Abstract
Social networks, online communities, mobile devices, and instant messaging applications generate complex, unstructured data at a high rate, resulting in large volumes of data. This poses new challenges for data management systems that aim to ingest, store, index, and analyze such data efficiently. In response, we released the first public version of AsterixDB, an open-source Big Data Management System (BDMS), in June of 2013. This paper describes the storage management layer of AsterixDB, providing a detailed description of its ingestion-oriented approach to local storage and a set of initial measurements of its ingestion-related performance characteristics.
In order to support high frequency insertions, AsterixDB has wholly adopted Log-Structured Merge-trees as the storage technology for all of its index structures. We describe how the AsterixDB software framework enables "LSM-ification" (conversion from an in-place update, disk-based data structure to a deferred-update, append-only data structure) of any kind of index structure that supports certain primitive operations, enabling the index to ingest data efficiently. We also describe how AsterixDB ensures the ACID properties for operations involving multiple heterogeneous LSM-based indexes. Lastly, we highlight the challenges related to managing the resources of a system when many LSM indexes are used concurrently and present AsterixDB's initial solution.