diff --git a/blog/6-streampark-usercase-joyme.md b/blog/6-streampark-usercase-joyme.md
index f983b3a16..511527813 100644
--- a/blog/6-streampark-usercase-joyme.md
+++ b/blog/6-streampark-usercase-joyme.md
@@ -1,46 +1,46 @@
 ---
 slug: streampark-usercase-joyme
-title: StreamPark 在 Joyme 的生产实践
-tags: [StreamPark, 生产实践, FlinkSQL]
+title: StreamPark's Production Practice in Joyme
+tags: [StreamPark, Production Practice, FlinkSQL]
 ---
 
-**摘要：** 本文带来 StreamPark 在 Joyme 中的生产实践, 作者是 Joyme 的大数据工程师秦基勇, 主要内容为:
+**Abstract:** This article presents the production practices of StreamPark at Joyme, written by Qin Jiyong, a big data engineer at Joyme. The main contents include:
 
-- 遇见StreamPark
-- Flink Sql 作业开发
-- Custom code 作业开发
-- 监控告警
-- 常见问题
-- 社区印象
-- 总结
+- Encountering StreamPark
+- Flink SQL job development
+- Custom code job development
+- Monitoring and alerting
+- Common issues
+- Community impressions
+- Summary
 
 <!-- truncate -->
 
-## 1 遇见 StreamPark
+## 1 Encountering StreamPark
 
-遇见 StreamPark 是必然的，基于我们现有的实时作业开发模式，不得不寻找一个开源的平台来支撑我司的实时业务。我们的现状如下:
+Encountering StreamPark was inevitable. Based on our existing real-time job development mode, we had to find an open-source platform to support our company's real-time business. Our current situation was as follows:
 
-- 编写作业打包到服务器，然后执行 Flink run 命令进行提交，过程繁琐，效率低下
-- Flink Sql 通过自研的老平台提交，老平台开发人员已离职，后续的代码无人维护，即便有人维护也不得不面对维护成本高的问题
-- 其中一部分作者是 SparkStreaming 作业，两套流引擎，框架不统一，开发成本大
-- 实时作业有 Scala 和 Java 开发，语言和技术栈不统一
+- We wrote jobs and packaged them to servers, then executed the Flink run command to submit them, which was a cumbersome and inefficient process.
+- Flink SQL was submitted through a self-developed old platform. The developers of the old platform had left, and no one maintained the subsequent code, even if someone did, they would have to face the problem of high maintenance costs.
+- Some of the authors were SparkStreaming jobs, with two sets of streaming engines and frameworks not unified, resulting in high development costs.
+- Real-time jobs were developed in Scala and Java, with languages and technology stacks not unified.
 
-基于以上种种原因，我们需要一个开源平台来管理我们的实时作业，同时我们也需要进行重构，统一开发模式，统一开发语言，将项目集中管理。
+For all these reasons, we needed an open-source platform to manage our real-time jobs, and we also needed to refactor to unify the development mode and language and centralize project management.
 
-第一次遇见 StreamPark 就基本确定了，我们根据官网的文档快速进行了部署安装，搭建以后进行了一些操作，界面友好，Flink 多版本支持，权限管理，作业监控等一系列功能已能较好的满足我们的需求，进一步了解到其社区也很活跃，从 1.1.0 版本开始见证了 StreamPark 功能完善的过程，开发团队是非常有追求的，相信会不断的完善。
+The first encounter with StreamPark basically confirmed our choice. We quickly deployed and installed according to the official documentation, performed some operations after setting up, and were greeted with a user-friendly interface. StreamPark's support for multiple versions of Flink, permission management, job monitoring, and other series of functions already met our needs well. Further understanding revealed that its community is also very active. We have witnessed the process of StreamPark's feature completion since version 1.1.0. The development team is very ambitious, and we believe they will continue to improve.
 
-## 2 Flink SQL 作业开发
+## 2 Development of Flink SQL Jobs
 
-Flink Sql 开发模式带来了很大的便利，对于一些简单的指标开发，只需要简单的 Sql 就可以完成，不需要写一行代码。Flink Sql 方便了很多同学的开发工作，毕竟一些做仓库的同学在编写代码方面还是有些难度。
+The Flink SQL development mode has brought great convenience. For some simple metric developments, it is possible to complete them with just a few SQL statements, without the need to write a single line of code. Flink SQL has facilitated the development work for many colleagues, especially since writing code can be somewhat difficult for those who work on warehouses.
 
-打开 StreamPark 的任务新增界面进行添加新任务，默认 Development Mode 就是 Flink Sql 模式。直接在 Flink Sql 部分编写Sql 逻辑。
+To add a new task, you open the task addition interface of StreamPark, where the default Development Mode is Flink SQL mode. You can write the SQL logic directly in the Flink SQL section.
 
-Flink Sql 部分，按照 Flink 官网的文档逐步编写逻辑 Sql 即可，对于我司来说，一般就三部分: 接入 Source ，中间逻辑处理，最后 Sink。基本上 Source 都是消费 kafka 的数据，逻辑处理层会有关联 MySQL 去做维表查询，最后 Sink 部分大多是 Es，Redis，MySQL。
+For the Flink SQL part, you can progressively write the logic SQL following the Flink official website's documentation. Generally, for our company, it consists of three parts: the Source connection, intermediate logic processing, and finally the Sink. Essentially, the Source is consuming data from Kafka, the logic layer will involve MySQL for dimension table queries, and the Sink part is mostly Elasticsearch, Redis, or MySQL.
 
-### **1. 编写SQL**
+### **1. Writing SQL**
 
 ```sql
--- 连接kafka
+-- Connect kafka
 CREATE TABLE source_table (
  `Data` ROW<uid STRING>
 ) WITH (
@@ -55,7 +55,7 @@ CREATE TABLE source_table (
 'format.derive-schema' = 'true'
 );
 
--- 落地表sink
+-- Landing table sink
 CREATE TABLE sink_table (
 `uid` STRING
 ) WITH (
@@ -69,93 +69,93 @@ CREATE TABLE sink_table (
 'connector.write.max-retries' = '3'
 );
 
--- 代码逻辑过
+-- Code logic pass
 INSERT INTO sink_table
 SELECT  Data.uid  FROM source_table;
 ```
 
-### **2. 添加依赖**
+### **2. Add Dependency**
 
-关于依赖这块是 StreamPark 里特有的，在 StreamPark 中创新型的将一个完整的 Flink Sql 任务拆分成两部分组成: Sql 和 依赖, Sql 很好理解不多啰嗦, 依赖是 Sql 里需要用到的一些 Connector 的 Jar, 如 Sql 里用到了 Kafka 和 MySQL 的 Connector, 那就需要引入这两个 Connector 的依赖, 在 StreamPark 中添加依赖两种方式，一种是基于标准的 Maven pom 坐标方式，另一种是从本地上传需要的 Jar 。这两种也可以混着用，按需添加，点击应用即可， 在提交作业的时候就会自动加载这些依赖。
+In terms of dependencies, it's an unique feature to Streampark. A complete Flink SQL job is innovatively split into two components within StreamPark: the SQL and the dependencies. The SQL part is easy to understand and requires no further explanation, but the dependencies are the various Connector JARs needed within the SQL, such as Kafka and MySQL Connectors. If these are used within the SQL, then these Connector dependencies must be introduced. In StreamPark, there are two ways to add dependencies: one is based on the standard Maven pom coordinates, and the other is by uploading the required Jars from a local source. These two methods can also be mixed and used as needed; simply apply, and these dependencies will be automatically loaded when the job is submitted.
 
 ![](/blog/joyme/add_dependency.png)
 
-### **3. 参数配置**
+### **3. Parameter Configuration**
 
-在任务的添加和修改页面中已经罗列了一些常用的参数设置，更多的参数设置则提供了一个 yaml 配置文件，我们这里只是设置了 checkpoint 和 savepoint 这两个配置。一是 checkpoint 的位置，二是 执行 checkpoint 的频率。其他的配置基本没有动，这部分用户可以根据自己的需要按需配置。
+The task addition and modification page has listed some common parameter settings, and for more extensive configuration options, a yaml configuration file is provided. Here, we've only set up checkpoint and savepoint configurations. The first is the location of the checkpoint, and the second is the frequency of checkpoint execution. We haven't changed other configurations much; users can customize these settings as per their needs.
 
-剩下的一些参数设置就要根据作业的具体情况去对症下药的配置了，处理的数据量大了，逻辑复杂了，可能就需要更多的内存，并行度给多一些。有时候需要根据作业的运行情况进行多次调整。
+The rest of the parameter settings should be configured according to the specific circumstances of the job. If the volume of data processed is large or the logic is complex, it might require more memory and higher parallelism. Sometimes, adjustments need to be made multiple times based on the job's operational performance.
 
 ![](/blog/joyme/checkpoint_configuration.png)
 
-### **4. 动态参数设置**
+### **4. Dynamic Parameter Settings**
 
-由于我们的模式部署是 on Yarn，在动态选项配置里配置了 Yarn 的队列名称。也有一些配置了开启增量的 Checkpoint 选项和状态过期时间，基本的这些参数都可以从 Flink 的官网去查询到。之前有一些作业确实经常出现内存溢出的问题，加上增量参数和过期参数以后，作业的运行情况好多了。还有就是 Flink Sql 作业设计到状态这种比较大和逻辑复杂的情况下，我个人感觉还是用 Streaming 代码来实现比较好控制一些。
+Since our deployment mode is on Yarn, we configured the name of the Yarn queue in the dynamic options. Some configurations have also been set to enable incremental checkpoints and state TTL (time-to-live), all of which can be found on Flink's official website. Before, some jobs frequently encountered out-of-memory issues. After adding the incremental parameter and TTL, the job operation improved significantly. Also, in cases where the Flink SQL job involves larger states and complex logic, I personally feel that it's better to implement them through streaming code for more control.
 
-- -Dyarn.application.queue= yarn队列名称
+- -Dyarn.application.queue=Yarn queue name
 - -Dstate.backend.incremental=true
-- -Dtable.exec.state.ttl=过期时间
+- -Dtable.exec.state.ttl=expiration time
 
-完成配置以后提交，然后在 application 界面进行部署。
+After completing the configuration, submit & deploy it from the application interface.
 
 ![](/blog/joyme/application_job.png)
 
-## 3 Custom Code 作业开发
+## 3 Custom Code Job Development
 
-Streaming 作业我们是使用 Flink java 进行开发，将之前 Spark scala，Flink scala，Flink java 的作业进行了重构，然后工程整合到了一起，目的就是为了维护起来方便。Custom code 作业需要提交代码到 Git，然后配置项目:
+For streaming jobs, we use Flink Java for development, having refactored previous Spark Scala, Flink Scala, and Flink Java jobs and then integrated these projects together. The purpose of this integration is to facilitate maintenance. Custom code jobs require the submission of code to Git, followed by project configuration:
 
 ![](/blog/joyme/project_configuration.png)
 
-配置完成以后，根据对应的项目进行编译，也就完成项目的打包环节。这样后面的 Constom code 作业也可以引用。每次需要上线都需要进行编译才可以，否则只能是上次编译的代码。这里有个问题，为了安全，我司的 gitlab 账号密码都是定期更新的。这样就会导致，StreamPark 已经配置好的项目还是之前的密码，结果导致编译时从 git 里拉取项目失败，导致整个编译环节失败，针对这个问题，我们联系到社区，了解到这部分已经在后续的 1.2.1 版本中支持了项目的修改操作。
+Once the configuration is completed, compile the corresponding project to finish the packaging phase. Thus, the Custom code jobs can also reference it. Compilation is required every time the code needs to go live; otherwise, only the last compiled code is available. Here's an issue: for security reasons, our company’s GitLab account passwords are regularly updated. This leads to a situation where the StreamPark projects have the old passwords configured, resulting in a failure when pulling projects from Git during compilation. To address this problem, we contacted the community and learned that the capability to modify projects has been added in the subsequent version 1.2.1.
 
 ![](/blog/joyme/flink_system.png)
 
-新建任务，选择 Custom code ，选择 Flink 版本，选择项目以及模块 Jar 包，选择开发的应用模式为 Apache Flink (标准的 Flink 程序)，程序主函数入口类，任务的名称。
+To create a new task, select Custom code, choose the Flink version, select the project and the module Jar package, and choose the development application mode as Apache Flink (standard Flink program), program main function entry class, and the task's name.
 
 ![](/blog/joyme/add_projectconfiguration.png)
 
-以及任务的并行度，监控的方式等，内存大小根据任务需要进行配置。Program Args 程序的参数则根据程序需要自行定义入口参数,比如：我们统一启动类是 StartJobApp，那么启动作业就需要传入作业的 Full name 告诉启动类要去找哪个类来启动此次任务，也就是一个反射机制，作业配置完成以后同样也是 Submit 提交，然后在 application 界面部署任务。
+As well as the task’s parallelism, monitoring method, etc., memory size should be configured based on the needs of the task. Program Args, the program parameters, are defined according to the program's needs. For example: If our unified startup class is StartJobApp, to start a job, it's necessary to pass the full name of the job, informing the startup class which class to find to launch the task—essentially, which is a reflection mechanism. After the job configuration is complete, it is also submitted with Submit and then deployed from the application interface.
 
 ![](/blog/joyme/application_interface.png)
 
-## 4 监控告警
+## 4 Monitoring and Alerts
 
-StreamPark 的监控需要在 setting 模块去配置发送邮件的基本信息。
+The monitoring in StreamPark requires configuration in the setting module to set up the basic information for sending emails.
 
 ![](/blog/joyme/system_setting.png)
 
-然后在任务里配置重启策略：监控在多久内几次异常，然后是报警还是重启的策略，同时发送报警要发到哪个邮箱。目前我司使用版本是 1.2.1 只支持邮件发送。
+Then, within the tasks, configure the restart strategy: monitor how many times an exception occurs within a certain time, and then decide whether the strategy is to alert or restart, as well as which email address should receive the alert notifications. The version currently used by our company is 1.2.1, which supports only email sending.
 
 ![](/blog/joyme/email_setting.png)
 
-当我们的作业出现失败的情况下，就可以接收到报警邮箱。这报警还是很好看的有木有，可以清楚看到我们的哪个作业，什么状态。也可以点击下面的具体地址进行查看。
+When our jobs fail, we can receive alerts through email. These alerts are quite clear, showing which job is in what state. You can also click on the specific address below to view details.
 
 ![](/blog/joyme/alarm_eamil.png)
 
-关于报警这一块目前我们基于 StreamPark 的 t_flink_app 表进行了一个定时任务的开发。为什么要这么做？因为发送邮件这种通知，大部分人可能不会去及时去看。所以我们选择监控每个任务的状态去把对应的监控信息发送我们的飞书报警群，这样可以及时发现问题去解决任务。一个简单的 python 脚本，然后配置了 crontab 去定时执行。
+Regarding alerts, we have developed a scheduled task based on StreamPark's t_flink_app table. Why do this? Because most people might not check their emails promptly when it comes to email notifications. Therefore, we opted to monitor the status of each task and send corresponding monitoring information to our Lark (Feishu) alert group, enabling us to promptly identify and address issues with the tasks. It's a simple Python script, then configured with crontab to execute at scheduled times.
 
-## 5 常见问题
+## 5 Common Issues
 
-关于作业的异常问题，我们归纳分析了基本分为这么几种情况:
+When it comes to the abnormal issues of jobs, we have summarized them into the following categories:
 
-### **1. 作业启动失败**
+### **1. Job Launch Failure**
 
-作业启动失败的问题，就是点击启动运行部署。发现起不来，这时候需要看界面的详情信息的日志。在我们的任务列表中有一个眼睛的按钮，点进去。在start logs 中会找到提交的作业日志信息，点进去查看，如果有明显的提示信息，直接解决就可以了。如果没有，就需要去查看后台部署任务的目录 logs/下面的 streamx.out，打开以后会找到启动失败的日志信息。
+The issue of job launch failure refers to the situation where the job does not start upon initiation. In this case, one needs to check the detailed information logs on the interface. There is an eye-shaped button in our task list; by clicking on it, one can find the submitted job log information in the start logs. If there is clear prompt information, the issue can be directly addressed. If not, one has to check the streamx.out file under the logs/ directory of the backend deployment task to find the log information regarding the launch failure.
 
 ![](/blog/joyme/start_log.png)
 
-### **2. 作业运行失败**
+### **2. Job Running Failure**
 
-如果是任务已经起来了，但是在运行阶段失败了。这种情况表面看上去和上面的情况一样，实则完全不同，这种情况是已经将任务提交给集群了，但是任务运行不起来，那就是我们的任务自身有问题了。同样可以用上面第一种情况的排查方式打开作业的具体日志，找到任务在 yarn 上运行的信息，根据日志里记录的 yarn 的 tackurl 去 yarn 的日志里查看具体的原因，是 Sql 的 Connector 不存在，还是代码的哪行代码空指针了，都可以看到具体的堆栈信息。有了具体信息，就可以对症下药了。
+If the task has started but fails during the running phase, this situation might seem similar to the first, but is actually entirely different. This means that the task has been submitted to the cluster, but the task itself has problems running. One can still apply the troubleshooting method from the first scenario to open the specific logs of the job, find the task information on yarn, and then go to yarn's logs with the yarn's tackurl recorded in the logs to look for the specific reasons. Whether it is the absence of the Sql's Connector or a null pointer in a line of code, one can find the detailed stack information. With specific information, one can find the right remedy.
 
 ![](/blog/joyme/yarn_log.png)
 
-## 6 社区印象
+## 6 Community Impression
 
-很多时候我们在 StreamPark 用户群里讨论问题，都会得到社区小伙伴的即时响应。提交的一些 issue 在当下不能解决的，基本也会在下一个版本或者最新的代码分支中进行修复。在群里，我们也看到很多不是社区的小伙伴，也在积极互相帮助去解决问题。群里也有很多其他社区的大佬，很多小伙伴也积极加入了社区的开发工作。整个社区给我的感觉还是很活跃！
+Often when we discuss issues in the StreamPark user group, we get immediate responses from community members. Issues that cannot be resolved at the moment are generally fixed in the next version or the latest code branch. In the group, we also see many non-community members actively helping each other out. There are many big names from other communities as well, and many members actively join the community development work. The whole community feels very active to me!
 
-## 7 总结
+## 7 Conclusion
 
-目前我司线上运行 60 个实时作业，Flink sql 与 Custom-code 差不多各一半。后续也会有更多的实时任务进行上线。很多同学都会担心 StreamPark 稳不稳定的问题，就我司根据几个月的生产实践而言，StreamPark 只是一个帮助你开发作业，部署，监控和管理的一个平台。到底稳不稳，还是要看自家的 Hadoop yarn 集群稳不稳定（我们用的onyan模式），其实已经跟 StreamPark关系不大了。还有就是你写的 Flink Sql 或者是代码健不健壮。更多的是这两方面应该是大家要考虑的，这两方面没问题再充分利用 StreamPark 的灵活性才能让作业更好的运行，单从一方面说 StreamPark 稳不稳定，实属偏激。
+Currently, our company runs 60 real-time jobs online, with Flink SQL and custom code each making up about half. More real-time tasks will be put online subsequently. Many colleagues worry about the stability of StreamPark, but based on several months of production practice in our company, StreamPark is just a platform to help you develop, deploy, monitor, and manage jobs. Whether it is stable or not depends on the stability of our own Hadoop Yarn cluster (we use the onyan mode) and has little to do with StreamPark itself. It also depends on the robustness of the Flink SQL or code you write. These two aspects should be the primary concerns. Only when these two aspects are problem-free can the flexibility of StreamPark be fully utilized to improve job performance. To discuss the stability of StreamPark in isolation is somewhat extreme.
 
-以上就是 StreamPark 在乐我无限的全部分享内容，感谢大家看到这里。非常感谢 StreamPark 提供给我们这么优秀的产品，这就是做的利他人之事。从1.0 到 1.2.1 平时遇到那些bug都会被即时的修复，每一个issue都被认真对待。目前我们还是 onyarn的部署模式，重启yarn还是会导致作业的lost状态，重启yarn也不是天天都干的事，关于这个社区也会尽早的会去修复这个问题。相信 StreamPark 会越来越好，未来可期。
+That is all the content shared by StreamPark at Joyme. Thank you for reading this article. We are very grateful for such an excellent product provided by StreamPark, which is a true act of benefiting others. From version 1.0 to 1.2.1, the bugs encountered are promptly fixed, and every issue is taken seriously. We are still using the on yarn deployment mode. Restarting yarn will still cause jobs to be lost, but restarting yarn is not something we do every day. The community will also look to fix this problem as soon as possible. I believe that StreamPark will get better and better, with a promising future ahead.
\ No newline at end of file