Current flow of the project that I'm working on involves pushing to a local kafka using ruby-kafka gem.
Now the need arose to add producer for the remote kafka, and duplicate also messages there.
And I'm looking for a better way, than calling Kafka.new(...) twice...
Could you please help me, and do you happen to have any ideas?
Another approach to consider would be writing the data once from your application, and then asynchronously replicating the message from one Kafka cluster to another. There are multiple ways of doing this including Apache Kafka's MirrorMaker, Confluent's Replicator, Uber's uReplicator etc.
Disclaimer: I work for Confluent.
Within a cloud application I'm using NiFi (=> I'm a newbee) to work with data streams published by a mqtt broker. So far so good.
In the end I want to stream into an InfluxDB. That's the point I'm struggling with.
Does anybody have some experiences with a processor for such a setup? Is there a suitable processor for writing data into an InfluxDB?
Thanks a lot.
Kind regards,
T_F
There is a PutInfluxDB processor which accepts the incoming flowfile and writes the content as 'line content' in InfluxDB.
I'm searching for a simple event- / datastream generator for the Kafka-broker to run some performance tests on the streaming tools of the Hadoop framework. Found nothing suitable so far. It should be able to send a lot of (mostly equal) messages in a very short periot of time (milliseconds).
Thanks!
You can use kafka-producer-perf-test.sh tool that is shipped with Kafka 0.9+. It allows you to produce a number of messages of a given size. This tool is just a producer that allows you send a batch of messages and collect statistics.
In our performance tests we use this tool (or our own performance producer but that is similar to this) and when we want to increase the load we run several instances in parallel into different hosts.
I have a problem using real time UDP stream processing with the map reduce system. Actually I am doing a university project and I want to use mapreduce to process this data. UDP stream is about ship data from several AIS devices.
As far as I am aware, Apache Storm will be the solution for that. But I dont know that I can incorporate mapreduce to the Storm . I want to incorporate mapreduce concepts and ultimately I want to learn it.
Also I want to have some advice about the system architecture, the normal procedure is this,
UDP stream received by the system
decode the stream
real time analytic should be shown
stored for future data retrial purposes.
so can anyone suggest what is the best way to do this? can Apache Storm do this?
I'll answer the easy question first: Yes, Apache Storm can do what you want it to do.
That said, any of other 'big data' streaming tools can do this data processing as well. These tools include Storm, but also Spark and Samza.
If I were building this myself, I'd push the streaming data into a messaging queue, probably Kafka, then use Storm to pull individual messages out and process them. You can then store the result however you want. That could be onto disk, back into Kafka, or whatever makes sense in your case.
Finally, it doesn't seem that mapreduce is a good fit to your problem. Mapreduce is for batch processing, which isn't what you are describing as your problem.
Apache Kafka: Distributed messaging system
Apache Storm: Real Time Message Processing
How we can use both technologies in a real-time data pipeline for processing event data?
In terms of real time data pipeline both seems to me do the job identical. How can we use both the technologies on a data pipeline?
You use Apache Kafka as a distributed and robust queue that can handle high volume data and enables you to pass messages from one end-point to another.
Storm is not a queue. It is a system that has distributed real time processing abilities, meaning you can execute all kind of manipulations on real time data in parallel.
The common flow of these tools (as I know it) goes as follows:
real-time-system --> Kafka --> Storm --> NoSql --> BI(optional)
So you have your real time app handling high volume data, sends it to Kafka queue. Storm pulls the data from kafka and applies some required manipulation. At this point you usually like to get some benefits from this data, so you either send it to some Nosql db for additional BI calculations, or you could simply query this NoSql from any other system.
I know that this is an older thread and the comparisons of Apache Kafka and Storm were valid and correct when they were written but it is worth noting that Apache Kafka has evolved a lot over the years and since version 0.10 (April 2016) Kafka has included a Kafka Streams API which provides stream processing capabilities without the need for any additional software such as Storm. Kafka also includes the Connect API for connecting into various sources and sinks (destinations) of data.
Announcement blog - https://www.confluent.io/blog/introducing-kafka-streams-stream-processing-made-simple/
Current Apache documentation - https://kafka.apache.org/documentation/streams/
In 0.11 Kafka the stream processing functionality was further expanded to provide Exactly Once Semantics and Transactions.
https://www.confluent.io/blog/exactly-once-semantics-are-possible-heres-how-apache-kafka-does-it/
Kafka and Storm have a slightly different purpose:
Kafka is a distributed message broker which can handle big amount of messages per second. It uses publish-subscribe paradigm and relies on topics and partitions. Kafka uses Zookeeper to share and save state between brokers. So Kafka is basically responsible for transferring messages from one machine to another.
Storm is a scalable, fault-tolerant, real-time analytic system (think like Hadoop in realtime). It consumes data from sources (Spouts) and passes it to pipeline (Bolts). You can combine them in the topology. So Storm is basically a computation unit (aggregation, machine learning).
But you can use them together: for example your application uses kafka to send data to other servers which uses storm to make some computation on it.
This is how it works
Kafka - To provide a realtime stream
Storm - To perform some operations on that stream
You might take a look at the GitHub project https://github.com/abhishekgoel137/kafka-nodejs-d3js.
(D3js is a graph-representation library)
Ideal case:
Realtime application -> Kafka -> Storm -> NoSQL -> d3js
This repository is based on:
Realtime application -> Kafka -> <plain Node.js> -> NoSQL -> d3js
As every one explain you that
Apache Kafka: is continuous messaging queue
Apache Storm: is continuous processing tool
here in this aspect Kafka will get the data from any website like FB,Twitter by using API's and that data is processed by using Apache Storm and you can store the processed data in either in any databases you like.
https://github.com/miguno/kafka-storm-starter
Just follow it you will get some idea
When I have a use case that requires me to visualize or alert on patterns (think of twitter trends), while continuing to process the events, I have a several patterns.
NiFi would allow me to process an event and update a persistent data store with low(er) batch aggregation with very, very little custom coding.
Storm (lots of custom coding) allows me nearly real time access to the trending events.
If I can wait for many seconds, then I can batch out of kafka, into hdfs (Parquet) and process.
If I need to know in seconds, I need NiFi, and probably even Storm. (Think of monitoring thousands of earth stations, where I need to see small region weather conditions for tornado warnings).
Simply Kafka send the messages from node to another , and Storm processing the messages . Check this example of how you can Integration Apache Kafka With Storm