Category: Implementing a Key-Value Store

Implementing a Key-Value Store – Part 10: High-Performance Networking: KingServer vs. Nginx

Published by Emmanuel Goossaert on July 21, 2016

This is Part 10 of the IKVS series, “Implementing a Key-Value Store”. You can also check the Table of Contents for other parts. In this series of articles, I describe the research and process through which I am implementing a key-value database, which I have named “KingDB”. The source code is available at http://kingdb.org. Please note that you do not need to read the previous parts to be able to follow what is going on here. The previous parts were mostly exploratory, and starting with Part 8 is perfectly fine.

In this article, I explain the model and inner workings of KingServer, the network server for KingDB. In order to put things into perspective I also cover Nginx, the high-performance HTTP server well-known for its network stack, and how it differs from KingDB.

Implementing a Key-Value Store – Part 9: Data Format and Memory Management in KingDB

Published by Emmanuel Goossaert on August 3, 2015

This is Part 9 of the IKVS series, “Implementing a Key-Value Store”. You can also check the Table of Contents for other parts. In this series of articles, I describe the research and process through which I am implementing a key-value database, which I have named “KingDB”. The source code is available at http://kingdb.org. Please note that you do not need to read the previous parts to be able to follow. The previous parts were mostly exploratory, and starting with Part 8 is perfectly fine.

In this article, I explain how the storage engine of KingDB works, including details about the data format. I also cover how memory management is done through the use of a compaction process.

Implementing a Key-Value Store – Part 8: Architecture of KingDB

Published by Emmanuel Goossaert on May 25, 2015

This is Part 8 of the IKVS series, “Implementing a Key-Value Store”. You can also check the Table of Contents for other parts. In this series of articles, I describe the research and process through which I am implementing a key-value database, which I have named “KingDB”. The source code is available at http://kingdb.org. Please note that you do not need to read the previous parts to be able to follow. The previous parts were mostly exploratory, and starting with Part 8 is perfectly fine.

In the previous articles, I have laid out the research and discussion around what needs to be considered when implementing a new key-value store. In this article, I will present the architecture of KingDB, the key-value store of this article series that I have finally finished implementing.

Introducing KingDB v0.9.0

Published by Emmanuel Goossaert on May 14, 2015

I am pleased to announce that I am releasing the very first version of KingDB, the fast persisted key-value store. KingDB is a side-project that I have been hacking on intermittently over the last couple of years. It has taken a lot of my personal time, therefore I am very…

Implementing a Key-Value Store – Part 7: Optimizing Data Structures for SSDs

Published by Emmanuel Goossaert on October 18, 2014

This is Part 7 of the IKVS series, “Implementing a Key-Value Store”. You can also check the Table of Contents for other parts. In this series of articles, I describe the research and process through which I am implementing a key-value store database, which I have named “KingDB”.

In the previous articles, I have spent a fair amount of time reviewing existing key-value stores, interfaces, architectures, and I focused greatly on hash tables. In this article, I will talk about hardware considerations for storing data structures on solid-state drives (SSDs), and I will share a few notes regarding file I/O optimizations in Unix-based operating systems.

This article will cover:

1. Fast data structures on SSDs
2. File I/O optimizations
3. Done is better than perfect
4. References

Implementing a Key-Value Store – Part 6: Open-Addressing Hash Tables

Published by Emmanuel Goossaert on May 7, 2014

This is Part 6 of the IKVS series, “Implementing a Key-Value Store”. You can also check the Table of Contents for other parts.

In this article, I will compare several open-addressing hash tables: Linear Probing, Hopscotch hashing, and Robin Hood hashing. I have already done some work on this topic, and in this article I want to gather data for more metrics in order to decide which hash table I will use for my key-value store.

The result section also contains an interesting observation about the maximum DIB for Robin Hood hashing, which originated from Kristofer Karlsson, a software engineer at Spotify and the author of the key-value store Sparkey.

This article will cover:

1. Open-addressing hash tables
2. Metrics
3. Experimental Protocol
4. Results and Discussion
5. Conclusion
6. References

Implementing a Key-Value Store – Part 5: Hash table implementations

Published by Emmanuel Goossaert on May 13, 2013

This is Part 5 of the IKVS series, “Implementing a Key-Value Store”. You can also check the Table of Contents for other parts.

In this article, I will study the actual implementations of hash tables in C++ to understand where are the bottlenecks. Hash functions are CPU-intensive and should be optimized for that. However, most of the inner mechanisms of hash tables are just about efficient memory and I/O access, which will be the main focus of this article. I will study three different hash table implementations in C++, both in-memory and on-disk, and take a look at how the data are organized and accessed. This article will cover:

1. Hash tables
    1.1 Quick introduction to hash tables
    1.2 Hash functions
2. Implementations
    2.1 unordered_map from TR1
    2.2 dense_hash_map from SparseHash
    2.3 HashDB from Kyoto Cabinet
3. Conclusion
4. References

Implementing a Key-Value Store – Part 4: API Design

Published by Emmanuel Goossaert on April 3, 2013

This is Part 4 of the IKVS series, “Implementing a Key-Value Store”. You can also check the Table of Contents for other parts.

I finally settled on a name for this whole key-value store project, which from now on will be referred as ~~FelixDB~~ KingDB.

In this article, I will take a look at the APIs of four key-value stores and database systems: LevelDB, Kyoto Cabinet, BerkekeyDB and SQLite3. For each major functionality in their APIs, I will compare the naming conventions and method prototypes, to balance the pros and cons and design the API for the key-value store I am currently developing, KingDB. This article will cover:

1. General principles for API design
2. Defining the functionalities for the public API of KingDB
3. Comparing the APIs of existing databases
    3.1 Opening and closing a database
    3.2 Reads and Writes
    3.3 Iteration
    3.4 Parametrization
    3.5 Error management
4. Conclusion
5. References

Implementing a Key-Value Store – Part 3: Comparative Analysis of the Architectures of Kyoto Cabinet and LevelDB

Published by Emmanuel Goossaert on December 30, 2012

This is Part 3 of the IKVS series, “Implementing a Key-Value Store”. You can also check the Table of Contents for other parts.

In this article, I will walk through the architectures of Kyoto Cabinet and LevelDB, component by component. The goal, as stated in Part 2 of the IKVS series, is to get insights at how I should create the architecture my own key-value store by analyzing the architectures of existing key-value stores. This article will cover:

1. Intent and methodology of this architecture analysis
2. Overview of the Components of a Key-Value Store
3. Structural and conceptual analysis of Kyoto Cabinet and LevelDB
    3.1 Create a map of the code with Doxygen
    3.2 Overall architecture
    3.3 Interface
    3.4 Parametrization
    3.5 String
    3.6 Error Management
    3.7 Memory Management
    3.8 Data Storage
4. Code review
    4.1 Organization of declarations and definitions
    4.2 Naming
    4.3 Code duplication

Implementing a Key-Value Store – Part 2: Using existing key-value stores as models

Published by Emmanuel Goossaert on December 3, 2012

This is Part 2 of the IKVS series, “Implementing a Key-Value Store”. You can also check the Table of Contents for other parts.

In this article, I will start by explaining why I think it is important to use models for this project and not start completely from scratch. I will describe a set of criteria for selecting key-value store models. Finally, I will go over some well-known key-value store projects, and select a few of them as models using the presented criteria. This article will cover:

1. Not reinventing the wheel
2. Model candidates and selection criteria
3. Overview of the selected key-value stores