Skip to main content

Database Performance considerations while Architecting

Performance considerations while architecting database access

  1. Turning on features that application only.
  2. N-Tier strategy gives maximum performance as opposed to a direct client-to-database connection.
  3. Use Optimal Managed Provider such as System.Data.SqlClient to gain performance.
  4. Stored Procedures usage where ever possible.
  5. Avoid Auto-Generated commands of data adapter. Gives better performance in critical applications.
  6. Prefer Data Reader, when you dont need to cache your data. using data reader can provide you with an enormous performance boost.
  7. Data Reader's CommandBehavior.Sequential Access can be used often as possible. If you dont need to work the whole object at once, will give you much better performance.
  8. Keep your Datasets lean.
  9. Particularly when designing for disconnected approach, make several connections in sequence rather than holding a single connection open for a long time.

Comments

Post a Comment

Popular posts from this blog

DDSP: DIFFERENTIABLE DIGITAL SIGNAL PROCESSING

 -By Google Research {jesseengel,hanoih,gcj,adarob}@google.com Paper Link Code Demo Abstract Most generative models of audio directly generate samples in one of two domains: time or frequency. While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived. A third approach (vocoders/synthesizers) successfully incorporates strong domain knowledge of signal processing and perception, but has been less actively researched due to limited expressivity and difficulty integrating with modern auto-differentiation-based machine learning methods. In this paper, we introduce the Differentiable Digital Signal Processing (DDSP) library, which enables direct integration of classic signal processing elements with deep learning methods. Focusing on audio synthesis, we achieve high-fidelity generation without the need for large autoregressive models or adversarial losses, demonstrating that DDSP enable...
Post a Comment
Read more

Capsule Neural Networks

-By Sara Sabour, Nicholas Frosst,  Geoffrey E. Hinton  Google Brain Toronto Paper Link Abstract   A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We show that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher...
1 comment
Read more

Detecting Anomaly in Big Data System Logs Using Convolutional Neural Network

By - Siyang Lu, Xiang Wei, Yandong Li, Liqiang Wang Department of Computer Science, University of Central Florida, Orlando, FL, USA School of Software Engineering, Beijing Jiaotong University, China Paper Link Previous week we did a scan of the survey of Anomaly detection   , that covers breath of the topic (Types of Anomalies, Types of Models and their applications). In this summary paper, we look in to the core Heavy lifting part of Detecting Anomaly in Big Data System Logs Using Convolutional Neural Network. Abstract Nowadays, big data systems are being widely adopted by many domains for offering effective data solutions, such as manufacturing, healthcare, education, and media. Big data systems produce tons of unstructured logs that contain buried valuable information. However, it is a daunting task to manually unearth the information and detect system anomalies. A few automatic methods have been developed, where the cutting-edge machine lear...
Post a Comment
Read more