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.

Other suggestions welcome, as well as

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

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 ?

Apache ShardingSphere
Apache ShardingSphere is a distributed database middleware ecosystem, including 2 independent products, ShardingSphere JDBC and ShardingSphere Proxy presently. They all provide functions of data sharding, distributed transaction, and database orchestration.
Page: https://shardingsphere.apache.org
Github: https://github.com/apache/shardingsphere

 Background

ShardingSphere parser engine helps users parse a SQL to get the AST (Abstract Syntax Tree) and visit this tree to get SQLStatement (Java Object). At present, this parser engine can handle SQLs for `MySQL`, `PostgreSQL`, `SQLServer`, `openGauss` and `Oracle`, which means we have to understand different database dialect SQLs.
More details:
https://shardingsphere.apache.org/document/current/en/reference/sharding/parse/

Task

This issue is to solve the unsupported postgres sql about alter in this file . * ALTER OPERATOR

• ALTER POLICY

• ALTER PUBLICATION

• ALTER ROUTINE

• ALTER RULE

• ALTER SCHEMA

• ALTER SEQUENCE

• ALTER SERVER

• ALTER STATISTICS

• ALTER SUBSCRIPTION

• ALTER TABLE

• ALTER TEXT SEARCH

• ALTER TRIGGER

• ALTER TYPE

• ALTER VIEW

You can learn more here. *
You may need to try to get why it's not supported.(antlr4 grammar? or not implement visit method) You can use antlr4 plugins to help you to analyze. You may need to visit an official doc to check the grammar.

• 
  After you fix it, remember to add a new corresponding SQL case in SQL Cases and the expected parsed result in Expected Statment XML.

• Run SQLParserParameterizedTest and UnsupportedSQLParserParameterizedTest to make sure no exceptions.

Notice, these issues can be a good example.
support alter foreign table for pg/og
support alter materialized view for pg/og.

Relevant Skills

1. Master JAVA language
2. Have a basic understanding of Antlr g4 file
3. Be familiar with Postgres SQLs

Targets files

1. Postgres SQLs g4 file: https://github.com/apache/shardingsphere/blob/master/shardingsphere-sql-parser/shardingsphere-sql-parser-dialect/shardingsphere-sql-parser-postgresql/src/main/antlr4/org/apache/shardingsphere/sql/parser/autogen/PostgreSQLStatement.g4

Mentor

Trista Pan, PMC of Apache ShardingSphere, https://tristazero.github.io

Zhengqiang Duan, Committer of ApacheShardingSphere, https://github.com/strongduanmu

Hello Students! We are the Apache CloudStack project. From our project website: "Apache CloudStack is open source software designed to deploy and manage large networks of virtual machines, as a highly available, highly scalable Infrastructure as a Service (IaaS) cloud computing platform. CloudStack is used by a number of service providers to offer public cloud services, and by many companies to provide an on-premises (private) cloud offering, or as part of a hybrid cloud solution."

2-min video on the Apache CloudStack project - https://www.youtube.com/watch?v=oJ4b8HFmFTc 

Here's about an hour-long intro to what is CloudStack - https://www.youtube.com/watch?v=4qFFwyK9hos 

The general skills student would need are - Java, Python, JavaScript/Vue. Idea-specific requirements are mentioned on the idea issue.  We're a diverse and welcoming community and we encourage interested students to join the dev ML: http://cloudstack.apache.org/mailing-lists.html  (dev@cloudstack.apache.org)

All our Apache CloudStack GSoC2022 ideas are tracked on the project's Github issue: 

https://github.com/apache/cloudstack/issues?q=is%3Aissue+is%3Aopen+label%3Agsoc2022

The existing compaction strategies have a number of drawbacks that make all three unsuitable as a general use compaction strategy, for example STCS creates giant files that are hard to back up, mess with read performance and the page cache, and led to many of the early re-open bugs. LCS improved dramatically on this but also has various issues e.g. lack of performant full compaction or due to the strict leveling with e.g. bulk loading when writes exceed the rate we can do the L0 - L1 promotion.

In this talk I proposed a novel compaction strategy that aims to expose a single tunable that the user can control for the read amplification. Raise the min_threshold_levels and you tradeoff read/space performance for write performance. Since then a proof of concept patch has been published along with some rudimentary documentation but this is still not tracked in Jira.

The remaining work here is

1. Validate the algorithm is correct via test suites and performance testing stress testing and benchmarking with OSS tools (e.g. cassandra-stress, tlp-stress, or ndbench). When issues are found (there likely will be issues as the patch is a PoC), devise how to adjust the algorithm and implementation appropriately. Key metric of success is we can run Cassandra stably for more than 24 hours while applying sustained load, with minimal compaction load (and also compaction can keep up).

2. Do more in depth experiments measuring performance across a wide range of workloads (e.g. write heavy, read heavy, balanced, time series, register update, etc ...) and in comparison with LCS (leveled), STCS (size tiered), and TWCS (time window). Key metrics of success are establishing that as we tune max_read_per_read we should get more predictable read latency under low system load (ρ < 30%) while not degrading at high system load (ρ > 70%), and we should match LCS performance under low load while doing better at high load.

Once this is validated a Cassandra blog post reporting on the findings (positive or negative) may be advisable.

It would be desirable to develop a Beam Sink that supports all of the 'best practices' like throttling, auto-sharding, exactly-once capabilities, etc.

A design proposal is here: https://docs.google.com/document/d/1UIWv6wnD86GYAkeqbVWCG3mx4dTZ9WstUUThPWQmcFM/edit#heading=h.smc16ifdre2

A prototype for the API and parts of implementation is here: https://github.com/apache/beam/pull/16763

Contact Pablo Estrada on dev@beam.apache.org if you have questions, or comment here.