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]>
