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A Guide to Threading in Python

In Computer Science, a thread is defined as the smallest unit of execution with the independent set of instructions. In simple terms, it is a separate flow of instruction. The advantage of threading is that it allows a user to run different parts of the program in a concurrent manner and make the design of the program simpler.  During threading, different processors run on a single program and each one of them performs an independent task simultaneously. However, if you want to perform multiprocessing, then you need to execute your code in a different language or use the multiprocessing module. In the CPython implementation of Python, interactions are made with the Global Interpreter Lock (GIL) which always limits one Python thread to run at a time. In threading, good candidates are considered those who spend much of their time waiting for external events. These are all true in the case when the code is written in Python. However, in the case of threading in C other than Python, they have the ability to release GIL and run in a concurrent manner.  Basically, building up your program to use threading will help to make the design clearer and easier to reason about. Let us see how to start a thread in Python. How to Start a Thread? The Python Standard Library contains a module named threading which comprises all the basics needed to understand the process of threading better. By this module, you can easily encapsulate threads and provide a clean interface to work with them.  If you want to start a thread, first you need to create a Thread instance and then implement .start(): import logging import threading import time def thread_func(name): logging.info("Thread %s: starting...",name) time.sleep(2) logging.info("Thread %s: finishing...",name) if __name__ == "__main__": format = "%(asctime)s: %(message)s" logging.basicConfig(format=format,level=logging.INFO, datefmt="%H:%M:%S") logging.info("Main    : before creating thread...") t = threading.Thread(target=thread_function,args=(1,)) logging.info("Main    : before running thread...") t.start() logging.info("Main    : wait for the thread to finish...") # t.join() logging.info("Main    : all done...")It is observable that the main section is responsible for creating and initiating the thread: t = threading.Thread(target=thread_function, args=(1,)) t.start()When a Thread is created, a function and a list of arguments to that function are passed. In the example above, thread_function() is being run and 1 is passed as an argument. The function, however, simply logs messages with a time.sleep() in between them.The output of the code above  will be displayed as:$ ./single_thread.py Main    : before creating thread... Main    : before running thread... Thread 1: starting... Main    : wait for the thread to finish... Main    : all done... Thread 1: finishing...The Thread gets finished only after the Main section of the code.Daemon ThreadsIn terms of computer science, a daemon is a computer program that runs as a background process. It is basically a thread that runs in the background without worrying about shutting it down. A daemon thread will shut down immediately when the program terminates. However, if a program is running non-Daemon threads, then the program will wait for those threads to complete before it ends.  In the example code above, you might have noticed that there is a pause of about 2 seconds after the main function has printed the all done message and before the thread is finished. This is because Python waits for the non-daemonic thread to complete. threading.shutdown() goes through all of the running threads and calls .join on every non-daemonic thread. You can understand it better if you look at the source of Python threading.  Let us the example we did before with a daemon thread by adding the daemon=True flag:t = threading.Thread(target=thread_function, args=(1,),daemon=True)Now if you run your program, the output will be as follows: $ ./daemon_thread.py  Main    : before creating thread...  Main    : before running thread...  Thread 1: starting...  Main    : wait for the thread to finish...  Main    : all done... The basic difference here is that the final line of output is missing. This is because when the main function reached the end of code, the daemon was killed.Multiple ThreadingThe process of executing multiple threads in a parallel manner is called multithreading. It enhances the performance of the program and Python multithreading is quite easy to learn.Let us start understanding multithreading using the example we used earlier:import logging import threading import time def thread_func(name): logging.info("Thread %s: starting...", name) time.sleep(2) logging.info("Thread %s: finishing...", name) if __name__ == "__main__": format = "%(asctime)s: %(message)s" logging.basicConfig(format=format,level=logging.INFO, datefmt="%H:%M:%S")     multiple_threads = list() for index in range(3): logging.info("Main    : create and start thread %d...",index) t = threading.Thread(target=thread_function,args=(index,)) threads.append(x) t.start() for index, thread in enumerate(multiple_threads): logging.info("Main    : before joining thread %d...",index) thread.join() logging.info("Main    : thread %d done...",index)This code will work in the same way as it was in the process to start a thread. First, we need to create a Thread object and then call the .start() object. The program then keeps a list of Thread objects. It then waits for them using .join(). If we run this code multiple times, the output will be as below: $ ./multiple_threads.py Main    : create and start thread 0... Thread 0: starting... Main    : create and start thread 1... Thread 1: starting... Main    : create and start thread 2...  Thread 2: starting...  Main    : before joining thread 0...  Thread 2: finishing...  Thread 1: finishing...  Thread 0: finishing...  Main    : thread 0 done...  Main    : before joining thread 1...  Main    : thread 1 done...  Main    : before joining thread 2...  Main    : thread 2 done... The threads are sequenced in the opposite order in this example. This is because multithreading generates different orderings. The Thread x: finishing message informs when each of the thread is done. The thread order is determined by the operating system, so it is essential to know the algorithm design that uses the threading process.  A ThreadPool ExecutorUsing a ThreadpoolExecutor is an easier way to start up a group of threads. It is contained in the Python Standard Library in concurrent.futures. You can create it as a context manager using the help of with statement. It will help in managing and destructing the pool. Example to illustrate a ThreadpoolExecutor (only the main section): import concurrent.futures if __name__ == "__main__":      format = "%(asctime)s: %(message)s"      logging.basicConfig(format=format,level=logging.INFO, datefmt="%H:%M:%S") with concurrent.futures.ThreadPoolExecutor(max_workers=3) asexecutor: executor.map(thread_function,range(3))The code above creates a ThreadpoolExecutor and informs how many worker threads it needs in the pool and then .map() is used to iterate through a list of things. When the with block ends, .join() is used on each of the threads in the pool. It is recommended to use ThreadpoolExecutor whenever possible so that you never forget to .join() the threads.The output of the code will look as follows:$ ./executor.py  Thread 0: starting... Thread 1: starting... Thread 2: starting... Thread 1: finishing... Thread 0: finishing... Thread 2: finishing…Race Conditions When multiple threads try to access a shared piece of data or resource, race conditions occur. Race conditions produce results that are confusing for a user to understand and it occurs rarely and is very difficult to debug.Let us try to understand a race condition using a class with a false database:class FalseDatabase: def race(self): self.value = 0 def update(self,name): logging.info("Thread %s: starting update...",name) local_copy_value = self.value local_copy_value += 1 time.sleep(0.1) self.value = local_copy_value logging.info("Thread %s: finishing update...",name)The class FalseDatabase holds the shared data value on which the race condition will occur. The function race simply intializes .value to zero.  The work of .update() is to analyze a database, perform some computation and then rewrite a value to the database. However, reading from the database means just copying .value to a local variable. Computation means adding a single value and then .sleep() for a little bit and then the value is written back by copying the local value back to .value().The main section of FalseDatabase:if __name__ == "__main__": format = "%(asctime)s: %(message)s" logging.basicConfig(format=format, level=logging.INFO, datefmt="%H:%M:%S") dtb = FalseDatabase() logging.info("Testing update. Starting value is %d...",dtb.value) with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor: for index in range(2): executor.submit(dtb.update,index) logging.info("Testing update. Ending value is %d...", dtb.value)The programs create a ThreadPoolExecutor with two threads and calls .submit()and then runs database.update()..submit() contains two arguments: both positional and named arguments are passed to the function running in the thread: .submit(function, *args, **kwargs)The output will look like as follows: $ ./racecond.py Testing unlocked update... Starting value is 0... Thread 0: starting update... Thread 1: starting update... Thread 0: finishing update... Thread 1: finishing update... Testing unlocked update... Ending value is 1...One ThreadIn this section, we would be discussing how threads work in a simplified manner.  When the ThreadPoolExecutor is informed to run each thread, we are basically telling it to which function to run and what are the parameters to be passed: executor.submit(database.update, index). This will allow each thread in the pool to call the executor.submit(index). The database is a reference to the FalseDatabase object that was created in main function.Each of the threads will have a reference to the database and also a unique index value which will make the log statements readable. The thread contains its own version of all the data local to the function. This is called local_copy in case of .update(). This is an advantage that allows all the local variables to a function to be thread-safe.Two ThreadsIf we consider the race condition again, the two threads will run concurrently. They will each point to the same object database and will have their own version of local_copy. The database object will be the reason for the problems.  The program will start with Thread 1 running .update() and then the thread will call time.sleep() and allows other threads to take its place and start running. Now Thread 2 performs all the same operations just like Thread 1. It also copies database.value into its local_copy but database.value does not get updated.  Now when Thread 2 ends, the shared database.value still contains zero and both versions of local_copy have the value one. Finally, Thread 1 again wakes up and it terminates by saving its local_copy which gives a chance to Thread 2 to run. On the other hand,  Thread 2 is unaware of Thread 1 and the updated database.value.  Thread 2 also then stores its version of local_copy into database.value.  The race condition occurs here in the sense that Thread 1 and Thread 2 have interleaving access to a single shared object and they overwrite each other’s results. Race condition can also occur when one thread releases memory or closes a file handle before the work of another thread. Basic Synchronization in ThreadingYou can solve race conditions with the help of Lock. A Lock is an object that acts like a hall pass which will allow only one thread at a time to enter the read-modify-write section of the code. If any other thread wants to enter at the same time, it has to wait until the current owner of the Lock gives it up.  The basic functions are .acquire() and .release(). A thread will call my_lock.acquire() to get the Lock. However, this thread will have to wait if the Lock is held by another thread until it releases it. The Lock in Python also works as a context manager and can be used within a with statement and will be released automatically with the exit of with block. Let us take the previous FalseDatabase class and add Lock to it:class FalseDatabase: def race(self): self.value = 0 self._lock = threading.Lock() def locked_update(self, name): logging.info("Thread %s: starting update...",name) logging.debug("Thread %s about to lock...",name) with self._lock: logging.debug("Thread %s has lock...",name) local_copy = self.value local_copy += 1 time.sleep(0.1) self.value = local_copy logging.debug("Thread %s about to release lock...",name) logging.debug("Thread %s after release...",name) logging.info("Thread %s: finishing update...",name)._lock is a part of the threading.Lock() object and is initialized in the unlocked state and later released with the help of with statement. The output of the code above with logging set to warning level will be as follows: $ ./fixingracecondition.py Testing locked update. Starting value is 0. Thread 0: starting update... Thread 1: starting update... Thread 0: finishing update... Thread 1: finishing update... Testing locked update. Ending value is 2.The output of the code with full logging by setting the level to DEBUG:$ ./fixingracecondition.py Testing locked update. Starting value is 0. Thread 0: starting update... Thread 0 about to lock... Thread 0 has lock... Thread 1: starting update... Thread 1 about to lock... Thread 0 about to release lock... Thread 0 after release... Thread 0: finishing update... Thread 1 has lock... Thread 1 about to release lock... Thread 1 after release... Thread 1: finishing update... Testing locked update. Ending value is 2.The Lock provides a mutual exclusion between the threads.The Producer-Consumer Threading ProblemIn Computer Science, the Producer-Consumer Threading Problem is a classic example of a multi-process synchronization problem.  Consider a program that has to read messages and write them to disk. It will listen and accept messages as they coming in bursts and not at regular intervals. This part of the program is termed as the producer.  On the other hand, you need to write the message to the database once you have it. This database access is slow because of bursts of messages coming in. This part of the program is called the consumer.  A pipeline has to be created between the producer and consumer that will act as the changing part as you gather more knowledge about various synchronization objects.  Using LockThe basic design is a producer thread that will read from a false network and put the message into the pipeline: import random Sentinel = object() def producer(pipeline): """Pretend we're getting a message from the network.""" for index in range(10): msg = random.randint(1,101) logging.info("Producer got message: %s",msg) pipeline.set_msg(msg,"Producer") # Send a sentinel message to tell consumer we're done  pipeline.set_msg(SENTINEL,"Producer")The producer gets a random number between 1 and 100 and calls the .set_message() on the pipeline to send it to the consumer: def consumer(pipeline):     """Pretend we're saving a number in the database.""" msg = 0 while msg is not Sentinel: msg = pipeline.get_msg("Consumer") if msg is not Sentinel: logging.info("Consumer storing message: %s",msg)The consumer reads a message from the pipeline and displays the false database.The main section of the section is as follows:if __name__ == "__main__": format = "%(asctime)s: %(message)s" logging.basicConfig(format=format,level=logging.INFO, datefmt="%H:%M:%S") # logging.getLogger().setLevel(logging.DEBUG) pipeline = Pipeline() with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor: executor.submit(producer, pipeline) executor.submit(consumer, pipeline)Now let us see the code of Pipeline that will pass messages from the producer to consumer: class Pipeline:  """Class to allow a single element pipeline between producer and consumer."""  def pipeline_message(self):  self.msg = 0 self.producer_lock = threading.Lock() self.consumer_lock = threading.Lock() self.consumer_lock.acquire() def get_msg(self, name): logging.debug("%s:about to acquire getlock...",name) self.consumer_lock.acquire() logging.debug("%s:have getlock...",name) msg = self.msg logging.debug("%s:about to release setlock...",name) self.producer_lock.release() logging.debug("%s:setlock released...",name) return msg def set_msg(self, msg, name): logging.debug("%s:about to acquire setlock...",name) self.producer_lock.acquire() logging.debug("%s:have setlock...",name) self.msg=msg logging.debug("%s:about to release getlock...",name) self.consumer_lock.release() logging.debug("%s:getlock released...", name)The members of Pipeline are: .msg - It stores the message to pass..producer_lock - It is a threading.Lock object that does not allow access to the message by the producer..consumer_lock - It is a threading.Lock that does not allow to access the message by the consumer.The function pipeline_message initializes the three members and then calls .acquire() on the .consumer_lock. Now the producer has the allowance to add a message and the consumer has to wait until the message is present.  .get_msg calls .acquire on the consumer_lock and then the consumer copies the value in .msg and then calls .release() on the .producer_lock. After the lock is released, the producer can insert the message into the pipeline. Now the producer will call the .set_msg() and it will acquire the .producer_lock and set the .msg and then the lock is released and the consumer can read the value. The output of the code with the logging set to WARNING: $ ./producerconsumer_lock.py Producer got data 43  Producer got data 45  Consumer storing data: 43  Producer got data 86  Consumer storing data: 45  Producer got data 40  Consumer storing data: 86  Producer got data 62  Consumer storing data: 40  Producer got data 15  Consumer storing data: 62  Producer got data 16  Consumer storing data: 15  Producer got data 61  Consumer storing data: 16  Producer got data 73  Consumer storing data: 61  Producer got data 22  Consumer storing data: 73  Consumer storing data: 22 Objects in Threading Python consists of few more threading modules which can be handy to use in different cases. Some of which are discussed below. Semaphore A semaphore is a counter module with few unique properties. The first property is that its counting is atomic which means that the operating system will not swap the thread while incrementing or decrementing the counter. The internal counter increments when .release() is called and decremented when .acquire() is called.  The other property is that if a thread calls .acquire() while the counter is zero, then the thread will be blocked until another thread calls .release(). The main work of semaphores is to protect a resource having a limited capacity. It is used in cases where you have a pool of connections and you want to limit the size of the pool to a particular number. Timer The Timer module is used to schedule a function that is to be called after a certain amount of time has passed. You need to pass a number of seconds to wait and a function to call to create a Timer:t = threading.Timer(20.0,my_timer_function) The timer is started by calling the .start function and you can stop it by calling  .cancel(). A Timer prompts for action after a particular amount of time.  Summary In this article we have covered most of the topics associated with threading in Python. We have discussed:What is Threading Creating and starting a Thread Multiple threading Race Conditions and how to prevent them Threading Objects We hope you are now well aware of Python threading and how to build threaded programs and the problems they approach to solve. You have also gained knowledge of the problems that arise when writing and debugging different types of threaded programs.  For more information about threading and its uses in the real-world applications, you may refer to the official documentation of Python threading.  To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course. 

A Guide to Threading in Python

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A Guide to Threading in Python

In Computer Science, a thread is defined as the smallest unit of execution with the independent set of instructions. In simple terms, it is a separate flow of instruction. The advantage of threading is that it allows a user to run different parts of the program in a concurrent manner and make the design of the program simpler.  

During threading, different processors run on a single program and each one of them performs an independent task simultaneously. However, if you want to perform multiprocessing, then you need to execute your code in a different language or use the multiprocessing module. 

In the CPython implementation of Python, interactions are made with the Global Interpreter Lock (GIL) which always limits one Python thread to run at a time. In threading, good candidates are considered those who spend much of their time waiting for external events. These are all true in the case when the code is written in Python. However, in the case of threading in C other than Python, they have the ability to release GIL and run in a concurrent manner.  

Basically, building up your program to use threading will help to make the design clearer and easier to reason about. Let us see how to start a thread in Python. 

How to Start a Thread? 

The Python Standard Library contains a module named threading which comprises all the basics needed to understand the process of threading better. By this module, you can easily encapsulate threads and provide a clean interface to work with them.  

If you want to start a thread, first you need to create a Thread instance and then implement .start()

import logging
import threading
import time

def thread_func(name):
     logging.info("Thread %s: starting...",name)
     time.sleep(2)
     logging.info("Thread %s: finishing...",name)

if __name__ == "__main__":
     format = "%(asctime)s: %(message)s"
     logging.basicConfig(format=format,level=logging.INFO,
                         datefmt="%H:%M:%S")
     logging.info("Main    : before creating thread...")
     t = threading.Thread(target=thread_function,args=(1,))
     logging.info("Main    : before running thread...")
      t.start()
     logging.info("Main    : wait for the thread to finish...")
     # t.join()
     logging.info("Main    : all done...")

It is observable that the main section is responsible for creating and initiating the thread: 

t = threading.Thread(target=thread_function, args=(1,))
t.start()

When a Thread is created, a function and a list of arguments to that function are passed. In the example above, thread_function() is being run and 1 is passed as an argument. The function, however, simply logs messages with a time.sleep() in between them.

The output of the code above  will be displayed as:

$ ./single_thread.py
Main    : before creating thread...
Main    : before running thread...
Thread 1: starting...
Main    : wait for the thread to finish...
Main    : all done...
Thread 1: finishing...

The Thread gets finished only after the Main section of the code.

Daemon Threads

In terms of computer science, a daemon is a computer program that runs as a background process. It is basically a thread that runs in the background without worrying about shutting it down. A daemon thread will shut down immediately when the program terminates. However, if a program is running non-Daemon threads, then the program will wait for those threads to complete before it ends.  

In the example code above, you might have noticed that there is a pause of about 2 seconds after the main function has printed the all done message and before the thread is finished. This is because Python waits for the non-daemonic thread to complete. 

threading.shutdown() goes through all of the running threads and calls .join on every non-daemonic thread. You can understand it better if you look at the source of Python threading.  

Let us the example we did before with a daemon thread by adding the daemon=True flag:

t = threading.Thread(target=thread_function, args=(1,),daemon=True)

Now if you run your program, the output will be as follows: 

$ ./daemon_thread.py 
Main    : before creating thread... 
Main    : before running thread... 
Thread 1: starting... 
Main    : wait for the thread to finish... 
Main    : all done... 

The basic difference here is that the final line of output is missing. This is because when the main function reached the end of code, the daemon was killed.

Multiple Threading

Multiple Threading Process in Python

The process of executing multiple threads in a parallel manner is called multithreading. It enhances the performance of the program and Python multithreading is quite easy to learn.

Let us start understanding multithreading using the example we used earlier:

import logging
import threading
import time

def thread_func(name):
    logging.info("Thread %s: starting...", name)
    time.sleep(2)
    logging.info("Thread %s: finishing...", name)

if __name__ == "__main__":
    format = "%(asctime)s: %(message)s"
    logging.basicConfig(format=format,level=logging.INFO,
                        datefmt="%H:%M:%S")

    multiple_threads = list()
    for index in range(3):
            logging.info("Main    : create and start thread %d...",index)
        t = threading.Thread(target=thread_function,args=(index,))
        threads.append(x)
        t.start()

    for index, thread in enumerate(multiple_threads):
        logging.info("Main    : before joining thread %d...",index)
        thread.join()
        logging.info("Main    : thread %d done...",index)

This code will work in the same way as it was in the process to start a thread. First, we need to create a Thread object and then call the .start() object. The program then keeps a list of Thread objects. It then waits for them using .join(). If we run this code multiple times, the output will be as below: 

$ ./multiple_threads.py
Main    : create and start thread 0...
Thread 0: starting...
Main    : create and start thread 1...
Thread 1: starting...
Main    : create and start thread 2... 
Thread 2: starting... 
Main    : before joining thread 0... 
Thread 2: finishing... 
Thread 1: finishing... 
Thread 0: finishing... 
Main    : thread 0 done... 
Main    : before joining thread 1... 
Main    : thread 1 done... 
Main    : before joining thread 2... 
Main    : thread 2 done... 

The threads are sequenced in the opposite order in this example. This is because multithreading generates different orderings. The Thread x: finishing message informs when each of the thread is done. The thread order is determined by the operating system, so it is essential to know the algorithm design that uses the threading process.  

A ThreadPool Executor

Using a ThreadpoolExecutor is an easier way to start up a group of threads. It is contained in the Python Standard Library in concurrent.futures. You can create it as a context manager using the help of with statement. It will help in managing and destructing the pool. 

Example to illustrate a ThreadpoolExecutor (only the main section): 

import concurrent.futures

if __name__ == "__main__":
     format = "%(asctime)s: %(message)s" 
     logging.basicConfig(format=format,level=logging.INFO,
                         datefmt="%H:%M:%S")
        with concurrent.futures.ThreadPoolExecutor(max_workers=3) asexecutor:
        executor.map(thread_function,range(3))

The code above creates a ThreadpoolExecutor and informs how many worker threads it needs in the pool and then .map() is used to iterate through a list of things. When the with block ends, .join() is used on each of the threads in the pool. It is recommended to use ThreadpoolExecutor whenever possible so that you never forget to .join() the threads.

The output of the code will look as follows:

$ ./executor.py 
Thread 0: starting...
Thread 1: starting...
Thread 2: starting...
Thread 1: finishing...
Thread 0: finishing...
Thread 2: finishing…

Race Conditions 

When multiple threads try to access a shared piece of data or resource, race conditions occur. Race conditions produce results that are confusing for a user to understand and it occurs rarely and is very difficult to debug.

Let us try to understand a race condition using a class with a false database:

class FalseDatabase:
    def race(self):
        self.value = 0

    def update(self,name):
        logging.info("Thread %s: starting update...",name)
        local_copy_value = self.value
        local_copy_value += 1
        time.sleep(0.1)
        self.value = local_copy_value
        logging.info("Thread %s: finishing update...",name)

The class FalseDatabase holds the shared data value on which the race condition will occur. The function race simply intializes .value to zero.  

The work of .update() is to analyze a database, perform some computation and then rewrite a value to the database. However, reading from the database means just copying .value to a local variable. Computation means adding a single value and then .sleep() for a little bit and then the value is written back by copying the local value back to .value().

The main section of FalseDatabase:

if __name__ == "__main__":
    format = "%(asctime)s: %(message)s"
    logging.basicConfig(format=format, level=logging.INFO,
                        datefmt="%H:%M:%S")
    dtb = FalseDatabase()
          logging.info("Testing update. Starting value is %d...",dtb.value)
          with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
          for index in range(2):
              executor.submit(dtb.update,index)
    logging.info("Testing update. Ending value is %d...", dtb.value)

The programs create a ThreadPoolExecutor with two threads and calls .submit()and then runs database.update().

.submit() contains two arguments: both positional and named arguments are passed to the function running in the thread: 

.submit(function, *args, **kwargs)

The output will look like as follows: 

$ ./racecond.py
Testing unlocked update... Starting value is 0...
Thread 0: starting update...
Thread 1: starting update...
Thread 0: finishing update...
Thread 1: finishing update...
Testing unlocked update... Ending value is 1...

One Thread

In this section, we would be discussing how threads work in a simplified manner.  

When the ThreadPoolExecutor is informed to run each thread, we are basically telling it to which function to run and what are the parameters to be passed: executor.submit(database.update, index)This will allow each thread in the pool to call the executor.submit(index). The database is a reference to the FalseDatabase object that was created in main function.

Each of the threads will have a reference to the database and also a unique index value which will make the log statements readable. The thread contains its own version of all the data local to the function. This is called local_copy in case of .update(). This is an advantage that allows all the local variables to a function to be thread-safe.

Two Threads

If we consider the race condition again, the two threads will run concurrently. They will each point to the same object database and will have their own version of local_copy. The database object will be the reason for the problems.  

The program will start with Thread 1 running .update() and then the thread will call time.sleep() and allows other threads to take its place and start running. Now Thread 2 performs all the same operations just like Thread 1. It also copies database.value into its local_copy but database.value does not get updated.  

Now when Thread 2 ends, the shared database.value still contains zero and both versions of local_copy have the value one. Finally, Thread 1 again wakes up and it terminates by saving its local_copy which gives a chance to Thread 2 to run. On the other hand,  Thread 2 is unaware of Thread 1 and the updated database.value.  Thread 2 also then stores its version of local_copy into database.value.  

The race condition occurs here in the sense that Thread 1 and Thread 2 have interleaving access to a single shared object and they overwrite each other’s results. Race condition can also occur when one thread releases memory or closes a file handle before the work of another thread. 

Basic Synchronization in Threading

You can solve race conditions with the help of Lock. A Lock is an object that acts like a hall pass which will allow only one thread at a time to enter the read-modify-write section of the code. If any other thread wants to enter at the same time, it has to wait until the current owner of the Lock gives it up.  

The basic functions are .acquire() and .release(). A thread will call my_lock.acquire() to get the Lock. However, this thread will have to wait if the Lock is held by another thread until it releases it. 

The Lock in Python also works as a context manager and can be used within a with statement and will be released automatically with the exit of with block. Let us take the previous FalseDatabase class and add Lock to it:

class FalseDatabase:
    def race(self):
        self.value = 0
        self._lock = threading.Lock()

    def locked_update(self, name):
        logging.info("Thread %s: starting update...",name)
        logging.debug("Thread %s about to lock...",name)
        with self._lock:
            logging.debug("Thread %s has lock...",name)
            local_copy = self.value
            local_copy += 1
            time.sleep(0.1)
            self.value = local_copy
            logging.debug("Thread %s about to release lock...",name)
       logging.debug("Thread %s after release...",name)
       logging.info("Thread %s: finishing update...",name)

._lock is a part of the threading.Lock() object and is initialized in the unlocked state and later released with the help of with statement. 

The output of the code above with logging set to warning level will be as follows: 

$ ./fixingracecondition.py
Testing locked update. Starting value is 0.
Thread 0: starting update...
Thread 1: starting update...
Thread 0: finishing update...
Thread 1: finishing update...
Testing locked update. Ending value is 2.

The output of the code with full logging by setting the level to DEBUG:

$ ./fixingracecondition.py
Testing locked update. Starting value is 0.
Thread 0: starting update...
Thread 0 about to lock...
Thread 0 has lock...
Thread 1: starting update...
Thread 1 about to lock...
Thread 0 about to release lock...
Thread 0 after release...
Thread 0: finishing update...
Thread 1 has lock...
Thread 1 about to release lock...
Thread 1 after release...
Thread 1: finishing update...
Testing locked update. Ending value is 2.

The Lock provides a mutual exclusion between the threads.

The Producer-Consumer Threading Problem

In Computer Science, the Producer-Consumer Threading Problem is a classic example of a multi-process synchronization problem.  

Consider a program that has to read messages and write them to disk. It will listen and accept messages as they coming in bursts and not at regular intervals. This part of the program is termed as the producer.  

On the other hand, you need to write the message to the database once you have it. This database access is slow because of bursts of messages coming in. This part of the program is called the consumer.  

A pipeline has to be created between the producer and consumer that will act as the changing part as you gather more knowledge about various synchronization objects.  

Using Lock

The basic design is a producer thread that will read from a false network and put the message into the pipeline

import random
Sentinel = object()

def producer(pipeline):
    """Pretend we're getting a message from the network."""
    for index in range(10):
        msg = random.randint(1,101)
        logging.info("Producer got message: %s",msg)
        pipeline.set_msg(msg,"Producer")

    # Send a sentinel message to tell consumer we're done 
    pipeline.set_msg(SENTINEL,"Producer")

The producer gets a random number between 1 and 100 and calls the .set_message() on the pipeline to send it to the consumer

def consumer(pipeline):
    """Pretend we're saving a number in the database."""
    msg = 0
    while msg is not Sentinel:
       msg = pipeline.get_msg("Consumer")
       if msg is not Sentinel:
           logging.info("Consumer storing message: %s",msg)

The consumer reads a message from the pipeline and displays the false database.

The main section of the section is as follows:

if __name__ == "__main__":
    format = "%(asctime)s: %(message)s"
    logging.basicConfig(format=format,level=logging.INFO,
                        datefmt="%H:%M:%S")
    # logging.getLogger().setLevel(logging.DEBUG)

    pipeline = Pipeline()
         with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
         executor.submit(producer, pipeline)
         executor.submit(consumer, pipeline)

Now let us see the code of Pipeline that will pass messages from the producer to consumer

class Pipeline:
    """Class to allow a single element pipeline between producer and consumer.""" 
   def pipeline_message(self): 
     self.msg = 0
     self.producer_lock = threading.Lock()
     self.consumer_lock = threading.Lock()
     self.consumer_lock.acquire()

  def get_msg(self, name):
      logging.debug("%s:about to acquire getlock...",name)
      self.consumer_lock.acquire()
      logging.debug("%s:have getlock...",name)
      msg = self.msg
      logging.debug("%s:about to release setlock...",name)
      self.producer_lock.release()
      logging.debug("%s:setlock released...",name)
      return msg

  def set_msg(self, msg, name):
      logging.debug("%s:about to acquire setlock...",name)
      self.producer_lock.acquire()
      logging.debug("%s:have setlock...",name)
      self.msg=msg
      logging.debug("%s:about to release getlock...",name)
      self.consumer_lock.release()
      logging.debug("%s:getlock released...", name)

The members of Pipeline are: 

  • .msg - It stores the message to pass.
  • .producer_lock - It is a threading.Lock object that does not allow access to the message by the producer.
  • .consumer_lock - It is a threading.Lock that does not allow to access the message by the consumer.

The function pipeline_message initializes the three members and then calls .acquire() on the .consumer_lock. Now the producer has the allowance to add a message and the consumer has to wait until the message is present.  

.get_msg calls .acquire on the consumer_lock and then the consumer copies the value in .msg and then calls .release() on the .producer_lock. After the lock is released, the producer can insert the message into the pipeline. Now the producer will call the .set_msg() and it will acquire the .producer_lock and set the .msg and then the lock is released and the consumer can read the value. 

The output of the code with the logging set to WARNING

$ ./producerconsumer_lock.py
Producer got data 43 
Producer got data 45 
Consumer storing data: 43 
Producer got data 86 
Consumer storing data: 45 
Producer got data 40 
Consumer storing data: 86 
Producer got data 62 
Consumer storing data: 40 
Producer got data 15 
Consumer storing data: 62 
Producer got data 16 
Consumer storing data: 15 
Producer got data 61 
Consumer storing data: 16 
Producer got data 73 
Consumer storing data: 61 
Producer got data 22 
Consumer storing data: 73 
Consumer storing data: 22 

Objects in Threading 

Python consists of few more threading modules which can be handy to use in different cases. Some of which are discussed below. 

Semaphore 

A semaphore is a counter module with few unique properties. The first property is that its counting is atomic which means that the operating system will not swap the thread while incrementing or decrementing the counter. The internal counter increments when .release() is called and decremented when .acquire() is called.  

The other property is that if a thread calls .acquire() while the counter is zero, then the thread will be blocked until another thread calls .release()

The main work of semaphores is to protect a resource having a limited capacity. It is used in cases where you have a pool of connections and you want to limit the size of the pool to a particular number. 

Timer 

The Timer module is used to schedule a function that is to be called after a certain amount of time has passed. You need to pass a number of seconds to wait and a function to call to create a Timer:

t = threading.Timer(20.0,my_timer_function) 

The timer is started by calling the .start function and you can stop it by calling  .cancel(). A Timer prompts for action after a particular amount of time.  

Summary 

In this article we have covered most of the topics associated with threading in Python. We have discussed:

  • What is Threading 
  • Creating and starting a Thread 
  • Multiple threading 
  • Race Conditions and how to prevent them 
  • Threading Objects 

We hope you are now well aware of Python threading and how to build threaded programs and the problems they approach to solve. You have also gained knowledge of the problems that arise when writing and debugging different types of threaded programs.  

For more information about threading and its uses in the real-world applications, you may refer to the official documentation of Python threading.  To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course

Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

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Top 12 Python Packages for Machine Learning

Lovers of vintage movies would have definitely heard of the Monty Python series. The programming language that it inspired continues to remain among the most popular languages. Guess why Python has consistently topped the charts of the most popular programming languages? Because of its rich environment of libraries and tools, its easy code readability and the fact that it is so easy to pick up.  You name the domain, and you will get Python libraries available, to help you out in solving problems. Right from Artificial Intelligence, Data Science, Machine Learning, Image Processing, Speech Recognition, Computer Vision and more, Python has numerous uses. These libraries and frameworks are open source and can be easily integrated with the development environment that one has.These software frameworks, the platforms which provides necessary libraries and code components, are backbones for devloping applications. Read on to see which are the top ML frameworks and libraries in Python.1. Numpy As the name implies, this is the library which supports numerical calculations and tasks. It supports array operations and basic mathematical functions on the array and other data types of Python. The basic data type of this library is ndArray object.   Numpy has many advantagesThe base data structure is N –Dimensional array. Rich functions to handle the N-dimensional array effectively. Supports integration of C, C++ and other language code fragments. Supports many functions related to linear algebra, random numbers, transforms, statistics etc. DisadvantagesNo GPU and TPU support. Cannot automatically calculate the derivatives which is required in all ML algorithms. Numpy performance goes down when high complex calculations are required. 2. PandasThis is the most useful library for data preprocessing and preparing the data for the Machine Learning algorithms. The data from various files like CSV, Excel, Data etc. can be easily read using Pandas. The data is available in a spreadsheet like area, which makes processing easy. There are three basic data structures at the core of Pandas library: Series - One-dimensional array like object containing data and label (or index). Dataframe - Spreadsheet-like data structure containing an order collection of columns. It has both a row and column index. Panel – Collection of dataframes but rarely used data structure. AdvantagesStructured data can be read easily. Great tool for handling of data. Strong functions for manipulation and preprocessing of data. Data Exploration functions help in better understanding data. Data preprocessing capabilities help in making data ready for the application of ML algorithms. Basic Plotting functions are provided for visualization of data.  Datasets can be easily joined or merged. The functions of Pandas are optimized for large datasets. DisadvantagesGetting to know the Pandas functionalities is time consuming. The syntax is complex when multiple operations are required. Support for 3D metrics is poor. Proper documentation is not available for study. 3. MatplotlibMatplotlib is an important Python library which helps in data visualization. Understanding the data is very important for a data scientist before devising any machine learning based model. This library helps in understanding the data in a visual way. Data can be visualized using various graphical methods like line graph, bar graph, pie chart etc. This is a 2D visualization library with numerous ways of visualizing data. Image SourceAdvantagesSimple and easy to learn for beginners. Integrated with Pandas for visualization of data in effective way. Various plots are provided for better understanding of data like Bar Chart, Stacked Bar chart, Pie chart, Scatter Plot etc. Forms a base for many advanced plotting libraries. Supports storing of the various graphs as images so that they can be integrated with other applications. Can plot timeseries data (with date) very easily. DisadvantagesComplex Syntax for plotting simple graphs. The code becomes lengthy and complex for visualizations. Support for plotting of categorial data is not provided. It is a 2D visualization library. When multiple fields are required to be plotted and visualized effectively, matplotlib code can become lengthy. Managing multiple figures is difficult. 4. Seaborn Visualizations are made simpler and more advanced with the help of Seaborn library. The base for Seaborn is Matplotlib. It is a boon for programmers as statistical visualizations are simplified. Image sourceAdvantagesBest high-level interface for drawing statistical graphics. Provides support for plotting of categorial data effectively. The library provides default themes and many visualization patterns. Multiple figures are automatically created. The syntax is very simple and compact. There are many methods to integrate with Pandas dataframe, making this library most useful for visualization. DisadvantagesMemory issues due to creation of multiple figures. Less customizable and flexible as compared to Matplotlib. Scalability issues. 5. Scipy   Scipy is a Scientific Python library based on Numpy. It has functions which are best suitable for Mathematics, Science and Engineering. Many libraries are provided for Image and Signal Processing, Fourier Transform, Linear Algebra, Integration and Optimization. The functions are useful for ML algorithms and programs. AdvantagesThe base library is Numpy. Many ML related functions are provided like Linear Algebra, Optimization, Compressed Sparce Data Structure etc. Useful Linear Algebra functions are available which are required for implementation of ML related algorithms. The functions can be applied with Pandas Dataframe directly. DisadvantagesComplex functions are available and domain knowledge is needed to understand and implement these functions. There are performance issues when data size increases. Many other effective alternative libraries are available with the needed functionality. 6. Scikit-Learn Scikit-Learn is a useful open access library for use to Python developers. It is an extensive and popular library with many Machine Learning Supervised and Unsupervised algorithms implemented. These algorithms can be fine-tuned with the help of hyperparameters. This library contains many useful functions for preprocessing of data, useful metrics to measure performance of algorithms and optimization techniques.  AdvantagesIt is a general Machine Learning library built on top of Numpy, Pandas and Matplotlib. Simple to understand and use even for novice programmers. Useful Machine Learning Algorithms, both Supervised and Unsupervised, are implemented. Popular library for doing Machine Learning related tasks. Rich in Data Preprocessing and Data Sampling functions and techniques. Plethora of evaluation measures implemented to track the performance of algorithms. Very effective for quick coding and building Machine Learning Models. DisadvantagesScikit learn, as is based on Numpy, requires additional support to run on GTP and TPU Performance is an issue with size of data. Best suitable for basic Machine Learning applications. This library may be useful if one wants to write easy code, but it’s not the best choice for more detailed learning. 7. NLTK Natural Language processing is a great field of study for developers who like to research and challenge themselves. This library provides a base for Natural Language processing by providing simple functionalities to work with and understand languages.AdvantagesVery simple to use for processing natural language data. Many basic functionalities like tokenizing the words, removal of stop words, conversion to word vectors etc. are provided which forms the basis to start with natural language processing models. It is an amazing library to play with natural language using Python. It has more than 50 trained models and lexical resources like wordnet available for use. Rich discussion forums and many examples are available to discuss how to use this library effectively. DisadvantagesIt is based on string processing, which itself has many limitations. Slower as compared to other Natural Language processing libraries like Spacy.8. Keras Keras is a library written in Python for Neural Network programming. It offers a very simple interface to code the neural network and related algorithms. It is an incredibly popular library for Deep Learning algorithms, models and applications and can also be combined with various deep learning frameworks. It provides support for GPU and TPU computation of algorithms. The API provided is simple, same as Scikit-learn. Keras is totally based on Models and Graphs. A model has Input, output and intermediate layers to perform the various tasks as per requirement. Effective functionalities and models provided to code deep learning algorithms like Neural Network, Recurrent Neural Network, Long Short-Term memory, Autoencoders etc. Allows to create products easily supporting multiple backends Supports multi-platform use. Can be used with TensorFlow, can be used in browser using web based keras and provides native ML support for iPhone app development. 9. TensorFlow TensorFlow is the talk of the town because of its capabilities suitable for Machine Learning and Deep Learning models. It is one of the best, and most popular frameworks, adopted by companies around the world for Machine Learning and Deep Learning. Its support for Web as well as Mobile application coupled with Deep Learning models has made it popular among engineers and researchers. Many giants like IBM, Dropbox, Nvidia etc. use TensorFlow for creating and deploying Machine Learning Models. This library has many applications like image recognition, video analysis, speech recognition, Natural Language Processing, Recommendation System etc. TensorFlow lite and TensorFlow JS has made it more popular for web applications and Mobile Applications. Advantages Developed by Google, it is one of the best deep learning frameworks. Simple Machine Learning tasks are also supported in TensorFlow. Supports many famous libraries like scikit learn, Keras etc. which are part of TensorFlow. The basic unit is Tensor which is an n-dimensional array. The basic derivatives are inherently computed which helps in developing many Machine learning Models easily. The models developed are supported on CPT, TPU and GPU. Tensorboard is the effective tool for data visualization. Many other supported tools are available to facilitate Web Development, App Development and IoT Applications using Machine Learning. Disadvantages Understanding Tensor and computational graphs is tedious. Computational graphs make the code complex and sometimes face performance problems. 10. Pytorch A popular Python framework, Pytorch supports machine learning and deep learning algorithms and is a scientific computing framework. This is a framework which is widely used by Twitter, Google and Facebook. The library supports complex Tensor computations and is used to construct deep neural networks. AdvantagesThe power of Pytorch lies in construction of Deep Neural Networks. Rich functions and utilities are provided to construct and use Neural Networks. Powerful when it comes to creation of production ready models. It supports GPU operations with rich math-based library functions. Unlike Numpy, it provides the functions which calculates gradient of the function, useful for the construction of the neural network. Provides support for Gradient based optimization which helps in scaling up the models easily to large data. Disadvantages It is a complex framework, so learning is difficult. Documentation support for learning is not readily available. Scalability may be an issue as compared to TensorFlow. 11. Theano Theano is a library for evaluating and optimizing the mathematical computations. It is based on NumPy but provides support for both the GPU and CPU. AdvantagesIt is a fast computation library in Python. Uses native libraries like BIAS to turn the code in faster computation. Best suited to handle computations in Deep Learning algorithms. Industry standard for Deep Learning research and development. Disadvantages It is not very popular among researchers as it is one of the older frameworks. It is not as easy to use as TensorFlow.12. CNTK CNTK is Microsoft’s Cognitive Toolkit for the development of Deep Learning based models. It is a commercial distributed deep learning tool. AdvantagesIt is a distributed open-source deep learning framework. Popular models like Deep Neural Network, Convolutional Neural Network models can be combined easily to form new models. Provides interface with C, C++ and Java to include Machine Learning models. Can be used to build reinforcement learning models as wide functions are available. Can be used to develop GAN (Generative Adversarial Networks). Provides various ways to measure the performance of the models built. High accuracy parallel computation on Multiple GPU is provided. Disadvantages Proper documentation is not available. There is inadequate community support. ConclusionPython, being one of the most popular languages for the development of Machine Learning models, has a plethora of tools and frameworks available for use. The choice of tool depends on the developer’s experience as well as the type of application to be developed. Every tool has some strong points and some weaknesses, so one has to carefully choose the tool or framework for the development of Machine Learning based applications. The documentation and support available are also important criteria to be kept in mind while choosing the most appropriate tool. 
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Top 12 Python Packages for Machine Learning

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Python released in 1991 and within 5 to 6 years, this programming language become the most popular and widely used programming language in various disciplines. Today, companies use Python for GUI and CLI-based software development, web development (server-side), data science, machine learning, AI, robotics, drone systems, developing cyber-security tools, mathematics, system scripting, etc. PCAP is a professional Python certification credential that measures your competency in using the Python language to create code and your fundamental understanding of object-oriented programming.It comprises of topics likeBasic concepts of PythonOperators & data typesControl and EvaluationsModules and PackagesData AggregatesException HandlingStringsFunctions and ModulesObject-Oriented ProgrammingList Comprehensions, Lambdas, Closures, and I/O OperationsClasses, Objects, and ExceptionsDemand and Benefits: Having a Python certification verifies that the programmer or the aspirant has all the necessary and essential skills needed to become an expert Python developer. This certification also helps in getting an internship or entry-level jobs in different organizations. The average entry-level salary of a Python developer starts at around $100k per annum. With a few years of experience, the average salary hikes to $ 105k annually.Top companies and organizations hiring certified Python programmers are Bank of America, Atlassian, Google, Adobe, Apple, Cisco Systems, Intel, Lyft, IBM, etc.Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for beginners in Python programming. It has hands-on learning with 24 hours of instructor-led online lectures. Apart from that, the course has 100 hours of MCQs and three live projects.Who should take the Training (roles) for Certification: Any programmer, graduate, post graduate student, or computer science aspirant - who wants to pursue a career as a Python developer or  Python programmer can opt for this certification training. There is no other prerequisite to appear for this exam.Course fees for Certification:  $ 295Exam fee for certification: $ 295Retake fee for certification: If a candidate fails the exam, he/she has to wait for 15 days before being allowed to retake the exam for free. There is no limit to the number of times a candidate may retake an exam.4. MongoDB Certified Developer Associate ExamMongoDB is a NoSQL, document-based high-volume heterogeneous database system. Instead of having tables with rows and columns, MongoDB uses a collection of documents. It is a database development system that provides scalability and flexibility as per query requirements. Its document models are easy to implement for developers and can meet complex demands at scale.MongoDB created this MongoDB Certified Developer Associate Exam for individuals who require to verify their knowledge on fundamentals of designing and building applications using MongoDB. They recommend this certification for those who want to become software engineers and have a solid understanding of core MongoDB along with professional experience.It comprises of topics likeMongoDB BasicsCRUDIndexing and PerformanceThe MongoDB Aggregation FrameworkBasic Cluster AdministrationAggregation & ReplicationShardingMongoDB Performance  MongoDB for Python DevelopersMongoDB for Java Developers or MongoDB for JavaScript DevelopersData ModelingDemand and Benefits: Having a MongoDB Certified Developer Associate Exam certification verifies that the programmer or the aspirant has all the necessary and essential skills to become a NoSQL database expert. The MongoDB certification is inexpensive and in demand. The average salary for a software developer with MongoDB skills starts from $ 8200 per annum.Top companies and organizations hiring certified MongoDB developers are Accenture, Collabera, Leoforce LLC., Adobe, Trigent Software, Lyft, etc.Where to take Training for Certification: KnowledgeHut has a comprehensive course structure for those who want to learn MongoDB & Mongodb Administrator. It has 24+ hours of instructor-led online lectures and 80+ hours of hands-on with cloud labs. This self-paced course also includes capstone projects to give participants a feel of real world working.  Who should take the Training (roles) for Certification: Any programmer, graduate, post graduate student, experienced developer or computer science aspirant - who wants to embark on a career as a MongoDB developer or start his/her career as a NoSQL database expert or do better in their current role as a MongoDB developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification:  $ 150Exam fee for certification: $ 150Retake fee for certification: MongoDB University is no longer allowing a free retake with the exam fee. The candidate has to pay an additional $10 to reschedule or retake the exam.5. R Programming CertificationIt is a part of the data science specialization from Johns Hopkins University under Coursera. This course teaches R programming for efficient data analysis. It covers different R programming concepts like building blocks of R, datatypes, reading data into R from external files, accessing packages, writing functions, debugging techniques, profiling R code, and performing analysis.It comprises of topics like:Basic building blocks in RData types in RControl StructuresScoping Rules - OptimizationCoding StandardsDates and TimesFunctionsLoopingDebugging toolsSimulating data in RR ProfilerDemand and Benefits: Having an R Programming certification verifies that the programmer or the aspirant has all the necessary and essential skills require to get a job role as data analyst. This certification also helps in getting an internship or entry-level jobs in different organizations and firms. The average salary of a certified R programmer with this certification is ₹ 508,224 per annum.Top companies and industries hiring certified R programmers are Technovatrix, CGI Group Inc., Amazon, Sparx IT Solutions, Accenture, Uber, etc.Where to take Training for Certification: KnowledgeHut has a fascinating training course for those who wants to become a R programmer. It has 22+ hours of instructor-led live training and three self-paced live projects.Who should take the Training (roles) for Certification: Any data analyst, graduate, post graduate student, experienced data analyst or computer science aspirant - who wants to settle as a R programmer or data analyst can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: FreeFee for certification: $ 60 (Coursera Plus Monthly)Retake fee for certification: Free6. Oracle MySQL Database Administration Training and Certification (CMDBA)It is another course offered by Oracle for SQL developers. Oracle University designed this course for database administrators who want to validate their skills with developing performance, blending business processes, and accomplishing data processing work. Structured Query Language (SQL) is one of the top database management query languages that allows us to access and manipulate databases. If you want to verify your database skills during a job interview or impress your peers at your workplace then this certification is worth getting. This certification path includes Professional, Specialist, and Developer levels. The candidate should pass the MySQL Database Administrator Certified Professional Exam Part 1 & Part 2 to earn the certification.It comprises of topics likeInstalling MySQLMySQL ArchitectureConfiguring MySQLUser ManagementMySQL SecurityMaintaining a Stable SystemOptimizing Query PerformanceBackup StrategiesConfiguring a Replication TopologyDemand and Benefits: Having an CMDBA certification verifies that the programmer or the aspirant has all the necessary and essential skills required to get a job role as SQL developer. This certification also helps in getting an internship or entry-level jobs in different organizations and firms. The average salary of a certified MySQL DBA or backend developer with this certification is $ 66,470 per annum.Top companies and industries hiring Certified MySQL database administrators are Fiserv, IBM, HCL, Adobe, Microsoft, Apple, Accenture, Collabera, and more.Where to take Training for Certification: KnowledgeHut has a cutting-edge curriculum for those who want to become  MySQL database administrators. It has 16+ hours of instructor-led online lectures and 80+ hours of hands-on lab. Apart from that, this self-paced course has Capstone projects.Who should take the Training (roles) for Certification: Any developer, graduate, post graduate student, experienced developer or computer science aspirant - who wants to pursue a career as a DBA or backend developer or start his/her career in database management or backend software development can opt for this certification course. There is no other prerequisite to appear for this exam or course.Course fees for Certification: $ 255Exam fee for certification: $ 255Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days after the initial attempt.7. CCA Spark and Hadoop DeveloperWith the exponential growth in data, IT firms and organizations have to manage this tremendous amount of data generated. So, many companies are actively looking for Big data and Spark developers who can optimize performance. Big Data is the term used to describe enormous volumes of data. Apache Spark supports data management as it is an open-source centralized analytics engine that handles large-scale data processing.It requires prerequisite knowledge of Scala and Python. This certification also verifies and showcases your skills through Spark and Hadoop projects. Passing this certification course gives you a logo and a license to authenticate your CCA status.It comprises of topics likeLoad data from HDFS for use in Spark applicationsWrite the results back into HDFS using SparkRead and write files in a variety of file formatsPerform standard extract, transform, load (ETL) processes on data using the Spark APIUse metastore tables as an input source or an output sink for Spark applicationsUnderstand the fundamentals of querying datasets in SparkFilter data using SparkWrite queries that calculate aggregate statisticsJoin disparate datasets using SparkProduce ranked or sorted dataSupply command-line options to change your application configuration, such as increasing available memoryDemand and Benefits: Passing the CCA Spark and Hadoop Developer Exam (CCA175) by Cloudera verifies that you have all the essential skills required to get a job as a Hadoop developer and handle Big data projects. The average salary of a certified CCA Spark and Hadoop Developer with this certification is $ 74,200 per annum.Top companies and industries hiring Certified Spark and Hadoop Developers are Primus Global, IBM, Collabera, CorroHealth, Genpact, Xerox, Accenture, and more.Where to take Training for Certification: KnowledgeHut has extensive courses for those who want to become Big Data experts and want to work as Hadoop developers. It has different courses on Big Data Analytics, Apache Storm, Hadoop Administration, Apache Spark & Scala, Big Data with Hadoop, and more.Who should take the Training (roles) for Certification: Any Big Data developer, graduate & post graduate students, Hadoop developer or computer science aspirant - who wants to make a career in Big data development or start his/her career as a Big Data or Hadoop project developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification: $ 295Application fee for certification: $ 295Exam fee for certification: $ 295Retake fee for certification: Within 30 to 60 minutes of exam completion, Cloudera will send a scorecard mail with a pass or fail status. If the candidate fails the exam, then they have to wait for 30 days for another try.  Cloudera gives additional discounts on retakes.ConclusionWhether you are starting your career as a coder or are an experienced programmer looking to grow in the industry, having a certification and proper knowledge of any popular programming language is one of the most proven ways to elevate your programming career.  We trust that this article will help you to understand your area of interest. Choose the programming language you wish to make a career in, wisely. This would also depend on your pre-existing knowledge. If you aren't sure which resource will be more informative for doing your certification as per your area of interest, KnowledgeHut (https://www.knowledgehut.com/) has all the support and expert trainers who can guide you, from start to finish—that is in clearing the exam and helping you gain sound knowledge of your preferred subject.Receiving a programming certification is an added bonus which will make you stand out from the rest. Proper training from an institute such as KnowledgeHut will help you gain skills that are relevant and in demand in the industry.
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Top-Paying Programming Certifications for 2021

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Top IT Certifications for Java Developers in 2021

Programming languages are at the heart of computer science and software development. They help developers write efficient code for developing digital solutions through applications and websites. Programming helps in automating, maintaining, assembling, and measuring the processed data.  Java is one such popular programming language. It is a robust, high-level, general-purpose, pure object-oriented programming language developed by Sun Microsystems (now part of Oracle). James Gosling is the creator of Java which was earlier named Oak. Java ranks high in the top programming languages list and is one of the most extensively used software development platforms. It is well suited to developing software solutions and other innovative projects and simulations.  Since Oracle acquired Sun Microsystems in January 2010, they have been responsible for the further development of the Java platform. All the mentioned top Java certifications verify a specific expertise level and knowledge of the Java platform highlighting particular domains. Without further due, let us now dig into the top 5 Java certifications and their details. About Oracle’s Java CertificationsOrganizations and industries consider certifications as proof of knowledge, especially when the certifications are from a recognized body or firm. Aspirants and professionals looking for possibilities in the Java development domain can avail of a plethora of benefits through the certifications mentioned in this article. There are six levels of Oracle Java Certification based on job roles, skills, and responsibilities: Oracle Certified Junior Associate (OCJA) Oracle Certified Associate (OCA) Oracle Certified Professional (OCP) Oracle Certified Specialist (OCS) Oracle Certified Expert (OCE) Oracle Certified Master (OCM) Among them, the top five Java certifications that are in demand for the year 2021 are – 1. Oracle Certified Associate Java Programmer OCAJPIt is the preliminary and most basic certification provided by Oracle for Java. It helps you gain fundamental understanding of Java programming and build a foundation in Java and other general programming concepts. There are two subcategories in this certification – OCAJP Java Standard Edition 8 (OCAJP 8) and  OCAJP Java Standard Edition 11 (OCAJP 11) OCAJP8 comprises of topics like  Creating and Using Arrays Handling Exceptions Java Basics Using Loop Constructs Using Operators and Decision Constructs Working with Inheritance Working with Java Data Types Working with Methods and Encapsulation Working with Selected classes from the Java API OCAJP11 comprises of topics like Applying Encapsulation Creating and Using Methods Creating Simple Java Programs Describing and Using Objects and Classes Handling Exceptions Java Technology and the Java Development Environment Programming Abstractly Through Interfaces Reusing Implementations Through Inheritance Understanding Modules Using Operators and Decision Constructs Working with Java Arrays Working with Java Primitive Data Types and String APIs Demand and Benefits: Having an OCAJP certification verifies that the programmer or the aspirant has all the necessary and essential skills to become an expert Java developer. This certification also helps in getting an internship or entry-level jobs in different organizations. The entry-level salary of a junior Java developer with this certification is $ 3670 per annum; when the candidate gathers two to three years of experience, the average salary hikes to $ 5430 annually.   (Source: Glassdoor) Top companies and industries hiring Oracle Certified Associate Java Programmers are Smart Monitor Pvt. Ltd., Fiserv, Micron Semiconductor Asia Pvt. Ltd., and more. Where to take Training for Certification: KnowledgeHut has a fascinating course, designed for beginners in Java programming. It offers hands-on learning with 40 hours of instructor-led online lectures. Apart from that, Oracle also provides exam vouchers for this certification course. Who should take the Training (roles) for Certification: Any programmer or computer science aspirant - who wants to be a Java developer or start his/her career as a Java programmer can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. 2) Oracle Certified Professional Java Programmer OCPJPIt is a professional-level certification program provided by Oracle for Java developers. It verifies the candidates' knowledge and professional expertise. Using this certification, aspirants and other hard-core Java programmers can distinguish themselves from those Java professionals who are not certified. It comes in the second level of Oracle's Java Certification list. There are two subcategories of this certification – OCPJP Java Standard Edition 8 (OCPJP 8) and  OCPJP Java Standard Edition 11 (OCPJP 11) This certification is preferable if someone has professional experience with Java or has already worked for some years in Java technology.  OCPJP8 comprises of topics like: Advanced Class Design Building Database Applications with JDBC Concurrency Exceptions and Assertions Generics and Collections Java Class Design Java File I/O (NIO.2) Java I/O Fundamentals Java Stream API Lambda Built-in Functional Interfaces Localization Use Java SE 8 Date/Time API OCPJP11 comprises of topics like: Annotations Built-in Functional Interfaces Concurrency Database Applications with JDBC Exception Handling and Assertions Functional Interface and Lambda Expressions Generics and Collections I/O (Fundamentals and NIO.2) Java Fundamentals Java Interfaces Java Stream API Lambda Operations on Streams Localization Migration to a Modular Application Parallel Systems Secure Coding in Java SE Application Services in a Modular ApplicationDemand and Benefits: Once you are a certified Professional Java Programmer (OCPJP), you can switch to better salary slabs and organizations that hire senior Java developers. This certification also helps in getting internal promotions as Java developers in different organizations and firms. The average salary of a certified professional Java developer is $ 5300 - $ 8610 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are Oracle, Capgemini, Morgan Stanley, Chetu, Mphasis, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for Java developers and professionals for learning intermediate Java topics. It has hands-on learning with 32 hours of instructor-led online lectures. Apart from that, Oracle also provides exam vouchers for this certification course. Who should take the Training (roles) for Certification: Any Java programmer who wants to apply for a senior Java developer's role or start his/her career as a Java programmer can opt for this professional certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days.3. Oracle Certified Expert - Web Component Developer OCEWCDIt is an intermediate-level course offered by Oracle for Java web developers. The Oracle Certified Expert Web Component Developer is for web developers who want to write web applications using Java. Through this course, they can prove their expertise in developing web apps using JSP and Servlet technologies. It verifies your expertise in Servlet 3.0 and helps in creating dynamic Web content and Web services.  It comprises of topics like Understanding Java EE Architecture Managing Persistence using JPA entities and Bean Validation Implementing business logic using EJBs Using Java Message Service API Implement SOAP Services using JAX-WS and JAXB APIs Creating Java Web Applications using Servlets and JSPs Implementing REST Services using JAX-RS API Creating Java Applications using WebSockets Developing Web Applications using JSFs Securing Java EE 7 Applications Using CDI Beans Demand and Benefits: You can opt for this course once you are a certified Professional Java Programmer (OCPJP) or certified associated Java programmer. This certification course will help you get a job in organizations having rigorous work in Servlet, Java Server Page, JSF, and web microservices. The average salary of a certified professional Java developer is $ 8,850 - $ 11,930 per annum. Top companies and industries hiring Oracle Certified Web Component Developers are Amdocs, IBM, Oracle, Capgemini, SAP, Shine, Byjus, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for Java web developers (. It has hands-on learning with instructor-led online lectures and live projects. Apart from this, you can get online training from Oracle University as wellWho should take the Training (roles) for Certification: Any programmer or computer science aspirant who wants to settle as a Java web developer or start his/her career as a Java web content and web service developer can opt for this certification course. As a prerequisite, you have to pass the OCPJP to opt for this certification.  Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. 4. Oracle Certified Professional Java Application Developer (OCPJAD)It is an advanced-level course offered by Oracle for Java application developers. The Oracle Certified Professional Java Application Developer (OCPJAD) is for software developers who want to write different applications and automation tools using Java. Through this course, developers can prove their expertise and abilities to develop and deploy applications through Java Enterprise Edition 7. OCPJAD is ideal for desktop application developers, frontend + backend app developers, software engineers, and application architects. It comprises of topics like Creating Batch API Developing CDI Beans Concepts of Concurrency Creating Java Applications with Web-Sockets Creating Java Web Applications with JSPs Developing Java Web Applications with Servlets Developing Web Applications with JSFs Implementing Business Logic with EJBs Performing REST Services with JAX-RS API Implementing SOAP Services with JAX-WS and JAXB APIs Java EE 7 system architecture Java EE 7 Security Techniques Java Message Service API Managing Persistence with JPA Entities and Bean-ValidationDemand and Benefits: Once you pass the Certified Professional Java Application Developer (OCPJAD), you can seek employment in organizations that work on critical application development and command higher salaries. This professional certification will give you exposure to develop APIs, implementing business logic using EJBs, create message services, and apply security systems. The average salary of a certified professional application developer is $ 9,800 - $ 13,910 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are Oracle, Capgemini, NetSuite Inc., SAP, Cognizant, etc. Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity with hands-on learning exposure and live projects. Apart from this, you can get online training from Oracle University as well. Who should take the Training (roles) for Certification: Any Java developer or full-stack application developer who wants to become a certified Java application developer or move to the specialized sector of API development using REST, security architect or software engineer can opt for this certification course. As a prerequisite, you should have passed the OCAJP certification.  Course fees for Certification:  $ 245 Application fee for certification: $ 245 Exam fee for certification: $ 245 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days.5. Oracle Certified Master Java Enterprise Architect (OCMJEA)Large-scale development and service firms have different critical applications and systems to develop, manage, and maintain. Such systems require full-stack developers and specialized professionals with proven skills. Such organizations and MNCs hire only highly experienced professionals and specialists who can supervise the extensive operation, architect the defects, and define & develop systems as per requirements. The Oracle Certified Master Java Enterprise Architect (OCMJEA) is one of the most prestigious Java certifications a Java developer can achieve.  It comprises of topics like Architect Enterprise Applications through Java EE Developing Applications for the Java EE 6 Developing Applications for the Java EE 7 Developing Applications with Java EE 6 on WebLogic Server 12c Java Design Patterns Java EE 6: Develop Business Components with JMS & EJBs Java EE 6: Develop Database Applications with JPA Java EE 6: Develop Web Services with JAX-WS & JAX-RS Java EE 7: New Features Java SE 7: Develop Rich Client Applications Java SE 8: Programming Java SE 8 Fundamentals Object-Oriented Analysis and Design Using UML, etc. Demand and Benefits: Once you pass the Certified Master Java Enterprise Architect course, you get the essential skills and understanding of how to execute application development on an enterprise level. Such an experienced professional gains full-stack Java development skills. They get hired with the responsibility of undertaking Java projects from the very start to their final delivery. Many Certified Master Java Enterprise Architects work as managers or senior managerial roles in industries and firms. The average salary of a certified professional application developer is $ 14,000 - $ 19,210 per annum. Top companies and industries hiring Oracle Certified Professional Java Programmers are IBM, Oracle, Microsoft, HCL, Capgemini, NetSuite Inc., SAP, Cognizant, Atlassian, etc. Where to take Training for Certification: KnowledgeHut has a fascinating Java course  with hands-on learning exposure and a live project. Apart from that, a professional can train himself through ILT (Instructor-Led-in-Class), Learning Subscription, TOD (Training on Demand), LVC (Live Virtual Class), or classes delivered by Oracle Authorized Education Center . Other Oracle Authorized Partner Oracle Academy, Oracle University Training Center, or Oracle Workforce Development Program can also benefit and train you in this course.  Who should take the Training (roles) for Certification: Any Java developer or full-stack application developer who wants to move to a senior role in the enterprise-level or want to become a manager or team lead can opt for this certification course. As a prerequisite, you need to have passed the OCPJP certification.  Course fees for Certification:  $248 Application fee for certification: $ 248 Exam fee for certification: $ 248 Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days. Java is an evergreen programming language and is here to stay, at least for the next couple of decades. A vast community of professionals and entry-level aspirants enjoy the benefit of this pure object-oriented, class-based, multi-paradigm, high-level programming language. Java Certification requires proper training.KnowledgeHut has the required infrastructure and quality education faculty, both online and offline, to train aspirants for these Oracle Certifications. It caters to well-structured, industry-oriented Java certification training, explicitly designed to serve the candidates according to the latest industry needs. Getting proper training from KnowledgeHut will help aspirants master core knowledge of Java plus equip themselves with the industry standards to manage large projects. 
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Top IT Certifications for Java Developers in 2021

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