适配Nacos动态更新schema(GAL-144)
This commit is contained in:
39
pom.xml
39
pom.xml
@@ -6,7 +6,7 @@
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<groupId>com.zdjizhi</groupId>
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<artifactId>log-olap-analysis-schema</artifactId>
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<version>220316-encryption</version>
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<version>220323-nacos</version>
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<name>log-olap-analysis-schema</name>
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<url>http://www.example.com</url>
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@@ -38,6 +38,8 @@
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<hadoop.version>2.7.1</hadoop.version>
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<kafka.version>1.0.0</kafka.version>
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<hbase.version>2.2.3</hbase.version>
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<nacos.version>1.2.0</nacos.version>
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<zdjz.tools.version>1.0.8</zdjz.tools.version>
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<scope.type>provided</scope.type>
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<!--<scope.type>compile</scope.type>-->
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</properties>
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@@ -116,7 +118,7 @@
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<dependency>
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<groupId>com.zdjizhi</groupId>
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<artifactId>galaxy</artifactId>
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<version>1.0.7</version>
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<version>${zdjz.tools.version}</version>
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<exclusions>
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<exclusion>
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<artifactId>slf4j-log4j12</artifactId>
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@@ -183,30 +185,32 @@
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<scope>compile</scope>
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</dependency>
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<dependency>
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<groupId>org.apache.httpcomponents</groupId>
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<artifactId>httpclient</artifactId>
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<version>4.5.2</version>
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</dependency>
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<dependency>
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<groupId>com.jayway.jsonpath</groupId>
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<artifactId>json-path</artifactId>
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<version>2.4.0</version>
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</dependency>
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<dependency>
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<groupId>io.prometheus</groupId>
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<artifactId>simpleclient_pushgateway</artifactId>
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<version>0.9.0</version>
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</dependency>
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<dependency>
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<groupId>cn.hutool</groupId>
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<artifactId>hutool-all</artifactId>
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<version>5.5.2</version>
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</dependency>
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<!-- https://mvnrepository.com/artifact/org.jasypt/jasypt -->
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<dependency>
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<groupId>org.jasypt</groupId>
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<artifactId>jasypt</artifactId>
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<version>1.9.3</version>
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</dependency>
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<!-- https://mvnrepository.com/artifact/com.alibaba.nacos/nacos-client -->
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<dependency>
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<groupId>com.alibaba.nacos</groupId>
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<artifactId>nacos-client</artifactId>
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<version>${nacos.version}</version>
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</dependency>
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<dependency>
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<groupId>org.slf4j</groupId>
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<artifactId>slf4j-api</artifactId>
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@@ -226,13 +230,6 @@
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<scope>test</scope>
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</dependency>
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<!-- https://mvnrepository.com/artifact/org.jasypt/jasypt -->
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<dependency>
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<groupId>org.jasypt</groupId>
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<artifactId>jasypt</artifactId>
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<version>1.9.3</version>
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</dependency>
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</dependencies>
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</project>
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@@ -7,6 +7,7 @@ max.poll.records=3000
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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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retries=0
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@@ -27,19 +28,34 @@ 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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#10M
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max.request.size=10485760
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#生产者压缩模式 none or snappy
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producer.kafka.compression.type=none
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#生产者ack
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producer.ack=1
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#====================kafka default====================#
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#kafka SASL验证用户名-加密
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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.pin=6MleDyA3Z73HSaXiKsDJ2k7Ys8YWLhEJ
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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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#两个输出之间的最大时间(单位milliseconds)
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buffer.timeout=100
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#第一次随机分组random范围
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random.range.num=40
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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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random.range.num=40
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@@ -1,57 +1,56 @@
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#--------------------------------地址配置------------------------------#
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#管理kafka地址
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source.kafka.servers=192.168.44.12:9094
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source.kafka.servers=192.168.40.223:9094,192.168.40.151:9094,192.168.40.152:9094
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#管理输出kafka地址
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sink.kafka.servers=192.168.44.12:9094
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sink.kafka.servers=192.168.40.223:9094,192.168.40.151:9094,192.168.40.152:9094
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#--------------------------------nacos配置------------------------------#
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#nacos 地址
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nacos.server=192.168.44.12:8848
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#nacos namespace
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nacos.schema.namespace=flink
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#nacos data id
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nacos.data.id=liveChart_session.json
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#--------------------------------HTTP------------------------------#
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#kafka 证书地址
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tools.library=D:\\workerspace\\dat\\
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#网关的schema位置
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schema.http=http://192.168.44.67:9999/metadata/schema/v1/fields/liveChart_session
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#网关APP_ID 获取接口
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app.id.http=http://192.168.44.67:9999/open-api/appDicList
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tools.library=/home/tsg/olap/topology/dat/
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#--------------------------------Kafka消费组信息------------------------------#
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#kafka 接收数据topic
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source.kafka.topic=test
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source.kafka.topic=SESSION-RECORD
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#补全数据 输出 topic
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sink.kafka.topic=test-result
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sink.kafka.topic=TRAFFIC-PROTOCOL-STAT
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#读取topic,存储该spout id的消费offset信息,可通过该拓扑命名;具体存储offset的位置,确定下次读取不重复的数据;
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group.id=mytest-211119-1
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#生产者压缩模式 none or snappy
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producer.kafka.compression.type=none
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#生产者ack
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producer.ack=1
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group.id=liveCharts-session-20211105-1
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#--------------------------------topology配置------------------------------#
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#consumer 并行度
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source.parallelism=1
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source.parallelism=9
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#map函数并行度
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parse.parallelism=2
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parse.parallelism=27
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#first count 函数并行度
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first.window.parallelism=2
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#第一次窗口计算并行度
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first.window.parallelism=27
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#second count 函数并行度
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second.window.parallelism=2
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#第二次窗口计算并行度
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second.window.parallelism=27
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#producer 并行度
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sink.parallelism=1
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sink.parallelism=9
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#初次随机预聚合窗口时间
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##初次随机预聚合窗口时间
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first.count.window.time=5
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#二次聚合窗口时间
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second.count.window.time=15
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@@ -18,6 +18,16 @@ public class StreamAggregateConfig {
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public static final String FORMAT_SPLITTER = ",";
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public static final String PROTOCOL_SPLITTER = "\\.";
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/**
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* Nacos
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*/
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public static final String NACOS_SERVER = StreamAggregateConfigurations.getStringProperty(0, "nacos.server");
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public static final String NACOS_SCHEMA_NAMESPACE = StreamAggregateConfigurations.getStringProperty(0, "nacos.schema.namespace");
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public static final String NACOS_DATA_ID = StreamAggregateConfigurations.getStringProperty(0, "nacos.data.id");
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public static final String NACOS_PIN = StreamAggregateConfigurations.getStringProperty(1, "nacos.pin");
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public static final String NACOS_GROUP = StreamAggregateConfigurations.getStringProperty(1, "nacos.group");
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public static final String NACOS_USERNAME = StreamAggregateConfigurations.getStringProperty(1, "nacos.username");
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/**
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* System
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*/
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@@ -25,7 +35,6 @@ public class StreamAggregateConfig {
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public static final Integer PARSE_PARALLELISM = StreamAggregateConfigurations.getIntProperty(0, "parse.parallelism");
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public static final Integer FIRST_WINDOW_PARALLELISM = StreamAggregateConfigurations.getIntProperty(0, "first.window.parallelism");
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public static final Integer SECOND_WINDOW_PARALLELISM = StreamAggregateConfigurations.getIntProperty(0, "second.window.parallelism");
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public static final Integer APP_TICK_TUPLE_FREQ_SECS = StreamAggregateConfigurations.getIntProperty(1, "app.tick.tuple.freq.secs");
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public static final Integer FIRST_COUNT_WINDOW_TIME = StreamAggregateConfigurations.getIntProperty(0, "first.count.window.time");
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public static final Integer SECOND_COUNT_WINDOW_TIME = StreamAggregateConfigurations.getIntProperty(0, "second.count.window.time");
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public static final String TOOLS_LIBRARY = StreamAggregateConfigurations.getStringProperty(0, "tools.library");
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@@ -45,7 +54,7 @@ public class StreamAggregateConfig {
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*/
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public static final String SINK_KAFKA_SERVERS = StreamAggregateConfigurations.getStringProperty(0, "sink.kafka.servers");
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public static final String SINK_KAFKA_TOPIC = StreamAggregateConfigurations.getStringProperty(0, "sink.kafka.topic");
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public static final String PRODUCER_ACK = StreamAggregateConfigurations.getStringProperty(0, "producer.ack");
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public static final String PRODUCER_ACK = StreamAggregateConfigurations.getStringProperty(1, "producer.ack");
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public static final String RETRIES = StreamAggregateConfigurations.getStringProperty(1, "retries");
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public static final String LINGER_MS = StreamAggregateConfigurations.getStringProperty(1, "linger.ms");
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public static final Integer REQUEST_TIMEOUT_MS = StreamAggregateConfigurations.getIntProperty(1, "request.timeout.ms");
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@@ -68,13 +77,7 @@ public class StreamAggregateConfig {
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/**
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* kafka限流配置-20201117
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*/
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public static final String PRODUCER_KAFKA_COMPRESSION_TYPE = StreamAggregateConfigurations.getStringProperty(0, "producer.kafka.compression.type");
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/**
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* http
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*/
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public static final String SCHEMA_HTTP = StreamAggregateConfigurations.getStringProperty(0, "schema.http");
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public static final String APP_ID_HTTP = StreamAggregateConfigurations.getStringProperty(0, "app.id.http");
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public static final String PRODUCER_KAFKA_COMPRESSION_TYPE = StreamAggregateConfigurations.getStringProperty(1, "producer.kafka.compression.type");
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}
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@@ -30,13 +30,11 @@ public class StreamAggregateTopology {
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try {
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final StreamExecutionEnvironment environment = StreamExecutionEnvironment.getExecutionEnvironment();
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// environment.enableCheckpointing(5000);
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//两个输出之间的最大时间 (单位milliseconds)
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environment.setBufferTimeout(StreamAggregateConfig.BUFFER_TIMEOUT);
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DataStream<String> streamSource = environment.addSource(KafkaConsumer.getKafkaConsumer())
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.setParallelism(StreamAggregateConfig.SOURCE_PARALLELISM);
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.setParallelism(StreamAggregateConfig.SOURCE_PARALLELISM).name(StreamAggregateConfig.SOURCE_KAFKA_TOPIC);
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SingleOutputStreamOperator<Tuple3<String, String, String>> parseDataMap = streamSource.map(new ParseMapFunction())
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.name("ParseDataMap")
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@@ -55,8 +53,11 @@ public class StreamAggregateTopology {
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SingleOutputStreamOperator<String> secondCountWindow = secondWindow.process(new SecondCountWindowFunction())
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.name("SecondCountWindow").setParallelism(StreamAggregateConfig.SECOND_WINDOW_PARALLELISM);
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secondCountWindow.flatMap(new ResultFlatMapFunction()).name("ResultFlatMap").setParallelism(StreamAggregateConfig.SINK_PARALLELISM)
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.addSink(KafkaProducer.getKafkaProducer()).name("LogSinkKafka").setParallelism(StreamAggregateConfig.SINK_PARALLELISM);
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SingleOutputStreamOperator<String> resultFlatMap = secondCountWindow.flatMap(new ResultFlatMapFunction())
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.name("ResultFlatMap").setParallelism(StreamAggregateConfig.SINK_PARALLELISM);
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resultFlatMap.addSink(KafkaProducer.getKafkaProducer()).name("LogSinkKafka")
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.setParallelism(StreamAggregateConfig.SINK_PARALLELISM).name(StreamAggregateConfig.SINK_KAFKA_TOPIC);
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environment.execute(args[0]);
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} catch (Exception e) {
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@@ -24,21 +24,21 @@ import java.util.Map;
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public class FirstCountWindowFunction extends ProcessWindowFunction<Tuple3<String, String, String>, Tuple2<String, String>, String, TimeWindow> {
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private static final Logger logger = LoggerFactory.getLogger(FirstCountWindowFunction.class);
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private static HashMap<String, String[]> metricsMap = JsonParseUtil.getMetricsMap();
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private static HashMap<String, String[]> actionMap = JsonParseUtil.getActionMap();
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private HashMap<String, Map<String, Object>> cacheMap = new HashMap<>(320);
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@Override
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@SuppressWarnings("unchecked")
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public void process(String key, Context context, Iterable<Tuple3<String, String, String>> input, Collector<Tuple2<String, String>> output) {
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try {
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HashMap<String, String[]> metricsMap = JsonParseUtil.getMetricFunctionsMap();
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HashMap<String, String[]> actionMap = JsonParseUtil.getActionMap();
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for (Tuple3<String, String, String> tuple : input) {
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String label = tuple.f0;
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String dimensions = tuple.f1;
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String message = tuple.f2;
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String l7_Protocol = label.substring(0, label.indexOf("@"));
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String l7Protocol = label.substring(0, label.indexOf("@"));
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//action中某个协议的所有function,如果没有就默认
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String[] metricNames = actionMap.getOrDefault(l7_Protocol, actionMap.get("Default"));
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String[] metricNames = actionMap.getOrDefault(l7Protocol, actionMap.get("Default"));
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if (StringUtil.isNotBlank(message)) {
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Map<String, Object> dimensionsObj = (Map<String, Object>) JsonMapper.fromJsonString(dimensions, Map.class);
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Map<String, Object> object = (Map<String, Object>) JsonMapper.fromJsonString(message, Map.class);
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@@ -29,17 +29,14 @@ import java.util.concurrent.ThreadLocalRandom;
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public class ParseMapFunction implements MapFunction<String, Tuple3<String, String, String>> {
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private static final Logger logger = LoggerFactory.getLogger(FirstCountWindowFunction.class);
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private static ArrayList<String[]> jobList = JsonParseUtil.getTransformsList();
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private static HashMap<String, String> dimensionsMap = JsonParseUtil.getDimensionsMap();
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@Override
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@SuppressWarnings("unchecked")
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public Tuple3<String, String, String> map(String message) {
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try {
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ArrayList<String[]> jobList = JsonParseUtil.getTransformsList();
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HashMap<String, String> dimensionsMap = JsonParseUtil.getDimensionsMap();
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if (StringUtil.isNotBlank(message)) {
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Map<String, Object> object = (Map<String, Object>) JsonMapper.fromJsonString(message, Map.class);
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// String streamTraceId = JsonMapperParseUtil.getString(object, "common_stream_trace_id");
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Map<String, Object> dimensionsObj = ParseFunctions.transDimensions(dimensionsMap, object);
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if (ParseFunctions.filterLogs(object)) {
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for (String[] strings : jobList) {
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@@ -126,7 +123,6 @@ public class ParseMapFunction implements MapFunction<String, Tuple3<String, Stri
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} else {
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dimensions.put(resultKeyName, combinationField);
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JsonParseUtil.setValue(message, fieldName, combinationField);
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}
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}
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}
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@@ -25,14 +25,13 @@ import java.util.concurrent.ConcurrentHashMap;
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public class SecondCountWindowFunction extends ProcessWindowFunction<Tuple2<String, String>, String, String, TimeWindow> {
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private static final Logger logger = LoggerFactory.getLogger(SecondCountWindowFunction.class);
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private static HashMap<String, String[]> metricsMap = JsonParseUtil.getMetricsMap();
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private HashMap<String, Map<String, Object>> cacheMap = new HashMap<>(320);
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private static String resultTimeKey = JsonParseUtil.getTimeKey();
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@Override
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@SuppressWarnings("unchecked")
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public void process(String key, Context context, Iterable<Tuple2<String, String>> input, Collector<String> output) {
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try {
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HashMap<String, String[]> metricsMap = JsonParseUtil.getMetricFunctionsMap();
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for (Tuple2<String, String> tuple : input) {
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String dimensions = tuple.f0;
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String message = tuple.f1;
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@@ -57,7 +56,7 @@ public class SecondCountWindowFunction extends ProcessWindowFunction<Tuple2<Stri
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for (String countKey : cacheMap.keySet()) {
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Map<String, Object> resultMap = cacheMap.get(countKey);
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JsonParseUtil.setValue(resultMap, resultTimeKey, endTime);
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JsonParseUtil.setValue(resultMap, JsonParseUtil.getResultTimeKey(), endTime);
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output.collect(JsonMapper.toJsonString(resultMap));
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}
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}
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@@ -1,7 +1,7 @@
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package com.zdjizhi.utils.general;
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import com.zdjizhi.utils.json.JsonTypeUtils;
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import com.zdjizhi.utils.json.JsonTypeUtil;
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/**
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* @author qidaijie
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@@ -18,8 +18,8 @@ public class MetricFunctions {
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* @return value1 + value2
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*/
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public static Long longSum(Object value1, Object value2) {
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Long res1 = JsonTypeUtils.checkLongValue(value1);
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Long res2 = JsonTypeUtils.checkLongValue(value2);
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Long res1 = JsonTypeUtil.checkLongValue(value1);
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Long res2 = JsonTypeUtil.checkLongValue(value2);
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return res1 + res2;
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}
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@@ -32,6 +32,6 @@ public class MetricFunctions {
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*/
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public static Long count(Object count) {
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return JsonTypeUtils.checkLongValue(count) + 1L;
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return JsonTypeUtil.checkLongValue(count) + 1L;
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}
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}
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@@ -18,12 +18,6 @@ import java.util.Map;
|
||||
* @Version V1.0
|
||||
**/
|
||||
public class ParseFunctions {
|
||||
/**
|
||||
* 获取filters条件map
|
||||
*/
|
||||
private static HashMap<String, String> filtersMap = JsonParseUtil.getFiltersMap();
|
||||
|
||||
private static ArrayList<String> metricsList = JsonParseUtil.getLogMetrics();
|
||||
|
||||
/**
|
||||
* 解析 dimensions 字段集
|
||||
@@ -50,7 +44,7 @@ public class ParseFunctions {
|
||||
*/
|
||||
public static boolean filterLogs(Map<String, Object> object) {
|
||||
boolean available = false;
|
||||
|
||||
HashMap<String, String> filtersMap = JsonParseUtil.getFiltersMap();
|
||||
for (String key : filtersMap.keySet()) {
|
||||
switch (key) {
|
||||
case "notempty":
|
||||
@@ -65,11 +59,16 @@ public class ParseFunctions {
|
||||
return available;
|
||||
}
|
||||
|
||||
/**
|
||||
* 根据原始日志字段,生成schema内指定的metrics指标json。
|
||||
*
|
||||
* @param object 原始日志json
|
||||
* @return 统计metrics json
|
||||
*/
|
||||
public static String getMetricsLog(Map<String, Object> object) {
|
||||
|
||||
Map<String, Object> json = new HashMap<>(16);
|
||||
|
||||
for (String fileName : metricsList) {
|
||||
for (String fileName : JsonParseUtil.getMetricsFiledNameList()) {
|
||||
json.put(fileName, object.get(fileName));
|
||||
}
|
||||
|
||||
|
||||
@@ -1,77 +0,0 @@
|
||||
package com.zdjizhi.utils.http;
|
||||
|
||||
import cn.hutool.log.Log;
|
||||
import cn.hutool.log.LogFactory;
|
||||
import org.apache.commons.io.IOUtils;
|
||||
import org.apache.http.HttpEntity;
|
||||
import org.apache.http.client.methods.CloseableHttpResponse;
|
||||
import org.apache.http.client.methods.HttpGet;
|
||||
import org.apache.http.impl.client.CloseableHttpClient;
|
||||
import org.apache.http.impl.client.HttpClients;
|
||||
|
||||
import java.io.BufferedReader;
|
||||
import java.io.IOException;
|
||||
import java.io.InputStreamReader;
|
||||
|
||||
/**
|
||||
* 获取网关schema的工具类
|
||||
*
|
||||
* @author qidaijie
|
||||
*/
|
||||
public class HttpClientUtil {
|
||||
private static final Log logger = LogFactory.get();
|
||||
|
||||
/**
|
||||
* 请求网关获取schema
|
||||
*
|
||||
* @param http 网关url
|
||||
* @return schema
|
||||
*/
|
||||
public static String requestByGetMethod(String http) {
|
||||
CloseableHttpClient httpClient = HttpClients.createDefault();
|
||||
StringBuilder entityStringBuilder;
|
||||
|
||||
HttpGet get = new HttpGet(http);
|
||||
BufferedReader bufferedReader = null;
|
||||
CloseableHttpResponse httpResponse = null;
|
||||
try {
|
||||
httpResponse = httpClient.execute(get);
|
||||
HttpEntity entity = httpResponse.getEntity();
|
||||
entityStringBuilder = new StringBuilder();
|
||||
if (null != entity) {
|
||||
bufferedReader = new BufferedReader(new InputStreamReader(httpResponse.getEntity().getContent(), "UTF-8"), 8 * 1024);
|
||||
int intC;
|
||||
while ((intC = bufferedReader.read()) != -1) {
|
||||
char c = (char) intC;
|
||||
if (c == '\n') {
|
||||
break;
|
||||
}
|
||||
entityStringBuilder.append(c);
|
||||
}
|
||||
|
||||
return entityStringBuilder.toString();
|
||||
}
|
||||
} catch (IOException e) {
|
||||
logger.error("Get Schema from Query engine ERROR! Exception message is:" + e);
|
||||
} finally {
|
||||
if (httpClient != null) {
|
||||
try {
|
||||
httpClient.close();
|
||||
} catch (IOException e) {
|
||||
logger.error("Close HTTP Client ERROR! Exception messgae is:" + e);
|
||||
}
|
||||
}
|
||||
if (httpResponse != null) {
|
||||
try {
|
||||
httpResponse.close();
|
||||
} catch (IOException e) {
|
||||
logger.error("Close httpResponse ERROR! Exception messgae is:" + e);
|
||||
}
|
||||
}
|
||||
if (bufferedReader != null) {
|
||||
IOUtils.closeQuietly(bufferedReader);
|
||||
}
|
||||
}
|
||||
return "";
|
||||
}
|
||||
}
|
||||
@@ -1,17 +1,21 @@
|
||||
package com.zdjizhi.utils.json;
|
||||
|
||||
|
||||
import cn.hutool.log.Log;
|
||||
import cn.hutool.log.LogFactory;
|
||||
import com.alibaba.nacos.api.NacosFactory;
|
||||
import com.alibaba.nacos.api.PropertyKeyConst;
|
||||
import com.alibaba.nacos.api.config.ConfigService;
|
||||
import com.alibaba.nacos.api.config.listener.Listener;
|
||||
import com.alibaba.nacos.api.exception.NacosException;
|
||||
import com.jayway.jsonpath.DocumentContext;
|
||||
import com.jayway.jsonpath.JsonPath;
|
||||
import com.zdjizhi.common.StreamAggregateConfig;
|
||||
import com.zdjizhi.utils.JsonMapper;
|
||||
import com.zdjizhi.utils.http.HttpClientUtil;
|
||||
import net.sf.cglib.beans.BeanGenerator;
|
||||
import com.zdjizhi.utils.StringUtil;
|
||||
import net.sf.cglib.beans.BeanMap;
|
||||
|
||||
import java.util.*;
|
||||
import java.util.concurrent.Executor;
|
||||
|
||||
/**
|
||||
* 使用FastJson解析json的工具类
|
||||
@@ -21,74 +25,76 @@ import java.util.*;
|
||||
public class JsonParseUtil {
|
||||
|
||||
private static final Log logger = LogFactory.get();
|
||||
private static Properties propNacos = new Properties();
|
||||
|
||||
/**
|
||||
* 模式匹配,给定一个类型字符串返回一个类类型
|
||||
*
|
||||
* @param type 类型
|
||||
* @return 类类型
|
||||
* 获取actions所有的计算函数
|
||||
*/
|
||||
|
||||
public static Class getClassName(String type) {
|
||||
Class clazz;
|
||||
|
||||
switch (type) {
|
||||
case "int":
|
||||
clazz = Integer.class;
|
||||
break;
|
||||
case "string":
|
||||
clazz = String.class;
|
||||
break;
|
||||
case "long":
|
||||
clazz = long.class;
|
||||
break;
|
||||
case "array":
|
||||
clazz = List.class;
|
||||
break;
|
||||
case "double":
|
||||
clazz = double.class;
|
||||
break;
|
||||
case "float":
|
||||
clazz = float.class;
|
||||
break;
|
||||
case "char":
|
||||
clazz = char.class;
|
||||
break;
|
||||
case "byte":
|
||||
clazz = byte.class;
|
||||
break;
|
||||
case "boolean":
|
||||
clazz = boolean.class;
|
||||
break;
|
||||
case "short":
|
||||
clazz = short.class;
|
||||
break;
|
||||
default:
|
||||
clazz = String.class;
|
||||
}
|
||||
return clazz;
|
||||
}
|
||||
|
||||
private static HashMap<String, String[]> actionMap = new HashMap<>(16);
|
||||
|
||||
/**
|
||||
* 获取属性值的方法
|
||||
*
|
||||
* @param obj 对象
|
||||
* @param property key
|
||||
* @return 属性的值
|
||||
* 解析metrics指标字段信息
|
||||
*/
|
||||
public static Object getValue(Object obj, String property) {
|
||||
private static HashMap<String, String[]> metricFunctionsMap = new HashMap<>(16);
|
||||
|
||||
/**
|
||||
* 解析dimensions维度字段信息
|
||||
*/
|
||||
private static HashMap<String, String> dimensionsMap = new HashMap<>(16);
|
||||
|
||||
/**
|
||||
* 解析filters过滤信息
|
||||
*/
|
||||
private static HashMap<String, String> filtersMap = new HashMap<>(16);
|
||||
|
||||
/**
|
||||
* 解析transforms转换函数信息
|
||||
*/
|
||||
private static ArrayList<String[]> transformsList = new ArrayList<>();
|
||||
|
||||
/**
|
||||
* 解析metrics指标字段集
|
||||
*/
|
||||
private static ArrayList<String> metricsFiledNameList = new ArrayList<>();
|
||||
|
||||
/**
|
||||
* 解析hierarchy函数,获取切分信息
|
||||
*/
|
||||
private static String[] hierarchy;
|
||||
|
||||
/**
|
||||
* 解析时间戳字段名称
|
||||
*/
|
||||
private static String resultTimeKey = "stat_time";
|
||||
|
||||
static {
|
||||
propNacos.setProperty(PropertyKeyConst.SERVER_ADDR, StreamAggregateConfig.NACOS_SERVER);
|
||||
propNacos.setProperty(PropertyKeyConst.NAMESPACE, StreamAggregateConfig.NACOS_SCHEMA_NAMESPACE);
|
||||
propNacos.setProperty(PropertyKeyConst.USERNAME, StreamAggregateConfig.NACOS_USERNAME);
|
||||
propNacos.setProperty(PropertyKeyConst.PASSWORD, StreamAggregateConfig.NACOS_PIN);
|
||||
try {
|
||||
BeanMap beanMap = BeanMap.create(obj);
|
||||
if (beanMap.containsKey(property)) {
|
||||
return beanMap.get(property);
|
||||
} else {
|
||||
return null;
|
||||
ConfigService configService = NacosFactory.createConfigService(propNacos);
|
||||
String dataId = StreamAggregateConfig.NACOS_DATA_ID;
|
||||
String group = StreamAggregateConfig.NACOS_GROUP;
|
||||
String schema = configService.getConfig(dataId, group, 5000);
|
||||
if (StringUtil.isNotBlank(schema)) {
|
||||
parseSchema(schema);
|
||||
}
|
||||
} catch (RuntimeException e) {
|
||||
logger.error("获取json-value异常,异常key:" + property + "异常信息为:" + e);
|
||||
return null;
|
||||
configService.addListener(dataId, group, new Listener() {
|
||||
@Override
|
||||
public Executor getExecutor() {
|
||||
return null;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void receiveConfigInfo(String configMsg) {
|
||||
if (StringUtil.isNotBlank(configMsg)) {
|
||||
parseSchema(configMsg);
|
||||
}
|
||||
}
|
||||
});
|
||||
} catch (NacosException e) {
|
||||
logger.error("Get Schema config from Nacos error,The exception message is :" + e.getMessage());
|
||||
}
|
||||
}
|
||||
|
||||
@@ -103,7 +109,7 @@ public class JsonParseUtil {
|
||||
try {
|
||||
return jsonMap.getOrDefault(property, null);
|
||||
} catch (RuntimeException e) {
|
||||
logger.error("获取json-value异常,异常key:" + property + "异常信息为:" + e);
|
||||
logger.error("Get the JSON value is abnormal,The key is :" + property + "error message is :" + e);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
@@ -152,7 +158,7 @@ public class JsonParseUtil {
|
||||
try {
|
||||
jsonMap.put(property, value);
|
||||
} catch (RuntimeException e) {
|
||||
logger.error("赋予实体类错误类型数据", e);
|
||||
logger.error("The JSON set value is abnormal,the error message is :", e);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -172,204 +178,130 @@ public class JsonParseUtil {
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 根据反射生成对象的方法
|
||||
*
|
||||
* @param properties 反射类用的map
|
||||
* @return 生成的Object类型的对象
|
||||
*/
|
||||
public static Object generateObject(Map properties) {
|
||||
BeanGenerator generator = new BeanGenerator();
|
||||
Set keySet = properties.keySet();
|
||||
for (Object aKeySet : keySet) {
|
||||
String key = (String) aKeySet;
|
||||
generator.addProperty(key, (Class) properties.get(key));
|
||||
}
|
||||
return generator.create();
|
||||
}
|
||||
|
||||
/**
|
||||
* 通过获取String类型的网关schema链接来获取map,用于生成一个Object类型的对象
|
||||
*
|
||||
* @return 用于反射生成schema类型的对象的一个map集合
|
||||
* 用于反射生成schema类型的对象的一个map集合
|
||||
*/
|
||||
public static HashMap<String, String[]> getActionMap() {
|
||||
HashMap<String, String[]> map = new HashMap<>(16);
|
||||
|
||||
String schema = HttpClientUtil.requestByGetMethod(StreamAggregateConfig.SCHEMA_HTTP);
|
||||
private static void parseSchema(String schema) {
|
||||
clearCacheMap();
|
||||
DocumentContext parse = JsonPath.parse(schema);
|
||||
|
||||
List<Object> actions = parse.read("$.data.doc.action[*]");
|
||||
|
||||
List<Object> actions = parse.read("$.doc.action[*]");
|
||||
for (Object action : actions) {
|
||||
map.put(JsonPath.read(action, "$.label"),
|
||||
actionMap.put(JsonPath.read(action, "$.label"),
|
||||
JsonPath.read(action, "$.metrics").toString().split(StreamAggregateConfig.FORMAT_SPLITTER));
|
||||
// System.out.println(JsonPath.read(action, "$.label")+JsonPath.read(action, "$.metrics").toString());
|
||||
}
|
||||
|
||||
return map;
|
||||
}
|
||||
|
||||
/**
|
||||
* 通过获取String类型的网关schema链接来获取map,用于生成一个Object类型的对象
|
||||
*
|
||||
* @return 用于反射生成schema类型的对象的一个map集合
|
||||
*/
|
||||
public static HashMap<String, String[]> getMetricsMap() {
|
||||
HashMap<String, String[]> map = new HashMap<>(16);
|
||||
|
||||
String schema = HttpClientUtil.requestByGetMethod(StreamAggregateConfig.SCHEMA_HTTP);
|
||||
DocumentContext parse = JsonPath.parse(schema);
|
||||
|
||||
List<Object> metrics = parse.read("$.data.doc.metrics[*]");
|
||||
|
||||
for (Object metric : metrics) {
|
||||
map.put(JsonPath.read(metric, "$.name"),
|
||||
List<Object> metricFunctions = parse.read("$.doc.metrics[*]");
|
||||
for (Object metric : metricFunctions) {
|
||||
metricFunctionsMap.put(JsonPath.read(metric, "$.name"),
|
||||
new String[]{JsonPath.read(metric, "$.function"), JsonPath.read(metric, "$.fieldName")}
|
||||
);
|
||||
}
|
||||
return map;
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取Metrics内指标,用于过滤原始日志
|
||||
*
|
||||
* @return 指标列原始名称
|
||||
*/
|
||||
public static ArrayList<String> getLogMetrics() {
|
||||
ArrayList<String> list = new ArrayList<>();
|
||||
String schema = HttpClientUtil.requestByGetMethod(StreamAggregateConfig.SCHEMA_HTTP);
|
||||
DocumentContext parse = JsonPath.parse(schema);
|
||||
|
||||
List<Object> metrics = parse.read("$.data.doc.metrics[*]");
|
||||
|
||||
List<Object> metrics = parse.read("$.doc.metrics[*]");
|
||||
for (Object metric : metrics) {
|
||||
list.add(JsonPath.read(metric, "$.fieldName"));
|
||||
metricsFiledNameList.add(JsonPath.read(metric, "$.fieldName"));
|
||||
}
|
||||
return list;
|
||||
}
|
||||
|
||||
/**
|
||||
* 通过获取String类型的网关schema链接来获取map,用于生成一个Object类型的对象
|
||||
*
|
||||
* @return 用于反射生成schema类型的对象的一个map集合
|
||||
*/
|
||||
public static String getTimeKey() {
|
||||
|
||||
String schema = HttpClientUtil.requestByGetMethod(StreamAggregateConfig.SCHEMA_HTTP);
|
||||
|
||||
return JsonPath.read(schema, "$.data.doc.timestamp.name");
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 通过获取String类型的网关schema链接来获取map,用于生成一个Object类型的对象
|
||||
*
|
||||
* @return 用于反射生成schema类型的对象的一个map集合
|
||||
*/
|
||||
public static HashMap<String, Class> getResultLogMap() {
|
||||
HashMap<String, Class> map = new HashMap<>(16);
|
||||
|
||||
String schema = HttpClientUtil.requestByGetMethod(StreamAggregateConfig.SCHEMA_HTTP);
|
||||
DocumentContext parse = JsonPath.parse(schema);
|
||||
|
||||
List<Object> dimensions = parse.read("$.data.doc.dimensions[*]");
|
||||
|
||||
List<Object> dimensions = parse.read("$.doc.dimensions[*]");
|
||||
for (Object dimension : dimensions) {
|
||||
map.put(JsonPath.read(dimension, "$.name"),
|
||||
JsonParseUtil.getClassName(JsonPath.read(dimension, "$.type")));
|
||||
}
|
||||
|
||||
List<Object> metrics = parse.read("$.data.doc.metrics[*]");
|
||||
for (Object metric : metrics) {
|
||||
map.put(JsonPath.read(metric, "$.name"),
|
||||
JsonParseUtil.getClassName(JsonPath.read(metric, "$.type")));
|
||||
}
|
||||
|
||||
return map;
|
||||
}
|
||||
|
||||
/**
|
||||
* 通过获取String类型的网关schema链接来获取map,用于生成一个Object类型的对象
|
||||
*
|
||||
* @return 用于反射生成schema类型的对象的一个map集合
|
||||
*/
|
||||
public static HashMap<String, String> getDimensionsMap() {
|
||||
HashMap<String, String> map = new HashMap<>(16);
|
||||
|
||||
String schema = HttpClientUtil.requestByGetMethod(StreamAggregateConfig.SCHEMA_HTTP);
|
||||
DocumentContext parse = JsonPath.parse(schema);
|
||||
|
||||
List<Object> dimensions = parse.read("$.data.doc.dimensions[*]");
|
||||
|
||||
for (Object dimension : dimensions) {
|
||||
map.put(JsonPath.read(dimension, "$.name"),
|
||||
dimensionsMap.put(JsonPath.read(dimension, "$.name"),
|
||||
JsonPath.read(dimension, "$.fieldName"));
|
||||
}
|
||||
|
||||
return map;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 通过获取String类型的网关schema链接来获取map,用于生成一个Object类型的对象
|
||||
*
|
||||
* @return 用于反射生成schema类型的对象的一个map集合
|
||||
*/
|
||||
public static HashMap<String, String> getFiltersMap() {
|
||||
HashMap<String, String> map = new HashMap<>(16);
|
||||
String schema = HttpClientUtil.requestByGetMethod(StreamAggregateConfig.SCHEMA_HTTP);
|
||||
DocumentContext parse = JsonPath.parse(schema);
|
||||
|
||||
List<Object> filters = parse.read("$.data.doc.filters[*]");
|
||||
List<Object> filters = parse.read("$.doc.filters[*]");
|
||||
for (Object filter : filters) {
|
||||
map.put(JsonPath.read(filter, "$.type"), JsonPath.read(filter, "$.fieldName"));
|
||||
filtersMap.put(JsonPath.read(filter, "$.type"), JsonPath.read(filter, "$.fieldName"));
|
||||
}
|
||||
|
||||
return map;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 根据http链接获取schema,解析之后返回一个任务列表 (useList toList funcList paramlist)
|
||||
*
|
||||
* @return 任务列表
|
||||
*/
|
||||
public static ArrayList<String[]> getTransformsList() {
|
||||
ArrayList<String[]> list = new ArrayList<>();
|
||||
String schema = HttpClientUtil.requestByGetMethod(StreamAggregateConfig.SCHEMA_HTTP);
|
||||
DocumentContext parse = JsonPath.parse(schema);
|
||||
|
||||
List<Object> transforms = parse.read("$.data.doc.transforms[*]");
|
||||
List<Object> transforms = parse.read("$.doc.transforms[*]");
|
||||
for (Object transform : transforms) {
|
||||
String function = JsonPath.read(transform, "$.function").toString();
|
||||
String name = JsonPath.read(transform, "$.name").toString();
|
||||
String fieldName = JsonPath.read(transform, "$.fieldName").toString();
|
||||
String parameters = JsonPath.read(transform, "$.parameters").toString();
|
||||
list.add(new String[]{function, name, fieldName, parameters});
|
||||
transformsList.add(new String[]{function, name, fieldName, parameters});
|
||||
}
|
||||
|
||||
return list;
|
||||
}
|
||||
|
||||
/**
|
||||
* 根据http链接获取schema,解析之后返回一个任务列表 (useList toList funcList paramlist)
|
||||
*
|
||||
* @return 任务列表
|
||||
*/
|
||||
public static String[] getHierarchy() {
|
||||
String schema = HttpClientUtil.requestByGetMethod(StreamAggregateConfig.SCHEMA_HTTP);
|
||||
DocumentContext parse = JsonPath.parse(schema);
|
||||
List<Object> transforms = parse.read("$.data.doc.transforms[*]");
|
||||
for (Object transform : transforms) {
|
||||
List<Object> hierarchyList = parse.read("$.doc.transforms[*]");
|
||||
for (Object transform : hierarchyList) {
|
||||
String function = JsonPath.read(transform, "$.function").toString();
|
||||
if ("hierarchy".equals(function)) {
|
||||
String name = JsonPath.read(transform, "$.name").toString();
|
||||
String parameters = JsonPath.read(transform, "$.parameters").toString();
|
||||
return new String[]{name, parameters};
|
||||
hierarchy = new String[]{name, parameters};
|
||||
}
|
||||
}
|
||||
return null;
|
||||
|
||||
resultTimeKey = JsonPath.read(schema, "$.doc.timestamp.name");
|
||||
}
|
||||
|
||||
/**
|
||||
* @return 解析schema获取的actions集合
|
||||
*/
|
||||
public static HashMap<String, String[]> getActionMap() {
|
||||
return actionMap;
|
||||
}
|
||||
|
||||
/**
|
||||
* @return 解析schema获取的指标统计方式集合
|
||||
*/
|
||||
public static HashMap<String, String[]> getMetricFunctionsMap() {
|
||||
return metricFunctionsMap;
|
||||
}
|
||||
|
||||
/**
|
||||
* @return 解析schema获取的维度集合
|
||||
*/
|
||||
public static HashMap<String, String> getDimensionsMap() {
|
||||
return dimensionsMap;
|
||||
}
|
||||
|
||||
/**
|
||||
* @return 解析schema获取的过滤规则集合
|
||||
*/
|
||||
public static HashMap<String, String> getFiltersMap() {
|
||||
return filtersMap;
|
||||
}
|
||||
|
||||
/**
|
||||
* @return 解析schema获取的操作集合
|
||||
*/
|
||||
public static ArrayList<String[]> getTransformsList() {
|
||||
return transformsList;
|
||||
}
|
||||
|
||||
/**
|
||||
* @return 解析schema获取的指标字段集合
|
||||
*/
|
||||
public static ArrayList<String> getMetricsFiledNameList() {
|
||||
return metricsFiledNameList;
|
||||
}
|
||||
|
||||
/**
|
||||
* @return 解析schema获取的拆解函数
|
||||
*/
|
||||
public static String[] getHierarchy() {
|
||||
return hierarchy;
|
||||
}
|
||||
|
||||
/**
|
||||
* @return 解析schema获取的时间字段的key
|
||||
*/
|
||||
public static String getResultTimeKey() {
|
||||
return resultTimeKey;
|
||||
}
|
||||
|
||||
/**
|
||||
* 在配置变化时清空缓存,重新解析schema更新缓存
|
||||
*/
|
||||
private static void clearCacheMap() {
|
||||
actionMap.clear();
|
||||
metricFunctionsMap.clear();
|
||||
dimensionsMap.clear();
|
||||
filtersMap.clear();
|
||||
transformsList.clear();
|
||||
metricsFiledNameList.clear();
|
||||
}
|
||||
}
|
||||
@@ -1,7 +1,6 @@
|
||||
package com.zdjizhi.utils.json;
|
||||
|
||||
import cn.hutool.log.Log;
|
||||
import cn.hutool.log.LogFactory;
|
||||
|
||||
import com.zdjizhi.utils.JsonMapper;
|
||||
import com.zdjizhi.utils.exception.AnalysisException;
|
||||
|
||||
@@ -14,8 +13,7 @@ import java.util.Map;
|
||||
* @Description:
|
||||
* @date 2021/7/1217:34
|
||||
*/
|
||||
public class JsonTypeUtils {
|
||||
private static final Log logger = LogFactory.get();
|
||||
public class JsonTypeUtil {
|
||||
/**
|
||||
* String 类型检验转换方法
|
||||
*
|
||||
@@ -22,19 +22,25 @@ public class KafkaConsumer {
|
||||
properties.put("session.timeout.ms", StreamAggregateConfig.SESSION_TIMEOUT_MS);
|
||||
properties.put("max.poll.records", StreamAggregateConfig.MAX_POLL_RECORDS);
|
||||
properties.put("max.partition.fetch.bytes", StreamAggregateConfig.MAX_PARTITION_FETCH_BYTES);
|
||||
properties.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
|
||||
properties.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
|
||||
|
||||
CertUtils.chooseCert(StreamAggregateConfig.SOURCE_KAFKA_SERVERS, properties);
|
||||
|
||||
return properties;
|
||||
}
|
||||
|
||||
/**
|
||||
* 官方序列化kafka数据
|
||||
*
|
||||
* @return kafka logs
|
||||
*/
|
||||
public static FlinkKafkaConsumer<String> getKafkaConsumer() {
|
||||
FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>(StreamAggregateConfig.SOURCE_KAFKA_TOPIC,
|
||||
new SimpleStringSchema(), createConsumerConfig());
|
||||
|
||||
//随着checkpoint提交,将offset提交到kafka
|
||||
kafkaConsumer.setCommitOffsetsOnCheckpoints(true);
|
||||
|
||||
//从消费组当前的offset开始消费
|
||||
kafkaConsumer.setStartFromGroupOffsets();
|
||||
|
||||
return kafkaConsumer;
|
||||
|
||||
@@ -42,11 +42,7 @@ public class KafkaProducer {
|
||||
createProducerConfig(), Optional.empty());
|
||||
|
||||
//启用此选项将使生产者仅记录失败日志而不是捕获和重新抛出它们
|
||||
kafkaProducer.setLogFailuresOnly(false);
|
||||
|
||||
//写入kafka的消息携带时间戳
|
||||
// kafkaProducer.setWriteTimestampToKafka(true);
|
||||
|
||||
kafkaProducer.setLogFailuresOnly(true);
|
||||
|
||||
return kafkaProducer;
|
||||
}
|
||||
|
||||
@@ -1,14 +1,14 @@
|
||||
#Log4j
|
||||
log4j.rootLogger=info,console,file
|
||||
log4j.rootLogger=error,console,file
|
||||
# 控制台日志设置
|
||||
log4j.appender.console=org.apache.log4j.ConsoleAppender
|
||||
log4j.appender.console.Threshold=info
|
||||
log4j.appender.console.Threshold=error
|
||||
log4j.appender.console.layout=org.apache.log4j.PatternLayout
|
||||
log4j.appender.console.layout.ConversionPattern=[%d{yyyy-MM-dd HH\:mm\:ss}] [%-5p] [Thread\:%t] %l %x - <%m>%n
|
||||
|
||||
# 文件日志设置
|
||||
log4j.appender.file=org.apache.log4j.DailyRollingFileAppender
|
||||
log4j.appender.file.Threshold=info
|
||||
log4j.appender.file.Threshold=error
|
||||
log4j.appender.file.encoding=UTF-8
|
||||
log4j.appender.file.Append=true
|
||||
#路径请用相对路径,做好相关测试输出到应用目下
|
||||
@@ -18,8 +18,8 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout
|
||||
#log4j.appender.file.layout.ConversionPattern=%d{HH:mm:ss} %X{ip} [%t] %5p %c{1} %m%n
|
||||
log4j.appender.file.layout.ConversionPattern=[%d{yyyy-MM-dd HH\:mm\:ss}] [%-5p] %X{ip} [Thread\:%t] %l %x - %m%n
|
||||
#MyBatis 配置,com.nis.web.dao是mybatis接口所在包
|
||||
log4j.logger.com.nis.web.dao=debug
|
||||
log4j.logger.com.nis.web.dao=error
|
||||
#bonecp数据源配置
|
||||
log4j.category.com.jolbox=debug,console
|
||||
log4j.category.com.jolbox=error,console
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user