druid/protobuf.md at c068906fca4082737e059418d369eb0711500c07

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id	title
protobuf	Protobuf

This Apache Druid extension enables Druid to ingest and understand the Protobuf data format. Make sure to include druid-protobuf-extensions as an extension.

The druid-protobuf-extensions provides the Protobuf Parser for stream ingestion. See corresponding docs for details.

Example: Load Protobuf messages from Kafka

This example demonstrates how to load Protobuf messages from Kafka. Please read the Load from Kafka tutorial first, and see Kafka Indexing Service documentation for more details.

The files used in this example are found at ./examples/quickstart/protobuf in your Druid directory.

For this example:

Kafka broker host is localhost:9092
Kafka topic is metrics_pb
Datasource name is metrics-protobuf

Here is a JSON example of the 'metrics' data schema used in the example.

{
  "unit": "milliseconds",
  "http_method": "GET",
  "value": 44,
  "timestamp": "2017-04-06T02:36:22Z",
  "http_code": "200",
  "page": "/",
  "metricType": "request/latency",
  "server": "www1.example.com"
}

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.

syntax = "proto3";
message Metrics {
  string unit = 1;
  string http_method = 2;
  int32 value = 3;
  string timestamp = 4;
  string http_code = 5;
  string page = 6;
  string metricType = 7;
  string server = 8;
}

When using a descriptor file

Next, we use the protoc Protobuf compiler to generate the descriptor file and save it as metrics.desc. The descriptor file must be either in the classpath or reachable by URL. In this example the descriptor file was saved at /tmp/metrics.desc, however this file is also available in the example files. From your Druid install directory:

protoc -o /tmp/metrics.desc ./quickstart/protobuf/metrics.proto

When using Schema Registry

Make sure your Schema Registry version is later than 5.5. Next, we can post a schema to add it to the registry:

POST /subjects/test/versions HTTP/1.1
Host: schemaregistry.example1.com
Accept: application/vnd.schemaregistry.v1+json, application/vnd.schemaregistry+json, application/json

{
    "schemaType": "PROTOBUF",
    "schema": "syntax = \"proto3\";\nmessage Metrics {\n  string unit = 1;\n  string http_method = 2;\n  int32 value = 3;\n string timestamp = 4;\n string http_code = 5;\n string page = 6;\n string metricType = 7;\n string server = 8;\n}\n"
}

This feature uses Confluent's Protobuf provider which is not included in the Druid distribution and must be installed separately. You can fetch it and its dependencies from the Confluent repository and Maven Central at:

Copy or symlink those files to extensions/protobuf-extensions under the distribution root directory.

Create Kafka Supervisor

Below is the complete Supervisor spec JSON to be submitted to the Overlord. Make sure these keys are properly configured for successful ingestion.

When using a descriptor file

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

{
  "type": "kafka",
  "dataSchema": {
    "dataSource": "metrics-protobuf",
    "parser": {
      "type": "protobuf",
      "protoBytesDecoder": {
        "type": "file",
        "descriptor": "file:///tmp/metrics.desc",
        "protoMessageType": "Metrics"
      },
      "parseSpec": {
        "format": "json",
        "timestampSpec": {
          "column": "timestamp",
          "format": "auto"
        },
        "dimensionsSpec": {
          "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"
    }
  },
  "tuningConfig": {
    "type": "kafka",
    "maxRowsPerSegment": 5000000
  },
  "ioConfig": {
    "topic": "metrics_pb",
    "consumerProperties": {
      "bootstrap.servers": "localhost:9092"
    },
    "taskCount": 1,
    "replicas": 1,
    "taskDuration": "PT1H"
  }
}

To adopt to old version. You can use old parser style, which also works.

{
  "parser": {
    "type": "protobuf",
    "descriptor": "file:///tmp/metrics.desc",
    "protoMessageType": "Metrics"
  }
}

When using Schema Registry

Important supervisor properties

protoBytesDecoder.url for the schema registry URL with single instance.
protoBytesDecoder.urls for the schema registry URLs with multi instances.
protoBytesDecoder.capacity capacity for schema registry cached schemas.
protoBytesDecoder.config to send additional configurations, configured for Schema Registry.
protoBytesDecoder.headers to send headers to the Schema Registry.
protoBytesDecoder.type set to schema_registry, indicate use schema registry to decode Protobuf file.
parser should have type set to protobuf, but note that the format of the parseSpec must be json.

{
  "parser": {
    "type": "protobuf",
    "protoBytesDecoder": {
      "urls": ["http://schemaregistry.example1.com:8081","http://schemaregistry.example2.com:8081"],
      "type": "schema_registry",
      "capacity": 100,
      "config" : {
           "basic.auth.credentials.source": "USER_INFO",
           "basic.auth.user.info": "fred:letmein",
           "schema.registry.ssl.truststore.location": "/some/secrets/kafka.client.truststore.jks",
           "schema.registry.ssl.truststore.password": "<password>",
           "schema.registry.ssl.keystore.location": "/some/secrets/kafka.client.keystore.jks",
           "schema.registry.ssl.keystore.password": "<password>",
           "schema.registry.ssl.key.password": "<password>",
             ... 
      },
      "headers": {
          "traceID" : "b29c5de2-0db4-490b-b421",
          "timeStamp" : "1577191871865",
          ...
      }
    }
  }
}

Adding Protobuf messages to Kafka

If necessary, from your Kafka installation directory run the following command to create the Kafka topic

./bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic metrics_pb

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

You can confirm that data has been inserted to your Kafka topic using the following command from your Kafka installation directory

./bin/kafka-console-consumer --zookeeper localhost --topic metrics_pb

which should print messages like this

millisecondsGETR"2017-04-06T03:23:56Z*2002/list:request/latencyBwww1.example.com

If your supervisor created in the previous step is running, the indexing tasks should begin producing the messages and the data will soon be available for querying in Druid.

Generating the example files

The files provided in the example quickstart can be generated in the following manner starting with only metrics.proto.

`metrics.desc`

The descriptor file is generated using protoc Protobuf compiler. Given a .proto file, a .desc file can be generated like so.

protoc -o metrics.desc metrics.proto

`metrics_pb2.py`

metrics_pb2.py is also generated with protoc

 protoc -o metrics.desc metrics.proto --python_out=.

`pb_publisher.py`

After metrics_pb2.py is generated, another script can be constructed to parse JSON data, convert it to Protobuf, and produce to a Kafka topic

#!/usr/bin/env python

import sys
import json

from kafka import KafkaProducer
from metrics_pb2 import Metrics


producer = KafkaProducer(bootstrap_servers='localhost:9092')
topic = 'metrics_pb'

for row in iter(sys.stdin):
    d = json.loads(row)
    metrics = Metrics()
    for k, v in d.items():
        setattr(metrics, k, v)
    pb = metrics.SerializeToString()
    producer.send(topic, pb)

producer.flush()

10 KiB Raw Blame History

Example: Load Protobuf messages from Kafka

Proto file

When using a descriptor file

When using Schema Registry

Create Kafka Supervisor

When using a descriptor file

When using Schema Registry

Adding Protobuf messages to Kafka

Generating the example files

metrics.desc

metrics_pb2.py

pb_publisher.py

10 KiB

Raw Blame History

`metrics.desc`

`metrics_pb2.py`

`pb_publisher.py`