видавнича справа

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Borys Grinchenko Kyiv University

Київський університет імені Бориса Грінченка Напрям підготовки : Видавнича справа і редагування

Видавнича справа і редагування ,[object Object],[object Object]

Видавнича справа і редагування ,[object Object],[object Object],[object Object],[object Object]

Видавнича справа і редагування ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Investment banks rely extensively on grids to dramatically increase throughput for their calculations for analytics (especially risk). The traditional design pattern involves executing compute intensive workflows where jobs require movement of large data files to the compute nodes, calculation results creating files which then are again consumed by the next job in the flow. Increasingly, the pattern is shifting to running short lived tasks where the bottleneck is data i.e. the time spent to move data back and forth between compute nodes can be overwhelming - turning a compute bound job to be a IO bound one. For instance, real time pricing for financial derivative instruments could just take a few milliseconds, but, the time required for the data transfer could be hundreds of milliseconds. The talk focuses on one architectural pattern gaining popularity - move the compute to the data. The data is partitioned in grid memory across many nodes and the compute task is routed to the node with the right data set provisioned based on the data hints it provides during launch. We discuss the features of the main-memory based data grid solution that uses different data partitioning policies such as hashing or data relationship based to manage data across a large cluster of nodes. We also discuss techniques for rebalancing data and behavior across the Grid nodes to achieve the best throughput and lowest latency.

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Flexible OLTP data models in the future ================================= There has been a flurry of highly scalable data stores and a dramatic spike in the interest level. The solutions with the most mindshare seem to be inspired by Dynamo's (Amazon) eventually consistency model or a data model that promotes nested, self-describing data structures like BigTable from Google. At the same time you see projects within these corporations evolving to architectures like MegaStore and Dremel (Google) where features from the column-oriented data model is blended together with the relational model. The shift from just highly structured data to unstructured and semistructured content is evident. New applications are being developed or existing applications are being modified at break neck speed. Developers want the data model evolution to be extremely simple and want support for nested structures so they can map to representations like JSON with ease so there is little impedance between the application programming model and the database. Next generation enterprise applications will increasingly work with structured and semi-structured data from a multitude of data sources. A pure relational model is too rigid and a pure BigTable like model has too many shortcomings and cannot be integrated with existing relational databases systems. In this talk, I walk through an alternative. We prefer the familiar "row oriented" over "column oriented" approach but still tilt the relational model - mostly the schema definition to support partitioning and colocation, redundancy level and support for dynamic and nested columns. Each of these extensions will support different desired attributes - partitioning and colocation primitives cover horizontal scaling, availability primitives allow explicit support for replication model and the placement policies (local vs across data centers), dynamic columns will address flexibility for schema evolution (different rows have different columns and added with no DDL requirements) and nested columns that support organizing data in a hierarchy. We draw inspiration for the data model from Pat helland's 'Life beyond distributed transactions' by adopting entity groups as a first class artifact designers start with, and define relationships between entities within the group (associations based on reference as well as containment). Rationalizing the design around entity groups will force the designer to think about data access patterns and how the data will be colocated in partitions. We then cover why ACID properties and sophiticated querying becomes significantly less challenging to accomplish. There are many ideas around partitioning policies, tradeoffs in supporting transactions and joins across entity groups that are worth discussion. The idea is to present a model and generate discussion on how to achieve the best of both worlds. Flexible schemas without losing referential integrity, support for associations and the po

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VMWare vFabric SQLFire - scalable SQL instead of NoSQL There is quite a bit of buzz thesedays on "NoSQL" databases. The lack of transactions and good support for querying (SQL) has been a problem for many to adopt these solutions. This talk presents, VMWare SQLFire, a distributed SQL data management solution that melds Apache Derby (borrowing SQL drivers, parsing and some aspects of the engine) and an object data grid (GemFire) to offer a horizontally scalable, memory oriented data management system where developers can continue to use SQL. We focus on new primitives that extend the well known SQL Data definition syntax for data partitioning and replication strategies but leaving the "select" and data manipulation part of SQL intact so it only minimally impacts your application. I gave this presentation at What's next, Paris 2011(http://www.whatsnextparis.com/abouttheseminar.html).

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