ePipe: Near Real-Time Polyglot Persistence of HopsFS Metadata

Mahmoud Ismail, Mikael Ronström, Seif Haridi, J. Dowling
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引用次数: 3

Abstract

Distributed OLTP databases are now used to manage metadata for distributed file systems, but they cannot also efficiently support complex queries or aggregations. To solve this problem, we introduce ePipe, a databus that both creates a consistent change stream for a distributed, hierarchical file system (HopsFS) and eventually delivers the correctly ordered stream with low latency to downstream clients. ePipe can be used to provide polyglot storage for file system metadata, allowing metadata queries to be handled by the most efficient engine for that query. For file system notifications, we show that ePipe achieves up to 56X throughput improvement over HDFS INotify and Trumpet with up to 3 orders of magnitude lower latency. For Spotify's Hadoop workload, we show that ePipe can replicate all file system changes from HopsFS to Elasticsearch with an average replication lag of only 330 ms.
ePipe: HopsFS元数据的近实时多语言持久性
分布式OLTP数据库现在用于管理分布式文件系统的元数据,但是它们也不能有效地支持复杂的查询或聚合。为了解决这个问题,我们引入了ePipe,它是一种数据总线,既可以为分布式分层文件系统(HopsFS)创建一致的更改流,又可以最终以低延迟向下游客户端提供正确排序的流。ePipe可用于为文件系统元数据提供多语言存储,允许元数据查询由最有效的引擎处理。对于文件系统通知,我们发现ePipe比HDFS INotify和Trumpet的吞吐量提高了56X,延迟降低了3个数量级。对于Spotify的Hadoop工作负载,我们展示了ePipe可以复制从HopsFS到Elasticsearch的所有文件系统更改,平均复制延迟仅为330毫秒。
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