优化代码：去除无使用的类
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zhanghongqing
@@ -8,58 +8,40 @@ max.poll.records=5000
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#kafka source poll bytes
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max.partition.fetch.bytes=31457280
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#====================Kafka KafkaProducer====================#
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#producer重试的次数设置
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#producer\u91CD\u8BD5\u7684\u6B21\u6570\u8BBE\u7F6E
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retries=0
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#他的含义就是说一个Batch被创建之后，最多过多久，不管这个Batch有没有写满，都必须发送出去了
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#\u4ED6\u7684\u542B\u4E49\u5C31\u662F\u8BF4\u4E00\u4E2ABatch\u88AB\u521B\u5EFA\u4E4B\u540E\uFF0C\u6700\u591A\u8FC7\u591A\u4E45\uFF0C\u4E0D\u7BA1\u8FD9\u4E2ABatch\u6709\u6CA1\u6709\u5199\u6EE1\uFF0C\u90FD\u5FC5\u987B\u53D1\u9001\u51FA\u53BB\u4E86
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linger.ms=10
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#如果在超时之前未收到响应，客户端将在必要时重新发送请求
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#\u5982\u679C\u5728\u8D85\u65F6\u4E4B\u524D\u672A\u6536\u5230\u54CD\u5E94\uFF0C\u5BA2\u6237\u7AEF\u5C06\u5728\u5FC5\u8981\u65F6\u91CD\u65B0\u53D1\u9001\u8BF7\u6C42
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request.timeout.ms=30000
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#producer都是按照batch进行发送的,批次大小，默认:16384
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#producer\u90FD\u662F\u6309\u7167batch\u8FDB\u884C\u53D1\u9001\u7684,\u6279\u6B21\u5927\u5C0F\uFF0C\u9ED8\u8BA4:16384
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batch.size=262144
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#Producer端用于缓存消息的缓冲区大小
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#Producer\u7AEF\u7528\u4E8E\u7F13\u5B58\u6D88\u606F\u7684\u7F13\u51B2\u533A\u5927\u5C0F
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#128M
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buffer.memory=134217728
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#这个参数决定了每次发送给Kafka服务器请求的最大大小,默认1048576
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#\u8FD9\u4E2A\u53C2\u6570\u51B3\u5B9A\u4E86\u6BCF\u6B21\u53D1\u9001\u7ED9Kafka\u670D\u52A1\u5668\u8BF7\u6C42\u7684\u6700\u5927\u5927\u5C0F,\u9ED8\u8BA41048576
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#10M
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max.request.size=10485760
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#====================kafka default====================#
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#kafka SASL验证用户名-加密
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#kafka SASL\u9A8C\u8BC1\u7528\u6237\u540D-\u52A0\u5BC6
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kafka.user=nsyGpHKGFA4KW0zro9MDdw==
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#kafka SASL及SSL验证密码-加密
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#kafka SASL\u53CASSL\u9A8C\u8BC1\u5BC6\u7801-\u52A0\u5BC6
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kafka.pin=6MleDyA3Z73HSaXiKsDJ2k7Ys8YWLhEJ
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#生产者ack
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#\u751F\u4EA7\u8005ack
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producer.ack=1
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#====================nacos default====================#
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#nacos username
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nacos.username=nacos
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#nacos password
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nacos.pin=nacos
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#nacos group
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nacos.group=Galaxy
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#====================Topology Default====================#
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#hbase table name
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hbase.table.name=tsg_galaxy:relation_framedip_account
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#邮件默认编码
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#\u90AE\u4EF6\u9ED8\u8BA4\u7F16\u7801
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mail.default.charset=UTF-8
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#0不做任何校验，1弱类型校验
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#0\u4E0D\u505A\u4EFB\u4F55\u6821\u9A8C\uFF0C1\u5F31\u7C7B\u578B\u6821\u9A8C
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log.transform.type=1
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#两个输出之间的最大时间(单位milliseconds)
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#\u4E24\u4E2A\u8F93\u51FA\u4E4B\u95F4\u7684\u6700\u5927\u65F6\u95F4(\u5355\u4F4Dmilliseconds)
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buffer.timeout=5000
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#====================临时配置-待删除====================#
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#网关APP_ID 获取接口
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app.id.http=http://192.168.44.20:9999/open-api/appDicList
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#app_id 更新时间，如填写0则不更新缓存
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app.tick.tuple.freq.secs=0
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@@ -8,51 +8,25 @@ sink.kafka.servers=192.168.45.102:9092
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#zookeeper \u5730\u5740 \u7528\u4E8E\u914D\u7F6Elog_id
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zookeeper.servers=192.168.45.102:2181
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#hbase zookeeper\u5730\u5740 \u7528\u4E8E\u8FDE\u63A5HBase
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hbase.zookeeper.servers=192.168.45.102:2181
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#--------------------------------HTTP/\u5B9A\u4F4D\u5E93------------------------------#
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#\u5B9A\u4F4D\u5E93\u5730\u5740
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tools.library=D:\\workerspace\\dat\\
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#--------------------------------nacos\u914D\u7F6E------------------------------#
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#nacos \u5730\u5740
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nacos.server=192.168.45.102:8848
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#nacos namespace
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nacos.schema.namespace=prod
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#nacos data id
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nacos.data.id=session_record.json
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#--------------------------------Kafka\u6D88\u8D39/\u751F\u4EA7\u914D\u7F6E------------------------------#
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#kafka \u63A5\u6536\u6570\u636Etopic
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source.kafka.topic=atest
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#\u8865\u5168\u6570\u636E \u8F93\u51FA topic
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sink.kafka.topic=atest2
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#\u8BFB\u53D6topic,\u5B58\u50A8\u8BE5spout id\u7684\u6D88\u8D39offset\u4FE1\u606F\uFF0C\u53EF\u901A\u8FC7\u8BE5\u62D3\u6251\u547D\u540D;\u5177\u4F53\u5B58\u50A8offset\u7684\u4F4D\u7F6E\uFF0C\u786E\u5B9A\u4E0B\u6B21\u8BFB\u53D6\u4E0D\u91CD\u590D\u7684\u6570\u636E\uFF1B
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group.id=flinktest-102
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group.id=knowledge-group
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#--------------------------------topology\u914D\u7F6E------------------------------#
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#consumer \u5E76\u884C\u5EA6
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source.parallelism=1
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#\u8F6C\u6362\u51FD\u6570\u5E76\u884C\u5EA6
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transform.parallelism=1
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#kafka producer \u5E76\u884C\u5EA6
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sink.parallelism=1
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#\u6570\u636E\u4E2D\u5FC3\uFF0C\u53D6\u503C\u8303\u56F4(0-31)
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data.center.id.num=0
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#hbase \u66F4\u65B0\u65F6\u95F4\uFF0C\u5982\u586B\u51990\u5219\u4E0D\u66F4\u65B0\u7F13\u5B58
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hbase.tick.tuple.freq.secs=180
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#--------------------------------\u9ED8\u8BA4\u503C\u914D\u7F6E------------------------------#
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#1 connection\u65E5\u5FD7 \uFF0C2 dns\u65E5\u5FD7
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log.need.complete=2
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@@ -60,7 +34,6 @@ log.need.complete=2
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#\u751F\u4EA7\u8005\u538B\u7F29\u6A21\u5F0F none or snappy
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producer.kafka.compression.type=none
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source.kafka.topic.connection=connection_record_log
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source.kafka.topic.sketch=connection_sketch_record_log
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source.kafka.topic.dns=dns_record_log
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@@ -78,36 +51,30 @@ sink.arango.table.r.resolve.domain2ip=R_RESOLVE_DOMAIN2IP
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sink.arango.table.r.nx.domain2domain=R_NX_DOMAIN2DOMAIN
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#clickhouse \u5165\u5E93
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ck.hosts=192.168.45.102:8123
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ck.hosts=192.168.45.102:8123,192.168.45.102:8123
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ck.database=tsg_galaxy_v3
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ck.username=default
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ck.pin=galaxy2019
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#\u5355\u4F4D\u6BEB\u79D2
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ck.connection.timeout=10000
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ck.socket.timeout=300000
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ck.batch=10000
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#connection_record_log
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flink.watermark.max.orderness=50
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#\u7EDF\u8BA1\u65F6\u95F4\u95F4\u9694 \u5355\u4F4Ds
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#flink \u65E5\u5FD7\u5EF6\u8FDF\u8D85\u65F6\u65F6\u95F4
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flink.watermark.max.delay.time=50
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#ck relation\u7EDF\u8BA1\u65F6\u95F4\u95F4\u9694 \u5355\u4F4Ds
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log.aggregate.duration=5
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#arangodb \u7EDF\u8BA1\u65F6\u95F4\u95F4\u9694 \u5355\u4F4Ds
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log.aggregate.duration.graph=5
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#arangoDB\u53C2\u6570\u914D\u7F6E
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arangoDB.host=192.168.45.102
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#arangoDB.host=192.168.40.224
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arangoDB.port=8529
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arangoDB.user=root
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arangoDB.password=galaxy_2019
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arangoDB.DB.name=knowledge
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arangoDB.batch=100000
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arangoDB.ttl=3600
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arangoDB.read.limit=
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update.arango.batch=10000
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thread.pool.number=10
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thread.await.termination.time=10
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sink.batch.time.out=5
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sink.batch=10000
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sink.batch.delay.time=5
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