A B-tree index must write every piece of data at least twice: once to the write-ahead log, and once to the tree page itself (and perhaps again as pages are split). There is also overhead from having to write an entire page at a time, even if only a few bytes in that page changed. Some storage engines even overwrite the same page twice in order to avoid ending up with a partially updated page in the event of a power failure.
LSM-trees are typically able to sustain higher write throughput than B-trees, partly because they sometimes have lower write amplification, and partly because they sequentially write compact SSTable files rather than having to overwrite several pages in the tree.
LSM-trees can be compressed better, and thus often produce smaller files on disk than B-trees. B-tree storage engines leave some disk space unused due to fragmentation: when a page is split or when a row cannot fit into an existing page, some space in a page remains unused. Since LSM-trees are not page-oriented and periodically rewrite SSTables to remove fragmentation, they have lower storage overheads, especially when using leveled compaction.
On many SSDs, the firmware internally uses a log-structured algorithm to turn random writes into sequential writes on the underlying storage chips, so the impact of the storage engine’s write pattern is less pronounced. However, lower write amplification and reduced fragmentation are still advantageous on SSDs: representing data more compactly allows more read and write requests within the available I/O bandwidth.
Downsides of LSM-trees:
A downside of log-structured storage is that the compaction process can sometimes interfere with the performance of ongoing reads and writes.
Another issue with compaction arises at high write throughput: the disk’s finite write bandwidth needs to be shared between the initial write (logging and flushing a memtable to disk) and the compaction threads running in the background. When writing to an empty database, the full disk bandwidth can be used for the initial write, but the bigger the database gets, the more disk bandwidth is required for compaction.
An advantage of B-trees is that each key exists in exactly one place in the index, whereas a log-structured storage engine may have multiple copies of the same key in different segments. This aspect makes B-trees attractive in databases that want to offer strong transactional semantics: in many relational databases, transaction isolation is implemented using locks on ranges of keys, and in a B-tree index, those locks can be directly attached to the tree.
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