Efficient Fault Tolerance for Pipelined Query Engines via Write-ahead Lineage
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909135091531776 |
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| author | Wang, Ziheng Aiken, Alex |
| author_facet | Wang, Ziheng Aiken, Alex |
| contents | Modern distributed pipelined query engines either do not support intra-query fault tolerance or employ high-overhead approaches such as persisting intermediate outputs or checkpointing state. In this work, we present write-ahead lineage, a novel fault recovery technique that combines Spark's lineage-based replay and write-ahead logging. Unlike Spark, where the lineage is determined before query execution, write-ahead lineage persistently logs lineage at runtime to support dynamic task dependencies in pipelined query engines. Since only KB-sized lineages are persisted instead of MB-sized intermediate outputs, the normal execution overhead is minimal compared to spooling or checkpointing based approaches. To ensure fast fault recovery times, tasks only consume intermediate outputs with persisted lineage, preventing global rollbacks upon failure. In addition, lost tasks from different stages can be recovered in a pipelined parallel manner. We implement write-ahead lineage in a distributed pipelined query engine called Quokka. We show that Quokka is around 2x faster than SparkSQL on the TPC-H benchmark with similar fault recovery performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08062 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Fault Tolerance for Pipelined Query Engines via Write-ahead Lineage Wang, Ziheng Aiken, Alex Distributed, Parallel, and Cluster Computing Databases Modern distributed pipelined query engines either do not support intra-query fault tolerance or employ high-overhead approaches such as persisting intermediate outputs or checkpointing state. In this work, we present write-ahead lineage, a novel fault recovery technique that combines Spark's lineage-based replay and write-ahead logging. Unlike Spark, where the lineage is determined before query execution, write-ahead lineage persistently logs lineage at runtime to support dynamic task dependencies in pipelined query engines. Since only KB-sized lineages are persisted instead of MB-sized intermediate outputs, the normal execution overhead is minimal compared to spooling or checkpointing based approaches. To ensure fast fault recovery times, tasks only consume intermediate outputs with persisted lineage, preventing global rollbacks upon failure. In addition, lost tasks from different stages can be recovered in a pipelined parallel manner. We implement write-ahead lineage in a distributed pipelined query engine called Quokka. We show that Quokka is around 2x faster than SparkSQL on the TPC-H benchmark with similar fault recovery performance. |
| title | Efficient Fault Tolerance for Pipelined Query Engines via Write-ahead Lineage |
| topic | Distributed, Parallel, and Cluster Computing Databases |
| url | https://arxiv.org/abs/2403.08062 |