Thanks Mark. The workaround to have intermediate split text to split few lines works well, as you said, the throughput is not quite there. I think it serves our purpose as of now.
From: Mark Payne <[email protected]<mailto:[email protected]>> Reply-To: "[email protected]<mailto:[email protected]>" <[email protected]<mailto:[email protected]>> Date: Tuesday, November 10, 2015 at 7:12 PM To: "[email protected]<mailto:[email protected]>" <[email protected]<mailto:[email protected]>> Subject: Re: Memory Issues on Split Text Naveen, There is a ticket [1] that will make this work more cleanly so that we can use SplitText to split a large file into millions of FlowFiles. Right now, as you noted you will end up running out of memory. There are a few possible solutions that you can use. If you need to split each line into a separate FlowFile, the easiest way is to use two SplitText processors. The first would be configured with a Line Split Count of say 10,000. Then, the "splits" relationship is routed to a second SplitText processor with the Line Split Count set to 1. This prevents the processor from holding those millions of FlowFiles in memory. The only downside here is that if you create a FlowFile for every single message, your throughput will not be quite as good. The next approach is to just send the entire 2 GB FlowFile to PutKafka and set the Message Delimiter to "\n". This will send each line in the FlowFile to Kafka as a separate message. The down side here is that if you have sent say 1 million messages to Kafka and then NiFi is restarted, it doesn't know that those 1 million messages have been sent, so you will end up sending all of the data again and will duplicate a lot of the messages. The third approach is a hybrid of the two. You can use SplitText to split the FlowFile into 10,000 lines each. Then, instead of sending to another SplitText, you can send the "splits" relationship to PutKafka with a Message Delimiter of "\n". This way, you will still get great throughput by not splitting each FlowFile into millions of FlowFiles, but you will avoid duplicating millions of messages (you'll duplicate at the very most 10,000 messages in this example). So you can use any of these approaches. You just have to consider the pro's and con's of each and decide which trade-offs you want to make. Thanks -Mark [1] https://issues.apache.org/jira/browse/NIFI-1008 On Nov 10, 2015, at 5:28 PM, Madhire, Naveen <[email protected]<mailto:[email protected]>> wrote: Hi, I am reading a 2 GB file from local and putting the data into a Kafka topic. Since GetFile only creates one flow file per file, I am making use of SplitText processor to split the file into one flow file per line before inserting the data into a Kafka topic. I am seeing a lot of “GC Overhead limit exceeded errors” on SplitText processor. I am running Nifi on a single linux server with 16 GB memory. Is this the right approach of reading and putting into Kafka? Or there is any better approach? Thanks, Naveen ________________________________ The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates and may only be used solely in performance of work or services for Capital One. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer. ________________________________________________________ The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates and may only be used solely in performance of work or services for Capital One. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer.
