Analyzing and understanding the characteristics of the incoming workload is crucial in unraveling trends and tuning the performance of a database system. In this work, we present Oracle Workload Intelligence (WI), a tool for workload modeling and mining, as our attempt to infer the processes that generate a given workload. WI consists of two main functionalities. First, WI derives a model that captures the main characteristics of the workload without overfitting, which makes it likely to generalize well to unseen instances of the workload. Such a model provides insights into the most frequent code paths in the application that drives the workload, and also enables optimizations inside the database system that target sequences of query statements. Second, WI can compare the models of different snapshots of the workload to detect whether the workload has changed. Such changes might indicate new trends, regressions, problems, or even security issues. We demonstrate the effectiveness of WI with an experimental study on synthetic workloads and customer-provided application benchmarks.

Compression has historically been used to reduce the cost of storage, I/Os from that storage, and buffer pool utilization, at the expense of the CPU required to decompress data every time it is queried. However, significant additional CPU efficiencies can be achieved by deferring decompression as late in query processing as possible and performing query processing operations directly on the still-compressed data. In this paper, we investigate the benefits and challenges of performing joins on compressed (or encoded) data. We demonstrate the benefit of independently optimizing the compression scheme of each join column, even though join predicates relating values from multiple columns may require translation of the encoding of one join column into the encoding of the other. We also show the benefit of compressing "payload" data other than the join columns "on the fly," to minimize the size of hash tables used in the join. By partitioning the domain of each column and defining separate dictionaries for each partition, we can achieve even better overall compression as well as increased flexibility in dealing with new values introduced by updates. Instead of decompressing both join columns participating in a join to resolve their different compression schemes, our system performs a light-weight mapping of only qualifying rows from one of the join columns to the encoding space of the other at run time. Consequently, join predicates can be applied directly on the compressed data. We call this procedure encoding translation. Two alternatives of encoding translation are developed and compared in the paper. We provide a comprehensive evaluation of these alternatives using product implementations of each on the TPC-H data set, and demonstrate that performing joins on encoded and partitioned data achieves both superior performance and excellent compression.