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Auflistung nach Autor:in "Stolze, Knut"

1 - 10 von 12
  • Zeitschriftenartikel
    Architecture of a data analytics service in hybrid cloud environments
    (it - Information Technology: Vol. 59, No. 5, 2017) Beier, Felix; Stolze, Knut
    DB2 for z/OS is the backbone of many transactional systems in the world. IBM DB2 Analytics Accelerator (IDAA) is IBM's approach to enhance DB2 for z/OS with very fast processing of OLAP and analytical SQL workload. While IDAA was originally designed as an appliance to be connected directly to System z, the trend in the IT industry is towards cloud environments. That offers a broad range of tools for analytical data processing tasks.
  • Konferenzbeitrag
    Architecture of a highly scalable data warehouse appliance integrated to mainframe database systems
    (Datenbanksysteme für Business, Technologie und Web (BTW), 2011) Stolze, Knut; Beier, Felix; Sattler, Kai-Uwe; Sprenger, Sebastian; Grolimund, Carlos Caballero; Czech, Marco
  • Konferenzbeitrag
    Autonomous Data Ingestion Tuning in Data Warehouse Accelerators
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017) Stolze, Knut; Beier, Felix; Müller, Jens
    The IBM DB2 Analytics Accelerator (IDAA) is a state-of-the art hybrid database system that seamlessly extends the strong transactional capabilities of DB2 for z/OS with very fast processing of OLAP and analytical SQL workload in Netezza. IDAA copies the data from DB2 for z/OS into its Netezza backend, and customers can tailor data maintenance according to their needs. This copy process, the data load, can be done on a whole table or just a physical table partition. IDAA also o ers an incremental update feature, which employs replication technologies for low-latency data synchronization. The accelerator targets big relational databases with several TBs of data. Therefore, the data load is performance-critical, not only for the data transfer itself, but the system has to be able to scale up to a large number of tables, i. e., tens of thousands to be loaded at the same time, as well. The administrative overhead for such a number of tables has to be minimized. In this paper, we present our work on a prototype, which is geared towards e ciently loading data for many tables, where each table may store only a comparably small amount of data. A new load scheduler has been introduced for handling all concurrent load requests for disjoint sets of tables. That is not only required for a multi-tenant setup, but also a significant improvement for attaching an accelerator to a single DB2 for z/OS system. In this paper, we present architecture and implementation aspects of the new and improved load mechanism and results of some initial performance evaluations.
  • Konferenzbeitrag
    Bringing BLINK closer to the full power of SQL
    (Datenbanksysteme in Business, Technologie und Web (BTW) – 13. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2009) Stolze, Knut; Raman, Vijayshankar; Sidle, Richard; Draese, Oliver
    BLINK is a prototype of an in-memory based query processor that exploits heavily the underlying CPU infrastructure. It is very sensitive to the processor's caches and instruction set. In this paper, we describe how to close two major functional gaps in BL
  • Konferenzbeitrag
    Evolutionary integration of in-memory database technology into IBM's enterprise DB2 database systems
    (INFORMATIK 2013 – Informatik angepasst an Mensch, Organisation und Umwelt, 2013) Stolze, Knut; Lohman, Guy; Raman, Vijayshankar; Sidle, Richard; Beier, Felix
    Recently, IBM announced Blink Ultra (BLU) as an in-memory enhancement for DB2 for Linux, Unix, and Windows. The technology implemented in BLU was tested in various stages until it founds its current form as DB2 feature. In this paper, we give a brief summary on the origins of BLU and the adoption process from the BLINK prototype over the IBM Smart Analytics Optimizer to BLU itself.
  • Konferenzbeitrag
    IBM Data Gate: Making On-Premises Mainframe Databases Available to Cloud Applications
    (BTW 2023, 2023) Stolze, Knut; Beier, Felix; Dimov, Vassil; Kalogeiton, Eirini; Toši?, Mateo
    Many companies use databases on the mainframe for their mission critical applications. They will continue to do so in the future. It is important to exploit this existing data for analysis and business decisions via modern applications that are often built for cloud environments. IBM Db2 for z/OS Data Gate (Data Gate) is bridging the gap between mainframe databases and such cloud-native applications. It offers high-performance data synchronization connecting both worlds, while providing data coherence at the level of individual transactions.Data Gate is a hybrid cloud solution, which protects existing systems and applications (and investments into those) while enabling new use cases to work with and analyze mainframe data. It evolved from the IBM Db2 Analytics Accelerator (IDAA) technology by adjusting the architecture and some of the functionality. In this paper, we give an overview of Data Gate and how it addresses typical ETL issues like code page conversions, data coherence, encryption or integration with other cloud services. We also describe how Data Gate can be used to handle query acceleration or archiving of cold data -just like IDAA did. Along the lines, we highlight key differences between the two products.
  • Zeitschriftenartikel
    Integrating Cluster-Based Main-Memory Accelerators in Relational Data Warehouse Systems
    (Datenbank-Spektrum: Vol. 11, No. 2, 2011) Stolze, Knut; Beier, Felix; Koeth, Oliver; Sattler, Kai-Uwe
    Today, data warehouse systems are faced with challenges for providing nearly realtime response times even for complex analytical queries on enormous data volumes. Highly scalable computing clusters in combination with parallel in-memory processing of compressed data are valuable techniques to address these challenges. In this paper, we give an overview on core techniques of the IBM Smart Analytics Optimizer—an accelerator engine for IBM’s mainframe database system DB2 for z/OS. We particularly discuss aspects of a seamless integration between the two worlds and describe techniques exploiting features of modern hardware such as parallel processing, cache utilization, and SIMD. We describe issues encountered during the development and evaluation of our system and outline current research activities for solving them.
  • Konferenzbeitrag
    Integrating the relational interval tree into IBM's DB2 universal database server
    (Datenbanksysteme in Business, Technologie und Web, 11. Fachtagung des GIFachbereichs “Datenbanken und Informationssysteme” (DBIS), 2005) Brochhaus, Christoph; Enderle, Jost; Schlosser, Achim; Seidl, Thomas; Stolze, Knut
    User-defined data types such as intervals require specialized access methods to be efficiently searched and queried. As database implementors cannot provide appropriate index structures and query processing methods for each conceivable data type, present-day object-relational database systems offer extensible indexing frameworks that enable developers to extend the set of built-in index structures by custom access methods. Although these frameworks permit a seamless integration of user-defined indexing techniques into query processing they do not facilitate the actual implementation of the access method itself. In order to leverage the applicability of indexing frameworks, relational access methods such as the Relational Interval Tree (RI-tree), an efficient index structure to process interval intersection queries, mainly rely on the functionality, robustness and performance of built-in indexes, thus simplifying the index implementation significantly. To investigate the behavior and performance of the recently released IBM DB2 indexing framework we use this interface to integrate the RI-tree into the DB2 server. The standard implementation of the RI-tree, however, does not fit to the narrow corset of the DB2 framework which is restricted to the use of a single index only. We therefore present our adaptation of the originally two-tree technique to the single index constraint. As experimental results with interval intersection queries show, the plugged-in access method delivers excellent performance compared to other techniques.
  • Konferenzbeitrag
    Integration of spatial data in database acceleration for analytics
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015), 2015) Stolze, Knut; Rückert, Josephine; Butt, Frank
    The IBM DB2 Analytics Accelerator (IDAA) integrates the strong OLTP capabilities of DB2 for z/OS with very fast processing of OLAP and analytical SQL workload in Netezza. Today, not all data types supported by DB2 are available for acceleration. One step to narrow this gap is to enable spatial data - in form of polygons, linestrings, and points - for OLAP processing. Since DB2 and Netezza have very different internal architectures and different limitations, a straight-forward mapping from the DB2 data type to Netezza data types is not possible. We developed a prototype geared toward performance and simplicity for spatial data support in IDAA. In particular, all SQL statement that work against DB2 shall work against the accelerator without any changes. In this paper, we describe how the integration with the accelerator is accomplished so that ingestion and access to the spatial data becomes as seamless and transparent as possible for existing applications. The architecture and implementation aspects for spatial data representation in Netezza as well as the access during query processing are discussed in detail.
  • Textdokument
    Partial Reload of Incrementally Updated Tables in Analytic Database Accelerators
    (BTW 2019, 2019) Stolze, Knut; Beier, Felix; Müller, Jens
    The IBM Db2 Analytics Accelerator (IDAA) is a state-of-the art hybrid database system that seamlessly extends the strong transactional capabilities of Db2 for z/OS (Db2z) with very fast column-store processing in Db2 Database for Linux, Unix, and Windows. IDAA maintains a copy of the data from Db2z in its backend database. The data can be synchronized in batch with a granularity of table partitions, or incrementally using replication technology for individual rows. In this paper we present the enablement of combining the batch loading of a true subset of a table’s partitions for replicated tables. The primary goal for such an integration is to ensure data consistency. A specific challenge is that no duplicated rows stemming from the two data transfer paths come into existence. We present a robust and yet simple approach that is based on IDAA’s implementation of multi-version concurrency control.