The Material for the Project Support BloomFilter for kvrocks-Toy模板网

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Here is the information about the project “Support BloomFilter for kvrocks” from the current web page context:

Field	Value
项目编号	239430147
项目难度	Advanced
支持语言	English
项目社区导师	Xuwei Fu
导师联系邮箱	maplewish117@gmail.com
技术领域	C++, Database
开源协议	Apache-2.0 License
项目简述	kvrocks is a distributed key value NoSQL database that uses RocksDB as storage engine and is compatible with Redis protocol. Kvrocks intends to decrease the cost of memory and increase the capacity while compared to Redis. This project intent to port BloomFilter and Cuckoo Filter to kvrocks, like RedisBloom(https://redis.io/docs/stack/bloom/).
Deliverables	The code and testing for BloomFilter and Cuckoo Filter implementions. Performance benchmark for them.
Required Skills	Medium knowledge of C++. Basic understanding of RocksDB and Redis. Basic understanding of Git.
项目产出要求	The code, testing and benchmark for BloomFilter and Cuckoo Filter.
项目技术要求	C++
项目成果仓库	https://github.com/apache/incubator-kvrocks

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The framework for its application document

Here is the framework for the project “Support BloomFilter for kvrocks” translated into academic English:

Project Background
1.1 Basic Project Requirements
1.2 Technical Field Tags
Technical Methods and Feasibility
2.1 BloomFilter and Cuckoo Filter Related
2.2 RocksDB and Redis Related
2.3 C++ Related
Project Implementation Plan
3.1 Phase Division
3.2 Milestones
Expected Project Outcomes
Project Risks and Countermeasures
5.1 Technical Risks
5.2 Time Risks
Participated Relevant Projects

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The First Version for Application Document

Here is an application document for the project “Support BloomFilter for kvrocks” based on our previous conversation:

Application for the Project “Support BloomFilter for kvrocks”

1. Project Background

1.1 Basic Project Requirements

The project “Support BloomFilter for kvrocks” aims to port BloomFilter and Cuckoo Filter to kvrocks, like RedisBloom. The project is not hard and includes implementing RedisBloom and Cuckoo Filter. The theory and code could be easy and clear.

1.2 Technical Field Tags

C++
Database

2. Technical Methods and Feasibility

2.1 BloomFilter and Cuckoo Filter Related

The project includes implementing RedisBloom and Cuckoo Filter. You can learn some optimization about the size of the bloom filter or cache locality of the bloom filter, like Block Split Bloom Filter.

2.2 RocksDB and Redis Related

Kvrocks is a distributed key value NoSQL database that uses RocksDB as storage engine and is compatible with Redis protocol.

2.3 C++ Related

The project requires medium knowledge of C++.

3. Project Implementation Plan

3.1 Phase Division

Phase 1: Implementing RedisBloom and Cuckoo Filter.
Phase 2: Testing and benchmarking the implementations.
Phase 3: Optimizing the size of the bloom filter or cache locality of the bloom filter.

3.2 Milestones

Milestone 1: Completion of RedisBloom and Cuckoo Filter implementations.
Milestone 2: Completion of testing and benchmarking.
Milestone 3: Completion of optimizations.

4. Expected Project Outcomes

The expected outcomes of the project include the code, testing, and benchmark for BloomFilter and Cuckoo Filter.

5. Project Risks and Countermeasures

5.1 Technical Risks

Difficulty in implementing RedisBloom and Cuckoo Filter.
Difficulty in optimizing the size of the bloom filter or cache locality of the bloom filter.

5.2 Time Risks

Delays in completing the implementations.
Delays in completing the testing and benchmarking.

6. Participated Relevant Projects

I have participted a project and its related and detailed information is as follows:
Project Type: National Natural Science Foundation of China General Program; Project Name: Research on Symmetric Encryption Schemes for Searchable Boolean Retrieval and K-Nearest Neighbor Retrieval; Project Funding: 610,000 RMB; Research Period: January 2020 - December 2023; Affiliated Institution: Dongguan University of Technology; Project Number: 61972089. In this project I have study the basic principle of bloom filter and Cuckoo Filter implements. In addition, I used them into the searchable encrypted database.
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