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 <obaidc...@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
