What are the main differences, advantages, and disadvantages between LSM-Tree and B(+)-Tree?

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This topic has been translated from a Chinese forum by GPT and might contain errors.

Original topic: LSM-Tree和B(+)-Tree的主要区别以及优劣势是什么

| username: alfred

The main differences, advantages, and disadvantages between LSM-Tree and B(+)-Tree are as follows:

  1. Structure:

    • LSM-Tree (Log-Structured Merge-Tree): Optimized for write-heavy workloads. It uses a sequence of components (levels) where data is first written to an in-memory structure and then periodically merged and flushed to disk.
    • B(+)-Tree: A balanced tree data structure that maintains sorted data and allows searches, sequential access, insertions, and deletions in logarithmic time. It is optimized for read-heavy workloads.

  2. Write Performance:

    • LSM-Tree: Generally has better write performance due to its append-only nature and batched writes. Writes are first accumulated in memory and then written to disk in bulk.
    • B(+)-Tree: Writes can be slower because they often require multiple disk I/O operations to maintain the tree’s balance.

  3. Read Performance:

    • LSM-Tree: Read performance can be slower due to the need to search through multiple levels of data. However, this can be mitigated with Bloom filters and other optimizations.
    • B(+)-Tree: Typically offers faster read performance as data is more uniformly distributed and can be accessed directly.

  4. Space Efficiency:

    • LSM-Tree: Can be less space-efficient due to the need for multiple copies of data at different levels and the overhead of compaction processes.
    • B(+)-Tree: Generally more space-efficient as it maintains a single, balanced structure.

  5. Compaction and Maintenance:

    • LSM-Tree: Requires periodic compaction to merge and sort data, which can be resource-intensive.
    • B(+)-Tree: Requires rebalancing operations to maintain its structure, but these are typically less resource-intensive compared to LSM-Tree compactions.

  6. Use Cases:

    • LSM-Tree: Suitable for applications with high write throughput and where write performance is critical, such as logging systems and time-series databases.
    • B(+)-Tree: Suitable for applications with high read throughput and where read performance is critical, such as traditional relational databases.

In summary, LSM-Trees are generally better for write-heavy workloads, while B(+)-Trees are better for read-heavy workloads. The choice between the two depends on the specific requirements of the application.

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| username: 胡杨树旁 | Original post link

I remember the teacher saying in the video that LSM-Tree trades space for time and is write-friendly. I understand that LSM-Tree is sequential writing, similar to how all the data I write is piled up, whereas B(+)-Tree has data in their respective positions. It is not write-friendly but is read-friendly.

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| username: buddyyuan | Original post link

By comparing the various amplifications of B+ trees and LSM-trees, we can conclude that LSM-trees have better write performance than B+ trees, while their read performance is not as good. The main reason TiKV uses LSM-trees instead of B-trees as its underlying storage engine is that it is much easier to improve read performance using caching techniques than to improve write performance.

You can read this article:

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| username: 近墨者zyl | Original post link

Sequential writes and random writes target memory, not disk. Whether writing or reading, operations are directly performed on memory. When it comes to the file system layer or storage layer, all reads and writes are random. LSM is easy to understand as sequential writes: like logs, it writes sequentially in memory. B+TREE involves random writes: taking Oracle as an example, the smallest read/write unit of Oracle’s underlying storage engine is a block, and different tables correspond to different blocks. When cached into memory, it becomes random reads and writes.

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| username: system | Original post link

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