John, Just field METRON-318. Is this something you would like to work on? Would you like help from us to get started?
Thanks, James 12.07.2016, 11:53, "[email protected]" <[email protected]>: > Hi All, > > Has there been any additional discussion or development regarding this? I > did take a brief look around the jira and didn't see anything regarding > this, but I may have missed it. Thanks, > > Jon > > On Fri, Apr 15, 2016 at 2:01 PM Nick Allen <[email protected]> wrote: > >> I definitely agree that you need this level of understanding of your >> cluster. It definitely could work the way that you describe. >> >> I was thinking of it slightly differently though. The metrics for this >> purpose (understanding performance of existing cluster) should come from >> the actual sensors themselves. For example, I need to instrument the >> packet capture process so that it kicks out time-series-ish metrics that >> you can monitor in a dashboard over time. >> >> On Fri, Apr 15, 2016 at 1:40 PM, [email protected] <[email protected]> >> wrote: >> >> > However, it would be handy to have something like this perpetually >> running >> > so you know when to scale up/out/down/in a cluster. >> > >> > On Fri, Apr 15, 2016, 13:35 Nick Allen <[email protected]> wrote: >> > >> > > I think it is slightly different. I don't even want to install minimal >> > > Kafka infrastructure (Look ma, no Kafka!) >> > > >> > > The exact implementation would differ based on the data inputs that you >> > are >> > > trying to measure, but for example... >> > > >> > > - To understand raw packet rates I would have a specialized sensor >> > that >> > > counts packets and size on the wire. It doesn't do anything more >> than >> > > that. >> > > - To understand Netflow rates, it would watch for Netflow packets >> and >> > > count those. >> > > - To understand sizing around application logs, a sensor would watch >> > for >> > > Syslog packets and count those. >> > > >> > > The implementation would be more similar to raw packet capture with >> some >> > > DPI. No Hadoop-y components required. >> > > >> > > >> > > >> > > On Fri, Apr 15, 2016 at 1:10 PM, James Sirota <[email protected] >> > >> > > wrote: >> > > >> > > > So this is exactly what I am proposing. Calculate the metrics on the >> > fly >> > > > without landing any data in the cluster. The problem is that that >> > > > enterprise data volumes are so large you can’t just point them at a >> > Java >> > > or >> > > > a C++ program or sensor. You either need an existing minimal Kafka >> > > > infrastructure to take that load or sample the data. >> > > > >> > > > Thanks, >> > > > James >> > > > >> > > > >> > > > >> > > > >> > > > On 4/15/16, 9:54 AM, "Nick Allen" <[email protected]> wrote: >> > > > >> > > > >Or we have the assessment tool not actually land any data. The >> > > assessment >> > > > >tool becomes a 'sensor' in its own right. You just point the input >> > data >> > > > >sets at the assessment tool, it builds metrics on the input (for >> > > example: >> > > > >count the number of packets per second) and then we use those >> metrics >> > to >> > > > >estimate cluster size. >> > > > > >> > > > >On Wed, Apr 13, 2016 at 5:45 PM, James Sirota < >> > [email protected]> >> > > > >wrote: >> > > > > >> > > > >> That’s an excellent point. So I think there are three ways >> forward. >> > > > >> >> > > > >> One is we can assume that there has to be at least a minimal >> > > > >> infrastructure in place (at least a subset of Kafka and Storm >> > > > resources) to >> > > > >> run a full-scale assessment. If you point something that blasts >> > > > millions >> > > > >> of messages per second at something like ActiveMQ you are going to >> > > blow >> > > > >> up. So the infrastructure to at least receive these kinds of >> > message >> > > > >> volumes has to exist as a pre-requisite. There is no way to get >> > around >> > > > that. >> > > > >> >> > > > >> The second approach I see is sampling. Sampling is a lot less >> > precise >> > > > and >> > > > >> you can miss peaks that fall outside of your sampling windows. >> But >> > > the >> > > > >> obvious benefit is that you don’t need a cluster to process these >> > > > streams. >> > > > >> You can probably perform most of your calculations with a >> > > multithreaded >> > > > >> java program. Sampling poses a few design challenges. First, >> where >> > > do >> > > > you >> > > > >> sample? Do you sample on the sensor? (the implication here is >> that >> > we >> > > > have >> > > > >> to program some sort of sampling capability in our sensors) . Do >> you >> > > > sample >> > > > >> on transport? (maybe a Flume interceptor or NiFi processor). >> There >> > is >> > > > also >> > > > >> a question of what the sampling rate should be. Not knowing >> > > statistical >> > > > >> properties of a stream ahead of time it’s hard to make that call. >> > > > >> >> > > > >> The third option I think is MR job. We can blast the data into >> HDFS >> > > and >> > > > >> then go over it with MR to derive the metrics we are looking for. >> > > Then >> > > > we >> > > > >> don’t have to sample or setup expensive infrastructure to receive >> a >> > > > deluge >> > > > >> of data. But then we run into the chicken and the egg problem >> that >> > in >> > > > >> order to size your HDFS you need to have data in HDFS. Ideally >> you >> > > > need to >> > > > >> capture at least one full weeks worth of logs because patterns >> > > > throughout >> > > > >> the day as well as every day of the week have different >> statistical >> > > > >> properties. So you need off peak, on peak, weekdays and weekends >> to >> > > > derive >> > > > >> these stats in batch. >> > > > >> >> > > > >> Any other design ideas? >> > > > >> >> > > > >> Thanks, >> > > > >> James >> > > > >> >> > > > >> >> > > > >> >> > > > >> >> > > > >> >> > > > >> On 4/13/16, 1:59 PM, "Nick Allen" <[email protected]> wrote: >> > > > >> >> > > > >> >If the tool starts at Kafka, the user would have to already have >> > > > committed >> > > > >> >to the investment in the infrastructure and time to setup the >> > sensors >> > > > that >> > > > >> >feed Kafka and Kafka itself. Maybe it would need to be further >> > > > upstream? >> > > > >> >On Apr 13, 2016 1:05 PM, "James Sirota" <[email protected] >> > >> > > > wrote: >> > > > >> > >> > > > >> >> Hi Goerge, >> > > > >> >> >> > > > >> >> This article defines micro-tuning of the existing cluster. >> What >> > I >> > > am >> > > > >> >> proposing is a level up from that. When you start with Metron >> > how >> > > do >> > > > >> you >> > > > >> >> even know how many nodes you need? And of these nodes how many >> > do >> > > > you >> > > > >> >> allocate to Storm, indexing, storage? How much storage do you >> > > need? >> > > > >> >> Tuning would be the next step in the process, but this tool >> would >> > > > answer >> > > > >> >> more fundamental questions about what a Metron deployment >> should >> > > look >> > > > >> like >> > > > >> >> given the number of telemetries and retention policies of the >> > > > >> enterprise. >> > > > >> >> >> > > > >> >> The best way to get this data (in my opinion) is to have some >> > tool >> > > > that >> > > > >> we >> > > > >> >> can plug into Metron’s point of ingest (kafka topics) and run >> > that >> > > > for >> > > > >> >> about a week or a month to be able to figure that out and spit >> > out >> > > > these >> > > > >> >> relevant metrics. Based on these metrics we can figure out the >> > > > >> fundamental >> > > > >> >> things about what metron should look like. Tuning would be the >> > > next >> > > > >> step. >> > > > >> >> >> > > > >> >> Thanks, >> > > > >> >> James >> > > > >> >> >> > > > >> >> >> > > > >> >> >> > > > >> >> >> > > > >> >> On 4/13/16, 9:52 AM, "George Vetticaden" < >> > > > [email protected]> >> > > > >> >> wrote: >> > > > >> >> >> > > > >> >> >I have used the following Kafka and Storm Best Practices guide >> > at >> > > > >> numerous >> > > > >> >> >customer implementations. >> > > > >> >> > >> > > > >> >> >> > > > >> >> > > > >> > > >> > >> https://community.hortonworks.com/articles/550/unofficial-storm-and-kafka-b >> > > > >> >> >est-practices-guide.html >> > > > >> >> > >> > > > >> >> > >> > > > >> >> >We need to have something similar and prescriptive for Metron >> > > based >> > > > on: >> > > > >> >> >1. What data sources are we enabling >> > > > >> >> >2. What enrichment services are we enabling >> > > > >> >> >3. What threat intel services are we enabling >> > > > >> >> >4. What are we indexing into Solr/Elastic and how long >> > > > >> >> >5. What are we persisting into HDFS.. >> > > > >> >> > >> > > > >> >> >Ideally, the The metron assessment tool combined with an >> > > > introspection >> > > > >> of >> > > > >> >> >the user’s ansible configuration should drive what ambari >> > > blueprint >> > > > >> type >> > > > >> >> >and configuration should be used when the cluster is spun up >> and >> > > the >> > > > >> storm >> > > > >> >> >topology is deployed. >> > > > >> >> > >> > > > >> >> > >> > > > >> >> >-- >> > > > >> >> >George VetticadenPrincipal, COE >> > > > >> >> >[email protected] >> > > > >> >> >(630) 909-9138 >> > > > >> >> > >> > > > >> >> > >> > > > >> >> > >> > > > >> >> > >> > > > >> >> > >> > > > >> >> >On 4/13/16, 11:40 AM, "George Vetticaden" < >> > > > [email protected] >> > > > >> > >> > > > >> >> >wrote: >> > > > >> >> > >> > > > >> >> >>+ 1 to James suggestion. >> > > > >> >> >>We also need to consider not just the data volume and storage >> > > > >> >> requirements >> > > > >> >> >>for proper cluster sizing but also processing requirements as >> > > well. >> > > > >> Given >> > > > >> >> >>that in the new architecture, we have moved to single >> > enrichment >> > > > >> topology >> > > > >> >> >>that will support all data sources, proper sizing of the >> > > enrichment >> > > > >> >> >>topology will be even more crucial to maintain SLAs and HA >> > > > >> requirements. >> > > > >> >> >>The following key questions will apply to each parser >> topology >> > > and >> > > > >> single >> > > > >> >> >>enrichment topology >> > > > >> >> >> >> > > > >> >> >>1. Number of workers? >> > > > >> >> >>2. Number of workers per machine? >> > > > >> >> >>3. Size of each workers (in memory)? >> > > > >> >> >>4. Supervisor memory settings >> > > > >> >> >> >> > > > >> >> >>The assessment tool should also be used to size topologies >> > > > correctly >> > > > >> as >> > > > >> >> >>well. >> > > > >> >> >> >> > > > >> >> >>Tuning Kafka, Hbase and Solr/Elastic should also be governed >> by >> > > the >> > > > >> >> Metron >> > > > >> >> >>assessment tool. >> > > > >> >> >> >> > > > >> >> >> >> > > > >> >> >>-- >> > > > >> >> >>George Vetticaden >> > > > >> >> >> >> > > > >> >> >> >> > > > >> >> >> >> > > > >> >> >> >> > > > >> >> >> >> > > > >> >> >> >> > > > >> >> >> >> > > > >> >> >>On 4/13/16, 11:28 AM, "James Sirota" < >> [email protected]> >> > > > wrote: >> > > > >> >> >> >> > > > >> >> >>>Prior to adoption of Metron each adopting entity needs to >> > > > guesstimate >> > > > >> >> >>>it¹s data volume and data storage requirements so they can >> > size >> > > > their >> > > > >> >> >>>cluster properly. I propose a creation of an assessment >> tool >> > > that >> > > > >> can >> > > > >> >> >>>plug in to a Kafka topic for a given telemetry and over time >> > > > produce >> > > > >> >> >>>statistics for ingest volumes and storage requirement. The >> > idea >> > > > is >> > > > >> that >> > > > >> >> >>>prior to adoption of Metron someone can set up all the feeds >> > and >> > > > >> kafka >> > > > >> >> >>>topics, but instead of deploying Metron right away they >> would >> > > > deploy >> > > > >> >> this >> > > > >> >> >>>tool. This tool would then produce statistics for data >> > > > >> ingest/storage >> > > > >> >> >>>requirement, and all relevant information needed for cluster >> > > > sizing. >> > > > >> >> >>> >> > > > >> >> >>>Some of the metrics that can be recorded are: >> > > > >> >> >>> >> > > > >> >> >>> * Number of system events per second (average, max, >> mean, >> > > > >> standard >> > > > >> >> >>>dev) >> > > > >> >> >>> * Message size (average, max, mean, standard dev) >> > > > >> >> >>> * Average number of peaks >> > > > >> >> >>> * Duration of peaks (average, max, mean, standard dev) >> > > > >> >> >>> >> > > > >> >> >>>If the parser for a telemetry exist the tool can produce >> > > > additional >> > > > >> >> >>>statistics >> > > > >> >> >>> >> > > > >> >> >>> * Number of keys/fields parsed (average, max, mean, >> > standard >> > > > dev) >> > > > >> >> >>> * Length of field parsed (average, max, mean, standard >> > dev) >> > > > >> >> >>> * Length of key parsed (average, max, mean, standard >> dev) >> > > > >> >> >>> >> > > > >> >> >>>The tool can run for a week or a month and produce these >> kinds >> > > of >> > > > >> >> >>>statistics. Then once the statistics are available we can >> > come >> > > up >> > > > >> with >> > > > >> >> a >> > > > >> >> >>>guidance documentation of recommended cluster setup. >> > Otherwise >> > > > it¹s >> > > > >> >> hard >> > > > >> >> >>>to properly size a cluster and setup streaming parallelism >> not >> > > > >> knowing >> > > > >> >> >>>these metrics. >> > > > >> >> >>> >> > > > >> >> >>> >> > > > >> >> >>>Thoughts/ideas? >> > > > >> >> >>> >> > > > >> >> >>>Thanks, >> > > > >> >> >>>James >> > > > >> >> >> >> > > > >> >> >> >> > > > >> >> > >> > > > >> >> >> > > > >> >> > > > > >> > > > > >> > > > > >> > > > >-- >> > > > >Nick Allen <[email protected]> >> > > > >> > > >> > > >> > > >> > > -- >> > > Nick Allen <[email protected]> >> > > >> > -- >> > >> > Jon >> > >> >> -- >> Nick Allen <[email protected]> > -- > > Jon ------------------- Thank you, James Sirota PPMC- Apache Metron (Incubating) jsirota AT apache DOT org
