Over the past several decades, the amount of data being stored and analyzed has increased exponentially. Businesses across industries and sectors have begun relying on this data to improve products, evaluate performance, and make business-critical decisions. However, as data volumes have increasingly become internet-scale, businesses have needed to manage historical and new data in a cost-effective and scalable manner, while analyzing it using a high number of concurrent queries and an expectation of real-time latencies (e.g. less than one second, depending on the use case). This paper presents an overview of ClickHouse, a popular open-source OLAP database designed for high-performance analytics over petabyte-scale data sets with high ingestion rates. Its storage layer combines a data format based on traditional log-structured merge (LSM) trees with novel techniques for continuous transformation (e.g. aggregation, archiving) of historical data in the background. Queries are written in a convenient SQL dialect and processed by a state-of-the-art vectorized query execution engine with optional code compilation. ClickHouse makes aggressive use of pruning techniques to avoid evaluating irrelevant data in queries. Other data management systems can be integrated at the table function, table engine, or database engine level. Real-world benchmarks demonstrate that ClickHouse is amongst the fastest analytical databases on the market.

We demonstrate Habitat, a declarative observational debugger for SQL. Habitat facilitates true language-level (not: plan-level) debugging of, probably flawed, SQL queries that yield unexpected results. Users may mark arbitrary SQL subexpressions---ranging from literals, over fragments of predicates, to entire subquery blocks---to observe whether these evaluate as expected.

We report on a query compilation technique that enables the construction of alternative efficient query providers for Microsoft's Language Integrated Query (LINQ) framework. LINQ programs are mapped into an intermediate algebraic form, suitable for execution on any SQL:1999-capable relational database system. This compilation technique leads to query providers that (1) faithfully preserve list order and nesting, both being core features of the LINQ data model, (2) support the complete family of LINQ's Standard Query Operators, (3) bring database support to LINQ to XML where the original provider performs in-memory query evaluation, and, most importantly, (4) emit SQL statement sequences whose size is only determined by the input query's result type (and thus independent of the database size). A sample query scenario uses this LINQ provider to marry database-resident TPC-H and XMark data---resulting in a unique query experience that exhibits quite promising performance characteristics, especially for large data instances.

We demonstrate an efficient LINQ to SQL provider and its significant impact on the runtime performance of LINQ programs that process large data volumes. This alternative provider is based on Ferry, compilation technology that lets relational database systems participate in the evaluation of first-order functional programs over nested, ordered data structures. The Ferry-based provider seamlessly hooks into the .NET LINQ framework and generates SQL code that strictly adheres to the semantics of the LINQ data model. Ferry comes with strong code size guarantees and complete support for the LINQ Standard Query Operator family, enabling a truly interactive and compelling LINQ demonstration. A variety of inspection holes may be opened to learn about the internals of the Ferry-based LINQ to SQL provider.

We demonstrate the language Ferry and its editing, compilation, and execution environment FerryDeck. Ferry's type system and operations match those of scripting or programming languages; its compiler has been designed to emit (bundles of) compliant and efficient SQL:1999 statements. Ferry acts as glue that permits a programming style in which developers access database tables using their programming language's own syntax and idioms -- the Ferry-expressible fragments of such programs may be executed by a relational database back-end, i.e., close to the data. The demonstrator FerryDeck implements compile-and-execute-as-you-type interactivity for Ferry and offers a variety of (graphical) hooks to explore and inspect this approach to database-supported program execution.