Excellent information here. Thanks Lee and Peter. 

Andy LoPresto
alopresto@apache.org
alopresto.apache@gmail.com
PGP Fingerprint: 70EC B3E5 98A6 5A3F D3C4  BACE 3C6E F65B 2F7D EF69

On Oct 24, 2016, at 6:57 AM, Peter Wicks (pwicks) <pwicks@micron.com> wrote:

Prabhu,
 
Lee mentioned making sure you have good indexes, but I would caution you on this point.  If you have a unique constraint then SQL Server will build an index on this automatically, but I would suggest dropping all other indexes that aren’t related to data integrity. Each time SQL Server updates a column that is indexed it’s going to be updating that index also.  This will add a lot of overhead.
 
You might be thinking that you need these indexes though for user queries. To work around this I often see the use of a staging table. This table has no indexes beyond the absolute minimum to ensure data integrity, and sometimes even these are removed and data integrity/duplicate removal is handled through the use of SQL or a Stored Procedure.  A periodic job will move all data from this staging table into a final table.  If you execute the copy and a truncate in a single transaction it allows you to do this safely:
 
INSERT INTO “Some_Final_Table” SELECT * FROM “Staging_Table_With_Exact_Same_schema”;TRUNCATE TABLE “Staging_Table_With_Exact_Same_schema”;
 
If you do it this way you can keep the indexes you need for user access while still allowing maximum data throughput to SQL Server.
 
I’ve seen a lot of comments online about batch sizing around 500 being optimal, but of course this will vary on the system configuration; both your NiFi server and the SQL Server.
 
I have had issues getting good performance out of PutSQL even with the above, I don’t think this is the fault of the processor, but more due to the volume of data and JDBC batch row processing not really being designed for this kind of volume. In my case I was trying to push about 10M rows over a longer time period, but was still running into trouble. After working on the issue for a while I found that a database specific loader was needed. I am loading to Teradata, so I wrote up a Teradata FastLoad processor.  In your case the MS SQL Server JDBC Driver includes a `SQLServerBulkCopy` loader, https://msdn.microsoft.com/en-us/library/mt221490%28v=sql.110%29.aspx.  Unfortunately, this would require writing code either through a scripted processor, or as a whole new processor.
 
Since writing a custom processor may be more than you want to jump into right now you should probably take a look at `bcp`.  I didn’t catch if you were on Windows or a Unix platform, but if you are on Windows I’d check out the command line based Bulk Copy Program for MS SQL:https://msdn.microsoft.com/en-us/library/ms162802.aspx.  Using this would allow you to prepare your data into an intermediary format, like CSV first, then send it all at once through `bcp`.
 
 
Thanks,
  Peter Wicks
 
From: Lee Laim [mailto:lee.laim@gmail.com] 
Sent: Monday, October 24, 2016 7:17 AM
To: users@nifi.apache.org
Subject: Re: How to increase the processing speed of the ExtractText and ReplaceText Processor?
 
Hello Prabhu,
 
50 minutes is a good start! Now we have to determine where the next bottleneck is -check to see where the flow files are queueing.  You can also check the "average task duration" statistic for each processor.  I suspect the bottleneck is at  PutSQL and will carry this assumption forward.  
 
There are several knobs you can adjust at the assumed PutSQL bottleneck:
1.  Increase the run duration and keep the PutSQL processor running for 2 secondsbefore releasing the thread.  
2. Set Fragmented Transactions to false.  This removes constraints that take time to check.
3. Consider changing batch size, systematically and observe throughput changes. I'd move up in increments of 100. 
4*. Increase the number of concurrent tasks for the bottleneck processor to 3 or higher.  Increase, systematically to observe if you get more flow files through.   You can increase the max timer driven threads of the NiFi instance in the NiFi Flow Settings (top right of the canvas).  you can set the max to 25, but you are truly limited by hardware here. Consider a more powerful system to manage this flow, especially with the time constraint you need. It is often easier to throw more hardware at the problem than to debug.  
Other areas:
5. On the output of the last SplitText processor,  Invoke back pressure object threshold = 1.  This will slow (temporarily stop) the first split text processor and reduce the number of overall flow files to manage.  It also reduces the NiFi processor demand for the cpu threads.   
6. Increase nifi.queue.swap.threshold in nifi.properties-  reduce disk access.
7. Check connection/load on the SQL server.    
 
To address your queries,I used the same expression you provided: (.+)[|](.+)[|](.+)[|](.+)  
You can use an ExtractStreamCommand processor to 'extract text', but in this case, with small flow files, it won't offer much gain.  
 
*With an i5 processor, you have 4 cpu threads to process flow files, manage NiFi, read/write to disk, and handle all other non-NiFi processes.  Moving to an i7 or Xeon, hyper threading will provide NiFi more resources to really get work done.  While clustering is great for increasing throughput, I wouldn't recommend clustering on a set of smaller i5 systems, as there is added communication overhead that will need to be handled by the cpu.  It can be done, but there are easier ways to increase throughput at this point in development.
 
Hope this helps.  Also, if there is anything I stated that is contrary to what others have observed, please chime in.
 
Thanks,
Lee
 
 
On Thu, Oct 20, 2016 at 6:02 PM, prabhu Mahendran <prabhuu161994@gmail.com> wrote:

Lee,

I have tried your suggested flow which can able to insert the data into sql server in 50 minutes And it also take long time.

==>your Query:You might be processing the entire dat file (instead of a single row) for each record.

  How can i process entire dat file into SQL Server?

==>Query:Without any new optimizations you'll need ~25 threads and sufficient memory to feed the threads.

  My processors runs in 10 threads only by setting concurrent threads,How to increase it to be 25 threads.

If you try quick test then please share "what is regex which you have used?"

Is there any other processor having functionality like extract text?

Thanks

 
On Wed, Oct 19, 2016 at 11:29 PM, Lee Laim <lee.laim@gmail.com> wrote:
Prabu,
 
In order to move 3M rows in 10 minutes, you'll need to process 5000 rows/second.
During your 4 hour run, you were processing ~200 rows/second.
 
Without any new optimizations you'll need ~25 threads and sufficient memory to feed the threads.  I agree with Mark and you should be able to get far more than 200 rows/second.  
 
I ran a quick test using your ExtractText regex on similar data I was able to process over 100,000 rows/minute through the extract text processor.  The input data was a single row of 4 fields delimited by the "|" symbol. 
 
You might be processing the entire dat file (instead of a single row) for each record.
Can you check the flow file attributes and content going into ExtractText?  
 
 
Here is the flow with some notes:
 
1.GetFile (a 30 MB .dat file consisting of 3M rows; each row is about 10 bytes)
 
2 SplitText -> SplitText  (to break the 3M rows down to manageable chunks of 10,000 lines per flow file, then split again to 1 line per flow file)
 
3. ExtractText to extract the 4 fields
 
4. ReplaceText to generate json (You can alternatively use AttributesToJson here)
 
5. ConvertJSONtoSQL
 
6. PutSQL - (This should be true bottleneck; Index the DB well and use many threads) 
 
If my assumptions are incorrect, please let me know.  
 
Thanks,
Lee
 
On Thu, Oct 20, 2016 at 1:43 AM, Kevin Verhoeven <Kevin.Verhoeven@ds-iq.com> wrote:
I’m not clear on how much data you are processing, does the data(.dat) file have 3,00,000 rows?
 
Kevin
 
From: prabhu Mahendran [mailto:prabhuu161994@gmail.com] 
Sent: Wednesday, October 19, 2016 2:05 AM
To: users@nifi.apache.org
Subject: Re: How to increase the processing speed of the ExtractText and ReplaceText Processor?
 

Mark,

Thanks for the response.

My Sample input data(.dat) like below..,

1|2|3|4
6|7|8|9
11|12|13|14

In Extract Text,i have add input row only with addition of default properties like below screenshot.

<image001.png>
In Replace text ,

just replace value like {"data1":"${inputrow.1}","data2":"${inputrow.2}","data3":"${inputrow.3}","data4":"${inputrow.4}"}
<image002.png>


Here there is no bulletins indicates back pressure on processors.

Can i know prerequisites needed for move the 3,00,000 data into sql server in duration 10-20 minutes?
What are the number of CPU' s needed?
How much heap size and perm gen size we need to set for move that data into sql server? 

Thanks

 
On Tue, Oct 18, 2016 at 7:05 PM, Mark Payne <markap14@hotmail.com> wrote:
Prabhu,
 
Thanks for the details. All of this seems fairly normal. Given that you have only a single core,
I don't think multiple concurrent tasks will help you. Can you share your configuration for ExtractText
and ReplaceText? Depending on the regex'es being used, they can be extremely expensive to evaluate.
The regex that you mentioned in the other email - "(.+)[|](.+)[|](.+)[|](.+)" is in fact extremely expensive.
Any time that you have ".*" or ".+" in your regex, it is going to be extremely expensive, especially with
longer FlowFile content.
 
Also, do you see any bulletins indicating that the provenance repository is applying backpressure? Given
that you are splitting your FlowFiles into individual lines, the provenance repository may be under a lot
of pressure.
 
Another thing to check, is how much garbage collection is occurring. This can certainly destroy your performance
quickly. You can get this information by going to the "Summary Table" in the top-right of the UI and then clicking the
"System Diagnostics" link in the bottom-right corner of that Summary Table.
 
Thanks
-Mark
 
 
On Oct 18, 2016, at 1:31 AM, prabhu Mahendran <prabhuu161994@gmail.com> wrote:
 
Mark,

Thanks for your response.

Please find the response for your questions.

==>The first processor that you see that exhibits poor performance is ExtractText, correct?
                             Yes,Extract Text exhibits poor performance.

==>How big is your Java heap?
                            I have set 1 GB for java heap.

==>
Do you have back pressure configured on the connection between ExtractText and ReplaceText?
                           There is no back pressure between extract and replace text.

==>when you say that you specify concurrent tasks, what are you configuring the concurrent tasks
to be?
                          I have specify concurrent tasks to be 2 for the extract text processor due to slower processing rate.Which                           is specified in Concurrent Task Text box.

==>Have you changed the maximum number of concurrent tasks available to your dataflow?
                         No i haven't changed.

==>How many CPU's are available on this machine?
                        Only single cpu are available in this machine with core i5 processor CPU @2.20Ghz.

==> Are these the only processors in your flow, or do you have other dataflows going on in the
same instance as NiFi?
                       Yes this is the only processor in work flow which is running and no other instances are running.

Thanks
 
On Mon, Oct 17, 2016 at 6:08 PM, Mark Payne <markap14@hotmail.com> wrote:
Prabhu,
 
Certainly, the performance that you are seeing, taking 4-5 hours to move 3M rows into SQLServer is far from
ideal, but the good news is that it is also far from typical. You should be able to see far better results.
 
To help us understand what is limiting the performance, and to make sure that we understand what you are seeing, 
I have a series of questions that would help us to understand what is going on.
 
The first processor that you see that exhibits poor performance is ExtractText, correct?
Can you share the configuration that you have for that processor?
 
How big is your Java heap? This is configured in conf/bootstrap.conf; by default it is configured as:
java.arg.2=-Xms512m
java.arg.3=-Xmx512m
 
Do you have backpressure configured on the connection between ExtractText and ReplaceText?
 
Also, when you say that you specify concurrent tasks, what are you configuring the concurrent tasks
to be? Have you changed the maximum number of concurrent tasks available to your dataflow? By default, NiFi will
use only 10 threads max. How many CPU's are available on this machine?
 
And finally, are these the only processors in your flow, or do you have other dataflows going on in the
same instance as NiFi?
 
Thanks
-Mark
 
 
On Oct 17, 2016, at 3:35 AM, prabhu Mahendran <prabhuu161994@gmail.com> wrote:
 

Hi All,

I have tried to perform the below operation.

dat file(input)-->JSON-->SQL-->SQLServer


GetFile-->SplitText-->SplitText-->ExtractText-->ReplaceText-->ConvertJsonToSQL-->PutSQL.

My Input File(.dat)-->3,00,000 rows.

Objective: Move the data from '.dat' file into SQLServer.

I can able to Store the data in SQL Server by using combination of above processors.But it takes almost 4-5 hrs to move complete data into SQLServer.

Combination of SplitText's perform data read quickly.But Extract Text takes long time to pass given data matches with user defined expression.If input comes 107 MB but it send outputs in KB size only even ReplaceText processor also processing data in KB Size only.

In accordance with above slow processing leads the more time taken for data into SQLsever. 


Extract Text,ReplaceText,ConvertJsonToSQL processors send's outgoing flow file in Kilobytes only.

If i have specify concurrent tasks for those ExtractText,ReplaceText,ConvertJsonToSQL then it occupy the 100% cpu and disk usage.

It just 30 MB data ,But processors takes 6 hrs for data movement into SQLServer.

Faced Problem is..,

  1.        Almost 6 hrs taken for move the 3lakhs data into SQL Server.
  2.        ExtractText,ReplaceText take long time for processing data(it send output flowfile kb size only).

Can anyone help me to solve below requirement?

Need to reduce the number of time taken by the processors for move the lakhs of data into SQL Server.



If anything i'm done wrong,please help me to done it right.