In Java, the finalize method has been part of the language since its early days, offering a mechanism to perform cleanup activities before an object is garbage collected. However, using finalizers has come under scrutiny due to several performance-related concerns. As of Java 9, the finalize method has been deprecated, and its use is highly discouraged.
![Using finalize() method?]()
Delayed Garbage Collection
Finalizers can substantially slow down the garbage collection process. When an object is ready to be collected but has the finalize method, the garbage collector must call this method and then re-check the object in the next garbage collection cycle. This two-step process delays memory reclamation, leading to increased memory usage and potential memory leaks.
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Welcome to the distributed systems series. In this article, we are going to learn about consistent hashing and its usage in distributed systems. Why consistent hashing is important and how it plays a role in designing distributed systems such as databases, cache, etc. Let’s first understand what is hashing and how it is used to distribute data across machines. Then, we will understand what is consistent hashing.
Hashing
Hashing is a technique that generates a unique ID for an object. A simple example would be the hashcode function in Java, which returns a unique ID for an immutable object. This returned ID is used to choose the bucket from an array of buckets for storage and retrieval. In order for this hashing function to return the correct value, the object or key that we use to hash should be immutable. This is how hashing works in Java to store and retrieve the value in the HashMap data structure. If you know how hashmap works, the concept is pretty similar in distributed systems. In distributed systems, we have an array of machines to store the data, and we have to decide which machines should hold the specific data. The following diagram explains how hashing is used to store {key, value} data on different machines.
Is a generative AI preamble necessary for a newsletter focused on Knowledge Graphs, Graph Databases, Graph Analytics, and Graph AI? Normally, it should not be. However, the influence of generative AI on the items included in this issue was overwhelming. There is a simple explanation for that.
It's been a year since Generative AI burst into the mainstream with the release of ChatGPT. Notwithstanding a rather spotty record both in terms of technical performance and accuracy as well as in terms of business reliability, there's no denying that Generative AI has captured the attention of executives worldwide.
In 2023, the rise of Large Language Models (LLMs) like Alpaca, Falcon, Llama 2, and GPT-4 indicates a trend toward AI democratization. This allows even small companies to afford customized models, promoting widespread adoption. However, challenges persist, such as restricted licensing for open-source models and the costs of fine-tuning and maintenance, which are manageable mainly for large enterprises or research institutes.
The key to maximizing LLM potential is in fine-tuning and customizing pre-trained models for specific tasks. This approach aligns with individual requirements, providing innovative and tailored solutions. Fine-tuning not only enhances model efficiency and accuracy but also optimizes system resource utilization, requiring less computational power than training from scratch.
Delayed Garbage Collection
