leveled/README.md

# LevelEd - An Erlang Key-Value store

## Introduction

LevelEd is a work-in-progress prototype of a simple Key-Value store based on the concept of Log-Structured Merge Trees, with the following characteristics:

- Optimised for workloads with larger values (e.g. > 4KB).
- Explicitly supports HEAD requests in addition to GET requests. 
  - Splits the storage of value between key/metadata and body, and allowing for HEAD requests which have lower overheads than GET requests, and queries which traverse keys/metadatas to be supported with fewer side effects on the page cache.
- Support for tagging of object types and the implementation of alternative store behaviour based on type.
  - Potentially usable for objects with special retention or merge properties.
- Support for low-cost clones without locking to provide for scanning queries.
  - Low cost specifically where there is a need to scan across keys and metadata (not values).
- Written in Erlang as a message passing system between Actors.

The store has been developed with a focus on being a potential backend to a Riak KV database, rather than as a generic store.  

The primary aim of developing (yet another) Riak backend is to examine the potential to reduce the broader costs providing sustained throughput in Riak i.e. to provide equivalent throughput on cheaper hardware.  It is also anticipated in having a fully-featured pure Erlang backend may assist in evolving new features through the Riak ecosystem  which require end-to-end changes, rather than requiring context switching between C++ and Erlang based components.

The store is not expected to offer lower median latency than leveldb (the primary fully-featured Riak backend available today), but it is likely in some cases to offer improvements in throughput.

## More Details

For more details on the store:

- An [introduction](docs/INTRO.md) to LevelEd covers some context to the factors motivating design trade-offs in the store.

- The [design overview](docs/DESIGN.md) explains the actor model used and the basic flow of requests through the store.

- [Future work](docs/FUTURE.md) covers new features being implemented at present, and improvements necessary to make the system production ready.

- There is also a ["Why"](docs/WHY.md) section looking at lower level design choices and the rationale that supports them.

## Is this interesting?

At the initiation of the project I accepted that making a positive contribution to this space is hard - given the superior brainpower and experience of those that have contributed to the KV store problem space in general, and the Riak backend space in particular.

The target at inception was to do something interesting, something that articulates through working software the potential for improvement to exist by re-thinking certain key assumptions and trade-offs.

[Initial volume tests](docs/VOLUME.md) indicate that it is at least interesting.  With improvements in throughput multiple configurations, with the improvement becoming more marked as the test progresses (and the base data volume becomes more realistic).  

The delta in the table below  is the comparison in Riak performance between Leveled and Leveldb.

Test Description                 | Hardware       | Duration |Avg TPS    | Delta (Overall)  | Delta (Last Hour)
:-------------------------------:|:--------------:|:--------:|:---------:|:----------------:|:------------------:
8MB Object, 60 workers, sync     | 5 x i2.2xlarge | 4 hr     |           |                  |
8MB Object, 100 workers, no_sync | 5 x i2.2xlarge | 6 hr     | 14,100.19 | <b>+ 16.15%</b>  | <b>+ 35.92%</b>
8MB Object, 50 workers, no_sync  | 5 x d2.2xlarge | 6 hr     | 10,400.29 | <b>+  8.37%</b>  | <b>+ 23.51%</b> 

Tests generally show a 5:1 improvement in tail latency for LevelEd.

All tests have in common:

- Target Key volume - 200M with pareto distribution of load
- 5 GETs per 1 update 
- RAID 10 (software) drives
- allow_mult=false, lww=false
- modified riak optimised for leveled used in leveled tests


The throughput in leveled is generally CPU-bound, whereas in comparative tests for leveledb the throughput was disk bound.  This potentially makes capacity planning simpler, and opens up the possibility of scaling out to equivalent throughput at much lower cost (as CPU is relatively low cost when compared to disk space at high I/O) - [offering better alignment between resource constraints and the cost of resource](docs/INTRO.md).

More information can be found in the [volume testing section](docs/VOLUME.md).