[Docs] Update transform about and faq related docs info

tcodehuber · Jul 12, 2024 · 413a918 · 413a918
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diff --git a/docs/en/about.md b/docs/en/about.md
@@ -9,7 +9,7 @@ SeaTunnel is a very easy-to-use, ultra-high-performance, distributed data integr
 synchronization of massive data. It can synchronize tens of billions of data stably and efficiently every day, and has
 been used in production by nearly 100 companies.
 
-## Why we need SeaTunnel
+## Why We Need SeaTunnel
 
 SeaTunnel focuses on data integration and data synchronization, and is mainly designed to solve common problems in the field of data integration:
 
@@ -18,45 +18,45 @@ SeaTunnel focuses on data integration and data synchronization, and is mainly de
 - High resource demand: Existing data integration and data synchronization tools often require vast computing resources or JDBC connection resources to complete real-time synchronization of massive small tables. This has increased the burden on enterprises.
 - Lack of quality and monitoring: Data integration and synchronization processes often experience loss or duplication of data. The synchronization process lacks monitoring, and it is impossible to intuitively understand the real situation of the data during the task process.
 - Complex technology stack: The technology components used by enterprises are different, and users need to develop corresponding synchronization programs for different components to complete data integration.
-- Difficulty in management and maintenance: Limited to different underlying technology components (Flink/Spark), offline synchronization and real-time synchronization often have be developed and managed separately, which increases the difficulty of management and maintainance.
+- Difficulty in management and maintenance: Limited to different underlying technology components (Flink/Spark), offline synchronization and real-time synchronization often have be developed and managed separately, which increases the difficulty of management and maintenance.
 
-## Features of SeaTunnel
+## Features Of SeaTunnel
 
-- Rich and extensible Connector: SeaTunnel provides a Connector API that does not depend on a specific execution engine. Connectors (Source, Transform, Sink) developed based on this API can run on many different engines, such as SeaTunnel Engine, Flink, and Spark, that are currently supported.
-- Connector plug-in: The plug-in design allows users to easily develop their own Connector and integrate it into the SeaTunnel project. Currently, SeaTunnel supports more than 100 Connectors, and the number is surging. Here is the list of [currently-supported connectors](Connector-v2-release-state.md)
+- Rich and extensible Connector: SeaTunnel provides a Connector API that does not depend on a specific execution engine. Connectors (Source, Transform, Sink) developed based on this API can run on many different engines, such as SeaTunnel Engine(Zeta), Flink, and Spark.
+- Connector plugin: The plugin design allows users to easily develop their own Connector and integrate it into the SeaTunnel project. Currently, SeaTunnel supports more than 100 Connectors, and the number is surging. Here is the list of [Currently Supported Connectors](Connector-v2-release-state.md)
 - Batch-stream integration: Connectors developed based on the SeaTunnel Connector API are perfectly compatible with offline synchronization, real-time synchronization, full-synchronization, incremental synchronization and other scenarios. They greatly reduce the difficulty of managing data integration tasks.
 - Supports a distributed snapshot algorithm to ensure data consistency.
-- Multi-engine support: SeaTunnel uses the SeaTunnel Engine for data synchronization by default. SeaTunnel also supports the use of Flink or Spark as the execution engine of the Connector to adapt to the existing technical components of the enterprise. SeaTunnel supports multiple versions of Spark and Flink.
+- Multi-engine support: SeaTunnel uses the SeaTunnel Engine(Zeta) for data synchronization by default. SeaTunnel also supports the use of Flink or Spark as the execution engine of the Connector to adapt to the enterprise's existing technical components. SeaTunnel supports multiple versions of Spark and Flink.
 - JDBC multiplexing, database log multi-table parsing: SeaTunnel supports multi-table or whole database synchronization, which solves the problem of over-JDBC connections; and supports multi-table or whole database log reading and parsing, which solves the need for CDC multi-table synchronization scenarios to deal with problems with repeated reading and parsing of logs.
 - High throughput and low latency: SeaTunnel supports parallel reading and writing, providing stable and reliable data synchronization capabilities with high throughput and low latency.
 - Perfect real-time monitoring: SeaTunnel supports detailed monitoring information of each step in the data synchronization process, allowing users to easily understand the number of data, data size, QPS and other information read and written by the synchronization task.
 - Two job development methods are supported: coding and canvas design. The SeaTunnel web project https://github.com/apache/seatunnel-web provides visual management of jobs, scheduling, running and monitoring capabilities.
 
-## SeaTunnel work flowchart
+## SeaTunnel Work Flowchart
 
-![SeaTunnel work flowchart](../images/architecture_diagram.png)
+![SeaTunnel Work Flowchart](../images/architecture_diagram.png)
 
 The runtime process of SeaTunnel is shown in the figure above.
 
 The user configures the job information and selects the execution engine to submit the job.
 
-The Source Connector is responsible for parallel reading the data and sending the data to the downstream Transform or directly to the Sink, and the Sink writes the data to the destination. It is worth noting that Source, Transform and Sink can be easily developed and extended by yourself.
+The Source Connector is responsible for parallel reading and sending the data to the downstream Transform or directly to the Sink, and the Sink writes the data to the destination. It is worth noting that Source, Transform and Sink can be easily developed and extended by yourself.
 
 SeaTunnel is an EL(T) data integration platform. Therefore, in SeaTunnel, Transform can only be used to perform some simple transformations on data, such as converting the data of a column to uppercase or lowercase, changing the column name, or splitting a column into multiple columns.
 
 The default engine use by SeaTunnel is [SeaTunnel Engine](seatunnel-engine/about.md). If you choose to use the Flink or Spark engine, SeaTunnel will package the Connector into a Flink or Spark program and submit it to Flink or Spark to run.
 
 ## Connector
 
-- **Source Connectors** SeaTunnel supports reading data from various relational, graph, NoSQL, document, and memory databases; distributed file systems such as HDFS; and a variety of cloud storage solutions, such as S3 and OSS. We also support data reading of many common SaaS services. You can access the detailed list [here](connector-v2/source). If you want, You can develop your own source connector and easily integrate it into SeaTunnel.
+- **Source Connectors** SeaTunnel supports reading data from various relational, graph, NoSQL, document, and memory databases; distributed file systems such as HDFS; and a variety of cloud storage solutions, such as S3 and OSS. We also support data reading of many common SaaS services. You can access the detailed list [Here](connector-v2/source). If you want, You can develop your own source connector and easily integrate it into SeaTunnel.
 
 - **Transform Connector** If the schema is different between source and Sink, You can use the Transform Connector to change the schema read from source and make it the same as the Sink schema.
 
-- **Sink Connector** SeaTunnel supports writing data to various relational, graph, NoSQL, document, and memory databases; distributed file systems such as HDFS; and a variety of cloud storage solutions, such as S3 and OSS. We also support writing data to many common SaaS services. You can access the detailed list [here](connector-v2/sink). If you want, you can develop your own Sink connector and easily integrate it into SeaTunnel.
+- **Sink Connector** SeaTunnel supports writing data to various relational, graph, NoSQL, document, and memory databases; distributed file systems such as HDFS; and a variety of cloud storage solutions, such as S3 and OSS. We also support writing data to many common SaaS services. You can access the detailed list [Here](connector-v2/sink). If you want, you can develop your own Sink connector and easily integrate it into SeaTunnel.
 
-## Who uses SeaTunnel
+## Who Uses SeaTunnel
 
-SeaTunnel has lots of users. You can find more information about them in [users](https://seatunnel.apache.org/user).
+SeaTunnel has lots of users. You can find more information about them in [Users](https://seatunnel.apache.org/user).
 
 ## Landscapes
 

diff --git a/docs/en/faq.md b/docs/en/faq.md
@@ -6,7 +6,7 @@ SeaTunnel now uses computing engines such as Spark and Flink to complete resourc
 
 ## I have a question, and I cannot solve it by myself
 
-I have encountered a problem when using SeaTunnel and I cannot solve it by myself. What should I do? First, search in [Issue list](https://github.com/apache/seatunnel/issues) or [mailing list](https://lists.apache.org/[email protected]) to see if someone has already asked the same question and got an answer. If you cannot find an answer to your question, you can contact community members for help in [these ways](https://github.com/apache/seatunnel#contact-us).
+I have encountered a problem when using SeaTunnel and I cannot solve it by myself. What should I do? First, search in [Issue List](https://github.com/apache/seatunnel/issues) or [Mailing List](https://lists.apache.org/[email protected]) to see if someone has already asked the same question and got an answer. If you cannot find an answer to your question, you can contact community members for help in [These Ways](https://github.com/apache/seatunnel#contact-us).
 
 ## How do I declare a variable?
 
@@ -61,7 +61,7 @@ your string 1
 
 Refer to: [lightbend/config#456](https://github.com/lightbend/config/issues/456).
 
-## Is SeaTunnel supportted in Azkaban, Oozie, DolphinScheduler?
+## Is SeaTunnel supported in Azkaban, Oozie, DolphinScheduler?
 
 Of course! See the screenshot below:
 
@@ -93,7 +93,7 @@ sink {
 
 ## Are there any HBase plugins?
 
-There is an hbase input plugin. You can download it from here: https://github.com/garyelephant/waterdrop-input-hbase .
+There is a HBase input plugin. You can download it from here: https://github.com/garyelephant/waterdrop-input-hbase .
 
 ## How can I use SeaTunnel to write data to Hive?
 
@@ -184,7 +184,7 @@ The following conclusions can be drawn:
 
 3. In general, both M and N are determined, and the conclusion can be drawn from 2: The size of `spark.streaming.kafka.maxRatePerPartition` is positively correlated with the size of `spark.executor.cores` * `spark.executor.instances`, and it can be increased while increasing the resource `maxRatePerPartition` to speed up consumption.
 
-![kafka](../images/kafka.png)
+![Kafka](../images/kafka.png)
 
 ## How can I solve the Error `Exception in thread "main" java.lang.NoSuchFieldError: INSTANCE`?
 
@@ -203,11 +203,11 @@ spark {
 }
 ```
 
-## How do I specify a different JDK version for SeaTunnel on Yarn?
+## How do I specify a different JDK version for SeaTunnel on YARN?
 
 For example, if you want to set the JDK version to JDK8, there are two cases:
 
-- The Yarn cluster has deployed JDK8, but the default JDK is not JDK8. Add two configurations to the SeaTunnel config file:
+- The YARN cluster has deployed JDK8, but the default JDK is not JDK8. Add two configurations to the SeaTunnel config file:
 
   ```
     env {
@@ -217,12 +217,12 @@ For example, if you want to set the JDK version to JDK8, there are two cases:
    ...
   }
   ```
-- Yarn cluster does not deploy JDK8. At this time, start SeaTunnel attached with JDK8. For detailed operations, see:
+- YARN cluster does not deploy JDK8. At this time, start SeaTunnel attached with JDK8. For detailed operations, see:
   https://www.cnblogs.com/jasondan/p/spark-specific-jdk-version.html
 
 ## What should I do if OOM always appears when running SeaTunnel in Spark local[*] mode?
 
-If you run in local mode, you need to modify the `start-seatunnel.sh` startup script. After `spark-submit`, add a parameter `--driver-memory 4g` . Under normal circumstances, local mode is not used in the production environment. Therefore, this parameter generally does not need to be set during On Yarn. See: [Application Properties](https://spark.apache.org/docs/latest/configuration.html#application-properties) for details.
+If you run in local mode, you need to modify the `start-seatunnel.sh` startup script. After `spark-submit`, add a parameter `--driver-memory 4g` . Under normal circumstances, local mode is not used in the production environment. Therefore, this parameter generally does not need to be set during On YARN. See: [Application Properties](https://spark.apache.org/docs/latest/configuration.html#application-properties) for details.
 
 ## Where can I place self-written plugins or third-party jdbc.jars to be loaded by SeaTunnel?
 
@@ -236,14 +236,14 @@ cp third-part.jar plugins/my_plugins/lib
 
 `my_plugins` can be any string.
 
-## How do I configure logging-related parameters in SeaTunnel-v1(Spark)?
+## How do I configure logging-related parameters in SeaTunnel-V1(Spark)?
 
 There are three ways to configure logging-related parameters (such as Log Level):
 
 - [Not recommended] Change the default `$SPARK_HOME/conf/log4j.properties`.
   - This will affect all programs submitted via `$SPARK_HOME/bin/spark-submit`.
 - [Not recommended] Modify logging related parameters directly in the Spark code of SeaTunnel.
-  - This is equivalent to writing dead, and each change needs to be recompiled.
+  - This is equivalent to hardcoding, and each change needs to be recompiled.
 - [Recommended] Use the following methods to change the logging configuration in the SeaTunnel configuration file (The change only takes effect if SeaTunnel >= 1.5.5 ):
 
   ```
@@ -283,7 +283,7 @@ log4j.appender.console.layout=org.apache.log4j.PatternLayout
 log4j.appender.console.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1}:%L - %m%n
 ```
 
-## How do I configure logging related parameters in SeaTunnel-v2(Spark, Flink)?
+## How do I configure logging related parameters in SeaTunnel-V2(Spark, Flink)?
 
 Currently, they cannot be set directly. you need to modify the SeaTunnel startup script. The relevant parameters are specified in the task submission command. For specific parameters, please refer to the official documents:
 
@@ -309,7 +309,7 @@ For example, if you want to output more detailed logs of E2E Test, just downgrad
 In SeaTunnel, the data type will not be actively converted. After the Input reads the data, the corresponding
 Schema. When writing ClickHouse, the field type needs to be strictly matched, and the mismatch needs to be resolved.
 
-Data conversion can be achieved through the following two plug-ins:
+Data conversion can be achieved through the following two plugins:
 
 1. Filter Convert plugin
 2. Filter Sql plugin

diff --git a/docs/en/transform-v2/common-options.md b/docs/en/transform-v2/common-options.md
@@ -4,7 +4,7 @@
 
 |       Name        |  Type  | Required | Default |                                                                                                                                                                                                                                                              Description                                                                                                                                                                                                                                                              |
 |-------------------|--------|----------|---------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| result_table_name | String | No       | -       | When `source_table_name` is not specified, the current plug-in processes the data set `(dataset)` output by the previous plug-in in the configuration file; <br/>When `source_table_name` is specified, the current plugin is processing the data set corresponding to this parameter.                                                                                                                                                                                                                                                |
+| result_table_name | String | No       | -       | When `source_table_name` is not specified, the current plugin processes the data set `(dataset)` output by the previous plugin in the configuration file; <br/>When `source_table_name` is specified, the current plugin is processing the data set corresponding to this parameter.                                                                                                                                                                                                                                                  |
 | source_table_name | String | No       | -       | When `result_table_name` is not specified, the data processed by this plugin will not be registered as a data set that can be directly accessed by other plugins, or called a temporary table `(table)`; <br/>When `result_table_name` is specified, the data processed by this plugin will be registered as a data set `(dataset)` that can be directly accessed by other plugins, or called a temporary table `(table)` . The dataset registered here can be directly accessed by other plugins by specifying `source_table_name` . |
 
 ## Task Example

diff --git a/docs/en/transform-v2/sql-udf.md b/docs/en/transform-v2/sql-udf.md
@@ -4,7 +4,7 @@
 
 ## Description
 
-Use UDF SPI to extends the SQL transform functions lib.
+Use UDF SPI to extend the SQL transform functions lib.
 
 ## UDF API