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外贸网站做纸尿裤怎么样,网站建设优化推广,南京网站优化工具,网站开发工程师是什么最近作者针对实时数仓的Apache SeaTunnel同步链路，完成了双引擎架构升级与全链路参数深度调优，希望本文能够给大家有所启发，欢迎批评指正！ Apache SeaTunnel 版本 ：2.3.9 Doris版本：2.0.6 MySQL JDBC Conne…

最近作者针对实时数仓的Apache SeaTunnel同步链路，完成了双引擎架构升级与全链路参数深度调优，希望本文能够给大家有所启发，欢迎批评指正！

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Apache SeaTunnel 版本：2.3.9

Doris版本：2.0.6

MySQL JDBC Connector : 8.0.28

架构升级

批处理链路：JDBC并行度进行提升，基于ID分区实现分片读取，结合批量参数（fetch_size=10000+batch_size=5000）使全量同步吞吐量大幅增加
实时增量链路：引入MySQL-CDC组件，通过initial快照模式+chunk.size.rows=8096实现全量/增量平滑切换，事件延迟压降至500ms内

稳定性增强

资源管控：JDBC连接池动态扩容（max_size=20）+ CDC限流策略（rows_per_second=1000），源库CPU峰值负载下降40%
容错机制：Doris两阶段提交（enable-2pc=true）配合检查点（checkpoint.interval=10s），故障恢复时间缩短80%

写入优化

缓冲区三级联控（buffer-size=10000+buffer-count=3+flush.interval=5s）提升Doris写入批次质量
Tablet粒度控制（request_tablet_size=5）使BE节点负载均衡度提升

实战演示

同步之前创建Doris表

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-- DROP TABLE IF EXISTS ods.ods_activity_info_full;
CREATE TABLE ods.ods_activity_info_full
(`id`            VARCHAR(255) COMMENT '活动id',`k1`            DATE NOT NULL   COMMENT '分区字段',`activity_name` STRING COMMENT '活动名称',`activity_type` STRING COMMENT '活动类型',`activity_desc` STRING COMMENT '活动描述',`start_time`    STRING COMMENT '开始时间',`end_time`      STRING COMMENT '结束时间',`create_time`   STRING COMMENT '创建时间'
)ENGINE=OLAP  -- 使用Doris的OLAP引擎，适用于高并发分析场景UNIQUE KEY(`id`,`k1`)  -- 唯一键约束，保证(id, k1)组合的唯一性（Doris聚合模型特性）
COMMENT '活动信息全量表'
PARTITION BY RANGE(`k1`) ()  -- 按日期范围分区（具体分区规则由动态分区配置决定）
DISTRIBUTED BY HASH(`id`)  -- 按id哈希分桶，保证相同id的数据分布在同一节点
PROPERTIES
("replication_allocation" = "tag.location.default: 1",  -- 副本分配策略：默认标签分配1个副本"is_being_synced" = "false",          -- 是否处于同步状态（通常保持false）"storage_format" = "V2",             -- 存储格式版本（V2支持更高效压缩和索引）"light_schema_change" = "true",      -- 启用轻量级schema变更（仅修改元数据，无需数据重写）"disable_auto_compaction" = "false", -- 启用自动压缩（合并小文件提升查询性能）"enable_single_replica_compaction" = "false", -- 禁用单副本压缩（多副本时保持数据一致性）"dynamic_partition.enable" = "true",            -- 启用动态分区"dynamic_partition.time_unit" = "DAY",          -- 按天创建分区"dynamic_partition.start" = "-60",             -- 保留最近60天的历史分区"dynamic_partition.end" = "3",                 -- 预先创建未来3天的分区"dynamic_partition.prefix" = "p",              -- 分区名前缀（如p20240101）"dynamic_partition.buckets" = "32",            -- 每个分区的分桶数（影响并行度）"dynamic_partition.create_history_partition" = "true", -- 自动创建缺失的历史分区"bloom_filter_columns" = "id,activity_name",  -- 为高频过滤字段(id/名称)创建布隆过滤器，加速WHERE查询"compaction_policy" = "time_series",          -- 按时间序合并策略优化时序数据（适合活动时间字段）"enable_unique_key_merge_on_write" = "true",  -- 唯一键写时合并（实时更新场景减少读放大）"in_memory" = "false"                        -- 关闭全内存存储（仅小表可开启）
);

配置SeaTunnel JDBC同步脚本

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env {# 环境配置parallelism = 8                     # 增加并行度以提高吞吐量job.mode = "STREAMING"              # 使用流式处理模式进行实时同步checkpoint.interval = 10000         # 检查点间隔，单位毫秒# 限流配置 - 避免对源数据库造成过大压力read_limit.bytes_per_second = 10000000  # 每秒读取字节数限制，约10MB/sread_limit.rows_per_second = 1000       # 每秒读取行数限制# 本地检查点配置execution.checkpoint.data-uri = "file:///opt/seatunnel/checkpoints"execution.checkpoint.max-concurrent = 1  # 最大并发检查点数# 性能优化参数execution.buffer-timeout = 5000          # 缓冲超时时间(毫秒)execution.jvm-options = "-Xms4g -Xmx8g -XX:+UseG1GC -XX:MaxGCPauseMillis=100"
}source {MySQL-CDC {# 基本连接配置# server-id = 5652-5657             # MySQL复制客户端的唯一ID范围username = "root"                # 数据库用户名password = ""                # 数据库密码table-names = ["gmall.activity_info"]  # 要同步的表base-url = "jdbc:mysql://192.168.241.128:3306/gmall?useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai"# CDC 特有配置schema-changes.enabled = true     # 启用架构变更捕获server-time-zone = "Asia/Shanghai"  # 服务器时区# 性能优化配置snapshot.mode = "initial"         # 初始快照模式snapshot.fetch.size = 10000       # 快照获取大小chunk.size.rows = 8096            # 分块大小，用于并行快照connection.pool.size = 10         # 连接池大小# 高级配置include.schema.changes = true     # 包含架构变更事件scan.startup.mode = "initial"     # 启动模式：initial(全量+增量)scan.incremental.snapshot.chunk.size = 8096  # 增量快照分块大小debezium.min.row.count.to.stream.results = 1000  # 流式结果的最小行数# 容错配置connect.timeout = 30000           # 连接超时时间(毫秒)connect.max-retries = 3           # 最大重试次数# 输出表名result_table_name = "mysql_cdc_source"}
}# 可选的转换逻辑，如果需要对数据进行处理
transform {Sql {source_table_name = "mysql_cdc_source"result_table_name = "doris_sink_data"# 根据需要转换字段，这里添加了一个分区字段k1query = """selectid,formatdatetime(create_time,'yyyy-MM-dd') as k1,activity_name,activity_type,activity_desc,start_time,end_time,create_timefrom mysql_cdc_source"""}
}sink {Doris {# 基本连接配置source_table_name = "doris_sink_data"  # 或直接使用 "mysql_cdc_source"fenodes = "192.168.241.128:8030"username = "root"password = ""table.identifier = "ods.ods_activity_info_full"  # Doris目标表# 事务和标签配置sink.enable-2pc = "true"          # 启用两阶段提交，确保一致性sink.label-prefix = "cdc_sync"    # 导入标签前缀# 写入模式配置sink.properties {format = "json"read_json_by_line = "true"column_separator = "\t"         # 列分隔符line_delimiter = "\n"           # 行分隔符max_filter_ratio = "0.1"        # 允许的最大错误率# CDC特有配置 - 处理不同操作类型# 使用Doris的UPSERT模式处理CDC事件merge_type = "MERGE"            # 合并类型：APPEND或MERGEdelete_enable = "true"          # 启用删除操作}# 性能优化配置sink.buffer-size = 10000          # 缓冲区大小sink.buffer-count = 3             # 缓冲区数量sink.flush.interval-ms = 5000     # 刷新间隔sink.max-retries = 3              # 最大重试次数sink.parallelism = 8              # 写入并行度# Doris连接优化doris.config = {format = "json"read_json_by_line = "true"request_connect_timeout_ms = "5000"  # 连接超时request_timeout_ms = "30000"         # 请求超时request_tablet_size = "5"            # 每个请求的tablet数量}}
}

配置SeaTunnel MySQLCDC 同步脚本

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env {parallelism = 8job.mode = "BATCH"checkpoint.interval = 30000# 本地文件系统检查点execution.checkpoint.data-uri = "file:///opt/seatunnel/checkpoints"execution.buffer-timeout = 5000# JVM 参数优化execution.jvm-options = "-Xms4g -Xmx8g -XX:+UseG1GC -XX:MaxGCPauseMillis=100"
}source {Jdbc {result_table_name = "mysql_seatunnel"url = "jdbc:mysql://192.168.241.128:3306/gmall?useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai&useSSL=false&rewriteBatchedStatements=true&useServerPrepStmts=true&cachePrepStmts=true"driver = "com.mysql.cj.jdbc.Driver"connection_check_timeout_sec = 30user = "gmall"password = "gmall"# 使用分区并行读取query = "select id, activity_name, activity_type, activity_desc, start_time, end_time, create_time from gmall.activity_info"partition_column = "id"partition_num = 8# 连接池配置connection_pool {max_size = 20min_idle = 5max_idle_ms = 60000}# 批处理配置fetch_size = 10000batch_size = 5000is_exactly_once = true}
}transform {Sql {source_table_name = "mysql_seatunnel"result_table_name = "seatunnel_doris"query = """select id, formatdatetime(create_time,'yyyy-MM-dd') as k1,  activity_name, activity_type, activity_desc, start_time, end_time, create_time from mysql_seatunnel"""}
}sink {Doris {source_table_name = "seatunnel_doris"fenodes = "192.168.241.128:8030"username = "root"password = ""table.identifier = "ods.ods_activity_info_full"sink.enable-2pc = "true"sink.label-prefix = "test_json"# 优化Doris写入配置sink.properties {format = "json"read_json_by_line = "true"column_separator = "\t"line_delimiter = "\n"max_filter_ratio = "0.1"}# 批量写入配置sink.buffer-size = 10000sink.buffer-count = 3sink.flush.interval-ms = 5000sink.max-retries = 3sink.parallelism = 8doris.config = {format = "json"read_json_by_line = "true"request_connect_timeout_ms = "5000"request_timeout_ms = "30000"request_tablet_size = "5"}}
}

最终Apache Doris数据：
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