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 ?

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://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.

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

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^{Image Added}

Trista Pan, PMC of Apache ShardingSphere, panjuan@apache.org^{Image Added}

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:

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.

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

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@apachezhonghongsheng@apache.org

Zhengqiang DuanXinze Guo, PMC Committer of Apache ShardingSphere, duanzhengqiang@apacheazexin@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 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^{Image Removed}

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^{Image Added}

Chuxin Chen, Committer of Apache ShardingSphere, tuichenchuxin@apacheTrista Pan, PMC of Apache ShardingSphere, panjuan@apache.org^{Image Modified}

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^{Image Removed}

Chuxin Chen, Committer of Apache ShardingSphere, tuichenchuxin@apache.org^{Image Removed}

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^{Image Removed}

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

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^{Image Added}

Trista Pan, PMC of Apache ShardingSphere, panjuan@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 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 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 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^{Image Removed}

Yangkun Ai, PMC of Apache RocketMQ, aaronai@apache.org^{Image Removed}

 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^{Image Removed}

[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^{Image Removed}

• Mailing List: dev@rocketmq.apache.org^{Image Removed}

• 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
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^{Image Removed}
 
Mentor
Zhouxiang Zhan, committer of Apache RocketMQ, zhouxzhan@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

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/

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

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

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 ^{Image Removed}
• Mentor: Haopeng Li, Apache Doris PMC member & Committer, lihaopeng@apache.org^{Image Removed}  
• 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

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 ^{Image Removed}
• Mentor: Zhijing Lu, Apache Doris Committer, luzhijing@apache.org^{Image Removed}  
• Mailing List: dev@doris.apache.org

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.