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​本月初，SeaTunnel同步计算引擎STE 2.3.0 beta2（commit id 7393c47）在社区的共同努力之下正式发布。与此同时，社区对大家期待的性能指标进行了测试。

为了让大家对测试结果有一个更直观的概念，我们采用了对比测试的方法。熟悉数据集成领域的人应该了解，DataX是目前数据开源同步引擎里，性能较好的同步工具之一，这次SeaTunnel做对比的对象，正是这款目前在数据集成领域使用较多的开源同步引擎。

为了保证对比测试的准确性，我们选取了相同的测试场景：在相同的资源情况下，测试DataX和SeaTunnel将数据批量从MySQL同步到HDFS，以Text格式保存，所需要花费的时间，并进行对比。

测试环境

MySQL

阿里云RDS MySQL 8Core 32G

HDFS

CPU：Intel(R) Xeon(R) Platinum 8369B CPU @ 2.70GHz

Memory：32G

节点数：3

NameNode -Xmx4G

DataNode -Xmx16G

测试数据

列数：31

行数：32226320 （3000万条）

大小：数据写入HDFS（text格式）大小为18G

我们在Mysql中创建了一张包含了31个字段的表，主键选择递增的id，其他所有字段采用随机的方式生成，除了主键外均不设置索引。

建表语句为

 create table test.type_source_table ( id int auto_increment primary key, f_binary binary(64) null, f_blob blob null, f_long_varbinary mediumblob null, f_longblob longblob null, f_tinyblob tinyblob null, f_varbinary varbinary(100) null, f_smallint smallint null, f_smallint_unsigned smallint unsigned null, f_mediumint mediumint null, f_mediumint_unsigned mediumint unsigned null, f_int int null, f_int_unsigned int unsigned null, f_integer int null, f_integer_unsigned int unsigned null, f_bigint bigint null, f_bigint_unsigned bigint unsigned null, f_numeric decimal null, f_decimal decimal null, f_float float null, f_double double null, f_double_precision double null, f_longtext longtext null, f_mediumtext mediumtext null, f_text text null, f_tinytext tinytext null, f_varchar varchar(100) null, f_date date null, f_datetime datetime null, f_time time null, f_timestamp timestamp null ); 

DataX任务配置

为了充分利用DataX提供的特性，我们采用了DataX提供的splitPk的特性，将单个Job对应的分片进行拆分，产生一定数量的子任务。具体配置如下：

 { "job": { "content": [ { "reader": { "name": "mysqlreader", "parameter": { "column": [ "id", "f_binary", "f_blob", "f_long_varbinary", "f_longblob", "f_tinyblob", "f_varbinary", "f_smallint", "f_smallint_unsigned", "f_mediumint", "f_mediumint_unsigned", "f_int", "f_int_unsigned", "f_integer", "f_integer_unsigned", "f_bigint", "f_bigint_unsigned", "f_numeric", "f_decimal", "f_float", "f_double", "f_double_precision", "f_longtext", "f_mediumtext", "f_text", "f_tinytext", "f_varchar", "f_date", "f_datetime", "f_time", "f_timestamp" ], "connection": [ { "jdbcUrl": [ "jdbc:mysql://seatunnel.rds.aliyuncs.com:3306/test" ], "table": [ "type_source_table" ] } ], "password": "password", "username": "root", "splitPk": "id" } }, "writer": { "name": "hdfswriter", "parameter": { "column": [ { "name": "id", "type": "INT" }, { "name": "f_binary", "type": "STRING" }, { "name": "f_blob", "type": "STRING" }, { "name": "f_long_varbinary", "type": "STRING" }, { "name": "f_longblob", "type": "STRING" }, { "name": "f_tinyblob", "type": "STRING" }, { "name": "f_varbinary", "type": "STRING" }, { "name": "f_smallint", "type": "SMALLINT" }, { "name": "f_smallint_unsigned", "type": "SMALLINT" }, { "name": "f_mediumint", "type": "SMALLINT" }, { "name": "f_mediumint_unsigned", "type": "SMALLINT" }, { "name": "f_int", "type": "INT" }, { "name": "f_int_unsigned", "type": "INT" }, { "name": "f_integer", "type": "INT" }, { "name": "f_integer_unsigned", "type": "INT" }, { "name": "f_bigint", "type": "BIGINT" }, { "name": "f_bigint_unsigned", "type": "BIGINT" }, { "name": "f_numeric", "type": "DOUBLE" }, { "name": "f_decimal", "type": "DOUBLE" }, { "name": "f_float", "type": "FLOAT" }, { "name": "f_double", "type": "DOUBLE" }, { "name": "f_double_precision", "type": "DOUBLE" }, { "name": "f_longtext", "type": "STRING" }, { "name": "f_mediumtext", "type": "STRING" }, { "name": "f_text", "type": "STRING" }, { "name": "f_tinytext", "type": "STRING" }, { "name": "f_varchar", "type": "STRING" }, { "name": "f_date", "type": "DATE" }, { "name": "f_datetime", "type": "TIMESTAMP" }, { "name": "f_time", "type": "DATE" }, { "name": "f_timestamp", "type": "TIMESTAMP" } ], "defaultFS": "hdfs://hadoop1:9000", "fieldDelimiter": ",", "fileName": "result", "fileType": "text", "path": "/test/result", "writeMode": "append" } } } ], "setting": { "speed": { "channel": 8 } } } } 

在固定JVM内存为8G的情况下，得到最佳的channel数为8。同时固定channel数的情况下，得到最佳的内存大小为2G，用时114S完成同步。基于该结论，我们在相同的内存和并发数上，测试SeaTunnel能够达到的速度。

SeaTunnel Engine任务配置

在SeaTunnel中，我们同样使用和DataX类似的特性，根据ID字段来进行数据拆分，分成多个子任务进行数据处理。

下面是SeaTunnel的配置文件：

 env { # You can set engine configuration here job.mode = "BATCH" checkpoint.interval = 300000 #execution.checkpoint.data-uri = "hdfs://localhost:9000/checkpoint" } source { # This is a example source plugin **only for test and demonstrate the feature source plugin** jdbc{ url = "jdbc:mysql://seatunnel.mysql.rds.aliyuncs.com:3306/test" driver = "com.mysql.cj.jdbc.Driver" user = "root" password = "password" query = "select * from type_source_table" partition_column = "id" parallelism = 8 } } transform { } sink { HdfsFile { fs.defaultFS="hdfs://hadoop1:9000" path="/test/result/" field_delimiter="\\t" row_delimiter="\\n" file_name_expression="${transactionId}_${now}" file_format="text" filename_time_format="yyyy.MM.dd" is_enable_transaction=true } } 

在相同的2G，8线程的情况下，SeaTunnel Engine比DataX快20%，具体对比见后表。

结论

在对比了最佳的配置之后，我们针对不同的内存大小，不同的线程数进行了更加深入的对比。在相同的环境下，重复测试得到如下对比结果图表。

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单位：秒

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从上表可以看出，在相同测试环境下，最新发布的同步计算引擎 SeaTunnel Engine 均比DataX同步数据的速度更快，甚至在内存吃紧的情况下，内存的降低对SeaTunnel Engine没有显著影响。这得益于SeaTunnel优秀的架构和高效的代码逻辑。

值得注意的是，这只是单机版本测试，DataX也支持单机版本，而SeaTunnel新引擎是支持集群版本的，单机性能差异就如此之大，可想而知SeaTunnel集群会给用户带来多大的性能提升！Note：本次对比基于DataX: datax_v202209. SeaTunnel: commit id 7393c47，欢迎大家下载测试！