In-situ processing has been proposed as a novel data exploration solution in many domains generating massive amounts of raw data, e.g., astronomy, since it provides immediate SQL querying over raw files. The performance of in-situ processing across a query workload is, however, limited by the speed of full scan, tokenizing, and parsing of the entire data. Online aggregation (OLA) has been introduced as an efficient method for data exploration that identifies uninteresting patterns faster by continuously estimating the result of a computation during the actual processing---the computation can be stopped as early as the estimate is accurate enough to be deemed uninteresting. However, existing OLA solutions have a high upfront cost of randomly shuffling and/or sampling the data.

Traditional databases are not equipped with the adequate functionality to handle the volume and variety of "Big Data". Strict schema definition and data loading are prerequisites even for the most primitive query session. Raw data processing has been proposed as a schema-on-demand alternative that provides instant access to the data. When loading is an option, it is driven exclusively by the current-running query, resulting in sub-optimal performance across a query workload. In this paper, we investigate the problem of workload-driven raw data processing with partial loading. We model loading as fully-replicated binary vertical partitioning. We provide a linear mixed integer programming optimization formulation that we prove to be NP-hard. We design a two-stage heuristic that comes within close range of the optimal solution in a fraction of the time. We extend the optimization formulation and the heuristic to pipelined raw data processing, scenario in which data access and extraction are executed concurrently. We provide three case-studies over real data formats that confirm the accuracy of the model when implemented in a state-of-the-art pipelined operator for raw data processing.

Traditional databases incur a significant data-to-query delay due to the requirement to load data inside the system before querying. Since this is not acceptable in many domains generating massive amounts of raw data (e.g., genomics), databases are entirely discarded. External tables , on the other hand, provide instant SQL querying over raw files. Their performance across a query workload is limited though by the speed of repeated full scans, tokenizing, and parsing of the entire file. In this article, we propose SCANRAW, a novel database meta-operator for in-situ processing over raw files that integrates data loading and external tables seamlessly, while preserving their advantages: optimal performance across a query workload and zero time-to-query. We decompose loading and external table processing into atomic stages in order to identify common functionality. We analyze alternative implementations and discuss possible optimizations for each stage. Our major contribution is a parallel superscalar pipeline implementation that allows SCANRAW to take advantage of the current many- and multicore processors by overlapping the execution of independent stages. Moreover, SCANRAW overlaps query processing with loading by speculatively using the additional I/O bandwidth arising during the conversion process for storing data into the database, such that subsequent queries execute faster. As a result, SCANRAW makes intelligent use of the available system resources—CPU cycles and I/O bandwidth—by switching dynamically between tasks to ensure that optimal performance is achieved. We implement SCANRAW in a state-of-the-art database system and evaluate its performance across a variety of synthetic and real-world datasets. Our results show that SCANRAW with speculative loading achieves the best-possible performance for a query sequence at any point in the processing. Moreover, SCANRAW maximizes resource utilization for the entire workload execution while speculatively loading data and without interfering with normal query processing.

Traditional databases incur a significant data-to-query delay due to the requirement to load data inside the system before querying. Since this is not acceptable in many domains generating massive amounts of raw data, e.g., genomics, databases are entirely discarded. External tables, on the other hand, provide instant SQL querying over raw files. Their performance across a query workload is limited though by the speed of repeated full scans, tokenizing, and parsing of the entire file. In this paper, we propose SCANRAW, a novel database physical operator for in-situ processing over raw files that integrates data loading and external tables seamlessly while preserving their advantages: optimal performance across a query workload and zero time-to-query. Our major contribution is a parallel super-scalar pipeline implementation that allows SCANRAW to take advantage of the current many- and multi-core processors by overlapping the execution of independent stages. Moreover, SCANRAW overlaps query processing with loading by speculatively using the additional I/O bandwidth arising during the conversion process for storing data into the database such that subsequent queries execute faster. As a result, SCANRAW makes optimal use of the available system resources -- CPU cycles and I/O bandwidth -- by switching dynamically between tasks to ensure that optimal performance is achieved. We implement SCANRAW in a state-of-the-art database system and evaluate its performance across a variety of synthetic and real-world datasets. Our results show that SCANRAW with speculative loading achieves optimal performance for a query sequence at any point in the processing. Moreover, SCANRAW maximizes resource utilization for the entire workload execution while speculatively loading data and without interfering with normal query processing.

Scientific data have dual structure. Raw data are preponderantly ordered multi-dimensional arrays or sequences while metadata and derived data are best represented as unordered relations. Scientific data processing requires complex operations over arrays and relations. These operations cannot be expressed using only standard linear and relational algebra operators, respectively. Existing scientific data processing systems are designed for a single data model and handle complex processing at the application level.

We present GLADE, a scalable distributed system for large scale data analytics. GLADE takes analytical functions expressed through the User-Defined Aggregate (UDA) interface and executes them efficiently on the input data. The entire computation is encapsulated in a single class which requires the definition of four methods. The runtime takes the user code and executes it right near the data by taking full advantage of the parallelism available inside a single machine as well as across a cluster of computing nodes.