在前面:微服務調用鏈追蹤中心搭建 一文中咱們利用Zipkin搭建了一個微服務調用鏈的追蹤中心,而且模擬了微服務調用的實驗場景。利用Zipkin的庫Brave,咱們能夠收集一個客戶端請求從發出到被響應 經歷了哪些組件、哪些微服務、請求總時長、每一個組件所花時長 等信息。java
本文將講述如何利用Zipkin對Mysql數據庫的調用進行追蹤,這裏一樣藉助OpenZipkin庫Brave來完成。mysql
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ZipkinTool是在《微服務調用鏈追蹤中心搭建》一文中編寫的與Zipkin通訊的工具組件,利用其追蹤微服務調用鏈的,如今咱們想追蹤Mysql數據庫調用鏈的話,能夠擴展一下其功能。github
<dependency>
<groupId>io.zipkin.brave</groupId>
<artifactId>brave-mysql</artifactId>
<version>4.0.6</version>
</dependency>
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@Bean
public MySQLStatementInterceptorManagementBean mySQLStatementInterceptorManagementBean() {
return new MySQLStatementInterceptorManagementBean(brave().clientTracer());
}
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依然繼承前文:《微服務調用鏈追蹤中心搭建》,咱們改造一下文中的ServiceC這個微服務,在其中添加與Mysql數據庫的交互。spring
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-jdbc</artifactId>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<scope>runtime</scope>
</dependency>
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spring.datasource.driver-class-name=com.mysql.jdbc.Driver
spring.datasource.url=jdbc:mysql://你的Mysql服務所在IP:3307/test?useSSL=false\
&statementInterceptors=com.github.kristofa.brave.mysql.MySQLStatementInterceptor\
&zipkinServiceName=mysqlService
spring.datasource.username=root
spring.datasource.password=XXXXXX
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@GetMapping("/mysqltest」) public String mysqlTest() { String name = jdbcTemplate.queryForObject( "SELECT name FROM user WHERE id = 1", String.class ); return "Welcome " + name; } 複製代碼
1. 啓動Mysql容器sql
docker run -d -p 3307:3306 \
-v ~/mysql/data:/var/lib/mysql \
-v ~/mysql/conf:/etc/mysql/conf.d \
-e MYSQL_ROOT_PASSWORD=XXXXXX \
--name mysql mysql
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2. 再啓動一個Mysql容器,接入其中作一些設置docker
docker run -it --rm \
--link mysql:mysql mysql \
mysql -hmysql -u root -p
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CREATE DATABASE `zipkin`
CREATE TABLE IF NOT EXISTS zipkin_spans (
`trace_id_high` BIGINT NOT NULL DEFAULT 0 COMMENT 'If non zero, this means the trace uses 128 bit traceIds instead of 64 bit’, `trace_id` BIGINT NOT NULL, `id` BIGINT NOT NULL, `name` VARCHAR(255) NOT NULL, `parent_id` BIGINT, `debug` BIT(1), `start_ts` BIGINT COMMENT 'Span.timestamp(): epoch micros used for endTs query and to implement TTL’,
`duration` BIGINT COMMENT 'Span.duration(): micros used for minDuration and maxDuration query’ ) ENGINE=InnoDB ROW_FORMAT=COMPRESSED CHARACTER SET=utf8 COLLATE utf8_general_ci; ALTER TABLE zipkin_spans ADD UNIQUE KEY(`trace_id_high`, `trace_id`, `id`) COMMENT 'ignore insert on duplicate’;
ALTER TABLE zipkin_spans ADD INDEX(`trace_id_high`, `trace_id`, `id`) COMMENT 'for joining with zipkin_annotations’; ALTER TABLE zipkin_spans ADD INDEX(`trace_id_high`, `trace_id`) COMMENT 'for getTracesByIds’;
ALTER TABLE zipkin_spans ADD INDEX(`name`) COMMENT 'for getTraces and getSpanNames’; ALTER TABLE zipkin_spans ADD INDEX(`start_ts`) COMMENT 'for getTraces ordering and range’;
CREATE TABLE IF NOT EXISTS zipkin_annotations (
`trace_id_high` BIGINT NOT NULL DEFAULT 0 COMMENT 'If non zero, this means the trace uses 128 bit traceIds instead of 64 bit’, `trace_id` BIGINT NOT NULL COMMENT 'coincides with zipkin_spans.trace_id’,
`span_id` BIGINT NOT NULL COMMENT 'coincides with zipkin_spans.id’, `a_key` VARCHAR(255) NOT NULL COMMENT 'BinaryAnnotation.key or Annotation.value if type == -1’,
`a_value` BLOB COMMENT 'BinaryAnnotation.value(), which must be smaller than 64KB’, `a_type` INT NOT NULL COMMENT 'BinaryAnnotation.type() or -1 if Annotation’,
`a_timestamp` BIGINT COMMENT 'Used to implement TTL; Annotation.timestamp or zipkin_spans.timestamp’, `endpoint_ipv4` INT COMMENT 'Null when Binary/Annotation.endpoint is null’,
`endpoint_ipv6` BINARY(16) COMMENT 'Null when Binary/Annotation.endpoint is null, or no IPv6 address’, `endpoint_port` SMALLINT COMMENT 'Null when Binary/Annotation.endpoint is null’,
`endpoint_service_name` VARCHAR(255) COMMENT 'Null when Binary/Annotation.endpoint is null’ ) ENGINE=InnoDB ROW_FORMAT=COMPRESSED CHARACTER SET=utf8 COLLATE utf8_general_ci; ALTER TABLE zipkin_annotations ADD UNIQUE KEY(`trace_id_high`, `trace_id`, `span_id`, `a_key`, `a_timestamp`) COMMENT 'Ignore insert on duplicate’;
ALTER TABLE zipkin_annotations ADD INDEX(`trace_id_high`, `trace_id`, `span_id`) COMMENT 'for joining with zipkin_spans’; ALTER TABLE zipkin_annotations ADD INDEX(`trace_id_high`, `trace_id`) COMMENT 'for getTraces/ByIds’;
ALTER TABLE zipkin_annotations ADD INDEX(`endpoint_service_name`) COMMENT 'for getTraces and getServiceNames’; ALTER TABLE zipkin_annotations ADD INDEX(`a_type`) COMMENT 'for getTraces’;
ALTER TABLE zipkin_annotations ADD INDEX(`a_key`) COMMENT 'for getTraces’; ALTER TABLE zipkin_annotations ADD INDEX(`trace_id`, `span_id`, `a_key`) COMMENT 'for dependencies job’;
CREATE TABLE IF NOT EXISTS zipkin_dependencies (
`day` DATE NOT NULL,
`parent` VARCHAR(255) NOT NULL,
`child` VARCHAR(255) NOT NULL,
`call_count` BIGINT,
`error_count` BIGINT
) ENGINE=InnoDB ROW_FORMAT=COMPRESSED CHARACTER SET=utf8 COLLATE utf8_general_ci;
ALTER TABLE zipkin_dependencies ADD UNIQUE KEY(`day`, `parent`, `child`);
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這裏建立了三個數據表。數據庫
該Sql文件能夠從如下連接得到:https://github.com/openzipkin/zipkin/blob/master/zipkin-storage/mysql/src/main/resources/mysql.sql編程
Sql腳本執行完成後,能夠看到zipkin相關的三個表已經建成:c#
CREATE DATABASE `test`
CREATE TABLE `user` (
`id` int(11) unsigned NOT NULL auto_increment,
`name` varchar(100) DEFAULT NULL ,
PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET = utf8;
insert into user values (1,」hansonwang99」)
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這裏插入了一條數據用於實驗。
docker run -d -p 9411:9411 \
--link mysql:mysql \
-e STORAGE_TYPE=mysql \
-e MYSQL_HOST=mysql \
-e MYSQL_TCP_PORT=3306 \
-e MYSQL_DB=zipkin \
-e MYSQL_USER=root \
-e MYSQL_PASS=XXXXXX \
--name zipkin openzipkin/zipkin
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在瀏覽器中輸入:localhost:8883/mysqltest,若是看到如下輸出,就能夠證實數據庫調用操做已經成功了!
打開Zipkin Web UI,點擊服務名下拉列表能看見已經成功識別了Mysql數據庫調用服務
能夠看到 首次查詢 Mysql的調用鏈追蹤信息,有不少
隨便點開某一個查看:
目的是再次觸發Mysql的調用,而後再次Find Traces,能夠看到追蹤數據相似下圖:包含兩次Mysql的query動做:
點開第一個query查看,其其實是在 嘗試鏈接Mysql數據庫
點開第二個query查看,發現這裏纔是 實際查詢業務
從圖形化界面上能夠清楚地知道每一個階段的詳細步驟與耗時,所以能夠用來分析哪一個SQL語句執行相對較慢。
本文實驗所用源碼已經開源,須要的話請 自取。
做者更多的SpringBt實踐文章在此:
若是有興趣,也能夠抽點時間看看做者一些關於容器化、微服務化方面的文章: