Maven : Copy Dependencies in Target Folder

How do I get my project’s runtime dependencies copied into the target/lib folder?

Copy below XML code into pom.xml


Run mvn install command on shell

Now Run copy denpendencies command
mvn install dependency:copy-dependencies


That’s it you re done!!

YACassandraPDO : How to fetch UUID column from cassandra table

It is observer that when we fetch the UUID field using YACassandraPDO driver in PHP it fetches the garbage. for example

select dateof(uuidfield) as theTimeStamp from table;

output will be :

array(1) {
array(2) {
string(8) "�;e$��"
string(8) "�;e$��"


array(1) {
array(2) {
string(8) "2012-12-04 10:00:00+0100"
string(8) "2012-12-04 10:00:00+0100"

For timestamp, pdo returning the hexodecimal string. You can use below function to convert it back to date string.

function getDateStringFromHex($str) {
$date = unpack('H*', $str);
$time = hexdec($date[1]) / 1000;
$dateStr = date('Y-m-d H:i:s', $time);
return $dateStr;

Definition Storm Data Model and Topology

The Storm data model

The basic unit of data that can be processed by a Storm application is called a tuple.Each tuple consists of a predefined list of fields. The value of each field can be a byte,char, integer, long, float, double, Boolean, or byte array. Storm also provides an API to define your own data types, which can be serialized as fields in a tuple.

A tuple is dynamically typed, that is, you just need to define the names of the fields in a tuple and not their data type. The choice of dynamic typing helps to simplify the API and makes it easy to use. Also, since a processing unit in Storm can process multiple types of tuples, it’s not practical to declare field types.

Each of the fields in a tuple can be accessed by its name getValueByField(String) or its positional index getValue(int) in the tuple. Tuples also provide convenient methods such as getIntegerByField(String) that save you from typecasting the objects. For example, if you have a Fraction(numerator, denominator) tuple,representing fractional numbers, then you can get the value of the numerator by
either using getIntegerByField(“numerator”) or getInteger(0).

Definition of a Storm topology
In Storm terminology, a topology is an abstraction that defines the graph of the computation. You create a Storm topology and deploy it on a Storm cluster to process the data. A topology can be represented by a direct acyclic graph, where each node does some kind of processing and forwards it to the next node(s) in the flow.

The following is a sample Storm topology:


The following are the components of a Storm topology:

Stream: The key abstraction in Storm is that of a stream. A stream is an unbounded sequence of tuples that can be processed in parallel by Storm.Each stream can be processed by a single or multiple types of bolts. Each stream in a Storm application is given an ID and the bolts can produce and consume tuples from these streams on the basis of their ID. Each stream also has an associated schema for the tuples that will flow through it.

Spout: A spout is the source of tuples in a Storm topology. It is responsible for reading or listening to data from an external source, for example,by reading from a logfile or listening for new messages in a queue and publishing them—emitting, in Storm terminology—into streams. A spout can emit multiple streams, each of different schemas. For example, it can read 10-field records from a logfile and emit them as different streams of 7-tuples and 4-tuples each. The backtype.storm.spout.ISpout interface is the interface used to define spouts. If you are writing your topology in Java, then you should use backtype.storm.topology.IRichSpout as it declares methods to use the TopologyBuilder API. Whenever a spout emits a tuple, Storm tracks all the tuples generated while processing this tuple, and when the execution of all the tuples in the graph of this source tuple is complete, it will send back an acknowledgement to the spout. This tracking happens only if a message ID was provided while emitting the tuple. If null was used as message ID, this tracking will not happen.

A tuple-processing timeout can also be defined for a topology, and if a tuple is not processed within the specified timeout, a fail message will be sent back to the spout. Again, this will happen only if you define a message ID. A small performance gain can be extracted out of Storm at the risk of some data loss by disabling the message acknowledgements, which can be done by skipping the message ID while emitting tuples.

The important methods of spout are:

nextTuple(): This method is called by Storm to get the next tuple from the input source. Inside this method, you will have the logic of reading data from the external sources and emitting them to an instance of backtype.storm.spout.ISpoutOutputCollector.The schema for streams can be declared by using the declareStream method of backtype.storm.topology.OutputFieldsDeclarer. If a spout wants to emit data to more than one stream, it can declare multiple streams using the declareStream method and specify a stream ID while emitting the tuple. If there are no more tuples to emit at the moment, this method would not be blocked. Also, if this method does not emit a tuple, then Storm will wait for 1 millisecond before calling it again. This waiting time can be configured using the setting.

ack(Object msgId): This method is invoked by Storm when the tuple with the given message ID is completely processed by the topology. At this point, the user should mark the message as processed and do the required cleaning up such as removing the message from the message queue so that it does not get processed again.

fail(Object msgId): This method is invoked by Storm when it identifies that the tuple with the given message ID has not been processed successfully or has timed out of the configured interval. In such scenarios, the user should do the required processing so that the messages can be emitted again by the nextTuple method.A common way to do this is to put the message back in the incoming message queue.

open(): This method is called only once—when the spout is initialized.If it is required to connect to an external source for the input data,define the logic to connect to the external source in the open method, and then keep fetching the data from this external source in the nextTuple method to emit it further.Another point to note while writing your spout is that none of the methods should be blocking, as Storm calls all the methods in the same thread. Every spout has an internal buffer to keep track of the status of the tuples emitted so far. The spout will keep the tuples in this buffer until they are either acknowledged or failed, calling the ack or fail method respectively. Storm will call the nextTuple method only when this buffer is not full.

Bolt: A bolt is the processing powerhouse of a Storm topology and is responsible for transforming a stream. Ideally, each bolt in the topology should be doing a simple transformation of the tuples, and many such bolts can coordinate with each other to exhibit a complex transformation.

The backtype.storm.task.IBolt interface is preferably used to define bolts, and if a topology is written in Java, you should use the backtype.storm.topology.IRichBolt interface. A bolt can subscribe to multiple streams of other components—either spouts or other bolts—in the topology and similarly can emit output to multiple streams. Output streams can be declared using the declareStream method of backtype.storm.topology.OutputFieldsDeclarer.

The important methods of a bolt are:

execute(Tuple input): This method is executed for each tuple that comes through the subscribed input streams. In this method,you can do whatever processing is required for the tuple and then produce the output either in the form of emitting more tuples to the declared output streams or other things such as persisting the results in a database.

You are not required to process the tuple as soon as this method is called, and the tuples can be held until required. For example, while joining two streams, when a tuple arrives, you can hold it until its counterpart also comes, and then you can emit the joined tuple.The metadata associated with the tuple can be retrieved by the various methods defined in the Tuple interface. If a message ID is associated with a tuple, the execute method must publish an ack or fail event using OutputCollector for the bolt or else Storm will not know whether the tuple was processed successfully or not. The backtype.storm.topology.IBasicBolt interface is a convenient interface that sends an acknowledgement automatically after the completion of the execute method. In the case that a fail event is to be sent, this method should throw backtype.storm.topology.FailedException.

prepare(Map stormConf, TopologyContext context,OutputCollector collector): A bolt can be executed by multiple workers in a Storm topology. The instance of a bolt is created on the client machine and then serialized and submitted to Nimbus. When Nimbus creates the worker instances for the topology, it sends this serialized bolt to the workers. The work will desterilize the bolt and call the prepare method. In this method, you should make sure the bolt is properly configured to execute tuples now. Any state that you want to maintain can be stored as instance variables for the bolt that can be serialized/deserialized later.

Storm Components

A Storm cluster follows a master-slave model where the master and slave processes are coordinated through ZooKeeper. The following are the components of a Storm cluster.

The Nimbus node is the master in a Storm cluster. It is responsible for distributing the application code across various worker nodes, assigning tasks to different machines, monitoring tasks for any failures, and restarting them as and when required.Nimbus is stateless and stores all of its data in ZooKeeper. There is a single Nimbus node in a Storm cluster. It is designed to be fail-fast, so when Nimbus dies, it can be restarted without having any effects on the already running tasks on the worker nodes. This is unlike Hadoop, where if the JobTracker dies, all the running jobs are left in an inconsistent state and need to be executed again.

Supervisor nodes

Supervisor nodes are the worker nodes in a Storm cluster. Each supervisor node runs a supervisor daemon that is responsible for creating, starting, and stopping worker processes to execute the tasks assigned to that node. Like Nimbus, a supervisor daemon is also fail-fast and stores all of its state in ZooKeeper so that it can be restarted
without any state loss. A single supervisor daemon normally handles multiple worker processes running on that machine.

The ZooKeeper cluster
In any distributed application, various processes need to coordinate with each other and share some configuration information. ZooKeeper is an application that provides all these services in a reliable manner. Being a distributed application, Storm also uses a ZooKeeper cluster to coordinate various processes. All of the states associated with
the cluster and the various tasks submitted to the Storm are stored in ZooKeeper. Nimbus and supervisor nodes do not communicate directly with each other but through ZooKeeper. As all data is stored in ZooKeeper, both Nimbus and the supervisor daemons can be killed abruptly without adversely affecting the cluster.




Apache Storm Introduction

Apache Storm has emerged as the platform of choice for the industry leaders to develop such distributed, real-time, data processing platforms.

Stream processing: Storm is used to process a stream of data and update a variety of databases in real time. This processing occurs in real time and the processing speed needs to match the input data speed.

Continuous computation: Storm can do continuous computation on data streams and stream the results into clients in real time. This might require processing each message as it comes or creating small batches over a little time. An example of continuous computation is streaming trending topics on Twitter into browsers.

Distributed RPC: Storm can parallelize an intense query so that you can compute it in real time.

Real-time analytics: Storm can analyze and respond to data that comes from different data sources as they happen in real time.

Features of Storm

Fast: Storm has been reported to process up to 1 million tuples per second per node.

Horizontally scalable: Being fast is a necessary feature to build a high volume/velocity data processing platform, but a single-node will have an upper limit on the number of events that it can process per second. A node represents a single machine in your setup that execute Storm applications. Storm, being a distributed platform, allows you to add more nodes to your Storm cluster and increase the processing capacity of your application. Also, it is linearly scalable, which means that you can double the processing capacity by doubling the nodes.

Fault tolerant: Units of work are executed by worker processes in a Storm cluster. When a worker dies, Storm will restart that worker, and if the node on which the worker is running dies, Storm will restart that worker on some other node in the cluster.

Guaranteed data processing: Storm provides strong guarantees that each message passed on to it to process will be processed at least once. In the event of failures, Storm will replay the lost tuples. Also, it can be configured so that each message will be processed only once.

Easy to operate: Storm is simple to deploy and manage. Once the cluster is deployed, it requires little maintenance.

Programming language agnostic: Even though the Storm platform runs on Java Virtual Machine, the applications that run over it can be written in any programming language that can read and write to standard input and output streams.