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From Joe Witt <joe.w...@gmail.com>
Subject Re: Data Ingestion forLarge Source Files and Masking
Date Thu, 14 Jan 2016 03:51:50 GMT
Hello

Let's narrow in on potential issues.  So while this process is running
and appears sluggish in nature please run the following on the command
line

'jps'

This command will tell you the process id of NiFi.  You'll want the
pid associated with the Java process other than what is called 'jps'
presuming there aren't other things running than NiFi at the time.

Lets say the result is a pid of '12345'

Then run this command

'jstat -gcutil 12345 1000'

This will generate garbage collection information every one second
until you decide to stop it with cntl-c.  So let that run for a while
say 30 seconds or so then hit cntl-c.  Can you please paste that
output in response.  That will show us how the general health of GC
is.

Another really important/powerful set of output can be gleaned by
running 'iostat' which gives you statistics about input/output to
things like the underlying storage system.  That is part of the
'sysstat' package in case you need to install that.  But then you can
run

''iostat xmh 1"

Or something even as simple as 'iostat 1'.  Your specific command
string may vary.  Please let that run for say 10-20 seconds and paste
those results as well.  That will give a sense of io utilization while
the operation is running.

Between these two outputs (Garbage Collection/IO) we should have a
pretty good idea of where to focus the effort to find why it is slow.

Thanks
Joe


On Wed, Jan 13, 2016 at 9:23 PM, obaidul karim <obaidcuet@gmail.com> wrote:
> Hi Joe & Others,
>
> Thanks for all of your suggestions.
>
> Now I am using below code:
> 1. Buffered reader (I tried to use NLKBufferedReader, but it requires too
> many libs & Nifi failed to start. I was lost.)
> 2. Buffered writer
> 3. Using appending line end instead to concat new line
>
> Still no performance gain. Am I doing something wrong, anything else I can
> change here.
>
> flowfile = session.write(flowfile, new StreamCallback() {
> @Override
> public void process(InputStream in, OutputStream out) throws IOException {
>     try (BufferedReader reader = new BufferedReader(new
> InputStreamReader(in, charset), maxBufferSize);
>         BufferedWriter writer = new BufferedWriter(new
> OutputStreamWriter(out, charset));) {
>
> if(skipHeader == true && headerExists==true) { // to skip header, do an
> additional line fetch before going to next step
> if(reader.ready())   reader.readLine();
> } else if( skipHeader == false && headerExists == true) { // if header is
> not skipped then no need to mask, just pass through
> if(reader.ready())  {
> writer.write(reader.readLine());
> writer.write(lineEndingBuilder.toString());
> }
> }
> // decide about empty line earlier
> String line;
> while ((line = reader.readLine()) != null) {
> writer.write(parseLine(line, seperator, quote, escape, maskColumns));
> writer.write(lineEndingBuilder.toString());
> };
> writer.flush();
>         }
> }
>
> });
>
>
> -Obaid
>
> On Wed, Jan 13, 2016 at 1:38 PM, Joe Witt <joe.witt@gmail.com> wrote:
>>
>> Hello
>>
>> So the performance went from what sounded pretty good to what sounds
>> pretty problematic.  The rate now sounds like it is around 5MB/s which
>> is indeed quite poor.  Building on what Bryan said there does appear
>> to be some good opportunities to improve the performance.  The link he
>> provided just expanded to cover the full range to look at is here [1].
>>
>> Couple key points to note:
>> 1) Use of a buffered line oriented reader than preserves the new lines
>> 2) write to a buffered writer that accepts strings and understands
>> which charset you intend to write out
>> 3) avoid strong concat with newline
>>
>> Also keep in mind you how large any single line could be because if
>> they can be quite large you may need to consider the GC pressure that
>> can be caused.  But let's take a look at how things are after these
>> easier steps first.
>>
>> [1]
>> https://github.com/apache/nifi/blob/ee14d8f9dd0c3f18920d910fcddd6d79b8b9f9cf/nifi-nar-bundles/nifi-standard-bundle/nifi-standard-processors/src/main/java/org/apache/nifi/processors/standard/ReplaceText.java#L334-L361
>>
>> Thanks
>> Joe
>>
>> On Tue, Jan 12, 2016 at 10:35 PM, Juan Sequeiros <hellojuan@gmail.com>
>> wrote:
>> > Obaid,
>> >
>> > Since you mention that you will have dedicated ETL servers and assume
>> > they
>> > will also have a decent amount of ram on them, then I would not shy away
>> > from increasing your threads.
>> >
>> > Also in your staging directory if you do not need to keep originals,
>> > then
>> > might consider GetFile and on that one use one thread.
>> >
>> > Hi Joe,
>> >
>> > Yes, I took consideration of existinh RAID and HW settings. We have 10G
>> > NIC
>> > for all hadoop intra-connectivity and the server in question is an edge
>> > node
>> > of our hadoop cluster.
>> > In production scenario we will use dedicated ETL servers having high
>> > performance(>500MB/s) local disks.
>> >
>> > Sharing a good news, I have successfully mask & load to HDFS 110 GB data
>> > using below flow:
>> >
>> > ExecuteProcess(touch and mv to input dir) > ListFile (1 thread) >
>> > FetchFile
>> > (1 thread) > maskColumn(4 threads) > PutHDFS (1 threads).
>> >
>> > * used 4 threads for masking and 1 for other because I found it is the
>> > slowest component.
>> >
>> > However, It seems to be too slow. It was processing 2GB files in  6
>> > minutes.
>> > It may be because of my masking algorithm(although masking algorithm is
>> > pretty simple FPE with some simple twist).
>> > However I want to be sure that the way I have written custom processor
>> > is
>> > the most efficient way. Please below code chunk and let me know whether
>> > it
>> > is the fastest way to process flowfiles (csv source files) which needs
>> > modifications on specific columns:
>> >
>> > * parseLine method contains logic for masking.
>> >
>> >        flowfile = session.write(flowfile, new StreamCallback() {
>> >         @Override
>> >            public void process(InputStream in, OutputStream out) throws
>> > IOException {
>> >
>> >         BufferedReader reader = new BufferedReader(new
>> > InputStreamReader(in));
>> >         String line;
>> >         if(skipHeader == true && headerExists==true) { // to skip
>> > header, do
>> > an additional line fetch before going to next step
>> >         if(reader.ready())   reader.readLine();
>> >         } else if( skipHeader == false && headerExists == true) { //
if
>> > header is not skipped then no need to mask, just pass through
>> >         if(reader.ready())
>> > out.write((reader.readLine()+"\n").getBytes());
>> >         }
>> >
>> >         // decide about empty line earlier
>> >         while ((line = reader.readLine()) != null) {
>> >         if(line.trim().length() > 0 ) {
>> >         out.write( parseLine(line, seperator, quote, escape,
>> > maskColumns).getBytes() );
>> >         }
>> > };
>> > out.flush();
>> >            }
>> >        });
>> >
>> >
>> >
>> >
>> > Thanks in advance.
>> > -Obaid
>> >
>> >
>> > On Tue, Jan 5, 2016 at 12:36 PM, Joe Witt <joe.witt@gmail.com> wrote:
>> >>
>> >> 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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