├── .gitignore ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── images └── diagram.png └── stackdriverdataflowbigquery.py /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # How to Contribute 2 | 3 | We'd love to accept your patches and contributions to this project. There are 4 | just a few small guidelines you need to follow. 5 | 6 | ## Contributor License Agreement 7 | 8 | Contributions to this project must be accompanied by a Contributor License 9 | Agreement (CLA). You (or your employer) retain the copyright to your 10 | contribution; this simply gives us permission to use and redistribute your 11 | contributions as part of the project. 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We also recommend that a 186 | file or class name and description of purpose be included on the 187 | same "printed page" as the copyright notice for easier 188 | identification within third-party archives. 189 | 190 | Copyright [yyyy] [name of copyright owner] 191 | 192 | Licensed under the Apache License, Version 2.0 (the "License"); 193 | you may not use this file except in compliance with the License. 194 | You may obtain a copy of the License at 195 | 196 | http://www.apache.org/licenses/LICENSE-2.0 197 | 198 | Unless required by applicable law or agreed to in writing, software 199 | distributed under the License is distributed on an "AS IS" BASIS, 200 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 201 | See the License for the specific language governing permissions and 202 | limitations under the License. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Dialogflow Log Parser 2 | 3 | This repository contains an example of how to leverage Cloud Dataflow and BigQuery to view Dialogflow interactions. 4 | 5 | The Pipeline Steps are as follows: 6 | 7 | 1. Dialogflow Interactions are logged to Google Cloud Logging 8 | 2. A Cloud Logging sink sends the log messages to Cloud Pub/Sub 9 | 3. Dataflow process the __textpayload__ and streams it to BigQuery 10 | 4. Access to the log interactions are now available in BigQuery 11 | 12 | ![Dialogflow Log Parser Diagram](images/diagram.png) 13 | 14 | __Note:__ Dialogflow Interactions Logging is sent to Cloud Logging as a Text Payload, this code will parse the Text Payload to a structured format within BigQuery which is defined in the Dataflow code. 15 | 16 | ## BigQuery Schema 17 | You can change the schema as required in the Dataflow code to include other key:value pairs extracted from Cloud Logging. Here is a reference to the current schema: 18 | 19 | | Field name | Type | 20 | | ----------- | ----------- | 21 | | session_id | STRING | 22 | | trace | STRING | 23 | | caller_id | STRING | 24 | | email | STRING | 25 | | timestamp | TIMESTAMP | 26 | | receiveTimestamp | TIMESTAMP | 27 | | resolved_query | STRING | 28 | | string_value | STRING | 29 | | speech | STRING | 30 | | is_fallback_intent | STRING | 31 | | webhook_for_slot_filling_used | STRING | 32 | | webhook_used | STRING | 33 | | intent_name | STRING | 34 | | intent_id | STRING | 35 | | action | STRING | 36 | | source | STRING | 37 | | error_type | STRING | 38 | | code | STRING | 39 | | insertId | STRING | 40 | | logName | STRING | 41 | | lang | STRING | 42 | | textPayload | STRING | 43 | 44 | ## Installation 45 | 46 | 1. Enable the Dataflow API 47 | ```sh 48 | gcloud services enable dataflow 49 | ``` 50 | 51 | 2. Create a Storage Bucket for Dataflow Staging 52 | 53 | ```sh 54 | gsutil mb gs://[BUCKET_NAME]/ 55 | ``` 56 | 57 | 3. Create a folder in the newly created bucket in the Google Cloud Console Storage Browser called __tmp__ 58 | 59 | 4. Create a Pub/Sub Topic 60 | ```sh 61 | gcloud pubsub topics create [TOPIC_NAME] 62 | ``` 63 | 5. Create a Cloud Logging sink 64 | ```sh 65 | gcloud logging sinks create [SINK_NAME] pubsub.googleapis.com/projects/[PROJECT_ID]/topics/[TOPIC_NAME] --log-filter="resource.type=global" 66 | ``` 67 | 68 | 6. Install the Apache Beam GCP Library 69 | ```sh 70 | python3 -m virtualenv tempenv 71 | source tempenv/bin/activate 72 | pip install apache-beam[gcp] 73 | ``` 74 | 75 | 7. Create BigQuery dataset 76 | 77 | 8. Deploy Dataflow Job 78 | ```sh 79 | python3 stackdriverdataflowbigquery.py --project=[YOUR_PROJECT_ID] \ 80 | --input_topic=projects/[YOUR_PROJECT_ID]/topics/[YOUR_TOPIC_NAME] \ 81 | --runner=DataflowRunner --temp_location=gs://[YOUR_DATAFLOW_STAGING_BUCKET]/tmp \ 82 | --output_bigquery=[YOUR_BIGQUERY_DATASET.YOUR BIGQUERY_TABLE] --region=us-central1 83 | ``` 84 | 85 | 9. Enable Dialogflow Logs to Cloud Logging 86 | 87 | Enable Log interactions to Dialogflow and Google Cloud 88 | https://cloud.google.com/dialogflow/docs/history#access_all_logs 89 | 90 | Once you enable Enable Log interactions, your new Dialogflow interactions will be available in BigQuery 91 | 92 | **This is not an officially supported Google product** -------------------------------------------------------------------------------- /images/diagram.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GoogleCloudPlatform/dialogflow-log-parser-dataflow-bigquery/040985ead97d04ee435b8390cb472e28ba34d210/images/diagram.png -------------------------------------------------------------------------------- /stackdriverdataflowbigquery.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Google LLC. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | # [START parsing Stackdriver logs from pubsub_to_bigquery] 16 | import argparse 17 | import logging 18 | import json 19 | import re 20 | import ast 21 | 22 | import apache_beam as beam 23 | from apache_beam.options.pipeline_options import PipelineOptions 24 | from apache_beam.options.pipeline_options import SetupOptions 25 | from apache_beam.options.pipeline_options import StandardOptions 26 | 27 | def iterate_multidimensional(my_dict): 28 | return_json = { 29 | "insertId": None, 30 | "logName": None, 31 | "receiveTimestamp": None, 32 | "textPayload": None, 33 | "timestamp": None, 34 | "trace": None 35 | } 36 | for k,v in my_dict.items(): 37 | if(isinstance(v,dict)): 38 | iterate_multidimensional(v) 39 | continue 40 | if k in return_json: 41 | return_json[k] = v 42 | return return_json 43 | 44 | def iterate_textpayload_multidimensional(my_dict): 45 | return_dict = { 46 | "error_type": None, 47 | "session_id": None, 48 | "caller_id": None, 49 | "email": None, 50 | "code": None, 51 | "string_value": None, 52 | "lang": None, 53 | "speech": None, 54 | "is_fallback_intent": None, 55 | "webhook_for_slot_filling_used": None, 56 | "webhook_used": None, 57 | "intent_name": None, 58 | "intent_id": None, 59 | "score": None, 60 | "action": None, 61 | "resolved_query": None, 62 | "source": None 63 | } 64 | 65 | for k,v in my_dict.items(): 66 | if(isinstance(v,dict)): 67 | iterate_textpayload_multidimensional(v) 68 | continue 69 | if k in return_dict: 70 | return_dict[k] = v 71 | return return_dict 72 | 73 | def iterate_textpayload(my_list): 74 | res = [] 75 | for item in my_list: 76 | my_list_item = item.replace('"', '') 77 | if ':' in my_list_item: 78 | res.append(map(str.strip, my_list_item.split(":", 1))) 79 | return dict(res) 80 | 81 | # function to get response body data from pub/sub message and build structure for BigQuery load 82 | def parse_transform_response(data): 83 | logging.info('--- START parse_transform_response Function ---') 84 | pub_sub_data = json.loads(data) 85 | fullpayload_dict = iterate_multidimensional(pub_sub_data) 86 | # Clean textPlayload from Stackdriver - not a valid JSON object 87 | text_payload = fullpayload_dict['textPayload'] 88 | return_merged_payload = None 89 | 90 | if text_payload != None: 91 | regex = re.compile(r'''[\S]+:(?:\s(?!\S+:)\S+)+''', re.VERBOSE) 92 | matches = regex.findall(pub_sub_data["textPayload"]) 93 | iterate_textpayload_response = iterate_textpayload(matches) 94 | textpayload_dict = iterate_textpayload_multidimensional(iterate_textpayload_response) 95 | if textpayload_dict["error_type"] is not None: 96 | textpayload_dict["error_type"] = textpayload_dict["error_type"].replace("\n", "").replace("}", "").strip() 97 | return_merged_payload = dict(list(fullpayload_dict.items()) + list(textpayload_dict.items())) 98 | if return_merged_payload is not None: 99 | logging.info('--- END parse_transform_response Function ---') 100 | logging.info(return_merged_payload) 101 | return return_merged_payload 102 | else: 103 | logging.info('--- END parse_transform_response Function ---') 104 | logging.info(fullpayload_dict) 105 | return fullpayload_dict 106 | 107 | def run(argv=None, save_main_session=True): 108 | """Build and run the pipeline.""" 109 | parser = argparse.ArgumentParser() 110 | group = parser.add_mutually_exclusive_group(required=True) 111 | group.add_argument( 112 | '--input_topic', 113 | help=('Input PubSub topic of the form ' 114 | '"projects//topics/".')) 115 | group.add_argument( 116 | '--input_subscription', 117 | help=('Input PubSub subscription of the form ' 118 | '"projects//subscriptions/."')) 119 | parser.add_argument('--output_bigquery', required=True, 120 | help='Output BQ table to write results to ' 121 | '"PROJECT_ID:DATASET.TABLE"') 122 | known_args, pipeline_args = parser.parse_known_args(argv) 123 | 124 | pipeline_options = PipelineOptions(pipeline_args) 125 | pipeline_options.view_as(SetupOptions).save_main_session = save_main_session 126 | pipeline_options.view_as(StandardOptions).streaming = True 127 | p = beam.Pipeline(options=pipeline_options) 128 | 129 | # Read from PubSub into a PCollection. 130 | if known_args.input_subscription: 131 | messages = (p 132 | | beam.io.ReadFromPubSub( 133 | subscription=known_args.input_subscription) 134 | .with_output_types(bytes)) 135 | else: 136 | messages = (p 137 | | beam.io.ReadFromPubSub(topic=known_args.input_topic) 138 | .with_output_types(bytes)) 139 | 140 | decode_messages = messages | 'DecodePubSubMessages' >> beam.Map(lambda x: x.decode('utf-8')) 141 | 142 | # Parse response body data from pub/sub message and build structure for BigQuery load 143 | output = decode_messages | 'ParseTransformResponse' >> beam.Map(parse_transform_response) 144 | 145 | # Write to BigQuery 146 | bigquery_table_schema = { 147 | "fields": [ 148 | { 149 | "mode": "NULLABLE", 150 | "name": "session_id", 151 | "type": "STRING" 152 | }, 153 | { 154 | "mode": "NULLABLE", 155 | "name": "trace", 156 | "type": "STRING" 157 | }, 158 | { 159 | "mode": "NULLABLE", 160 | "name": "caller_id", 161 | "type": "STRING" 162 | }, 163 | { 164 | "mode": "NULLABLE", 165 | "name": "email", 166 | "type": "STRING" 167 | }, 168 | { 169 | "mode": "NULLABLE", 170 | "name": "timestamp", 171 | "type": "TIMESTAMP" 172 | }, 173 | { 174 | "mode": "NULLABLE", 175 | "name": "receiveTimestamp", 176 | "type": "TIMESTAMP" 177 | }, 178 | { 179 | "mode": "NULLABLE", 180 | "name": "resolved_query", 181 | "type": "STRING" 182 | }, 183 | { 184 | "mode": "NULLABLE", 185 | "name": "string_value", 186 | "type": "STRING" 187 | }, 188 | { 189 | "mode": "NULLABLE", 190 | "name": "speech", 191 | "type": "STRING" 192 | }, 193 | { 194 | "mode": "NULLABLE", 195 | "name": "is_fallback_intent", 196 | "type": "STRING" 197 | }, 198 | { 199 | "mode": "NULLABLE", 200 | "name": "webhook_for_slot_filling_used", 201 | "type": "STRING" 202 | }, 203 | { 204 | "mode": "NULLABLE", 205 | "name": "webhook_used", 206 | "type": "STRING" 207 | }, 208 | { 209 | "mode": "NULLABLE", 210 | "name": "intent_name", 211 | "type": "STRING" 212 | }, 213 | { 214 | "mode": "NULLABLE", 215 | "name": "intent_id", 216 | "type": "STRING" 217 | }, 218 | { 219 | "mode": "NULLABLE", 220 | "name": "score", 221 | "type": "STRING" 222 | }, 223 | { 224 | "mode": "NULLABLE", 225 | "name": "action", 226 | "type": "STRING" 227 | }, 228 | { 229 | "mode": "NULLABLE", 230 | "name": "source", 231 | "type": "STRING" 232 | }, 233 | { 234 | "mode": "NULLABLE", 235 | "name": "error_type", 236 | "type": "STRING" 237 | }, 238 | { 239 | "mode": "NULLABLE", 240 | "name": "code", 241 | "type": "STRING" 242 | }, 243 | { 244 | "mode": "NULLABLE", 245 | "name": "insertId", 246 | "type": "STRING" 247 | }, 248 | { 249 | "mode": "NULLABLE", 250 | "name": "logName", 251 | "type": "STRING" 252 | }, 253 | { 254 | "mode": "NULLABLE", 255 | "name": "lang", 256 | "type": "STRING" 257 | }, 258 | { 259 | "mode": "NULLABLE", 260 | "name": "textPayload", 261 | "type": "STRING" 262 | } 263 | 264 | ] 265 | } 266 | output | 'WriteToBigQuery' >> beam.io.WriteToBigQuery( 267 | known_args.output_bigquery, 268 | schema=bigquery_table_schema, 269 | create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED, 270 | write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND) 271 | 272 | p.run() 273 | 274 | if __name__ == '__main__': 275 | logging.getLogger().setLevel(logging.DEBUG) 276 | run() 277 | # [END parsing Stackdriver logs from pubsub_to_bigquery] --------------------------------------------------------------------------------