A Strategy for Reducing I/O and Improving Query Processing Time in an Oracle Data Warehouse Environment

In the current information age as the saying goes, time is money. For the modern information worker, decisions must often be made quickly. Every extra minute spent waiting for critical data could mean the difference between financial gain and financial ruin. Despite the importance of timely data retrieval, many organizations lack even a basic strategy for improving the performance of their data warehouse based reporting systems. This project explores the idea that a strategy making use of three database performance improvement techniques can reduce I/O (input/output operations) and improve query processing time in an information system designed for reporting. To demonstrate that these performance improvement goals can be achieved, queries were run on ordinary tables and then on tables utilizing the performance improvement techniques. The I/O statistics and processing times for the queries were compared to measure the amount of performance improvement. The measurements were also used to explain how these techniques may be more or less effective under certain circumstances, such as when a particular type of query is run. The collected I/O and time based measurements showed a varying degree of improvement for each technique based on the query used. A need to match the types of queries commonly run on the system to the performance improvement technique being implemented was found to be an important consideration. The results indicated that in a reporting environment these performance improvement techniques have the potential to reduce I/O and improve query performance

Decoding billions of integers per second through vectorization

In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized scheme called SIMD-BP128 that improves over previously proposed vectorized approaches. It is nearly twice as fast as the previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the same time, SIMD-BP128 saves up to 2 bits per integer. For even better compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while being two times faster during decoding.Comment: For software, see https://github.com/lemire/FastPFor, For data, see http://boytsov.info/datasets/clueweb09gap

Building Efficient Query Engines in a High-Level Language

Abstraction without regret refers to the vision of using high-level programming languages for systems development without experiencing a negative impact on performance. A database system designed according to this vision offers both increased productivity and high performance, instead of sacrificing the former for the latter as is the case with existing, monolithic implementations that are hard to maintain and extend. In this article, we realize this vision in the domain of analytical query processing. We present LegoBase, a query engine written in the high-level language Scala. The key technique to regain efficiency is to apply generative programming: LegoBase performs source-to-source compilation and optimizes the entire query engine by converting the high-level Scala code to specialized, low-level C code. We show how generative programming allows to easily implement a wide spectrum of optimizations, such as introducing data partitioning or switching from a row to a column data layout, which are difficult to achieve with existing low-level query compilers that handle only queries. We demonstrate that sufficiently powerful abstractions are essential for dealing with the complexity of the optimization effort, shielding developers from compiler internals and decoupling individual optimizations from each other. We evaluate our approach with the TPC-H benchmark and show that: (a) With all optimizations enabled, LegoBase significantly outperforms a commercial database and an existing query compiler. (b) Programmers need to provide just a few hundred lines of high-level code for implementing the optimizations, instead of complicated low-level code that is required by existing query compilation approaches. (c) The compilation overhead is low compared to the overall execution time, thus making our approach usable in practice for compiling query engines

Self-organizing tuple reconstruction in column-stores

Column-stores gained popularity as a promising physical design alternative. Each attribute of a relation is physically stored as a separate column allowing queries to load only the required attributes. The overhead incurred is on-the-fly tuple reconstruction for multi-attribute queries. Each tuple reconstruction is a join of two columns based on tuple IDs, making it a significant cost component. The ultimate physical design is to have multiple presorted copies of each base table such that tuples are already appropriately organized in multiple different orders across the various columns. This requires the ability to predict the workload, idle time to prepare, and infrequent updates. In this paper, we propose a novel design, \emph{partial sideways cracking}, that minimizes the tuple rec

Building Efficient Query Engines in a High-Level Language

In this paper we advocate that it is time for a radical rethinking of database systems design. Developers should be able to leverage high-level programming languages without having to pay a price in efficiency. To realize our vision of abstraction without regret, we present LegoBase, a query engine written in the high-level programming language Scala. The key technique to regain efficiency is to apply generative programming: the Scala code that constitutes the query engine, despite its high-level appearance, is actually a program generator that emits specialized, low-level C code. We show how the combination of high-level and generative programming allows to easily implement a wide spectrum of optimizations that are difficult to achieve with existing low-level query compilers, and how it can continuously optimize the query engine. We evaluate our approach with the TPC-H benchmark and show that: (a) with all optimizations enabled, our architecture significantly outperforms a commercial in-memory database system as well as an existing query compiler, (b) these performance improvements require programming just a few hundred lines of high-level code instead of complicated low-level code that is required by existing query compilers and, finally, that (c) the compilation overhead is low compared to the overall execution time, thus making our approach usable in practice for efficiently compiling query engines

Self-organizing tuple reconstruction in column-stores

Column-stores gained popularity as a promising physical de-sign alternative. Each attribute of a relation is physically stored as a separate column allowing queries to load only the required attributes. The overhead incurred is on-the-fly tuple reconstruction for multi-attribute queries. Each tu-ple reconstruction is a join of two columns based on tuple IDs, making it a significant cost component. The ultimate physical design is to have multiple presorted copies of each base table such that tuples are already appropriately orga-nized in multiple different orders across the various columns. This requires the ability to predict the workload, idle time to prepare, and infrequent updates. In this paper, we propose a novel design, partial side-ways cracking, that minimizes the tuple reconstruction cost in a self-organizing way. It achieves performance similar to using presorted data, but without requiring the heavy initial presorting step itself. Instead, it handles dynamic, unpredictable workloads with no idle time and frequent up-dates. Auxiliary dynamic data structures, called cracker maps, provide a direct mapping between pairs of attributes used together in queries for tuple reconstruction. A map is continuously physically reorganized as an integral part of query evaluation, providing faster and reduced data access for future queries. To enable flexible and self-organizing be-havior in storage-limited environments, maps are material-ized only partially as demanded by the workload. Each map is a collection of separate chunks that are individually reor-ganized, dropped or recreated as needed. We implemented partial sideways cracking in an open-source column-store. A detailed experimental analysis demonstrates that it brings significant performance benefits for multi-attribute queries

H2O: A Hands-free Adaptive Store

Modern state-of-the-art database systems are designed around a single data storage layout. This is a fixed decision that drives the whole architectural design of a database system, i.e., row-stores, column-stores. However, none of those choices is a universally good solution; different workloads require different storage layouts and data access methods in order to achieve good performance. In this paper, we present the H2O system which introduces two novel concepts. First, it is flexible to support multiple storage layouts and data access patterns in a single engine. Second, and most importantly, it decides on-the-fly, i.e., during query processing, which design is best for classes of queries and the respective data parts. At any given point in time, parts of the data might be materialized in various patterns purely depending on the query workload; as the workload changes and with every single query, the storage and access patterns continuously adapt. In this way, H2O makes no a priori and fixed decisions on how data should be stored, allowing each single query to enjoy a storage and access pattern which is tailored to its specific properties. We present a detailed analysis of H2O using both synthetic benchmarks and realistic scientific workloads. We demonstrate that while existing systems cannot achieve maximum performance across all workloads, H2O can always match the best case performance without requiring any tuning or workload knowledge