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The following commit(s) were added to refs/heads/dev by this push:
new cb75755a [Improve] intro doc update style issue fixed (#392)
cb75755a is described below
commit cb75755a0eda95c9cda698e3570566ae0cb502f5
Author: benjobs <[email protected]>
AuthorDate: Sun Jun 30 19:15:57 2024 +0800
[Improve] intro doc update style issue fixed (#392)
* [Improve] intro doc update style issue fixed
* [Improve] flink and spark naming minor improvement
---
blog/10-streampark-flink-with-paimon-in-ziru.md | 4 +--
blog/11-streampark-usercase-tianyancha.md | 2 +-
blog/5-streampark-usercase-dustess.md | 6 ++---
blog/6-streampark-usercase-joyme.md | 2 +-
blog/8-streampark-usercase-ziru.md | 2 +-
docs/framework/connector/1.kafka.md | 2 +-
docs/framework/connector/2.jdbc.md | 4 +--
docs/framework/connector/3.clickhouse.md | 2 +-
docs/framework/connector/5.es.md | 2 +-
docs/framework/connector/6.hbase.md | 2 +-
docs/framework/connector/7.http.md | 4 +--
docs/framework/connector/8.redis.md | 2 +-
docs/get-started/1.intro.md | 31 +++++++++-------------
docs/get-started/2.deployment.md | 2 +-
docs/get-started/4.platformBasicUsage.md | 2 +-
docs/platform/k8s/2.hadoop-resource-integration.md | 4 +--
docusaurus.config.js | 2 +-
.../10-streampark-flink-with-paimon-in-ziru.md | 6 ++---
.../11-streampark-usercase-tianyancha.md | 4 +--
.../5-streampark-usercase-dustess.md | 8 +++---
.../6-streampark-usercase-joyme.md | 2 +-
.../8-streampark-usercase-ziru.md | 4 +--
.../submit_guide/documentation-style-guide.md | 14 +++++-----
.../current/flinksql/format/10-parquet.md | 2 +-
.../current/framework/connector/1.kafka.md | 2 +-
.../current/framework/connector/2.jdbc.md | 2 +-
.../current/framework/connector/3.clickhouse.md | 2 +-
.../current/framework/connector/5.es.md | 2 +-
.../current/framework/connector/6.hbase.md | 2 +-
.../current/framework/connector/7.http.md | 2 +-
.../current/framework/connector/8.redis.md | 2 +-
.../current/get-started/1.intro.md | 8 +++---
.../current/get-started/4.platformBasicUsage.md | 2 +-
33 files changed, 66 insertions(+), 73 deletions(-)
diff --git a/blog/10-streampark-flink-with-paimon-in-ziru.md
b/blog/10-streampark-flink-with-paimon-in-ziru.md
index fd43cdff..733db419 100644
--- a/blog/10-streampark-flink-with-paimon-in-ziru.md
+++ b/blog/10-streampark-flink-with-paimon-in-ziru.md
@@ -78,13 +78,13 @@ Paimon can be used in conjunction with Apache Spark. Our
scenario is Paimon comb
Apache StreamPark is a stream processing development and management framework
that provides a set of fast APIs for developing Flink/Spark jobs. In addition,
it also provides a one-stop stream processing job development and management
platform, covering the entire life cycle from stream processing job development
to launch. Cycles are supported. StreamPark mainly includes the following core
features:
-- **Stream processing application development framework**: Based on
StreamPark, developers can easily build and manage stream processing
applications, and better utilize Apache Flink to write stream processing
applications.
+- **Stream processing application development framework**: Based on
StreamPark, developers can easily build and manage stream processing
applications, and better utilize Apache Flink® to write stream processing
applications.
- **Perfect management capabilities**: StreamPark provides a one-stop
streaming task development and management platform that supports the full life
cycle of Flink/Spark from application development to debugging, deployment,
operation and maintenance, allowing Flink/Spark jobs to Make it simple.
- **High degree of completion**: StreamPark supports multiple versions of
Flink, allowing flexible switching of one platform. It also supports Flink’s
deployment mode, effectively solving the problem of too cumbersome Flink on
YARN/K8s deployment. Through automated processes, It simplifies the process of
building, testing and deploying tasks and improves development efficiency.
-- **Rich management API**: StreamPark provides APIs for job operations,
including job creation, copy, build, deployment, stop and start based on
checkpoint/savepoint, etc., making it easy to implement external system calls
to Apache Flink tasks. .
+- **Rich management API**: StreamPark provides APIs for job operations,
including job creation, copy, build, deployment, stop and start based on
checkpoint/savepoint, etc., making it easy to implement external system calls
to Apache Flink® tasks. .
## **3. StreamPark + Paimon Practice**
diff --git a/blog/11-streampark-usercase-tianyancha.md
b/blog/11-streampark-usercase-tianyancha.md
index 804aa127..9238406f 100644
--- a/blog/11-streampark-usercase-tianyancha.md
+++ b/blog/11-streampark-usercase-tianyancha.md
@@ -162,7 +162,7 @@ In the process of intensive use, we also found some
problems. In order to better
## **Benefits derived**
-Apache StreamPark has brought us significant benefits,**mainly in its one-stop
service capability, which enables business developers to complete the
development, compilation, submission and management of Flink jobs** on a
unified platform. It greatly saves our time on Flink job development and
deployment, significantly improves development efficiency, and realizes
full-process automation from user rights management to Git deployment, task
submission, alerting, and automatic recovery, eff [...]
+Apache StreamPark has brought us significant benefits,**mainly in its one-stop
service capability, which enables business developers to complete the
development, compilation, submission and management of Flink jobs** on a
unified platform. It greatly saves our time on Flink job development and
deployment, significantly improves development efficiency, and realizes
full-process automation from user rights management to Git deployment, task
submission, alerting, and automatic recovery, eff [...]
diff --git a/blog/5-streampark-usercase-dustess.md
b/blog/5-streampark-usercase-dustess.md
index e83f7a13..ed369431 100644
--- a/blog/5-streampark-usercase-dustess.md
+++ b/blog/5-streampark-usercase-dustess.md
@@ -111,7 +111,7 @@ https://streampark.apache.org/docs/development/config
In addition:
-StreamPark also **supports Apache Flink native tasks**. The parameter
configuration can be statically maintained within the Java task internal code,
covering a wide range of scenarios, such as seamless migration of existing
Flink tasks, etc.
+StreamPark also **supports Apache Flink® native tasks**. The parameter
configuration can be statically maintained within the Java task internal code,
covering a wide range of scenarios, such as seamless migration of existing
Flink tasks, etc.
#### **7. Checkpoint Management**
@@ -182,7 +182,7 @@ Currently, we are researching and designing solutions
related to metadata, permi
Since most of the data development team members have a background in Java and
Scala, we've implemented Jar-based builds for more flexible development,
transparent tuning of Flink tasks, and to cover more scenarios. Our
implementation was in two phases:
-**First Phase:** StreamPark provides support for native Apache Flink projects.
We configured our existing tasks' Git addresses in StreamPark, used Maven to
package them as Jar files, and created StreamPark Apache Flink tasks for
seamless migration. In this process, StreamPark was merely used as a platform
tool for task submission and state maintenance, without leveraging the other
features mentioned above.
+**First Phase:** StreamPark provides support for native Apache Flink®
projects. We configured our existing tasks' Git addresses in StreamPark, used
Maven to package them as Jar files, and created StreamPark Apache Flink® tasks
for seamless migration. In this process, StreamPark was merely used as a
platform tool for task submission and state maintenance, without leveraging the
other features mentioned above.
**Second Phase:** After migrating tasks to StreamPark in the first phase and
having them run on the platform, the tasks' configurations, such as checkpoint,
fault tolerance, and adjustments to business parameters within Flink tasks,
required source code modifications, pushes, and builds. This was very
inefficient and opaque.
@@ -259,7 +259,7 @@ Regarding the unification of stream and batch, I am also
currently researching a
## **05 Closing Words**
-That's all for the sharing of StreamPark in the production practice at Dustess
Info. Thank you all for reading this far. The original intention of writing
this article was to bring a bit of StreamPark's production practice experience
and reference to everyone, and together with the buddies in the StreamPark
community, to jointly build StreamPark. In the future, I plan to participate
and contribute more. A big thank you to the developers of StreamPark for
providing such an excellent produ [...]
+That's all for the sharing of StreamPark in the production practice at Dustess
Info. Thank you all for reading this far. The original intention of writing
this article was to bring a bit of StreamPark's production practice experience
and reference to everyone, and together with the buddies in the StreamPark
community, to jointly build StreamPark. In the future, I plan to participate
and contribute more. A big thank you to the developers of StreamPark for
providing such an excellent produ [...]
diff --git a/blog/6-streampark-usercase-joyme.md
b/blog/6-streampark-usercase-joyme.md
index 6d1e2df4..fac16e5f 100644
--- a/blog/6-streampark-usercase-joyme.md
+++ b/blog/6-streampark-usercase-joyme.md
@@ -110,7 +110,7 @@ Once the configuration is completed, compile the
corresponding project to finish
-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.
+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.
diff --git a/blog/8-streampark-usercase-ziru.md
b/blog/8-streampark-usercase-ziru.md
index 36851202..e4f36d88 100644
--- a/blog/8-streampark-usercase-ziru.md
+++ b/blog/8-streampark-usercase-ziru.md
@@ -540,7 +540,7 @@ netstat -tlnp | grep 10002
## **Benefits Brought**
-Our team has been using StreamX (the predecessor of StreamPark) and, after
more than a year of practice and refinement, StreamPark has significantly
improved our challenges in developing, managing, and operating Apache Flink
jobs. As a one-stop service platform, StreamPark greatly simplifies the entire
development process. Now, we can complete job development, compilation, and
release directly on the StreamPark platform, not only lowering the management
and deployment threshold of Flink [...]
+Our team has been using StreamX (the predecessor of StreamPark) and, after
more than a year of practice and refinement, StreamPark has significantly
improved our challenges in developing, managing, and operating Apache Flink®
jobs. As a one-stop service platform, StreamPark greatly simplifies the entire
development process. Now, we can complete job development, compilation, and
release directly on the StreamPark platform, not only lowering the management
and deployment threshold of Flink [...]
Since deploying StreamPark, we have been using the platform on a large scale
in a production environment. From initially managing over 50 FlinkSQL jobs to
nearly 500 jobs now, as shown in the diagram, StreamPark is divided into 7
teams, each with dozens of jobs. This transformation not only demonstrates
StreamPark's scalability and efficiency but also fully proves its strong
practical value in actual business.
diff --git a/docs/framework/connector/1.kafka.md
b/docs/framework/connector/1.kafka.md
index ba24e65e..4a82922d 100644
--- a/docs/framework/connector/1.kafka.md
+++ b/docs/framework/connector/1.kafka.md
@@ -6,7 +6,7 @@ sidebar_position: 1
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-[Apache Flink
officially](https://ci.apache.org/projects/flink/flink-docs-release-1.12/zh/dev/connectors/kafka.html)
provides a connector to [Apache Kafka](https://kafka.apache.org) connector for
reading from or writing to a Kafka topic, providing **exactly once** processing
semantics.
+[Apache Flink®
officially](https://ci.apache.org/projects/flink/flink-docs-release-1.12/zh/dev/connectors/kafka.html)
provides a connector to [Apache Kafka](https://kafka.apache.org) connector for
reading from or writing to a Kafka topic, providing **exactly once** processing
semantics.
`KafkaSource` and `KafkaSink` in `StreamPark` are further encapsulated based
on `kafka connector` from the official website, simplifying the development
steps, making it easier to read and write data.
diff --git a/docs/framework/connector/2.jdbc.md
b/docs/framework/connector/2.jdbc.md
index b7420fc0..2d94f27c 100755
--- a/docs/framework/connector/2.jdbc.md
+++ b/docs/framework/connector/2.jdbc.md
@@ -7,7 +7,7 @@ sidebar_position: 2
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-Apache Flink officially provides the
[JDBC](https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/connectors/jdbc.html)
connector for reading from or writing to JDBC, which can provides
**AT_LEAST_ONCE** (at least once) processing semantics.
+Apache Flink® officially provides the
[JDBC](https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/connectors/jdbc.html)
connector for reading from or writing to JDBC, which can provides
**AT_LEAST_ONCE** (at least once) processing semantics.
Apache StreamPark implements **EXACTLY_ONCE** (Exactly Once) semantics of
`JdbcSink` based on two-stage commit, and uses
[`HikariCP`](https://github.com/brettwooldridge/HikariCP) as connection pool to
make data reading and write data more easily and accurately.
@@ -42,7 +42,7 @@ The parameter `semantic` is the semantics when writing in
`JdbcSink`, only effec
#### EXACTLY_ONCE
-If `JdbcSink` is configured with `EXACTLY_ONCE` semantics, the underlying
two-phase commit implementation is used to complete the write, at this time to
Apache Flink with `Checkpointing` to take effect, how to open checkpoint please
refer to Chapter 2 on [checkpoint](/docs/model/conf) configuration section
+If `JdbcSink` is configured with `EXACTLY_ONCE` semantics, the underlying
two-phase commit implementation is used to complete the write, at this time to
Apache Flink® with `Checkpointing` to take effect, how to open checkpoint
please refer to Chapter 2 on [checkpoint](/docs/model/conf) configuration
section
#### AT_LEAST_ONCE && NONE
diff --git a/docs/framework/connector/3.clickhouse.md
b/docs/framework/connector/3.clickhouse.md
index 3301bdff..837887be 100755
--- a/docs/framework/connector/3.clickhouse.md
+++ b/docs/framework/connector/3.clickhouse.md
@@ -9,7 +9,7 @@ import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
[ClickHouse](https://clickhouse.com/) is a columnar database management system
(DBMS) for online analytics (OLAP).
-Currently, Apache Flink does not officially provide a connector for writing to
ClickHouse and reading from ClickHouse.
+Currently, Apache Flink® does not officially provide a connector for writing
to ClickHouse and reading from ClickHouse.
Based on the access form supported by [ClickHouse - HTTP
client](https://clickhouse.com/docs/zh/interfaces/http/)
and [JDBC driver](https://clickhouse.com/docs/zh/interfaces/jdbc), StreamPark
encapsulates ClickHouseSink for writing data to ClickHouse in real-time.
diff --git a/docs/framework/connector/5.es.md b/docs/framework/connector/5.es.md
index f57ec2b0..830fc1b4 100755
--- a/docs/framework/connector/5.es.md
+++ b/docs/framework/connector/5.es.md
@@ -8,7 +8,7 @@ sidebar_position: 5
import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';
[Elasticsearch](https://www.elastic.co/cn/elasticsearch/) is a distributed,
RESTful style search and data analysis
-engine. [Apache Flink
officially](https://nightlies.apache.org/flink/flink-docs-release-1.14/zh/docs/connectors/)
provides a connector for Elasticsearch, which is used to write data to
Elasticsearch, which can provide ** at least once** Semantics.
+engine. [Apache Flink®
officially](https://nightlies.apache.org/flink/flink-docs-release-1.14/zh/docs/connectors/)
provides a connector for Elasticsearch, which is used to write data to
Elasticsearch, which can provide ** at least once** Semantics.
ElasticsearchSink uses TransportClient (before 6.x) or RestHighLevelClient
(starting with 6.x) to communicate with the Elasticsearch cluster. Apache
StreamPark further encapsulates Flink-connector-elasticsearch6, shields
development details, and simplifies write operations for Elasticsearch6 and
above.
diff --git a/docs/framework/connector/6.hbase.md
b/docs/framework/connector/6.hbase.md
index 38985118..a9318d5f 100755
--- a/docs/framework/connector/6.hbase.md
+++ b/docs/framework/connector/6.hbase.md
@@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem';
[Apache HBase](https://hbase.apache.org/book.html) is a highly reliable,
high-performance, column-oriented, and scalable distributed storage system.
Using HBase technology, large-scale structured storage clusters can be built on
cheap PC Servers. Unlike general relational databases, Apache HBase is a
database suitable for unstructured data storage because HBase storage is based
on a column rather than a row-based schema.
-Apache Flink does not officially provide a connector for HBase DataStream.
Apache StreamPark encapsulates HBaseSource and HBaseSink based on
`HBase-client`. It supports automatic connection creation based on
configuration and simplifies development. StreamPark reading Apache HBase can
record the latest status of the read data when the checkpoint is enabled,
+Apache Flink® does not officially provide a connector for HBase DataStream.
Apache StreamPark encapsulates HBaseSource and HBaseSink based on
`HBase-client`. It supports automatic connection creation based on
configuration and simplifies development. StreamPark reading Apache HBase can
record the latest status of the read data when the checkpoint is enabled,
and the offset corresponding to the source can be restored through the data
itself. Implement source-side AT_LEAST_ONCE.
HBaseSource implements Flink Async I/O to improve streaming throughput. The
sink side supports AT_LEAST_ONCE by default.
diff --git a/docs/framework/connector/7.http.md
b/docs/framework/connector/7.http.md
index 9fa927f3..a4e412dd 100755
--- a/docs/framework/connector/7.http.md
+++ b/docs/framework/connector/7.http.md
@@ -8,8 +8,8 @@ sidebar_position: 7
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-Some background services receive data through HTTP requests. In this scenario,
Apache Flink can write result data through HTTP
-requests. Currently, Apache Flink officially does not provide a connector for
writing data through HTTP requests. Apache StreamPark
+Some background services receive data through HTTP requests. In this scenario,
Apache Flink® can write result data through HTTP
+requests. Currently, Apache Flink® officially does not provide a connector for
writing data through HTTP requests. Apache StreamPark
encapsulates HttpSink to write data asynchronously in real-time based on
asynchttpclient.
`HttpSink` writes do not support transactions, writing data to the target
service provides AT_LEAST_ONCE semantics. Data
diff --git a/docs/framework/connector/8.redis.md
b/docs/framework/connector/8.redis.md
index 720da559..4d0aad36 100644
--- a/docs/framework/connector/8.redis.md
+++ b/docs/framework/connector/8.redis.md
@@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem';
[Redis](http://www.redis.cn/) is an open source in-memory data structure
storage system that can be used as a database, cache, and messaging middleware.
It supports many types of data structures such as strings, hashes, lists, sets,
ordered sets and range queries, bitmaps, hyperlogloglogs and geospatial index
radius queries. Redis has built-in transactions and various levels of disk
persistence, and provides high availability through Redis Sentinel and Cluster.
-Apache Flink does not officially provide a connector for writing reids data.
Apache StreamPark is based on [Flink Connector
Redis](https://bahir.apache.org/docs/flink/current/flink-streaming-redis/).
+Apache Flink® does not officially provide a connector for writing reids data.
Apache StreamPark is based on [Flink Connector
Redis](https://bahir.apache.org/docs/flink/current/flink-streaming-redis/).
It encapsulates RedisSink, configures redis connection parameters, and
automatically creates redis connections to simplify development. Currently,
RedisSink supports the following connection methods: single-node mode, sentinel
mode, and cluster mode because it does not support transactions.
diff --git a/docs/get-started/1.intro.md b/docs/get-started/1.intro.md
index 3ce0fb72..24ea6346 100644
--- a/docs/get-started/1.intro.md
+++ b/docs/get-started/1.intro.md
@@ -10,23 +10,21 @@ Make stream processing easier!
## 🚀 What is Apache StreamPark™
-Apache StreamPark is an easy-to-use stream processing application development
framework and one-stop stream processing operation platform. Aimed to make it
easy to build and manage streaming applications, StreamPark provides
scaffolding for writing streaming process logic with Apache Flink and Apache
Spark.
-
-StreamPark also provides a professional task management module including task
development, scheduling, interactive queries, deployment, operations, and
maintenance.
+Apache StreamPark™ is a streaming application development framework that
provides a development framework for developing stream processing application
with Apache Flink® and Apache Spark™, Also, StreamPark is a professional
management platform for streaming application, Its core capabilities include
application development, debugging, deployment, operation, etc. Drastically
simplifies streaming apps’ development and operations, enabling enterprises to
derive immediate insight from their [...]
## Why Apache StreamPark™?
-Apache Flink and Apache Spark are widely used as the next generation of big
data streaming computing engines. Based on a foundation of excellent
experiences combined with best practices, we extracted the task deployment and
runtime parameters into the configuration files. In this way, an easy-to-use
`RuntimeContext` with out-of-the-box connectors can bring an easier and more
efficient task development experience. It reduces the learning cost and
development barriers, so developers can fo [...]
+Apache Flink® and Apache Spark™ are widely used as the next generation of big
data streaming computing engines. Based on a foundation of excellent
experiences combined with best practices, we extracted the task deployment and
runtime parameters into the configuration files. This way, an easy-to-use
RuntimeContext with out-of-the-box connectors can bring an easier and more
efficient task development experience. It reduces the learning cost and
development barriers so developers can focus [...]
-On the other hand, It can be challenge for enterprises to use Apache Flink &
Apache Spark if there is no professional management platform for Flink & Spark
tasks during the deployment phase. StreamPark provides such a professional task
management platform as described above.
+However, enterprises may struggle to use Apache Flink® and Apache Spark™
without a professional management platform for Flink and Spark tasks during the
deployment phase. StreamPark offers a professional task management platform to
address this need.
## 🎉 Features
-* Apache Flink & Apache Spark application development scaffold
-* Supports multiple versions of Apache Flink & Apache Spark
-* Wide range of out-of-the-box connectors
+* Easy-to-use Apache Flink® and Apache Spark™ application development framework
* One-stop stream processing operation platform
-* Supports catalog, OLAP, streaming warehouse, etc.
+* Support multiple versions of Apache Flink® & Apache Spark™
+* Out-of-the-box connectors
+* Support catalog、olap、streaming-warehouse etc.
## 🏳🌈 Architecture of Apache StreamPark™
@@ -34,22 +32,18 @@ The overall architecture of Apache StreamPark is shown in
the following figure.
-### 1️⃣ streampark-core
-
-`streampark-core` is a framework used during development. It supports coding
development, regulates configuration files, and follows the 'convention over
configuration' principle.
+### 1. streampark-core
-`streampark-core` provides development-time Runtime Content and a series of
out-of-the-box Connectors. Cumbersome operations are simplified by extending
DataStream-related methods and integrating DataStream and the Flink SQL API.
This improves development efficiency and developer experience, because users
can focus on the business logic.
+`streampark-core` is a framework used during development. It supports coding
development, regulates configuration files, and follows the 'convention over
configuration' principle. `streampark-core` provides development-time Runtime
Content and a series of out-of-the-box Connectors. Cumbersome operations are
simplified by extending DataStream-related methods and integrating DataStream
and the Flink SQL API. This improves development efficiency and developer
experience because users can fo [...]
-### 2️⃣ streampark-console
+### 2. streampark-console
-`streampark-console` is a comprehensive real-time Low Code data platform that
can manage Flink tasks more convenient.
-It integrates the experience of many best practices and integrates many
functions such as project compilation, release,
-parameter configuration, startup, savepoint, flame graph, Flink SQL,
monitoring, etc., which greatly simplifies the daily operation of Flink tasks
and maintenance. The ultimate goal is to create a one-stop big data platform,
which can provide a solution that integrates flow and batch, and integrates
lake and warehouse.
+`streampark-console` is a comprehensive real-time Low Code data platform that
can manage Flink tasks more convenient. It integrates the experience of many
best practices and integrates many functions such as project compilation,
release, parameter configuration, startup, savepoint, Flink SQL, monitoring,
etc., which greatly simplifies the daily operation of Flink tasks and
maintenance. The ultimate goal is to create a one-stop big data platform, which
can provide a solution that integrat [...]
This platform uses technologies including, but not limited to:
* [Apache Flink](http://flink.apache.org)
-* [Apache Spark](http://spark.apache.org)
+* [Apache Spark™](http://spark.apache.org)
* [Apache YARN](http://hadoop.apache.org)
* [Spring Boot](https://spring.io/projects/spring-boot/)
* [Mybatis](http://www.mybatis.org)
@@ -58,7 +52,6 @@ This platform uses technologies including, but not limited to:
* [VuePress](https://vuepress.vuejs.org/)
* [Ant Design of Vue](https://antdv.com/)
* [ANTD PRO VUE](https://pro.antdv)
-* [xterm.js](https://xtermjs.org/)
* [Monaco Editor](https://microsoft.github.io/monaco-editor/)
Thanks for the support and inspiration given by the above excellent open
source projects and many other excellent open source projects not mentioned
here!
diff --git a/docs/get-started/2.deployment.md b/docs/get-started/2.deployment.md
index 7646ebfd..473e0090 100755
--- a/docs/get-started/2.deployment.md
+++ b/docs/get-started/2.deployment.md
@@ -8,7 +8,7 @@ import { DeploymentEnvs } from '../components/TableData.jsx';
The overall component stack structure of StreamPark consists of two major
parts: streampark-core and streampark-console.
-streampark-console is positioned as an integrated, real-time data platform,
streaming data warehouse Platform, Low Code, Apache Flink & Apache Spark task
hosting platform. It can manage Flink tasks better, and integrate project
compilation, publishing, parameter configuration, startup, savepoint, flame
graph, Flink SQL, monitoring and many other functions, which greatly simplifies
the daily operation and maintenance of Flink tasks and integrates many best
practices.
+streampark-console is positioned as an integrated, real-time data platform,
streaming data warehouse Platform, Low Code, Apache Flink® & Apache Spark™ task
hosting platform. It can manage Flink tasks better, and integrate project
compilation, publishing, parameter configuration, startup, savepoint, flame
graph, Flink SQL, monitoring and many other functions, which greatly simplifies
the daily operation and maintenance of Flink tasks and integrates many best
practices.
The goal is to create a one-stop big data solution that integrates real-time
data warehouses and batches.
diff --git a/docs/get-started/4.platformBasicUsage.md
b/docs/get-started/4.platformBasicUsage.md
index d4de73eb..f98def91 100644
--- a/docs/get-started/4.platformBasicUsage.md
+++ b/docs/get-started/4.platformBasicUsage.md
@@ -322,7 +322,7 @@ flink run-application -t yarn-application \
## Job Details
### Native Flink Job Details
-> View through “[Apache Flink
Dashboard](http://hadoop:8088/proxy/application_1701871016253_0004/#/)”
+> View through “[Apache Flink®
Dashboard](http://hadoop:8088/proxy/application_1701871016253_0004/#/)”
diff --git a/docs/platform/k8s/2.hadoop-resource-integration.md
b/docs/platform/k8s/2.hadoop-resource-integration.md
index 412409cc..da6c6f88 100644
--- a/docs/platform/k8s/2.hadoop-resource-integration.md
+++ b/docs/platform/k8s/2.hadoop-resource-integration.md
@@ -116,7 +116,7 @@ public static String getHadoopConfConfigMapName(String
clusterId) {
### 2. Apache Hive
-To sink data to Apache Hive or use Hive Metastore for Flink's metadata, it is
necessary to open the path from Apache Flink to Apache Hive, which also needs
to go through the following two steps:
+To sink data to Apache Hive or use Hive Metastore for Flink's metadata, it is
necessary to open the path from Apache Flink® to Apache Hive, which also needs
to go through the following two steps:
#### 2.1. Add Hive-related jars
@@ -161,7 +161,7 @@ spec:
#### Conclusion
-Through the above method, Apache Flink can be connected with Apache Hadoop and
Hive. This method can be extended to general, that is, flink and external
systems such as redis, mongo, etc., generally require the following two steps:
+Through the above method, Apache Flink® can be connected with Apache Hadoop
and Hive. This method can be extended to general, that is, flink and external
systems such as redis, mongo, etc., generally require the following two steps:
1. Load the connector jar of the specified external service;
2. If there is, load the specified configuration file into the Flink system.
diff --git a/docusaurus.config.js b/docusaurus.config.js
index 6c27df96..3481e432 100644
--- a/docusaurus.config.js
+++ b/docusaurus.config.js
@@ -23,7 +23,7 @@ const darkTheme =
require('prism-react-renderer/themes/vsDark')
/** @type {import('@docusaurus/types').Config} */
const config = {
title: 'Apache StreamPark (incubating)',
- tagline: 'Apache StreamPark - Make stream processing easier! Easy-to-use
streaming application development framework and operation platform, with Apache
Flink and Apache Spark supported.',
+ tagline: 'Apache StreamPark - Make stream processing easier! Easy-to-use
streaming application development framework and operation platform, with Apache
Flink® and Apache Spark™ supported.',
url: 'https://streampark.apache.org/',
baseUrl: '/',
onBrokenLinks: 'ignore',
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-blog/10-streampark-flink-with-paimon-in-ziru.md
b/i18n/zh-CN/docusaurus-plugin-content-blog/10-streampark-flink-with-paimon-in-ziru.md
index cce070ec..850c661b 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-blog/10-streampark-flink-with-paimon-in-ziru.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-blog/10-streampark-flink-with-paimon-in-ziru.md
@@ -72,19 +72,19 @@ Paimon: https://github.com/apache/paimon
- **支持表结构变更同步**：当数据源表结构发生变化时，Paimon 能自动识别并同步这些变化。
-Paimon 可以结合 Apache Spark 来使用，我们场景是 Paimon 结合 Flink 的方式，这样一来 “**如何管理 4000+个
Flink 数据同步作业**” 将会是我们面临的新问题。在全面调研了相关项目，经过各项维度综合评估后，**我们决定采用
StreamPark**，那么为什么选择 StremaPark 呢？
+Paimon 可以结合 Apache Spark™ 来使用，我们场景是 Paimon 结合 Flink 的方式，这样一来 “**如何管理 4000+个
Flink 数据同步作业**” 将会是我们面临的新问题。在全面调研了相关项目，经过各项维度综合评估后，**我们决定采用
StreamPark**，那么为什么选择 StremaPark 呢？
### **StreamPark 的核心特性**
Apache StreamPark 是一个流处理开发管理框架，提供了一套快捷的API 用来开发 Flink/Spark
作业，此外还提供了一个一站式的流处理作业开发管理平台，从流处理作业开发到上线全生命周期都做了支持，StreamPark 主要包含下面这些核心特点：
-- **流处理应用开发框架**：基于 StreamPark，开发者可以轻松构建和管理流处理应用程序，更好地利用 Apache Flink
去编写流处理应用程序。
+- **流处理应用开发框架**：基于 StreamPark，开发者可以轻松构建和管理流处理应用程序，更好地利用 Apache Flink®
去编写流处理应用程序。
- **完善的作为管理能力**：StreamPark 提供一站式流任务开发管理平台，支持了 Flink / Spark
从应用开发到调试、部署、运维等全生命周期的能力支持，让 Flink / Spark 作业变得简单。
- **完成度高**：StreamPark 支持了 Flink 多版本，可以做到一个平台灵活切换，同时支持 Flink 所的部署模式，有效解决了 Flink
on YARN / K8s 部署过于繁琐的问题，通过自动化流程，简化了任务的构建、测试和部署流程，并提高了开发效率。
-- **丰富的管理 API**：StreamPark 提供了作业操作的 API，包括作业创建、拷贝、构建、部署、基于
checkpoint/savepoint 的停止和启动等功能，使外部系统调用 Apache Flink 任务变得易于实现。
+- **丰富的管理 API**：StreamPark 提供了作业操作的 API，包括作业创建、拷贝、构建、部署、基于
checkpoint/savepoint 的停止和启动等功能，使外部系统调用 Apache Flink® 任务变得易于实现。
## **3. StreamPark + Paimon 实践**
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-blog/11-streampark-usercase-tianyancha.md
b/i18n/zh-CN/docusaurus-plugin-content-blog/11-streampark-usercase-tianyancha.md
index ab11939d..68b321d4 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-blog/11-streampark-usercase-tianyancha.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-blog/11-streampark-usercase-tianyancha.md
@@ -30,7 +30,7 @@ Github: https://github.com/apache/streampark
## **业务背景与挑战**
-天眼查有着庞大的用户基础和多样的业务维度，我们通过 Apache Flink
这一强大的实时计算引擎，为用户提供更优质的产品和服务体验，我们的实时计算业务主要涵盖以下几个场景：
+天眼查有着庞大的用户基础和多样的业务维度，我们通过 Apache Flink®
这一强大的实时计算引擎，为用户提供更优质的产品和服务体验，我们的实时计算业务主要涵盖以下几个场景：
- 实时数据 ETL 处理和数据传输。
@@ -162,7 +162,7 @@ woodstox-core-5.0.3.jar
## **带来的收益**
-Apache StreamPark 为我们带来了显著的收益，**主要体现在其一站式服务能力，使得业务开发人员能够在一个统一的平台上完成 Flink
作业的开发、编译、提交和管理**。极大地节省了我们在 Flink 作业开发和部署上的时间，显著地提升了开发效率，并实现了从用户权限管理到 Git
部署、任务提交、告警、自动恢复的全流程自动化，有效解决了 Apache Flink 运维的复杂性。
+Apache StreamPark 为我们带来了显著的收益，**主要体现在其一站式服务能力，使得业务开发人员能够在一个统一的平台上完成 Flink
作业的开发、编译、提交和管理**。极大地节省了我们在 Flink 作业开发和部署上的时间，显著地提升了开发效率，并实现了从用户权限管理到 Git
部署、任务提交、告警、自动恢复的全流程自动化，有效解决了 Apache Flink® 运维的复杂性。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-blog/5-streampark-usercase-dustess.md
b/i18n/zh-CN/docusaurus-plugin-content-blog/5-streampark-usercase-dustess.md
index 73fb8130..cabc70a8 100644
--- a/i18n/zh-CN/docusaurus-plugin-content-blog/5-streampark-usercase-dustess.md
+++ b/i18n/zh-CN/docusaurus-plugin-content-blog/5-streampark-usercase-dustess.md
@@ -111,7 +111,7 @@ https://streampark.apache.org/docs/development/config
除此之外：
-StreamPark 也**支持Apache Flink 原生任务**，参数配置可以由 Java 任务内部代码静态维护，可以覆盖非常多的场景，比如存量
Flink 任务无缝迁移等等
+StreamPark 也**支持Apache Flink® 原生任务**，参数配置可以由 Java 任务内部代码静态维护，可以覆盖非常多的场景，比如存量
Flink 任务无缝迁移等等
#### **7. Checkpoint 管理**
@@ -182,7 +182,7 @@ StreamPark 非常贴心的准备了 Demo SQL 任务，可以直接在刚搭建
由于目前团队的数据开发同学大多有 Java 和 Scala 语言基础，为了更加灵活的开发、更加透明的调优 Flink
任务及覆盖更多场景，我们也快速的落地了基于 Jar 包的构建方式。我们落地分为了两个阶段
-第一阶段：**StreamPark 提供了原生 Apache Flink 项目的支持**，我们将存量的任务Git地址配置至 StreamPark，底层使用
Maven 打包为 Jar 包，创建 StreamPark 的 Apache Flink任务，无缝的进行了迁移。在这个过程中，StreamPark
只是作为了任务提交和状态维护的一个平台工具，远远没有使用到上面提到的其他功能。
+第一阶段：**StreamPark 提供了原生 Apache Flink® 项目的支持**，我们将存量的任务Git地址配置至 StreamPark，底层使用
Maven 打包为 Jar 包，创建 StreamPark 的 Apache Flink任务，无缝的进行了迁移。在这个过程中，StreamPark
只是作为了任务提交和状态维护的一个平台工具，远远没有使用到上面提到的其他功能。
第二阶段：第一阶段将任务都迁移至 StreamPark 上之后，任务已经在平台上运行，但是任务的配置，如 checkpoint，容错以及 Flink
任务内部的业务参数的调整都需要修改源码 push 及 build，效率十分低下且不透明。
@@ -201,7 +201,7 @@ StreamingContext = ParameterTool +
StreamExecutionEnvironment
String value = ParameterTool.get("${user.custom.key}")
```
-- StreamExecutionEnvironment 为 Apache Flink 原生任务上下文
+- StreamExecutionEnvironment 为 Apache Flink® 原生任务上下文
## **03 业务支撑 & 能力开放**
@@ -259,7 +259,7 @@ StreamingContext = ParameterTool +
StreamExecutionEnvironment
## **05 结束语**
-以上就是 StreamPark 在尘锋信息生产实践的全部分享内容，感谢大家看到这里。写这篇文章的初心是为大家带来一点 StreamPark
的生产实践的经验和参考，并且和 StreamPark 社区的小伙伴们一道，共同建设 StreamPark ，未来也准备会有更多的参与和建设。非常感谢
StreamPark
的开发者们，能够提供这样优秀的产品，足够多的细节都感受到了大家的用心。虽然目前公司生产使用的（1.2.0-release）版本，在任务分组检索，编辑返回跳页等交互体验上还有些许不足，但瑕不掩瑜，相信
StreamPark 会越来越好，**也相信 StreamPark 会推动 Apache Flink 的普及**。最后用 Apache Flink
社区的一句话来作为结束吧：实时即未来！
+以上就是 StreamPark 在尘锋信息生产实践的全部分享内容，感谢大家看到这里。写这篇文章的初心是为大家带来一点 StreamPark
的生产实践的经验和参考，并且和 StreamPark 社区的小伙伴们一道，共同建设 StreamPark ，未来也准备会有更多的参与和建设。非常感谢
StreamPark
的开发者们，能够提供这样优秀的产品，足够多的细节都感受到了大家的用心。虽然目前公司生产使用的（1.2.0-release）版本，在任务分组检索，编辑返回跳页等交互体验上还有些许不足，但瑕不掩瑜，相信
StreamPark 会越来越好，**也相信 StreamPark 会推动 Apache Flink® 的普及**。最后用 Apache Flink®
社区的一句话来作为结束吧：实时即未来！
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-blog/6-streampark-usercase-joyme.md
b/i18n/zh-CN/docusaurus-plugin-content-blog/6-streampark-usercase-joyme.md
index a795e5c2..0cd1af61 100644
--- a/i18n/zh-CN/docusaurus-plugin-content-blog/6-streampark-usercase-joyme.md
+++ b/i18n/zh-CN/docusaurus-plugin-content-blog/6-streampark-usercase-joyme.md
@@ -110,7 +110,7 @@ Streaming 作业我们是使用 Flink java 进行开发，将之前 Spark scala
-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。
+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。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-blog/8-streampark-usercase-ziru.md
b/i18n/zh-CN/docusaurus-plugin-content-blog/8-streampark-usercase-ziru.md
index 2a343db3..e5f26299 100644
--- a/i18n/zh-CN/docusaurus-plugin-content-blog/8-streampark-usercase-ziru.md
+++ b/i18n/zh-CN/docusaurus-plugin-content-blog/8-streampark-usercase-ziru.md
@@ -44,7 +44,7 @@ tags: [StreamPark, 生产实践]
### **03 作业维护困难**
-在自如有多个不同版本的 Flink 作业在运行，由于 Apache Flink 的 API
在大版本升级中经常会发生变动，且不保证向下兼容性，这直接导致流作业项目代码的升级成本变得很高。因此如何管理这些不同版本的作业成了头痛的问题。
+在自如有多个不同版本的 Flink 作业在运行，由于 Apache Flink® 的 API
在大版本升级中经常会发生变动，且不保证向下兼容性，这直接导致流作业项目代码的升级成本变得很高。因此如何管理这些不同版本的作业成了头痛的问题。
由于没有统一的作业平台，这些作业在提交时，只能通过执行脚本的形式进行提交。不同的作业有不同重要程度和数据量级，作业所需资源和运行参数也都各不相同，都需要相应的修改。我们可以通过修改提交脚本或直接在代码中设置参数来进行修改，但这使得配置信息的获取变得困难，尤其是当作业出现重启或失败时，FlinkUI
无法打开，配置信息变成一个黑盒。因此，亟需建立一个更加高效、支持配置实时计算平台。
@@ -533,7 +533,7 @@ netstat -tlnp | grep 10002
## **带来的收益**
-我们的团队从 StreamX（即 StreamPark 的前身）开始使用，经过一年多的实践和磨合，StreamPark 显著改善了我们在 Apache
Flink 作业的开发管理和运维上的诸多挑战。StreamPark 作为一站式服务平台，极大地简化了整个开发流程。现在，我们可以直接在 StreamPark
平台上完成作业的开发、编译和发布，这不仅降低了 Flink 的管理和部署门槛，还显著提高了开发效率。
+我们的团队从 StreamX（即 StreamPark 的前身）开始使用，经过一年多的实践和磨合，StreamPark 显著改善了我们在 Apache
Flink® 作业的开发管理和运维上的诸多挑战。StreamPark 作为一站式服务平台，极大地简化了整个开发流程。现在，我们可以直接在 StreamPark
平台上完成作业的开发、编译和发布，这不仅降低了 Flink 的管理和部署门槛，还显著提高了开发效率。
自从部署 StreamPark 以来，我们已经在生产环境中大规模使用该平台。从最初管理的 50 多个 FlinkSQL 作业，增长到目前近 500
个作业，如图在 StreamPark 上划分为 7 个 team，每个 team 中有几十个作业。这一转变不仅展示了 StreamPark
的可扩展性和高效性，也充分证明了它在实际业务中的强大应用价值。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/submit_guide/documentation-style-guide.md
b/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/submit_guide/documentation-style-guide.md
index adaf7aa0..93385cc4 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/submit_guide/documentation-style-guide.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs-community/current/submit_guide/documentation-style-guide.md
@@ -93,11 +93,11 @@ sidebar_position: 4
正例：
-> Apache Flink 是 Apache StreamPark 支持的计算引擎之一。
+> Apache Flink® 是 Apache StreamPark 支持的计算引擎之一。
反例：
-> Apache Flink 是 Apache StreamPark支持的计算引擎之一。
+> Apache Flink® 是 Apache StreamPark支持的计算引擎之一。
>
> Apache Flink是 Apache StreamPark 支持的计算引擎之一。
@@ -107,13 +107,13 @@ sidebar_position: 4
正例：
-> 某个公司的 Apache StreamPark 平台运行了 5000 个 Apache Flink 作业。
+> 某个公司的 Apache StreamPark 平台运行了 5000 个 Apache Flink® 作业。
反例：
-> 某个公司的 Apache StreamPark 平台运行了5000 个 Apache Flink 作业。
+> 某个公司的 Apache StreamPark 平台运行了5000 个 Apache Flink® 作业。
>
-> 某个公司的 Apache StreamPark 平台运行了 5000个 Apache Flink 作业。
+> 某个公司的 Apache StreamPark 平台运行了 5000个 Apache Flink® 作业。
#### 2.1.3 数字和单位之间
@@ -131,13 +131,13 @@ sidebar_position: 4
> 角度为 90° 的角，就是直角。
>
-> Apache StreamPark 可以给 Apache Flink 作业管理带来约 15% 的效率提升。
+> Apache StreamPark 可以给 Apache Flink® 作业管理带来约 15% 的效率提升。
反例：
> 角度为 90 ° 的角，就是直角。
>
-> Apache StreamPark 可以给 Apache Flink 作业管理带来约 15 % 的效率提升。
+> Apache StreamPark 可以给 Apache Flink® 作业管理带来约 15 % 的效率提升。
#### 2.1.4 全角标点符号和其他字符之间不需要空格
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/flinksql/format/10-parquet.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/flinksql/format/10-parquet.md
index 27cafdad..3376fe11 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/flinksql/format/10-parquet.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/flinksql/format/10-parquet.md
@@ -57,7 +57,7 @@ Parquet 格式也支持 `ParquetOutputFormat` 的配置。 例如, 可以配置
## 数据类型映射
-目前，Parquet 格式类型映射与 Apache Hive 兼容，但与 Apache Spark 有所不同：
+目前，Parquet 格式类型映射与 Apache Hive 兼容，但与 Apache Spark™ 有所不同：
* Timestamp：不参考精度，直接映射 timestamp 类型至 int96。
* Decimal：根据精度，映射 decimal 类型至固定长度字节的数组。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/1.kafka.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/1.kafka.md
index ae3f84b2..8db7a211 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/1.kafka.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/1.kafka.md
@@ -6,7 +6,7 @@ sidebar_position: 1
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-[Apache Flink
官方](https://ci.apache.org/projects/flink/flink-docs-release-1.12/zh/dev/connectors/kafka.html)提供了
[Apache Kafka](http://kafka.apache.org) 的连接器，用于从 Kafka
主题中读取或者向其中写入数据，可提供**精确一次**的处理语义。
+[Apache Flink®
官方](https://ci.apache.org/projects/flink/flink-docs-release-1.12/zh/dev/connectors/kafka.html)提供了
[Apache Kafka](http://kafka.apache.org) 的连接器，用于从 Kafka
主题中读取或者向其中写入数据，可提供**精确一次**的处理语义。
Apache StreamPark 中 `KafkaSource` 和 `KafkaSink` 基于官网的 Kafka Connector
进一步封装，屏蔽了很多细节，简化开发步骤，让数据的读取和写入更简单。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/2.jdbc.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/2.jdbc.md
index 4d44563a..70fe25d8 100755
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/2.jdbc.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/2.jdbc.md
@@ -7,7 +7,7 @@ sidebar_position: 2
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-Apache Flink
官方提供了[JDBC](https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/connectors/jdbc.html)的连接器，用于从
JDBC 中读取或者向其中写入数据，可提供至少一次（**AT_LEAST_ONCE**）的处理语义。
+Apache Flink®
官方提供了[JDBC](https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/connectors/jdbc.html)的连接器，用于从
JDBC 中读取或者向其中写入数据，可提供至少一次（**AT_LEAST_ONCE**）的处理语义。
Apache StreamPark 中基于两阶段提交实现了精确一次（**EXACTLY_ONCE**）语义的 `JdbcSink` 类，并且采用
[`HikariCP`](https://github.com/brettwooldridge/HikariCP) 为连接池,让数据的读取和写入更简单更准确。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/3.clickhouse.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/3.clickhouse.md
index 4ccdef9d..cb9810d2 100755
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/3.clickhouse.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/3.clickhouse.md
@@ -8,7 +8,7 @@ sidebar_position: 3
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-[ClickHouse](https://clickhouse.com/) 是一个用于联机分析（OLAP）的列式数据库管理系统，主要面向 OLAP
场景。目前 Apache Flink 官方未提供写入
+[ClickHouse](https://clickhouse.com/) 是一个用于联机分析（OLAP）的列式数据库管理系统，主要面向 OLAP
场景。目前 Apache Flink® 官方未提供写入
读取 ClickHouse 数据的连接器。Apache StreamPark 基于 ClickHouse 支持的访问形式 [HTTP
客户端](https://clickhouse.com/docs/zh/interfaces/http/)、[JDBC
驱动](https://clickhouse.com/docs/zh/interfaces/jdbc/)封装了 `ClickHouseSink` 用于向
ClickHouse 实时写入数据。
ClickHouse 写入不支持事务，使用 JDBC 向其中写入数据可提供至少一次的处理语义。使用 HTTP
客户端异步写入，对异步写入重试多次失败的数据会写入外部组件,最终通过人为介入来恢复数据，实现最终数据一致。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/5.es.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/5.es.md
index 894b3d45..9ff17c5a 100755
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/5.es.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/5.es.md
@@ -8,7 +8,7 @@ sidebar_position: 5
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-[Elasticsearch](https://www.elastic.co/cn/elasticsearch/) 是一个分布式的、RESTful
风格的搜索和数据分析引擎。[Apache Flink
官方](https://nightlies.apache.org/flink/flink-docs-release-1.14/zh/docs/connectors/)提供了
[Elasticsearch](https://nightlies.apache.org/flink/flink-docs-release-1.14/zh/docs/connectors/datastream/elasticsearch/)
的连接器，用于向 ElasticSearch 中写入数据,可提供 **至少一次** 的处理语义。
+[Elasticsearch](https://www.elastic.co/cn/elasticsearch/) 是一个分布式的、RESTful
风格的搜索和数据分析引擎。[Apache Flink®
官方](https://nightlies.apache.org/flink/flink-docs-release-1.14/zh/docs/connectors/)提供了
[Elasticsearch](https://nightlies.apache.org/flink/flink-docs-release-1.14/zh/docs/connectors/datastream/elasticsearch/)
的连接器，用于向 ElasticSearch 中写入数据,可提供 **至少一次** 的处理语义。
ElasticsearchSink 使用 TransportClient（6.x 之前）或者 RestHighLevelClient（6.x 开始）和
Elasticsearch 集群进行通信，Apache StreamPark 对 flink-connector-elasticsearch6
进一步封装，屏蔽开发细节，简化 Elasticsearch6 及以上的写入操作。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/6.hbase.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/6.hbase.md
index 2c8c8806..043224cf 100755
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/6.hbase.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/6.hbase.md
@@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem';
[Apache HBase](https://hbase.apache.org/book.html)
是一个高可靠性、高性能、面向列、可伸缩的分布式存储系统，利用 HBase
技术可在廉价服务器上搭建起大规模结构化存储集群。HBase不同于一般的关系数据库，它是一个适合于非结构化数据存储的数据库，HBase
基于列的而不是基于行的模式。
-Apache Flink 官方未提供 HBase DataStream 的连接器。Apache StreamPark 基于 HBase client 封装了
HBaseSource、HBaseSink，支持依据配置自动创建连接，简化开发。StreamPark 读取 HBase 在开启 chekpoint
情况下可以记录读取数据的最新状态，通过数据本身标识可以恢复 source 对应偏移量。实现 source 端至少一次语义。
+Apache Flink® 官方未提供 HBase DataStream 的连接器。Apache StreamPark 基于 HBase client
封装了 HBaseSource、HBaseSink，支持依据配置自动创建连接，简化开发。StreamPark 读取 HBase 在开启 chekpoint
情况下可以记录读取数据的最新状态，通过数据本身标识可以恢复 source 对应偏移量。实现 source 端至少一次语义。
HBaseSource 实现了 Flink 的 Async I/O 接口，可以提升流处理的吞吐量。Sink 端默认支持至少一次的处理语义。在开启
checkpoint 情况下支持精确一次语义。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/7.http.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/7.http.md
index 842d521f..770b61e1 100755
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/7.http.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/7.http.md
@@ -8,7 +8,7 @@ sidebar_position: 7
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-一些后台服务通过 HTTP 请求接收数据，这种场景下 Apache Flink 可以通过 HTTP 请求写入结果数据，目前 Apache Flink
官方未提供通过 HTTP 请求写入
+一些后台服务通过 HTTP 请求接收数据，这种场景下 Apache Flink® 可以通过 HTTP 请求写入结果数据，目前 Apache Flink®
官方未提供通过 HTTP 请求写入
数据的连接器。Apache StreamPark 基于 asynchttpclient 封装了 HttpSink 异步实时写入数据。
`HttpSink`
写入不支持事务，向目标服务写入数据可提供至少一次的处理语义。异步写入重试多次失败的数据会写入外部组件，最终通过人为介入来恢复数据，达到最终数据一致。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/8.redis.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/8.redis.md
index 1c6eee65..d04558e7 100755
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/8.redis.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/framework/connector/8.redis.md
@@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem';
[Redis](http://www.redis.cn/)是一个开源内存数据结构存储系统，它可以用作数据库、缓存和消息中间件。
它支持多种类型的数据结构，如字符串（strings）， 散列（hashes）， 列表（lists）， 集合（sets）， 有序集合（sorted
sets）与范围查询，bitmaps、hyperloglogs 和地理空间（geospatial） 索引半径查询。 Redis
内置了事务（transactions） 和不同级别的磁盘持久化（persistence），并通过
Redis哨兵（Sentinel）和自动分区（Cluster）提供高可用性（high availability）。
-Apache Flink 官方未提供写入 Reids 数据的连接器。Apache StreamPark 基于 [Flink Connector
Redis](https://bahir.apache.org/docs/flink/current/flink-streaming-redis/) 封装了
RedisSink、配置 redis 连接参数，即可自动创建 redis 连接简化开发。目前 RedisSink
支持连接方式有：单节点模式、哨兵模式，因集群模式不支持事务，目前未支持。
+Apache Flink® 官方未提供写入 Reids 数据的连接器。Apache StreamPark 基于 [Flink Connector
Redis](https://bahir.apache.org/docs/flink/current/flink-streaming-redis/) 封装了
RedisSink、配置 redis 连接参数，即可自动创建 redis 连接简化开发。目前 RedisSink
支持连接方式有：单节点模式、哨兵模式，因集群模式不支持事务，目前未支持。
StreamPark 使用Redis的 **MULTI** 命令开启事务，**EXEC**
命令提交事务，细节见链接：http://www.redis.cn/topics/transactions.html。RedisSink 默认支持
AT_LEAST_ONCE 的处理语义，在开启 checkpoint 情况下支持 EXACTLY_ONCE 语义。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/get-started/1.intro.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/get-started/1.intro.md
index 5d4bcbad..845fff3e 100644
--- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/get-started/1.intro.md
+++ b/i18n/zh-CN/docusaurus-plugin-content-docs/current/get-started/1.intro.md
@@ -10,16 +10,16 @@ sidebar_position: 1
## 🚀 什么是 Apache StreamPark™
-实时即未来,在实时处理流域 Apache Spark 和 Apache Flink 是一个伟大的进步，尤其是 Flink 被普遍认为是下一代大数据流计算引擎。
+实时即未来,在实时处理流域 Apache Spark™ 和 Apache Flink® 是一个伟大的进步，尤其是 Flink
被普遍认为是下一代大数据流计算引擎。
-在使用 Apache Flink 和 Apache Spark 时，我们发现从编程模型,
启动配置到运维管理都有很多可以抽象共用的地方。于是，我们将一些好的经验固化下来并结合业内的最佳实践, 通过不断努力诞生了今天的框架：Apache
StreamPark。项目的初衷是：让流处理更简单！
+在使用 Apache Flink® 和 Apache Spark™ 时，我们发现从编程模型,
启动配置到运维管理都有很多可以抽象共用的地方。于是，我们将一些好的经验固化下来并结合业内的最佳实践, 通过不断努力诞生了今天的框架：Apache
StreamPark。项目的初衷是：让流处理更简单！
使用 StreamPark 开发流处理作业, 可以极大降低学习成本和开发门槛, 让开发者只用关心最核心的业务，StreamPark
规范了项目的配置、鼓励函数式编程、定义了最佳的编程方式，提供了一系列开箱即用的连接器（Connector），标准化了配置、开发、测试、部署、监控、运维的整个过程，提供了
Scala 和 Java 两套接口，并且提供了一个一站式的流处理作业开发管理平台，从流处理作业开发到上线全生命周期都做了支持，是一个一站式的流处理计算平台。
## 🎉 Features
-* Apache Flink 和 Apache Spark 应用程序开发脚手架
-* 支持多个版本的 Apache Flink 和 Apache Spark
+* Apache Flink® 和 Apache Spark™ 应用程序开发脚手架
+* 支持多个版本的 Apache Flink® 和 Apache Spark
* 一系列开箱即用的连接器
* 一站式流处理运营平台
* 支持 Catalog / OLAP / Streaming Warehouse 等场景
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/get-started/4.platformBasicUsage.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/get-started/4.platformBasicUsage.md
index cea63156..6d89f3cd 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/get-started/4.platformBasicUsage.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/get-started/4.platformBasicUsage.md
@@ -335,7 +335,7 @@ flink run-application -t yarn-application \
## 作业详情
### 原生flink作业详情
-> 通过 “[Apache Flink
Dashboard](http://hadoop:8088/proxy/application_1701871016253_0004/#/)”查看
+> 通过 “[Apache Flink®
Dashboard](http://hadoop:8088/proxy/application_1701871016253_0004/#/)”查看
