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From Joe Witt <joe.w...@gmail.com>
Subject Re: Data Ingestion forLarge Source Files and Masking
Date Tue, 05 Jan 2016 04:36:42 GMT
Obaid,

Really happy you're seeing the performance you need.  That works out
to about 110MB/s on average over that period.  Any chance you have a
1GB NIC?  If you really want to have fun with performance tuning you
can use things like iostat and other commands to observe disk,
network, cpu.  Something else to consider too is the potential
throughput gains of multiple RAID-1 containers rather than RAID-5
since NiFi can use both in parallel.  Depends on your goals/workload
so just an FYI.

A good reference for how to build a processor which does altering of
the data (transformation) is here [1].  It is a good idea to do a
quick read through that document.  Also, one of the great things you
can do as well is look at existing processors.  Some good examples
relevant to transformation are [2], [3], and [4] which are quite
simple stream transform types. Or take a look at [5] which is a more
complicated example.  You might also be excited to know that there is
some really cool work done to bring various languages into NiFi which
looks on track to be available in the upcoming 0.5.0 release which is
NIFI-210 [6].  That will provide a really great option to quickly
build transforms using languages like Groovy, JRuby, Javascript,
Scala, Lua, Javascript, and Jython.

[1] https://nifi.apache.org/docs/nifi-docs/html/developer-guide.html#enrich-modify-content

[2] https://github.com/apache/nifi/blob/master/nifi-nar-bundles/nifi-standard-bundle/nifi-standard-processors/src/main/java/org/apache/nifi/processors/standard/Base64EncodeContent.java

[3] https://github.com/apache/nifi/blob/master/nifi-nar-bundles/nifi-standard-bundle/nifi-standard-processors/src/main/java/org/apache/nifi/processors/standard/TransformXml.java

[4] https://github.com/apache/nifi/blob/master/nifi-nar-bundles/nifi-standard-bundle/nifi-standard-processors/src/main/java/org/apache/nifi/processors/standard/ModifyBytes.java

[5] https://github.com/apache/nifi/blob/master/nifi-nar-bundles/nifi-standard-bundle/nifi-standard-processors/src/main/java/org/apache/nifi/processors/standard/ReplaceText.java

[6] https://issues.apache.org/jira/browse/NIFI-210

Thanks
Joe

On Mon, Jan 4, 2016 at 9:32 PM, obaidul karim <obaidcuet@gmail.com> wrote:
> Hi Joe,
>
> Just completed by test with 100GB data (on a local RAID 5 disk on a single
> server).
>
> I was able to load 100GB data within 15 minutes(awesome!!) using below flow.
> This throughput is enough to load 10TB data in a day with a single and
> simple machine.
> During the test, server disk I/O went up to 200MB/s.
>
>     ExecuteProcess(touch and mv to input dir) > ListFile > FetchFile (4
> threads) > PutHDFS (4 threads)
>
> My Next action is to incorporate my java code for column masking with a
> custom processor.
> I am now exploring on that. However, if you have any good reference on
> custom processor(altering actual data) please let  me know.
>
> Thanks,
> Obaid
>
>
>
> On Mon, Jan 4, 2016 at 9:11 AM, obaidul karim <obaidcuet@gmail.com> wrote:
>>
>> Hi Joe,
>>
>> Yes, symlink is another option I was thinking when I was trying to use
>> getfile.
>> Thanks for your insights, I will update you on this mail chain when my
>> entire workflow completes. So that thus could be an reference for other :).
>>
>> -Obaid
>>
>> On Monday, January 4, 2016, Joe Witt <joe.witt@gmail.com> wrote:
>>>
>>> Obaid,
>>>
>>> You make a great point.
>>>
>>> I agree we will ultimately need to do more to make that very valid
>>> approach work easily.  The downside is that puts the onus on NiFi to
>>> keep track of a variety of potentially quite large state about the
>>> directory.  One way to avoid that expense is if NiFi can pull a copy
>>> of then delete the source file.  If you'd like to keep a copy around I
>>> wonder if a good approach is to simply create a symlink to the
>>> original file you want NiFi to pull but have the symlink in the NiFi
>>> pickup directory.  NiFi is then free to read and delete which means it
>>> simply pulls whatever shows up in that directory and doesn't have to
>>> keep state about filenames and checksums.
>>>
>>> I realize we still need to do what you're suggesting as well but
>>> thought I'd run this by you.
>>>
>>> Joe
>>>
>>> On Sun, Jan 3, 2016 at 6:43 PM, obaidul karim <obaidcuet@gmail.com>
>>> wrote:
>>> > Hi Joe,
>>> >
>>> > Condider a scenerio, where we need to feed some older files and we are
>>> > using
>>> > "mv" to feed files to input directory( to reduce IO we may use "mv").
>>> > If we
>>> > use "mv", last modified date will not changed. And this is very common
>>> > on a
>>> > busy file collection system.
>>> >
>>> > However, I think I can still manage it by adding additional "touch"
>>> > before
>>> > moving fole in the target directory.
>>> >
>>> > So, my suggestion is to add file selection criteria as an configurable
>>> > option in listfile process on workflow. Options could be last modified
>>> > date(as current one) unique file names, checksum etc.
>>> >
>>> > Thanks again man.
>>> > -Obaid
>>> >
>>> >
>>> > On Monday, January 4, 2016, Joe Witt <joe.witt@gmail.com> wrote:
>>> >>
>>> >> Hello Obaid,
>>> >>
>>> >> The default behavior of the ListFile processor is to keep track of the
>>> >> last modified time of the files it lists.  When you changed the name
>>> >> of the file that doesn't change the last modified time as tracked by
>>> >> the OS but when you altered content it does.  Simply 'touch' on the
>>> >> file would do it too.
>>> >>
>>> >> I believe we could observe the last modified time of the directory in
>>> >> which the file lives to detect something like a rename.  However, we'd
>>> >> not know which file was renamed just that something was changed.  So
>>> >> it require keeping some potentially problematic state to deconflict
or
>>> >> requiring the user to have a duplicate detection process afterwards.
>>> >>
>>> >> So with that in mind is the current behavior sufficient for your case?
>>> >>
>>> >> Thanks
>>> >> Joe
>>> >>
>>> >> On Sun, Jan 3, 2016 at 6:17 AM, obaidul karim <obaidcuet@gmail.com>
>>> >> wrote:
>>> >> > Hi Joe,
>>> >> >
>>> >> > I am now exploring your solution.
>>> >> > Starting with below flow:
>>> >> >
>>> >> > ListFIle > FetchFile > CompressContent > PutFile.
>>> >> >
>>> >> > Seems all fine. Except some confusion with how ListFile identifies
>>> >> > new
>>> >> > files.
>>> >> > In order to test, I renamed a already processed file and put in
in
>>> >> > input
>>> >> > folder and found that the file is not processing.
>>> >> > Then I randomly changed the content of the file and it was
>>> >> > immediately
>>> >> > processed.
>>> >> >
>>> >> > My question is what is the new file selection criteria for
>>> >> > "ListFile" ?
>>> >> > Can
>>> >> > I change it only to file name ?
>>> >> >
>>> >> > Thanks in advance.
>>> >> >
>>> >> > -Obaid
>>> >> >
>>> >> >
>>> >> >
>>> >> >
>>> >> >
>>> >> >
>>> >> >
>>> >> > On Fri, Jan 1, 2016 at 10:43 PM, Joe Witt <joe.witt@gmail.com>
>>> >> > wrote:
>>> >> >>
>>> >> >> Hello Obaid,
>>> >> >>
>>> >> >> At 6 TB/day and average size of 2-3GB per dataset you're looking
at
>>> >> >> a
>>> >> >> sustained rate of 70+MB/s and a pretty low transaction rate.
 So
>>> >> >> well
>>> >> >> within a good range to work with on a single system.
>>> >> >>
>>> >> >> 'I's there any way to by pass writing flow files on disk or
>>> >> >> directly
>>> >> >> pass those files to HDFS as it is ?"
>>> >> >>
>>> >> >>   There is no way to bypass NiFi taking a copy of that data
by
>>> >> >> design.
>>> >> >> NiFi is helping you formulate a graph of dataflow requirements
from
>>> >> >> a
>>> >> >> given source(s) through given processing steps and ultimate
driving
>>> >> >> data into given destination systems.  As a result it takes
on the
>>> >> >> challenge of handling transactionality of each interaction
and the
>>> >> >> buffering and backpressure to deal with the realities of different
>>> >> >> production/consumption patterns.
>>> >> >>
>>> >> >> "If the files on the spool directory are compressed(zip/gzip),
can
>>> >> >> we
>>> >> >> store files on HDFS as uncompressed ?"
>>> >> >>
>>> >> >>   Certainly.  Both of those formats (zip/gzip) are supported
in
>>> >> >> NiFi
>>> >> >> out of the box.  You simply run the data through the proper
process
>>> >> >> prior to the PutHDFS process to unpack (zip) or decompress
(gzip)
>>> >> >> as
>>> >> >> needed.
>>> >> >>
>>> >> >> "2.a Can we use our existing java code for masking ? if yes
then
>>> >> >> how ?
>>> >> >> 2.b For this Scenario we also want to bypass storing flow files
on
>>> >> >> disk. Can we do it on the fly, masking and storing on HDFS
?
>>> >> >> 2.c If the source files are compressed (zip/gzip), is there
any
>>> >> >> issue
>>> >> >> for masking here ?"
>>> >> >>
>>> >> >>   You would build a custom NiFi processor that leverages your
>>> >> >> existing
>>> >> >> code.  If your code is able to operate on an InputStream and
writes
>>> >> >> to
>>> >> >> an OutputStream then it is very likely you'll be able to handle
>>> >> >> arbitrarily large objects with zero negative impact to the
JVM Heap
>>> >> >> as
>>> >> >> well.  This is thanks to the fact that the data is present
in
>>> >> >> NiFi's
>>> >> >> repository with copy-on-write/pass-by-reference semantics and
that
>>> >> >> the
>>> >> >> API is exposing those streams to your code in a transactional
>>> >> >> manner.
>>> >> >>
>>> >> >>   If you want the process of writing to HDFS to also do
>>> >> >> decompression
>>> >> >> and masking in one pass you'll need to extend/alter the PutHDFS
>>> >> >> process to do that.  It is probably best to implement the flow
>>> >> >> using
>>> >> >> cohesive processors (grab files, decompress files, mask files,
>>> >> >> write
>>> >> >> to hdfs).  Given how the repository construct in NiFi works
and
>>> >> >> given
>>> >> >> how caching in Linux works it is very possible you'll be quite
>>> >> >> surprised by the throughput you'll see.  Even then you can
optimize
>>> >> >> once you're sure you need to.  The other thing to keep in mind
here
>>> >> >> is
>>> >> >> that often a flow that starts out as specific as this turns
into a
>>> >> >> great place to tap the stream of data to feed some new system
or
>>> >> >> new
>>> >> >> algorithm with a different format or protocol.  At that moment
the
>>> >> >> benefits become even more obvious.
>>> >> >>
>>> >> >> Regarding the Flume processes in NiFi and their memory usage.
 NiFi
>>> >> >> offers a nice hosting mechanism for the Flume processes and
brings
>>> >> >> some of the benefits of NiFi's UI, provenance, repository concept.
>>> >> >> However, we're still largely limited to the design assumptions
one
>>> >> >> gets when building a Flume process and that can be quite memory
>>> >> >> limiting.  We see what we have today as a great way to help
people
>>> >> >> transition their existing Flume flows into NiFi by leveraging
their
>>> >> >> existing code but would recommend working to phase the use
of those
>>> >> >> out in time so that you can take full benefit of what NiFi
brings
>>> >> >> over
>>> >> >> Flume.
>>> >> >>
>>> >> >> Thanks
>>> >> >> Joe
>>> >> >>
>>> >> >>
>>> >> >> On Fri, Jan 1, 2016 at 4:18 AM, obaidul karim <obaidcuet@gmail.com>
>>> >> >> wrote:
>>> >> >> > Hi,
>>> >> >> >
>>> >> >> > I am new in Nifi and exploring it as open source ETL tool.
>>> >> >> >
>>> >> >> > As per my understanding, flow files are stored on local
disk and
>>> >> >> > it
>>> >> >> > contains
>>> >> >> > actual data.
>>> >> >> > If above is true, lets consider a below scenario:
>>> >> >> >
>>> >> >> > Scenario 1:
>>> >> >> > - In a spool directory we have terabytes(5-6TB/day) of
files
>>> >> >> > coming
>>> >> >> > from
>>> >> >> > external sources
>>> >> >> > - I want to push those files to HDFS as it is without
any changes
>>> >> >> >
>>> >> >> > Scenario 2:
>>> >> >> > - In a spool directory we have terabytes(5-6TB/day) of
files
>>> >> >> > coming
>>> >> >> > from
>>> >> >> > external sources
>>> >> >> > - I want to mask some of the sensitive columns
>>> >> >> > - Then send one copy to HDFS and another copy to Kafka
>>> >> >> >
>>> >> >> > Question for Scenario 1:
>>> >> >> > 1.a In that case those 5-6TB data will be again written
on local
>>> >> >> > disk
>>> >> >> > as
>>> >> >> > flow files and will cause double I/O. Which eventually
may cause
>>> >> >> > slower
>>> >> >> > performance due to I/O bottleneck.
>>> >> >> > Is there any way to by pass writing flow files on disk
or
>>> >> >> > directly
>>> >> >> > pass
>>> >> >> > those files to HDFS as it is ?
>>> >> >> > 1.b If the files on the spool directory are compressed(zip/gzip),
>>> >> >> > can
>>> >> >> > we
>>> >> >> > store files on HDFS as uncompressed ?
>>> >> >> >
>>> >> >> > Question for Scenario 2:
>>> >> >> > 2.a Can we use our existing java code for masking ? if
yes then
>>> >> >> > how ?
>>> >> >> > 2.b For this Scenario we also want to bypass storing flow
files
>>> >> >> > on
>>> >> >> > disk.
>>> >> >> > Can
>>> >> >> > we do it on the fly, masking and storing on HDFS ?
>>> >> >> > 2.c If the source files are compressed (zip/gzip), is
there any
>>> >> >> > issue
>>> >> >> > for
>>> >> >> > masking here ?
>>> >> >> >
>>> >> >> >
>>> >> >> > In fact, I tried above using flume+flume interceptors.
Everything
>>> >> >> > working
>>> >> >> > fine with smaller files. But when source files greater
that 50MB
>>> >> >> > flume
>>> >> >> > chocks :(.
>>> >> >> > So, I am exploring options in NiFi. Hope I will get some
>>> >> >> > guideline
>>> >> >> > from
>>> >> >> > you
>>> >> >> > guys.
>>> >> >> >
>>> >> >> >
>>> >> >> > Thanks in advance.
>>> >> >> > -Obaid
>>> >> >
>>> >> >
>
>

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