MonetDB/X100 - A DBMS in the CPU cache

X100 is a new execution engine for the MonetDB system, that improves execution speed and overcomes its main memory limitation. It introduces t

Positional Delta Trees to reconcile updates with read-optimized data storage

We investigate techniques that marry the high readonly analytical query performance of compressed, replicated column storage (“read-optimized” databases) with the ability to handle a high-throughput update workload. Today’s large RAM sizes and the growing gap between sequential vs. random IO disk throughput, bring this once elusive goal in reach, as it has become possible to buffer enough updates in memory to allow background migration of these updates to disk, where efficient sequential IO is amortized among many updates. Our key goal is that read-only queries always see the latest database state, yet are not (significantly) slowed down by the update processing. To this end, we propose the Positional Delta Tree (PDT), that is designed to minimize the overhead of on-the-fly merging of differential updates into (index) scans on stale disk-based data. We describe the PDT data structure and its basic operations (lookup, insert, delete, modify) and provide an in-detail study of their performance. Further, we propose a storage architecture called Replicated Mirrors, that replicates tables in multiple orders, storing each table copy mirrored in both column- and row-wise data formats, and uses PDTs to handle updates. Experiments in the MonetDB/X100 system show that this integrated architecture is able to achieve our main goals

Flexible and efficient IR using array databases

textabstractThe Matrix Framework is a recent proposal by IR researchers to flexibly represent all important information retrieval models in a single multi-dimensional array framework. Computational support for exactly this framework is provided by the array database system SRAM (Sparse Relational Array Mapping) that works on top of a DBMS. Information retrieval models can be specified in its comprehension-based array query language, in a way that directly corresponds to the underlying mathematical formulas. SRAM efficiently stores sparse arrays in (compressed) relational tables and translates and optimizes array queries into relational queries. In this work, we describe a number of array query optimization rules and demonstrate their effect on text retrieval in the TREC TeraByte track (TREC-TB) efficiency task, using the Okapi BM25 model as our example. It turns out that these optimization rules enable SRAM to automatically translate the BM25 array queries into the relational equivalent of inverted list processing including compression, score materialization and quantization, such as employed by custom-built IR systems. The use of the high-performance MonetDB/X100 relational backend, that provides transparent database compression, allows the system to achieve very fast response times with good precision and low resource usage

Super-scalar RAM-CPU cache compression

High-performance data-intensive query processing tasks like OLAP, data mining or scientific data analysis can be severely I/O bound, even when high-e

Super-Scalar RAM-CPU Cache Compression

High-performance data-intensive query processing tasks like OLAP, data mining or scientific data analysis can be severely I/O bound, even whe

Super-Scalar RAM-CPU Cache Compression

textabstractHigh-performance data-intensive query processing tasks like OLAP, data mining or scientific data analysis can be severely I/O bound, even when high-end RAID storage systems are used. Compression can alleviate this bottleneck only if encoding and decoding speeds significantly exceed RAID I/O bandwidth. For this purpose, we propose three new versatile compression schemes (PDICT, PFOR, and PFOR-DELTA) that are specifically designed to extract maximum IPC from modern CPUs. We compare these algorithms with compression techniques used in (commercial) database and information retrieval systems. Our experiments on the MonetDB/X100 database system, using both DSM and PAX disk storage, show that these techniques strongly accelerate TPC-H performance to the point that the I/O bottleneck is eliminated

Flexible and efficient IR using array databases

The Matrix Framework is a recent proposal by IR researchers to flexibly represent all important information retrieval models in a single multi-dimensional array framework. Computational support for exactly this framework is provided by the array database system SRAM (Sparse Relational Array Mapping) that works on top of a DBMS. Information retrieval models can be specified in its comprehension-based array query language, in a way that directly corresponds to the underlying mathematical formulas. SRAM efficiently stores sparse arrays in (compressed) relational tables and translates and optimizes array queries into relational queries. In this work, we describe a number of array query optimization rules and demonstrate their effect on text retrieval in the TREC TeraByte track (TREC-TB) efficiency task, using the Okapi BM25 model as our example. It turns out that these optimization rules enable SRAM to automatically translate the BM25 array queries into the relational equivalent of inverted list processing including compression, score materialization and quantization, such as employed by custom-built IR systems. The use of the high-performance MonetDB/X100 relational backend, that provides transparent database compression, allows the system to achieve very fast response times with good precision and low resource usage