Figure 2 illustrates the organization of an in-memory store implementing the KVL model based on the fundamental structure of existing in-memory key-value stores.
Therefore, using KVL data model with good data schema design could significantly boost the system performance without spending much effort on optimizing the in-memory store implementation.
Apart from the simple university example in the previous section, we found that the KVL is also applicable to a wide range of applications.
The KVL model, on the other hand, is very similar to the concept of Graph database since this model itself is a network of items.
The KVL model is well fit for caching such kind of dataset since it is also a graph in nature.
streets), KVL model is a promising candidate for representing its content in ITS systems.
We have implemented an in-memory store utilizing the KVL model named KELI (stands for KEy-value-with-Link In-memory Store).
During this process, items are transformed from their original format on disk to KVL format.
In this section, we will illustrate the benefits of employing the KVL model for RDMA-based in-memory stores by comparing the performance of KELI with another in-memory key-value store.
Particularly, we use the KVL model to represent several real-life datasets and let KELI manage them.
In the text bellow, we will illustrate those datasets and describe how to use the KVL model to model them.
Figure 5 shows how the dataset is modeled by the KVL model.