From e47dc1d7c3026d19fd0af53fa5e590bf3fc3e23f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BA=91=E6=97=B6?= Date: Thu, 7 Jun 2018 16:30:06 +0800 Subject: [PATCH] add datax plugin dev wiki --- README | 0 README.md | 2 +- dataxPluginDev.md | 471 ++++++++++++++++++++++++++++++++++ images/plugin_dev_guide_1.png | Bin 0 -> 36660 bytes images/plugin_dev_guide_2.png | Bin 0 -> 74094 bytes images/plugin_dev_guide_3.png | Bin 0 -> 56187 bytes userGuid.md | 2 +- 7 files changed, 473 insertions(+), 2 deletions(-) delete mode 100755 README create mode 100644 dataxPluginDev.md create mode 100644 images/plugin_dev_guide_1.png create mode 100644 images/plugin_dev_guide_2.png create mode 100644 images/plugin_dev_guide_3.png diff --git a/README b/README deleted file mode 100755 index e69de29bb2..0000000000 diff --git a/README.md b/README.md index d0f8187957..b64734ed1f 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,7 @@ DataX目前已经有了比较全面的插件体系,主流的RDBMS数据库、N | | Elasticsearch | | √ |![读]() 、![写]()| # 我要开发新的插件 -请点击:[DataX插件开发宝典](xxx) +请点击:[DataX插件开发宝典](https://github.com/alibaba/DataX/blob/master/dataxPluginDev.md) # 项目成员 diff --git a/dataxPluginDev.md b/dataxPluginDev.md new file mode 100644 index 0000000000..3cb5a63e80 --- /dev/null +++ b/dataxPluginDev.md @@ -0,0 +1,471 @@ +# DataX插件开发宝典 + +本文面向DataX插件开发人员,尝试尽可能全面地阐述开发一个DataX插件所经过的历程,力求消除开发者的困惑,让插件开发变得简单。 + +## 一、开发之前 + +> 路走对了,就不怕远。✓ +> 路走远了,就不管对不对。✕ + +当你打开这篇文档,想必已经不用在此解释什么是`DataX`了。那下一个问题便是: + +### `DataX`为什么要使用插件机制? + +从设计之初,`DataX`就把异构数据源同步作为自身的使命,为了应对不同数据源的差异、同时提供一致的同步原语和扩展能力,`DataX`自然而然地采用了`框架` + `插件` 的模式: + +- 插件只需关心数据的读取或者写入本身。 +- 而同步的共性问题,比如:类型转换、性能、统计,则交由框架来处理。 + +作为插件开发人员,则需要关注两个问题: + +1. 数据源本身的读写数据正确性。 +2. 如何与框架沟通、合理正确地使用框架。 + +### 开工前需要想明白的问题 + +就插件本身而言,希望在您动手coding之前,能够回答我们列举的这些问题,不然路走远了发现没走对,就尴尬了。 + +## 二、插件视角看框架 + +### 逻辑执行模型 + +插件开发者不用关心太多,基本只需要关注特定系统读和写,以及自己的代码在逻辑上是怎样被执行的,哪一个方法是在什么时候被调用的。在此之前,需要明确以下概念: + +- `Job`: `Job`是DataX用以描述从一个源头到一个目的端的同步作业,是DataX数据同步的最小业务单元。比如:从一张mysql的表同步到odps的一个表的特定分区。 +- `Task`: `Task`是为最大化而把`Job`拆分得到的最小执行单元。比如:读一张有1024个分表的mysql分库分表的`Job`,拆分成1024个读`Task`,用若干个并发执行。 +- `TaskGroup`: 描述的是一组`Task`集合。在同一个`TaskGroupContainer`执行下的`Task`集合称之为`TaskGroup` +- `JobContainer`: `Job`执行器,负责`Job`全局拆分、调度、前置语句和后置语句等工作的工作单元。类似Yarn中的JobTracker +- `TaskGroupContainer`: `TaskGroup`执行器,负责执行一组`Task`的工作单元,类似Yarn中的TaskTracker。 + +简而言之, **`Job`拆分成`Task`,在分别在框架提供的容器中执行,插件只需要实现`Job`和`Task`两部分逻辑**。 + +### 物理执行模型 + +框架为插件提供物理上的执行能力(线程)。`DataX`框架有三种运行模式: + +- `Standalone`: 单进程运行,没有外部依赖。 +- `Local`: 单进程运行,统计信息、错误信息汇报到集中存储。 +- `Distrubuted`: 分布式多进程运行,依赖`DataX Service`服务。 + +当然,上述三种模式对插件的编写而言没有什么区别,你只需要避开一些小错误,插件就能够在单机/分布式之间无缝切换了。 +当`JobContainer`和`TaskGroupContainer`运行在同一个进程内时,就是单机模式(`Standalone`和`Local`);当它们分布在不同的进程中执行时,就是分布式(`Distributed`)模式。 + +是不是很简单? + +### 编程接口 + +那么,`Job`和`Task`的逻辑应是怎么对应到具体的代码中的? + +首先,插件的入口类必须扩展`Reader`或`Writer`抽象类,并且实现分别实现`Job`和`Task`两个内部抽象类,`Job`和`Task`的实现必须是 **内部类** 的形式,原因见 **加载原理** 一节。以Reader为例: + +```java +public class SomeReader extends Reader { + public static class Job extends Reader.Job { + + @Override + public void init() { + } + + @Override + public void prepare() { + } + + @Override + public List split(int adviceNumber) { + return null; + } + + @Override + public void post() { + } + + @Override + public void destroy() { + } + + } + + public static class Task extends Reader.Task { + + @Override + public void init() { + } + + @Override + public void prepare() { + } + + @Override + public void startRead(RecordSender recordSender) { + } + + @Override + public void post() { + } + + @Override + public void destroy() { + } + } +} +``` + +`Job`接口功能如下: +- `init`: Job对象初始化工作,测试可以通过`super.getPluginJobConf()`获取与本插件相关的配置。读插件获得配置中`reader`部分,写插件获得`writer`部分。 +- `prepare`: 全局准备工作,比如odpswriter清空目标表。 +- `split`: 拆分`Task`。参数`adviceNumber`框架建议的拆分数,一般是运行时所配置的并发度。值返回的是`Task`的配置列表。 +- `post`: 全局的后置工作,比如mysqlwriter同步完影子表后的rename操作。 +- `destroy`: Job对象自身的销毁工作。 + +`Task`接口功能如下: +- `init`:Task对象的初始化。此时可以通过`super.getPluginJobConf()`获取与本`Task`相关的配置。这里的配置是`Job`的`split`方法返回的配置列表中的其中一个。 +- `prepare`:局部的准备工作。 +- `startRead`: 从数据源读数据,写入到`RecordSender`中。`RecordSender`会把数据写入连接Reader和Writer的缓存队列。 +- `startWrite`:从`RecordReceiver`中读取数据,写入目标数据源。`RecordReceiver`中的数据来自Reader和Writer之间的缓存队列。 +- `post`: 局部的后置工作。 +- `destroy`: Task象自身的销毁工作。 + +需要注意的是: +- `Job`和`Task`之间一定不能有共享变量,因为分布式运行时不能保证共享变量会被正确初始化。两者之间只能通过配置文件进行依赖。 +- `prepare`和`post`在`Job`和`Task`中都存在,插件需要根据实际情况确定在什么地方执行操作。 + +框架按照如下的顺序执行`Job`和`Task`的接口: + +![DataXReaderWriter (2)](https://github.com/alibaba/DataX/blob/master/images/plugin_dev_guide_1.jpg) + +上图中,黄色表示`Job`部分的执行阶段,蓝色表示`Task`部分的执行阶段,绿色表示框架执行阶段。 + +相关类关系如下: + +![DataX](https://github.com/alibaba/DataX/blob/master/images/plugin_dev_guide_2.jpg) + +### 插件定义 + +代码写好了,有没有想过框架是怎么找到插件的入口类的?框架是如何加载插件的呢? + +在每个插件的项目中,都有一个`plugin.json`文件,这个文件定义了插件的相关信息,包括入口类。例如: + +```json +{ + "name": "mysqlwriter", + "class": "com.alibaba.datax.plugin.writer.mysqlwriter.MysqlWriter", + "description": "Use Jdbc connect to database, execute insert sql.", + "developer": "alibaba" +} +``` + +- `name`: 插件名称,大小写敏感。框架根据用户在配置文件中指定的名称来搜寻插件。 **十分重要** 。 +- `class`: 入口类的全限定名称,框架通过反射穿件入口类的实例。**十分重要** 。 +- `description`: 描述信息。 +- `developer`: 开发人员。 + +### 打包发布 + +`DataX`使用`assembly`打包,`assembly`的使用方法请咨询谷哥或者度娘。打包命令如下: + +```bash +mvn clean package -DskipTests assembly:assembly +``` + +`DataX`插件需要遵循统一的目录结构: + +``` +${DATAX_HOME} +|-- bin +| `-- datax.py +|-- conf +| |-- core.json +| `-- logback.xml +|-- lib +| `-- datax-core-dependencies.jar +`-- plugin + |-- reader + | `-- mysqlreader + | |-- libs + | | `-- mysql-reader-plugin-dependencies.jar + | |-- mysqlreader-0.0.1-SNAPSHOT.jar + | `-- plugin.json + `-- writer + |-- mysqlwriter + | |-- libs + | | `-- mysql-writer-plugin-dependencies.jar + | |-- mysqlwriter-0.0.1-SNAPSHOT.jar + | `-- plugin.json + |-- oceanbasewriter + `-- odpswriter +``` + +- `${DATAX_HOME}/bin`: 可执行程序目录。 +- `${DATAX_HOME}/conf`: 框架配置目录。 +- `${DATAX_HOME}/lib`: 框架依赖库目录。 +- `${DATAX_HOME}/plugin`: 插件目录。 + +插件目录分为`reader`和`writer`子目录,读写插件分别存放。插件目录规范如下: + +- `${PLUGIN_HOME}/libs`: 插件的依赖库。 +- `${PLUGIN_HOME}/plugin-name-version.jar`: 插件本身的jar。 +- `${PLUGIN_HOME}/plugin.json`: 插件描述文件。 + +尽管框架加载插件时,会把`${PLUGIN_HOME}`下所有的jar放到`classpath`,但还是推荐依赖库的jar和插件本身的jar分开存放。 + +注意: +**插件的目录名字必须和`plugin.json`中定义的插件名称一致。** + +### 配置文件 + +`DataX`使用`json`作为配置文件的格式。一个典型的`DataX`任务配置如下: + +```json +{ + "job": { + "content": [ + { + "reader": { + "name": "odpsreader", + "parameter": { + "accessKey": "", + "accessId": "", + "column": [""], + "isCompress": "", + "odpsServer": "", + "partition": [ + "" + ], + "project": "", + "table": "", + "tunnelServer": "" + } + }, + "writer": { + "name": "oraclewriter", + "parameter": { + "username": "", + "password": "", + "column": ["*"], + "connection": [ + { + "jdbcUrl": "", + "table": [ + "" + ] + } + ] + } + } + } + ] + } +} +``` + +`DataX`框架有`core.json`配置文件,指定了框架的默认行为。任务的配置里头可以指定框架中已经存在的配置项,而且具有更高的优先级,会覆盖`core.json`中的默认值。 + +**配置中`job.content.reader.parameter`的value部分会传给`Reader.Job`;`job.content.writer.parameter`的value部分会传给`Writer.Job`** ,`Reader.Job`和`Writer.Job`可以通过`super.getPluginJobConf()`来获取。 + +`DataX`框架支持对特定的配置项进行RSA加密,例子中以`*`开头的项目便是加密后的值。 **配置项加密解密过程对插件是透明,插件仍然以不带`*`的key来查询配置和操作配置项** 。 + +#### 如何设计配置参数 + +> 配置文件的设计是插件开发的第一步! + +任务配置中`reader`和`writer`下`parameter`部分是插件的配置参数,插件的配置参数应当遵循以下原则: + +- 驼峰命名:所有配置项采用驼峰命名法,首字母小写,单词首字母大写。 +- 正交原则:配置项必须正交,功能没有重复,没有潜规则。 +- 富类型:合理使用json的类型,减少无谓的处理逻辑,减少出错的可能。 + - 使用正确的数据类型。比如,bool类型的值使用`true`/`false`,而非`"yes"`/`"true"`/`0`等。 + - 合理使用集合类型,比如,用数组替代有分隔符的字符串。 +- 类似通用:遵守同一类型的插件的习惯,比如关系型数据库的`connection`参数都是如下结构: + + ```json + { + "connection": [ + { + "table": [ + "table_1", + "table_2" + ], + "jdbcUrl": [ + "jdbc:mysql://127.0.0.1:3306/database_1", + "jdbc:mysql://127.0.0.2:3306/database_1_slave" + ] + }, + { + "table": [ + "table_3", + "table_4" + ], + "jdbcUrl": [ + "jdbc:mysql://127.0.0.3:3306/database_2", + "jdbc:mysql://127.0.0.4:3306/database_2_slave" + ] + } + ] + } + ``` +- ... + +#### 如何使用`Configuration`类 + +为了简化对json的操作,`DataX`提供了简单的DSL配合`Configuration`类使用。 + +`Configuration`提供了常见的`get`, `带类型get`,`带默认值get`,`set`等读写配置项的操作,以及`clone`, `toJSON`等方法。配置项读写操作都需要传入一个`path`做为参数,这个`path`就是`DataX`定义的DSL。语法有两条: + +1. 子map用`.key`表示,`path`的第一个点省略。 +2. 数组元素用`[index]`表示。 + +比如操作如下json: + +```json +{ + "a": { + "b": { + "c": 2 + }, + "f": [ + 1, + 2, + { + "g": true, + "h": false + }, + 4 + ] + }, + "x": 4 +} +``` + +比如调用`configuration.get(path)`方法,当path为如下值的时候得到的结果为: + +- `x`:`4` +- `a.b.c`:`2` +- `a.b.c.d`:`null` +- `a.b.f[0]`:`1` +- `a.b.f[2].g`:`true` + +注意,因为插件看到的配置只是整个配置的一部分。使用`Configuration`对象时,需要注意当前的根路径是什么。 + +更多`Configuration`的操作请参考`ConfigurationTest.java`。 + +### 插件数据传输 + +跟一般的`生产者-消费者`模式一样,`Reader`插件和`Writer`插件之间也是通过`channel`来实现数据的传输的。`channel`可以是内存的,也可能是持久化的,插件不必关心。插件通过`RecordSender`往`channel`写入数据,通过`RecordReceiver`从`channel`读取数据。 + + +`channel`中的一条数据为一个`Record`的对象,`Record`中可以放多个`Column`对象,这可以简单理解为数据库中的记录和列。 + +`Record`有如下方法: + +```java +public interface Record { + // 加入一个列,放在最后的位置 + void addColumn(Column column); + // 在指定下标处放置一个列 + void setColumn(int i, final Column column); + // 获取一个列 + Column getColumn(int i); + // 转换为json String + String toString(); + // 获取总列数 + int getColumnNumber(); + // 计算整条记录在内存中占用的字节数 + int getByteSize(); +} +``` + +因为`Record`是一个接口,`Reader`插件首先调用`RecordSender.createRecord()`创建一个`Record`实例,然后把`Column`一个个添加到`Record`中。 + +`Writer`插件调用`RecordReceiver.getFromReader()`方法获取`Record`,然后把`Column`遍历出来,写入目标存储中。当`Reader`尚未退出,传输还在进行时,如果暂时没有数据`RecordReceiver.getFromReader()`方法会阻塞直到有数据。如果传输已经结束,会返回`null`,`Writer`插件可以据此判断是否结束`startWrite`方法。 + +`Column`的构造和操作,我们在《类型转换》一节介绍。 + +### 类型转换 + +为了规范源端和目的端类型转换操作,保证数据不失真,DataX支持六种内部数据类型: + +- `Long`:定点数(Int、Short、Long、BigInteger等)。 +- `Double`:浮点数(Float、Double、BigDecimal(无限精度)等)。 +- `String`:字符串类型,底层不限长,使用通用字符集(Unicode)。 +- `Date`:日期类型。 +- `Bool`:布尔值。 +- `Bytes`:二进制,可以存放诸如MP3等非结构化数据。 + + +对应地,有`DateColumn`、`LongColumn`、`DoubleColumn`、`BytesColumn`、`StringColumn`和`BoolColumn`六种`Column`的实现。 + + +`Column`除了提供数据相关的方法外,还提供一系列以`as`开头的数据类型转换转换方法。 + +![Columns](https://github.com/alibaba/DataX/blob/master/images/plugin_dev_guide_3.jpg) + + +DataX的内部类型在实现上会选用不同的java类型: + +| 内部类型 | 实现类型 | 备注 | +| ----- | -------- | ----- | +| Date | java.util.Date | | +| Long | java.math.BigInteger| 使用无限精度的大整数,保证不失真 | +| Double| java.lang.String| 用String表示,保证不失真 | +| Bytes | byte[]| | +| String| java.lang.String | | +| Bool | java.lang.Boolean | | + +类型之间相互转换的关系如下: + +| from\to | Date | Long | Double | Bytes | String | Bool | +| ----- | -------- | ----- | ------ | -------- | ----- | ----- | +| Date | - | 使用毫秒时间戳 | 不支持 | 不支持 | 使用系统配置的date/time/datetime格式转换 | 不支持 | +| Long | 作为毫秒时间戳构造Date | - | BigInteger转为BigDecimal,然后BigDecimal.doubleValue() | 不支持 | BigInteger.toString() | 0为false,否则true | +| Double | 不支持 | 内部String构造BigDecimal,然后BigDecimal.longValue() | - | 不支持 | 直接返回内部String | | +| Bytes | 不支持 | 不支持 | 不支持 | - | 按照`common.column.encoding`配置的编码转换为String,默认`utf-8` | 不支持 | +| String | 按照配置的date/time/datetime/extra格式解析 | 用String构造BigDecimal,然后取longValue() | 用String构造BigDecimal,然后取doubleValue(),会正确处理`NaN`/`Infinity`/`-Infinity` | 按照`common.column.encoding`配置的编码转换为byte[],默认`utf-8` | - | "true"为`true`, "false"为`false`,大小写不敏感。其他字符串不支持 | +| Bool | 不支持 | `true`为`1L`,否则`0L` | | `true`为`1.0`,否则`0.0` | 不支持 | - | + + +### 脏数据处理 + +#### 什么是脏数据? + +目前主要有三类脏数据: + +1. Reader读到不支持的类型、不合法的值。 +1. 不支持的类型转换,比如:`Bytes`转换为`Date`。 +2. 写入目标端失败,比如:写mysql整型长度超长。 + +#### 如何处理脏数据 + +在`Reader.Task`和`Writer.Task`中,功过`AbstractTaskPlugin.getPluginCollector()`可以拿到一个`TaskPluginCollector`,它提供了一系列`collectDirtyRecord`的方法。当脏数据出现时,只需要调用合适的`collectDirtyRecord`方法,把被认为是脏数据的`Record`传入即可。 + +用户可以在任务的配置中指定脏数据限制条数或者百分比限制,当脏数据超出限制时,框架会结束同步任务,退出。插件需要保证脏数据都被收集到,其他工作交给框架就好。 + +### 加载原理 + + +1. 框架扫描`plugin/reader`和`plugin/writer`目录,加载每个插件的`plugin.json`文件。 +2. 以`plugin.json`文件中`name`为key,索引所有的插件配置。如果发现重名的插件,框架会异常退出。 +3. 用户在插件中在`reader`/`writer`配置的`name`字段指定插件名字。框架根据插件的类型(`reader`/`writer`)和插件名称去插件的路径下扫描所有的jar,加入`classpath`。 +4. 根据插件配置中定义的入口类,框架通过反射实例化对应的`Job`和`Task`对象。 + + +## 三、Last but not Least + +> 文档是工程师的良知。 + +每个插件都必须在`DataX`官方wiki中有一篇文档,文档需要包括但不限于以下内容: + +1. **快速介绍**:介绍插件的使用场景,特点等。 +2. **实现原理**:介绍插件实现的底层原理,比如`mysqlwriter`通过`insert into`和`replace into`来实现插入,`tair`插件通过tair客户端实现写入。 +3. **配置说明** + - 给出典型场景下的同步任务的json配置文件。 + - 介绍每个参数的含义、是否必选、默认值、取值范围和其他约束。 +4. **类型转换** + - 插件是如何在实际的存储类型和`DataX`的内部类型之间进行转换的。 + - 以及是否存在特殊处理。 +5. **性能报告** + - 软硬件环境,系统版本,java版本,CPU、内存等。 + - 数据特征,记录大小等。 + - 测试参数集(多组),系统参数(比如并发数),插件参数(比如batchSize) + - 不同参数下同步速度(Rec/s, MB/s),机器负载(load, cpu)等,对数据源压力(load, cpu, mem等)。 +6. **约束限制**:是否存在其他的使用限制条件。 +7. **FQA**:用户经常会遇到的问题。 diff --git a/images/plugin_dev_guide_1.png b/images/plugin_dev_guide_1.png new file mode 100644 index 0000000000000000000000000000000000000000..04978a022a8dc4259461c8f1bd9baeefdcd0001c GIT binary patch literal 36660 zcmcG#Wl$YW)HRB`ySsa^V8PuTf?II6;KAM99fG?A2=4CgPH=bmhUdL+)&2h8d#b29 z!%Uy)>FMsh_F8L)DauPA!r{SzfPf%MNs20ifPkt4f9)_(z<=Z!^m;%*NI;}Sg;d=@ zPutBB35H#m9)Fm+FV3r-F-{5g8XG{H`ugD-@KGYsD(|7UHx!Cm#7w+TGruh`XVte! zlqYShQhy7dn+nTEH5;l;#eD>Y0vbG+v#-1yU#q2go>x|hyY#wzEh`&c%bX3U7#j-3 z*BAA1-yMj;4>!bt+CzbW02LBM8NvgzTyTt!{O6b-Ot@kO`hWVv3Vi<#rCNvnU;jeY zo?S^YLo^4ifCx#R_m{G2s}c<{^0~_hwD9B z=bt~xu_E@XYFNjXtvL%iI*{-=t-FA+QvyCHG$?WR4-dObwZ>eY_YBW3(5VLS!hbU1 z47;qGayvSR@V^$X^zB~lHS{?j2v=B8NWX4wZyUZ|r*!spc)v`ot_Dd`Am}P2yH(z3 zf18Q4R_B$^;Sa+9YP%()|BOaQPk%$-2QGbbdfKP(>zR#!*A*im;4M^~oF(!ar`A4W ziGa)dnZkCxHHg{gD&o?k9a)gJEd?0;2s5Dr@$`SS#)DTAymlgL5?74^-@}^3d^E>q zUYD(F-1x%qzd~J|dum*sx7ihxmg0Ke^Fc@S$B!GCCTWnMbiKd5)c*Q_E-6>JDq^+~ zG!FPpB8ORDwuFE_h%Tkm#^d|&pxdu)*WSN!ax*C~d6154`sKIDwdpu)(~buX0n+YJ z961g?e&5&5TDJaU$d z-N#*Kfha=W6@#BB(pr{Bod5Q)tMyr({8{+()o}T`4;cER{qP+Uzg{NK(Tx6Nn*gj}HI8!vw zlIDDScSni=*Zt4gwC&&TJq@OuO4=l*X5u|il(1Ocao=(20F0tFk|ASn%q1JDu0J8} zMDU%}G}mBxO&S=G>b5U0@rnJA2~q+5`J{27hx^9VeOg%S$9k0iAQQ-&VNeFxiD=lH ziyiG=oZoJynS?7^yay?M3g!Mq$-TIj<=>t63UfCQS=oR9vn`{dTb`n7Nx;x``r=`#ru>Z{qr?}_i-Cp zfs`N`JeB<(%&hCqt((dv@sH*uD z7Y)Tp-aSVNd0loRuALG5(5Obl$Gf~8FiqvaG&V+ky^o3@iRZAOTd20{YO;afa-P`q z4}YO!AA}dKaDtF08YKc7F7$Kb>&Am`PetaHJPGTMKjjoXEGR&AYj;fyPrj+~z}|Jy zl3tl5?8>myF+%UYKZ9ps!RebOZ;q2a3Z-_5j1v38j?oGMJIMH}p?zY4m@LlH?-NR!yQEzMu}3{4i+pIe zhSOE;;}Q)XMR2iOI9lE zf#I-!4B~oby^Bl~N7h(WOq&}BZ*@IAG{Mt_$ejMXB$uU^-~W?T67HcD`CFPFN(MBtq%+Hv3xSueL~rPuG?{O-nVYOx5NUv#4>KH_^VM7Tp6;QA-V_ zkW>l&`ftyCRy-?xk@dZX!Y@VwuRhQjP!;NI)#EuoyGP|gAm!vR&9#pPfzk4DlPk_w zLl`56ECE->aXl)fkl9GHYzr}C6-e>m-qZ2vrK4>ry5`mJ15@UZn}WE|{))WXS}+cd zmMESi=E6YDo1lh7HhPE|P&O)Jk0HrUj8lgM7ls~mH5EneLvb73gJf!t5w#Hz0(tsm z=IYUswC?wjJwip@-HAW)ze_!5nXvX1NEh<0n`Y3`*%fE_X9p8+B59OLoeT5m6giSY zV*CUXzlwVWzF6^SKYi7Jv0q)c>ZI-GJKgB5QgHv{_G@TQ?W#$4SW>=z2&oP7pBnFd zm{S=^mp<}P8>=-nXh|Z5Fy~ExV?OH^>-B=?!$d!XVtLaGN}>9yjIKVaQE2lMowNNlRNJ{| z>RK7kJ>^DJ-!h+V7#@pVZqywguJ(Yyh<2+MMz*#&u9B%&@1evK9pYu_b(9PA4CUnE zB`y^CD&EjhiTuTxo6v(btUC{d43*HfnfLHT7qv;aVt=l;uR zI6wwX7!I5-*Vt{_d}G@1yztKCf1|bCB-&b+K2#`sO_HTpSz7Yd&|CID%-M{9gvYWM z%+ZknAevPm^B zq%)@$5eOOhppy8PmY45#J|z|MNi$df>%KNuDJ=pp*ccmt%x=~|p6If{kY<%8IwGIC zSGGtdN^$CqEJl$Jf;7S#ytW&q7AW&!Bg^Onfg(0(IG-Cw%Dzs2`3kfUN$x-;(uB*$>F&l%qw$ekv3I{%xg`@XF@bjC_t z2U|MDk%dlh!cJ#cKVSfq?-d z6m1g>2*o91dW+mTao;iQ!XLB5S)-&d81TSr>7P;lL1X5z6JD0>t{1E*nG}Hl`UMi> zdq7g%ANKD_3(8@V1=B}`1JZ}=%i=5a>*OC(!Z~Af`%Uq!-|zsKFR@Gh^ClETPMwg? 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