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Airavata
Local user interaface for Airavata MFT
NOte: This is an issue in github - https://github.com/apache/airavata-mft/issues/114 cross posting in Jira for GSoC purposes.
Currently, Airavata MFT can be accessed through its command line interface and the gRPC API. However, it is really easy if a Docker desktop-like user interface is provided for a locally running Airavata MFT. The functionalities of such an interface can be summarized as follows
- Start / Stop MFT Instance
- Register/ List/ Remove Storage endpoints
- Access data (list, download, delete, upload) in configured storage endpoints
- Move data between storage endpoints
- Search data across multiple storage endpoints
- Analytics - Performance numbers (data transfer rates in each agent)
We can use ElectonJS to develop this cross-platform user interface. The node.js backend of ElectronJS can use gRPC to connect to Airavata MFT to perform management operations
Apache NuttX
NuttX NAND Flash Subsystem
Currently NuttX has support only for NOR Flash and eMMC as solid state storage.
Although for low-end embedded systems NOR Flash still much used, for some devices that need bigger storage, NAND Flash is a better option, because its price per MB is very low.
In the other NAND Flash brings many challenges: you need to map and track all the bad-blocks, you need to have a good filesystem for wear leveling. Currently the SmartFS and LittleFS offer some kind wear leveling for NOR Flash. It needs to be adapted to NAND Flash.
Rust integration on NuttX
The Rust language is gain some momentum as an alternative to C and C++ for embedded system (https://www.rust-lang.org/what/embedded) and it should be very useful to be able to develop NuttX applications using Rust language.
Sometime Yoshiro Sugino already ported the Rust standard libraries, but it was not a complete port and wasn't integrated on NuttX. Anyway this initial port could be used as starting point for some student willing to add official support on NuttX.
Also it needs to pave the way to support developing NuttX driver in Rust and an complement to C drivers.
Device Tree support for NuttX
Device Tree will simplify the way as boards are configured to support NuttX. Currently for each board the developer/user need to manually create an initialization file for each feature or device (expect when the device is already in the common board folder).
Matias Nitsche (aka v0id) create a very descriptive and information explanation here: https://github.com/apache/incubator-nuttx/issues/1020
The goal for this project is to add Device Tree support for NuttX and let it to be configurable (low end board should be able to avoid using Device Tree for instance).
Micro-ROS integration on NuttX
Micro-ROS (https://micro.ros.org) is a ROS2 support to Microcontrollers. Initially the project was developed over NuttX by Bosch and other EU organizations. Later on they added support to FreeRTOS and Zephyr. After that NuttX support started ageing and we didn't get anyone working to fix it (with few exceptions like Roberto Bucher work to test it with pysimCoder).
Add X11 graphic support on NuttX using NanoX
NanoX/Microwindows is a small graphic library what allow Unix/Linux X11 application to run on embedded systems that cannot support X-Server because it is too big. Add it to NuttX will allow many applications to be ported to NuttX. More importantly: it will allow FLTK 1.3 run on NuttX and that could big Dillo web browser.
TinyGL support on NuttX
TinyGL is a small 3D graphical library created by Fabrice Bellard (same creator of QEMU) designed for embedded system. Currently NuttX RTOS doesn´t have a 3D library and this could enable people to add more 3D programs on NuttX.
SkyWalking
[GSOC] [SkyWalking] Self-Observability of the query subsystem in BanyanDB
Background
SkyWalking BanyanDB is an observability database, aims to ingest, analyze and store Metrics, Tracing and Logging data.
Objectives
- Support EXPLAIN[1] for both measure query and stream query
- Add self-observability including trace and metrics for query subsystem
- Support EXPLAIN in the client SDK & CLI and add query plan visualization in the UI
[1]: EXPLAIN in MySQL
Recommended Skills
- Familiar with Go
- Have a basic understanding of database query engine
- Have an experience of Apache SkyWalking or other APMs
Mentor
- Mentor: Jiajing Lu, Apache SkyWalking PMC, lujiajing@apache.org
- Mentor: Hongtao Gao, Apache SkyWalking PMC, Apache ShardingSphere PMC, hanahmily@apache.org
- Mailing List: dev@skywalking.apache.org
Doris
[GSoC][Doris]Dictionary encoding optimization
Background
Apache Doris is a modern data warehouse for real-time analytics.
It delivers lightning-fast analytics on real-time data at scale.
Objectives
Dictionary encoding optimization
To save storage space, Doris uses dictionary encoding when storing string-type data in the storage layer if the cardinality is relatively low. Dictionary encoding involves mapping string values to integer values using a dictionary. The data can be stored directly as integers, and the dictionary information is stored separately. When reading the data, the integers are converted back to their corresponding string values based on the dictionary.
The storage layer doesn't know whether a column has low or high cardinality when the data comes in. Currently, the implementation encodes the first page using dictionary encoding, and if the dictionary becomes too large, it indicates a column with high cardinality. Subsequent pages will not use dictionary encoding. However, even for columns with high cardinality, a dictionary page is still retained, which doesn't save storage space and adds additional memory overhead during reading as well as extra CPU overhead during decoding.
Optimizations can be made to improve the memory and CPU overhead caused by dictionary encoding.
Recommended Skills
Familiar with C++ programming
Familiar with the storage layer of Doris
Mentor
Mentor: Xin Liao, Apache Doris Committer, liaoxinbit@gmail.com
Mentor: YongQiang Yang, Apache Doris PMC Member, dataroaring@gmail.com
Mailing List: dev@doris.apache.org
Website: https://doris.apache.org
Source Code: https://github.com/apache/doris
[GSoC][Doris]Support UPDATE for Doris Duplicate Key Table
Objectives
Support UPDATE for Doris Duplicate Key Table
Currently, Doris supports three data models, Duplicate Key / Aggregate Key / Unique Key, of which Unique Key has perfect data update support (including UPDATE statement). With the widespread popularity of Doris, users have more demands on Doris. For example, some user needs to perform ETL processing operations inside Doris, but they uses Duplicate Key table and hopes that Duplicate Key can also support UPDATE. For Duplicate Key, since there is no primary key can help we locate one specific row, UPDATE is low efficient. The usual practice is to rewrite all the data, even if the user only updates one field of a row of data, he must rewrite at least the segment file it is in. Another potentially more efficient solution is to implement Duplicate Key by combining Unique Key's Merge-on-Write, and the auto_increment column. i.e., let's change the underlying implementation of Duplicate Key to use Unique Key MoW, and add a hidden auto_increment column in the primary key, so that all the keys written by the user to the Unique Key MoW table are not duplicated, which realizes the semantics of Duplicate Key, and since each row of data has a unique primary key, we can reuse the UPDATE capability of Unique Key to support the Duplicate Key's UPDATE
We would like participants to help design and implement the solution, and perform performance testing for comparison and performance optimization.
Recommended Skills
Familiar with C++ programming
Familiar with the storage layer of Doris
Mentor
Mentor: Chen Zhang, Apache Doris Committer, chzhang1987@gmail.com
Mentor: Guolei Yi, Apache Doris PMC Member, yiguolei@gmail.com
Mailing List: dev@doris.apache.org
Website: https://doris.apache.org
Openmeetings
Add blur background filter options on video sharing - AI-ML
OpenMeetings uses webRTC and HTML5 video to share audio video. Purely browser based.
One feature missing is the ability to blur your webcam's camera background.
There are multiple ways to achieve it, Google Meet seems to use: https://www.tensorflow.org/
Tensorflow are AI/ML models, they provide precompiled models into JS, for detection of face/body it seems: https://github.com/tensorflow/tfjs-models/tree/master/body-segmentation is the best model.
Since Chrome 14 there is also a Background Blur API (relying on operating system APIs): https://developer.chrome.com/blog/background-blur - but that doesn't seem to be widely or reliable supported by operating systems yet.
The project would be about adding the background blur into a simple demo and then integrate into the OpenMeetings project. Additionally other types of backgrounds can be added.
Tensorflow TFJS is under the Apache 2.0 License (See LICENSE) and should be possible to redistribute with Apache OpenMeetings.
Other live demos and examples:
https://blog.francium.tech/edit-live-video-background-with-webrtc-and-tensorflow-js-c67f92307ac5