I have given a task to load a json file into titandb with dynamodb as back end.Is there any java tutorial or if possible please upload java sample coding...
thanks.
Titan is an abstraction layer so whether you use Cassandra, dynamo, hbase, etc, you merely need to find Titan data loading instructions. They are a bit dated but you might want to start with these blog posts:
http://thinkaurelius.com/2014/05/29/powers-of-ten-part-i/
http://thinkaurelius.com/2014/06/02/powers-of-ten-part-ii/
The code examples work with an older version of Titan (the schema portion) but the concepts still apply.
You will find that the strategy for data loading with Titan has a lot to do with the size of your graph. You said you are loading "a JSON file" so I imagine you have a smaller graph in the millions of edges. In this case, a simple groovy script will likely suffice. Write a script to parse your JSON and write the data to the Titan.
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I am trying to move my informatica pipelines in PC 10.1 to Azure Data Factory/ Synapse pipelines. Other than rewriting them from scratch, is there a way to migrate them somehow.. I am not finding any tools to achieve this as well. Has anyone faced this problem. Any leads on how to proceed ahead.
Thanks
There are no out of box solutions available to complete this migration. Unfortunately, you will have to author them again.
Informatica PowerCenter pipelines are a physical implementation of an Extract Transform Load (ETL) process. Each provider has different approaches to the implementations and they do not necessarily map well from one to another. Core Azure Data Factory (ADF) is actually more suited to Extract, Load and Transform (ELT), unless of course you use Data Flows.
So what you have to do is:
map out physically what your current pipeline is doing, if you don't have that documentation already. A simple spreadsheet template mapping out the components of the existing pipeline, tracking source, target plus any transformations will suffice
logically map out what the pipeline is doing; ie without using PowerCenter- specific terminology lay out what the "as is" pipeline is doing. A data flow diagram is a great way to do this
logically map out what the "to be" pipeline should do; ie without using any ADF-specific terminology, attempt to refine the "as is" pipeline to its simplest form
using expert knowledge of the ADF components (eg Copy, Lookup, Notebook, Stored Proc to name but a few) map from the logical "to be" to the physical (in the loosest sense of the word, it's all cloud now right : ), eg move data from place to place with the Copy activity, transform data in a SQL database using the Stored Proc activity, a repeated activity might use a For Each loop (bear in mind these execute in parallel), do sophisticated transformations or processing using Databricks notebooks if required and so on. If you require a low-code approach, consider Data Flows.
So you can see it's just a few simple steps. Good luck!
I'm working on a project that uses parallel methods to convert text from one form to another. We're going to implement a CSV to JSON converter to demonstrate the speedups that are possible using our parallel framework.
We want to benchmark our converter once it's finished. What are the fastest libraries/stand-alone programs/etc out there that are capable of doing CSV-JSON conversion? I found a list of potential candidates here:Large CSV to JSON/Object in Node.js, but I'm not sure how fast the listed options are. In the worst case I'll benchmark them myself, but if someone already knows what the "best in class" converters are it'd save me some time.
Looks like the maintainer of csvtojson has developed a benchmark application. I think I can add my csv to json converter to his benchmark project to test my converter.
if your project can consider in-browser apps, I suggest csvtojson as it is by far the speediest converter on the market as of 2017.
I created it myself so I may be a bit biaised, but I specifically developed it for a bigger project that required big csv to json crunching.
Tell me if it served.
I have the following setup (that I cannot change) and I'd like some advice from people who have been down that road. I'm not sure if this is the right place to ask, but here goes anyway.
Various JSON messages are placed on a different channels of a JMS queue (Universal Messaging/webMethods).
Before the data can be stored in relational-style DBs it has to be transformed: renamed, arrays flattened and some structures from nested objects extracted.
Data has to be appended to MySQL (as a serving layer for a visualization tool) and Hive (for long-term storage).
We're stuck on Spark 1.4.1 and may move to 1.6.0 in a few months' time. So, structured streaming is not (yet) an option.
At some point the events will be streamed directly to real-time dashboards, so having something in place that is capable of doing that now would be ideal.
Ideally coding is done in Scala (because we already have considerable batch-based repo with Spark and Scala), so the minimal requirement is JVM-based.
I've looked at Spark Streaming but it does not have a JMS adapter and as far as I can tell operating on JSON would be done using a SQLContext instance on the DStream's RDDs. I understand that it's possible to write a custom adapter, but then I'm not sure if Spark is still the best/easiest solution. I've also looked at the doc for Samza and Flink but did not find much for JMS and/or JSON, at least not natively.
Apache Camel seems like it might have a substantial set of connectors but I'm not too familiar with it, and I get the impression it does not do the streaming part, 'just' the bit where you connect to various systems. There's also Akka although I get the impression it's more of a replacement for messaging systems and JMS is set.
There is an almost bewildering amount of available tools and I'm at this point at a loss what to look at or what to look out for. What do you recommend based on your experience that I use to pick up the messages, transform, and insert into Hive and MySQL?
I was consulting several references to discover how I may output trained Weka models into Java source code so that I may use the classifiers I am training in actual code for research applications I have been developing.
As I was playing with Weka 3.7, I noticed that while it does output Java code to its main text buffer when use simpler classification (supervised in my case this time) methods such as J48 decision tree, it removes the option (rather, it voids it by removing the ability to checkmark it and fades the text) to output Java code for RandomTree and RandomForest (which are the ones that give me the best performance in my situation).
Note: I am clicking on the "More Options" button and checking "Output source code:".
Does Weka not allow you to output RandomTree or RandomForest as Java code? If so, why? Or if it does and just doesn't put it in the output buffer (since RF is multiple decision trees which I imagine it doesn't want to waste buffer space), how does one go digging up where in the file system Weka outputs java code by default?
Are there any tricks to get Weka to give me my trained RandomForest as Java code? Or is Serialization of the output *.model files my only hope when it comes to RF and RandomTree?
Thanks in advance to those who provide help.
NOTE: (As an addendum to the answer provided below) If you run across a similar situation (requiring you to use your trained classifier/ML model in your code), I recommend following the links posted in the answer that was provided in response to my question. If you do not specifically need the Java code for the RandomForest, as an example, de-serializing the model works quite nicely and fits into Java application code, fulfilling its task as a trained model/hardened algorithm meant to predict future unlabelled instances.
RandomTree and RandomForest can't be output as Java code. I'm not sure for the reasoning why, but they don't implement the "Sourceable" interface.
This explains a little about outputting a classifier as Java code: Link 1
This shows which classifiers can be output as Java code: Link 2
Unfortunately I think the easiest route will be Serialization, although, you could maybe try implementing "Sourceable" for other classifiers on your own.
Another, but perhaps inconvenient solution, would be to use Weka to build the classifier every time you use it. You wouldn't need to load the ".model" file, but you would need to load your training data and relearn the model. Here is a starters guide to building classifiers in your own java code http://weka.wikispaces.com/Use+WEKA+in+your+Java+code.
Solved the problem for myself by turning the output of WEKA's -printTrees option of the RandomForest classifier into Java source code.
http://pielot.org/2015/06/exporting-randomforest-models-to-java-source-code/
Since I am using classifiers with Android, all of the existing options had disadvantages:
shipping Android apps with serialized models didn't reliably work across devices
computing the model on the phone took too much resources
The final code will consist of three classes only: the class with the generated model + two classes to make the classification work.
I'd like to compute very large JSON files (about 400 MB each) in Scala.
My use-case is batch-processing. I can receive several very big files (up to 20 GB, then cut to be processed) at the same moment and I really want to process them quickly as a queue (but it's not the subject of this post!). So it's really about distributed architecture and performance issues.
My JSON file format is an array of objects, each JSON object contains at least 20 fields. My flow is composed of two major steps. The first one is the mapping of the JSON object into a Scala object. And the second step is some transformations I'm making on the Scala object data.
To avoid loading all the file in memory, I'd like a parsing library where I can have incremental parsing. There are so many libraries (Play-JSON, Jerkson, Lift-JSON, the built in scala.util.parsing.json.JSON, Gson) and I cannot figure out which one to take, with the requirement to minimize dependencies.
Do you have any ideas of a library I can use for high-volume parsing with good performances?
Also, I'm searching a way to process in parallel the mapping of the JSON file and the transformations made on the fields (between several nodes).
Do you think I can use Apache Spark to do it? Or are there alternative ways to accelerate/distribute the mapping/transformation?
Thanks for any help.
Best regards, Thomas
Considering a scenario without Spark, I would advise to stream the json with Jackson Streaming (Java) (see for example there), map each Json object to a Scala case class and send them to an Akka router with several routees that do the transformation part in parallel.