Databases that handle tons of writes (like saving millions of tweets or bank transactions) need special designs to stay fast. Log-Structured Merge (LSM) Trees are one such design, perfect for write-heavy workloads because they make writing data super quick. They’re different from B+ Trees, which are better for reading data. In this guide, we’ll break down LSM Trees and related concepts in a beginner-friendly way, using diagrams to make things clear. Let’s dive in!
1. Request Condensing
What is it?
When lots of write requests (like updating your profile name) come in, the database can get overwhelmed. Request condensing is like combining multiple updates into one to save effort. For example, if you change your name from “John” to “Jon” to “Johnny” in a second, the database might just save “Johnny” once.
Why it matters: Condensing reduces the number of writes, making the database faster for write-heavy apps like social media.
Explanation: The diagram shows multiple updates being condensed into one write, saving time and space.
2. Log Using Linked List
What is a log? A log is like a diary where the database records every write (e.g., “save user ID 123”) in order. It’s stored in memory as a linked list, a chain where each piece of data points to the next.
Why it’s fast: Adding data to the end of a linked list (called a memtable in LSM Trees) is quick because you don’t need to sort or rearrange anything.
Analogy: Think of a cashier scribbling every sale at the bottom of a notebook—no organizing, just writing.
3. B+ Tree vs. Linked List
B+ Tree:
A B+ Tree is like a library catalog, organized in a tree shape where data is sorted for fast reading. It’s great for finding data quickly but slow for writes because every new piece of data requires rearranging the tree to keep it sorted.
Linked List (in LSM Trees):
In LSM Trees, the linked list (memtable) is unsorted and fast for writes because you just add data to the end. Reading is slower since you might need to scan the list.
Why LSM Trees use linked lists: They prioritize fast writes, saving sorting for later.
Explanation: The diagram compares B+ Trees (sorted, read-optimized) with linked lists (append-only, write-optimized).
4. Sorted String Table (SSTable)
What is an SSTable?
A Sorted String Table (SSTable) is a sorted file on disk, like a binder where data (e.g., user IDs and their info) is organized alphabetically. When the in-memory linked list (memtable) gets full, it’s sorted and saved as an SSTable.
Why sort?
Sorting makes it faster to find data later (e.g., searching for “user ID 123” is quick in a sorted file).
How it works:
Writes go to the memtable (linked list).
When full, the memtable is sorted and written to disk as an SSTable.
Multiple SSTables pile up over time.
Explanation: The diagram shows writes going to the memtable, which is then sorted and saved as an SSTable on disk.
5. Compaction
What is compaction?
Compaction is like cleaning up multiple binders (SSTables) by combining them into one, keeping only the latest data and removing duplicates or old entries (e.g., outdated profile updates).
Why it’s needed: Over time, you get many SSTables with overlapping data, which wastes space and slows down reads. Compaction merges them to keep things efficient.
Explanation: The diagram shows multiple SSTables being merged into one, keeping only the latest data.
6. Bloom Filters
What is a Bloom Filter?
A Bloom Filter is like a quick checklist that tells you if a piece of data (e.g., “user ID 123”) might be in an SSTable without checking the actual file. It’s a small, in-memory tool that saves time.
How it works:
It says either “definitely not in this SSTable” (100% accurate) or “maybe in there” (you check the SSTable).
This avoids slow disk reads for data that isn’t there.
Analogy: It’s like checking an index card to see if a book is on a shelf before searching the shelf.
Why LSM Trees Are Write-Friendly
LSM Trees are awesome for write-heavy workloads because:
1. Fast writes: They append to a log (linked list) in memory, which is quick.
2. Batching: Writes are collected in the memtable and flushed to disk as SSTables, reducing disk work.
3. Compaction: Merges SSTables to keep the system tidy and efficient.
4. Bloom Filters: Speed up reads by avoiding pointless disk checks.
B+ Trees, on the other hand, are slower for writes because they need to keep everything sorted on disk, which takes more effort.
Example: Databases like Cassandra or RocksDB use LSM Trees for apps with tons of writes, like logging tweets or sensor data.
Conclusion
LSM Trees make databases lightning-fast for apps with lots of writes, like social media or IoT systems. By using logs to quickly jot down data, sorting it into SSTables, cleaning up with compaction, and speeding up searches with Bloom Filters, LSM Trees strike a balance between fast writes and efficient reads. Unlike B+ Trees, which are great for reading but slower for writing, LSM Trees are built for speed in write-heavy scenarios. As you explore databases, try sketching how an app like Twitter might use LSM Trees to handle millions of posts. With practice, these concepts will feel like second nature!
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