Nanokristalle werden beispielsweise für eine Anwendung in Solarzellen, Lichtemittern und nichtflüchtigen Datenspeichern diskutiert. Damit diese Anwendungen funktionieren können, ist eine genaue Kontrolle der Kristallitgröße sowie der Flächendichte und Lage der Kristallite in der Matrix wichtig. Zudem sollte die Matrix amorph sein, da amorphe Matrixmaterialien die Nanokristall-Oberfläche besser passivieren und beständiger gegen Leckströme sind. In dieser Arbeit werden Ge-Nanokristalle in die Hoch-Epsilon-Dielektrika ZrO2 und TaZrOx eingebettet. Im System Ge/ZrO2 kristallisieren die Ge-Cluster und die ZrO2-Matrix bei der gleichen Temperatur. Aufgrund der kristallinen Matrix weicht die Form der Ge-Nanokristalle von einer Kugel ab, worunter unter anderem die Größenkontrolle leidet. Die Beimischung von Ta2O5 stabilisiert die amorphe Phase des ZrO2 und verhindert dadurch die gemeinsame Kristallisation. Dadurch wird es im System Ge/TaZrOx möglich, kugelförmige Ge-Nanokristalle im Größenbereich von 3 nm bis 6 nm positionskontrolliert in eine amorphe Matrix einzubetten. Für die Untersuchung einer möglichen Anwendung des Materialsystems wurden Speicherzellen eines nichtflüchtigen Datenspeichers auf Basis von Ge-Nanokristallen hergestellt. Dabei zeigte sich, dass das System Ge/TaZrOx überdurchschnittlich viele Ladungen speichert und daher für diese Anwendung vielversprechend ist. Zudem stabilisiert die Beimischung von Ta2O5 eine extrem seltene orthorhombische Modifikation des ZrO2. Für ferroelektrische Datenspeicher könnte diese Phase eine aussichtsreiche Alternative zum HfO2 sein.

Database systems have long been optimized to hide the higher latency of storage media, yielding complex persistence mechanisms. With the advent of large DRAM capacities, it became possible to keep a full copy of the data in DRAM. Systems that leverage this possibility, such as main-memory databases, keep two copies of the data in two different formats: one in main memory and the other one in storage. The two copies are kept synchronized using snapshotting and logging. This main-memory-centric architecture yields nearly two orders of magnitude faster analytical processing than traditional, disk-centric ones. The rise of Big Data emphasized the importance of such systems with an ever-increasing need for more main memory. However, DRAM is hitting its scalability limits: It is intrinsically hard to further increase its density. Storage-Class Memory (SCM) is a group of novel memory technologies that promise to alleviate DRAM’s scalability limits. They combine the non-volatility, density, and economic characteristics of storage media with the byte-addressability and a latency close to that of DRAM. Therefore, SCM can serve as persistent main memory, thereby bridging the gap between main memory and storage. In this dissertation, we explore the impact of SCM as persistent main memory on database systems. Assuming a hybrid SCM-DRAM hardware architecture, we propose a novel software architecture for database systems that places primary data in SCM and directly operates on it, eliminating the need for explicit IO. This architecture yields many benefits: First, it obviates the need to reload data from storage to main memory during recovery, as data is discovered and accessed directly in SCM. Second, it allows replacing the traditional logging infrastructure by fine-grained, cheap micro-logging at data-structure level. Third, secondary data can be stored in DRAM and reconstructed during recovery. Fourth, system runtime information can be stored in SCM to improve recovery time. Finally, the system may retain and continue in-flight transactions in case of system failures. However, SCM is no panacea as it raises unprecedented programming challenges. Given its byte-addressability and low latency, processors can access, read, modify, and persist data in SCM using load/store instructions at a CPU cache line granularity. The path from CPU registers to SCM is long and mostly volatile, including store buffers and CPU caches, leaving the programmer with little control over when data is persisted. Therefore, there is a need to enforce the order and durability of SCM writes using persistence primitives, such as cache line flushing instructions. This in turn creates new failure scenarios, such as missing or misplaced persistence primitives. We devise several building blocks to overcome these challenges. First, we identify the programming challenges of SCM and present a sound programming model that solves them. Then, we tackle memory management, as the first required building block to build a database system, by designing a highly scalable SCM allocator, named PAllocator, that fulfills the versatile needs of database systems. Thereafter, we propose the FPTree, a highly scalable hybrid SCM-DRAM persistent B+-Tree that bridges the gap between the performance of transient and persistent B+-Trees. Using these building blocks, we realize our envisioned database architecture in SOFORT, a hybrid SCM-DRAM columnar transactional engine. We propose an SCM-optimized MVCC scheme that eliminates write-ahead logging from the critical path of transactions. Since SCM -resident data is near-instantly available upon recovery, the new recovery bottleneck is rebuilding DRAM-based data. To alleviate this bottleneck, we propose a novel recovery technique that achieves nearly instant responsiveness of the database by accepting queries right after recovering SCM -based data, while rebuilding DRAM -based data in the background. Additionally, SCM brings new failure scenarios that existing testing tools cannot detect. Hence, we propose an online testing framework that is able to automatically simulate power failures and detect missing or misplaced persistence primitives. Finally, our proposed building blocks can serve to build more complex systems, paving the way for future database systems on SCM.

Database systems have long been optimized to hide the higher latency of storage media, yielding complex persistence mechanisms. With the advent of large DRAM capacities, it became possible to keep a full copy of the data in DRAM. Systems that leverage this possibility, such as main-memory databases, keep two copies of the data in two different formats: one in main memory and the other one in storage. The two copies are kept synchronized using snapshotting and logging. This main-memory-centric architecture yields nearly two orders of magnitude faster analytical processing than traditional, disk-centric ones. The rise of Big Data emphasized the importance of such systems with an ever-increasing need for more main memory. However, DRAM is hitting its scalability limits: It is intrinsically hard to further increase its density. Storage-Class Memory (SCM) is a group of novel memory technologies that promise to alleviate DRAM’s scalability limits. They combine the non-volatility, density, and economic characteristics of storage media with the byte-addressability and a latency close to that of DRAM. Therefore, SCM can serve as persistent main memory, thereby bridging the gap between main memory and storage. In this dissertation, we explore the impact of SCM as persistent main memory on database systems. Assuming a hybrid SCM-DRAM hardware architecture, we propose a novel software architecture for database systems that places primary data in SCM and directly operates on it, eliminating the need for explicit IO. This architecture yields many benefits: First, it obviates the need to reload data from storage to main memory during recovery, as data is discovered and accessed directly in SCM. Second, it allows replacing the traditional logging infrastructure by fine-grained, cheap micro-logging at data-structure level. Third, secondary data can be stored in DRAM and reconstructed during recovery. Fourth, system runtime information can be stored in SCM to improve recovery time. Finally, the system may retain and continue in-flight transactions in case of system failures. However, SCM is no panacea as it raises unprecedented programming challenges. Given its byte-addressability and low latency, processors can access, read, modify, and persist data in SCM using load/store instructions at a CPU cache line granularity. The path from CPU registers to SCM is long and mostly volatile, including store buffers and CPU caches, leaving the programmer with little control over when data is persisted. Therefore, there is a need to enforce the order and durability of SCM writes using persistence primitives, such as cache line flushing instructions. This in turn creates new failure scenarios, such as missing or misplaced persistence primitives. We devise several building blocks to overcome these challenges. First, we identify the programming challenges of SCM and present a sound programming model that solves them. Then, we tackle memory management, as the first required building block to build a database system, by designing a highly scalable SCM allocator, named PAllocator, that fulfills the versatile needs of database systems. Thereafter, we propose the FPTree, a highly scalable hybrid SCM-DRAM persistent B+-Tree that bridges the gap between the performance of transient and persistent B+-Trees. Using these building blocks, we realize our envisioned database architecture in SOFORT, a hybrid SCM-DRAM columnar transactional engine. We propose an SCM-optimized MVCC scheme that eliminates write-ahead logging from the critical path of transactions. Since SCM -resident data is near-instantly available upon recovery, the new recovery bottleneck is rebuilding DRAM-based data. To alleviate this bottleneck, we propose a novel recovery technique that achieves nearly instant responsiveness of the database by accepting queries right after recovering SCM -based data, while rebuilding DRAM -based data in the background. Additionally, SCM brings new failure scenarios that existing testing tools cannot detect. Hence, we propose an online testing framework that is able to automatically simulate power failures and detect missing or misplaced persistence primitives. Finally, our proposed building blocks can serve to build more complex systems, paving the way for future database systems on SCM.

Nanokristalle werden beispielsweise für eine Anwendung in Solarzellen, Lichtemittern und nichtflüchtigen Datenspeichern diskutiert. Damit diese Anwendungen funktionieren können, ist eine genaue Kontrolle der Kristallitgröße sowie der Flächendichte und Lage der Kristallite in der Matrix wichtig. Zudem sollte die Matrix amorph sein, da amorphe Matrixmaterialien die Nanokristall-Oberfläche besser passivieren und beständiger gegen Leckströme sind. In dieser Arbeit werden Ge-Nanokristalle in die Hoch-Epsilon-Dielektrika ZrO2 und TaZrOx eingebettet. Im System Ge/ZrO2 kristallisieren die Ge-Cluster und die ZrO2-Matrix bei der gleichen Temperatur. Aufgrund der kristallinen Matrix weicht die Form der Ge-Nanokristalle von einer Kugel ab, worunter unter anderem die Größenkontrolle leidet. Die Beimischung von Ta2O5 stabilisiert die amorphe Phase des ZrO2 und verhindert dadurch die gemeinsame Kristallisation. Dadurch wird es im System Ge/TaZrOx möglich, kugelförmige Ge-Nanokristalle im Größenbereich von 3 nm bis 6 nm positionskontrolliert in eine amorphe Matrix einzubetten. Für die Untersuchung einer möglichen Anwendung des Materialsystems wurden Speicherzellen eines nichtflüchtigen Datenspeichers auf Basis von Ge-Nanokristallen hergestellt. Dabei zeigte sich, dass das System Ge/TaZrOx überdurchschnittlich viele Ladungen speichert und daher für diese Anwendung vielversprechend ist. Zudem stabilisiert die Beimischung von Ta2O5 eine extrem seltene orthorhombische Modifikation des ZrO2. Für ferroelektrische Datenspeicher könnte diese Phase eine aussichtsreiche Alternative zum HfO2 sein.