https://www.mail-archive.com/server-dev@james.apache.org/msg71462.html

A good long term objective for the PMC is to drop RabbitMQ in
favor of pulsar (third parties could package their own components using
RabbitMQ if they wishes...)

This means:

• Solve the bugs that were found during the Pulsar MailQueue review
• Pulsar MailQueue need to allow listing blobs in order to be
  deduplication friendly.
• Provide an event bus based on Pulsar
• Provide a task manager based on Pulsar
• Package a distributed server backed by pulsar, deprecate then replace
  the current one.
• (optionally) support mail queue priorities

While contributions would of course be welcomed on this topic, we could
offer it as part of GSOC 2022, and we could co-mentor it with mentors of
the Pulsar community (see [3])

[3] https://lists.apache.org/thread/y9s7f6hmh51ky30l20yx0dlz458gw259

Would such a plan gain traction around here ?

Why ?

Alternatives like Postfix...

• Do not offer a unified view of the mail queue across nodes
• Requires statefull persistent storage

Given Apache James recent push to adopt a distributed mail queue based on Pulsar supporting delays (JAMES-3687), it starts making sense developing tooling for MX related tooling.

I propose myself to mentor a Gsoc on this topic.

Benefits for the student

At the end of this GSOC you will...

• Have a solid understanding of email relaying and associated mechanics
• Understand James modular architecture (mailet/ matcher / routes)
• Have a hands-on expertise in SQL / NoSQL working with technologies like Cassandra, Redis, JPA...
• Identify fix and solve architecture problems.
• Conduct performance tests and develop an operational mindset

Inventory...

James ships a couple of MX related tools within smtp-hooks/mailets in default packages. It would make sense to me to move those as an extension.

James supports today...

checks agains DNS blacklists. `DNSRBLHandler` or `URIRBLHandler` smtp hook for instance. This can be moved as an extension IMO.

We would need a little performance benchmark to document performance implications of activating DNS-RBL.

Finally as quoted by a gitter guy: it would make more sens to have this done as a MailHook rather as a RcptHook as it would avoid doing the same job again and over again for each recipients. See JAMES-3820 .

Grey listing. There's an existing implementation using JDBC as an underlying storage.

Move it as an extension.

Remove JDBC storage, propose 2 storage possibilities: in-memory for single node, REDIS for a distributed topology.

Some work around whitelist mailets? Move it as an extension, propose JPA, Cassandra, and XML configured implementations ? With a route to manage entries in there for JPA + Cassandra ?

I would expect a student to do his own little audit and come up with extra suggestions!

James today provides a command line tool to do administration tasks like creating a domain, listing users, setting quota, etc.
It requires access to JMX port and even if lot of admins are confortable with such tools, to make our user base broader, we probably should expose the same commands in Rest and provide a fancy default web ui.
The task would need some basic skills on frontend tools to design an administration board, knowledge on what REST mean and enough Java understanding to add commands to existing Rest backend.
In the team, we have a strong focus on test (who want a mail server that is not tested enough ?) so we will explain and/or teach the student how to have the right test coverage of the features using modern tools like Cucumber, Selenium, rest-assured, etc.

Background
Apache Traffic Control is a Content Delivery Network (CDN) control plane for large scale content distribution.

Traffic Control currently requires Apache Traffic Server as the underlying cache. Help us expand the scope by integrating with the very popular Varnish Cache.

There are multiple aspects to this project:

• Configuration Generation: Write software to build Varnish configuration files (VCL). This code will be implemented in our Traffic Ops and cache client side utilities, both written in Go.

• Health Monitoring: Implement monitoring of the Varnish cache health and performance. This code will run both in the Traffic Monitor component and within Varnish. Traffic Monitor is written in Go and Varnish is written in C.

• Testing: Adding automated tests for new code

Skills:

• Proficiency in Go is required
• A basic knowledge of HTTP and caching is preferred, but not required for this project.

https://github.com/apache/trafficcontrol/issues/7076

[GSoC] RocketMQ TieredStore Integration with HDFS

Github Issue: https://github.com/apache/rocketmq/issues/6282

Apache RocketMQ and HDFS

• Apache RocketMQ is a cloud native messaging and streaming platform, making it simple to build event-driven applications.

• Hadoop Distributed File System (HDFS) is a distributed file system designed to store and manage large data sets across multiple servers or clusters. HDFS provides a reliable, scalable, and fault-tolerant platform for storing and accessing data that can be accessed by a variety of applications running on the hadoop cluster.

Background

High-speed storage media, such as solid-state drives (SSDs), are typically more expensive than traditional hard disk drives (HDDs). To minimize storage costs, the local data disk size of a rocketmq broker is often limited. HDFS can store large amounts of data at a lower cost, it has better support for storing and retrieving data sequentially rather than randomly. In order to preserve message data over a long period or facilitate message export, the RocketMQ project previously introduced a tiered storage plugin. Now it is necessary to implement a storage plugin to save data on hdfs.

Relevant Skills

• Interest in messging middleware and distributed storage system

• Java development skills

• Having a good understanding of rocketmq and hdfs models

Anyways, the most important relevant skill is motivation and readiness to learn during the project!

Tasks

• understand the basic concepts and principles in distributed systems

• provide related design documents

• develop one that uses hdfs as the backend storage plugin to store rocketmq message data

• write effective unit test code

• *suggest improvements to the tiered storage interface

• *what ever comes in your mind further ideas are always welcome

Learning Material

• RocketMQ HomePage (https://rocketmq.apache.org) Github: https://github.com/apache/rocketmq

• RocketMQ Tiered Storage Design (https://github.com/apache/rocketmq/wiki/RIP-57-Tiered-storage-for-RocketMQ)

• HDFS HomePage (https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsUserGuide.html)

Name and contact information

• Mentor: Zhimin Li, Apache RocketMQ Committer, lizhimin@apache.org

• Mailing List: dev@rocketmq.apache.org

• Website: https://rocketmq.apache.org/ and https://hadoop.apache.org/

Apache RocketMQ

Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity and flexible scalability.

Page: https://rocketmq.apache.org

Background

RocketMQ 5.0 has released various language clients including Java, CPP, and Golang, to cover all major programming languages, a Python client needs to be implemented.

Related Repo: https://github.com/apache/rocketmq-clients

Task

The developer is required to be familiar with the Java implementation and capable of developing a Python client, while ensuring consistent functionality and semantics.

Relevant Skills
Python language
Basic knowledge of RocketMQ 5.0

Mentor

Yangkun Ai, PMC of Apache RocketMQ, aaronai@apache.org

 Apache RocketMQ

Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity and flexible scalability.

Page: https://rocketmq.apache.org
Github: https://github.com/apache/rocketmq

Background

RocketMQ is a widely used message middleware system in the Java community, which mainly supports Java8. As Java has evolved many new features and improvements have been added to the language and the Java Virtual Machine (JVM). However, RocketMQ still lacks compatibility with the latest Java versions, preventing users from taking advantage of new features and performance improvements. Therefore, we are seeking community support to upgrade RocketMQ to support higher versions of Java and enable the use of new features and JVM parameters.

Task

We aim to update the RocketMQ codebase to support newer versions of Java in a cross-compile manner. The goal is to enable RocketMQ to work with Java17, while maintaining backward compatibility with previous versions of Java. This will involve identifying and updating any dependencies that need to be changed to support the new Java versions, as well as testing and verifying that the new version of RocketMQ works correctly. With these updates, users will be able to take advantage of the latest Java features and performance improvements. We hope that the community can come together to support this task and make RocketMQ a more versatile and powerful middleware system.

Relevant Skills

1. Java language
2. Having a good understanding of the new features in higher versions of Java, particularly LTS versions.

Mentor

Yangkun Ai, PMC of Apache RocketMQ, aaronai@apache.org

Apache RocketMQ

Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity, and flexible scalability.

Page: https://rocketmq.apache.org
Repo: https://github.com/apache/rocketmq

Background

RocketMQ 5.0 has released a new module called `proxy`, which supports gRPC and remoting protocol. Additionally, it can be deployed in two modes, namely Local and Cluster modes. The performance tuning task will provide contributors with a comprehensive understanding of Apache RocketMQ and its intricate data flow, presenting a unique opportunity for beginners to acquaint themselves with and actively participate in our community.

Task

The task is to tune RocketMQ proxy for optimal performance involves latency and throughput. possess a thorough knowledge of Java implementation and possess the ability to fine-tune Netty, gRPC, the operating system, and RocketMQ itself. We anticipate that the developer responsible for this task will provide a performance report about measurements of both latency and throughput.

Relevant Skills

Basic knowledge of RocketMQ 5.0, Netty, gRPC, and operating system.
 

Mailing List: dev@rocketmq.apache.org
 
Mentor
Zhouxiang Zhan, committer of Apache RocketMQ, zhouxzhan@apache.org

Apache RocketMQ

Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity and flexible scalability.

Page: https://rocketmq.apache.org
Github: https://github.com/apache/rocketmq

Background

RocketMQ 5.0 client has been released recently, we need to integrate it with Spring.

Related issue: https://github.com/apache/rocketmq-clients/issues/275

Task

1. Familiar with RocketMQ 5.0 java client usage, you could see more details from https://github.com/apache/rocketmq-clients/tree/master/java and https://rocketmq.apache.org/docs/quickStart/01quickstart
2. Integrate with Spring.

Relevant Skills

1. Java language
2. Basic knowledge of RocketMQ 5.0
3. Spring

Mentor

Rongtong Jin, PMC of Apache RocketMQ, jinrongtong@apache.org

Yangkun Ai, PMC of Apache RocketMQ, aaronai@apache.org

Apache RocketMQ{}

Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity and flexible scalability.

Page: https://rocketmq.apache.org

Background

With the official release of RocketMQ 5.1.0, tiered storage has arrived as a new independent module in the Technical Preview milestone. This allows users to unload messages from local disks to other cheaper storage, extending message retention time at a lower cost.

Reference RIP-57: https://github.com/apache/rocketmq/wiki/RIP-57-Tiered-storage-for-RocketMQ

In addition, RocketMQ introduced a new high availability architecture in version 5.0.

Reference RIP-44: https://github.com/apache/rocketmq/wiki/RIP-44-Support-DLedger-Controller

However, currently RocketMQ tiered storage only supports single replicas.

Task

Currently, tiered storage only supports single replicas, and there are still the following issues in the integration with the high availability architecture:

• Metadata synchronization: how to reliably synchronize metadata between master and slave nodes.
• Disallowing message uploads beyond the confirm offset: to avoid message rollback, the maximum uploaded offset cannot exceed the confirm offset.
• Starting multi-tier storage upload when the slave changes to master, and stopping tiered storage upload when the master becomes the slave: only the master node has write and delete permissions, and after the slave node is promoted, it needs to quickly resume tiered storage breakpoint resumption.
• Design of slave pull protocol: how a newly launched empty slave can properly synchronize data through the tiered storage architecture. (If synchronization is performed based on the first or last file, resumption of breakpoints may not be possible when switching again).

So you need to provide a complete plan to solve the above issues and ultimately complete the integration of tiered storage and high availability architecture, while verifying it through the existing tiered storage file version and OpenChaos testing.

Relevant Skills

• Interest in messaging middleware and distributed storage systems
• Java development skills
• Having a good understanding of RocketMQ tiered storage and high availability architecture

Apache RocketMQ

Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity, and flexible scalability.
Page: https://rocketmq.apache.org
Repo: https://github.com/apache/rocketmq

Background

RocketMQ 5.0 introduced a new component, the controller, which controls the high availability master-slave switch in multi-replica scenarios. It uses the DLedger Raft library as a consensus replication state machine for metadata. As a completely independent component, it can run normally in some scenarios, but in large-scale clusters, it is necessary to maintain a large number of broker groups, which is a great challenge for operational capabilities and resource waste. When dealing with a large number of Broker groups, we need to optimize performance in large-scale scenarios, leveraging the high-performance writing of DLedger itself and performing some optimization for the current Controller architecture.

Task

1. Polish the usage of DLedger

Currently, on the Controller side, a task queue single thread is used for requesting reads and writes to DLedger, that is, only one read/write request can be processed at a time. However, DLedger itself implements many optimizations for multi-client reads and writes and can ensure linear consistency reading. However, now all read and write processing is performed using a single logical DLedger client, which will become a serious performance bottleneck in large-scale scenarios.

2. Optimization of DLedger features usage

DLedger itself can implement many optimizations, such as ReadIndex read and FollowerRead read. After implementation, we can fully leverage the performance of reads. Currently, all Broker nodes communicate with the Leader node of the Controller. In large-scale scenarios, this will cause the requests of each Controller group to be concentrated on the Leader node, and the other Follower nodes will not share the request processing of the Leader, which will cause single-point performance bottlenecks for the Leader.

3. Full asynchronous + parallel processing

Currently, DLedger itself is fully asynchronous, but on the Controller side, all requests for the DLedger side are synchronized, and many Controller-side operations are performed synchronously in a single thread, such as heartbeat checks and other timed tasks. In large-scale scenarios, the logic of these single-threaded synchronous operations will block a large number of requests from Broker-side, so asynchronous + parallel processing can be used for optimization.

4. Correctness testing and performance testing

After completing the above optimizations, it is necessary to conduct correctness testing on the new version and use distributed chaos testing frameworks such as OpenChaos to verify correct operation under fault scenarios such as network partition and random crashes.
After completing the correctness testing, a detailed performance testing report can be produced by comparing the new and old versions.

Skills Required

• Strong interest in message middleware and distributed storage systems
• Proficient in Java development
• In-depth understanding of distributed consensus algorithms
• In-depth understanding of the high-availability module of RockeetMQ and the DLedger library
• Understanding of distributed chaos testing and performance testing.

RocketMQ Streams 

RocketMQ Streams is a lightweight stream processing framework, application gains the stream processing ability by depending on RocketMQ Streams as an SDK.

Background

Repo of RocketMQ Streams:  https://github.com/apache/rocketmq-streams
The architecture document of RocketMQ Streams: RocketMQ Streams examples,

architecture doc

Task

The observability needs to be enhanced in the following aspects:

• The metric of client/processor/thread/state/rocksdb;
• The topology of streaming process;
• Mutli-output of metrics;

This task need you to study the implementation details of RocketMQ Streams streams, and find out the key indicators in the stream processing process. Design and implement a complete set of observability solutions, and finally use it to complete runtime problem diagnosis;

Mentor

nize, Committer of of Apache RocketMQ, karp@apache.org

Apache StreamPipes

Apache StreamPipes (incubating) is a self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams. StreamPipes offers several modules including StreamPipes Connect to easily connect data from industrial IoT sources, the Pipeline Editor to quickly create processing pipelines and several visualization modules for live and historic data exploration. Under the hood, StreamPipes utilizes an event-driven microservice paradigm of standalone, so-called analytics microservices making the system easy to extend for individual needs.

Background

StreamPipes has grown significantly throughout recent years. We were able to introduce a lot of new features and attracted both users and contributors. Putting the cherry on the cake, we were graduated as an Apache top level project in December 2022. We will of course continue developing new features and never rest to make StreamPipes even more amazing. Although, since we are approaching with full stream towards our `1.0` release, we want to project also to get more mature. Therefore, we want to address one of our Achilles' heels: our test coverage.

Don't worry, this issue is not about implementing myriads of tests for our code base. As a first step, we would like to make the status quo transparent. That means we want to measure our code coverage consistently across the whole codebase (Backend, UI, Python library) and report the coverage to codecov. Furthermore, to benchmark ourselves and motivate us to provide tests with every contributing, we would like to lock the current test coverage as an lower threshold that we always want to achieve (meaning in case we drop CI builds fail etc). With time we then can increase the required coverage lever step to step.

More than monitoring our test coverage, we also want to invest in better and more clean code. Therefore, we would like to adopt sonarcloud for our repository.

Tasks

• [ ] calculate test coverage for all main parts of the repo
• [ ] send coverage to codeCov
• [ ] determine coverage threshold and let CI fail if below
• [ ] include sonarcloud in CI setup
• [ ] include automatic coverage report in PR validation (see an example here ) -> optional
• [ ] include automatic sonarcloud report in PR validation -> optional
• [ ] what ever comes in your mind 💡 further ideas are always welcome

❗Important Note❗

Do not create any account in behalf of Apache StreamPipes in Sonarcloud or in CodeCov or using the name of Apache StreamPipes for any account creation. Your mentor will take care of it.

Relevant Skills

• basic knowledge about GitHub worfklows

Learning Material

• GitHub workflow docs
• Apache StreamPipes workflows
• Sonarcloud for Monorepos
• Using code cov for a monorepo: https://www.curtiscode.dev/post/tools/codecov-monorepo/ & https://docs.codecov.com/docs/flags

References

You can find our corresponding issue on GitHub here

Name and Contact Information

Name: Tim Bossenmaier

email:  bossenti[at]apache.org

community: dev[at]streampipes.apache.org

website: https://streampipes.apache.org/

Apache StreamPipes

Apache StreamPipes (incubating) is a self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams. StreamPipes offers several modules including StreamPipes Connect to easily connect data from industrial IoT sources, the Pipeline Editor to quickly create processing pipelines and several visualization modules for live and historic data exploration. Under the hood, StreamPipes utilizes an event-driven microservice paradigm of standalone, so-called analytics microservices making the system easy to extend for individual needs.

Background

StreamPipes has grown significantly over the past few years, with new features and contributors joining the project. However, as the project continues to evolve, e2e test coverage must also be improved to ensure that all features remain functional. Modern frameworks, such as Cypress, make it quite easy and fun to automatically test even complex application functionalities. As StreamPipes approaches its 1.0 release, it is important to improve e2e testing to ensure the robustness of the project and its use in real-world scenarios.

Tasks

• [ ] Write e2e tests using Cypress to cover most functionalities and user interface components of StreamPipes.
• [ ] Add more complex testing scenarios to ensure the reliability and robustness of StreamPipes in real-world use cases (e.g. automated tests for version updates)
• [ ] Add e2e tests for the new Python client to ensure its integration with the main system and its functionalities ([#774 | https://github.com/apache/streampipes/issues/774]])
• [ ] Document the testing infrastructure and the testing approach to allow for easy maintenance and future contributions.

  ❗ ***Important Note*** ❗

Do not create any account on behalf of Apache StreamPipes in Cypress or using the name of Apache StreamPipes for any account creation. Your mentor will take care of it.

Relevant Skills

• Familiarity with testing frameworks, such as Cypress or Selenium
• Experience with TypeScript or Java
• Basic knowledge of Angular is helpful
• Familiarity with Docker and containerization is a plus

  Learning Material

• Cypress docs
• Learn Cypress
• Angular docs

References

You can find our corresponding issue on GitHub here

Name and Contact Information

Name: Philipp Zehnder

email:  zehnder[at]apache.org

community: dev[at]streampipes.apache.org

website: https://streampipes.apache.org/

Apache StreamPipes

Apache StreamPipes (incubating) is a self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams. StreamPipes offers several modules including StreamPipes Connect to easily connect data from industrial IoT sources, the Pipeline Editor to quickly create processing pipelines and several visualization modules for live and historic data exploration. Under the hood, StreamPipes utilizes an event-driven microservice paradigm of standalone, so-called analytics microservices making the system easy to extend for individual needs.

Background

StreamPipes is grown significantly throughout recent years. We were able to introduce a lot of new features and attracted both users and contributors. Putting the cherry on the cake, we were graduated as an Apache top level project in December 2022. We will of course continue developing new features and never rest to make StreamPipes even more amazing. 

StreamPipes really shines when connecting Industrial IoT data. Such data sources typically originate from machine controllers, called PLCs (e.g., Siemens S7). But there are also new protocols such as OPC-UA which allow to browse available data within the controller. Our goal is to make connectivity of industrial data sources a matter of minutes.

Currently, data sources can be connected using the built-in module `StreamPipes Connect` from the UI. We provide a set of adapters for popular protocols that can be customized, e.g., connection details can be added. 

To make it even easier to connect industrial data sources with StreamPipes, we plan to add an OPC-UA browser. This will be part of the entry page of StreamPipes connect and should allow users to enter connection details of an existing OPC-UA server. Afterwards, a new view in the UI shows available data nodes from the server, their status and current value. Users should be able to select values that should be part of a new adapter. Afterwards, a new adapter can be created by reusing the current workflow to create an OPC-UA data source.

This is a really cool project for participants interested in full-stack development who would like to get a deeper understanding of industrial IoT protocols. Have fun! 

Tasks

• [ ] get familiar with the OPC-UA protocol
• [ ] develop mockups which demonstrate the user workflow
• [ ] develop a data model for discovering data from OPC-UA
• [ ] create the backend business logic for the OPC-UA browser 
• [ ] create the frontend views to asynchronously browse data and to create a new adapter
• [ ] write Junit, Component and E2E tests
• [ ] what ever comes in your mind 💡 further ideas are always welcome

   

 Relevant Skills

• interest in Industrial IoT and procotols such as OPC-UA

• Java development skills
• Angular/Typescript development skills

Anyways, the most important relevant skill is motivation and readiness to learn during the project!

Learning Material

• StreamPipes documentation (https://streampipes.apache.org/docs/docs/user-guide-introduction.html)
• [ur current OPC-UA adapter (https://github.com/apache/streampipes/tree/dev/streampipes-extensions/streampipes-connect-adapters-iiot/src/main/java/org/apache/streampipes/connect/iiot/adapters/opcua)
• Eclipse Milo, which we currently use for OPC-UA connectivity (https://github.com/eclipse/milo)
• Apache PLC4X, which has an API for browsing (https://plc4x.apache.org/) 

Reference

Github issue can be found here: https://github.com/apache/streampipes/issues/1390

Name and contact information

• Mentor: Dominik Riemer (riemer[at]apache.org).
• Mailing list: (dev[at]streampipes.apache.org)
• Website: streampipes.apache.org

Apache ShardingSphere

Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard layer and ecosystem above heterogeneous databases. It focuses on how to reuse existing databases and their respective upper layer, rather than creating a new database. The goal is to minimize or eliminate the challenges caused by underlying databases fragmentation.

Page: https://shardingsphere.apache.org
Github: https://github.com/apache/shardingsphere 

Background

ShardingSphere provides two adapters: ShardingSphere-JDBC and ShardingSphere-Proxy.

Now, ShardingSphere uses logback for logging, but consider the following situations:

• Users may need to switch the logging framework to meet special needs, such as log4j2 can provide better asynchronous performance;
• When using the JDBC adapter, the user application may not use logback, which may cause some conflicts.

Why doesn't the log facade suffice? Because ShardingSphere provides users with clustered logging configurations (such as changing the log level online), this requires dynamic construction of logger, which cannot be achieved with only the log facade.

Task

1. Design and implement logging SPI to support multiple logging frameworks (such as logback and log4j2)
2. Allow users to choose which logging framework to use through the logging rule

Relevant Skills

1. Master JAVA language

2. Basic knowledge of logback and log4j2

3. Maven

Mentor

Longtao Jiang, Committer of Apache ShardingSphere, jianglongtao@apache.org

Trista Pan, PMC of Apache ShardingSphere, panjuan@apache.org

Apache ShardingSphere

Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard layer and ecosystem above heterogeneous databases. It focuses on how to reuse existing databases and their respective upper layer, rather than creating a new database. The goal is to minimize or eliminate the challenges caused by underlying databases fragmentation.

Page: https://shardingsphere.apache.org
Github: https://github.com/apache/shardingsphere 

Background

The community just added CDC (change data capture) feature recently. Change feed will be published in created network connection after logging in, then it could be consumed.

Since Kafka is popular distributed event streaming platform, it's useful to import change feed into Kafka for later processing.

Task

1. Familiar with ShardingSphere CDC client usage, create publication and subscribe change feed.
2. Familiar with Kafka connector development, develop source connector, integrate with ShardingSphere CDC. Persist change feed to Kafka topics properly.
3. Add unit test and E2E integration test.

Relevant Skills

1. Java language

2. Basic knowledge of CDC and Kafka

3. Maven

References

• https://github.com/apache/shardingsphere/issues/22500
• https://kafka.apache.org/documentation/#connect_development
• https://github.com/apache/kafka/tree/trunk/connect/file/src
• https://github.com/confluentinc/kafka-connect-jdbc

Mentor

Hongsheng Zhong, PMC of Apache ShardingSphere, zhonghongsheng@apache.org

Xinze Guo, Committer of Apache ShardingSphere, azexin@apache.org

Apache ShardingSphere

Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard layer and ecosystem above heterogeneous databases. It focuses on how to reuse existing databases and their respective upper layer, rather than creating a new database. The goal is to minimize or eliminate the challenges caused by underlying databases fragmentation.

Page: https://shardingsphere.apache.org/
Github: https://github.com/apache/shardingsphere 

Background

There is a proposal about new CRD Cluster and ComputeNode as belows:

• WIP: [New Feature] Introduce new CRD Cluster #167
• [Feat] Introduce new CRD as ComputeNode for better usability #166

Currently we try to promote ComputeNode as major CRD to represent a special ShardingSphere Proxy deployment. And plan to use Cluster indicating a special ShardingSphere Proxy cluster.

Task

This issue is to enhance ComputeNode reconciliation availability. The specific case list is as follows.

•  Add IT test case for Deployment spec volume
•  Add IT test case for Deployment spec template init containers
•  Add IT test case for Deployment spec template spec containers
•  Add IT test case for Deployment spec volume mounts
•  Add IT test case for Deployment spec container ports
•  Add IT test case for Deployment spec container image tag
•  Add IT test case for Service spec ports
•  Add IT test case for ConfigMap data serverconfig
•  Add IT test case for ConfigMap data logback
   
  Notice, these issues can be a good example.
• chore: add more Ginkgo tests for ComputeNode #203

Relevant Skills

1. Master Go language, Ginkgo test framework
2. Have a basic understanding of Apache ShardingSphere Concepts
3. Be familiar with Kubernetes Operator, kubebuilder framework

Targets files

ComputeNode IT - https://github.com/apache/shardingsphere-on-cloud/blob/main/shardingsphere-operator/pkg/reconcile/computenode/compute_node_test.go

Mentor

Liyao Miao, Committer of Apache ShardingSphere,  miaoliyao@apache.org

Chuxin Chen, Committer of Apache ShardingSphere, tuichenchuxin@apache.org

Apache ShardingSphere

Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard layer and ecosystem above heterogeneous databases. It focuses on how to reuse existing databases and their respective upper layer, rather than creating a new database. The goal is to minimize or eliminate the challenges caused by underlying databases fragmentation.

Page: https://shardingsphere.apache.org
Github: https://github.com/apache/shardingsphere 

Background

ShardingSphere has designed its own metadata database to simulate metadata queries that support various databases.

More details:

https://github.com/apache/shardingsphere/issues/21268
https://github.com/apache/shardingsphere/issues/22052

Task

• Support PostgreSQL And openGauss `\d tableName`
• Support PostgreSQL And openGauss `\d+`
• Support PostgreSQL And openGauss `\d+ tableName`
• Support PostgreSQL And openGauss `l`
• Support query for MySQL metadata `TABLES`
• Support query for MySQL metadata `COLUMNS`
• Support query for MySQL metadata `schemata`
• Support query for MySQL metadata `ENGINES`
• Support query for MySQL metadata `FILES`
• Support query for MySQL metadata `VIEWS`

Notice, these issues can be a good example.

https://github.com/apache/shardingsphere/pull/22053
https://github.com/apache/shardingsphere/pull/22057/
https://github.com/apache/shardingsphere/pull/22166/
https://github.com/apache/shardingsphere/pull/22182

Relevant Skills

•  Master JAVA language
•  Have a basic understanding of Zookeeper
•  Be familiar with MySQL/Postgres SQLs 

Mentor

Chuxin Chen, Committer of Apache ShardingSphere, tuichenchuxin@apache.org

Zhengqiang Duan, PMC of Apache ShardingSphere, duanzhengqiang@apache.org

Apache ShardingSphere

Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard layer and ecosystem above heterogeneous databases. It focuses on how to reuse existing databases and their respective upper layer, rather than creating a new database. The goal is to minimize or eliminate the challenges caused by underlying databases fragmentation.

Page: https://shardingsphere.apache.org
Github: https://github.com/apache/shardingsphere 

Background

The ShardingSphere SQL federation engine provides support for complex SQL statements, and it can well support cross-database join queries, subqueries, aggregation queries and other statements. An important part of SQL federation engine is to convert the SQL statement parsed by ShardingSphere into SqlNode, so that Calcite can be used to implement SQL optimization and federated query.

Task

This issue is to solve the MySQL exception that occurs during SQLNodeConverterEngine conversion. The specific case list is as follows.

• select_char
• select_extract
• select_from_dual
• select_from_with_table
• select_group_by_with_having_and_window
• select_not_between_with_single_table
• select_not_in_with_single_table
• select_substring
• select_trim
• select_weight_string
• select_where_with_bit_expr_with_ampersand
• select_where_with_bit_expr_with_caret
• select_where_with_bit_expr_with_div
• select_where_with_bit_expr_with_minus_interval
• select_where_with_bit_expr_with_mod
• select_where_with_bit_expr_with_mod_sign
• select_where_with_bit_expr_with_plus_interval
• select_where_with_bit_expr_with_signed_left_shift
• select_where_with_bit_expr_with_signed_right_shift
• select_where_with_bit_expr_with_vertical_bar
• select_where_with_boolean_primary_with_comparison_subquery
• select_where_with_boolean_primary_with_is
• select_where_with_boolean_primary_with_is_not
• select_where_with_boolean_primary_with_null_safe
• select_where_with_expr_with_and_sign
• select_where_with_expr_with_is
• select_where_with_expr_with_is_not
• select_where_with_expr_with_not
• select_where_with_expr_with_not_sign
• select_where_with_expr_with_or_sign
• select_where_with_expr_with_xor
• select_where_with_predicate_with_in_subquery
• select_where_with_predicate_with_regexp
• select_where_with_predicate_with_sounds_like
• select_where_with_simple_expr_with_collate
• select_where_with_simple_expr_with_match
• select_where_with_simple_expr_with_not
• select_where_with_simple_expr_with_odbc_escape_syntax
• select_where_with_simple_expr_with_row
• select_where_with_simple_expr_with_tilde
• select_where_with_simple_expr_with_variable
• select_window_function
• select_with_assignment_operator
• select_with_assignment_operator_and_keyword
• select_with_case_expression
• select_with_collate_with_marker
• select_with_date_format_function
• select_with_exists_sub_query_with_project
• select_with_function_name
• select_with_json_value_return_type
• select_with_match_against
• select_with_regexp
• select_with_schema_name_in_column_projection
• select_with_schema_name_in_shorthand_projection
• select_with_spatial_function
• select_with_trim_expr
• select_with_trim_expr_from_expr

You need to compare the difference between actual and expected, and then correct the logic in SQLNodeConverterEngine so that actual can be consistent with expected.

After you make changes, remember to add case to SUPPORTED_SQL_CASE_IDS to ensure it can be tested.

 
Notice, these issues can be a good example.
https://github.com/apache/shardingsphere/pull/14492

Relevant Skills

 
1. Master JAVA language

2. Have a basic understanding of Antlr g4 file

3. Be familiar with MySQL and Calcite SqlNode

Targets files

 
SQLNodeConverterEngineIT

https://github.com/apache/shardingsphere/blob/master/test/it/optimizer/src/test/java/org/apache/shardingsphere/test/it/optimize/SQLNodeConverterEngineIT.java 

Mentor

Zhengqiang Duan, PMC of Apache ShardingSphere, duanzhengqiang@apache.org

Chuxin Chen, Committer of Apache ShardingSphere, tuichenchuxin@apache.org

Trista Pan, PMC of Apache ShardingSphere, panjuan@apache.org

Apache Doris
Apache Doris is a real-time analytical database based on MPP architecture. As a unified platform that supports multiple data processing scenarios, it ensures high performance for low-latency and high-throughput queries, allows for easy federated queries on data lakes, and supports various data ingestion methods.
Page: https://doris.apache.org

Github: https://github.com/apache/doris

Background

In Apache Doris, dictionary encoding is performed during data writing and compaction. Dictionary encoding will be implemented on string data types by default. The dictionary size of a column for one segment is 1M at most. The dictionary encoding technology accelerates strings during queries, converting them into INT, for example.
 

Task

• Phase One: Get familiar with the implementation of Apache Doris dictionary encoding; learning how Apache Doris dictionary encoding accelerates queries.
•  Phase Two: Evaluate the effectiveness of full dictionary encoding and figure out how to optimize memory in such a case.

Learning Material

Page: https://doris.apache.org
Github: https://github.com/apache/doris

Mentor

• Mentor: Chen Zhang, Apache Doris Committer, zhangchen@apache.org 
• Mentor: Zhijing Lu, Apache Doris Committer, luzhijing@apache.org  
• Mailing List: dev@doris.apache.org

Apache Doris
Apache Doris is a real-time analytical database based on MPP architecture. As a unified platform that supports multiple data processing scenarios, it ensures high performance for low-latency and high-throughput queries, allows for easy federated queries on data lakes, and supports various data ingestion methods.
Page: https://doris.apache.org
Github: https://github.com/apache/doris

Background

Apache Doris supports acceleration of queries on external data sources to meet users' needs for federated queries and analysis.
Currently, Apache Doris supports multiple external catalogs including those from Hive, Iceberg, Hudi, and JDBC. Developers can connect more data sources to Apache Doris based on a unified framework.

Objective

• Enable Apache Doris to access one or more of these data sources via the Multi-Catalog feature: BigQuery/Kudu/Cassandra/Druid;
• Compile relevant documentation. See an example here: https://doris.apache.org/docs/dev/lakehouse/multi-catalog/hive

Task
Phase One:

• Get familiar with the Multi-Catalog structure of Apache Doris, including the metadata synchronization mechanism in FE and the data reading mechanism of BE.
• Investigate how metadata should be acquired and how data access works regarding the picked data source(s); produce the corresponding design documentation.

Phase Two:

• Develop connections to the picked data source(s) and implement access to metadata and data.

Learning Material

Page: https://doris.apache.org
Github: https://github.com/apache/doris

Mentor

• Mentor: Mingyu Chen, Apache Doris PMC Member & Committer, morningman@apache.org 
• Mentor: Calvin Kirs, Apache Geode PMC & Committer, Kirs@apache.org
• Mailing List: dev@doris.apache.org

Apache Doris
Apache Doris is a real-time analytical database based on MPP architecture. As a unified platform that supports multiple data processing scenarios, it ensures high performance for low-latency and high-throughput queries, allows for easy federated queries on data lakes, and supports various data ingestion methods.
Page: https://doris.apache.org

Github: https://github.com/apache/doris

Background

Apache Doris accelerates high-concurrency queries utilizing page cache, where the decompressed data is stored.
Currently, the page cache in Apache Doris uses a simple LRU algorithm, which reveals a few problems: 

• Hot data will be phased out in large queries
• The page cache configuration is immutable and does not support GC.

Task

• Phase One: Identify the impacts on queries when the decompressed data is stored in memory and SSD, respectively, and then determine whether full page cache is required.

• Phase Two: Improve the cache strategy for Apache Doris based on the results from Phase One.

Learning Material

Page: https://doris.apache.org
Github: https://github.com/apache/doris

Mentor

• Mentor: Yongqiang Yang, Apache Doris PMC member & Committer, yangyongqiang@apache.org 
• Mentor: Haopeng Li, Apache Doris PMC member & Committer, lihaopeng@apache.org  
• Mailing List: dev@doris.apache.org

Apache SkyWalking is an application performance monitor tool for distributed systems, especially designed for microservices, cloud native and container-based (Kubernetes) architectures. This year we will proceed on log clustering implementation with a revised architecture and this task will require student to focus on algorithm optimiztion for the clustering technique.

Apache SkyWalking is an application performance monitor tool for distributed systems, especially designed for microservices, cloud native and container-based (Kubernetes) architectures. This year we will proceed on log clustering implementation with a revised architecture and this task will require student to focus on Flink and its integration with SkyWalking OAP.

Apache SkyWalking is an application performance monitor tool for distributed systems, especially designed for microservices, cloud native and container-based (Kubernetes) architectures. This task is about enhancing Python agent performance, the tracking issue can be seen here -< https://github.com/apache/skywalking/issues/10408

Apache SkyWalking is an application performance monitor tool for distributed systems, especially designed for microservices, cloud native and container-based (Kubernetes) architectures. This task is about a pending task on K8s.

Background

SkyWalking BanyanDB is an observability database, aims to ingest, analyze and store Metrics, Tracing and Logging data.

Objectives

1. Fully unify/merge the query planner and executor for Measure and TopN

Recommended Skills

1. Familiar with Go
2. Have a basic understanding of database query engine
3. Have an experience of Apache SkyWalking

Mentor

• Mentor: Jiajing Lu, Apache SkyWalking PMC, lujiajing@apache.org
• Mentor: Hongtao Gao, Apache SkyWalking PMC, Apache ShardingSphere PMC, hanahmily@apache.org
• Mailing List: dev@skywalking.apache.org

Now the deployment methods for SkyWalking are limited, we only have Helm Chart for users to deploy in Kubernetes, other users that are not using Kubernetes have to do all the house keeping stuffs to set up SkyWalking on, for example, VM.

This issue aims to add a Terraform provider, so that users can conveniently  spin up a cluster for demonstration or testing, we should evolve the provider and allow users to customize as their need and finally users can use this in their production environment.

In this task, we will mainly focus on the support for AWS. In the Terraform provider, users need to provide their access key / secret key, and the provider does the rest stuffs: create VMs, create database/OpenSearch or RDS, download SkyWalking tars, configure the SkyWalking, and start the SkyWalking components (OAP/UI), create public IPs/domain name, etc.

Background

SkyWalking BanyanDB is an observability database, aims to ingest, analyze and store Metrics, Tracing and Logging data.

Objectives

1. Support EXPLAIN[1] for both measure query and stream query
2. Add self-observability including trace and metrics for query subsystem
3. Support EXPLAIN in the client SDK & CLI and add query plan visualization in the UI

[1]: EXPLAIN in MySQL

Recommended Skills

1. Familiar with Go
2. Have a basic understanding of database query engine
3. Have an experience of Apache SkyWalking or other APMs

Mentor

• Mentor: Jiajing Lu, Apache SkyWalking PMC, lujiajing@apache.org
• Mentor: Hongtao Gao, Apache SkyWalking PMC, Apache ShardingSphere PMC, hanahmily@apache.org
• Mailing List: dev@skywalking.apache.org

Background:

Shenyu is a native API gateway for service proxy, protocol translation and API governance. but Shenyu lack of End-To-End Tests.

Relevant skills：

1.Understand the architecture of ShenYu

2.Understand SpringCloud micro-service and ShenYu SpringCloud proxy plugin.

3.Understand ShenYu e2e framework and architecture.

How to coding

1.please refer to org.apache.shenyu.e2e.testcase.plugin.DividePluginCases

How to test

1.start shenyu admin in docker

2.start shenyu boostrap in docker

3.run test case org.apache.shenyu.e2e.testcase.plugin.PluginsTest#testDivide

Task List

1.develop e2e tests of the springcloud plug-in.

2.write shenyu e2e springcloud plugin documentation in shenyu-website.

3.refactor the existing plugin test cases.

Links:

website： https://shenyu.apache.org/

issues: https://github.com/apache/shenyu/issues/4474

Background

Apache ShenYu is a Java native API Gateway for service proxy, protocol conversion and API governance. Currently, ShenYu has good usability and performance in microservice scenarios. However, ShenYu's support for Kubernetes is still relatively weak.

Tasks

1. Discuss with mentors, and complete the requirements design and technical design of shenyu-ingress-controller.
2. Complete the initial version of shenyu-ingress-controller, implement the reconcile of k8s ingress api, and make ShenYu as the ingress gateway of k8s.
3. Complete the ci test of shenyu-ingress-controller, verify the correctness of the code.

Relevant Skills

1. Know the use of Apache ShenYu
2. Familiar with Java and Golang
3. Familiar with Kubernetes and can use java or golang to develop Kubernetes Controller

Description

Issues ： https://github.com/apache/shenyu/issues/4438
website ： https://shenyu.apache.org/

Background

Shenyu is a native API gateway for service proxy, protocol translation and API governance. It can manage and maintain the API through Shenyu-admin, and support internationalization in Chinese and English. Unfortunately, Shenyu-admin is only internationalized on the front end. The message prompt returned by the back-end interface is still in English. Therefore, we need to implement internationalization support for the back-end interface.This will lay a good foundation for shenyu to move towards more language support.

Relevant skills

• Related skills spring resources
• Spring Internationalization
• Front-end react framework

API reference

            java.util.Locale;
            org.springframework.context.MessageSource;
            org.springframework.context.support.ResourceBundleMessageSource; 

Interface effect example

            ## zh request example
            POST http://localhost:9095/plugin
            Content-Type: application/json
            Location: cn-zh
            X-Access-Token: xxx
            {
            "name": "test-create-plugin",
            "role": "test-create-plugin",
            "enabled": true,
            "sort": 100
            }
            Respone
            {
            "code": 600,
            "message": "未登录"
            }
            
            ### en request example
            POST http://localhost:9095/plugin
            Content-Type: application/json
            Location: en
            X-Access-Token: xxx
            {
            "name": "test-create-plugin",
            "role": "test-create-plugin",
            "enabled": true,
            "sort": 100
            }
            Respone
            {
            "code": 600,
            "message": "token is error"
            } 

Task List

• The task discussed with the tutor how to achieve the internationalization of shenyu-admin background
• Some prompt message translation
• Get through the internationalization of front-end, obtain the client region information through http protocol, support the language of the corresponding region.
• Leave the extension of other multi-language internationalization support interface, so as to facilitate the localization transformation of subsequent users.

Background

Apache ShenYu is a Java native API Gateway for service proxy, protocol conversion and API governance. Currently, ShenYu has good usability and performance in microservice scenarios. However, ShenYu's support for Kubernetes is still relatively weak.

Tasks

1. Support the registration of microservices deployed in K8s Pod to shenyu-admin and use K8s as the register center.
2. Discuss with mentors, and complete the requirements design and technical design of Shenyu K8s Register Center.
3. Complete the initial version of Shenyu K8s Register Center.
4. Complete the CI test of Shenyu K8s Register Center, verify the correctness of the code.
5. Write the necessary documentation, deployment guides, and instructions for users to connect microservices running inside the K8s Pod to ShenYu

Relevant Skills

1. Know the use of Apache ShenYu, especially the register center
2. Familiar with Java and Golang
3. Familiar with Kubernetes and can use Java or Golang to develop

Apache EventMesh (incubating)
Apache EventMesh is a fully serverless platform used to build distributed event-driven applications.

Website: https://eventmesh.apache.org

GitHub: https://github.com/apache/incubator-eventmesh

Upstream Issue: https://github.com/apache/incubator-eventmesh/issues/3488

Background

We hope that the community can contribute to the maintenance of documents, including the archiving of Chinese and English content of documents of different release versions, the maintenance of official website documents, the improvement of project quick start documents, feature introduction, etc.

Task

1.Discuss with the mentors what you need to do

2. Learn the details of the Apache EventMesh project

3. Improve and supplement the content of documents on GitHub, maintain official website documents, record eventmesh quick user experience, and feature display videos

Recommended Skills

1.Familiar with MarkDown

2. Familiar with Java\Go

Mentor
Eason Chen, PPMC of Apache EventMesh, https://github.com/qqeasonchen, chenguangsheng@apache.org

Mike Xue, PPMC of Apache EventMesh, https://github.com/xwm1992, mikexue@apache.org

Apache EventMesh (incubating)
Apache EventMesh is a fully serverless platform used to build distributed event-driven applications.

Website: https://eventmesh.apache.org

GitHub: https://github.com/apache/incubator-eventmesh

Upstream Issue: https://github.com/apache/incubator-eventmesh/issues/3327

Background

Currently, EventMesh has good usability in microservice scenarios. However, EventMesh's support for Kubernetes is still relatively weak.We hope the community can contribute EventMesh integration with the k8s.

Task

1.Discuss with the mentors your implementation idea

2. Learn the details of the Apache EventMesh project

3. Integrate EventMesh with the k8s

Recommended Skills

1.Familiar with Java

2.Familiar with Kubernetes

Mentor
Eason Chen, PPMC of Apache EventMesh, https://github.com/qqeasonchen, chenguangsheng@apache.org

Mike Xue, PPMC of Apache EventMesh, https://github.com/xwm1992, mikexue@apache.org

Beam library developers and Beam users would appreciate this : )

This project involves prototyping a few different solutions, so it will be large.

Today, you can do all sorts of Machine Learning using Apache Beam (https://beam.apache.org/documentation/ml/overview/).
 
Many of our users, however, have a hard time getting started with ML and understanding how Beam can be applied to their day to day work. The goal of this project is to build out a series of Beam pipelines as Jupyter Notebooks demonstrating real world ML use cases, from NLP to image recognition to using large language models. As you go, there may be bugs or friction points as well which will provide opportunities to contribute back to Beam's core ML libraries.

Mentor for this will be Danny McCormick

Placeholder for tasks that could be undertaken in this year's GSoC.

Ideas:

• Design an updated summary statistics API for use with Java 8 streams based on the summary statistic implementations in the Commons Math stat.descriptive package including moments, rank and summary sub-packages.

Add implementations of extended precision floating point numbers.

An extended precision floating point number is a series of floating-point numbers that are non-overlapping such that:

double-double (a, b):
            |a| > |b|
            a == a + b

Common representations are double-double and quad-double (see for example David Bailey's paper on a quad-double library: QD).

Many computations in the Commons Numbers and Statistics libraries use extended precision computations where the accumulated error of a double would lead to complete cancellation of all significant bits; or create intermediate overflow of integer values.

This project would formalise the code underlying these use cases with a generic library applicable for use in the case where the result is expected to be a finite value and using Java's BigDecimal and/or BigInteger negatively impacts performance.

An example would be the average of long values where the intermediate sum overflows or the conversion to a double loses bits:

            long[] values = {Long.MAX_VALUE, Long.MAX_VALUE};
            System.out.println(Arrays.stream(values).average().getAsDouble()); System.out.println(Arrays.stream(values).mapToObj(BigDecimal::valueOf)
            .reduce(BigDecimal.ZERO, BigDecimal::add)
            .divide(BigDecimal.valueOf(values.length)).doubleValue());
            long[] values2 = {Long.MAX_VALUE, Long.MIN_VALUE};
            System.out.println(Arrays.stream(values2).asDoubleStream().average().getAsDouble()); System.out.println(Arrays.stream(values2).mapToObj(BigDecimal::valueOf)
               .reduce(BigDecimal.ZERO, BigDecimal::add)
            .divide(BigDecimal.valueOf(values2.length)).doubleValue());
            

Outputs:

-1.0
            9.223372036854776E18
            0.0
            -0.5

Placeholder for tasks that could be undertaken in this year's GSoC.

Ideas (extracted from the "dev" ML):

1. Redesign and modularize the "ml" package
   -> main goal: enable multi-thread usage.
2. Abstract the linear algebra utilities
   -> main goal: allow switching to alternative implementations.
3. Redesign and modularize the "random" package
   -> main goal: general support of low-discrepancy sequences.
4. Refactor and modularize the "special" package
   -> main goals: ensure accuracy and performance and better API,
   add other functions.
5. Upgrade the test suite to Junit 5
   -> additional goal: collect a list of "odd" expectations.
6. Review and finalize pending issues about the refactoring of the "genetic algorithm" functionality (cf. dedicated branch)

Other suggestions welcome, as well as

• delineating additional and/or intermediate goals,
• signalling potential pitfalls and/or alternative approaches to the intended goal(s).

As discussed extensively on the "dev" ML[1][2], there are two competing designs (please review them on the dedicated git branch) for the refactoring of the basic functionality currently implemented in the org.apache.commons.math4.legacy.genetics "legacy" package.

TL;DR;

• The discussion has pointed to major (from a maintenance POV) issues of the design proposed by the OP.
• The alternative (much simpler) design has been implemented as proof-of-concept (indicating that some corner might have been cut).
• The OP mentioned correctness issues in the "simple" design but did neither fix them nor provided answers on the LM to that effect.
• Questions concerning other possible "bloat" (e.g. on using a custom representation of the "binary chromosome" concept instead of the BitSet available from the JDK) were also left dangling.
• Refactoring of the "basic" GA functionality (the purpose of the "proof-of-concept") must be decoupled from the new feature which the OP wanted to implement ("adaptive probability generation").
• Unit tests (a.o. all those from the "legacy" code) must demonstrate that the refactored code does (or does not) behave correctly, and bugs should be fixed on the "simple" implementation, before implementing the new feature on top of it.

[1] https://markmail.org/message/qn7gq2y7xjoxukzp
[2] https://markmail.org/message/f66iii3a4kmjaprr

A placeholder ticket, to link other issues and organize tasks related to the 1.0 release of Commons Imaging.

The 1.0 release of Commons Imaging has been postponed several times. Now we have a more clear idea of what's necessary for the 1.0 (see issues with fixVersion 1.0 and 1.0-alpha3, and other open issues), and the tasks are interesting as it involves both basic and advanced programming for tasks such as organize how test images are loaded, or work on performance improvements at byte level and following image format specifications.

The tasks are not too hard to follow, as normally there are example images that need to work with Imaging, as well as other libraries in C, C++, Rust, PHP, etc., that process these images correctly. Our goal with this issue is to a) improve our docs, b) improve our tests, c) fix possible security issues, d) get the parsers in Commons Imaging ready for the 1.0 release.

Assigning the label for GSoC 2023, and full time. Although it would be possible to work on a smaller set of tasks for 1.0 as a part time too.

Github issue: https://github.com/apache/cloudstack/issues/7142

Description:

With regards to IP info reporting, Cloudstack relies entirely on it's DHCP data bases and so on. When this is not available (L2 networks etc) no IP information is shown for a given VM.

I propose we introduce a mechanism for "IP autodetection" and try to discover the IPs used inside the machines by means of querying the hypervisors. For example with KVM/libvirt we can simply do something like this:

 
{{root@fedora35 ~]# virsh domifaddr win2k22 --source agent
Name MAC address Protocol Address
-------------------------------------------------------------------------------
Ethernet 52:54:00:7b:23:6a ipv4 192.168.0.68/24
Loopback Pseudo-Interface 1 ipv6 ::1/128

• - ipv4 127.0.0.1/8}}
  The above command queries the qemu-guest-agent inside the Windows VM. The VM needs to have the qemu-guest-agent installed and running as well as the virtio serial drivers (easily done in this case with virtio-win-guest-tools.exe ) as well as a guest-agent socket channel defined in libvirt.

Once we have this information we could display it in the UI/API as "Autodetected VM IPs" or something like that.

I imagine it's very similar for VMWare and XCP-ng.

Thank you

Github issue: https://github.com/apache/cloudstack/issues/7127

Description:

The Import-Export functionality is only allowed for the Vmware hypervisor. The functionality is developed within a VM ingestion framework that allows the extension to other hypervisors. The Import-Export functionality consists on few APIs and the UI to interact with them:

• listUnmanagedInstances: Lists unmanaged virtual machines (not existing in CloudStack but existing on the hypervisor side)
• importUnmanagedInstance: Import an unmanaged VM into CloudStack (this implies populating the database with the corresponding data)
• unmanageVirtualMachine: Make CloudStack forget a VM but do not remove it on the hypervisor side

The complexity on KVM should be parsing the existing XML domains into different resources and map them in CloudStack to populate the database correctly.

Github issue: https://github.com/apache/cloudstack/issues/2872

ConfigDrive / cloud-init supports a network_data.json file which can contain network information for a VM.

By providing the network information using ConfigDrive to a VM we can eliminate the need for DHCP and thus the Virtual Router in some use-cases.

An example JSON file:

            {
            "links": [
            {
            "ethernet_mac_address": "52:54:00:0d:bf:93",
            "id": "eth0",
            "mtu": 1500,
            "type": "phy"
            }
            ],
            "networks": [
            {
            "id": "eth0",
            "ip_address": "192.168.200.200",
            "link": "eth0",
            "netmask": "255.255.255.0",
            "network_id": "dacd568d-5be6-4786-91fe-750c374b78b4",
            "routes": [
            {
            "gateway": "192.168.200.1",
            "netmask": "0.0.0.0",
            "network": "0.0.0.0"
            }
            ],
            "type": "ipv4"
            },
            {
            "id": "eth0",
            "ip_address": "2001:db8:100::1337",
            "link": "eth0",
            "netmask": "64",
            "network_id": "dacd568d-5be6-4786-91fe-750c374b78b4",
            "routes": [
            {
            "gateway": "2001:db8:100::1",
            "netmask": "0",
            "network": "::"
            }
            ],
            "type": "ipv6"
            }
            ],
            "services": [
            {
            "address": "8.8.8.8",
            "type": "dns"
            }
            ]
            }

In Basic Networking and Advanced Networking zones which are using a shared network you wouldn't require a VR anymore.

Github issue: https://github.com/apache/cloudstack/issues/6949

Github issue: https://github.com/apache/cloudstack/issues/6934

Please add a button to test the ldaps connection or a list button to list some user button.

Github issue: https://github.com/apache/cloudstack/issues/4482

NFS Primary Storage mounts are handled by libvirt.

Currently libvirt defaults to NFS version 3 when mounting while it does support NFS version 4 if provided in the XML definition: https://libvirt.org/formatstorage.html#StoragePoolSource

            <source>
            <host name='localhost'/>
            <dir path='/var/lib/libvirt/images'/>
            <format type='nfs'/>
            <protocol ver='4'/>
            </source>
            

 
Maybe pass the argument 'nfsvers' to the URL provided to the Management Server and then pass this down to the Hypervisors which generate the XML for libvirt.

Github issue: https://github.com/apache/cloudstack/issues/6637

Weave project are looking for maintainers, it may be worth exploring what CNI is widely used and standard/stable for CKS use-case.

Github issue: https://github.com/apache/cloudstack/issues/3141

New Global Option For Letsencrypt enable on console proxy. Letsencrypt domain name option for letsencrypt ssl auto renew

Github issue: https://github.com/apache/cloudstack/issues/3065

Extend the Direct Download functionality to work with Ceph storage

The load of streaming jobs usually fluctuate according to the input rate or operations (e.g., window). Supporting the automatic scaling could reduce the operational cost of running streaming applications, while minimizing the performance degradation that can be caused by the bursty loads. 

We can harness the cloud resources such as VMs and serverless frameworks to acquire computing resources on demand. To realize the automatic scaling, the following features should be implemented.

1) state migration: scaling jobs require moving tasks (or partitioning a task to multiple ones). In this situation, the internal state of the task should be serialized/deserialized. 

2) input/output rerouting: if a task is moved to a new worker, the input and output of the task should be redirected. 

3) dynamic Executor or Task creation/deletion: Executor}}s or {{Task can be dynamically created or deleted. 

4) scaling policy: a scaling policy that decides when and how to scale out/in should be implemented. 

This is an umbrella issue to keep track of the issues related to the dynamic task sizing feature on Nemo.

Dynamic task sizing needs to consider a workload and try to decide on the optimal task size based on the runtime metrics and characteristics. It should have an effect on the parallelism and the partitions, on how many partitions an intermediate data should be divided/shuffled into, and to effectively handle skews in the meanwhile.

We aim to handle the problem on throttled resources (heterogeneous resources) and skewed input data. In order to solve this problem, we suggest dynamic work stealing that can dynamically track task statuses and steal workloads among each other. To do this, we have the following action items:

• Dynamically collecting task statistics during execution
• Detecting skewed tasks periodically
• Splitting the data allocated in skewed tasks and reallocating them into new tasks
• Synchronizing the optimization procedure
• Evaluation of the resulting implementations

Missing a deadline often has significant consequences for the business. And simulator can contributes to other approach for optimization 

So Implement a Simulator for Stream Processing Based on Functional models. 

There are some requirements:

• Simulation should be able to execute before or during job execution
• When a simulation is executed during job is running, It must be fast enough not to affect the job. 
• Information about running environment is received through argument.
• At least network topology should be considered for the WAN environment. 

The current SimulatedTaskExecutor is hardly available. because it needs actual metric to predict execution time. To increase utilization, we need new model that predicts a task level execution time with statistical analysis. 

Some of the related TODOs are as follows:

• Find factors that affect a task level execution time. with loose grid search.
• Infer the most suitable model with tight grid search. 

Currently, Nemo doesn't have a spill mechanism. This makes executors prone to memory problems such as OOM(Out Of Memory) or GC when task data is large. For example, handling skewed shuffle data in Nemo results in OOM and executor failure, as all data has to be handled in-memory.

We need to spill in-memory data to secondary storage when there are not enough memory in executor.

There are some factors that can affect the stage group level simulation, such as a latency, the rate of skewed data and the error rate of the executor etc. It is required to find a reasonable distribution form for these factors. Such as the normal distribution or the landau distribution. In actual running, It makes it possible to approximate the model with a small amount of data.

In-memory caching and spilling are essential features in in-memory big data processing frameworks, and Nemo needs one.

• Identify and persist frequently used data and unpersist it when its usage ended
• Spill in-memory data to disk upon memory pressure

If the the compile time identifies what data can be cached, the runtime requires logic to make this happen.

Implementation needs:

• (Driver) receive and update the status of blocks from various Executors, right now this seems to be best implemented as part of BlockManagerMaster
• (Driver) communicate to the  Executors the availability, location and status of blocks
• Possible concurrency issues:

1. Concurrency in Driver when multiple Executors update/inquire the same block information
2. Concurrency in Executor when a single cached block is accessed simultaneously.

In Stream processing, we have many methods, starting from the primitive checkpoint-and-replay to a more fancy version of reconfiguration and reinitiation of stream workloads. We aim to find a way to find the most effective and efficient way of reconfiguring stream workloads. Sub-issues are to be created later on.

Issues for making it easy to install and use Nemo on Google Dataproc.

Background

Dubbo currently supports the rest protocol based on http1, and the triple protocol based on http2, but currently the two protocols based on the http protocol are implemented independently, and at the same time, they cannot replace the underlying implementation, and their respective implementation costs are relatively high.

Target

In order to reduce maintenance costs, we hope to be able to abstract http. The underlying implementation of the target implementation of http has nothing to do with the protocol, and we hope that different protocols can reuse related implementations.

As a development framework that is closely related to users, Dubbo may have a huge impact on users if any problems occur during the iteration process. Therefore, Dubbo needs a complete set of automated regression testing tools.
At present, Dubbo already has a set of testing tools based on docker-compose, but this set of tools cannot test the compatibility in the kubernetes environment. At the same time, we also need a more reliable test case construction system to ensure that the test cases are sufficiently complete.

As a development framework closely related to users, Dubbo provides many functional features (such as configuring timeouts, retries, etc.). We hope that a tool can be given to users to scan which features are used, which features are deprecated, which ones will be deprecated in the future, and so on. Based on this tool, we can provide users with a better migration solution.
Suggestion: You can consider based on static code scanning or javaagent implementation.

Dubbo supports the communication mode based on the gRPC protocol through Triple. For this reason, Dubbo has developed a compiling plug-in for proto files based on jprotoc. Due to the activeness of jprotoc, currently Dubbo compiler cannot run well on the latest protobuf version. Therefore, we need to consider implementing a new compiler with reference to gRPC.

Dubbo is a development framework that is closely related to users, and many usages by users may cause exceptions handled by Dubbo. Usually, in this case, users can only judge through logs. We hope to provide an i18n localized log output tool to provide users with a more friendly log troubleshooting experience.

As more and more projects start to develop based on Gradle and profit from Gradle, Dubbo also hopes to migrate to the Gradle project. This task requires you to transform the dubbo project[1] into a gradle project.

 [1] https://github.com/apache/dubbo

Dubbo Admin is a console of Dubbo. Today, Dubbo's observability is becoming more and more powerful. We need to directly observe some indicators of Dubbo on Dubbo Admin, and even put forward suggestions for users to improve problems.

Background

At present, the abstraction of connection by client in different protocols in Dubbo is not perfect. For example, there is a big discrepancy between the client abstraction of connection in dubbo and triple protocols. As a result, the enhancement of connection-related functions in the client is more complicated, and the implementation cannot be reused. At the same time, the client also needs to implement a lot of repetitive code when extending the protocol.

Target

Reduce the complexity of the client part when extending the protocol, and increase the reuse of connection-related modules.

Background

Dubbo currently supports protobuf as a serialization method. Protobuf relies on proto (Idl) for code generation, but currently lacks tools for managing Idl files. For example, for java users, proto files are used for each compilation. It is more troublesome, and everyone is used to using jar packages for dependencies.

Target

Implement an Idl management and control platform, support idl files to automatically generate dependency packages in various languages, and push them to relevant dependency warehouses

For a large number of monolithic applications, problems such as performance will be encountered during large-scale deployment. For interface-oriented programming languages, Dubbo provides the capability of RPC remote calls, and we can help applications decouple through interfaces. Therefore, we can provide a deployer to help users realize the decoupling and splitting of microservices during deployment, and quickly provide performance optimization capabilities.

Since Dubbo runs on a distributed architecture, it naturally has the problem of difficult API interface definition management. It is often difficult for us to know which interface is running in the production environment. So we can provide an API-defined reporting platform, and even a management platform. This platform can automatically collect all APIs of the cluster, or can be directly defined by the user, and then unified distribution management is carried out through a mechanism similar to git and maven package management.

Dubbo currently supports a large number of Java language features through hessian under the Java SDK, such as generics, interfaces, etc. These capabilities will not be compatible when calling across systems. Therefore, Dubbo needs to provide the ability to detect the interface definition and determine whether the interface published by the user can be described by native json.

Dubbo currently provides a very simple performance testing tool. But for such a complex framework as Dubbo, the functional coverage is very low. We urgently need a testing tool that can test multiple complex scenarios. In addition, we also hope that this set of testing tools can be run automatically, so that we can track the current performance of Dubbo in time.

WebAssembly (abbreviated Wasm) is a binary instruction format for a stack-based virtual machine. Many capabilities of Dubbo support extensions, such as custom interceptors, routing, load balancing, etc. In order to allow the user's implementation to be used on Dubbo's multiple language SDKs, we can implement cross-platform operation based on wasm capabilities.

The implementation of this topic needs to provide a set of mechanisms for Wasm on Dubbo, covering the implementation of Java and Go. Also supports at least Filter, Router and Loadbalance.

WebAssembly (abbreviated Wasm) is a binary instruction format for a stack-based virtual machine. For web client users, we can provide Dubbo's wasm client, so that front-end developers can simply initiate Dubbo requests in the browser, and realize Dubbo's full-link unification.

This task needs to be implemented on a browser such as Chrome to initiate a request to the Dubbo backend.

At present, Dubbo provides RPC capabilities and a large number of service governance capabilities. This has led to the fact that Dubbo cannot be used well if some of Dubbo's own components only need to use RPC capabilities or some users who need extreme lightweight.
Goal: To provide a Dubbo RPC kernel, users can directly program for service calls and focus on RPC.

HTTP/3 has been formalized as a standard in the last year. Dubbo, as a framework that supports publishing and invoking Web services, needs to support the HTTP/3 protocol.

This task needs to expand the implementation of the current rest protocol to support publishing HTTP/3 services and calling HTTP/3 services.

Our Commons components use Commons Skin, a skin, or theme, for Apache Maven Site.

Our skin uses Bootstrap 2.x, but Bootstrap is already at 5.x release, and we are missing several improvements (UIUX, accessibility, browser compatibility) and JS/CSS bugs fixed over the years.

Work happening on Apache Maven Skins. Maybe we could adapt/use that one?

https://issues.apache.org/jira/browse/MSKINS-97

The Airavata Django Portal [1] allows users to create, execute and monitor computational experiments. However, when a user wants to then post-process or visualize the output of that computational experiment they must then download the output files and run tools that they may have on their computer or other systems. By integrating with JupyterHub the Django Portal can give users an environment in which they can explore the experiment's output data and gain insights.

The main requirements are:

• from the Django Portal a user can click a button and navigate to a JupyterHub instance that the user is immediately logged into using single sign on
• the user can save the Jupyter notebook and later retrieve it
• the user's files are available within the context of the running Jupyter instance
  • ideally users can also generate new outputs in the Jupyter instance and have them saved back in their portal data storage
• users can share their notebooks with other portal users
• (bonus) portal admins can suggest notebooks to use with specific applications so that with one click a user can open an experiment in a provided notebook
• users can manage their notebooks and can, for example, clone a notebook

[1] https://github.com/apache/airavata-django-portal

Integrate Apache Superset (https://superset.apache.org/) to visualize Airavata Catalogs (https://github.com/apache/airavata/tree/master/modules/registry) 

• Examples like this and stack overflow threads seem to indicate it is possible - https://medium.com/@s.akashb/apache-superset-integration-with-keycloak-a302840c290c 

• Integrate with Custos 
• We can start out by directly interfacing with MariaDB, but need to explore if we can write a superset DB Driver following the Hive example - https://github.com/apache/superset/blob/0409b12a55e893d88f6e992a7df247841a2da8f0/superset/db_engines/hive.py

1. UI Framework 
   1. To host the jupyter environment we will need to envolop the notebooks in a user interface and connect it with Apache Airavata services 
   2. Leverage Airavata communications from within the Django Portal - https://github.com/apache/airavata-django-portal 
   3. Explore if the platform is better to be developed as VSCode extensions leveraging jupyter extensions like - https://github.com/Microsoft/vscode-jupyter
   4. Alternatively, explore developing a standalone native application using ElectronJS
2. Draft up a platform architecture - Airavata based infrastructure with functionality similar to collab. 
3. Authenticate with Airavata Custos Framework - https://github.com/apache/airavata-custos 
4. Extend Notebook filesystem using the virtual file system approaching integration with Airavata based storage and catalog
5. Make the notebooks registered with Airavata app catalog and experiment catalog. 

Advanced Possibilities:

Explore Multi-tenanted JupyterHub 

• Can K8 namespace isolation accomplish?
• Make deployment of Jupyter support as part of the default core
• Data and the user-level tenancy can be assumed, how to make sure infrastructure can isolate them, like not one gateway crashing a hosting environment.

1. How to leverage computational resources jupypter hub

Gateway admins need period reports for various reporting and planning. 

Features Include:

• Compute resources across that had at least one job submitted during the period <start date - End date>
• User groups created within a given period and how many users are in those and with permission levels and also number of jobs each user have submitted.
• List applications and number of jobs for each applications for a given period and group them by job status.
• Number of users that at least submitted a single job for the period <start date - End date>
• Total number of Unique Users
• User Registration Trends
• Number of experiments for a given period <Start date - End date> grouped by the experiment status
• The total cpu-hours used by a users, sorted, quarterly, plotted over a period of time
• The total cpu-hours consumed by application, sorted, quarterly, plotted over a period of time

As discussed on the architecture mailing list [1] and summarized at [2], Airavata will need to develop a metascheduler. In the short term, a user request (demeler, gobert) is to have airavata throttle jobs to resources. In the future more informed scheduling strategies needs to be integrated. Hopefully, the actual scheduling algorithms can be borrowed from third party implementations.

[1] - http://markmail.org/message/tdae5y3togyq4duv
[2] - https://cwiki.apache.org/confluence/display/AIRAVATA/Airavata+Metascheduler

Complete all transports in MFT

• Currently SCP, S3 is known to work
• Others need effort to optimize, test, and declare readiness
• Develop a complete a fully functional MFT Command-line interface
• Have a feature-complete Python SDK
• A minimum implementation will be prvoided, students need to complete it and test it. 

Custos does not have the capabilities to efficiently backup and restore a live instance. This is essential for high available services. 

Using SEAGrid Rich Client as an example, develop a native application based on electronJS to mimic Airavata Django Portal.

Reference example - https://github.com/SciGaP/seagrid-rich-client