I can’t remember for sure but I think if you use CompressContent the compressed 
file has to fit in a single HDFS file block in order to work. IIRC 
Hadoop-Snappy is different from regular Snappy in the sense that it puts the 
compression header in each block so the file can be reassembled and 
decompressed correctly.



> On Nov 11, 2019, at 10:30 AM, Shawn Weeks <swe...@weeksconsulting.us> wrote:
> 
> 
> I’m assuming your talking about the snappy problem. If you use compress 
> content prior to puthdfs you can compress with Snappy as it uses the Java 
> Native Snappy Lib. The HDFS processors are limited to the actual Hadoop 
> Libraries so they’d have to change from Native to get around this. I’m pretty 
> sure we need instance loading to handle the other issues mentioned.
>  
> Thanks
> Shawn
>  
> From: Joe Witt <joe.w...@gmail.com>
> Reply-To: "users@nifi.apache.org" <users@nifi.apache.org>
> Date: Monday, November 11, 2019 at 8:56 AM
> To: "users@nifi.apache.org" <users@nifi.apache.org>
> Subject: Re: Influence about removing RequiresInstanceClassLoading from 
> AbstractHadoopProcessor processor
>  
> Peter
>  
> The most common challenge is if two isolated instances both want to use a 
> native lib.  No two native libs with the same name can be in the same jvm.  
> We need to solve that for sure.
>  
> Thanks
>  
> On Mon, Nov 11, 2019 at 9:53 AM Peter Turcsanyi <turcsa...@apache.org> wrote:
> Hi Hai Luo,
>  
> @RequiresInstanceClassLoading makes possible to configure separate / isolated 
> "Additional Classpath Resources" settings on your HDFS processors (eg. S3 
> storage driver on one of your PutHDFS and Azure Blob on the other).
>  
> Is there any specific reason / use case why you are considering to remove it?
>  
> Regards,
> Peter Turcsanyi
>  
> On Mon, Nov 11, 2019 at 3:30 PM abellnotring <abellnotr...@sina.com> wrote:
> Hi,all
>      I’m considering removing the RequiresInstanceClassLoading annotation 
> from class AbstractHadoopProcessor,
>      Does anybody know the potential Influence?
>  
>     Thanks
>     By Hai Luo

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