Exploiting the power of relational databases for efficient stream processing

Abstract

Stream applications gained significant popularity over the last years that lead to the development of specialized stream engines. These systems are designed from scratch with a different philosophy than nowadays database engines in order to cope with the stream applications requirements. However, this means that they lack the power and sophisticated techniques of a full fledged database system that exploits techniques and algorithms accumulated over many years of database research.In this paper, we take the opposite route and design a stream engine directly on top of a database kernel. Incoming tuples are directly stored upon arrival in a new kind of system tables, called baskets. A continuous query can then be evaluated over its relevant baskets as a typical one-time query exploiting the power of the relational engine. Once a tuple has been seen by all relevant queries/operators, it is dropped from its basket. A basket can be the input to a single or multiple similar query plans. Furthermore, a query plan can be split into multiple parts each one with its own input/output baskets allowing for flexible load sharing query scheduling. Contrary to traditional stream engines, that process one tuple at a time, this model allows batch processing of tuples, e.g., query a basket only after x tuples arrive or after a time threshold has passed. Furthermore, we are not restricted to process tuples in the order they arrive. Instead, we can selectively pick tuples from a basket based on the query requirements exploiting a novel query component, the basket expressions.We investigate the opportunities and challenges that arise with such a direction and we show that it carries significant advantages. We propose a complete architecture, the DataCell, which we implemented on top of an open-source column-oriented DBMS. A detailed analysis and experimental evaluation of the core algorithms using both micro benchmarks and the standard Linear Road benchmark demonstrate the potential of this new approach.

Database support for processing complex aggregate queries over data streams

“…The potential of database systems in efficient processing of continuous queries over streaming data has been explored in [19,23,10]. Authors in [19] showed that, the performance of stream processing in a standard relational database can be improved significantly by appropriate tunning and use of existing features like indices and temporary tables.…”

“…Authors in [19] showed that, the performance of stream processing in a standard relational database can be improved significantly by appropriate tunning and use of existing features like indices and temporary tables. The work of [23,10] presented how to extend the MonetDB and PostgreSQL database systems to support stream processing, respectively. The major motivation of these works is that, by building SPEs separately from database systems, the opportunity of leveraging the existing sophisticated algorithms and techniques of databases is lost.…”

The database architectures research group at CWI

“…In the DataCell project [14] we take a different route by designing a stream engine on top of an existing relational database kernel [15]. This includes reuse of both its storage/execution engine and its optimizer infrastructure.…”

Maintaining consistent results of continuous queries under diverse window specifications

“…Following this policy, Gigascope regularly generates punctuations (''heartbeats'') in order to unblock operators like aggregation in query plans. Recently, a stream engine built on top of a column-oriented DBMS was presented in [20], proposing transitory storage of incoming tuples in suitable system tables called baskets. Items are then propagated to operators after filtering by means of predicate windows, which apply simple selection conditions on the basket data, even irrespective of their timestamp order.…”