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https://issues.apache.org/jira/browse/HDFS-2542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13147152#comment-13147152
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Robert Joseph Evans commented on HDFS-2542:
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To Jinglong:
I agree completely with you, I just wanted to be sure that any final solution
provided a generic solution. Something that can cleanly separate out
classification of hot vs. cold vs really cold data from any extra processing
that might happen when data goes from one classification to another. Access
time is a great start, but I can imagine a lot of potential innovation and
experimentation in this area. I can also see lots of different groups wanting
to do something when the classification changes. Like you said, compress the
data, possibly move it to a different disk, possibly apply RAID to it. What
ever we do it should be something that is also pluggable.
> Transparent compression storage in HDFS
> ---------------------------------------
>
> Key: HDFS-2542
> URL: https://issues.apache.org/jira/browse/HDFS-2542
> Project: Hadoop HDFS
> Issue Type: Bug
> Reporter: jinglong.liujl
>
> As HDFS-2115, we want to provide a mechanism to improve storage usage in hdfs
> by compression. Different from HDFS-2115, this issue focus on compress
> storage. Some idea like below:
> To do:
> 1. compress cold data.
> Cold data: After writing (or last read), data has not touched by anyone
> for a long time.
> Hot data: After writing, many client will read it , maybe it'll delele
> soon.
>
> Because hot data compression is not cost-effective, we only compress cold
> data.
> In some cases, some data in file can be access in high frequency, but in
> the same file, some data may be cold data.
> To distinguish them, we compress in block level.
> 2. compress data which has high compress ratio.
> To specify high/low compress ratio, we should try to compress data, if
> compress ratio is too low, we'll never compress them.
> 2. forward compatibility.
> After compression, data format in datanode has changed. Old client will
> not access them. To solve this issue, we provide a mechanism which decompress
> on datanode.
> 3. support random access and append.
> As HDFS-2115, random access can be support by index. We separate data
> before compress by fixed-length (we call these fixed-length data as "chunk"),
> every chunk has its index.
> When random access, we can seek to the nearest index, and read this chunk for
> precise position.
> 4. async compress to avoid compression slow down running job.
> In practice, we found the cluster CPU usage is not uniform. Some clusters
> are idle at night, and others are idle at afternoon. We should make compress
> task running in full speed when cluster idle, and in low speed when cluster
> busy.
> Will do:
> 1. client specific codec and support compress transmission.
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