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 ShenYu (incubating)

A High-performance,multi-protocol,extensible,responsive API Gateway. Compatible with a variety of mainstream framework systems, support hot plug, users can customize the development, meet the current situation and future needs of users in a variety of scenarios, experienced the temper of large-scale scenes

• Website: https://shenyu.apache.org
• GitHub: https://github.com/apache/incubator-shenyu
• Linked GitHub Issue: https://github.com/apache/incubator-shenyu/issues/2896

Description

1. Apache ShenYu uses java agent and bytecode enhancement technology to achieve seamless embedding, so that users can access third-party observability systems without introducing dependencies, and obtain Traces, Metrics and Logging.
2. Take the shenyu gateway log information, write it to elasticSearch and display it.
3. Can add module like this ：

               shenyu-agent
               ------ shenyu-agent-plugin-logging
               ----------------shenyu-agent-plugin-logging-elasticsearch

Task

• Add shenyu-agent-plugin-logging-elasticsearch module and impl write it to elasticSearch
• Complete unit test for this module
• Complete the integration for this module
• Complete doc for this module in shenyu website

Recommended Skills

•  Familiar with Java
•  Know the usage of java agent and bytebuddy
•  Know the usage of elasticSearch java client
•  Have some knowledge about  Docker

Mentor

XiaoYu, PPMC of Apache ShenYu, https://github.com/yu199195, [xiaoyu@apache.org](xiaoyu@apache.org)

Apache ShenYu (incubating)

A High-performance,multi-protocol,extensible,responsive API Gateway. Compatible with a variety of mainstream framework systems, support hot plug, users can customize the development, meet the current situation and future needs of users in a variety of scenarios, experienced the temper of large-scale scenes

Website: https://shenyu.apache.org

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

Linked GitHub Issue: https://github.com/apache/incubator-shenyu/issues/2890

Background

1. ShenYu is still vacant with helm deployment, so we need to write charts for it, and then complete the integration test.
2. Shenyu already has a relatively complete integration testing framework, but some plug-ins have not been tested, and some tests are not perfect.

Task

• Write helm chart for Apache ShenYu
• Complete the integration test of deploying Apache ShenYu with helm in Kubernetes
• Documentation for helm deployment
• Complete the integration test of the Oauth2 plugin
• Improve the integration test of other existing plugin

Recommended Skills

Familiar with Java

Know the usage of spring-framework

Have some knowledge about Kubernetes and Docker

Mentor

Kunshuai Zhu, Committer of Apache ShenYu, https://github.com/JooKS-me, jooks@apache.org

Apache ShenYu (incubating)

A High-performance,multi-protocol,extensible,responsive API Gateway. Compatible with a variety of mainstream framework systems, support hot plug, users can customize the development, meet the current situation and future needs of users in a variety of scenarios, experienced the temper of large-scale scenes

• Website: https://shenyu.apache.org
• GitHub: https://github.com/apache/incubator-shenyu
• Linked GitHub Issue: https://github.com/apache/incubator-shenyu/issues/2917

Description

1. Apache ShenYu uses java agent and bytecode enhancement technology to achieve seamless embedding, so that users can access third-party observability systems without introducing dependencies, and obtain Traces, Metrics and Logging.
2. Take the shenyu gateway log information, write it to Kafka and display it.
3. Can add module like this ：

               shenyu-agent
               ------ shenyu-agent-plugin-logging
               ----------------shenyu-agent-plugin-logging-kafka

Task

• Add shenyu-agent-plugin-logging-kafka module and impl write it to Kafka
• Complete unit test for this module
• Complete the integration for this module
• Complete doc for this module in shenyu website

Recommended Skills

•  Familiar with Java
•  Know the usage of java agent and bytebuddy
•  Know the usage of Kafka java client
•  Have some knowledge about  Docker

Mentor

Zhang Yonglun, PPMC of Apache ShenYu, https://github.com/tuohai666, [zhangyonglun@apache.org](zhangyonglun@apache.org)

Apache APISIX is a dynamic, real-time, high-performance API gateway.

It provides rich traffic management features such as load balancing, dynamic upstream, canary release, circuit breaking, authentication, observability, and more.

Page: https://apisix.apache.org/

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

Project title:

Datacenter and local file configuration conversion, export and import are supported via Apache APISIX CLI.

Background: 

Apache APISIX supports running in standalone mode. At this point, Apache APISIX will rely on the local configuration file `conf/apisix.yaml` for routing and policy settings.

Apache  APISIX CLI supports the conversion, import and export of data center and local file configuration data, making Apache APISIX easier to switch and apply between different environments and scenarios.

Task: 

Add two commands `bin/apisix conf_export` and `bin/apisix conf_import` to Apache APISIX CLI, and complete the conversion, import and export of remote data center and local file configuration data through the above commands.

Difficulty: Normal
Project size: ~350 hours.

References:

https://github.com/apache/apisix/blob/master/docs/en/latest/stand-alone.md

Background:

Some plugins require storing data. For example, limit-count needs to keep track of originators of requests to limit how many requests the same client can send.

The plugin provides several data stores: local, Redis single node, and Redis cluster.

Now, other plugins that need to store data would also need to provide such configuration. Moreover, what if users want to store the data in MongoDB, Hazelcast, or in a plain SQL database?

Tasks:

• Introduce a Storage abstraction, on the same level as Upstream
• Create Storage concretions for local, Redis single node, and Redis cluster
• Migrate the limit-count plugin to use this abstraction
• If time allows, create a new plugin that uses this abstraction
• It time allows, create a new Storage implementation

Who is a Potential Mentor: Bozhong Yu, email: imbozhong@gmail.com and  https://github.com/zaunist,

Difficulty: Normal
Project size: ~350 hours.

Project title:

Multiple programming languages client SDK support with OpenAPI generator.

Apache APISIX is a dynamic, real-time, high-performance API gateway.

It provides rich traffic management features such as load balancing, dynamic upstream, canary release, circuit breaking, authentication, observability, and more.

Page: https://apisix.apache.org/

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

Background:

OpenAPI Generator allows the generation of API client libraries (SDK generation), server stubs, documentation, and configuration automatically given an OpenAPI Spec.

We can use it to provide Apache APISIX Admin and Control API SDKs in multiple programming languages. In the future, we may potentially integrate Java SDK into Spring framework and the starter of Spring boot or even make integration with ASP .Net

Task:

Generate a multilingual SDK through the definition files of the OpenAPI specification and use the OpenAPI Generator tool to generate client SDKs for Admin and Control APIs.

Difficulty: Normal
Project size: ~350 hours.

References:

• https://swagger.io/tools/swagger-codegen/
• https://github.com/OpenAPITools/openapi-generator

Apache APISIX is a dynamic, real-time, high-performance API gateway.

It provides rich traffic management features such as load balancing, dynamic upstream, canary release, circuit breaking, authentication, observability, and more.

Page: https://apisix.apache.org/

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

Background: Elasticsearch is a widespread search engine based on Apache Lucene. It allows users to index, store, and search for data via a REST API. Data going through APISIX are good candidates to be transferred to Elasticsearch for later analysis.

Task: The intern should evaluate different possible designs, analyze their pros and cons, and implement at least one in agreement with the mentor.

In particular, the intern should investigate ES requirements for writing data (amount of data, frequency, etc.) prior to any development.

 
Difficulty: Normal
Project size: ~175 hours.
 
References:

• Elasticsearch website
• Elasticsearch API

Potential Mentor: ZhengSong Tu, https://github.com/tzssangglass

Background:

At the moment, the Java runner plugin requires you to use an existing template project and change it according to one’s needs.

Task:

Improve developer experience on the existing Java plugin runner so that we can attract and increase the number of users from the Java community.

Limitations:

• The architecture doesn’t manage multiple plugins. All need to be set in the same project
• The standard Java unit of deployment is the JAR.
• The plugin doesn’t allow for other widespread JVM-based languages (e.g., Scala, Kotlin, Clojure, Groovy). Though it would be technically feasible, we would need to change the template’s language

Requirements:

The new plugin runner:

• MUST use the JAR as the unit of deployment
• MUST not require the usage of a project template
• MAY require the plugin to follow a certain class hierarchy (i.e., extends JavaPlugin)
• MAY use a more specific format to enforce a structure
• MUST allow multiple plugins to be deployed

• MUST use isolated classloader for each plugin
• MUST allow any JVM-compatible bytecode to run, whatever the language it was generated from
• MAY allow hot reloading of Java plugins
• MAY require a single JAR per plugin (to ease the classpath management of shared libraries)
• MUST define a minimum JVM version

Difficulty: Normal
Project size: ~350 hours.

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 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

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 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 . * CALL

• CHECKPOINT

• CLOSE

• CLUSTER

• COMMENT

• COPY

• CREATE ACCESS METHOD

• CREATE AGGREGATE

• CREATE CAST

• CREATE COLLATION

• CREATE EVENT TRIGGER

• CREATE FOREIGN DATA WRAPPER

• CREATE FOREIGN TABLE

• CREATE GROUP

• CREATE MATERIALIZED VIEW

• CREATE OPERATOR

• CREATE POLICY

• CREATE PUBLICATION

 
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 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

Zhengqiang Duan, Committer of Apache ShardingSphere, duanzhengqiang@apache.org
Haoran Meng, PMC of Apache ShardingSphere, menghaoran@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

Since version 5.0.0, ShrdingSphere provides its own management language: DistSQL, which greatly facilitates users to manage distributed databases.
There are now many users who want to convert from legacy YAML configuration to DistSQL, and we want to design a tool to help them. (For ShardingSphere-Proxy only)
 
More details:
https://shardingsphere.apache.org/document/current/en/concepts/distsql/

Task

Design and implement a command line tool that allows the user to enter a path to a YAML configuration file and output a DistSQL script file.
This means that when a user uses the generated DistSQL script, it is possible to create a configuration result equivalent to a YAML file.

 
We have provided a DistSQL for exporting schema configuration, which is related to this issue, to help you understand this issue.

• The tool should convert both datasources and rule configuration in YAML to corresponding DistSQL RDL

• The tool needs to run independently, but it can depend on the jar package of ShardingSphere.
• When the tool starts, it is best to prompt the currently applicable ShardingSphere version.
• It is best to use the Java language, so that the jar package provided by ShardingSphere can be reused

 
Notice:

• There is currently no suitable module in the ShardingSphere repository for standalone tools, so a new module needs to be added.

Relevant Skills

 
1. Master JAVA language
2. Understand the schema configurations of ShardingSphere-Proxy
3. Understand DistSQL RDL 

Mentor

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

BanyanDB, as an observability database, aims to ingest, analyze and store Metrics, Tracing, and Logging data. It's designed to handle observability data generated by Apache SkyWalking. 

We need a web-based application to 

• Query the data from the banyandb's data nodes
• Monitor the performance of the backend
• Render the topology of server nodes

Currently Apache SkyWalking can collect logs from various sources like user agents and Envoy access logs, it also provides a log analysis language to analyze the logs and produce some metrics, with those metrics, users can configure rules to trigger alerts and react to those abnormal/exceptional logs.

But in reality, production environment exceptional logs are not known in advance and users can't enumerate all possible exceptional logs.

This task aims to add an algorithm that can identify outlier log(s) from the massive logs, and draw the users attention to see whether there is error in the system.

The algorithm should be able to learn from bot the history logs and streaming logs, and adjust itself to increase the accuracy.

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.

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.

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

Ideas:

• Update the support for complex numbers in the complex package to allow operations to be performed on lists of complex numbers. This requires abstracting the representation of multiple complex numbers into a list structure storing real and imaginary parts that can be efficiently iterated to apply all the operations supported by the Complex class. Operations should modify the numbers in place allowing efficient, zero allocation complex number math to be performed on large datasets.

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.

Other suggestions welcome, as well as

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

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

Ideas:

• Examine and potentially redesign the API and algorithms in the commons-geometry-enclosing module. The goal here is to make consistent use of the newer geometry API and ensure that the algorithms are sound.
• Examine and potentially redesign the API and algorithms in the commons-geometry-hull module. The goal here is to make consistent use of the newer geometry API and ensure that the algorithms are sound (see GEOMETRY-144).
• Design and implement a parser/writer for the PLY file format in the commons-geometry-io-euclidean module.
• Design an API for advanced 3D mesh data structures (e.g. halfedge meshes) and operations (e.g. surface subdivision, smoothing, etc). This may end up being another module, e.g. commons-geometry-mesh.
• Create a series of user guides and/or tutorials demonstrating best-practice use of the library.
• other ideas ... ?

As of now the CloudStack Terraform Provider does not support managing CKS clusters

This proposal aims to add support to the CloudStack Terraform Provider to manage CKS clusters

This would involve supporting the following actions on CKS clusters :

• Create
• Stop / Start
• Scale
• Upgrade
• Delete

[Optional]
Support the following actions on the binary ISOs :

• Register
• Enable / Disable
• Delete

Duration

• 175 hours

Potential Mentors

• Harikrishna Patnala
• David Jumani

References

• https://github.com/apache/cloudstack/issues/6040

As of now, when an admin encounters an issue or error in CloudStack, the maximum information they can immediately get is the API failure response which provides a reason for the failure. At times this might not be sufficinet to diagnose the error and would require the admin to investiage the CloudStack logs. This would require the admin or the sysadmin to log into the VM running CloudStack and either view or export the logs, and then dive into identifying the issue. This idea aims to eiliminate that step.

The goal of this is to provide admins the ability to view the logs directly in the UI. This would make diagnosing failures and RCAs much quicker.

Provide the ability display the logs in the UI

Add an API / WebSocket (and UI) support to :

• View the logs
• Live follow the logs (similar to 'tail -f')

Duration

• 175 hours

Potential Mentors

• David Jumani

References

• https://github.com/apache/cloudstack/issues/6011

Shutting down / Restarting Cloudstack is a necessary step in upgrades, system maintenance, etc. As of now, there is no way to safely shutdown or restart CloudStack. It is directly terminated via systemd. Since this is the case, any asyncronous job or background task is abrubptly terminated and can fail. As of now, CloudStack maintains a list of asynchronous jobs wihtin it's database along with their status.

This idea aims to provide a way to safely shutdown CloudStack. It involves two parts :

• Prevent new asynchronous jobs from being added to CloudStack when a safe shutdown is triggered
• Check the status of the async jobs and Shut down CloudStack when all the jobs have been completed

Provide the ability to safely shutdown CloudStack

Add API (and/or UI) support to :

• Trigger a safe shutdown
• (Optional) Support restarts
• (Optional) Support a forced shutdown when CloudStack will quit even if there are async jobs running

Duration

• Some Experience : 175 hours
• Newbie : 350 hours

Potential Mentors

• David Jumani

References

• https://github.com/apache/cloudstack/issues/6021

Background

Over recent years, Edge computing has been gaining popularity as it defines a model that brings compute and storage closer to
where they are consumed by the end-user. By being closer to the end-user a better experience can be provided with reduction on overall latency, lower bandwidth requirements, lower TCO, more flexible hardware/software model, while also ensuring security and reliability. To align ACS with this evolving cloud computing model we would like to propose an idea of supporting Edge Zones in CloudStack, which
can be also looked upon as a lightweight zone, with minimal resources.

Requirement

Today, when a Zone is setup in CloudStack, it by default comes up with a secondary storage VM(SSVM) and a console proxy VM(CPVM). As part of this project, we would need to define a new zone type to decide the change in workflow required to ensure that a CPVM & SSVM isn't spawned up by default. Basic characteristics of an Edge zone include:

• no need for Secondary Storage
• no Secondary Storage VM
• no Console Proxy VM
• Local storage only as typically an edge device comprises of a single compute node (host)
• And supports L2 and Isolated networks.

A high-level view of an edge zone would look something like:

Relevant Skills:

Java
MySQL
Vue.js (Basic)

Difficulty:

Medium

Project Duration:

175 hours

Potential Mentors:

Alex Mattioli
Nicolas Vazquez
Pearl Dsilva

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.

We should provide users a way to understand how their query will be executed and some information on the amount of work that will be performed.
Explain statements are the most common way to do that.
A CEP Draft has been open for that: (DRAFT) CEP-4: Explain. This draft propose to add support for EXPLAIN and EXPLAIN ANALYZE but I believe that we should split the work in 2 parts because a simple EXPLAIN would already provide relevant information.

To complete this work I believe that the following steps will be required:

• Rework and submit the CEP
• Add missing statistics
• Implements the logic behind the EXPLAIN statements

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.

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.

Nemo has a policy layer that allows powerful optimization with configurable runtime modules. In terms of caching, it is possible to identify frequently used data and decide to cache them in-memory ahead of execution, without user annotation.

Implementation would need:

• On policy layer, build compile-time pass that identify reused data and mark them as cached
• On runtime, design and implement caching mechanism that manages per-executor cached data and discard them when these are no longer used.

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.

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