I'm new to Marklogic and mlcp. I'm working on marklogin 9.0-8. I wnat to use mlcp to load content, but since some parameters may need to be dynamically built based on content, does anyone know if it is possible to call mlcp from java application?
Thanks a lot,
Helen
MarkLogic provides two Java-based ways to load content: MLCP and DMSDK. MLCP is intended to be used as a command-line tool (and I believe that's the only supported use).
The Data Movement SDK, on the other hand, is specifically intended to offer very similar functionality in the form of a JAR, making it easy to access from a Java application. I encourage you to look into using that instead.
tutorial
JavaDoc
Asynchronous Multi-Document Operations
12-minute video intro to DMSDK
common tasks made easier through ml-gradle
I am facing a task to analyze, document and visualize a rather large global network (more than 200 sites, various technologies) and wonder if opendaylight might help me with this. The documentation should mainly focus on Layer 3 and Layer 4 but may partially also include L2 topology. There is no need to actually use a controller to configure devices through southbound APIs, but I would like to use the benefits of a structured, consistent description (e.g. YANG) and a visualization (DLUX, NeXt) which ODL provides.
So here's my question: Is there any way to manually add a topology (nodes, links) through a graphical editor to ODL?
From the general project description and from the last 20 seconds of this video, NeXt in general seems to be able to add/modify topology information. How far does the NeXt integration with ODL go? Would I need to write my own app (which could use NeXt) which would need to use the RESTCONF APIs to add information to the topology? Or should I maybe create a virtual topology of the real network using mininet? Any other ideas?
I understand that an sdn controller is probably not the right kind of tool for this task, but other alternatives (e.g. Net2Plan, Visio, yEd) are basically local solutions, a bit too complicated or provide no standardized DSL for topologies. Besides that, the documentation covering ODL and NeXt integration is very limited - couldn't get a grasp of how that integration should work (I'll try harder).
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
Between Apache Oozie, Spotify/Luigi and airbnb/airflow, what are the pros and cons for each of them?
I have used oozie and airflow in the past for building a data ingestion pipeline using PIG and Hive. Currently, I am in the process of building a pipeline that looks at logs and extracts out useful events and puts them on redshift.
I found that airflow was much easier to use/test/setup. It has a much cooler UI and lets users perform actions from the UI itself, which is not the case with Oozie. Any information about Luigi or other insights regarding stability and issues are welcome.
Azkaban: Nice UI, relatively simple, accessible for non-programmers. Has a longish history at LinkedIn.
Check out the Azkaban CLI project for programmatic job creation. I have an Azkaban example workflows project on GitHub.
Airflow: Decent UI, Python-ish job definition, semi-accessible for non-programmers, dependency declaration syntax is weird.
Luigi: OK UI, workflows are pure Python, requires solid grasp of Python coding and object oriented concepts, hence not suitable for non-programmers.
Oozie: Insane XML based job definitions. Here be dragons. ;-)
IMHO, Azkaban enforces simplicity (can’t use features that don’t exist) and the others subtly encourage complexity.
Simpler pipelines are better than complex pipelines: Easier to create, easier to understand (especially when you didn’t create) and easier to debug/fix.
When complex actions are needed you want to encapsulate them in a way that either completely succeeds or completely fails.
If you can make it idempotent (running it again creates identical results) then that’s even better.
This post will give you an initial idea about different possible workflows
http://bytepawn.com/luigi-airflow-pinball.html
I have an academic course "Middleware" which covers different aspects of Distributed Software Systems including introduction to topics like [tag:Distributed File system]. This also involves introduction to hbase,hadoop,mapreduce,hiveql,piglatin.
I want to know, can I have a small project which tries to integrate above technologies. For starters, I am aware of vm provided by cloudera for having a feel of hadoop and playing around using Eclipse.
I was thinking on lines of implementing an application which accepts stream of events as an input, Analyses this and gives an output.
I have both windows/linux on my machine with i7 procoessor and 4Gb Ram.
Please let me know how to get started with everything and any suggestions for simple example application are welcome.
Here is a blog post on analyzing Tweets using Hive/HDFS. And here is a blog post on performing Clickstream analytics using Pig and Hive.
Check some of the Big Data use cases here and try to solve an interesting problem.