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Distributed Transactions at Scale in Amazon DynamoDB
Amazon DynamoDB supports transactions without sacrificing performance or availability. Akshat Vig explains how DynamoDB introduced TransactGetItems and TransactWriteItems for atomic operations, proving full ACID support in distributed transactions.
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Comparative Analysis of Major Distributed File System Architectures: GFS vs. Tectonic vs. JuiceFS
As storage needs continue to grow, traditional disk file systems have revealed their limitations. To address the growing storage demands, distributed file systems have emerged as dynamic and scalable solutions. In this article, we explore the design principles, innovations, and challenges addressed by three representative distributed file systems: Google File System (GFS), Tectonic, and JuiceFS.
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Create Your Distributed Database on Kubernetes with Existing Monolithic Databases
The next challenge for databases is to run them on Kubernetes to become cloud neutral. However, they are more difficult to manage than the application layer, since Kubernetes is designed for stateless applications. Apache ShardingSphere is the ecosystem to transform any database into a distributed database system and enhance it with sharding, elastic scaling, encryption features, and more.
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Location, Location, Location: MVA Considerations for Distributed Processing and Data
Even when designing a Minimum Viable Architecture (MVA), developers must consider resource location, especially when mobile apps are part of a distributed system. Distributing the data and processing can introduce new challenges if location is not part of the decision-making criteria.
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Raft Engine: a Log-Structured Embedded Storage Engine for Multi-Raft Logs in TiKV
In this article, authors discuss the design and implementation of Raft Engine, a log-structured embedded storage engine introduced in TiDB distributed, NewSQL database version 5.4. They also discuss the performance benefits of the engine compared to the previous implementation based on RocksDB.
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Developing Deep Learning Systems Using Institutional Incremental Learning
Institutional incremental learning promises to achieve collaborative learning. This form of learning can address data sharing and security issues, without bringing in the complexities of federated learning. This article talks about practical approaches which help in building an object detection system.
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Getting Ready for IoT’s Big Data Challenges with Couchbase Mobile
Our physical world is about to become digitally enabled and according to various predictions for example by Gartner or Cisco, there will be many billions of IoT devices going online and constantly gathering data in the coming years. We got in touch with Wayne Carter and Ali LeClerc of Couchbase to discuss how Couchbase Mobile is also ready for the upcoming era of Internet of Things.
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Distributing Complex Services in Cross-Geolocational IDCs
In this interview, first published on InfoQ China, Micro Sun describes some of the techniques Tencent use to scale Qzone, a social networking platform in China with over 600 million monthly active users,