Best situation to use Column database
Column oriented database or datastore as the name sounds it stores the data by column rather than by row. It has some advantages and disadvantages over traditional RDBMS. Developer should know the typical situation to choose column oriented database. Below is the Student table representation
1,James,12,MColumn oriented database will store the data as columns.
- Column oriented database are good in doing aggregates for many rows by reading / loading subset of columns. It will load columns which are required. It doesn't need to parse all the rows.
- More frequent updates on columns or adding a new column will be faster as it needs to write only that specific column. In case of row-oriented database, every single row is affected.
- Most of the column oriented database provides support for compressing the data. Each column is compressed and stored in disk. On request (when queried) particular column will be uncompressed and loaded to memory. This saves disk storage.
See also: Open source column-oriented database
comments powered by Disqus
OLAP (Online Analytical Processing), Reporting, Data mining related tasks are usually done by Business intelligence products. They do powerful Extraction, Transformation and Loading (ETL) the data and provides various reports. They use relational database as its back end. How could they generate better reports? Will column DB do a better job?
Each website / blog delivers useful content or service to its users. But website themselves requires some service to monitor and increase its presence. Here are few free services which could be used by Website / Blog. This will be very much helpful for small business owners.
Students ask this question frequently steps or methodology to learn from open source projects. There is no single answer or steps available. I listed the steps which i follow and i hope this will help for few.
Web developers most frequent question, Should user images be stored in database or file system? Which is the best way. Both has some pros and cons.
Traditionally Programmers used ODBC, JDBC, ADO etc to access database. Developers need to write SQL queries, process the result set and convert the data in the form of objects (Data model). I think most programmers would typically write a function to convert the object to query and result set to object. To overcome these difficulties, ORM provides a mechanism to directly use objects and interact with the database.
Most of the database has support of full text search, basically indexing and saarching. MySQL, Oracle and many more databases has in-built full text search. Then what is the need to go for external search engine like Lucene, Sphinx, Solr etc. Check out the advantage of using Searchengine.
Lucene and Solr are most popular and widely used search engine. It indexes the content and delivers the search result faster. It has all capabilities of NoSQL database. This article describes about its pros and cons.
MongoDB is the most exciting SQL-free database currently available in the market. The new kid on the block, called MongoDB is a scalable, high-performance, open source, schema free and document oriented database that focuses on the ideas of NoSQL Approach. Written in C++, it has taken rapid strides since its emergence into the public sphere as a popular way to build your database applications.
You may require GBs of data to do performance or load testing. How your app behaves when there is loads of data. You need to know the capacity of your application. This is the frequently asked question from the sales team "The customer is having 100GB of data and he wants to know whether our product will handle this? If so how much RAM / Disk storage required?". This article has pointers to the large data corpus.
Solr and Elastic Search are built on top of Lucene. Both are open source and both have extra features which makes programmer life easy. This article explains the difference and the best situation to choose between them.