{"action":"created","comment":{"url":"https://api.github.com/repos/GoogleCloudPlatform/professional-services/pulls/comments/193257578","pull_request_review_id":126184363,"id":193257578,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDE5MzI1NzU3OA==","diff_hunk":"@@ -0,0 +1,485 @@\n+# Copyright 2017 Google Inc.\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+#      http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+\"\"\"\n+data_generation_for_benchmarking.py is a Dataflow pipeline which reads\n+a schema to simulate or \"fake\" data from a json file and writes\n+random data of the schema's shape to a BigQuery table. This can be used\n+to ease apprehension about BQ costs, unblock integration testing before\n+real data can be provided by the business, or create dummy datasets for\n+stress testing in the event of large data growth.\n+\"\"\"\n+\n+from __future__ import absolute_import\n+import argparse\n+import datetime\n+import json\n+import logging\n+import math\n+import numpy as np\n+\n+import apache_beam as beam\n+from apache_beam.options.pipeline_options import PipelineOptions\n+from faker import Faker\n+from faker_schema.faker_schema import FakerSchema\n+from google.cloud import bigquery as bq\n+from google.cloud import storage as gcs\n+from google.cloud.exceptions import NotFound\n+\n+\n+class DataGenerator(object):\n+    \"\"\"\n+    A class which contains the logic for data generation.\n+\n+    Attributes:\n+        null_prob: A float specifying the desired sparsity of the generated data.\n+        n_keys: An integer specifying the cardinality of foreign key columns\n+                (for generating joinable schemas).\n+        min_date: A datetime.date object specifying the earliest date to generate.\n+        max_date: A datetime.date object specifying the latest date to generate.\n+        only_pos: A boolean specifying whether to allow negative numbers to be generated.\n+        max_int: An integer defining the upper bound for the range of integers to generate.\n+        max_float: A float specifying the upper bound for the range of floats to generate.\n+        float_precision: An integer specifying the desired precision for generated floats.\n+\n+    \"\"\"\n+    def __init__(self, bq_schema_filename=None, input_bq_table=None, p_null=0.1, n_keys=1000,\n+                 min_date='2000-01-01', max_date=datetime.date.today().strftime('%Y-%m-%d'),\n+                 only_pos=True, max_int=10**11, max_float=float(10**11), float_precision=2,\n+                 write_disp='WRITE_APPEND'):\n+        \"\"\"\n+        Args:\n+            bq_schema_filename: A string containing a path to a local or gcs file containing a\n+                                BigQuery schema in a json file.\n+            p_null: A float specifying the desired sparsity of the generated data.\n+            n_keys: An integer specifying the cardinality of foreign key columns\n+                    (for generating joinable schemas).\n+            min_date: A datetime.date object specifying the earliest date to generate.\n+            max_date: A datetime.date object specifying the latest date to generate.\n+            only_pos: A boolean specifying whether to allow negative numbers to be generated.\n+            max_int: An integer defining the upper bound for the range of integers to generate.\n+            max_float: A float specifying the upper bound for the range of floats to generate.\n+            float_precision: An integer specifying the desired precision for generated floats.\n+        \"\"\"\n+        if bq_schema_filename is not None:\n+            try:\n+                # Handles json from google cloud storage or local.\n+                if bq_schema_filename.find('gs://') == 0:\n+                    bkt, path = bq_schema_filename.strip('gs://').split('/', 1)\n+                    client = gcs.Client()\n+                    bucket = client.get_bucket(bkt)\n+\n+                    blob = bucket.get_blob(path)\n+                    self.schema = json.loads(blob.download_as_string())\n+                else:\n+                    with open(bq_schema_filename, 'r') as json_file:\n+                        self.schema = json.load(json_file)\n+            except ValueError:\n+                logging.error(\"Not a valid json file! \\n %s\", str(ValueError))\n+            except AttributeError:\n+                logging.error(\"Could not find gcs file %s\", str(bq_schema_filename))\n+        elif input_bq_table is not None:\n+            bq_cli = bq.Client()\n+\n+            dataset_name, table_name = input_bq_table.split('.')\n+            bq_dataset = bq_cli.dataset(dataset_name)\n+            bq_table_ref = bq_dataset.table(table_name)  # This forms a TableReference object.\n+            bq_table = bq_cli.get_table(bq_table_ref)    # Errors out if table doesn't exist.\n+\n+            # Quickly parse TableSchema object to list of dictionaries.\n+            self.schema = [\n+                {u'name': field.name,\n+                 u'type': field.field_type,\n+                 u'mode': field.mode\n+                }\n+                for field in bq_table.schema\n+            ]\n+\n+        self.null_prob = float(p_null)\n+        self.n_keys = int(n_keys)\n+        self.min_date = datetime.datetime.strptime(min_date, \"%Y-%m-%d\").date()\n+        self.max_date = datetime.datetime.strptime(max_date, \"%Y-%m-%d\").date()\n+        self.only_pos = bool(only_pos)\n+        self.max_int = int(max_int)\n+        self.max_float = float(max_float)\n+        self.float_precision = int(float_precision)\n+\n+        # Map the passed string representation of the desired disposition.\n+        # This will force early error if invalid write disposition.\n+        write_disp_map = {\n+            'WRITE_APPEND': beam.io.BigQueryDisposition.WRITE_APPEND,\n+            'WRITE_EMPTY': beam.io.BigQueryDisposition.WRITE_EMPTY,\n+            'WRITE_TRUNCATE': beam.io.BigQueryDisposition.WRITE_TRUNCATE\n+        }\n+\n+        self.write_disp = write_disp_map[write_disp]\n+\n+    def get_bq_schema_string(self):\n+        \"\"\"\n+        This helper function parses a 'FIELDNAME:DATATYPE' string for the BQ api.\n+        \"\"\"\n+        schema_string = ','.join([str(obj[u'name']) + ':' + str(obj[u'type'])\n+                                  for obj in self.schema])\n+        return schema_string\n+\n+    def get_faker_schema(self):\n+        \"\"\"\n+        This function casts the BigQuery schema to one that will be understood by Faker.\n+\n+        Returns:\n+            faker_schema: A dictionary mapping field names to Faker providers.\n+        \"\"\"\n+        # Parse faker_schema out of the DataGenerator object's schema.\n+        type_map = {\n+            'FLOAT': 'pyfloat',\n+            'INTEGER': 'random_number',\n+            'STRING': 'word',\n+            'DATE': 'date_this_century',\n+            'DATETIME': 'date_time_this_century',\n+            'BOOLEAN': 'boolean',\n+            'TIMESTAMP': 'date_time_this_century',\n+            'RECORD': 'pystruct',\n+            'ARRAY': 'pylist',\n+            'TIME': 'time',\n+            'BYTES': 'pystr'\n+        }\n+\n+        # Use more specific Faker providers by looking for these keys as a substring of the\n+        # field name in the schema.\n+        # (See documention at https://faker.readthedocs.io/en/latest/providers.html ).\n+        special_map = {\n+            'address': 'address',\n+            'file': 'file_name',\n+            'color': 'color_name',\n+            'zip': 'zipcode',\n+            'phone': 'phone_number',\n+            'name': 'name',\n+            'company': 'company',\n+            'month': 'month',\n+            'city': 'city',\n+            'state': 'state',\n+            'country': 'country',\n+            'username': 'user_name',\n+            'email': 'email',\n+            'num': 'random_number',\n+            'description': 'paragraph'\n+        }\n+\n+        faker_schema = {}\n+        for obj in self.schema:\n+            is_special = False\n+            for key in special_map:\n+                if key.lower() in obj['name'].lower():\n+                    faker_schema[obj['name']] = special_map[key]\n+                    is_special = True\n+                    break\n+            if not is_special:\n+                faker_schema[obj['name']] = type_map[obj['type']]\n+\n+        return faker_schema\n+\n+\n+class FakeRowGen(beam.DoFn):\n+    \"\"\"\n+    This class wraps the logic defined in DataGenerator object and generates a fake record for each\n+    element it is passed.\n+    \"\"\"\n+    def __init__(self, data_gen):\n+        \"\"\"\n+        This initiates some properties of the FakeRowGen DoFn including an instance of the\n+        DataGenerator class and the number of records should be generated for each element in the\n+        prior PCollection.\n+\n+        Attributes:\n+            data_gen: A DataGenerator object defining the shape of the data should be generated by\n+                      this DoFn.\n+            n: An integer specifying how many records to generate.\n+        \"\"\"\n+        self.data_gen = data_gen\n+\n+    # Helper function to get a single field dictionary from the schema for checking type and mode.\n+\n+    def get_field_dict(self, field_name):\n+        return filter(lambda f: f[u'name'] == field_name,\n+                      self.data_gen.schema)[0]\n+\n+    def sanity_check(self, record, fieldname):\n+        \"\"\"\n+        This function ensures that the data is all of types that BigQuery expects.\n+\n+        Args:\n+            record: A dictionary generated by faker_schema representing the candidate for a fake\n+                    row in our BigQuery table\n+            fieldname: name of field we are checking with this call.\n+        \"\"\"\n+        # Create a Faker instance for individual parameterized random generation\n+        # (ie. minimum date).\n+        faker = Faker()\n+\n+        field = self.get_field_dict(fieldname)\n+        # Below handles if the datatype got changed by the faker provider\n+        if field[u'type'] == 'STRING' or field[u'type'].find('TIME') > -1:","path":"data-analytics/dataflow-python-examples/dataflow_python_examples/data_generation_for_benchmarking.py","position":232,"original_position":232,"commit_id":"39dcbff607cb9b089dfbee6672991c77943c0d96","original_commit_id":"39dcbff607cb9b089dfbee6672991c77943c0d96","user":{"login":"dparrish","id":193603,"node_id":"MDQ6VXNlcjE5MzYwMw==","avatar_url":"https://avatars3.githubusercontent.com/u/193603?v=4","gravatar_id":"","url":"https://api.github.com/users/dparrish","html_url":"https://github.com/dparrish","followers_url":"https://api.github.com/users/dparrish/followers","following_url":"https://api.github.com/users/dparrish/following{/other_user}","gists_url":"https://api.github.com/users/dparrish/gists{/gist_id}","starred_url":"https://api.github.com/users/dparrish/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dparrish/subscriptions","organizations_url":"https://api.github.com/users/dparrish/orgs","repos_url":"https://api.github.com/users/dparrish/repos","events_url":"https://api.github.com/users/dparrish/events{/privacy}","received_events_url":"https://api.github.com/users/dparrish/received_events","type":"User","site_admin":false},"body":"I'd suggest using `in` instead of `find`:\r\n\r\n```\r\n... or 'TIME' in field['type']\r\n```\r\n","created_at":"2018-06-06T00:01:31Z","updated_at":"2018-06-06T00:38:06Z","html_url":"https://github.com/GoogleCloudPlatform/professional-services/pull/36#discussion_r193257578","pull_request_url":"https://api.github.com/repos/GoogleCloudPlatform/professional-services/pulls/36","author_association":"NONE","_links":{"self":{"href":"https://api.github.com/repos/GoogleCloudPlatform/professional-services/pulls/comments/193257578"},"html":{"href":"https://github.com/GoogleCloudPlatform/professional-services/pull/36#discussion_r193257578"},"pull_request":{"href":"https://api.github.com/repos/GoogleCloudPlatform/professional-services/pulls/36"}}},"pull_request":{"url":"https://api.github.com/repos/GoogleCloudPlatform/professional-services/pulls/36","id":187930052,"node_id":"MDExOlB1bGxSZXF1ZXN0MTg3OTMwMDUy","html_url":"https://github.com/GoogleCloudPlatform/professional-services/pull/36","diff_url":"https://github.com/GoogleCloudPlatform/professional-services/pull/36.diff","patch_url":"https://github.com/GoogleCloudPlatform/professional-services/pull/36.patch","issue_url":"https://api.github.com/repos/GoogleCloudPlatform/professional-services/issues/36","number":36,"state":"open","locked":false,"title":"Data Generator and Image Labeler Pipelines","user":{"login":"joshmcginley","id":8662458,"node_id":"MDQ6VXNlcjg2NjI0NTg=","avatar_url":"https://avatars3.githubusercontent.com/u/8662458?v=4","gravatar_id":"","url":"https://api.github.com/users/joshmcginley","html_url":"https://github.com/joshmcginley","followers_url":"https://api.github.com/users/joshmcginley/followers","following_url":"https://api.github.com/users/joshmcginley/following{/other_user}","gists_url":"https://api.github.com/users/joshmcginley/gists{/gist_id}","starred_url":"https://api.github.com/users/joshmcginley/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/joshmcginley/subscriptions","organizations_url":"https://api.github.com/users/joshmcginley/orgs","repos_url":"https://api.github.com/users/joshmcginley/repos","events_url":"https://api.github.com/users/joshmcginley/events{/privacy}","received_events_url":"https://api.github.com/users/joshmcginley/received_events","type":"User","site_admin":false},"body":"Add two new pipelines to our Python Dataflow examples:\r\n\r\n- A data generator used to generate fake data in BigQuery.  This is useful for quickly estimating costs and testing performance before uploading real data.  \r\n- An image labeler showing an example of using the ML Vision API.","created_at":"2018-05-14T19:42:01Z","updated_at":"2018-06-06T00:38:06Z","closed_at":null,"merged_at":null,"merge_commit_sha":"3ec39d5f865d8e3253848769b320d30bb94391b5","assignee":null,"assignees":[],"requested_reviewers":[],"requested_teams":[],"labels":[],"milestone":null,"commits_url":"https://api.github.com/repos/GoogleCloudPlatform/professional-services/pulls/36/commits","review_comments_url":"https://api.github.com/repos/GoogleCloudPlatform/professional-services/pulls/36/comments","review_comment_url":"https://api.github.com/repos/GoogleCloudPlatform/professional-services/pulls/comments{/number}","comments_url":"https://api.github.com/repos/GoogleCloudPlatform/professional-services/issues/36/comments","statuses_url":"https://api.github.com/repos/GoogleCloudPlatform/professional-services/statuses/39dcbff607cb9b089dfbee6672991c77943c0d96","head":{"label":"joshmcginley:master","ref":"master","sha":"39dcbff607cb9b089dfbee6672991c77943c0d96","user":{"login":"joshmcginley","id":8662458,"node_id":"MDQ6VXNlcjg2NjI0NTg=","avatar_url":"https://avatars3.githubusercontent.com/u/8662458?v=4","gravatar_id":"","url":"https://api.github.com/users/joshmcginley","html_url":"https://github.com/joshmcginley","followers_url":"https://api.github.com/users/joshmcginley/followers","following_url":"https://api.github.com/users/joshmcginley/following{/other_user}","gists_url":"https://api.github.com/users/joshmcginley/gists{/gist_id}","starred_url":"https://api.github.com/users/joshmcginley/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/joshmcginley/subscriptions","organizations_url":"https://api.github.com/users/joshmcginley/orgs","repos_url":"https://api.github.com/users/joshmcginley/repos","events_url":"https://api.github.com/users/joshmcginley/events{/privacy}","received_events_url":"https://api.github.com/users/joshmcginley/received_events","type":"User","site_admin":false},"repo":{"id":113065220,"node_id":"MDEwOlJlcG9zaXRvcnkxMTMwNjUyMjA=","name":"professional-services","full_name":"joshmcginley/professional-services","owner":{"login":"joshmcginley","id":8662458,"node_id":"MDQ6VXNlcjg2NjI0NTg=","avatar_url":"https://avatars3.githubusercontent.com/u/8662458?v=4","gravatar_id":"","url":"https://api.github.com/users/joshmcginley","html_url":"https://github.com/joshmcginley","followers_url":"https://api.github.com/users/joshmcginley/followers","following_url":"https://api.github.com/users/joshmcginley/following{/other_user}","gists_url":"https://api.github.com/users/joshmcginley/gists{/gist_id}","starred_url":"https://api.github.com/users/joshmcginley/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/joshmcginley/subscriptions","organizations_url":"https://api.github.com/users/joshmcginley/orgs","repos_url":"https://api.github.com/users/joshmcginley/repos","events_url":"https://api.github.com/users/joshmcginley/events{/privacy}","received_events_url":"https://api.github.com/users/joshmcginley/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/joshmcginley/professional-services","description":null,"fork":true,"url":"https://api.github.com/repos/joshmcginley/professional-services","forks_url":"https://api.github.com/repos/joshmcginley/professional-services/forks","keys_url":"https://api.github.com/repos/joshmcginley/professional-services/keys{/key_id}","collaborators_url":"https://api.github.com/repos/joshmcginley/professional-services/collaborators{/collaborator}","teams_url":"https://api.github.com/repos/joshmcginley/professional-services/teams","hooks_url":"https://api.github.com/repos/joshmcginley/professional-services/hooks","issue_events_url":"https://api.github.com/repos/joshmcginley/professional-services/issues/events{/number}","events_url":"https://api.github.com/repos/joshmcginley/professional-services/events","assignees_url":"https://api.github.com/repos/joshmcginley/professional-services/assignees{/user}","branches_url":"https://api.github.com/repos/joshmcginley/professional-services/branches{/branch}","tags_url":"https://api.github.com/repos/joshmcginley/professional-services/tags","blobs_url":"https://api.github.com/repos/joshmcginley/professional-services/git/blobs{/sha}","git_tags_url":"https://api.github.com/repos/joshmcginley/professional-services/git/tags{/sha}","git_refs_url":"https://api.github.com/repos/joshmcginley/professional-services/git/refs{/sha}","trees_url":"https://api.github.com/repos/joshmcginley/professional-services/git/trees{/sha}","statuses_url":"https://api.github.com/repos/joshmcginley/professional-services/statuses/{sha}","languages_url":"https://api.github.com/repos/joshmcginley/professional-services/languages","stargazers_url":"https://api.github.com/repos/joshmcginley/professional-services/stargazers","contributors_url":"https://api.github.com/repos/joshmcginley/professional-services/contributors","subscribers_url":"https://api.github.com/repos/joshmcginley/professional-services/subs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