Tool: Spring Cloud Data Flow
I have created a sample with Source, Processor and Sink.
The graphical view of the whole app is
As I have an existing application which contains multiple Processor in a single project and enabled at once using code below
#EnableBinding({Processor.class, Processor1.class, Processor2.class})
Then, is there any possibility or configuration required so Data Flow can display all processor from the project?
It's really helpful if the Data Flow display processor with boundary and contains multiple processors in it (Shown in below image)
The SCDF Dashboard doesn't yet support this functionality.
Though it is possible to build multi-input/output based processors (in the same app) using Spring Cloud Stream, in SCDF today, primarily, the data pipelines are linear, with one-input/output representation.
We are exploring ideas to support this in SCDF proper. Please feel free to open a new issue with your use-cases, and as much as details as possible of the requirements - we could use it for the acceptance.
I have questions related what APIs are available inside Hyperledger Composer's transaction processor. It looks like, the sample only provides obtain assetRegistry, and then registry.update(). I expect that transaction processor is what we call SmartContract. Suppose the transaction is supposed to change the owner. So I want to verity update such that, new owner exists. I wonder if I can use participantRegistry.get() operation inside transaction processor. I checked that resolve() function is not available. So I suspect, transaction processor provides APIs available on composer runtime. But there is no documentation as to what kind of APIs are available for transaction processor.
The runtime APIs are in the runtime module. However, the runtime modules appear to have been removed from the publication of the API on the website. Please create an issue for that.
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 am working in the logical architecture of a project that receives some info from users and processes it. One of the requirements is to expose an interface for external developers to add further functionalities. So far I have proposed a MVC 2-tier architecture, where the View and Controller run in the user's machine, and the Model is hosted in an Application server and remotely invoked. The requirement on functionalities suggests me to use a plugin pattern.
Additional steps selected by the user might be executed when processing the information, so I wanted to model them as plugins that will already exist when the application is released. This means that this plugins would affect the same resource (the processing flow), and I am uncertain about how to deal with this when both plugins are enabled.
Since I am not as familiar with the plugin pattern as with other patterns, the reading I did before asking made me try something similar to the Abstract Factory pattern. The problem is that, when two or more plugins are enabled, I would need mutiple inheritance. I also thought of the Builder pattern to model steps of the processing separately, but then an order among plugins would have to be defined and this would affect the independence of plugin's developers.
If I understand correctly, you want to be able to extend the same variation point with multiple independent plugins. If so, the pipes and filters pattern is an appropriate mechanism.
With this approach, plugins represent filters and you can design a plugin container that loads and then chains them. If no plugin is loaded for a given variation point, then you either short-circuit the variation point or provide some form of default filter.
Also, giving the plugins a mechanism to specify their position in the filter chain will be helpful, so think about that when designing the plugin-interface.
Thrift sounds awesome but can't find some basic stuff I'm used to in RPC frameworks (as HttpServlet). Example of the things I can't find: session management, filtering, upload/download progress.
I understand that the missing stuff might be a management layer on top of Thrift. If so, any example of such a layer? Perhaps AOP (Aspect Oriented)?
I can't imagine such a layer that compiles to all languages and that's I'm missing. Taking session management as an example, there might be several clients that all need to do some authentication and pass the session_id upon each RPC. I would expect a similar API for all languages doing so.
Anyone knows of a a management layer for Thrift?
So thrift itself is not going to help you out a lot here.
I have had similar desires, and have a few suggestions:
1. Put your management objects into the IDL
Simply add an api token or common transfer data struct as a parameter to all of your service methods. Set it as parameter id 15 so that it will always be the last parameter, even if you add others in the middle.
As the first step in your handler you can validate/store/do whatever with the extra data.
This has the advantage that it is valid in any platform that thrift supports.
2. Use thrift over http
If you use http as your transport, you can include whatever data as you want as http headers, and the thrift content as the body.
This will often require a custom http client for every platform you use to inject the data, and a custom handler on the server to use the data, but neither of those are prohibitively difficult.
3. Hack the protocol
It is possible to create your own custom protocol that wraps another protocol and injects custom data. Take a look at how the multiplexed protocol works in the thrift library for most languages:
c# here. It sends the method name across the wire as service:method. The multiplexed processor unwraps this encoding and passes it on to the appropriate processor.
I have used a similar method to encode arbitrary key/value pairs (like http headers) inside the method name.
The downside to this is that you need to write a more complicated extension for each platform you will be using. Once. It varies a bit from language to language how this works, but it is generally simple enough once you figure it out once.
These are just a few ideas I have had, and I am sure there are others. The nice thing about thrift is how the individual components are decoupled from each other. If you have special needs you can swap any of them out as you need to to add specific functionality.