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Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management Author: Raul Castro Fernandez, Matteo Migliavacca, et al. Published conference: SIGMOD’13 Reporter: Ma Yuanwen
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Introduction Stream – A sequence of tuples – Sensor network, stock trading system Query plan – A query is specified as a directed acyclic graph Distributed stream processing – A query is deployed on a set of nodes State management – Checkpoint, back up, restore, partition Scale out – Split the instance, when the instance is overload Fault tolerance – Recover from failures without affecting processing results
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Outline Background – Problem Statement – System Model State Management – Query state – State Operations Scale out and fault tolerance – Fault tolerance scale out algorithm – System architecture – Bottleneck detection and scaling policy Evaluation Conclusions
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Problem Statement Operators – Stateless operators (e.g. filter or map) and stateful operators(e.g. join or aggregation ) – Sliding window based state – Entire history based state Intra-query parallelism – Query graph and execution graph Fault tolerance – Passive standby strategy – Active standby strategy – Upstream backup strategy Report the words frequencies in the recent 1 hour about every 10 minutes
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System model(1) nameAgesex Li Lei16male Han Mei15female Jim17male nameagesex Li Lei16male nameagesex Han Mei15female nameagesex Jim17male
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System model(2)
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Query state (1)
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Query state (2)
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State operations Operator state backup and restore – Checkpoint the state of an operator and backup the state to an upstream operator – Restore state for failure and scale out Operator state partitioning – When a stateful operator scales out, it’s processing state must be split across the new partitioned operators
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Operator state backup and restore
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Operator state partitioning
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Scale out and Fault Tolerance Scale out – SPS partitions operator on-demand in response to bottleneck operators Fault Tolerance – If a node hosting an operator fails, the SPS must replace it with an operator on a new node Operator recover becomes special case of scale out, in which a failed Operator is scale out to a parallelization of 1
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Fault-tolerant scale out algorithm
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System architecture Query manager – Perform a mapping of query operators to nodes and maintain the execution graph Deployment manager – use the execution graph to initialize nodes, deploy operators, set up stream communication and start processing
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Bottleneck detection and scaling policy
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Goals and deployment of evaluation The goals of experimental evaluation are to investigate – The effectiveness of stateful operator scale out approach – The recovery time of the stateful recovery mechanism – The impact of state management approach on tuple processing latency Experiment deployment
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Experiment data Linear road benchmark (LRB) – It models a road toll network – Queries: (1) Provide toll notifications to vehicles within 5s; (2) detect accidents within 5s; (3) answer balance account queries about paid toll amounts – The input rate for a single express-way (L=1) begins at 15 tuples/s and increase to 1700 tuples/s Wikipedia – A map/reduce-style top-k query That outputs every 30 seconds the ranking of the most visited Wikipedia language versions based on Wikipedia data traces
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Dynamic scale out (1)
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Dynamic scale out (2)
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Failure recovery Word count
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State management overhead
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Conclusions Provide state management of stateful operators – Checkpoint, back up, restore, partition Present an integrated approach for scale out and failure recovery
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