MICA is a scalable in-memory key-value store that han-dles 65.6 to 76.9 million key-value operations per second using a single general-purpose multi-core system. MICA is over 4–13.5x faster than current state-of-the-art sys-tems, while providing consistently high throughput over a variety of mixed

We present Scalaris, an Erlang implementation of a distributed key/value store. It uses, on top of a structured overlay network, replication for data availability and majority based distributed transactions for data consistency. In combination, this implements the ACID properties on a scalable

this paper is to show that by imposing mild extra annotations one can have the best of both worlds: easy to write code which is amenable to equational reasoning, yet modifies its arguments in place and does not allocate heap space unless explicitly told to do so. An aside: a critic may argue that a

causality, either by removing entries from client-id based version vectors or using server-id based version vectors, which cause false con-flicts. We propose a new logical clock mechanism and a logical clock framework that together support a traditional key-value store API, while capturing causal-ity

for performance of analytical processing. Key-words: In-memory, DStore, NoSQL, vertical scaling, document-oriented store, versioning

Abstract-Distributed, replicated keyed-record stores are often used by applications that place a premium on high availability and scalability. Such systems provide fast access to stored records given a primary key value, but access without the primary key may be very slow and expensive

-isting SI implementations tends to be prohibitively expensive. We present a novel MVCC implementation for main-mem-ory database systems that has very little overhead compared to serial execution with single-version concurrency control, even when maintaining serializability guarantees. Updating data in-place

). Consequently, read access is slower comparing to write. These specific features of LSM, including no in-place update and asymmetric read/write performance raise unique challenges in index maintenance for LSM. The structural difference between LSM and B-Tree also prevents mature B-Tree based approaches from

ABSTRACT The Deuteronomy transactional key value store executes millions of serializable transactions/second by exploiting multi-version timestamp order concurrency control. However, it has not supported range operations, only individual record operations (e.g., create, read, update, delete

vertices) and performs up to two orders of magnitude faster than commercial key-value stores and graph databases.