Ashok Joshi

Fast-Start: quick fault recovery in oracle

Availability requirements for database systems are more stringent than ever before with the widespread use of databases as the foundation for ebusiness. This paper highlights Fast-Start#8482; Fault Recovery, an important availability feature in Oracle, designed to expedite recovery from unplanned outages. Fast-Start allows the administrator to configure a running system to impose predictable bounds on the time required for crash recovery. For instance, fast-start allows fine-grained control over the duration of the roll-forward phase of crash recovery by adaptively varying the rate of checkpointing with minimal impact on online performance. Persistent transaction locking in Oracle allows normal online processing to be resumed while the rollback phase of recovery is still in progress, and fast-start allows quick and transparent rollback of changes made by uncommitted transactions prior to a crash.

50,000 users on an Oracle8 universal server database

In this paper, we describe the Oracle Large User Population Demonstration and highlight the scalability mechanisms in the Oracle8 Universal Data Server which make it possible to support as many as 50,000 concurrent users on a single Oracle8 database without any middle-tier TP-monitor software. Supporting such large user populations requires many mechanisms for high concurrency and throughput. Algorithms in all areas of the server ranging from process and buffer management to SQL compilation and execution must be designed to be highly scalable. Efficient resource sharing mechanisms are required to prevent server-side resource requirements from growing unboundedly with the number of users. Parallel execution across multiple systems is necessary to allow user-population and throughput to scale beyond the restrictions of a single system. In addition to scalability, mechanisms for high availability, ease-of-use, and rich functionality are necessary for supporting complex user applications typical of realistic workloads. All mechanisms must be portable to a wide variety of installations ranging from desk-top systems to large scale enterprise servers and to a wide variety of operating systems.

Data Management --- A Look Back and a Look Ahead

The essence of data management is to store, manage and process data. In 1970, E.F. Codd developed the relational data model and the universal data language SQL for data access and management. Over the years, relational data management systems have become an integral part of every organizations data management portfolio. Today, the world is in the midst of an information explosion fueled by worldwide adaption of internet and increase in number of devices connected to the internet. The velocity, volume and velocity of data generated are beyond the capabilities of traditional relational database management systems. This explosive growth has encouraged the birth of new technologies like Hadoop and NoSQL. n nThis paper gives an overview of the technology trends in data management, some of the emerging technologies and related challenges and opportunities and eminent convergence of platforms for efficiency and effectiveness.

Benchmarking Internet of Things Solutions

The Internet of Things (IoT) is the network of physical objects accessed through the Internet, as defined by technology analysts and visionaries. These objects contain embedded technology allowing them to interact with the external environment. In other words, when objects can sense and communicate, it changes how and where decisions are made, and who makes them. In the coming years, the Internet of Things is expected to be much larger than the internet and world-wide web that we know today.

Cloudy skies for data management

Cloud computing is a fast emerging computing paradigm for which no commonly accepted definition has yet emerged in the research community. Often, cloud computing denotes a business or service model where a hosted software or infrastructure is made available on a subscription or pay-peruse basis. Due to the massive parallel scale of the hosting environment, cloud computing is often also associated with highly scalable, fault-tolerant, and adaptable computing on large clusters of off-the-shelf computers. The simplicity as perceived from a users perspective greatly distinguishes Cloud computing from grid computing. This is achieved by a much simpler API at a higher level of abstraction, which is used to deploy and to execute software and to access entire infrastructure stacks as services; the user pays for the service based on its usage. Another advantage of a Cloud computing platform is that the amount of resources required for performing a task does not have to be specified upfront; Cloud computing platforms are explicitly able to handle rapid scale-out. This flexibility allows for economic usage of resources based on demand. Currently, there is an active community evolving in the space of data management on cloud platforms, spurred to a large extent by the the Map/Reduce paradigm and the open-source framework Hadoop. Researchers explore query languages on top of the map/reduce paradigm, e.g., PigLatin, JAQL, Hive, optimization of map/reduce programs, and investigate and even teach map/reduce as a programming paradigm for web-scale computing. Researchers also explore multi-tenancy and the impact of the services model on infrastructure and application development.