Posts

Showing posts with the label apache-storm-topology-example

Featured Post

How to Work With Tuple in Python

Image
Tuple in python is one of the streaming datasets. The other streaming datasets are List and Dictionary. Operations that you can perform on it are shown here for your reference. Writing tuple is easy. It has values of comma separated, and enclosed with parenthesis '()'. The values in the tuple are immutable, which means you cannot replace with new values. #1. How to create a tuple Code: my_tuple=(1,2,3,4,5) print(my_tuple) Output: (1, 2, 3, 4, 5) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #2. How to read tuple values Code: print(my_tuple[0]) Output: 1 ** Process exited - Return Code: 0 ** Press Enter to exit terminal #3. How to add two tuples Code: a=(1,6,7,8) c=(3,4,5,6,7,8) d=print(a+c) Output: (1, 6, 7, 8, 3, 4, 5, 6, 7, 8) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #4.  How to count tuple values Here the count is not counting values; count the repetition of a given value. Code: sample=(1, 6, 7, 8, 3, 4, 5, 6, 7, 8) print(sample

Apache Storm Architecture Tutorial Flowchart

Image
There are two main reasons why Apache Storm is so popular. The number one is it can connect to many sources. The number two is scalable. The other advantage is fault-tolerant. That means, guaranteed data processing. The map-reduce jobs process data analytics in Hadoop. The topology in Storm is the real data processor. The co-ordination between Nimbus and Supervisor carried by Zookeeper Apache Storm The jobs in Hadoop are similar to the topology. The jobs run as per the schedule defined. In Storm, the topology runs forever. A topology consists of many worker processes spread across many machines.  A topology is a pre-defined design to get end product using your data. A topology comprises of 2 parts. These are Spout and bolts. The Spout is a funnel for topology Two nodes in Storm Master Node: similar to the Hadoop job tracker. It runs on a daemon called Nimbus. Worker Node: It runs on a daemon called Supervisor. The Supervisor listens to the work assigned to