AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B + -tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.

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.

At UC Irvine, we are building a next generation parallel database system, called ASTERIX, as our approach to addressing today's "Big Data" management challenges. ASTERIX aims to combine time-tested principles from parallel database systems with those of the Web-scale computing community, such as fault tolerance for long running jobs. In this demo, we present a whirlwind tour of ASTERIX, highlighting a few of its key features. We will demonstrate examples of our data definition language to model semi-structured data, and examples of interesting queries using our declarative query language. In particular, we will show the capabilities of ASTERIX for answering geo-spatial queries and fuzzy queries, as well as ASTERIX' data feed construct for continuously ingesting data.