
)
parser_table.add_argument(
"--project",
help="The Google Cloud project id to use as a parent resource.",
default=default_project,
)
parser_table.add_argument(
"--info_types",
action="append",
help="Strings representing info types to look for. A full list of "
"info categories and types is available from the API. Examples "
'include "FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS". '
"If unspecified, the three above examples will be used.",
default=["FIRST_NAME", "LAST_NAME", "EMAIL_ADDRESS"],
)
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