Using Infobright Community Edition For Analytics
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Using Infobright Community Edition For Analytics

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This is a benchmark of using Infobright Community Edition (ICE) to perform analytics on large datasets

This is a benchmark of using Infobright Community Edition (ICE) to perform analytics on large datasets

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    Using Infobright Community Edition For Analytics Using Infobright Community Edition For Analytics Document Transcript

    • Improving the performance of ad-hoc analysis of large datasets True North R&D: Evaluation of Infobright Community Edition
    • Situation Most organisations will have at least one data warehouse or data marts containing business data specific to a department. These databases typically feed management information (MIS) and/or business intelligence (BI) solutions and are in larger organisations are usually relational data stores optimised to perform particular tasks1. Often business users want to perform additional analysis on the data in the warehouse or mart in order to gain insights in to customer or employee behaviour. Examples of this might be “Who are my top 10 customers buying widgets in the following regions over the past six months?”; “Which employees over director grade and in the IT department spend the most on employee benefits”; “Which customers using the Safari browser who click on the Swedish landing page go on to spend over 100 krone”. The problem This desire to perform ad-hoc analysis or data mining can lead to difficulties for the teams that own and provide access to the data. This is because data marts are usually optimised for a particular set of use cases and hence are aggregated and indexed on the dimensions that match the use cases. So a Sales data mart may be built to query on dimensions of product code, region, sales manager, but may not be geared up to answer queries as to the marketing campaign code of the product. The data warehouse itself (if a traditional warehouse) will not make any optimisations along dimensions. For this reason, users are often discouraged or prevented from performing this type of analysis on data warehouses. If they are allowed access there are two opposing factors:  Long response times to ad-hoc queries lead to a poor user experience  Database optimisations (indexes and aggregate tables) greatly increase the amount of storage required2 Reason for this evaluation Several of our current clients would benefit from being able to mine their data marts in an efficient and productive (from a user experience perspective) manner. This document looks at a potential solution to part of that problem – in enabling efficient access to the data both from the point of view of storage and response times. This document was an evaluation of Infobright Community Edition (ICE) as a means of enabling ad- hoc analysis of metric data. 1 Smaller organisations often have their data warehouse made up of one or more spreadsheets 2 This has a knock-on effect of increasing the time required and complexity of populating the database
    • Scope This document is not a full evaluation of Infobright, nor is it an endorsement of the product. Rather it describes the reasons for, approach, and results of an evaluation of Infobright Community Edition with a limited number of real-life data queries. About Infobright Infobright is a database designed to solve analytical queries. It is built on MySQL but uses a different storage engine, Brighthouse, rather than one of the standard storage engines (e.g. MyISAM, InnoDB). Infobright does not use indexes or aggregate tables but instead relies on the fact that it is a column- oriented (columnar) database which is why it is more suited to aggregate analytics. This is for the most part invisible to the user (depending on which edition is used) and Infobright can be accessed through the same clients used for a regular MySQL instance. Infobright comes in two flavours. The Community Edition (ICE) is Open Source Software and the Enterprise Edition (IEE) is a commercial product. The chief differences between the two offerings are support for data loading and DML (i.e. INSERT, UPDATE, DELETE). Evaluation We performed a limited evaluation to determine whether ICE would provide benefits in a real-life situation. We used data from a warehouse that belonging to one of our clients and worked with them to understand analysis that they would like to be able to perform but up to now have not been able to. The data and problem domain has been made anonymous and generic within this report to protect client confidentiality. The key principles for the evaluation were:  Use real data volumes  Ask real questions of the data Aim The aim of the evaluation was intended to understand how an Infobright Community Edition (ICE) database compared to a standard MySQL database (using an InnoDB storage engine) over the following dimensions:  User response times to sample queries  Storage space required by the database Specifications Tests were performed on a desktop developer’s machine  Pentium Dual-core 2.16GHz, 3Gb RAM, Windows XP Professional  MySQL Community Edition 5.1
    • o Using InnoDB  Infobright Community Edition 3.3.1  HeidiSQL was used to run the queries  Approximately a year’s worth of historical data was loaded in to the databases. This equated to 1.3 million rows. Approach In both cases the databases were loaded with approximately a year’s worth of data – this equated to 1,291,062 rows. The time taken to load the databases was not compared as ICE only allows load from flat file3 although as a note it took 1’29” to load the data in to ICE. Test 1: Comparing storage requirements In this case, the same data was loaded in to both databases but the InnoDB database had no optimisations applied (i.e. no keys, indexes, aggregates, etc). This was in order to limit the space to only the data. Test 2: Comparing reponse times The second test was to compare the performance of an ICE database against that of an optimised InnoDB database. The database could not be optimised for all queries against it (as they are ad-hoc) but was optimised for only the selected queries. 3 IEE allows population through more means (e.g. using DML, binary dumps rather than ASCII). See more at http://bit.ly/aXQvKM
    • Results Test 1: ICE compared to a non-optimised InnoDb database Storage space Infobright needed 17.7Mb to store the 1,291,062 rows versus 203.8 Mb needed by InnoDB. Response times Query Infobright InnoDB x Faster top 10 customers by quantity 3.828 147.781 39 top 10 customers by revenue 7.734 124.703 16 top 10 customers with revenue between 300K and 8.109 160.094 20 600K top 10 customers by quantity between Jan and Apr 1.235 21.703 18
    • Test 2: ICE compared to an optimised InnoDb database Storage space Response times Conclusions About the author