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In this episode, Sean Donahue looks back at the tech stories from 2022. The highs, lows, and everything in between. What story did we miss? Which stories surprised you in 2022? Please leave your comments below.<\/p>

Have a wonderful 2023!<\/p>

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解决了

RF2 2或3个数据副本

v
UserLevel 2
徽章 +17
需要知道何时我们说RF2将创建多少个数据副本？一个副本，即一个主要的原始数据和次要数据。或一个主要的原始数据和2个次要副本。
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最好的答案乔恩2017年3月17日，00：07

\n
\nRF 3 = 3 copies.
\n
\ni.e: RF2 for random-file-name-here.exe, which is 10G would take up 20G of storage
\n
\nRF3 for that same file would be 30G of storage
\n
\nThis assumes no data reduction, or use of erasure coding.
\n
\nErasure coding, Compression, Dedupe, and Zero suppression could make that 20G (or 30G) of storage much, much, much less","className":"post__content__best_answer"}">
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谢谢
    该主题已关闭以供评论

    3个答复

    乔恩
    秩
    UserLevel 6
    徽章 +29
    RF 2 = 2副本



    RF 3 = 3副本。



    即：随机文件name-here.exe的RF2，即10克将占用20克存储



    同一文件的RF3将是30克存储



    这没有减少数据或使用擦除编码。



    擦除编码，压缩，杜佩和零抑制可能会使该存储的20克（或30克）大得多，更少
    乔恩·科勒|Nutanix工程技术总监|Nutanix NPX＃003，VCDX＃116 |@jonkohler |如果有用的话，请荣誉！

    \n

    \nRF 3 = 3 copies.

    \n

    \ni.e: RF2 for random-file-name-here.exe, which is 10G would take up 20G of storage

    \n

    \nRF3 for that same file would be 30G of storage

    \n

    \nThis assumes no data reduction, or use of erasure coding.

    \n

    \nErasure coding, Compression, Dedupe, and Zero suppression could make that 20G (or 30G) of storage much, much, much less","quoteUsername":"Jon","translations":{"Common":{"like":"Like","unlike":"Unlike"},"Forum":{"Quote":"Quote","Share":"Share"}}}">
    v
    UserLevel 2
    徽章 +17
    刻意说



    RF2：Orignal DATA实例 + IT的一份副本

    RF3：Orignal DATA实例 + 2副本



    正确的？
    谢谢

    \n

    \nRF2: orignal data instance + one copy of it

    \nRF3: Orignal data instance + 2 copies of it

    \n

    \nright?","quoteUsername":"vivekds","translations":{"Common":{"like":"Like","unlike":"Unlike"},"Forum":{"Quote":"Quote","Share":"Share"}}}">
    Patrbng
    UserLevel 2
    徽章 +19
    是的。这些副本的部分散布在整个群集中，但与原始节点从未在相同的节点上。
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