CERN’s Large Hadron Collider (LHC), the world’s largest high-energy physics (HEP) instrument, collects tens of petabytes of data per year. The LHC’s next phase is expected to produce up to ten times more data, which calls for novel, more efficient ways of storing and processing these data.HEP collider data are prepared and provided to physicists as read-only data sets, stored in a custom columnar data format. While traditionally all data needed for a particular analysis were captured in a single data set, the increasing scale of the LHC and the advent of modern analysis techniques now requires analysis workflows to use data from different data sets. However, the processing model established across the HEP community does not yet provide a straightforward way to achieve this and currently relies heavily on data duplication to produce the desired data sets. This leads to significant overhead in analysis workflows, both in runtime and storage.To reduce this overhead, we propose more efficient ways to combine HEP data sets. Specifically, we design union and join operations, as defined in relational algebra, to combine HEP data sets at runtime, eliminating therefore the need for data duplication. In this paper, we specify these operations for HEP data and introduce EVENTSETPROCESSOR – an engine that implements these operations for HEP data processing. Through a first prototype, we show that this engine integrates well in existing HEP workflows, and that it can perform up to twice as fast as the current approach.

Modern analytical database systems offer high-performance inmemory joins. However, if the build side of a join does not fit in RAM, performance degrades sharply due to switching to traditional external join algorithms such as sort-merge. In streaming query execution, this problem is worsened if multiple joins are evaluated simultaneously, as the database system must decide how to allocate memory to each join, which can greatly affect performance. We revisit larger-than-memory join processing on modern hardware, aiming for robust performance that avoids a "performance cliff" when memory runs out, even in query plans with many joins. To achieve this, we propose three techniques. First, an adaptive, external hash join algorithm that stores temporary data in a unified buffer pool that oversees temporary and persistent data. Second, an optimizer that creates expressions to compress columns at runtime, reducing the size of materialized temporary data. Third, a strategy for dynamically managing the memory of concurrent operators during query execution to reduce spilling. We integrate these techniques into DuckDB and experimentally show that when processing memory-intensive join query plans, our implementation gracefully degrades performance as the space requirement exceeds the memory limit. This greatly increases the size of datasets that can be processed on economical hardware.

Analytical database systems offer high-performance in-memory aggregation. If there are many unique groups, temporary query intermediates may not fit RAM, requiring the use of external storage. However, switching from an in-memory to an external algorithm can degrade performance sharply. We revisit external hash aggregation on modern hardware, aiming instead for robust performance that avoids a “performance cliff” when memory runs out. To achieve this, we introduce two techniques for handling temporary query intermediates. First, we propose unifying the memory management of temporary and persistent data. Second, we propose using a page layout that can be spilled to disk despite being optimized for main memory performance. These two techniques allow operator implementations to process larger-than-memory query intermediates with only minor modifications. We integrate these into DuckDB's parallel hash aggregation. Experimental results show that our implementation gracefully degrades performance as query intermediates exceed the available memory limit, while main memory performance is competitive with other analytical database systems.

Sorting is one of the most well-studied problems in computer science and a vital operation for relational database systems. Despite this, little research has been published on implementing an efficient relational sorting operator. In this work, we explore the design space of sorting in a relational database system. We use micro-benchmarks to explore how to sort relational data efficiently in analytical database systems, taking into account different query execution engines as well as row and columnar data formats. We show that, regardless of architectural differences between query engines, sorting rows is almost always more efficient than sorting columnar data, even if this requires converting the data from columns to rows and back. Sorting rows efficiently is challenging for systems with an interpreted execution engine, as their implementation has to stay generic. We show that these challenges can be overcome with several existing techniques. Based on our findings, we implement a highly optimized row-based sorting approach in the DuckDB open-source in-process analytical database management system, which has a vectorized interpreted query engine. We compare DuckDB with four analytical database systems and find that DuckDB’s sort implementation outperforms query engines that sort using a columnar data format.

We introduce DuckDB-Wasm, a WebAssembly version of the database system DuckDB, to provide fast analytical processing for the Web. DuckDB-Wasm evaluates SQL queries asynchronously in web workers, supports efficient user-defined functions written in JavaScript, and features a browser-agnostic filesystem that reads local and remote data in pages. DuckDB-Wasm outperforms previous data processing libraries for the Web in the TPC-H benchmark at multiple scale factors. We demonstrate the capabilities of an analytical database in the browser using an interactive SQL shell.

Tensor Processing Units are specialized hardware devices built to train and apply Machine Learning models at high speed through high-bandwidth memory and massive instruction parallelism. In this short paper, we investigate how relational operations can be translated to those devices. We present mapping of relational operators to TPU-supported TensorFlow operations and experimental results comparing with GPU and CPU implementations. Results show that while raw speeds are enticing, TPUs are unlikely to improve relational query processing for now due to a variety of issues.

The immense popularity of SQLite shows that there is a need for unobtrusive in-process data management solutions. However, there is no such system yet geared towards analytical workloads. We demonstrate DuckDB, a novel data management system designed to execute analytical SQL queries while embedded in another process. In our demonstration, we pit DuckDB against other data management solutions to showcase its performance in the embedded analytics scenario. DuckDB is available as Open Source software under a permissive license.

Interactive exploration of large volumes of data is increasingly common, as data scientists attempt to extract interesting information from large opaque data sets. This scenario presents a difficult challenge for traditional database systems, as (1) nothing is known about the query workload in advance, (2) the query workload is constantly changing, and (3) the system must provide interactive responses to the issued queries. This environment is challenging for index creation, as traditional database indexes require upfront creation, hence a priori workload knowledge, to be efficient. In this paper, we introduce Progressive Indexing , a novel performance-driven indexing technique that focuses on automatic index creation while providing interactive response times to incoming queries. Its design allows queries to have a limited budget to spend on index creation. The indexing budget is automatically tuned to each query before query processing. This allows for systems to provide interactive answers to queries during index creation while being robust against various workload patterns and data distributions.

We leverage vectorized User-Defined Functions (UDFs) to efficiently integrate unchanged machine learning pipelines into an analytical data management system.The entire pipelines including data, models, parameters and evaluation outcomes are stored and executed inside the database system.Experiments using our MonetDB/Python UDFs show greatly improved performance due to reduced data movement and parallel processing opportunities.In addition, this integration enables meta-analysis of models using relational queries.

Column Imprints is a pre-filtering secondary index for answering range queries. The main feature of imprints is that they are light-weight and are based on compressed bit-vectors, one per cacheline, that quickly determine if the values in that cacheline satisfy the predicates of a query. The main overhead of the imprints implementation is the many sequential value comparisons against the boundaries of a virtual equi-height histogram. Similarly, during query scans, many sequential value comparisons are performed to identify false positives. In this paper, we speed-up the process of imprints creation and querying by using advanced vectorization techniques. We also experimentally explore the benefits of stretching imprints to larger bit-vector sizes and blocks of data, using 256-bit SIMD registers. Our findings are very promising for both imprints and for future index design research that would employ advanced vectorization techniques and larger (up to 512-bit) and more (from 16 now to 32) SIMD registers.

Comma Separated Value (CSV) files are commonly used to represent data. CSV is a very simple format, yet we show that it gives rise to a surprisingly large amount of ambiguities in its parsing and interpretation. We summarize the state-of-the-art in CSV parsers, which typically make a linear series of parsing and interpretation decisions, such that any wrong decision at an earlier stage can negatively affect all downstream decisions. Since computation time is much less scarce than human time, we propose to turn CSV parsing into a ranking problem. Our quality-oriented multi-hypothesis CSV parsing approach generates several concurrent hypotheses about dialect, table structure, etc. and ranks these hypotheses based on quality features of the resulting table. This approach makes it possible to create an advanced CSV parser that makes many different decisions, yet keeps the overall parser code a simple plug-in infrastructure. The complex interactions between these decisions are taken care of by searching the hypothesis space rather than by having to program these many interactions in code. We show that our approach leads to better parsing results than the state of the art and facilitates the parsing of large corpora of heterogeneous CSV files.

Transferring a large amount of data from a database to a client program is a surprisingly expensive operation. The time this requires can easily dominate the query execution time for large result sets. This represents a significant hurdle for external data analysis, for example when using statistical software. In this paper, we explore and analyse the result set serialization design space. We present experimental results from a large chunk of the database market and show the inefficiencies of current approaches. We then propose a columnar serialization method that improves transmission performance by an order of magnitude.

Data Scientists rely on vector-based scripting languages such as R, Python and MATLAB to perform ad-hoc data analysis on potentially large data sets. When facing large data sets, they are only efficient when data is processed using vectorized or bulk operations. At the same time, overwhelming volume and variety of data as well as parsing overhead suggests that the use of specialized analytical data management systems would be beneficial. Data might also already be stored in a database. Efficient execution of data analysis programs such as data mining directly inside a database greatly improves analysis efficiency.

Earth observation sciences produce large sets of data which are inherently rich in spatial and geo-spatial information. Together with live data collected from monitoring systems and large collections of semantically rich objects they provide new opportunities for advanced eScience research on climatology, urban planing and smart cities. Such combination of heterogeneous data sets forms a new source of knowledge. Efficient knowledge extraction from them is an eScience challenge. It requires efficient bulk data injection from both static and streaming data sources, dynamic adaptation of the physical and logical schema, efficient methods to correlate spatial and temporal data, and flexibility to (re-)formulate the research question at any time. In this work, we present a data management layer over a column-oriented relational data management system that provides efficient analysis of spatiotemporal data. It provides fast data ingestion through different data loaders, tabular and array based storage, and a dynamic step-wise exploration.

We make the suggestion that instead of implementing custom index structures and query evaluation algorithms, IR researchers should simply store document representations in a column-oriented relational database and implement ranking models using SQL. For rapid prototyping, this is particularly advantageous since researchers can explore new scoring functions and features by simply issuing SQL queries, without needing to write imperative code. We demonstrate the feasibility of this approach by an implementation of conjunctive BM25 using two modern column stores. Experiments on a web collection show that a retrieval engine built in this manner achieves effectiveness and efficiency on par with custom-built retrieval engines, but provides many additional advantages, including cleaner query semantics, a simpler architecture, built-in support for error analysis, and the ability to exploit advances in database technology "for free".

Statistical analysts have long been struggling with evergrowing data volumes. While specialized data management systems such as relational databases would be able to handle the data, statistical analysis tools are far more convenient to express complex data analyses. An integration of these two classes of systems has the potential to overcome the data management issue while at the same time keeping analysis convenient. However, one must keep a careful eye on implementation overheads such as serialization. In this paper, we propose the in-process integration of data management and analytical tools. Furthermore, we argue that a zero-copy integration is feasible due to the omnipresence of C-style arrays containing native types. We discuss the general concept and present a prototype of this integration based on the columnar relational database MonetDB and the R environment for statistical computing. We evaluate the performance of this prototype in a series of micro-benchmarks of common data management tasks.

Many database systems share a need for large amounts of fast storage. However, economies of scale limit the utility of extending a single machine with an arbitrary amount of memory. The recent broad availability of the zero-copy data transfer protocol RDMA over low-latency and high-throughput network connections such as InfiniBand prompts us to revisit the long-proposed usage of memory provided by remote machines. In this paper, we present a solution to make use of remote memory without manipulation of the operating system, and investigate the impact on database performance.

Statistics software packages and relational database systems possess considerable overlap in the area of data loading, handling, and transformation. However, only databases are mainly optimized towards high performance in this area. In this paper, we present our approach on bringing the best of these two worlds together. We integrate the analytics-optimized database MonetDB and the R environment for statistical computing in a non-obtrusive, transparent and compatible way.

In many systems providing storage and retrieval operations on data, indices are used to make these operations more efficient. Distributed storage systems provide means to distribute the burden of storing and retrieving data onto multiple different computers. Routing indices can answer the central question in these systems: Where should one look for a specified data item? To be able to query for different columns in a relation or different entries in tuples, indexing for multiple dimensions is necessary. Our group applies a swarm-based approach to distributed storage leading to a new class of distributed systems, which are fully self-organized in their behavior and lack any shared global data structures. In this paper, we research whether multiple levels of routing indices can be maintained and used in such a distributed and self-organized storage service. To achieve this, we look into different types of indices and evaluate them in an experiment.

Traditional approaches for data storage and analysis are facing their limits when handling the enormous data amounts of today's applications. We believe that a radical departure from contemporary architectures of stores is necessary to satisfy that central scalability requirement. One of the most promising new schools of thought in system design are swarm intelligent and swarm-based approaches for data distribution and organization. In this paper, we describe our current work on a swarm-based storage service for Semantic Web data. This storage service utilizes algorithms discovered in the behavior of ant colonies. We describe these algorithms and our enhancements to them as well as our evaluation of the implementation.