This is an automated email from the ASF dual-hosted git repository.
zhongjiajie pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/dolphinscheduler-website.git
The following commit(s) were added to refs/heads/master by this push:
new 4208d73b1 Add blog dolphinscheduler 3.0 and T3 usage (#827)
4208d73b1 is described below
commit 4208d73b1daecdd97173c34cce168145457de511
Author: lifeng <[email protected]>
AuthorDate: Mon Sep 19 14:02:44 2022 +0800
Add blog dolphinscheduler 3.0 and T3 usage (#827)
---
blog/en-us/Apache_dolphinScheduler_3.0.0.md | 215 +++++++++++++++++++++
.../DolphinScheduler_in_T3Go_One-stop_Platform.md | 110 +++++++++++
site_config/blog.js | 14 ++
site_config/home.jsx | 25 +--
4 files changed, 352 insertions(+), 12 deletions(-)
diff --git a/blog/en-us/Apache_dolphinScheduler_3.0.0.md
b/blog/en-us/Apache_dolphinScheduler_3.0.0.md
new file mode 100644
index 000000000..afb3eec0a
--- /dev/null
+++ b/blog/en-us/Apache_dolphinScheduler_3.0.0.md
@@ -0,0 +1,215 @@
+---
+title:Apache DolphinScheduler 3.0.0 Official Version Released!
+keywords: Apache,DolphinScheduler,scheduler,big
data,ETL,airflow,hadoop,orchestration,dataops,Kubernetes
+description:The official version 3.0.0 has undergone the most significant
changes since its release
+---
+# Apache DolphinScheduler 3.0.0 Official Version Released!
+
+The official version 3.0.0 has undergone the most significant changes since
its release, adding many new functions and features, aiming to bring users a
brand-new experience and more value.
+
+The iterative 3.0.0 official version is roughly the same as the primary
function and feature updates, optimizations, and bug fixes described in the
previous 3.0.0 alpha version update, including the four keywords summary
“faster, stronger, more modern, and easier to maintain” of this version.
+
+For the new functions and optimizations after the version iteration, this
article will make supplements.
+
+## Keyword: faster, stronger, more modern, and easier to maintain
+* Faster and more modern: a reworked UI with a new interface, which is not
only tens of times more responsive for users and hundreds of times faster for
developers to build but also with a more modern page layout and icon style.
+* More powerful: bringing many exciting new features such as data quality
check, custom time zones, support for AWS, and the addition of multiple task
plugins and multiple alert plugins.
+* Easier to maintain: back-end service separation is more in line with the
trend toward containerization and microservices, and also makes maintenance
easier by clarifying the responsibilities of each service.
+
+## New features and functionality
+### New UI, more robust and faster front-end code
+The biggest changes in 3.0.0-alpha are the introduction of a new UI, which
eliminates the need to reload pages when switching languages, and the addition
of a dark theme. The new UI uses the Vue3, TSX, and Vite-related technology
stack. Compared to the earlier UI, the new UI is not only more modern and
user-friendly, but the front-end is also more robust, allowing users to check
interface parameters if they find problems in the code, resulting in more
robust front-end code.
+
+In addition, the new architecture and technology stack will not only allow
users to operate Apache DolphinScheduler tens of times more responsively, but
developers will also be hundreds of times faster at compiling and launching the
UI locally, which will significantly reduce the time it takes for developers to
debug and package their code.
+
+Experience the new UI:
+
+
+
+<u>Local launch time comparison</u>
+### Homepage:
+
+### Workflow instances
+
+### Shell Tasks page
+
+### MySQL Data Sources page
+
+
+## Support for AWS
+As the Apache DolphinScheduler user grows, it has attracted many overseas
users. However, during the research, users found that there were two
bottlenecks that affected their experience with Apache DolphinScheduler, one
was the time zone issue and the other was the lack of support for overseas
cloud vendors, especially AWS. For this reason, we decided to support the
significant components of AWS, and this is one of the most significant changes
in this release.
+
+Apache DolphinScheduler now supports AWS for both Amazon EMR and Amazon
Redshift task types and has implemented Resource Center support for Amazon S3
storage.
+
+* **For Amazon EMR,** we have created a new task type and provided its Run Job
Flow feature, which allows users to submit multiple steps jobs to Amazon EMR
and specify the number of resources to be used. Details can be found at:
[https://dolphinscheduler.apache.org/zh-cn/docs/latest/user_doc/guide/task/emr.html](https://dolphinscheduler.apache.org/zh-cn/docs/latest/user_doc/guide/task/emr.html)
+
+### Amazon EMR Task Definition
+
+For Amazon Redshift, we have now extended support for Amazon Redshift data
sources in the SQL task type and users can now run Amazon Redshift tasks by
selecting the Redshift data source in the SQL task.
+
+### Amazon Redshift support
+
+
+For **Amazon S3**, we have extended the Apache DolphinScheduler’s resource
center to support not only local resources, HDFS resource storage but also
Amazon S3 as a resource centre for storage. Details can be found at:
https://dolphinscheduler.apache.org/en-us/docs/latest/user_doc/guide/resource/intro.html
in `resource.storage.type` We will be supporting more AWS tasks as users need
them, so stay tuned.
+
+### Service Separation
+While the new UI is the biggest change to the front end of 3.0.0-alpha, the
biggest change to the back end is the separation of services. Given the growing
buzz around containers and microservices, the Apache DolphinScheduler
developers made the decision to class
https://dolphinscheduler.apache.org/en-us/docs/latest/user_doc/guide/resource/intro.html
ify the backend services. By function, we split the service into the following
parts.
+
+* master-server: master service
+* worker-server: worker service
+* api-server: API service
+* alert-server: alert service
+* standalone-server: standalone for a quick experience with Apache
DolphinScheduler functionality
+* UI: UI resources
+* bin: quick-start scripts, mainly scripts to start individual services
+* tools: tools-related scripts, mainly database creation and scripts update
+All services can be started or stopped by executing the following commands.
+
+Plain Text
+
+`bin/dolphinscheduler-daemon.sh <start|stop> <server-name>`
+
+## Data quality verification
+In this release, the long-awaited data quality verification comes online,
solving data quality issues such as the accuracy of the number of data items
synchronized from the source, and alarms for single or multiple tables with
weekly or monthly fluctuations exceeding thresholds. Earlier versions of Apache
DolphinScheduler solved the problem of running tasks in a specific order and
time, while it lacks a commonly used data quality measurement after it had been
run, which imposes an additi [...]
+
+Data quality verification is now natively supported in 3.0.0-alpha, with
support for a data quality check process before the workflow is run, enabling
strict control of data quality and monitoring of results during the task run by
user-defined data quality check rules in the data quality function module.
+
+
+
+
+## Task groups
+
+Task groups are used to control the concurrency of task instances and to
define the group's priority. When creating a new task definition, the user can
configure the task group corresponding to the current task and configure the
priority of the task to run within the task group. When a task is configured
with a task group, the task can be executed only meeting the condition that all
upstream tasks run successfully but also the task currently running in the task
group is smaller than the [...]
+
+
+
+
+### Customizing time zones
+In versions prior to 3.0.0-alpha, Apache DolphinScheduler defaulted to the
UTC+8 time zone, but as the user base expanded, overseas users and those doing
business across time zones overseas were often confused by the time zone.
3.0.0-alpha supports time zone switching, which solves the time zone problem
for overseas users. For example, if an enterprise business involves a time zone
of East 8 and West 5, if you want to use the same DolphinScheduler cluster, you
can create multiple users a [...]
+
+See link:
[https://dolphinscheduler.apache.org/zh-cn/docs/3.0.0/user_doc/guide/howto/general-setting.html](https://dolphinscheduler.apache.org/zh-cn/docs/3.0.0/user_doc/guide/howto/general-setting.html)
+
+## List of task definitions
+
+With previous versions of Apache DolphinScheduler 3.0.0-alpha, if a user
wanted to manipulate a task, they needed to find the corresponding workflow and
locate the task in the workflow before they could edit it. However, when the
number of workflows became large or when a single workflow had a large number
of tasks, the process of finding the corresponding task became very painful for
users, which was not in line with the easy-to-use philosophy of Apache
DolphinScheduler. Therefore, we h [...]
+
+See the link for more details:
[https://dolphinscheduler.apache.org/zh-cn/docs/latest/user_doc/guide/project/task-definition.html](https://dolphinscheduler.apache.org/zh-cn/docs/latest/user_doc/guide/project/task-definition.html)
+
+## New alert types
+The 3.0.0-alpha alert type adds support for Telegram and Webexteams alert
types.
+
+### New Python API Features
+In 3.0.0, the most significant change in the Python API is to integrate the
corresponding PythonGatewayServer into the API-Server service and rename it
PythonGatewayService. Now the user will start the PythonGatewayService by
default when starting the API-server; if you do not want to start the
PythonGatewayService, you can set python-gateway.enabled in application.yaml to
false.
+
+Additionally, the Python API adds CLI and configuration modules. The
Configuration module allows users to modify the default configuration of the
Python API, such as modifying the default user name of the workflow, worker
grouping, etc. The values can be changed through environment variables, direct
file modification, and Python dynamic modification.
+
+
+At present, the CLI only has two subcommands, version and config, which are
used to confirm the current version and add or delete configuration files. In
the future, we will introduce more functions to facilitate users to operate
DolphinScheduler through the command line.
+
+It is worth noting that the Python API also supports the function of adding
and uploading files in the resource center to facilitate resource management;
it also supports writing different names for different workflows of the same
project; adding integration tests to make the testing more convenient.
+
+<u>Unannounced functionality and feature updates from previous releases</u>
+
+### Support for Flink task types
+In this release, we have extended the Flink task type to support running Flink
SQL tasks, which use sql-client.sh to submit tasks. In the previous version, we
only supported submitting tasks through Flink cli. This method needs to combine
the resource center, submit the resource file to the resource center, and then
refer to the modified resource on the task definition page, which is not
friendly for versioning and user transparency. As Flink SQL gradually becomes
the mainstream of Flink [...]
+
+For more details, please refer to: [flink sql
client](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sqlclient/)
+
+Corresponding PR:
[https://github.com/apache/dolphinscheduler/pull/9840](https://github.com/apache/dolphinscheduler/pull/9840)
+
+
+### Add Zepplin task type
+In this release, we have added the Zeppelin task type for creating and
executing Zeppelin-type tasks. When the worker executes this task, it triggers
the Zeppelin Notebook section through the Zeppelin Client API.
+
+Corresponding PR:
[https://github.com/apache/dolphinscheduler/pull/9810](https://github.com/apache/dolphinscheduler/pull/9810)
+
+
+### Bash parameter passing function
+The new version also adds the function of passing parameters through bash. If
you want to use bash variables instead of constant value export parameters in
downstream tasks, you can achieve this by rough setValue and Bash variables,
which is more flexible and allows you to dynamically obtain Existing local or
HTTP resources to get set variables.
+
+A similar syntax can be used
+
+`lines_num=$(wget
https://raw.githubusercontent.com/apache/dolphinscheduler/dev/README.md -q -O -
| wc -l | xargs)echo "#{setValue(set_val_var=${lines_num})}"
+`
+
+### Allow users to upload files without a suffix
+Previously, the resource center could only upload files with suffixes. After
version 3.0.0, we support users to upload files without suffixes.
+
+### Other functional enhancements
+In addition to the above new functions, version 3.0.0 has also carried out
many detailed function enhancements, such as refactoring task plug-ins and data
source plug-in modules to make expansion easier; restoring the support for
Spark SQL; making the E2E testing perfectly compatible with the new UI, etc.
+## Key optimizations
+* [#8584] Task back-end plugin optimization, new plugins only need to modify
the plugin’s own module
+* [#8874] Verify the end time and start time when submitting/creating a cron
under workflow
+* [#9016] Dependent The global project can be selected when adding dependencies
+* [#9221] AlertSender optimization and gracefully close, such as MasterServer
+* [#9228] Increase the slot condition to check the database and reduce the
returned data records
+* [#9230] Slim dist package by migrating python gatewar into Episerver
+* [#9372] [python] Migrate pythonGatewayServer into API server
+* [#9443] [python] Add missing doc about config and connect remote server
+* [#8719] [Master/Worker] Change the task ack to runnning callback
+* [#9293] [Master] add task event thread pool
+
+## Major bug fixes
+
+* [#7236] Failed to create tenant using S3a Minio
+* [#7416] Text file busy
+* [#7896] When the project is authorized, it will generate a duplicate
authorized project
+* [#8089] start server failed because it can’t connect to PostgreSQL
+* [#8183] message:datasource plugin ‘spark’ is not found.
+* [#8202] MapReduce generated command built-in parameter location is wrong
+* [#8751] Change param user, queue do no work in ProcessDefinition
+* [#8756] Process using the dependence component cannot migrate between test
and prod environment
+* [#8760] Resource file deletion conditions
+* [#8791] Rectify the issue affecting the original node’s data when editing
the form of the copied node.
+* [#8951] Worker resources are exhausted and cause downtime
+* [#9243] Some types of alarms can’t display project name
+* Problems with each deployment method in 3.0.0
+* When the task group is empty, the page reports an error
+* treemap view depth error problem
+* The alarm information is not clear
+* Parameter verification problem: The parameter verification problem in the
data source center, the password is inconsistent when the password is changed,
and the alert script is verified before the alarm is sent.
+* Python API: The release state cannot be set, the local parameter has a value
but the verification fails
+* The token query doesn’t follow the timezone
+* Fix HTTPS and HTTP string recognition issues
+* Fix alert server health monitoring failure problem
+* Fix condition task branch failure problem
+* Fix the issue of docker image does not support multi-platform
+* Fix the problem that the database cannot be written correctly when the
workflow with task group priority is created
+* Invalidation of the master task
+* Fix the issue of serial wait not running
+* Time zone error: scheduling time zone error problem, log add time zone
support
+* Re-run, pause workflow instance failure problem
+* Resource Center instantiation failure problem
+* Fix the problem of dividing lines in the email alert template
+* Fix data initialization problem in Standalone mode
+* Fixed the page display error when the monitoring center DB does not exist
+* Fix the issue of invalid creation workflow parameters
+* Fixed the abnormal problem of zookeeper port during K8S deployment
+* Fix the problem that the service fails to start in Standalone mode
+* Fix LDAP login failure problem
+* Python API: fix the problem that the task component names of different
workflows under the same project do not support the same name
+* Python API: fix SQL task component SQL type error
+* Fix the abnormal problem of resource file renaming form
+* Fix the problem of getting the executable time of the workflow according to
the timing settings
+* Upgraded module dependencies such as Logback and Log4j
+* Fix mission failure issue
+* Fixe the issue of HDFS NPE
+* Fix the problem of master deadlock caused by task group exception
+* Fixed several stability issues
+
+## Document modification
+
+* Correct the deployment documentation
+* Repair and update some usage documents: WebexTeams Chinese version
documentation, local parameters, global parameter documentation, Kubernetes FAQ
documentation, Spark precautions documentation, DataX usage documentation,
delete Flink API documentation, fix the open-api errors, fix wrong
documentation in data quality; Add stand-alone switch database document; Add
document for judging Yarn running status in Shell; Add update system
screenshot; Upgrade documents for parameter transfer, g [...]
+* Corrected some development documents: clarify the supported operating
systems, fix development environment construction documents, and add self-build
docker image documents
+
+## Release Note
+GitHub:[https://github.com/apache/dolphinscheduler/releases/tag/3.0.0](https://github.com/apache/dolphinscheduler/releases/tag/3.0.0)
+
+Download:
[https://dolphinscheduler.apache.org/en-us/download/download.html](https://dolphinscheduler.apache.org/en-us/download/download.html)
+
+## Thanks to contributors
+
+In alphabetical order
+
+Aaron Lin、Amy0104、Assert、BaoLiang、Benedict Jin、BenjaminWenqiYu、Brennan
Fox、Dannila、Desperado2、Devosend、DingPengfei、DuChaoJiaYou、EdwardYang、Eric
Gao、Frank Chen、GaoTianDuo、HanayoZz、HeChuan、HomminLee、Hua
Jiang、Hwting、Ivan0626、Jeff Zhan、Jiajie Zhong、JieguangZhou、Jiezhi.G、JinYong
Li、J·Y、Kerwin、Kevin.Shin、KingsleyY、Kirs、KyoYang、LinKai、LiuBodong、LongJGun、Luke
Yan、Lyle Shaw、Manhua、Martin Huang、Maxwell、Molin Wang、Mr.An、OS、PJ Fanning、Paul
Zhang、QuakeWang、ReonYu、SbloodyS、Sheldon、Shiwen Cheng、ShuiMu [...]
\ No newline at end of file
diff --git a/blog/en-us/DolphinScheduler_in_T3Go_One-stop_Platform.md
b/blog/en-us/DolphinScheduler_in_T3Go_One-stop_Platform.md
new file mode 100644
index 000000000..2e1d3617c
--- /dev/null
+++ b/blog/en-us/DolphinScheduler_in_T3Go_One-stop_Platform.md
@@ -0,0 +1,110 @@
+---
+title:ApacheCon Asia 2022 Review | Application of DolphinScheduler in T3Go
One-stop Platform
+keywords: Apache,DolphinScheduler,scheduler,big
data,ETL,airflow,hadoop,orchestration,dataops,Kubernetes
+description:This sharing mainly focuses on the following four parts
+---
+# ApacheCon Asia 2022 Review | Application of DolphinScheduler in T3Go
One-stop Platform
+
+<u>At the ApacheCon Asia 2022, big data engineers Li Xinkai & Zhao Yuwei at
T3Go shared the company’s functional iteration and integration enhancement
based on Apache DolphinScheduler in their development of a one-stop
platform.</u>
+
+This sharing mainly focuses on the following four parts:
+
+* Why we choose Apache DolphinScheduler
+* Data Lake Architecture of T3Go
+* Difficulties in using Apache DolphinScheduler
+* Solutions to the issues
+
+## Why we choose Apache DolphinScheduler
+### What is DolphinScheduler
+Apache DolphinScheduler is a distributed, decentralized, and easily scalable
visual DAG workflow task scheduling platform committed to dealing with the
intricate dependencies in the data process so that the scheduling system can be
used out of the box.
+
+### 02 Why we choose DolphinScheduler
+
+
+
+We choose Apache DolphinScheduler mainly based on the following four reasons:
+
+* High availability
+Apache DolphinScheduler provides decentralized multi-worker and master with
higher availability, self-support, and overload handling.
+* User-friendly
+Since all process definitions are visualized, Apache DolphinScheduler reduces
learning costs by enabling people with no coding skills (such as data analysts)
to create complex workflows.
+* Rich scenarios
+Apache DolphinScheduler provides multiple task types, including Spark, Hive,
Python, Flink, MapReduce, and more. Additionally, the multi-tenancy of the
platform increases efficiency and provides a high level of scalability.
+* High scalability
+Apache DolphinScheduler can linearly increase its overall scheduling
capability with the rising volume of the cluster.
+### 03 Status of use of DolphinScheduler in T3Go
+
+Apache DolphinScheduler in T3Go is mainly applied to combine with Kyuubi on
Spark tasks to process 30,000+ offline scheduling tasks every day; at the same
time, it schedules 300+ Spark Streaming tasks, 100+ Flink tasks, and 500+
Kylin, ClickHouse, and Shell tasks every day.
+
+## 04 Optimization based on DolphinScheduler
+A huge volume of scheduling tasks of T3Go heavily relies on Apache
DolphinScheduler. This time, we’d like to share the following optimization work
we had done based on it:
+
+
+* **Added the function of linkage complement**. Our task flow handles some
upstream and downstream relationships across task flows through depends
components. The relationship between these tasks is relatively complex. If the
upstream complement is required, it is very complicated to sort out the
upstream and downstream relations manually. So we made the linkage complement
function.
+
+
+* **Added task view function. For example**, if a workflow is set to schedule
on T-1, then a work instance with the running type of scheduling execution will
be generated every day. After adding the task view function, you can see the
operation of the workflow in the scheduling cycle at a glance, such as whether
the scheduling is successful, whether there is a failure complement, recovery
failure, etc., to improve development efficiency.
+
+### Data Lake Architecture of T3 Go
+Next, I will introduce the data lake architecture of T3 travel.
+
+### 01 Data Lake Architecture
+
+
+The bottom-level data sources in the data lake architecture of T3Go are mainly
Kafka data and some database data subscribed by cannel. The data obtained by
CDC is mainly stored in OBS object storage in Hudi format, and the unified
resource arrangement of the cluster is performed through YARN. Computing
engines such as Spark, Hive, Flink, Presto, and ClickHouse are integrated
uniformly on the top of YARN and are connected to various businesses through
the computing middleware of Kyuubi an [...]
+
+### T-1 Scheduling Process
+
+
+The scheduling process of T-1 is to allocate different resource policies for
different tenants by connecting to Kyuubi. Apache DolphinScheduler connects
different tenants to Kyuubi and performs computations on the Spark engine. In
addition, we also did incremental data processing for the storage data mainly
based on Hudi.
+
+### Other usage scenarios
+
+
+At the same time, data development and some OLAP scenarios are mainly
performed by connecting to Kyuubi. The reporting system connects to the Presto
engine by connecting to Kyuubi to query data lake data. You can also connect to
the Spark engine to write Spark SQL for data exploration and data development.
+
+## Difficulties in using Apache DolphinScheduler
+In the process of using Apache DolphinScheduler, we encountered some
difficulties (only for version 1.3), as follows:
+* No dedicated development tools
+
+Firstly, there was no dedicated data development module in Apache
DolphinScheduler at the time, so we developed it on Hue originally installed on
the CDH cluster. Developed statements also need to be manually copied to GitLab
for version management.
+* Lack of big data management CI/CD
+
+Secondly, to publish the developed statement on Apache DolphinScheduler, we
needed to manually copy it to Apache DolphinScheduler for node configuration.
At the same time, the conversion and configuration of some parameters and
variables also require manual operations. The lack of an integrated CI/CD
process greatly increases the cost of operation and maintenance.
+* No version management & code sharing mechanism
+
+We previously used Apache DolphinScheduler version 1.3, which lacked the
workflow version management and code sharing mechanism. This results in the
inability to submit code to GitLab, version switching, code sharing in the same
group and department, and repeated wheel building.
+## Solutions to the issues
+For the above issues, we have invested a lot of energy to optimize, mainly
covers:
+### Introducing Datasphere Studio
+
+At the beginning of this year, we introduced the open source Datasphere Studio
(DSS) of WeBank as a one-stop application interaction platform and customized
development to the demands of the company’s business.
+### Code sharing
+
+We call the developed files by integrating GitLab’s SDK and submit the code to
the corresponding GitLab code branch and project. In this way, users in the
same workspace can see and share each other’s code, which helps to avoid
repeated wheels building.
+### DSS Workflow
+
+The DSS workflow supports version management, which is convenient for rollback
and version iteration. It supports the pick-up operation for the specified
corresponding version. When pulling and arranging the workflow, you can select
the associated script (the developed code files placed in those directories).
+
+
+
+We integrated the DSS workflow so that it can be published directly into the
Apache DolphinScheduler workflow.
+
+DSS workflow is similar to Apache DolphinScheduler, which is also in JSON
format, stored in MySQL, and read through saved task flow. We parse the JSON
fields in the DSS workflow and convert them into JSON format files that conform
to the DolphinScheduler task flow.
+
+After storage, a task flow that conforms to its style is formed in
DolphinScheduler. Now, the node types we supported cover SQL, shell scripts,
and depends components, SeaTunnel(watedrop) tasks, Spark tasks and Python, etc.
+
+
+We use DSS workflow for version management. In essence, it’s an
up-and-down-online process of multiple versions corresponding to the
DolphinScheduler task flow. If the online version is selected, the
corresponding version workflow on the DS side will go online, and the workflow
of the non-selected version will go offline.
+
+The compliment, timing schedule, and other operations are performed on
DolphinScheduler, which is much more powerful than DSS.
+
+Of course, the new version of DolphinScheduler already supports the version
switching function, and we will integrate it later.
+
+After integrating DSS, the changes to the T3Go data lake architecture are
shown in the figure.
+
+First, the bottom layer is still CDC in the data lake. Hudi is on top of
object storage, resource orchestration is still performed on YARN, and the
engine is still acted by Spark, Flink, Presto, and ClickHouse. Connection is
still conducted through computing middleware, and we replaced Hue with the DSS
scripts data development platform to connect with various business systems.
DSS’s single sign-on authentication process facilitates unified login.
+
+After the introduction of DSS in the T3Go big data platform, it complements
Apache DolphinScheduler and unifies the integration process of code development
and business online scheduling system, manages the big data CI/CD in a closed
loop, and lowers the business barriers by helping them focus on business and
demands and relieve the pressure of data development. With all these efforts,
we narrow the gap to the goal of building a one-stop development platform.
+
+Apache DolphinScheduler has developed rapidly in the past year and enhanced
the functions we lacked before, such as data quality and task flow version
management. We hope that these capabilities of Apache DolphinScheduler can be
applied in our usage scenarios in subsequent iterations, and we also expect
Apache DolphinScheduler, as one of the leaders of the scheduling platform, to
grow more powerful!
diff --git a/site_config/blog.js b/site_config/blog.js
index 51a05d5eb..381816a8f 100644
--- a/site_config/blog.js
+++ b/site_config/blog.js
@@ -3,6 +3,20 @@ export default {
barText: 'Blog',
postsTitle: 'All posts',
list: [
+ {
+ title: 'Apache DolphinScheduler 3.0.0 Official Version
Released!',
+ author: 'Debra Chen',
+ dateStr: '2022-9-2',
+ desc: 'The official version 3.0.0 has undergone the most
significant changes since its release... ',
+ link: '/en-us/blog/Apache_dolphinScheduler_3.0.0.html',
+ },
+ {
+ title: 'ApacheCon Asia 2022 Review | Application of
DolphinScheduler in T3Go One-stop Platform',
+ author: 'Debra Chen',
+ dateStr: '2022-8-30',
+ desc: 'At the ApacheCon Asia 2022, big data engineers Li
Xinkai & Zhao Yuwei at T3Go ... ',
+ link:
'/en-us/blog/DolphinScheduler_in_T3Go_One-stop_Platform.html',
+ },
{
title: 'pache DolphinScheduler Extends Capabilities Through
Python API and AWS Support',
author: 'Debra Chen',
diff --git a/site_config/home.jsx b/site_config/home.jsx
index 79945c250..d343a54f7 100644
--- a/site_config/home.jsx
+++ b/site_config/home.jsx
@@ -558,6 +558,14 @@ export default {
events: {
title: 'Events & News',
list: [
+ {
+ img: 'https://miro.medium.com/max/1260/1*zwulVh_I1ROhSYrlE3OW6A.png',
+ title: 'Apache DolphinScheduler 3.0.0 Official Version Released!',
+ content: 'The official version 3.0.0 has undergone the most
significant changes since its release ...',
+ dateStr: '2022-9-2',
+ link: '/en-us/blog/Apache_dolphinScheduler_3.0.0.html',
+ },
+
{
img: 'https://miro.medium.com/max/630/0*Ep9rZFSTviU8SjmP',
title: 'Apache DolphinScheduler Extends Capabilities Through Python
API and AWS Support',
@@ -566,18 +574,11 @@ export default {
link: '/en-us/blog/Python_API_and_AWS_Support.html',
},
{
- img: '/img/2022-05-25/en/1.jpg',
- title: 'Three scenarios and five optimizations of Apache
DolphinScheduler in XWBank for processing of task instances',
- content: 'At XWBank, a large number of task instances are generated
every day...',
- dateStr: '2022-5-25',
- link: '/en-us/blog/Apache_DolphinScheduler_in_XWBank.html',
- },
- {
- img: '/img/2020-04-25/en/1.png',
- title: '3.0.0 Alpha Release! Nine New Features and A Brand New UI
Unlock New Capabilities For the Scheduling System\n',
- content: 'On April 22, 2022, Apache DolphinScheduler officially
announced the release of alpha version 3.0.0! ...',
- dateStr: '2022-3-10',
- link: '/en-us/blog/Apache_dolphinScheduler_3.0.0_alpha.html',
+ img: 'https://miro.medium.com/max/1260/1*9rnrDrko9bRkgkICrPJbQA.png',
+ title: 'ApacheCon Asia 2022 Review | Application of DolphinScheduler
in T3Go One-stop Platform',
+ content: 'At the ApacheCon Asia 2022, big data engineers Li Xinkai &
Zhao Yuwei at T3Go shared...',
+ dateStr: '2022-8-26',
+ link: '/en-us/blog/DolphinScheduler_in_T3Go_One-stop_Platform.html',
},
],
