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 deferredupdate, 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.
Many Web sites support keyword search on their spatial data, such as business listings and photos. In these systems, inconsistencies and errors can exist in both queries and the data. To bridge the gap between queries and data, it is important to support approximate keyword search on spatial data. In this paper we study how to answer such queries efficiently. We focus on a natural index structure that augments a tree-based spatial index with capabilities for approximate keyword search. We systematically study how to efficiently combine these two types of indexes, and how to search the resulting index to find answers. We develop three algorithms for constructing the index, successively improving the time and space efficiency by exploiting the textual and spatial properties of the data. We experimentally demonstrate the efficiency of our techniques on real, large datasets.
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.
With the social-media data explosion, near real-time queries, particularly those of a spatio-temporal nature, can be challenging. In this paper, we show how to efficiently answer queries that target recent data within very large data sets. We describe a solution that exploits a natural partitioning property that LSM-based indexes have for components, allowing us to filter out many components when answering queries. Our solution is generalizable to any LSM-based index structure, and can be applied not just on temporal fields (e.g., based on recency), but on any "time-correlated fields" such as Universally Unique Identifiers (UUIDs), user-provided integer ids, etc. We have implemented and experimentally evaluated the solution in the context of the AsterixDB system.
Document database systems store self-describing semi-structured records, such as JSON, "as-is" without requiring the users to pre-define a schema. This provides users with the flexibility to change the structure of incoming records without worrying about taking the system offline or hindering the performance of currently running queries. However, the flexibility of such systems does not free. The large amount of redundancy in the records can introduce an unnecessary storage overhead and impact query performance. Our focus in this paper is to address the storage overhead issue by introducing a tuple compactor framework that infers and extracts the schema from self-describing semi-structured records during the data ingestion. As many prominent document stores, such as MongoDB and Couchbase, adopt Log Structured Merge (LSM) trees in their storage engines, our framework exploits LSM lifecycle events to piggyback the schema inference and extraction operations. We have implemented and empirically evaluated our approach to measure its impact on storage, data ingestion, and query performance in the context of Apache AsterixDB.
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