2026-09-18T19:14:34Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1242692026-06-06T01:03:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Robert T. Morris.
Yuan, Gina,M. Eng.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2020-03-24T15:37:14Z
2020-03-24T15:37:14Z
2019
2019
https://hdl.handle.net/1721.1/124269
1145279777
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 55-57).
Streaming dataflow systems offer an appealing alternative to classic MySQL / memcached web backend stacks. But websites must not go down, and current fault tolerance techniques for dataflow systems either come with long downtimes during recovery, or fail to scale to large deployments due to the overhead of global coordination. For example, in the failure of a single dataflow node, existing lineage-based techniques take a long time to recompute all lost and downstream state, while checkpointing techniques require costly global coordination for rollback recovery. This thesis presents a causal logging approach to fault tolerance that rolls back and replays the execution of only the failed node, without any global coordination. The key to knowing how to replay a valid execution while ensuring exactly-once semantics is a small, constant-size tree clock piggybacked onto each message, incurring runtime overheads that are low and scalable. After recovery, the state of the system is indistinguishable from one that never failed at all. We implement and evaluate the protocol on Noria, a streaming dataflow backend for read-heavy web applications. Compared to Noria's original protocol of lineage-based recovery, tree clock recovery time is constant in relation to state size and graph size. Experimental results show sub-second recovery times with 1.5ms runtime overheads, which translates to a 290x improvement in recovery time.
by Gina Yuan.
M.Eng. in Computer Science and Engineering
M.Eng.inComputerScienceandEngineering Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
57 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Scalable fault tolerance for high-performance streaming dataflow
Thesis
application/pdf
2020-03-24T15:37:13Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Streaming dataflow systems offer an appealing alternative to classic MySQL / memcached web backend stacks. But websites must not go down, and current fault tolerance techniques for dataflow systems either come with long downtimes during recovery, or fail to scale to large deployments due to the overhead of global coordination. For example, in the failure of a single dataflow node, existing lineage-based techniques take a long time to recompute all lost and downstream state, while checkpointing techniques require costly global coordination for rollback recovery. This thesis presents a causal logging approach to fault tolerance that rolls back and replays the execution of only the failed node, without any global coordination. The key to knowing how to replay a valid execution while ensuring exactly-once semantics is a small, constant-size tree clock piggybacked onto each message, incurring runtime overheads that are low and scalable. After recovery, the state of the system is indistinguishable from one that never failed at all. We implement and evaluate the protocol on Noria, a streaming dataflow backend for read-heavy web applications. Compared to Noria's original protocol of lineage-based recovery, tree clock recovery time is constant in relation to state size and graph size. Experimental results show sub-second recovery times with 1.5ms runtime overheads, which translates to a 290x improvement in recovery time.</Abstract>
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