Fix protobuf use case document

docs/development/extensions-core/protobuf.md

@@ -56,7 +56,7 @@ Here is a JSON example of the 'metrics' data schema used in the example.
 
 ### Proto file
 
-The corresponding proto file for our 'metrics' dataset looks like this. You can use Protobuf parser with a proto file or [Confluent Schema Registry](https://docs.confluent.io/platform/current/schema-registry/index.html).
+The corresponding proto file for our 'metrics' dataset looks like this. You can use Protobuf `inputFormat` with a proto file or [Confluent Schema Registry](https://docs.confluent.io/platform/current/schema-registry/index.html).
 ```
 syntax = "proto3";
 message Metrics {
@@ -112,82 +112,86 @@ Important supervisor properties
 - `protoBytesDecoder.descriptor` for the descriptor file URL
 - `protoBytesDecoder.protoMessageType` from the proto definition
 - `protoBytesDecoder.type` set to `file`, indicate use descriptor file to decode Protobuf file
-- `parser` should have `type` set to `protobuf`, but note that the `format` of the `parseSpec` must be `json`
+- `inputFormat` should have `type` set to `protobuf`
 
 ```json
 {
-  "type": "kafka",
-  "dataSchema": {
-    "dataSource": "metrics-protobuf",
-    "parser": {
-      "type": "protobuf",
-      "protoBytesDecoder": {
-        "type": "file",
-        "descriptor": "file:///tmp/metrics.desc",
-        "protoMessageType": "Metrics"
-      },
-      "parseSpec": {
-        "format": "json",
+"type": "kafka",
+"spec": {
+    "dataSchema": {
+        "dataSource": "metrics-protobuf",
         "timestampSpec": {
-          "column": "timestamp",
-          "format": "auto"
+            "column": "timestamp",
+            "format": "auto"
         },
         "dimensionsSpec": {
-          "dimensions": [
-            "unit",
-            "http_method",
-            "http_code",
-            "page",
-            "metricType",
-            "server"
-          ],
-          "dimensionExclusions": [
-            "timestamp",
-            "value"
-          ]
+            "dimensions": [
+                "unit",
+                "http_method",
+                "http_code",
+                "page",
+                "metricType",
+                "server"
+            ],
+            "dimensionExclusions": [
+                "timestamp",
+                "value"
+            ]
+        },
+        "metricsSpec": [
+            {
+                "name": "count",
+                "type": "count"
+            },
+            {
+                "name": "value_sum",
+                "fieldName": "value",
+                "type": "doubleSum"
+            },
+            {
+                "name": "value_min",
+                "fieldName": "value",
+                "type": "doubleMin"
+            },
+            {
+                "name": "value_max",
+                "fieldName": "value",
+                "type": "doubleMax"
+            }
+        ],
+        "granularitySpec": {
+            "type": "uniform",
+            "segmentGranularity": "HOUR",
+            "queryGranularity": "NONE"
         }
-      }
     },
-    "metricsSpec": [
-      {
-        "name": "count",
-        "type": "count"
-      },
-      {
-        "name": "value_sum",
-        "fieldName": "value",
-        "type": "doubleSum"
-      },
-      {
-        "name": "value_min",
-        "fieldName": "value",
-        "type": "doubleMin"
-      },
-      {
-        "name": "value_max",
-        "fieldName": "value",
-        "type": "doubleMax"
-      }
-    ],
-    "granularitySpec": {
-      "type": "uniform",
-      "segmentGranularity": "HOUR",
-      "queryGranularity": "NONE"
-    }
-  },
-  "tuningConfig": {
-    "type": "kafka",
-    "maxRowsPerSegment": 5000000
-  },
-  "ioConfig": {
-    "topic": "metrics_pb",
-    "consumerProperties": {
-      "bootstrap.servers": "localhost:9092"
+    "tuningConfig": {
+        "type": "kafka",
+        "maxRowsPerSegment": 5000000
     },
-    "taskCount": 1,
-    "replicas": 1,
-    "taskDuration": "PT1H"
-  }
+    "ioConfig": {
+        "topic": "metrics_pb",
+        "consumerProperties": {
+            "bootstrap.servers": "localhost:9092"
+        },
+        "inputFormat": {
+            "type": "protobuf",
+            "protoBytesDecoder": {
+                "type": "file",
+                "descriptor": "file:///tmp/metrics.desc",
+                "protoMessageType": "Metrics"
+            },
+            "flattenSpec": {
+                "useFieldDiscovery": true
+            },
+            "binaryAsString": false
+        },
+        "taskCount": 1,
+        "replicas": 1,
+        "taskDuration": "PT1H",
+        "type": "kafka"
+    }
+}
 }
 ```
 
@@ -253,7 +257,7 @@ If necessary, from your Kafka installation directory run the following command t
 This example script requires `protobuf` and `kafka-python` modules. With the topic in place, messages can be inserted running the following command from your Druid installation directory
 
 ```
-./bin/generate-example-metrics | ./quickstart/protobuf/pb_publisher.py
+./bin/generate-example-metrics | python /quickstart/protobuf/pb_publisher.py
 ```
 
 You can confirm that data has been inserted to your Kafka topic using the following command from your Kafka installation directory