LSM-Trees or Log-Structured Merge Trees are a data structure used in modern databases for efficient storage and retrieval. They optimize write-intensive workloads.
#Structure
- LSM-Trees consist of multiple levels, including memory (memtable) and disk-based (SSTables (Sorted String Tables)) components.
- Data is sorted and stored in a series of SSTables (Sorted String Tables).
#How it works
- Data is first written to a memory-based component called the memtable.
- When the memtable is full, it’s flushed to disk as an SSTable.
- Read operations involve checking both the memtable and SSTables.
- Periodic compaction merges and removes obsolete data from SSTables.
#Benefits and Challenges of LSM-Trees
#Benefits
- High Write Throughput
- LSM-Trees are optimized for write-heavy workloads due to their append-only nature.
- Efficient Compaction
- Compaction occurs at the background, reducing the impact on write performance.
- Range Queries
- Sorted SSTables enable efficient range queries.
#Challenges
- Read Amplification
- Multiple SSTables (Sorted String Tables) may need to be checked during reads, leading to read amplification.
- Complexity
- LSM-Tree management and compaction can be complex.
#LSM-Trees Use Cases
LSM-Trees are commonly used in distributed and NoSQL databases like Apache Cassandra, HBase, and LevelDB.
Ideal for applications with heavy write traffic, such as IoT data ingestion.
Introduction