Conrad, Typically, the way that we like to think about using NiFi vs. something like Spark or Storm is whether the processing is Simple Event Processing or Complex Event Processing. Simple Event Processing encapsulates those tasks where you are able to operate on a single piece of data by itself (or in correlation with an Enrichment Dataset). So tasks like enrichment, splitting, and transformation are squarely within the wheelhouse of NiFi.
When we talk about doing Complex Event Processing, we are generally talking about either processing data from multiple streams together (think JOIN operations) or analyzing data across time windows (think calculating norms, standard deviation, etc. over the last 30 minutes). The idea here is to derive a single new "insight" from windows of data or joined streams of data - not to transform or enrich individual pieces of data. For this, we would recommend something like Spark, Storm, Flink, etc. In terms of scalability, NiFi certainly was not designed to scale outward in the way that Spark was. With Spark you may be scaling to thousands of nodes, but with NiFi you would get a pretty poor user experience because each change in the UI must be replicated to all of those nodes. That being said, NiFi does scale up very well to take full advantage of however much CPU and disks you have available. We typically see processing of several terabytes of data per day on a single node, so we have generally not needed to scale out to hundreds or thousands of nodes. I hope this helps to clarify when/where to use each one. If there are things that are still unclear or if you have more questions, as always, don't hesitate to shoot another email! Thanks -Mark > On Jun 2, 2016, at 9:28 AM, Conrad Crampton <[email protected]> > wrote: > > Hi, > ListenSyslog (using the approach that is being discussed currently in another > thread – ListenSyslog running on primary node as RGP, all other nodes > connecting to the port that the RPG exposes). > Various enrichment, routing on attributes etc. and finally into HDFS as Avro. > I want to branch off at an appropriate point in the flow and do some further > realtime analysis – got the output to port feeding to Spark process working > fine (notwithstanding the issue that you have been so kind to help with > previously with the SSLContext), just thinking about if this is most > appropriate solution. > > I have dabbled with a custom processor (for enriching url splitting/ > enriching etc. – probably could have done with ExecuteScript processor in > hindsight) so am comfortable with going this route if that is deemed more > appropriate. > > Thanks > Conrad > > From: Bryan Bende <[email protected] <mailto:[email protected]>> > Reply-To: "[email protected] <mailto:[email protected]>" > <[email protected] <mailto:[email protected]>> > Date: Thursday, 2 June 2016 at 13:12 > To: "[email protected] <mailto:[email protected]>" > <[email protected] <mailto:[email protected]>> > Subject: Re: Spark or custom processor? > > Conrad, > > I would think that you could do this all in NiFi. > > How do the log files come into NiFi? TailFile, ListenUDP/ListenTCP, > List+FetchFile? > > -Bryan > > > On Thu, Jun 2, 2016 at 6:41 AM, Conrad Crampton <[email protected] > <mailto:[email protected]>> wrote: > Hi, > Any advice on ‘best’ architectural approach whereby some processing function > has to be applied to every flow file in a dataflow with some (possible) > output based on flowfile content. > e.g. inspect log files for specific ip then send message to syslog > > approach 1 > Spark > Output port from NiFi -> Spark listens to that stream -> processes and > outputs accordingly > Advantages – scale spark job on Yarn, decoupled (reusable) from NiFi > Disadvantages – adds complexity, decoupled from NiFi. > > Approach 2 > NiFi > Custom processor -> PutSyslog > Advantages – reuse existing NiFi processors/ capability, obvious flow (design > intent) > Disadvantages – scale?? > > Any comments/ advice/ experience of either approaches? > > Thanks > Conrad > > > > > SecureData, combating cyber threats > > The information contained in this message or any of its attachments may be > privileged and confidential and intended for the exclusive use of the > intended recipient. If you are not the intended recipient any disclosure, > reproduction, distribution or other dissemination or use of this > communications is strictly prohibited. The views expressed in this email are > those of the individual and not necessarily of SecureData Europe Ltd. Any > prices quoted are only valid if followed up by a formal written quote. > > SecureData Europe Limited. Registered in England & Wales 04365896. Registered > Address: SecureData House, Hermitage Court, Hermitage Lane, Maidstone, Kent, > ME16 9NT > > > > > ***This email originated outside SecureData*** > > Click here > <https://www.mailcontrol.com/sr/0Yez0Z9rJiDGX2PQPOmvUr11KAWLA5a39FXrkhyyO4eQg2DXa9Xl!rwzg+4hlLPKdufvfzcRzpTaNxM9hG2QrA==> > to report this email as spam. >
