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new 5090b614a0 [INLONG-962][Release] Add blog for the 1.13.0 release (#965)
5090b614a0 is described below
commit 5090b614a00bb42dc000066efab7bb05b1e2bf99
Author: fuweng11 <[email protected]>
AuthorDate: Fri Aug 2 17:45:14 2024 +0800
[INLONG-962][Release] Add blog for the 1.13.0 release (#965)
---
blog/2024-07-18-release-1.13.0.md | 146 +++++++++++++++++++++
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.../2024-07-18-release-1.13.0.md | 145 ++++++++++++++++++++
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diff --git a/blog/2024-07-18-release-1.13.0.md
b/blog/2024-07-18-release-1.13.0.md
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+---
+title: Release 1.13.0
+author: Wenkai Fu
+author_url: https://github.com/fuweng11
+author_image_url: https://avatars.githubusercontent.com/u/76141879?v=4?s=400
+tags: [Apache InLong, Version]
+---
+
+Apache InLong recently released version 1.13.0, which closed about 275+
issues, including 6+ major features and 100+ optimizations. The main features
include supporting SSH installation of agents, managing field templates,
support configuring offline synchronization tasks, and collecting PostgreSQL
through agents
+. After the release of 1.13.0, Apache InLong has enriched and optimized Agent
function scenarios, enhanced the accuracy of Audit data measurement, and
enriched the capabilities and applicable scenarios of Sort, solved the demand
for quick troubleshooting in development and operation, and optimized the user
experience of Apache InLong operation and maintenance.
+<!--truncate-->
+
+## About Apache InLong
+
+As the industry's first one-stop, full-scenario, open-source massive data
integration framework, Apache InLong provides automatic, safe, reliable, and
high-performance data transmission capabilities to facilitate businesses to
build stream-based data analysis, modeling, and applications quickly. At
present, InLong is widely used in various industries such as advertising,
payment, social networking, games, artificial intelligence, etc., serving
thousands of businesses, among which the sca [...]
+
+The core keywords of InLong project positioning are "one-stop" and "massive
data". For "one-stop", we hope to shield technical details, provide complete
data integration and support services, and implement out-of-the-box; With its
advantages, such as multi-cluster management, it can stably support
larger-scale data volumes based on trillions of lines per day.
+
+## 1.13.0 Version Overview
+
+Apache InLong recently released version 1.13.0, which closed about 275+
issues, including 6+ major features and 100+ optimizations. The main features
include supporting SSH installation of agents, managing field templates,
support configuring offline synchronization tasks, and collecting PostgreSQL
through agents
+. After the release of 1.13.0, Apache InLong has enriched and optimized Agent
function scenarios, enhanced the accuracy of Audit data measurement, and
enriched the capabilities and applicable scenarios of Sort, solved the demand
for quick troubleshooting in development and operation, and optimized the user
experience of Apache InLong operation and maintenance. In Apache InLong 1.13.0
version, a large number of other features have also been completed, mainly
including:
+
+### Agent Module
+- Support data version numbers to distinguish between normal data and
supplementary data
+- Location storage supports plugins, currently supporting Rocksdb and Zookeeper
+- Support configuration version number comparison to prevent repeated
configuration
+- Support minute level file collection
+- Add PostgreSQL and MongoDB data source collection
+
+### Manager Module
+- Support installing agents through SSH
+- Switch audit ID query from direct interaction with database to Audit SDK
+- Offline synchronization supports Pulsar -> MySQL
+- Support offline synchronous scheduling information management
+- File collection supports multi IP collection
+- Support obtaining Agent configuration information
+- Support automatic synchronization to Sink after modifying Stream field
information
+- Support field template management
+- Data preview supports KV format
+- Data preview supports querying based on field filtering criteria
+
+### Dashboard Module
+- Add Source Data Field Template Page
+- Add monitoring and auditing page
+- Support installing agents through SSH key based authentication
+- Audit supports displaying total and variance audit data
+- File type data stream supports minute level cycles
+
+### Audit Module
+- Unified allocation and management of audit items using the Audit SDK
+- The Audit SDK supports automatic management of Audit Proxy addresses
+- Audit Store supports the universal JDBC protocol
+- Restarting the Audit Store optimization process may lead to data loss issues
+- Audit Service optimizes thread pool management
+- Audit Service compatible with historical audit data with empty Audit Tag
+- Audit Service Optimization for OpenAPI Audit Transmission Delay Calculation
+
+### Sort Module
+- Add JDBC connector on Flink 1.15
+- Added Pulsar connector on Flink 1.18
+- Redis connector supports reporting audit information
+- Kafka connector supports reporting audit information
+- MongoDB connector supports reporting audit information
+- PostgreSQL connector supports reporting audit information
+
+## 1.13.0 Version Feature Introduction
+
+### Manager supports installing Agent by SSH
+Through this feature, operation and maintenance personnel can install agents
through the Dashboard, which currently supports SSH and manual installation
methods. Users can create a new Agent cluster on the cluster management page.
+
+
+
+Afterwards, enter the node, select the new node, and configure the SSH
username and password to achieve SSH installation agent capability.
+Thanks to @haifxu and @fuweng11. For more information, please refer to
INLONG-10409.
+
+
+
+### Manager supports field template management
+With this feature, users can pre-configure field templates, and when creating
a new Stream, they can select the pre-configured field templates, thus
achieving the purpose of reusing configurations across multiple Streams.
+Thanks to @kamianlaida and @fuweng11. For more information, please refer to:
INLONG-10330.
+
+
+
+### Support the construction of the underlying framework for configuring
offline synchronization tasks
+In version 1.13.0, Manager supports the configuration of offline
synchronization tasks. Compared to real-time synchronization, offline data
synchronization pays more attention to synchronization throughput and
efficiency.
+Real-time synchronization tasks run in the manner of Flink stream tasks, while
offline synchronization runs in the manner of Flink batch tasks. This approach
can ensure the consistency of real-time and offline synchronization tasks' code
as much as possible, reducing maintenance costs.
+The offline synchronization function of InLong will be combined with the
scheduling system to synchronize the complete or incremental data of the data
source information to the data target. The offline synchronization task is
created by InLong Manager (including scheduling information), and the specific
data synchronization logic is implemented through the InLong Sort module.
+
+
+
+Key Competency:
+- Job Type: Support single or periodic offline data synchronization.
+- Scheduling:The scheduling function is plugin based, with a built-in simple
periodic scheduling function that allows customization of third-party
scheduling systems to support more complex capabilities, such as task
dependencies.
+- Compute Engine: Flink
+- Offline Job Operation and Maintenance: Job start,stop and running status
monitoring
+- Special Handling: Dirty Data Processing Capability
+
+The following is the core process:
+
+
+
+Thanks to @aloyszhang. For more information, please refer to: INLONG-10054,
INLONG-10053, INLONG-10055, INLONG-10069.
+
+### Optimize the Sort Standalone configuration process
+In version 1.13.0, the distribution process of Sort Standalone configuration
was modified. In previous versions, there were the following issues with the
distribution of Sort Standalone configuration:
+- Configuration changes are unreliable. Configuration changes will be updated
in real-time to the manager's cache. Once the configuration is changed, Sort
Standalone will sense it and write data based on the new configuration without
verifying the configuration.
+- The configuration and construction process is repetitive and cumbersome.
When pulling the Sort configuration from the database, it is a full pull and
real-time build is performed after the pull is complete.
+
+In the new version, after modifying the data target, the Sort configuration
will not take effect in real time, but will need to be built and written into
the sort_config table after executing the workflow. The following figure shows
a process comparison:
+
+
+
+Thanks to @fuweng11, @vernedeng. For more information, please refer to:
INLONG-9867, INLONG-10017.
+
+### Agent ability for collecting data from PostgreSQL
+In version 1.13.0, Agent supports data collection from PostgreSQL. When
creating a data source, you can directly select PostgreSQL and fill all
relevant data source information to start using it. The parameters include:
+- Data source name: Used to distinguish between different data sources
+- Cluster name: The cluster of data source belongs
+- Data source IP: The IP information of data source
+- Server Host: PostgreSQL Server Host
+- Port: PostgreSQL port
+
+Thanks to @haifxu. For more information, please refer to: INLONG-10318.
+
+
+
+### Sort connectors reporting audit information supports exactly once
+Sort Connectors support for exactly once semantics in reporting audit
information.
+When an operator encounters an exception and the snapshot fails, each
Connector ensures that the audit information is reported exactly once.
+As shown in the following diagram, when data is transferred, the current data
will be written to the current operator's AuditBuffer and the corresponding
checkpointId. When the checkpoint is completed (within the
notifyCompleteCheckpoint method), the data written to the buffer will be
reported.
+
+
+
+During the execution of snapShot, the checkpointId written to the buffer will
be changed to the checkpointId in the snapShot, and when the checkpoint is
completed,
+the audit information corresponding to the checkpointId will be uploaded to
the Audit Server within the notifyCompleteCheckpoint method. In the Audit SDK,
+it is already supportedto report all information in the Audit buffer that is
less than the specified checkpointId.
+
+
+
+Thanks to @XiaoYou201. For more information, please refer to: INLONG-10311,
INLONG-10312, INLONG-10317, INLONG-10355, INLONG-10357, INLONG-10358,
INLONG-10401.
+
+## Future plans
+In version 1.13.0, the community refactored the process of Manager issuing
Agent, Sort tasks, enriched features such as Flink 1.15 Connector, Inlong Agent
collecting PostgreSQL and other functions. In subsequent versions, InLong will
continue to enrich Flink 1.15 Connector, enhance Transform capabilities,
support offline data integration, unify DataProxy data protocols, optimize
Dashboard experience, etc. We look forward to more developers participating and
contributing.
+
+
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diff --git
a/i18n/zh-CN/docusaurus-plugin-content-blog/2024-07-18-release-1.13.0.md
b/i18n/zh-CN/docusaurus-plugin-content-blog/2024-07-18-release-1.13.0.md
new file mode 100644
index 0000000000..b5d5e2ed3d
--- /dev/null
+++ b/i18n/zh-CN/docusaurus-plugin-content-blog/2024-07-18-release-1.13.0.md
@@ -0,0 +1,145 @@
+---
+title: 1.13.0 版本发布
+author: Wenkai Fu
+author_url: https://github.com/fuweng11
+author_image_url: https://avatars.githubusercontent.com/u/76141879?v=4?s=400
+tags: [Apache InLong, Version]
+---
+
+Apache InLong（应龙）最近发布了 1.13.0 版本，该版本关闭了 275+ 个 Issues ，包含 6+ 个大特性和 100+
个优化，主要完成了支持 SSH 安装 Agent、字段模板管理能力、支持配置离线同步任务、Agent 采集 PostgreSQL 等特性。1.13.0
发布后，Apache InLong 丰富并优化了 Agent 功能场景， 增强了 Audit 数据度量的准确性，丰富了 Sort 的能力和适用场景，同时优化了
Apache InLong 运营、运维过程中遇到的一些问题和使用体验。
+<!--truncate-->
+
+## 关于 Apache InLong
+作为业界首个一站式、全场景海量数据集成框架，Apache
InLong（应龙）提供了自动、安全、可靠和高性能的数据传输能力，方便业务快速构建基于流式的数据分析、建模和应用。目前 InLong
正广泛应用于广告、支付、社交、游戏、人工智能等各个行业领域，服务上千个业务，其中高性能场景数据规模超百万亿条/天，高可靠场景数据规模超十万亿条/天。
+
+InLong
项目定位的核心关键词是“一站式”、“全场景”和“海量数据”。对于“一站式”，我们希望屏蔽技术细节、提供完整数据集成及配套服务，实现开箱即用；对于“全场景”，我们希望提供全方位的解决方案，覆盖大数据领域常见的数据集成场景；对于“海量数据”，我们希望通过架构上的数据链路分层、全组件可扩展、自带多集群管理等优势，在百万亿条/天的基础上，稳定支持更大规模的数据量。
+
+## 1.13.0 版本总览
+Apache InLong（应龙）最近发布了 1.13.0 版本，该版本关闭了 275+ 个 Issues ，包含 6+ 个大特性和 100+
个优化，主要完成了支持 SSH 安装 Agent、字段模板管理能力、支持配置离线同步任务、Agent 采集 PostgreSQL 等特性。1.13.0
发布后，Apache InLong 丰富并优化了 Agent 功能场景，增强了 Audit 数据度量的准确性，丰富了 Sort 的能力和适用场景，同时优化了
Apache InLong 运营、运维过程中遇到的一些问题和使用体验。Apache InLong 1.13.0 版本中，还完成了大量其它特性，主要包括：
+
+### Agent 模块
+- 支持数据版本号，用于区分正常数据与补录数据
+- 位点存储支持插件化，目前支持 Rocksdb 与 Zookeeper
+- 支持配置版本号比对，防止配置反复
+- 支持分钟级文件采集
+- 增加 PostgreSQL 数据源采集
+
+### Manager 模块
+- 支持通过 SSH 的方式安装 Agent
+- 审计 ID 查询从直接与数据库交互切换至 Audit SDK
+- 离线同步支持 Pulsar -> MySQL
+- 支持离线同步调度信息管理
+- 文件采集支持多 IP 采集
+- 支持获取 Agent 配置信息
+- 支持修改 Stream 字段信息后自动同步至 Sink
+- 支持字段模板管理
+- 数据预览支持 KV 格式
+- 数据预览支持根据字段过滤条件查询
+
+### Dashboard 模块
+- 新增源数据字段模板页面
+- 新增监控审计页面
+- 支持通过基于 SSH 密钥的身份验证安装 Agent
+- 审计支持显示总计和差异的审计数据
+- 文件类型数据流支持分钟级周期
+
+### Audit 模块
+- Audit SDK 统一分配与管理审计项
+- Audit SDK 支持自动管理 Audit Proxy 地址
+- Audit Store 支持通用 JDBC 协议
+- Audit Store 优化进程重启可能导致丢数据的问题
+- Audit Service 优化线程池管理
+- Audit Service 兼容 Audit Tag 为空的历史审计数据
+- Audit Service 优化 OpenAPI 审计传输时延的计算
+
+### Sort 模块
+- 新增 JDBC connector on flink 1.15
+- 新增 Pulsar connector on flink 1.18
+- Redis connector支持 上报审计信息
+- Kafka connector 支持上报审计信息
+- MongoDB connector 支持上报审计信息
+- PostgreSQL connector 支持上报审计信息
+
+### SDK 模块
+- 新增 DataProxy Python SDK
+- DataProxy Python SDK 增强 Transform SDK SQL函数支持，新增8种算术函数(power, abs, sqrt, ln,
log10, log2, log, exp)
+- DataProxy Go SDK 连接池支持动态均衡及故障节点恢复探测
+- DataProxy Go SDK 修复 gnet 初始化顺序的问题，避免升级到新版本gnet会阻塞
+- DataProxy Go SDK 潜在的阻塞问题，避免更新连接时阻塞
+
+## 1.13.0 版本特性介绍
+
+### Manager 支持 SSH 安装 Agent
+通过此特性，运维人员可以通过 Dashboard 进行 Agent 的安装操作，目前支持通过 SSH 和手动安装的方式。用户可以在集群管理页面新建
Agent 集群。
+
+
+
+之后，进入节点，选择新建节点并配置好 SSH 用户名和密码后实现 SSH 安装 Agent 能力。
+感谢 @haifxu、@fuweng11 两位同学在 Dashboard 及 Manager 部分对此功能的贡献。具体可参考：INLONG-10409。
+
+
+
+### Manager 支持字段模板管理能力
+通过此特性，用户可以事先配置好字段模板，在新建 Stream 时，可以选择已配置好的字段模板，从而达到多个 Stream 复用配置的目的。
+感谢 @kamianlaida、@fuweng11 两位同学在 Dashboard 及 Manager
部分对此功能的贡献。具体可参考：INLONG-10330。
+
+
+
+### 支持配置离线同步任务底层框架搭建
+在 1.13.0 版本中，InLong 支持了离线同步任务的配置，与实时同步相比，离线数据同步更注重同步吞吐量和效率。
+该实现统一基于 Flink 计算引擎。实时同步任务以 Flink 流任务的方式运行，而离线同步则以 Flink
批处理任务的方式进行。这种方法可以尽可能地确保实时和离线同步任务代码的一致性，从而降低维护成本。
+InLong 的离线同步功能将与调度系统相结合，将数据源信息的完整或增量数据同步到数据目标，离线同步任务由 InLong Manager
创建（包括调度信息），具体的数据同步逻辑通过 InLong Sort 模块实现。
+
+
+
+关键能力:
+- 作业类型：支持单次或者周期的离线数据同步
+- 调度：调度功能插件化，内置简单的周期性调度功能，可以自定义第三方调度系统来支持更复杂的能力，比如任务依赖等
+- 计算引擎：Flink
+- 离线作业操作和维护：作业启动、停止和运行状态监控
+- 特殊处理：脏数据处理能力
+
+下图为核心流程：
+
+
+
+感谢 @aloyszhang 对此功能的贡献，具体请参考 INLONG-10054, INLONG-10053, INLONG-10055,
INLONG-10069。
+
+### 优化 Sort Standalone 配置流程
+在 1.13.0 版本中，修改了 Sort Standalone 配置下发流程，在之前的版本中，sort standalone 的配置下发存在以下问题:
+- 配置变更具有不可靠性。配置变化会实时更新到 manager 的缓存中，一旦配置变更，Sort Standalone
就会感知到，并根据新的配置写入数据，并没有进行对配置的校验。
+- 配置构建流程重复并且繁琐。从数据库中拉取 Sort 配置时是全量拉取并且在拉取完毕后进行实时构建。
+在新的版本中，修改数据目标后，Sort 配置将不会实时生效，而是需要在执行工作流后将 Sort 配置构建写入 sort_config 表中。下图为流程对比:
+
+
+
+感谢 @fuweng11, @vernedeng 对此功能的贡献，具体请参考 INLONG-9867, INLONG-10017。
+
+
+### Agent 支持 PostgreSQL 采集
+在 1.13.0 版本中，Agent 支持了从 PostgreSQL 采集数据，在数据源创建时可以直接选择
PostgreSQL，填写相关数据源信息即可开始使用，参数包括：
+- 数据源名称：便于快速区分不同的数据源
+- 集群名称：选择该数据源所属的集群
+- 数据源 IP：选择该数据源所属的机器
+- 服务器主机：PostgreSQL 服务器主机
+- 端口：PostgreSQL 端口
+
+感谢 @haifxu 对此功能的贡献，具体请参考 INLONG-10318。
+
+
+
+### Sort Connectors 上报审计信息支持 exactly once
+Sort Connectors 上报审计信息支持 exactly once 语义, 在算子遇见异常，snapshot 失败时，各个 Connector
保证上报审计信息 exactly once。
+如下图所示，当数据传输时，会把当前数据写入当前算子 AuditBuffer 以及对应的 checkpointId。当 checkpoint 完成时（在
notifyCompleteCheckpoint 方法中）将写入 buffer 的数据进行上报。
+
+
+
+在 snapShot 执行时，会将写入 buffer 的 checkpointId 变更为 snapShot 中的 checkpointId，并在
checkpoint 执行完成时，也就是 notifyCompleteCheckpoint 方法中将审计信息上传至 Audit Server 中。
+
+
+
+感谢 @XiaoYou201 对此功能的贡献，具体请参考 INLONG-10311, INLONG-10312, INLONG-10317,
INLONG-10355, INLONG-10357, INLONG-10358, INLONG-10401。
+
+
+## 未来规划
+在 1.13.0 版本中，社区重构了 Manager 下发 Agent、Sort 任务流程，丰富了 Flink 1.15 Connector、Inlong
Agent 采集 PostgreSQL 等功能。在后续的版本中，InLong 将继续丰富 Flink 1.15、1.18 Connector 、丰富
Transform 能力、支持离线集成、统一 DataProxy 数据协议、Dashboard 体验优化等，期待更多开发者参与贡献。
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