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.

Hyperparameter optimization is an essential component in many data science pipelines and typically entails exhaustive time and resource-consuming computations in order to explore the combinatorial search space. Similar to this problem, other key operations in data science pipelines exhibit the exact same properties. Important examples are: neural architecture search, where the goal is to identify the best design choices for a neural network, and query cardinality estimation, where given different predicate values for a SQL query the goal is to estimate the size of the output. In this paper, we abstract away those essential components of data science pipelines and we model them as instances of tensor completion, where each variable of the search space corresponds to one mode of the tensor. Now the goal is to identify all missing entries of the tensor, corresponding to all combinations of variable values, starting from a very small sample of observed entries. In order to do so, we first conduct a thorough experimental evaluation of existing state-of-the-art tensor completion techniques. We also introduce domaininspired adaptations (such as smoothness across the discretized variable space) and an ensemble technique which is able to achieve state-of-the-art performance. We extensively evaluate existing and proposed methods in a number of generated datasets corresponding to (a) hyperparameter optimization for non-neural network models, (b) neural architecture search, and (c) variants of query cardinality estimation. By doing this, we demonstrate the effectiveness of tensor completion as a tool for automating data science pipelines. Furthermore, we release our generated datasets and code in order to provide benchmarks for future work on this topic.

Database management systems that support hybrid workloads (i.e., HTAP or HOAP) first arose in the relational world. Such hybrid data management support in the document database (NoSQL) world is also gaining popularity in both commercial and research arenas. The CH2 benchmark was proposed in 2021 to evaluate such hybrid NoSQL platforms. In addition to operational and analytical services, full-text search is a key component of NoSQL platforms that provides a search engine-like query processing capability on JSON documents. In this paper, we present CH3, a mixed workload benchmark for evaluating scalable NoSQL platforms with OLTP, OLAP, and full-text search (FTS) workloads. Like CH2, the CH3 benchmark borrows from and extends both TPC-C and TPC-H. However, CH3 generates meaningful text content and includes FTS indexes and FTS queries on these indexes to model an FTS workload. This paper presents the required extensions from CH2 to address FTS workloads, the detailed design of CH3, and performance results obtained by running the CH3 benchmark against Couchbase Server (which offers Query, Analytics, and Search Services). The results provide insight into the performance of the Search Service, the performance isolation among OLTP, OLAP and FTS workloads, and the horizontal scalability of Couchbase Server as well as the effectiveness of CH3 for evaluating the mixed workload performance of such NoSQL platforms.