lsm-storage-engine-key-value-store 1.0.7

A persistent key-value storage engine based on LSM-tree architecture.

Simple Python Storage Engine

Light weight key value storage engine optimized for write heavy workloads.

What and How?

Simple LSM-tree based implementation of a key value store in python. Implemented this after reading chapter 3 of Designing Data Intensive Applications where the author talks about different data structures powering our database, one such data structure was the LSMtree. This projects implements a:

1. Write Ahead Log (For atomicity and durability)
2. In memory memtable (Where writes initially go to)
3. SSTables (sorted string tables) where items are sorted based on keys.

The memtable is implemented using the SortedDict library of python. This ensures that the traversal through the memtable will give the items sorted by keys. This is important to flush the memtable in sorted order to the SSTable.

The SStables are ordered by levels (L0 -> L1 -> ... ). There is a preset number of SSTables allowed within a level, after which the merging and compaction process gets applied and combines those SSTables into one and moves it to the next level.

The merging and compaction process is a simple k-way merge using a minheap, similar to the leetcode qn (merging k sorted arrays).

This project supports basic key-value operations like GET, PUT, DELETE, EXISTS, CREATE. A simple CLI based interface is implemented where users can interact with the kv store.

The writes are first pushed to the Sorted Container in the memtable (which is in memory). Once the memtable reaches a threshold size, it is flushed into a persistent SSTable, every write is recorded in the WAL as it comes, for atomicity and crash recovery.

The reading process is a bit more complicated. LSMTrees are more optimized for write workloads. When the SSTable gets created, there is an index file also created which stores keys in gaps of 10 (eg: 1st item, 10th item, 20th item etc..). This was implemented for range queries. A read request is first sent to the index file to find which range it lies in and then moves to the different SSTables. Reading is not a very easy process in LSMTrees as compared to B-Trees, which are optimized for read workloads.

How to install?

pip install lsm_storage_engine_key_value_store

How to use?

As of current release, 0.3.0, dated 4th Aug, 2025. The package can be used both as a CLI tool and as a library.

Usage as Library:

Released as a part of v0.3.0

from lsm_storage_engine import StorageManager
storage = StorageManager(base_data_path=DATA_DIRECTORY)

try:
    # Create and use a collection
    storage.create_collection("products")
    storage.use_collection("products")

    # Get the active collection object
    products_store = storage.get_active_collection()

    # Put and get data
    products_store.put("prod:456", "{'name': 'Super Widget', 'price': 99.99}")
    product = products_store.get("prod:456")

    print(f"Retrieved: {product}")

except CollectionExistsError as e:
    print(f"Error: {e}")
finally:
    # Close all file handles gracefully
    storage.close_all()

Usage as CLI:

Released initially, still supported

run the command

lsm-cli

This will print the cli help with all the functions, follow the instructions as per the cli help.

General rules:

1. Create collection
2. Use the created collection, if collection already exists, skip step 1 and use the existing collection
3. Currently supports functions like PUT,GET,DELETE,EXISTS
4. Ctrl + C or EXIT to terminate the program.

Update history:

6th Aug 2025, version 0.4.1: Update Notes: Cleaned up debug and other print statements and logs for public release

21 Aug 2025, version 0.4.2: Added a close command to close the active collection and use a new one, if by accident user opens some collection they didnt mean to.

2 Oct 2025, version 1.0.0: Lay off merging and compaction to background worker thread so that incoming write requests to the single thread are not blocked. Provides leveled compaction. The fundamental change is moving from a Simple, Synchronous, Write-Optimized model to a Hybrid, Asynchronous, Read-Optimized model. The main issue with the previous model is that, in a write heavy process, the single threaded model would get blocked instantly because the main thread had to perform the compaction, which will increase latency, therefore defeating the point. Another problem was read amplification, where to search for a key will lead to searching in the memtable, then 4 different L0 SStables.

New system->

1. The _flush_memtable() now only writes the new SSTable and then sends a task signal ("FLUSH_COMPLETE") to a separate, permanent thread (_compaction_worker_run). This gives the asynchronous abilities

2. Essentially, we are ensuring that in a certain level Li, the sstables are strictly non overlapping, so to search for a key, we can do less number of I/O operations because we can see the key ranges of each sstable (stored in the new .meta file) and we can use that info to find the file where our key is stored. For merging, we find one sstable from level Li and then use the key range information from .meta file to find which sstables from level Li+1 and then overlap only those sstables where there is an overlap and dont worry about the other sstables.

Contribution

Contributions, issues and feature requests are welcome!

LICENSE

This project is licensed under the MIT License. See the LICENSE file for details.