Yuliya-824 - Fault-Tolerance in the Borealis Distributed...

Info iconThis preview shows pages 1–10. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Fault-Tolerance in the Borealis Distributed Stream Processing System M. BALAZINSKA, H. BALAKRISHNAN, S. MADDEN, M. STONEBRAKER Presented by Yuliya Olmo CSCE 824 Outline • Introduction • Stream Processing Systems • Distributed Stream Processing Systems • Motivation and Goal • Technical issues • Dynamic query revision • Flexible and scalable optimization • Fault tolerance • Conclusion Outline • Introduction • Stream Processing Systems • Distributed Stream Processing Systems • Motivation and Goal • Technical issues • Dynamic query revision • Flexible and scalable optimization • Fault tolerance • Conclusion Data Streams: What , Where? Continuous, unbounded, rapid, time-varying streams of data elements (tuples). Occur in a variety of modern applications Network monitoring traffic engineering Sensor networks, RFID tags Financial applications Manufacturing processes (CBM systems) Proposed for VANET and in-vehicle applications DSMS = Data Stream Management System Data Streams: DBMS vs. DSMS • Persistent relations • One-time queries • Random access • Access plan determined by query processor and physical DB design • Transient streams • Continuous queries • Sequential access • Unpredictable data characteristics and arrival patterns Data Streams: Continuous Queries • One time queries – Run once to completion over the current data set. • Continuous queries – Issued once and then continuously evaluated over the data. • Example: • Notify me when the temperature drops below X • Tell me when prices of stock Y > 300 • Tell me if I am driving too fast on this curve • Notify me if the vehicle behind me is about to rear-end me Outline • Introduction • Stream Processing Systems • Distributed Stream Processing Systems • Motivation and Goal • Technical issues • Dynamic query revision • Flexible and scalable optimization • Fault tolerance • Conclusion Distributed Data Streams • Multiple data sources: lots of data from lots of places • Multiple processing units: central PU doesn’t 9 Distributed Data Streams Weather Local Weather Web sources Flight information Travel Agent Centralized DB What is the status of my flight?...
View Full Document

This note was uploaded on 12/13/2011 for the course CSCE 824 taught by Professor Staff during the Fall '11 term at South Carolina.

Page1 / 34

Yuliya-824 - Fault-Tolerance in the Borealis Distributed...

This preview shows document pages 1 - 10. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online