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4 Layers of AWS Architecture a Quick Answer

I have collected real interview questions on AWS key architecture components. Those are S3, EC2, SQS, and SimpleDB. AWS is one of the most popular skills in the area of Cloud computing. Many companies are recruiting software developers to work on cloud computing.

AWS Key Architecture Components AWS is the top cloud platform. The knowledge of this helpful to learn other cloud platforms. Below are the questions asked in interviews recently.
What are the components involved in AWS?Amazon S3.With this, one can retrieve the key information which is occupied in creating cloud structural design, and the amount of produced information also can be stored in this component that is the consequence of the key specified.Amazon EC2. Helpful to run a large distributed system on the Hadoop cluster. Automatic parallelization and job scheduling can be achieved by this component.Amazon SQS. This component acts as a mediator between different controllers. Also worn for cushioning requirements those are obt…

Oracle 12C 'Bitmap Index' benefits over B-tree Index

Oracle 12C 'Bitmap Index' benefits over B-tree Index
#Oracle 12C 'Bitmap Index' benefits over B-tree Index:
A bitmap index has a significantly different structure from a B-tree index in the leaf node of the index. It stores one string of bits for each possible value (the cardinality) of the column being indexed.

Note: One string of BITs means -Each tupple of possible value it assigns '1' bit in a string.So, all the BITs become a string ( This is an example, on which column you created BIT map index)

The length of the string of bits is the same as the number of rows in the table being indexed.

In addition to saving a tremendous amount of space compared to traditional indexes, a bitmap index can provide dramatic improvements in response time because Oracle can quickly remove potential rows from a query containing multiple WHERE clauses long before the table itself needs to be accessed.

Multiple bitmaps can use logical AND and OR operations to determine which rows to access from the table.

Although you can use a bitmap index on any column in a table, it is most efficient when the column being indexed has a low cardinality, or number of distinct values.

For example, the GENDER column in the PERS table will be either NULL, M, or F. The bitmap index on the GENDER column will have only three bitmaps stored in the index. On the other hand, a bitmap index on the LAST_NAME column will have close to the same number of bitmap strings as rows in the table itself. The queries looking for a particular last name will most likely take less time if a full table scan is performed instead of using an index. In this case, a traditional B-tree non-unique index makes more sense.

A variation of bitmap indexes called bitmap join indexes creates a bitmap index on a table column that is frequently joined with one or more other tables on the same column. This provides tremendous benefits in a data warehouse environment where a bitmap join index is created on a fact table and one or more dimension tables, essentially pre-joining those tables and saving CPU and I/O resources when an actual join is performed.

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