High-performance transaction system applications typically insert rows in a History table to provide an activity trace; at the same time the transaction system generates log records for purposes of system recovery. Both types of generated information can benefit from efficient indexing. An example in a well-known setting is the TPC-A benchmark application, modified to support efficient queries on the history for account activity for specific accounts. This requires an index by account-id on the fast-growing History table. Unfortunately, standard disk-based index structures such as the B-tree will effectively double the I/O cost of the transaction to maintain an index such as this in real time, increasing the total system cost up to fifty percent. Clearly a method for maintaining a real-time index at low cost is desirable. The log-structured mergetree (LSM-tree) is a disk-based data structure designed to provide low-cost indexing for a file experiencing a high rate of record inserts (and deletes) over an extended period. The LSM-tree uses an algorithm that defers and batches index changes, cascading the changes from a memory-based component through one or more disk components in an efficient manner reminiscent of merge sort. During this process all index values are continuously accessible to retrievals (aside from very short locking periods), either through the memory component or one of the disk components. The algorithm has greatly reduced disk arm movements compared to a traditional access methods such as B-trees, and will improve cost-performance in domains where disk arm costs for inserts with traditional access methods overwhelm storage media costs. The LSM-tree approach also generalizes to operations other than insert and delete. However, indexed finds requiring immediate response will lose I/O efficiency in some cases, so the LSM-tree is most useful in applications where index inserts are more common than finds that retrieve the entries. This seems to be a common property for history tables and log files, for example. The conclusions of Sect. 6 compare the hybrid use of memory and disk components in the LSM-tree access method with the commonly understood advantage of the hybrid method to buffer disk pages in memory.

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The log-structured merge-tree (LSM-tree)

There are three dominent themes in building high transaction rate multiprocessor systems, namely shared memory (e.g. Synapse, IBM/AP configurations), shared disk (e.g. VAX/cluster, any multi-ported disk system), and shared nothing (e.g. Tandem, Tolerant). This paper argues that shared nothing is the preferred approach.

The Case for Shared Nothing.

We present a novel external sorting algorithm using graphics processors (GPUs) on large databases composed of billions of records and wide keys. Our algorithm uses the data parallelism within a GPU along with task parallelism by scheduling some of the memory-intensive and compute-intensive threads on the GPU. Our new sorting architecture provides multiple memory interfaces on the same PC -- a fast and dedicated memory interface on the GPU along with the main memory interface for CPU computations. As a result, we achieve higher memory bandwidth as compared to CPU-based algorithms running on commodity PCs. Our approach takes into account the limited communication bandwidth between the CPU and the GPU, and reduces the data communication between the two processors. Our algorithm also improves the performance of disk transfers and achieves close to peak I/O performance. We have tested the performance of our algorithm on the SortBenchmark and applied it to large databases composed of a few hundred Gigabytes of data. Our results on a 3 GHz Pentium IV PC with $300 NVIDIA 7800 GT GPU indicate a significant performance improvement over optimized CPU-based algorithms on high-end PCs with 3.6 GHz Dual Xeon processors. Our implementation is able to outperform the current high-end PennySort benchmark and results in a higher performance to price ratio. Overall, our results indicate that using a GPU as a co-processor can significantly improve the performance of sorting algorithms on large databases.

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GPUTeraSort: high performance graphics co-processor sorting for large database management

In this paper we describe the architecture and design of a new file system, XFS, for Silicon Graphics' IRIX operating system It is a general purpose file system for use on both workstations and servers The focus of the paper is on the mechanisms used by XFS to scale capacity and performance in supporting very large file systems The large file system support includes mechanisms for managing large files, large numbers of files, large directories, and very high performance I/O

In discussing the mechanisms used for scalability we include both descriptions of the XFS on-disk data structures and analyses of why they were chosen We discuss in detail our use of B+ trees in place of many of the more traditional linear file system structures

XFS has been shipping to customers since December of 1994 in a version of IRIX 53, and we are continuing to improve its performance and add features in upcoming releases We include performance results from running on the latest version of XFS to demonstrate the viability of our design

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Scalability in the XFS file system

The design and implementation decisions made for the three-dimensional data manager POSTGRES are discussed. Attention is restricted to the DBMS backend functions. The POSTGRES data model and query language, the rules system, the storage system, the POSTGRES implementation and the current status and performance are discussed. >

The implementation of POSTGRES

Three benchmarks are defined: Sort, Scan and DebitCredit. The first two benchmarks measure a system's input/output performance. DebitCredit is a simple transaction processing application used to define a throughput measure -Transactions Per Second (TPS). These benchmarks measure the performance of diverse transaction processing systems. A standard system cost measure is stated and used to define price/performance metrics. A condensed version of this paper appears in Datamation, April 1, 1985

Omri Serlin

Papers

A measure of transaction processing power

MIPS, Dhrystones, and other tales

Parallel processing: fact or fancy?

Exploring the OLTP realm