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Data most essential parts of computer technology.

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Data base systems are one of the most essential parts of
computer technology. Relational database systems were being used widely. They
are being used in many domains like payroll, billing, medical records and now
in e-commerce as well.  When it comes to implementing
database systems several architectural styles are available, and they are implemented
as per business needs. For example, memory is shared among resources, which allows
improve performance and give a sense of parallel processing. The only challenge
encountered in this process is to keep track of update on the final database.
In case of shared nothing, no hardware is shared among the machines and they
run independently of each other. In case of shared disk all the machines are
able to share the same disk but an unable to share the RAM. One of the major
advantage of this is that even if one node fails it does not affect the other
nodes. The query processor that sits on the top of the data base management system,
takes input in SQL format and processes into executable plan and then execute
it. This called as query processing which is a single user and single threaded
task. The SQL query parsing check the correctness of SQL command, converts into
internal format and verifies whether the user is authorized to operate the command.
The first step in this entire process is the canonicalization of the query. It
invokes the catalog manager to check if the table is present. The next step is
to ensure correct permission on table i.e. SELECT/DELETE etc. After checking for
the constraints, if the query is passed, it is then send to rewrite module. The
rewrite module mainly handles the view expansion for various views and updates
them accordingly. It also aims at semantic optimization. The next step is
carried out by query optimizer which creates an execution plan i.e a data flow
diagram which shows how the data flows through the queries. Many query optimization
are based on Selinger’s principles however, there are certain exception which
does not apply this dynamic programing and instead use top down approach. One
of the major task of query optimizer is the speed up the task. In cases of
WRITE/UPDATE statements, the role of query optimizer becomes critical in the sense
to ensure correct updates on the data. Different techniques like to avoid updating
indexes on the updated columns or use batch read write scheme. The query
interpreter is a runtime interpreter which receives query from the optimizer
which is in low-level code i.e. the graph format and then invokes the procedure
resolving each flow. The data is stored in the format of tuples for input and
output which has an array reference to these columns and each iterator is
allocated a space in the database. These tuples may reside either in buffer pool
or in memory heap. The access methods are used to provide access to iterator.
The basic API for this is init() method.

For most applications one the metric used to estimate the
performance of query processing is the time take for entire query processing
i.e. time to query completion. However, different query performance measure are
also used in different cases. Large data warehouses which store historical data
form one of the essential part of this system. The initial system of data /batch
processing was replaced with OLTP and this was then replaced with ETL. However,
with data warehouse there are several issues associated. In this case the data
is first loaded and then the data is static for months while in the earlier
case the B+- trees are optimized for fast processing. Another problem with
warehouses is that they offer materialize view which leads to longer processing
time. Another problem is that data cubes in ROLAP can provide high performance
for know queries but do not provide the same for unknown query. Databases initially
were able to store more of numeric or facts and figure related data however,
they have evolved to store different data formats which has lead to data
extensibility. With this abstract data types, XML tags and full text search is
enabled.

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In most data base systems the architectures is similar to
the one that was discussed however finer details are modified as per business
requirements.  These systems have evolved
over the time and have been able to cater to newer demands. 

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