The Cache Augmented Data Store (CADS) architecture extends a persistent data store with an in-memory cache manager. It is widely deployed to support read-intensive workloads. However, its write-around and write-through policies prevent the caching tier from absorbing write load. This means the data store layer must scale to process writes even when the extra capacity is not needed for read load. We address this limitation by devising a write-back technique to enable the caching layer to process both reads and writes. This technique preserves ACID transactions. We present a client side implementation of write-back and evaluate it using the YCSB, BG, and TPC-C benchmarks. In addition, we compare our write-back with (a) write-back policy of a data store such as MongoDB and (b) write-back policy of a host-side cache such as Flashcache.

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Diverse applications implement read and write transactions using a data store. It is challenging to evaluate whether transactions that constitute an application provide strong consistency. It requires an end-to-end testing as an application may consist of several components that impact the consistency of data. Polygraph is a conceptual plug-n-play framework to quantify the amount of anomalies produced by an application. We show several use cases of Polygraph for two major application classes: e-commerce and cloud. One long-term objective of Polygraph is to reduce the cost and time required to test a data driven application, so that developers may focus more time and effort on applications' features and requirements.

Cache augmented SQL, CASQL, systems enhance the performance of simple operations that read and write a small amount of data from big data. They do so by looking up the results of computations that query the database in a key-value store (KVS) instead of processing them using a relational database management system (RDBMS). These systems incur undesirable race conditions that cause the KVS to produce stale data. This paper presents the IQ framework that provides strong consistency with no modification to the RDBMS. It consists of two non-blocking leases, Inhibit (I) and Quarantine (Q). Ratings obtained from a social networking benchmark named BG show the proposed framework has minimal impact on system performance while providing strong consistency guarantees.