I've developed a NiFi flow prototype for data ingestion in HDFS. Now I would like to improve the overall performances but it seems I cannot really move forward.
The flow takes in input csv files (each row has 80 fields), split them at row level, applies some transformations to the fields (using 4 custom processors executed sequentially), buffers the new rows into csv files, outputs them into HDFS. I've developed the processors in such a way the content of the flow file is accessed only once when each individual record is read and its fields are moved to flowfile attributes. Tests have been performed on a amazon EC2 m4.4xlarge instance (16 cores CPU, 64 GB RAM).
This is what I tried so far:
From the monitoring I've performed it looks like disks are not the bottleneck (they are basically idle a great part of the time, showing the computation is actually being performed in-memory) and the average CPU load is below 60%.
The most I can get is 215k rows/minute, which is 3,5k rows/second. In terms of volume, it's just 4,7 MB/s. I am aiming to something definitely greater than this. Just as a comparison, I created a flow that reads a file, splits it in rows, merges them together in blocks and outputs on disk. Here I get 12k rows/second, or 17 MB/s. Doesn't look surprisingly fast too and let me think that probably I am doing something wrong. Does anyone has suggestions about how to improve the performances? How much will I benefit from running NiFi on cluster instead of growing with the instance specs? Thank you all
It turned out the poor performances were a combination of both the custom processors developed, and the merge content built-in processor. The same question mirrored on the hortonworks community forum got interesting feedback.
Regarding the first issue, a suggestion is to add the SupportsBatching
annotation to the processors. This allows the processors to batch together several commits, and allows the NiFi user to favor latency or throughput with the processor execution from the configuration menu. Additional info can be found on the documentation here.
The other finding was that the MergeContent
built-in processor doesn't seem to have optimal performances itself, therefore if possible one should consider modifying the flow and avoid the merging phase.
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