Real-time analytics for large data sets

a technology for large data sets and real-time analytics, applied in the direction of digital data information retrieval, instruments, program control, etc., can solve the problems of inability to analyze extremely large sets of data in near real time, inability to perform query on extremely large data sets, and inability to operate in real-time over much smaller data sets. achieve fast write performance, fast processing of queries, and achieve the effect of reducing the number of data sets

Inactive Publication Date: 2013-07-25
EVOAPP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This technology allows users to quickly access huge amount of data from different sources on their own computer network instead of having them wait until they can actually use it again later when needed afterwards. It uses algorithms that sort through irrelevant records based upon certain criteria such as time stamps, location, and purpose. Users only have one request at once - making this quicker task possible with existing databases.

Problems solved by technology

The technical problem addressed in this patents relates to improving efficiency in accessing very big datasets without having to wait until all necessary parts get ready again after being analyzed. Cloud computing offers fast analysis capabilities compared to traditional methods due to its ability to execute complex analyzes more efficiently than older forms of software. Additionally, there has been research into developing efficient ways to search large numbers of data files within seconds rather than days before actually running them down.

Method used

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Examples

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Embodiment Construction

[0032]FIG. 9 is a simplified functional block diagram showing an overview of components of a cloud computing system according to an embodiment. The cloud computing system is comprised of cloud resources 900 that includes a set of compute nodes 910, each of which includes one or more processors 915 and local memory 920. The compute nodes 910 are coupled to persistent storage 930 comprising a collection of storage devices, for example, storage devices 931, 932, and 933. The cloud computing system 900 receives query 954 over network 940 from database client 952 residing within data center 950.

[0033]FIG. 1 is a simplified functional block diagram of an embodiment of a cloud computing system. Cloud computing system 100 includes functional modules comprising persistent storage 101, memory 102, network connector 103, blob storage manager 110, blob storage service 150, key management module 120, key management service 160, query manager 140, and de-serialization library generator 130.

[0034]Per

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Abstract

A cloud computing system is described herein that enables fast processing of queries over massive amounts of stored data. The system is characterized by the ability to scan tens of billions of data items and to perform aggregate calculations like counts, sums, and averages in real-time (less than three seconds). Ad hoc queries are supported including grouping, sorting, and filtering without the need to predefine queries by providing highly efficient loading and processing of data items across an arbitrarily large number of processors. The system does not require any fixed schema, thus the system supports any type of data. Calculations made to satisfy a query may be distributed across a large number of processors to parallelize the work. In addition, an optimal blob size for storing multiple serialized data items is determined, and existing blobs that are too large or too small are proactively redistributed or coalesced to increase performance.

Description

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Claims

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Application Information

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Owner EVOAPP
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