In this new era of "big data", traditional DBMSs are under attack from two sides. At one end of the spectrum, the use of document store NoSQL systems (e.g. MongoDB) threatens to move modern Web 2.0 applications away from traditional RDBMSs. At the other end of the spectrum, big data DSS analytics that used to be the domain of parallel RDBMSs is now under attack by another class of NoSQL data analytics systems, such as Hive on Hadoop. So, are the traditional RDBMSs, aka "big elephants", doomed as they are challenged from both ends of this "big data" spectrum? In this paper, we compare one representative NoSQL system from each end of this spectrum with SQL Server, and analyze the performance and scalability aspects of each of these approaches (NoSQL vs. SQL) on two workloads (decision support analysis and interactive data-serving) that represent the two ends of the application spectrum. We present insights from this evaluation and speculate on potential trends for the future.
Multi-pattern matching involves matching a data item against a large database of "signature" patterns. Existing algorithms for multi-pattern matching do not scale well as the size of the signature database increases. In this paper, we present sigMatch -- a fast, versatile, and scalable technique for multi-pattern signature matching. At its heart, sigMatch organizes the signature database into a (processor) cache-efficient q-gram index structure, called the sigTree. The sigTree groups patterns based on common sub-patterns, such that signatures that don't match can be quickly eliminated from the matching process. The sigTree also uses parallel Bloom filters and a technique to reduce imbalances across groups, for improved performance. Using extensive empirical evaluation across three diverse domains, we show that sigMatch often outperforms existing methods by an order of magnitude or more.
There is a growing interest in making relational DBMSs work synergistically with MapReduce systems. However, there are interesting technical challenges associated with figuring out the right balance between the use and co-deployment of these systems. This paper focuses on one specific aspect of this balance, namely how to leverage the superior indexing and query processing power of a relational DBMS for data that is often more cost-effectively stored in Hadoop/HDFS. We present a method to use conventional B+-tree indices in an RDBMS for data stored in HDFS and demonstrate that our approach is especially effective for highly selective queries.
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