Couchbase Capella is a scalable document-oriented database service in the cloud. Its existing Capella Operational service is based on a shared-nothing architecture and supports high volumes of low-latency queries and updates for JSON documents. Its new Capella Columnar cloud service complements the Operational service. The Capella Columnar service supports complex analytical queries (e.g., ad hoc joins and aggregations) over large collections of JSON documents that can originate from a variety of Couchbase and non-Couchbase data sources and formats and can either be stored and managed by the Capella Columnar service or externally stored and accessed on demand at query time. This paper describes the new Capella Columnar service, looking both over and under the hood.

In this paper we discuss the optimization of multiple-relation multiple-disjunct queries in a relational database system. Since optimization techniques for conjunctive (single disjunct) queries in relational databases are well known [Smith75, Wong76, Selinger79, Yao79, Youssefi79], the natural way to evaluate a multiple-disjunct query was to execute each disjunct independently [Bernstein81, Kerschberg82] However, evaluating each disjunct independently may be very inefficient. In this paper, we develop methods that merge two or more disjuncts to form a term. The advantage of merging disjuncts to form terms lies in the fact that each term can be evaluated with a single scan of each relation that is present in the term. In addition, the number of times a join is performed will also be reduced when two or more disjuncts are merged. The criteria for merging a set of disjuncts will be presented. As we will see, the number of times each relation in the query is scanned will be equal to the number of terms. Thus, minimizing the number of terms will minimize the number of scans for each relation. We will formulate the problem of minimizing the number of scans as one of covering a merge graph by a minimum number of complete merge graphs which are a restricted class of Cartesian product graphs. In general, the problem of minimizing the number of scans is NP-complete. We present polynomial time algorithms for special classes of merge graphs. We also present a heuristic for general merge graphs.

Multi-dimensional queries commonly occur in databases dealing with geographical, image, and VLSI databases.A typical two dimensional query in a geographical database might involve finding all cities within certain latitudinal and longitudinal bounds.Several multi-dimensional index struc tures for point data have been proposed in the literature.KDB trees [Robinson81] and and Grid files [Nievergelt84] are among the more popular ones.However, there has been no work in designing multi-dimensional histograms to aid in the optimization process using these multi-dimensional index structures.In order for an optimizer to select an appropriate access path for a multi-dimensional query, fairly accurate selectivity estimates must be available to it.Selectivity esti mates are also useful in determining appropriate join methods that follow the selections.In this paper we present an algorithm for generating equi-depth, multi-dimensional histograms.One might expect that the cost of building a D-dimensional histogram would be at least D times the cost of sorting the relation on a single attribute.We show, in our algorithm, that the sorting cost of building a D-dimensional histogram is significantly less than the cost of sorting the relation D times.We present a main memory data structure for storing the histograms and discuss two schemes for estimating the number of tuples that will be "etrieved by a given query.Experimental results are presented that show the efficacy of our histograms.The usefulness of a sampling technique in generating histograms at a very low cost is also explored. Related WorkThe System R optimizer [Selinger79] used simple statis tics, such as the minimum and maximum values in a given column, to estimate selectivity factors.Using such simple statistics will produce good selectivity estimates only if the attribute values are uniformly distributed.Since attribute values can have other distributions, it has become