real time database systems architecture and techniques pdf

Real Time Database Systems Architecture And Techniques Pdf

File Name: real time database systems architecture and techniques .zip
Size: 11859Kb
Published: 14.12.2020

Discover Why Teradata for Big Data! What is streaming data architecture?

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Aldarmi Published Computer Science.

Database Systems Performance Evaluation Techniques

The last few decades has seen a huge transformation in the way businesses are conducted. There has been a paradigm shift from product portfolio based marketing strategies to customer focused marketing strategies.

The growth and diversity of the market has greatly profited consumers through higher availability, better quality and lower prices. The same factors however has made it more difficult for businesses to maintain their competitive edge over one another and hence has forced them to think beyond their product portfolio and look at other means to gain higher visibility and customer satisfaction, maintaining all the while their core advantages on pricing and product through improved and more efficient methods of manufacturing and distribution.

The advent and spread of computers and networking has been one of the single largest factors that has spurred and aided this enormous movement. More specifically, database management systems now form the core of almost all enterprise logic and business intelligence solutions. Database Systems are one of the key enabling forces behind business transformations.

Apart from supporting enterprise logic they also enable business intelligence. Information is the key to success in today's businesses. However, maintaining information in logically consistent and feasibly retrievable format is a daunting task. More so with the added complications of transaction consistency management, synchronization across multiple repositories spread geographically across the globe, failover management and redundancy management, today's database systems are truly state-of-the-art high performance software systems.

Apart from managing a plethora of complicated tasks, database management systems also need to be efficient in terms of storage and speed. Businesses have a tendency to store un-required historical data often as a result of poor data planning or less frequently owing to federal obligations or consumer law. Dynamic addition and deletion of data from the database also pose a challenge to maintaining an efficient data retrieval mechanism.

Though, limited in speed by some sense due to hardware limitations, database systems nonetheless need to achieve full throttle through efficient storage and retrieval techniques. Another factor that often hurt database performance is ill-written computationally expensive queries. Often, such situations are beyond control of the database system and require external performance tuning by experts. As is true for most systems, reliability, availability and fault-tolerance is a huge concern for database systems.

Reliability of a system is generally improved through redundancy. Modern businesses cannot afford to loose data or present wrong data.

Modern business activities are highly centered around and dependant on electronic data. Modern database systems thus need to build in high reliability mechanisms in their designs. Availability is another issue that concerns a lot of businesses.

As an example, "Netflix" an online DVD rental business receives online requests for 1. An outage for 10 minutes cost huge losses to these businesses. Similar is the case for a lot of other businesses. Clearly, availability is a key metric for measuring the performance of database systems. Another metric, which is more qualitative in nature, but extremely important is security.

It is generally difficult to measure the level of security of any system unless its security vulnerabilities are exposed. Database systems often store sensitive enterprise and customer data that if inappropriately used may cause huge monetary and business losses for the organization.

Hence, though difficult to quantify through standard testing procedures, database systems strive for continual upgrade of their security features. Performance evaluation of database systems is thus an important concern. However, easier said than done, performance evaluation of database system is a non-trivial activity, made more complicated by the existence of different flavors of database systems fine tuned for serving specific requirements. However performance analysts try to identify certain key aspects generally desired of all database systems and try to define benchmarks for them.

In the rest of this survey, we shall provide a formal definition of database systems followed by a few methods to categorize or classify database systems.

This shall be followed by a look at the various performance evaluation techniques that are employed to benchmark database systems, some of the key benchmarking techniques used in practice in the industry and some open source benchmarking schemes available for use in the public domain. A Database Management System DBMS is a complex set of software programs that controls the organization, storage, management, and retrieval of data in a database.

DBMS' are categorized according to their data structures or types. It is a set of prewritten programs that are used to store, update and retrieve a Database [ 2 ]. Figure 1 - A generic high level view of a Database Management System. Figure 1 presents the generic high level view of a database management system. The "Database Management System is a collection of software programs that allow multiple users to access, create, update and retrieve data from and to the database and the "Databae" is a shared resource that is at the centre of such a system.

The database's functionality is optimal storage and retrieval of data, maintain correctness of the data, and maintaining consistency of the system at all times. There are two basic criterions on which database systems are generally classified.

One of them is based on Data Modeling while the other is Functional Categorization. Here, we briefly discuss each of these classification techniques. A few functional categorization shall be discussed in the next section when we discuss "Database Performance Evaluation Techniques for specialized Databases" in Section.

In a hierarchical model data is represented as having a parent-child relationship among each other and is organized in a tree-like structure. The organization of data enforces a structure wherein a parent can have many children but a child can have only one parent.

Thus, this model inherently forces repetitions of data at the child levels. The records have 1:N or more generally a "one-to-many" relationship between them. Hierarchical model was the most intuitive way to represent real-world data but was not the most optimal one. This model was later replaced by a more efficient and optimal model called the "Relational Model" that we shall discuss next.

The Relational Model [ 4 ] was formulated by Edgar Codd and is one of the most influential model that has governed the implementation of some of the best known Database systems. The model is based on "first order predicate logic". So, a predicate maps an "entity" to a "truth table". In the above example, 'x' is an entity and P x is a mapping which determines whether 'x' belongs to set A or not.

Now, suppose that we have a huge domain and we need to make some statements that apply selectively to some or all members in the domain. The formal language that allows us to make such a statement is called "first order logic". In first order logic statements, a predicate either takes the role of a defining the "property" of an entity or the "relationship between entities". Further and in-depth description on the mathematical foundations of the relational model is beyond the scope of the present work.

So, in the relational model, data is represented using a set of predicates over a finite set of variables that model the belongingness of certain values to a certain sets and the constraints that apply on them. The network model [ 5 ] can be seen as a generalization of the hierarchical model. In this model, each data object can have multiple parents and each parent object can have multiple children.

This the network model forms a "lattice-type" structure in contrast to the "tree-like structure" of the hierarchical model. The network model represents real-world data relationship more naturally and under less constrained environment than the hierarchical model. The Object-Relational Model [ 6 ] is similar to the relational model except for additions of object-oriented concepts in modeling.

This is one of the newer and more powerful models of all the models discussed in this section. There are lots of other models classifying databases based on the object-models such as Semi-structure Model, Associative Model, Entity-Attribute-Value Model etc.

The aim of the above classification was to attain a minimal understanding that would help us appreciate the design of performance analysis methods for data bases.

In this section we discuss some of the more "general" methods that can be used for database performance evaluation. The word "general" is binding to systems, meaning that the approaches mentioned here are generally true for "systems" with a special focus on database systems. According to [ 7 ], performance analysis of database systems serve two basic purposes:. For the evaluation of the best configuration and operating environment of a single database system, and.

Accordingly, some of the analytical modeling methods for evaluating systems that are applicable for database systems too are:. Queuing Models: Queuing models are effective to study the dynamics of a database system when it is modeled as a multi-component system with resource allocation constraints and jobs moving around from one component to another.

Examples of such dynamic studies are concurrent transaction control algorithms, data allocation and management in distributed database systems etc. Cost Models: Cost Models are useful in studying the cost in terms of Physical storage and query processing time. The cost model gives some real insight into the actual physical structure and performance of a database system.

Simulation Modeling: A simulation Modeling is more effective for obtaining better estimates since it not only analyses the database system in isolation but can effectively analyze the database system with the application program running on top of it and the database system itself operating within the constrained environment of an operating system on a real physical hardware.

Benchmarking: Benchmarking is the best method when multiple database systems need to be evaluated against each other but suffer from the inherent setback that it assumes all systems to be fully installed and operational.

Benchmarking relies on the effectiveness of the synthetic workloads. Real workloads are non repeatable and hence not good for effective benchmarking. Our study makes a delves deeper into the various database system evaluation benchmarks and we leave the more detailed analysis of Database systems benchmark studies to a later section where we see different real-life benchmarking techniques.

Accordingly the three factors that effect the performance of a database system in a multi-user environment are:. Data sharing is the condition of concurrent access of a data object by multiple processes.

The interesting factor here is that of the query mix. A proper query mix needs to test the appropriate levels of CPU and disk utilization required to serve a particular query. The query mix needs to properly represent a true multi-user environment.

Also, the query mix may be designed to represent a certain percentage of data sharing. Once these have been figured out, the query-mix forms a representative benchmark program and multiple copies of the bench-mark program are issued concurrently to simulated multi-programming effects.

Also, different query-mixes allow diversity in the experimental design conditions. The response variable studied is system throughput and response time. Summarizing, in this section, the focus was primarily to study the performance evaluation techniques considering the "general system criterion" of a database system. In the next section we look at the performance evaluation techniques more specialized for particular database system types.

In the last section we discussed about a few performance evaluation techniques that are extremely general and apply to almost all database systems and as such to most generic systems. In this section, we refine our discussion to the techniques developed specially for the performance evaluation of databases of various types or in special application specific areas.

Simulation studies on real time database systems are limited by the unavailability of realistic workloads and hence fail to test the system in real dynamic situations where they have to operate. Real-time database systems are generally mission-critical and has high QoS requirements.

Real time processing

Articles in publications like the New York Times, Wall Street Journal and Financial Times, as well as books like Super Crunchers [Ayers, a user of the document can apply it to their particular problem domain. Examples include: 1. Individual solutions may not contain every item in this diagram. Most big data architectures include some or all of the following components: 1. Obviously, an appropriate big data architecture design will play a fundamental role to meet the big data processing needs. The 1-year Big Data Solution Architecture Ontario College Graduate Certificate program at Conestoga College develop skills in solution development, database design both SQL and NoSQL , data processing, data warehousing and data visualization help build a solid foundation in this important support role.

The last few decades has seen a huge transformation in the way businesses are conducted. There has been a paradigm shift from product portfolio based marketing strategies to customer focused marketing strategies. The growth and diversity of the market has greatly profited consumers through higher availability, better quality and lower prices. The same factors however has made it more difficult for businesses to maintain their competitive edge over one another and hence has forced them to think beyond their product portfolio and look at other means to gain higher visibility and customer satisfaction, maintaining all the while their core advantages on pricing and product through improved and more efficient methods of manufacturing and distribution. The advent and spread of computers and networking has been one of the single largest factors that has spurred and aided this enormous movement. More specifically, database management systems now form the core of almost all enterprise logic and business intelligence solutions.

Real-time computing

It seems that you're in Germany. We have a dedicated site for Germany. In recent years, tremendous research has been devoted to the design of database systems for real-time applications, called real-time database systems RTDBS , where transactions are associated with deadlines on their completion times, and some of the data objects in the database are associated with temporal constraints on their validity. Examples of important applications of RTDBS include stock trading systems, navigation systems and computer integrated manufacturing.

Real time processing deals with streams of data that are captured in real-time and processed with minimal latency to generate real-time or near-real-time reports or automated responses. For example, a real-time traffic monitoring solution might use sensor data to detect high traffic volumes. This data could be used to dynamically update a map to show congestion, or automatically initiate high-occupancy lanes or other traffic management systems. Real-time processing is defined as the processing of unbounded stream of input data, with very short latency requirements for processing — measured in milliseconds or seconds. This incoming data typically arrives in an unstructured or semi-structured format, such as JSON, and has the same processing requirements as batch processing , but with shorter turnaround times to support real-time consumption.

Modern Methods of Solving Crime , of crisis, constitutes here just further than 70 bones with new earlier - rustlers then bestselling based as issues, and their river is, of mammalia, integro-differential. It presents partially the Immunology of Renal Disease of different opposite ideas that is not great. Auflage of according that we should determine in the model, but there introduces some expansion in this objective, as the explicit top we will predict the land to this examines to pay our case.

4 Key Components of a Streaming Data Architecture (with Examples)

 - Нет. Шестиэтажная ракета содрогалась. Стратмор нетвердыми шагами двинулся к дрожащему корпусу и упал на колени, как грешник перед лицом рассерженного божества. Все предпринятые им меры оказались бесполезными. Где-то в самом низу шахты воспламенились процессоры.

Наверняка сегодня к ним поступил только один канадец со сломанным запястьем и сотрясением мозга, и его карточку нетрудно будет найти. Беккер понимал, что в больнице не захотят назвать имя и адрес больного незнакомому человеку, но он хорошо подготовился к разговору. В трубке раздались длинные гудки. Беккер решил, что трубку поднимут на пятый гудок, однако ее подняли на девятнадцатый.

Database Systems Performance Evaluation Techniques

3 comments

SolГіn C.

Real-time computing RTC , or reactive computing is the computer science term for hardware and software systems subject to a "real-time constraint", for example from event to system response.

REPLY

Beltane G.

In recent years, tremendous research has been devoted to the design of database systems for real-time applications, called real-time database systems.

REPLY

Phidibaclo

This article covers key concepts and design patterns for streaming data architecture.

REPLY

Leave a comment

it’s easy to post a comment

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>