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https://issues.apache.org/jira/browse/HDFS-2542?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13145553#comment-13145553
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Robert Joseph Evans commented on HDFS-2542:
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I think the detection of hot vs. cold data is something important that should
be added in with or without transparent compression. It opens up a number of
possibilities for trying to improve storage efficiencies. In addition to
compression we could keep hot data in a cache of some sort SSD, Ram Disk, etc.
We could also migrate cold data to spinning disks, or even to something else
even slower and cheaper per GB if it is really never accessed. I am sure
others will have ideas as well about what to do with hot vs cold data. It
could be tied into some of the work for fsync and fadvise behind the scenes so
that we try to keep hot data in the disk cache and only run fadvise to flush
out the cache if it is not really hot.
> 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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