Summary
Two major hardware trends have a significant impact on the architecture of database management systems (DBMSs): First, main
memory sizes continue to grow significantly. Machines with 1TB of main memory and more are readily available at a relatively low
price. Second, the number of cores in a system continues to grow, from currently 64 and more to hundreds in the near future.
This trend offers radically new opportunities for both business and science. It promises to allow for information-at-your-fingertips, i.e., large volumes of data can be
analyzed and deeply explored online, in parallel to regular transaction processing. Currently, deep data exploration is performed
outside of the database system which necessitates huge data transfers. This impedes the processing such that real-time interactive
exploration is impossible. These new hardware capabilities now allow to build a true computational database system that integrates deep exploration functionality at the source
of the data. This will lead to a drastic shift in how users interact with data, as for the first time interactive data exploration
becomes possible at a massive scale.
Unfortunately, traditional DBMSs are simply not capable to tackle these new challenges.
Traditional techniques like interpreted code execution for query processing become a severe bottleneck in the presence of
such massive parallelism, causing poor utilization of the hardware. I pursue a radically different approach: Instead of adapting the
traditional, disk-based approaches, I am integrating a new just-in-time compilation framework into the in-memory database that
directly exploits the abundant, parallel hardware for large-scale data processing and exploration. By explicitly utilizing
cores, I will be able to build a powerful computational database engine that scales the entire spectrum of data processing - from
transactional to analytical to exploration workflows - far beyond traditional architectures.
memory sizes continue to grow significantly. Machines with 1TB of main memory and more are readily available at a relatively low
price. Second, the number of cores in a system continues to grow, from currently 64 and more to hundreds in the near future.
This trend offers radically new opportunities for both business and science. It promises to allow for information-at-your-fingertips, i.e., large volumes of data can be
analyzed and deeply explored online, in parallel to regular transaction processing. Currently, deep data exploration is performed
outside of the database system which necessitates huge data transfers. This impedes the processing such that real-time interactive
exploration is impossible. These new hardware capabilities now allow to build a true computational database system that integrates deep exploration functionality at the source
of the data. This will lead to a drastic shift in how users interact with data, as for the first time interactive data exploration
becomes possible at a massive scale.
Unfortunately, traditional DBMSs are simply not capable to tackle these new challenges.
Traditional techniques like interpreted code execution for query processing become a severe bottleneck in the presence of
such massive parallelism, causing poor utilization of the hardware. I pursue a radically different approach: Instead of adapting the
traditional, disk-based approaches, I am integrating a new just-in-time compilation framework into the in-memory database that
directly exploits the abundant, parallel hardware for large-scale data processing and exploration. By explicitly utilizing
cores, I will be able to build a powerful computational database engine that scales the entire spectrum of data processing - from
transactional to analytical to exploration workflows - far beyond traditional architectures.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/725286 |
| Start date: | 01-06-2017 |
| End date: | 31-05-2022 |
| Total budget - Public funding: | 1 918 750,00 Euro - 1 918 750,00 Euro |
Cordis data
Original description
Two major hardware trends have a significant impact on the architecture of database management systems (DBMSs): First, mainmemory sizes continue to grow significantly. Machines with 1TB of main memory and more are readily available at a relatively low
price. Second, the number of cores in a system continues to grow, from currently 64 and more to hundreds in the near future.
This trend offers radically new opportunities for both business and science. It promises to allow for information-at-your-fingertips, i.e., large volumes of data can be
analyzed and deeply explored online, in parallel to regular transaction processing. Currently, deep data exploration is performed
outside of the database system which necessitates huge data transfers. This impedes the processing such that real-time interactive
exploration is impossible. These new hardware capabilities now allow to build a true computational database system that integrates deep exploration functionality at the source
of the data. This will lead to a drastic shift in how users interact with data, as for the first time interactive data exploration
becomes possible at a massive scale.
Unfortunately, traditional DBMSs are simply not capable to tackle these new challenges.
Traditional techniques like interpreted code execution for query processing become a severe bottleneck in the presence of
such massive parallelism, causing poor utilization of the hardware. I pursue a radically different approach: Instead of adapting the
traditional, disk-based approaches, I am integrating a new just-in-time compilation framework into the in-memory database that
directly exploits the abundant, parallel hardware for large-scale data processing and exploration. By explicitly utilizing
cores, I will be able to build a powerful computational database engine that scales the entire spectrum of data processing - from
transactional to analytical to exploration workflows - far beyond traditional architectures.
Status
CLOSEDCall topic
ERC-2016-COGUpdate Date
27-04-2024
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