Optimize dictionary resetting

Oct 13, 2025 by thesweetdesigns.com 72 views

Introduction

In recent years, the use of compression algorithms has become increasingly important in various fields, including data storage and retrieval. One such algorithm is LZ4, which has gained popularity due to its high compression ratio and fast decompression speed. In this article, we will discuss the optimization of LZ4, specifically in the context of Apache Lucene, a popular search engine library. We will explore the benefits and drawbacks of using preset dictionaries with LZ4 and discuss potential solutions to improve performance.

Background

Since Lucene 8.7, preset dictionaries have been used with LZ4 for stored fields. This change has resulted in improved compression ratios, but at the cost of slightly slower fetch times. In our product, we upgraded Lucene from 8.6 to 9.x and observed a significant increase in IOPs and IO throughput usage. After investigating the issue, we found that the introduction of preset dictionaries was the primary cause.

Test Results

To better understand the impact of preset dictionaries on compression ratios, we conducted a test using the pmc dataset. The results are shown in the table below:

Method	Index Size (MB)
LZ4 with no preset dict (BEST_SPEED 16k, 128 doc)	2055
LZ4 with preset dict (BEST_SPEED 80k, 1024 doc)	1813

As we can see, the use of preset dictionaries resulted in a compression ratio improvement of 11.7%.

Questions and Solutions

Given these findings, we are faced with two questions:

Should we consider reusing the LZ4 with no preset dict algorithm as a viable alternative?

In this case, the answer is not a simple yes or no. While the LZ4 with no preset dict algorithm may offer better performance in terms of fetch times, it may not provide the same level of compression ratio improvement as the preset dictionary approach. Therefore, we need to weigh the trade-offs between compression ratio and performance.

Can we consider using space in exchange for time: Reversing a backup dictionary, cloning for each sub block?

To address the issue of resetting dictionaries for each sub block, we can explore the possibility of using a backup dictionary and cloning it for each sub block. This approach would allow us to reduce the time spent on resetting dictionaries, potentially improving performance.

Potential Solutions

Based on our analysis, we propose the following potential solutions:

Use a hybrid approach: Combine the benefits of both LZ4 with and without preset dictionaries. For example, we can use preset dictionaries for stored fields and LZ4 without preset dictionaries for other fields.
Optimize dictionary resetting: Implement a more efficient way to reset dictionaries for each sub block, such as using a backup dictionary and cloning it for each sub block.
Experiment with different compression ratios: Investigate the impact of different compression ratios on performance and compression ratio improvement.

Conclusion

In conclusion, the use of preset dictionaries with LZ4 has resulted in improved compression ratios, but at the cost of slightly slower fetch times. We have identified potential solutions to improve performance, including using a hybrid approach, optimizing dictionary resetting, and experimenting with different compression ratios. By carefully weighing the trade-offs between compression ratio and performance, we can make informed decisions about the best approach for our product.

Future Work

Future work includes:

Further experimentation: Conduct more experiments to better understand the impact of preset dictionaries on performance and compression ratio improvement.
Implementation of potential solutions: Implement the proposed solutions and evaluate their effectiveness.
Monitoring and evaluation: Continuously monitor and evaluate the performance of our product to ensure that it meets the required standards.

Introduction

In our previous article, we discussed the optimization of LZ4, specifically in the context of Apache Lucene. We explored the benefits and drawbacks of using preset dictionaries with LZ4 and proposed potential solutions to improve performance. In this article, we will answer some of the most frequently asked questions about LZ4 optimization.

Q&A

Q: What is LZ4 and why is it used in Lucene?

A: LZ4 is a compression algorithm that is widely used in various fields, including data storage and retrieval. In Lucene, LZ4 is used to compress stored fields, which can improve the compression ratio and reduce the storage space required.

Q: What are preset dictionaries and how do they affect LZ4 performance?

A: Preset dictionaries are pre-computed dictionaries that are used to improve the compression ratio of LZ4. They can be used to speed up the compression process, but they can also increase the memory usage and slow down the decompression process.

Q: Why did we observe a significant increase in IOPs and IO throughput usage after upgrading to Lucene 9.x?

A: The introduction of preset dictionaries in Lucene 9.x was the primary cause of the increase in IOPs and IO throughput usage. Preset dictionaries require more memory and can slow down the decompression process, which can lead to increased IOPs and IO throughput usage.

Q: Can we use LZ4 without preset dictionaries as a viable alternative?

A: Yes, we can use LZ4 without preset dictionaries as a viable alternative. However, it may not provide the same level of compression ratio improvement as the preset dictionary approach. We need to weigh the trade-offs between compression ratio and performance.

Q: How can we optimize dictionary resetting for each sub block?

A: We can use a backup dictionary and clone it for each sub block to optimize dictionary resetting. This approach can reduce the time spent on resetting dictionaries and improve performance.

Q: What are the potential solutions to improve LZ4 performance?

A: The potential solutions to improve LZ4 performance include:

Using a hybrid approach that combines the benefits of both LZ4 with and without preset dictionaries.
Optimizing dictionary resetting for each sub block.
Experimenting with different compression ratios to find the optimal balance between compression ratio and performance.

Q: How can we monitor and evaluate the performance of our product?

A: We can use various tools and metrics to monitor and evaluate the performance of our product, including:

IOPs and IO throughput usage.
Compression ratio improvement.
Decompression speed.
Memory usage.

Conclusion

In conclusion, LZ4 optimization is a complex topic that requires careful consideration of various factors, including compression ratio, performance, and memory usage. By answering these frequently asked questions, we hope to provide a better understanding of LZ4 optimization and help developers make informed decisions about the best approach for their product.

Future Work

Future work includes:

Further experimentation: Conduct more experiments to better understand the impact of preset dictionaries on performance and compression ratio improvement.
Implementation of potential solutions: Implement the proposed solutions and evaluate their effectiveness.
Monitoring and evaluation: Continuously monitor and evaluate the performance of our product to ensure that it meets the required standards.

By following this approach, we can ensure that our product remains competitive and efficient, while also providing the best possible user experience.