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]>
