Online transaction processing | EPFL Graph Search

Online transaction processing (OLTP) is a type of database system used in transaction-oriented applications, such as many operational systems. "Online" refers to that such systems are expected to respond to user requests and process them in real-time (process transactions). The term is contrasted with online analytical processing (OLAP) which instead focuses on data analysis (for example planning and management systems). The term transaction The term "transaction" can have two different meanings, both of which might apply: in the realm of computers or database transactions it denotes an atomic change of state, whereas in the realm of business or finance, the term typically denotes an exchange of economic entities (as used by, e.g., Transaction Processing Performance Council or commercial transactions.) OLTP may use transactions of the first type to record transactions of the second. Compared to OLAP OLTP is typically contrasted to online analytical processing (O

Transactions Chasing Scalability and Instruction Locality on Multicores

Pinar Tözün

For several decades, online transaction processing (OLTP) has been one of the main server applications that drives innovations in the data management ecosystem, and in turn the database and computer architecture communities. Recent hardware trends oblige software to overcome two major challenges against systems scalability on modern multicore processors: (1) exploiting the abundant thread-level parallelism across cores and (2) taking advantage of the implicit parallelism within a core. The traditional design of the OLTP systems, however, faces inherent scalability problems due to its tightly coupled components. In addition, OLTP cannot exploit the full capability of the micro-architectural resources of modern processors because of the conventional scheduling decisions that ignore the cache locality for transactions. As a result, today’s commonly used server hardware remains largely underutilized leading to a huge waste of hardware resources and energy. .... In this thesis, we first identify the unbounded critical sections of traditional OLTP systems as the main enemy of thread-level parallelism. We design an alternative shared-everything system based on physiological partitioning (PLP) to eliminate the unbounded critical sections while providing an infrastructure for low-cost dynamic repartitioning and without introducing high-cost distributed transactions. Then, we demonstrate that L1 instruction cache stalls are the dominant factor leading to underutilization in the commodity servers. However, we also observe that independently of their high-level functionality, transactions running in parallel on a multicore system share significant amount of common instructions. By adaptively spreading the execution of a transaction over multiple cores through thread migration or multiplexing transactions on one core, we enable both an ample L1 instruction cache capacity for a transaction and reuse of common instructions across concurrent transactions. .... As the hardware demands more from the software to exploit the complexity and parallelism it offers in the multicore era, this work would change the way we traditionally schedule transactions. Instead of viewing a transaction as a single big task, we split it into smaller parts that can exploit data and instruction locality through careful dynamic scheduling decisions. The methods this thesis presents are not only specific to OLTP systems, but they can also benefit other types of applications that have concurrent requests executing a series of actions from a predefined set and face similar scalability problems on emerging hardware.

EPFL2014

STREX: Boosting Instruction Cache Reuse in OLTP Workloads Through Stratified Transaction Execution

Anastasia Ailamaki, Xin Tong, Pinar Tözün

Online transaction processing (OLTP) workload performance suffers from instruction stalls; the instruction footprint of a typical transaction exceeds by far the capacity of an L1 cache, leading to ongoing cache thrashing. Several proposed techniques remove some instruction stalls in exchange for error-prone instrumentation to the code base, or for a sharp increase the L1-I cache unit area and power. Others reduce instruction miss latency by better utilizing a shared L2 cache. SLICC, a recently proposed thread migration technique that exploits transaction instruction locality, is promising for high core counts but performs sub-optimally or may hurt performance when running on few cores. This paper corroborates that OLTP transactions exhibit significant intra- and inter-thread overlap in their instruction footprint, and analyzes the instruction stall reduction benefits. This paper presents STREX, a hardware, programmer-transparent technique that exploits typical transaction behavior to improve instruction reuse in first level caches. STREX time-multiplexes the execution of similar transactions dynamically on a single core so that instructions fetched by one transaction are reused by all other transactions executing in the system as much as possible. STREX dynamically slices the execution of each transaction into cache-sized segments simply by observing when blocks are brought in the cache and when they are evicted. Experiments show that, when compared to baseline execution on 2 -- 16 cores, STREX consistently improves performance while reducing the number of L1 instruction and data misses by 37% and 14% on average, respectively. Finally, this paper proposes a practical hybrid technique that combines STREX and SLICC, thereby guaranteeing performance benefits regardless of the number of available cores and the workload's footprint.

2013