Cache
Caching is a technique used in computer science to store frequently accessed data in a location that can be accessed more quickly than the original source of the data. Caching has become an important tool for improving the performance of applications that need to access large amounts of data.
Caching is used because it can significantly improve the performance of applications that need to access large amounts of data. By storing frequently accessed data in a cache, the application can retrieve the data more quickly than if it had to access the original source every time.
The benefits of caching include:
- Improved performance: Caching can significantly improve the performance of applications by reducing the amount of time it takes to retrieve data.
- Reduced network traffic: Caching can reduce the amount of network traffic that an application generates which can be especially important in situations where network bandwidth is limited.
- Improved scalability: Caching can help applications scale to handle larger amounts of data and higher volumes of traffic.
Zarr is a Python package that provides an implementation of caching using a Least Recently Used (LRU) algorithm. The LRU algorithm keeps track of which data has been accessed most recently and stores that data in the cache. When the cache becomes full, the least recently accessed data is removed to make room for new data.
Now let’s dive into the experiments with caching.
In the zipfian_distribution.py script, we use a zipfian distribution to generate the access trace. A Zipfian distribution is a probability distribution where the probability of an item being accessed is proportional to its rank. This means that the most frequently accessed item is accessed much more frequently than the other items.
After running the script from the output shown below, we see that it is faster to fetch data from the LRU cache compared to the zipstore and in memory store.
In the uniform_distribution.py script, we use a uniform distribution. The uniform distribution is a probability distribution in which all values in the distribution have an equal probability of being chosen. In other words, each value in the distribution has an equal chance of being selected as any other value in the distribution. This means that the probability density function of the uniform distribution is constant over the range of the distribution.
After running the script from the output shown below, we see that it is faster to fetch data from the LRU cache compared to the zipstore and in memory store.
Here’s a code snippet from both scripts of the benchmark store with LRU caching:
These scripts use the NumPy library to generate random data and access traces. They create a dataset with a specified number of keys, array shape, and maximum number of groups for both the first and second levels. They then generate an access trace and access the dataset using the access trace. They compare the performance of three different stores: in-memory store, zipstore, and LRU caching. A cache will be slower the first time but after running both scripts after multiple times, it is evident that fetching data from the LRU cache is faster.
The size of the datasets used in the experiments is determined by the array shape and the number of keys. The array shape determines the size of each array, while the number of keys determines the number of arrays in the dataset. One can control the size of the dataset by adjusting the array shape and the number of keys. In these experiments, I use an array shape of (1000, 1000) and a maximum of 10,000 keys.
Overall, these experiments demonstrate the benefits of caching for improving the performance of applications that need to access large amounts of data. By storing frequently accessed data in a cache, we can reduce the amount of time it takes to retrieve data and improve the scalability of the application.