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SLSM-A Scalable Log Structured Merge Tree with Bloom Filters for Low Latency Analytics

机译:SLSM-A可伸缩日志结构合并树,具有盛开过滤器,用于低延迟分析

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In the era of Bigdata, millions of searches, queries etc. happens in a second. There is a need for next generation databases which can store and process bigdata more effectively. NoSQL databases are created to solve the problems of scalability issues related with traditional databases. Year by year the bigdata is getting new dimensions. HBase is a NoSQL database suitable for random, real-time read/write access to Big Data. LSM tree used in HBase helps to achieve this high performance. Commodity hardware have moderate RAM size. SLSM, an optimized Log Structured Merge Tree which dramatically reduces the read amplification and write amplification is proposed in this paper for commodity hardware. Incorporation of variant of bloom filters increases the read performance.
机译:在BigData的时代,百万搜索,查询等在一秒钟内发生。需要更有效地存储和处理BigData的下一代数据库。创建NoSQL数据库以解决与传统数据库相关的可扩展性问题的问题。年份BigData正在获得新的维度。 HBase是一个适用于大数据的随机,实时读/写访问的NoSQL数据库。在HBase中使用的LSM树有助于实现这种高性能。商品硬件具有中度的RAM尺寸。 SLSM,一个优化的日志结构合并树,其在本文中提出了商品硬件中提出了读取放大和写入放大。盛开滤波器变体的融合增加了读取性能。

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