I have got a requirement to show the management/ Client that the executor-memory, number of cores, default parallelism, number of shuffle partitions and other configuration properties for running the spark job are not excessive or more than required. I need a monitoring (with visualization) tool by which I can justify the memory usage in the spark job. Additionally it should give the kind of information like memory is not getting used properly or certain job requires more memory.
Please suggest some application or tool.
LinkedIn has created a tool that sounds very similar to what you're looking for
See for a presentation as an overview of that product
https://youtu.be/7KjnjwgZN7A?t=480
LinkedIn team has open-sourced Dr. Elephant here -
https://github.com/linkedin/dr-elephant
Give it a try.
Notice that this setup may require manual tweaking of Spark History Server as part of initial integration setup to get the information that Dr. Elephant requires.
I want suggestions for my application:
I have Multitenancy in Nifi. For each Process group, I have different Tenants/Users.
For any changes in one Tenant/user like in his custom processor(.nar file will create), we need to copy-paste that .nar file into lib folder and again restart the nifi. But due to this full Nifi server has restarted because of that Each Tenant/User and Processes group get restarted.
So, Please give Some Suggestions So that we can restart only one Tenant/user or process group Or Without Restart Nifi .nar file will reflect?
NiFi does not currently have the kind of warm restart option that you describe, however a lot of the base functionality needed to support it is in the code base and the concept is on the community roadmap.
Some options that might help you today:
Consider segregating the tenants with a high rate of code change into separate development environments. You could possibly leverage the Docker builds to provide flexibility and easy automation. You could then promote the end-of-day versions of your Nars into the 'Production' cluster each night, hopefully without disturbing users.
Consider utilising the NiFi Site-to-Site capability to have linked NiFi environments instead of a single shared one. Processors that change regularly could be called out to and updated in their own schedule
Consider why you are changing processor code so regularly, there may be a better approach than hard coding logic and parameters into the processors - the variable registry, various controller services, flow registry, etc. all provide a very rich featureset.
Can we monitor the elastic stack 6.0 and above(like elastic search..) without using the X-Pack?As we know many of the Features like security, machine learning, graph APIs don't be supported under BASIC(free Licence).
So I want to know if there are any APIs, without Licence limitation, can be used to implement those functionalities mentioned above?
All the information should be in the cluster APIs, you'll just lack the visualizations.
Monitoring (of the local cluster) is actually included in X-Pack Basic unlike the other features. Any reason you don't want to use it?
Alternatives include Kopf, Cerebro,... though you'll need to run them as a separate process and watch out for version compatibilities.
We've had success with ElasticHQ for Monitoring (requires python)
https://github.com/ElasticHQ/elasticsearch-HQ
And sentinl for setting up alerts/watchers (it is a plugin for kibana)
https://github.com/sirensolutions/sentinl/wiki
We have set up a reverse proxy to enable ssl/tls and use ubuntu user management to create logins, however, we do not limit access within Kibana itself.
We have little need for graph/machine learning so I am unaware of free alternatives.
The company I work for is heavily Open Source, so these projects suit us.
I am planning to do a class project and was going through few technologies where I can automate or set the flow of data between systems and found that there are couple of them i.e. Apache NiFi and StreamSets ( to my knowledge ). What I couldn't understand is the difference between them and use-cases where they can be used? I am new to this and if anyone can explain me a bit would be highly appreciated. Thanks
Suraj,
Great question.
My response is as a member of the open source Apache NiFi project management committee and as someone who is passionate about the dataflow management domain.
I've been involved in the NiFi project since it was started in 2006. My knowledge of Streamsets is relatively limited so I'll let them speak for it as they have.
The key thing to understand is that NiFi was built to do one really important thing really well and that is 'Dataflow Management'. It's design is based on a concept called Flow Based Programming which you may want to read about and reference for your project 'https://en.wikipedia.org/wiki/Flow-based_programming'
There are already many systems which produce data such as sensors and others. There are many systems which focus on data processing like Apache Storm, Spark, Flink, and others. And finally there are many systems which store data like HDFS, relational databases, and so on. NiFi purely focuses on the task of connecting those systems and providing the user experience and core functions necessary to do that well.
What are some of those key functions and design choices made to make that effective:
1) Interactive command and control
The job of someone trying to connect systems is to be able to rapidly and efficiently interact with the constant streams of data they see. NiFi's UI allows you do just that as the data is flowing you can add features to operate on it, fork off copies of data to try new approaches, adjust current settings, see recent and historical stats, helpful in-line documentation and more. Almost all other systems by comparison have a model that is design and deploy oriented meaning you make a series of changes and then deploy them. That model is fine and can be intuitive but for the dataflow management job it means you don't get the interactive change by change feedback that is so vital to quickly build new flows or to safely and efficiently correct or improve handling of existing data streams.
2) Data Provenance
A very unique capability of NiFi is its ability to generate fine grained and powerful traceability details for where your data comes from, what is done to it, where its sent and when it is done in the flow. This is essential to effective dataflow management for a number of reasons but for someone in the early exploration phases and working a project the most important thing this gives you is awesome debugging flexibility. You can setup your flows and let things run and then use provenance to actually prove that it did exactly what you wanted. If something didn't happen as you expected you can fix the flow and replay the object then repeat. Really helpful.
3) Purpose built data repositories
NiFi's out of the box experience offers very powerful performance even on really modest hardware or virtual environments. This is because of the flowfile and content repository design which gives us the high performance but transactional semantics we want as data works its way through the flow. The flowfile repository is a simple write ahead log implementation and the content repository provides an immutable versioned content store. That in turn means we can 'copy' data by only ever adding a new pointer (not actually copying bytes) or we can transform data by simply reading from the original and writing out a new version. Again very efficient. Couple that with the provenance stuff I mentioned a moment ago and it just provides a really powerful platform. Another really key thing to understand here is that in the business of connecting systems you don't always get to dictate things like size of data involved. The NiFi API was built to honor that fact and so our API lets processors do things like receive, transform, and send data without ever having to load the full objects in memory. These repositories also mean that in most flows the majority of processors do not even touch the content at all. However, you can easily see from the NiFi UI precisely how many bytes are actually being read or written so again you get really helpful information in establishing and observing your flows. This design also means NiFi can support back-pressure and pressure-release naturally and these are really critical features for a dataflow management system.
It was mentioned previously by the folks from the Streamsets company that NiFi is file oriented. I'm not really sure what the difference is between a file or a record or a tuple or an object or a message in generic terms but the reality is when data is in the flow then it is 'a thing that needs to be managed and delivered'. That is what NiFi does. Whether you have lots of really high speed tiny things or you have large things and whether they came from a live audio stream off the Internet or they come from a file sitting on your harddrive it doesn't matter. Once it is in the flow it is time to manage and deliver it. That is what NiFi does.
It was also mentioned by the Streamsets company that NiFi is schemaless. It is accurate that NiFi does not force conversion of data from whatever it is originally to some special NiFi format nor do we have to reconvert it back to some format for follow-on delivery. It would be pretty unfortunate if we did that because what this means is that even the most trivial of cases would have problematic performance implications and luckily NiFi does not have that problem. Further had we gone that route then it would mean handling diverse datasets like media (images, video, audio, and more) would be difficult but we're on the right track and NiFi is used for things like that all the time.
Finally, as you continue with your project and if you find there are things you'd like to see improved or that you'd like to contribute code we'd love to have your help. From https://nifi.apache.org you can quickly find information on how to file tickets, submit patches, email the mailing list, and more.
Here are a couple of fun recent NiFi projects to checkout:
https://www.linkedin.com/pulse/nifi-ocr-using-apache-read-childrens-books-jeremy-dyer
https://twitter.com/KayLerch/status/721455415456882689
Good luck on the class project! If you have any questions the users#nifi.apache.org mailing list would love to help.
Thanks
Joe
Both Apache NiFi and StreamSets Data Collector are Apache-licensed open source tools.
Hortonworks does have a commercially supported variant called Hortonworks DataFlow (HDF).
While both have a lot of similarities such as a web-based ui, both are used for ingesting data there are a few key differences. They also both consist of a processors linked together to perform transformations, serialization, etc.
NiFi processors are file-oriented and schemaless. This means that a piece of data is represented by a FlowFile (this could be an actual file on disk, or some blob of data acquired elsewhere). Each processor is responsible for understanding the content of the data in order to operate on it. Thus if one processor understands format A and another only understands format B, you may need to perform a data format conversion in between those two processors.
NiFi can be run standalone, or as a cluster using its own built-in clustering system.
StreamSets Data Collector (SDC) however, takes a record based approach. What this means is that as data enters your pipeline it (whether its JSON, CSV, etc) it is parsed into a common format so that the responsibility of understanding the data format is no longer placed on each individual processor and any processor can be connected to any other processor.
SDC also runs standalone, and also a clustered mode, but it runs atop Spark on YARN/Mesos instead, leveraging existing cluster resources you may have.
NiFi has been around for about the last 10 years (but less than 2 years in the open source community).
StreamSets was released to the open source community a little bit later in 2015. It is vendor agnostic, and as far as Hadoop goes Hortonworks, Cloudera, and MapR are all supported.
Full Disclosure: I am an engineer who works on StreamSets.
They are very similar for data ingest scenarios.
Apache NIFI(HDP) is more mature and StreamSets is more lightweight.
Both are easy to use, both have strong capability. And StreamSets could easily
They have companies behind, Hortonworks and Cloudera.
Obviously there are more contributors working on NIFI than StreamSets, of course, NIFI have more enterprise deployments in production.
Two of the key differentiators between the two IMHO are.
Apache NiFi is a Top Level Apache project, meaning it has gone through the incubation process described here, http://incubator.apache.org/policy/process.html, and can accept contributions from developers around the world who follow the standard Apache process which ensures software quality. StreamSets, is Apache LICENSED, meaning anyone can reuse the code, etc. But the project is not managed as an Apache project. In fact, in order to even contribute to Streamsets, you are REQUIRED to sign a contract. https://streamsets.com/contributing/ . Contrast this with the Apache NiFi contributor guide, which wasn't written by a lawyer. https://cwiki.apache.org/confluence/display/NIFI/Contributor+Guide#ContributorGuide-HowtocontributetoApacheNiFi
StreamSets "runs atop Spark on YARN/Mesos instead, leveraging existing cluster resources you may have." which imposes a bit of restriction if you want to deploy your dataflows further toward the Edge where the Devices that are generating the data live. Apache MiniFi, a sub-project of NiFi can run on a single Raspberry Pi, while I am fairly confident that StreamSets cannot, as YARN or Mesos require more resources than a Raspberry Pi provides.
Disclosure: I am a Hortonworks employee
I got information about haproxy from Stack Exchange Gives Back 2014 page. Stackoverflow is aso using this excellent application. After visiting haproxy website, I found its uses like - load balancer. So,
Does it work like F5 (reverse proxy) and can it replace F5?
Can someone list down its all features and similar competitors application?
You may check this list for alternative Load Balancing tools.
Cloud providers (Amazon, Rackspace, Google Compute Engine, Softlayer etc), but also some dedicated/VM server providers, usually offer some cheap Load-Balancing solutions as a service.
Haproxy currently seems to be one of the most popular opensource software for Reverse-Proxy, Load-Balancing and failover. Latest versions 1.5+ support SSL-offloading but it still doesn't support content caching (F5 does).
The usual configuration is combining it with a Content Caching system or/and CDN and using the last version you now don't even need a separate SSL-offloader. It offers a nice set of load-balancing rules (RR, leastconn, ipHash etc), apart from their website check this nicely-formatted link.