Question

我正在使用Kafka构建数据管道。数据流如下：捕获mongodb中的数据变化并将其发送到elasticsearch。

MongoDB的

版本3.6
分片群集

卡夫卡

Confuent Platform 4.1.0
mongoDB源连接器：debezium 0.7.5
elasticserach sink connector

Elasticsearch

版本6.1.0

由于我还在测试，Kafka相关系统正在单台服务器上运行。

启动zookeepr

$ bin/zookeeper-server-start etc/kafka/zookeeper.properties

启动引导服务器

$ bin/kafka-server-start etc/kafka/server.properties

启动注册表架构

$ bin/schema-registry-start etc/schema-registry/schema-registry.properties

启动mongodb source connetor

$ bin/connect-standalone \ 
  etc/schema-registry/connect-avro-standalone.properties \ 
  etc/kafka/connect-mongo-source.properties

$ cat etc/kafka/connect-mongo-source.properties
>>> 
name=mongodb-source-connector
connector.class=io.debezium.connector.mongodb.MongoDbConnector
mongodb.hosts=''
initial.sync.max.threads=1
tasks.max=1
mongodb.name=higee

$ cat etc/schema-registry/connect-avro-standalone.properties
>>>
bootstrap.servers=localhost:9092
key.converter=io.confluent.connect.avro.AvroConverter
key.converter.schema.registry.url=http://localhost:8081
value.converter=io.confluent.connect.avro.AvroConverter
value.converter.schema.registry.url=http://localhost:8081
internal.key.converter=org.apache.kafka.connect.json.JsonConverter
internal.value.converter=org.apache.kafka.connect.json.JsonConverter
internal.key.converter.schemas.enable=false
internal.value.converter.schemas.enable=false
rest.port=8083

启动elasticsearch sink连接器

$ bin/connect-standalone \ 
  etc/schema-registry/connect-avro-standalone2.properties  \ 
  etc/kafka-connect-elasticsearch/elasticsearch.properties

$ cat etc/kafka-connect-elasticsearch/elasticsearch.properties
>>>
name=elasticsearch-sink
connector.class=io.confluent.connect.elasticsearch.ElasticsearchSinkConnector
tasks.max=1
topics=higee.higee.higee
key.ignore=true
connection.url=''
type.name=kafka-connect

$ cat etc/schema-registry/connect-avro-standalone2.properties
>>>
bootstrap.servers=localhost:9092
key.converter=io.confluent.connect.avro.AvroConverter
key.converter.schema.registry.url=http://localhost:8081
value.converter=io.confluent.connect.avro.AvroConverter
value.converter.schema.registry.url=http://localhost:8081
internal.key.converter=org.apache.kafka.connect.json.JsonConverter
internal.value.converter=org.apache.kafka.connect.json.\ 
                      JsonConverter
internal.key.converter.schemas.enable=false
internal.value.converter.schemas.enable=false
rest.port=8084

以上系统一切都很好。 Kafka连接器捕获数据更改（CDC）并通过接收器连接器成功将其发送到elasticsearch。问题是我无法将字符串类型消息数据转换为结构化数据类型。例如，在对mongodb进行一些更改后，让我们使用主题数据。

    $ bin/kafka-avro-console-consumer \
    --bootstrap-server localhost:9092 \
    --topic higee.higee.higee --from-beginning | jq

然后，我得到以下结果。

    "after": null,
      "patch": {
        "string": "{\"_id\" : {\"$oid\" : \"5ad97f982a0f383bb638ecac\"},\"name\" : \"higee\",\"salary\" : 100,\"origin\" : \"South Korea\"}"
      },
      "source": {
        "version": {
          "string": "0.7.5"
        },
        "name": "higee",
        "rs": "172.31.50.13",
        "ns": "higee",
        "sec": 1524214412,
        "ord": 1,
        "h": {
          "long": -2379508538412995600
        },
        "initsync": {
          "boolean": false
        }
      },
      "op": {
        "string": "u"
      },
      "ts_ms": {
        "long": 1524214412159
      }
    }

然后，如果我去弹性搜索，我会得到以下结果。

    {
        "_index": "higee.higee.higee",
        "_type": "kafka-connect",
        "_id": "higee.higee.higee+0+3",
        "_score": 1,
        "_source": {
          "after": null,
          "patch": """{"_id" : {"$oid" : "5ad97f982a0f383bb638ecac"}, 
                       "name" : "higee", 
                       "salary" : 100,
                       "origin" : "South Korea"}""",
          "source": {
            "version": "0.7.5",
            "name": "higee",
            "rs": "172.31.50.13",
            "ns": "higee",
            "sec": 1524214412,
            "ord": 1,
            "h": -2379508538412995600,
            "initsync": false
          },
          "op": "u",
          "ts_ms": 1524214412159
        }
      }

我想要达到的目标如下

    {
        "_index": "higee.higee.higee",
        "_type": "kafka-connect",
        "_id": "higee.higee.higee+0+3",
        "_score": 1,
        "_source": {
          "oid" : "5ad97f982a0f383bb638ecac",
          "name" : "higee", 
          "salary" : 100,
          "origin" : "South Korea"
         }"
     }

我一直在尝试并仍在考虑的一些选项如下。

logstash

案例1：不知道如何解析这些字符（/ u0002，/ u0001）

logstash.conf

input {
  kafka {
    bootstrap_servers => ["localhost:9092"]
    topics => ["higee.higee.higee"]
    auto_offset_reset => "earliest"
    codec => json {
      charset => "UTF-8"
    }
  }
}

filter {
  json {
    source => "message"
  }
 }

output {
  stdout {
    codec => rubydebug
  }
}

结果

{
"message" => "H\u0002�\u0001{\"_id\" : \
    {\"$oid\" : \"5adafc0e2a0f383bb63910a6\"}, \
     \"name\" : \"higee\", \
     \"salary\" : 101, \
     \"origin\" : \"South Korea\"} \
     \u0002\n0.7.5\nhigee \ 
     \u0018172.31.50.13\u001Ahigee.higee2 \ 
     ��ح\v\u0002\u0002��̗���� \u0002\u0002u\u0002�����X",
"tags" => [[0] "_jsonparsefailure"]
}

案例2

logstash.conf

input {
  kafka {
    bootstrap_servers => ["localhost:9092"]
    topics => ["higee.higee.higee"]
    auto_offset_reset => "earliest"
    codec => avro {
      schema_uri => "./test.avsc"
    }
  }
}

filter {
  json {
    source => "message"
  }
}

output {
  stdout {
    codec => rubydebug
  }
}

test.avsc

{
    "namespace": "example",
    "type": "record",
    "name": "Higee",
    "fields": [
      {"name": "_id", "type": "string"},
      {"name": "name", "type": "string"},
      {"name": "salary",  "type": "int"},
      {"name": "origin", "type": "string"}
    ]
 }

结果

An unexpected error occurred! {:error=>#<NoMethodError: 
undefined method `type_sym' for nil:NilClass>, :backtrace=> 
["/home/ec2-user/logstash- 
6.1.0/vendor/bundle/jruby/2.3.0/gems/avro- 
1.8.2/lib/avro/io.rb:224:in `match_schemas'", "/home/ec2- 
user/logstash-6.1.0/vendor/bundle/jruby/2.3.0/gems/avro- 
1.8.2/lib/avro/io.rb:280:in `read_data'", "/home/ec2- 
user/logstash-6.1.0/vendor/bundle/jruby/2.3.0/gems/avro- 
1.8.2/lib/avro/io.rb:376:in `read_union'", "/home/ec2- 
user/logstash-6.1.0/vendor/bundle/jruby/2.3.0/gems/avro- 
1.8.2/lib/avro/io.rb:309:in `read_data'", "/home/ec2- 
user/logstash-6.1.0/vendor/bundle/jruby/2.3.0/gems/avro- 
1.8.2/lib/avro/io.rb:384:in `block in read_record'", 
"org/jruby/RubyArray.java:1734:in `each'", "/home/ec2- 
user/logstash-6.1.0/vendor/bundle/jruby/2.3.0/gems/avro- 
1.8.2/lib/avro/io.rb:382:in `read_record'", "/home/ec2- 
user/logstash-6.1.0/vendor/bundle/jruby/2.3.0/gems/avro- 
1.8.2/lib/avro/io.rb:310:in `read_data'", "/home/ec2- 
user/logstash-6.1.0/vendor/bundle/jruby/2.3.0/gems/avro- 
1.8.2/lib/avro/io.rb:275:in `read'", "/home/ec2- 
user/logstash-6.1.0/vendor/bundle/jruby/2.3.0/gems/ 
logstash-codec-avro-3.2.3-java/lib/logstash/codecs/ 
avro.rb:77:in `decode'", "/home/ec2-user/logstash-6.1.0/ 
vendor/bundle/jruby/2.3.0/gems/logstash-input-kafka- 
8.0.2/lib/ logstash/inputs/kafka.rb:254:in `block in 
thread_runner'", "/home/ec2-user/logstash- 
6.1.0/vendor/bundle/jruby/2.3.0/gems/logstash-input-kafka- 
8.0.2/lib/logstash/inputs/kafka.rb:253:in `block in 
thread_runner'"]}

python客户端

在一些数据操作之后使用不同主题名称来使用主题并生成，以便elasticsearch sink连接器可以只使用来自python操作主题的格式良好的消息

kafka库：无法解码消息

from kafka import KafkaConsumer

consumer = KafkaConsumer(
             topics='higee.higee.higee',
             auto_offset_reset='earliest'
           )

for message in consumer:
    message.value.decode('utf-8')

>>> 'utf-8' codec can't decode byte 0xe4 in position 6: 
    invalid continuation byte

confluent_kafka与python 3不兼容

知道如何在elasticsearch中对数据进行jsonify吗？以下是我搜索的来源。

提前致谢。

一些尝试

1）我已按如下方式更改了我的connect-mongo-source.properties文件，以测试转换。

    $ cat etc/kafka/connect-mongo-source.properties
    >>> 
    name=mongodb-source-connector
    connector.class=io.debezium.connector.mongodb.MongoDbConnector
    mongodb.hosts=''
    initial.sync.max.threads=1
    tasks.max=1
    mongodb.name=higee
    transforms=unwrap     
    transforms.unwrap.type = io.debezium.connector.mongodbtransforms.UnwrapFromMongoDbEnvelope

以下是我收到的错误日志。还不熟悉Kafka，更重要的是debezium平台，我无法调试此错误。

ERROR WorkerSourceTask{id=mongodb-source-connector-0} Task threw an uncaught and unrecoverable exception (org.apache.kafka.connect.runtime.WorkerTask:172)
org.bson.json.JsonParseException: JSON reader expected a string but found '0'.
    at org.bson.json.JsonReader.visitBinDataExtendedJson(JsonReader.java:904)
    at org.bson.json.JsonReader.visitExtendedJSON(JsonReader.java:570)
    at org.bson.json.JsonReader.readBsonType(JsonReader.java:145)
    at org.bson.codecs.BsonDocumentCodec.decode(BsonDocumentCodec.java:82)
    at org.bson.codecs.BsonDocumentCodec.decode(BsonDocumentCodec.java:41)
    at org.bson.codecs.BsonDocumentCodec.readValue(BsonDocumentCodec.java:101)
    at org.bson.codecs.BsonDocumentCodec.decode(BsonDocumentCodec.java:84)
    at org.bson.BsonDocument.parse(BsonDocument.java:62)
    at io.debezium.connector.mongodb.transforms.UnwrapFromMongoDbEnvelope.apply(UnwrapFromMongoDbEnvelope.java:45)
    at org.apache.kafka.connect.runtime.TransformationChain.apply(TransformationChain.java:38)
    at org.apache.kafka.connect.runtime.WorkerSourceTask.sendRecords(WorkerSourceTask.java:218)
    at org.apache.kafka.connect.runtime.WorkerSourceTask.execute(WorkerSourceTask.java:194)
    at org.apache.kafka.connect.runtime.WorkerTask.doRun(WorkerTask.java:170)
    at org.apache.kafka.connect.runtime.WorkerTask.run(WorkerTask.java:214)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
    at java.util.concurrent.FutureTask.run(FutureTask.java:266)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

2）在这段时间里，我改变了elasticsearch.properties并且没有对connect-mongo-source.properties进行更改。

$ cat connect-mongo-source.properties

    name=mongodb-source-connector
    connector.class=io.debezium.connector.mongodb.MongoDbConnector
    mongodb.hosts=''
    initial.sync.max.threads=1
    tasks.max=1
    mongodb.name=higee

$ cat elasticsearch.properties

    name=elasticsearch-sink
    connector.class = io.confluent.connect.elasticsearch.ElasticsearchSinkConnector
    tasks.max=1
    topics=higee.higee.higee
    key.ignore=true
    connection.url=''
    type.name=kafka-connect
    transforms=unwrap
    transforms.unwrap.type = io.debezium.connector.mongodb.transforms.UnwrapFromMongoDbEnvelope

我得到了以下错误。

ERROR WorkerSinkTask{id=elasticsearch-sink-0} Task threw an uncaught and unrecoverable exception (org.apache.kafka.connect.runtime.WorkerTask:172)
org.bson.BsonInvalidOperationException: Document does not contain key $set
    at org.bson.BsonDocument.throwIfKeyAbsent(BsonDocument.java:844)
    at org.bson.BsonDocument.getDocument(BsonDocument.java:135)
    at io.debezium.connector.mongodb.transforms.UnwrapFromMongoDbEnvelope.apply(UnwrapFromMongoDbEnvelope.java:53)
    at org.apache.kafka.connect.runtime.TransformationChain.apply(TransformationChain.java:38)
    at org.apache.kafka.connect.runtime.WorkerSinkTask.convertMessages(WorkerSinkTask.java:480)
    at org.apache.kafka.connect.runtime.WorkerSinkTask.poll(WorkerSinkTask.java:301)
    at org.apache.kafka.connect.runtime.WorkerSinkTask.iteration(WorkerSinkTask.java:205)
    at org.apache.kafka.connect.runtime.WorkerSinkTask.execute(WorkerSinkTask.java:173)
    at org.apache.kafka.connect.runtime.WorkerTask.doRun(WorkerTask.java:170)
    at org.apache.kafka.connect.runtime.WorkerTask.run(WorkerTask.java:214)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
    at java.util.concurrent.FutureTask.run(FutureTask.java:266)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

3）更改了test.avsc并运行了logstash。我没有收到任何错误消息，但结果并不是我所期待的origin，salary，name字段都是空的，即使它们已被提供非空值。我甚至能够通过控制台消费者正确地读取数据。

$ cat test.avsc
>>>
    {
      "type" : "record",
      "name" : "MongoEvent",
      "namespace" : "higee.higee",
      "fields" : [ {
        "name" : "_id",
        "type" : {
          "type" : "record",
          "name" : "HigeeEvent",
          "fields" : [ {
            "name" : "$oid",
            "type" : "string"
          }, {
            "name" : "salary",
            "type" : "long"
          }, {
            "name" : "origin",
            "type" : "string"
          }, {
            "name" : "name",
            "type" : "string"
          } ]
        }
      } ]
    }

$ cat logstash3.conf
>>>
    input {
      kafka {
        bootstrap_servers => ["localhost:9092"]
        topics => ["higee.higee.higee"]
        auto_offset_reset => "earliest"
        codec => avro {
          schema_uri => "./test.avsc"
        }
      }
    }

    output {
      stdout {
       codec => rubydebug
      }
    }

$ bin/logstash -f logstash3.conf
>>>
    {
    "@version" => "1",
    "_id" => {
      "salary" => 0,
      "origin" => "",
      "$oid" => "",
      "name" => ""
    },
    "@timestamp" => 2018-04-25T09:39:07.962Z
    }

Answer 1

Python客户端

您必须使用Avro Consumer，否则您将获得'utf-8' codec can't decode byte

Even this example will not work因为您仍需要架构注册表来查找架构。

The prerequisites of Confluent's Python Client says it works with Python 3.x

没有什么能阻止你使用不同的客户端，所以不确定为什么你只在尝试使用Python时就离开了它。

Logstash Avro Codec

JSON Codec无法解码Avro数据。我认为avro输入编解码器后面的json滤镜不会起作用
您的Avro架构错误 - 您错过$oid代替_id
“原始Avro”（包括消息本身内的架构）与Confluent的编码版本（仅包含注册表中的架构ID）之间存在差异。意思是，Logstash没有与Schema Registry集成......至少not without a plugin。

你的AVSC实际上应该是这样的

{
  "type" : "record",
  "name" : "MongoEvent",
  "namespace" : "higee.higee",
  "fields" : [ {
    "name" : "_id",
    "type" : {
      "type" : "record",
      "name" : "HigeeEvent",
      "fields" : [ {
        "name" : "$oid",
        "type" : "string"
      }, {
        "name" : "salary",
        "type" : "long"
      }, {
        "name" : "origin",
        "type" : "string"
      }, {
        "name" : "name",
        "type" : "string"
      } ]
    }
  } ]
}

然而，Avro doesn't allow for names starting with anything but a regex of [A-Za-z_]，$oid会成为问题。

虽然我不推荐它（也没有真正尝试过），但是从Avro控制台消费者那里获取JSON编码的Avro数据到Logstash的一种可能方法是使用Pipe输入插件

input {
  pipe {
    codec => json
    command => "/path/to/confluent/bin/kafka-avro-console-consumer --bootstrap-server localhost:9092 --topic higee.higee.higee --from-beginning" 
  }
}

Debezium

请注意，after值始终是一个字符串，按照惯例，它将包含文档的JSON表示

http://debezium.io/docs/connectors/mongodb/

我认为这也适用于patch值，但我不知道Debezium。

Kafka不会在不使用简单消息转换（SMT）的情况下解析JSON。阅读您链接的文档，您应该add these to your Connect Source properties

transforms=unwrap
transforms.unwrap.type=io.debezium.connector.mongodb.transforms.UnwrapFromMongoDbEnvelope

另外值得指出的是，字段展平是路线图 - DBZ-561

Kafka Connect Elasticsearch

Elasticsearch不会在不使用Logstash或其JSON Processor之类的情况下解析和处理编码的JSON字符串对象。相反，它只将它们作为整个字符串体索引。

如果我没记错的话，Connect只会将Elasticsearch映射应用于顶级Avro字段，而不是嵌套字段。

换句话说，生成的映射遵循此模式

"patch": {
    "string": "...some JSON object string here..."
  },

你真正需要这样的地方 - 也许是manually defining your ES index

"patch": {
   "properties": {
      "_id": {
        "properties" {
          "$oid" :  { "type": "text" }, 
          "name" :  { "type": "text" },
          "salary":  { "type": "int"  }, 
          "origin": { "type": "text" }
      },

同样，不确定是否允许美元符号。

Kafka Connect MongoDB Source

如果以上都不起作用，您可以尝试使用其他连接器

Answer 2

我能够使用python kafka客户端解决这个问题。以下是我的管道的新架构。

我使用了python 2，尽管Confluent文档说支持python3。主要原因是有一些python2语法代码。例如......（不完全遵循行但语法相似）

    except NameError, err:

为了与Python3一起使用，我需要将上面的行转换为：

    except NameError as err:

话虽如此，以下是我的python代码。请注意，此代码仅用于原型设计，而不适用于生产。

通过汇合消费者

消费消息

码

from confluent_kafka.avro import AvroConsumer

c = AvroConsumer({ 
       'bootstrap.servers': '',
       'group.id': 'groupid',
       'schema.registry.url': ''
    })

c.subscribe(['higee.higee.higee'])

x = True

while x:
    msg = c.poll(100)
    if msg:
        message = msg.value()
        print(message)
        x = False

c.close()

（在mongodb中更新文档后）让我们检查message变量

{u'after': None,
 u'op': u'u',
 u'patch': u'{
     "_id" : {"$oid" : "5adafc0e2a0f383bb63910a6"},
     "name" : "higee",
     "salary" : 100,
     "origin" : "S Korea"}',
 u'source': {
     u'h': 5734791721791032689L,
     u'initsync': False,
     u'name': u'higee',
     u'ns': u'higee.higee',
     u'ord': 1,
     u'rs': u'',
     u'sec': 1524362971,
     u'version': u'0.7.5'},
 u'ts_ms': 1524362971148
 }

操纵消耗的消息

码

patch = message['patch']
patch_dict = eval(patch)
patch_dict.pop('_id')

检查patch_dict

{'name': 'higee', 'origin': 'S Korea', 'salary': 100}

通过Confluent Producer生成消息

    from confluent_kafka import avro
    from confluent_kafka.avro import AvroProducer

    value_schema_str = """
    {
       "namespace": "higee.higee",
       "name": "MongoEvent",
       "type": "record",
       "fields" : [
           {
               "name" : "name",
               "type" : "string"
           },
           {
              "name" : "origin",
              "type" : "string"
           },
           {
               "name" : "salary",
               "type" : "int32"
           }
       ]
    }
    """
    AvroProducerConf = {
        'bootstrap.servers': '',
        'schema.registry.url': ''
    }

    value_schema = avro.load('./user.avsc')
    avroProducer = AvroProducer(
                       AvroProducerConf, 
                       default_value_schema=value_schema
                   )

    avroProducer.produce(topic='python', value=patch_dict)
    avroProducer.flush()

唯一剩下的就是让elasticsearch sink连接器响应新主题＆＃39; python＆＃39;通过以下格式设置配置。除topics之外，一切都保持不变。

    name=elasticsearch-sink
    connector.class= io.confluent.connect \ 
                     elasticsearch.ElasticsearchSinkConnector
    tasks.max=1
    topics=python
    key.ignore=true
    connection.url=''
    type.name=kafka-connect

然后运行elasticsearch sink连接器并在elasticsearch处检查它。

    {
        "_index": "zzzz",
        "_type": "kafka-connect",
        "_id": "zzzz+0+3",
        "_score": 1,
        "_source": {
          "name": "higee",
          "origin": "S Korea",
          "salary": 100
        }
      }

Answer 3

+1 @ cricket_007的建议 - 使用io.debezium.connector.mongodb.transforms.UnwrapFromMongoDbEnvelope单个邮件转换。您可以阅读有关SMT及其好处的更多信息here。

无法使用Confluent Elasticsearch接收器将Kafka主题数据转换为结构化JSON

3 个答案: