RDF is a data model for schema-free structured information that is gaining momentum in the context of Semantic-Web data, life sciences, and also Web 2.0 platforms. The "pay-as-you-go" nature of RDF and the flexible pattern-matching capabilities of its query language SPARQL entail efficiency and scalability challenges for complex queries including long join paths. This paper presents the RDF-3X engine, an implementation of SPARQL that achieves excellent performance by pursuing a RISC-style architecture with streamlined indexing and query processing. The physical design is identical for all RDF-3X databases regardless of their workloads, and completely eliminates the need for index tuning by exhaustive indexes for all permutations of subject-property-object triples and their binary and unary projections. These indexes are highly compressed, and the query processor can aggressively leverage fast merge joins with excellent performance of processor caches. The query optimizer is able to choose optimal join orders even for complex queries, with a cost model that includes statistical synopses for entire join paths. Although RDF-3X is optimized for queries, it also provides good support for efficient online updates by means of a staging architecture: direct updates to the main database indexes are deferred, and instead applied to compact differential indexes which are later merged into the main indexes in a batched manner. Experimental studies with several large-scale datasets with more than 50 million RDF triples and benchmark queries that include pattern matching, manyway star-joins, and long path-joins demonstrate that RDF-3X can outperform the previously best alternatives by one or two orders of magnitude.

/pdf/the-rdf-3x-engine-for-scalable-management-of-rdf-data-4khwpjqm1b.pdf

The RDF-3X engine for scalable management of RDF data

Data management workloads are increasingly write-intensive and subject to strict latency SLAs. This presents a dilemma: Update in place systems have unmatched latency but poor write throughput. In contrast, existing log structured techniques improve write throughput but sacrifice read performance and exhibit unacceptable latency spikes.We begin by presenting a new performance metric: read fanout, and argue that, with read and write amplification, it better characterizes real-world indexes than approaches such as asymptotic analysis and price/performance.We then present bLSM, a Log Structured Merge (LSM) tree with the advantages of B-Trees and log structured approaches: (1) Unlike existing log structured trees, bLSM has near-optimal read and scan performance, and (2) its new "spring and gear" merge scheduler bounds write latency without impacting throughput or allowing merges to block writes for extended periods of time. It does this by ensuring merges at each level of the tree make steady progress without resorting to techniques that degrade read performance.We use Bloom filters to improve index performance, and find a number of subtleties arise. First, we ensure reads can stop after finding one version of a record. Otherwise, frequently written items would incur multiple B-Tree lookups. Second, many applications check for existing values at insert. Avoiding the seek performed by the check is crucial.

bLSM: a general purpose log structured merge tree

A data backup system for use with a server running a storage application that writes and reads data blocks. The system includes a self archiving log structured volume for copying blocks from an active volume while the application is running without contending with the application for access to data blocks. The volume records the result of every write event in a new location in primary storage, forming a chronological log of the state changes the volume undergoes. The volume records in the log the points in time (synch events) when the blocks of the volume are in a consistent state with respect to the application. The system further includes backing storage to store the archived blocks and synch events of the volume. The volume migrates inactive segments of its log to the backing storage and ensures that a volume can be reconstructed from a fixed number of log segments.

Self archiving log structured volume with intrinsic data protection

Invented about 40 years ago and called ubiquitous less than 10 years later, B-tree indexes have been used in a wide variety of computing systems from handheld devices to mainframes and server farms. Over the years, many techniques have been added to the basic design in order to improve efficiency or to add functionality. Examples include separation of updates to structure or contents, utility operations such as non-logged yet transactional index creation, and robust query processing such as graceful degradation during index-to-index navigation.

This survey reviews the basics of B-trees and of B-tree indexes in databases, transactional techniques and query processing techniques related to B-trees, B-tree utilities essential for database operations, and many optimizations and improvements. It is intended both as a survey and as a reference, enabling researchers to compare index innovations with advanced B-tree techniques and enabling professionals to select features, functions, and tradeoffs most appropriate for their data management challenges.

/pdf/modern-b-tree-techniques-1f3zwajwmw.pdf

Modern B-Tree Techniques

Recently, the log-structured merge-tree (LSM-tree) has been widely adopted for use in the storage layer of modern NoSQL systems. Because of this, there have been a large number of research efforts, from both the database community and the operating systems community, that try to improve various aspects of LSM-trees. In this paper, we provide a survey of recent research efforts on LSM-trees so that readers can learn the state of the art in LSM-based storage techniques. We provide a general taxonomy to classify the literature of LSM-trees, survey the efforts in detail, and discuss their strengths and trade-offs. We further survey several representative LSM-based open-source NoSQL systems and discuss some potential future research directions resulting from the survey.

LSM-based storage techniques: a survey

Numerous applications such as stock market or medical information systems require that both historical and current data be logically integrated into a temporal database. The underlying access method must support different forms of “time-travel” queries, the migration of old record versions onto inexpensive archive media, and high insertion and update rates. This paper presents an access method for transaction-time temporal data, called the log-structured history data access method (LHAM) that meets these demands. The basic principle of LHAM is to partition the data into successive components based on the timestamps of the record versions. Components are assigned to different levels of a storage hierarchy, and incoming data is continuously migrated through the hierarchy. The paper discusses the LHAM concepts, including concurrency control and recovery, our full-fledged LHAM implementation, and experimental performance results based on this implementation. A detailed comparison with the TSB-tree, both analytically and based on experiments with real implementations, shows that LHAM is highly superior in terms of insert performance, while query performance is in almost all cases at least as good as for the TSB-tree; in many cases it is much better.

https://domino.mpi-inf.mpg.de/intranet/ag5/ag5publ.nsf/80255f02006a559a80255ef20056fc02/14008bbd691c7183c125713f00489b29/$FILE/MuthOPW00.pdf

The LHAM log-structured history data access method

Numerous applications such as stock market or medical information systems require that both historical and current data be logically integrated into a temporal database. The underlying access method must support different forms of “time-travel” queries, the migration of old record versions onto inexpensive archive media, and high insert and update rates. This paper introduces a new access method for transaction-time temporal data, called the Logstructured History Data Access Method (LHAM) that meets these demands. The basic principle of LHAM is to partition the data into successive components based on the timestamps of the record versions. Components are assigned to different levels of a storage hierarchy, and incoming data is continuously migrated through the hierarchy. The paper discusses the LHAM concepts, including concurrency control and recovery, our full-fledged LHAM implementation, and experimental performance results based on this implementation. A detailed comparison with the TSB-tree, both analytically and based on experiments with real implementations, shows that LHAM is highly superior in terms of insert performance while query performance is in almost all cases at least as good as for the TSB-tree; in many cases it is much better,

/pdf/design-implementation-and-performance-of-the-lham-log-cynq8e16pf.pdf

Design, Implementation, and Performance of the LHAM Log-Structured History Data Access Method

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The LHAM log-structured history data access method

Design, Implementation, and Performance of the LHAM Log-Structured History Data Access Method