'Data skew' diaporamas de présentation

Distributed Databases

Distributed Databases. CS347 Lecture 13 May 23, 2001. Expected Background. Basic SQL Relational algebra Following aspects of centralized DB Query processing: query plans, cost estimation, optimization Concurrency control techniques Recovery methods. Reading Material.

480 views • 46 slides

Improving MapReduce Performance Using Smart Speculative Execution Strategy

Improving MapReduce Performance Using Smart Speculative Execution Strategy. Qi Chen, Cheng Liu, and Zhen Xiao Oct 2013. To appear in IEEE Transactions on Computers. 2. Background. 6. Evaluation. 3. Previous work. 7. Conclusion. 4. Pitfalls. 5. Our Design. 1. Introduction. Outlines.

417 views • 39 slides

LIBRA: Lightweight Data Skew Mitigation in MapReduce

LIBRA: Lightweight Data Skew Mitigation in MapReduce. Qi Chen, Jinyu Yao, and Zhen Xiao Nov 2014. To appear in IEEE Transactions on Parallel and Distributed Systems. 2. Background. 5 . Evaluation. 3. Previous work. 6 . Conclusion. 4. System Design. 1. Introduction. Outlines. 0.

320 views • 31 slides

Genome-scale disk-based suffix tree indexing

403 views • 27 slides

Bad plan! Sit!

Bad plan! Sit!. Gail Shaw. Agenda. What exactly is a bad execution plan? Symptoms Possible causes Options for fixing. What is a bad execution plan. One that uses the wrong index? One that performs badly? One that uses the wrong joins? One that does table or index scans?

436 views • 26 slides

Data Streams, Data-flow parallelism, and Real-time Analytics

Data Streams, Data-flow parallelism, and Real-time Analytics. Christoph.Koch@epfl.ch -- EPFL DATA Lab. The Fail Whale. Science: sensors; feedback for experiment control Monitoring, log analysis, root cause analysis for failures Finance: algorithmic trading, risk management, OLTP

258 views • 24 slides

Judicious use of Histograms for Oracle Applications Tuning

Judicious use of Histograms for Oracle Applications Tuning. John Kanagaraj Cisco Systems Inc. ora_apps_dba_y@yahoo.com. Learning Objectives. As a result of this presentation, you will be able to know: What Histograms are How Histograms influence SQL performance

731 views • 20 slides

Self-tuning Histograms Building Histograms Without Looking at Data

Self-tuning Histograms Building Histograms Without Looking at Data. By Ashraf Aboulnage & Surajit Chaudhuri Represents By Miller Ofer. Traditional histograms. Histograms impose little cost at queries , especially in a large data base.

384 views • 20 slides

Azure SQL Data Warehouse: tips and tricks

Azure SQL Data Warehouse: tips and tricks. Kevin Ngo Program Manager Azure SQL Data Warehouse @ngovkevin. THR2181. SQL Data Warehouse Best Practices. Create and update statistics Enable auto-create stats (auto sample stats)

161 views • 14 slides

Four Effective Ways To Avoid Data Skew

For the organizations, which have a very large database CRM designing is a considerable and necessary aspect. They have to maintain hundreds or thousands of records. With database usually many accounts are associated with the main account and this association of many accounts with a single account is known as data skew.

120 views • 10 slides

Four Effective Ways To Avoid Data Skew

For the organizations, which have a very large database CRM designing is a considerable and necessary aspect. They have to maintain hundreds or thousands of records. With database usually many accounts are associated with the main account and this association of many accounts with a single account is known as data skew. Data skew can impact the performance of your CRM, so it is essential to prevent the data skew to get better performance of CRM by increasing the number of records, which may impact the CRM up to a great extent.

47 views • 3 slides

How To Avoid Data Skew In Salesforce And Improve The Performance Considerably

Every small decision that we make when designing or implementing a CRM system had a significant impact on the performance of Salesforce.

48 views • 3 slides