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README.md

Linkis

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Introduction

Linkis builds a layer of computation middleware between upper applications and underlying engines. By using standard interfaces such as REST/WS/JDBC provided by Linkis, the upper applications can easily access the underlying engines such as MySQL/Spark/Hive/Presto/Flink, etc., and achieve the intercommunication of user resources like unified variables, scripts, UDFs, functions and resource files at the same time.

As a computation middleware, Linkis provides powerful connectivity, reuse, orchestration, expansion, and governance capabilities. By decoupling the application layer and the engine layer, it simplifies the complex network call relationship, and thus reduces the overall complexity and saves the development and maintenance costs as well.

Since the first release of Linkis in 2019, it has accumulated more than 700 trial companies and 1000+ sandbox trial users, which involving diverse industries, from finance, banking, tele-communication, to manufactory, internet companies and so on. Lots of companies have already used Linkis as a unified entrance for the underlying computation and storage engines of the big data platform.

linkis-intro-01

linkis-intro-03

Features

  • Support for diverse underlying computation storage engines.
    Currently supported computation/storage engines: Spark, Hive, Flink, Python, Pipeline, Sqoop, openLooKeng, JDBC, Shell, etc.
    Computation/storage engines to be supported: Presto (planned 1.2.0), ElasticSearch (planned 1.2.0), etc. Supported scripting languages: SparkSQL, HiveQL, Python, Shell, Pyspark, R, Scala and JDBC, etc.

  • Powerful task/request governance capabilities. With services such as Orchestrator, Label Manager and customized Spring Cloud Gateway, Linkis is able to provide multi-level labels based, cross-cluster/cross-IDC fine-grained routing, load balance, multi-tenancy, traffic control, resource control, and orchestration strategies like dual-active, active-standby, etc.

  • Support full stack computation/storage engine. As a computation middleware, it will receive, execute and manage tasks and requests for various computation storage engines, including batch tasks, interactive query tasks, real-time streaming tasks and storage tasks;

  • Resource management capabilities. ResourceManager is not only capable of managing resources for Yarn and Linkis EngineManger, but also able to provide label-based multi-level resource allocation and recycling, allowing itself to have powerful resource management capabilities across mutiple Yarn clusters and mutiple computation resource types;

  • Unified Context Service. Generate Context ID for each task/request, associate and manage user and system resource files (JAR, ZIP, Properties, etc.), result set, parameter variable, function, etc., across user, system, and computing engine. Set in one place, automatic reference everywhere;

  • Unified materials. System and user-level unified material management, which can be shared and transferred across users and systems.

Supported Engine Types

Engine Name Suppor Component Version
(Default Dependent Version)
Linkis Version Requirements Included in Release Package
By Default
Description
Spark Apache 2.0.0~2.4.7,
CDH >= 5.4.0,
(default Apache Spark 2.4.3)
>=1.0.3 Yes Spark EngineConn, supports SQL , Scala, Pyspark and R code
Hive Apache >= 1.0.0,
CDH >= 5.4.0,
(default Apache Hive 2.3.3)
>=1.0.3 Yes Hive EngineConn, supports HiveQL code
Python Python >= 2.6,
(default Python2*)
>=1.0.3 Yes Python EngineConn, supports python code
Shell Bash >= 2.0 >=1.0.3 Yes Shell EngineConn, supports Bash shell code
JDBC MySQL >= 5.0, Hive >=1.2.1,
(default Hive-jdbc 2.3.4)
>=1.0.3 No JDBC EngineConn, already supports MySQL and HiveQL, can be extended quickly Support other engines with JDBC Driver package, such as Oracle
Flink Flink >= 1.12.2,
(default Apache Flink 1.12.2)
>=1.0.3 No Flink EngineConn, supports FlinkSQL code, also supports starting a new Yarn in the form of Flink Jar Application
Pipeline - >=1.0.3 No Pipeline EngineConn, supports file import and export
openLooKeng openLooKeng >= 1.5.0,
(default openLookEng 1.5.0)
>=1.1.1 No openLooKeng EngineConn, supports querying data virtualization engine with Sql openLooKeng
Sqoop Sqoop >= 1.4.6,
(default Apache Sqoop 1.4.6)
>=1.1.2 No Sqoop EngineConn, support data migration tool Sqoop engine
Impala Impala >= 3.2.0, CDH >=6.3.0 ongoing - Impala EngineConn, supports Impala SQL code
Presto Presto >= 0.180 ongoing - Presto EngineConn, supports Presto SQL code
ElasticSearch ElasticSearch >=6.0 ongoing - ElasticSearch EngineConn, supports SQL and DSL code
MLSQL MLSQL >=1.1.0 ongoing - MLSQL EngineConn, supports MLSQL code.
Hadoop Apache >=2.6.0,
CDH >=5.4.0
ongoing - Hadoop EngineConn, supports Hadoop MR/YARN application
TiSpark 1.1 ongoing - TiSpark EngineConn, supports querying TiDB with SparkSQL

Ecosystem

Component Description Linkis 1.x(recommend 1.1.1) Compatible
DataSphereStudio DataSphere Studio (DSS for short) is WeDataSphere, a one-stop data application development management portal. DSS 1.0.1[released][Linkis recommend 1.1.1]
Scriptis Support online script writing such as SQL, Pyspark, HiveQL, etc., submit to Linkis to perform data analysis web tools. In DSS 1.0.1[released]
Schedulis Workflow task scheduling system based on Azkaban secondary development, with financial-grade features such as high performance, high availability and multi-tenant resource isolation. Schedulis0.6.2 [released]
Qualitis Data quality verification tool, providing data verification capabilities such as data integrity and correctness Qualitis 0.9.1 [released]
Streamis Streaming application development management tool. It supports the release of Flink Jar and Flink SQL, and provides the development, debugging and production management capabilities of streaming applications, such as: start-stop, status monitoring, checkpoint, etc. Streamis 0.1.0 [released][Linkis recommend 1.1.0]
Exchangis A data exchange platform that supports data transmission between structured and unstructured heterogeneous data sources, the upcoming Exchangis1. 0, will be connected with DSS workflow Exchangis 1.0.0 [developing]
Visualis A data visualization BI tool based on the second development of Davinci, an open source project of CreditEase, provides users with financial-level data visualization capabilities in terms of data security. Visualis 1.0.0[developing]
Prophecis A one-stop machine learning platform that integrates multiple open source machine learning frameworks. Prophecis' MLFlow can be connected to DSS workflow through AppConn. Prophecis 0.3.0 [released]

Download

Please go to the Linkis Releases Page to download a compiled distribution or a source code package of Linkis.

Compile and Deploy

For more detailed guidance see: [Backend Compile] [Management Console Build]


## compile backend
### Mac OS/Linux
./mvnw -N install
./mvnw  clean install -Dmaven.javadoc.skip=true -Dmaven.test.skip=true


### Windows
mvnw.cmd -N install
mvnw.cmd clean install -Dmaven.javadoc.skip=true -Dmaven.test.skip=true

## compile web
cd incubator-linkis/web
npm install
npm run build

Please refer to Quick Deployment to do the deployment.

Examples and Guidance

Documentation & Vedio

Architecture

Linkis services could be divided into three categories: computation governance services, public enhancement services and microservice governance services.

  • The computation governance services, support the 3 major stages of processing a task/request: submission -> preparation -> execution;
  • The public enhancement services, including the material library service, context service, and data source service;
  • The microservice governance services, including Spring Cloud Gateway, Eureka and Open Feign.

Below is the Linkis architecture diagram. You can find more detailed architecture docs in Linkis-Doc/Architecture. architecture

Based on Linkis the computation middleware, we've built a lot of applications and tools on top of it in the big data platform suite WeDataSphere. Below are the currently available open-source projects. More projects upcoming, please stay tuned.

wedatasphere_stack_Linkis

Contributing

Contributions are always welcomed, we need more contributors to build Linkis together. either code, or doc, or other supports that could help the community.
For code and documentation contributions, please follow the contribution guide.

Contact Us

  • Any questions or suggestions please kindly submit an issue.
  • By mail dev@linkis.apache.org
  • You can scan the QR code below to join our WeChat group to get more immediate response.

wechatgroup

Who is Using Linkis

We opened an issue [Who is Using Linkis] for users to feedback and record who is using Linkis.
Since the first release of Linkis in 2019, it has accumulated more than 700 trial companies and 1000+ sandbox trial users, which involving diverse industries, from finance, banking, tele-communication, to manufactory, internet companies and so on.

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打通了多个计算存储引擎如:Spark、TiSpark、Hive、Python和HBase等,对外提供统一REST/WebSocket/JDBC接口,提交执行SQL、Pyspark、HiveQL、Scala等脚本的计算中间件。 expand collapse
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