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Python map() and lambda() Use Cases and Examples

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 In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work. Python map and lambda top use cases 1. Using map() with lambda The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list). Example: Doubling Numbers numbers = [ 1 , 2 , 3 , 4 , 5 ] doubled = list ( map ( lambda x: x * 2 , numbers)) print (doubled) # Output: [2, 4, 6, 8, 10] 2. Using map() to Convert Data Types Example: Converting Strings to Integers string_numbers = [ "1" , "2" , "3" , "4" , "5" ] integers = list ( map ( lambda x: int (x), string_numbers)) print (integers) # Output: [1, 2, 3, 4, 5] 3. Using map() with Multiple Iterables You can also use map() with more than one iterable. The lambda function can take multiple arguments. Example: Adding Two Lists Element-wise list1 = [ 1 , 2 , 3 ]
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How to Monitor Kafka-stream's Performance

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Kafka Streams API is a part of Kafka, it goes without saying that monitoring your application will require some monitoring of Kafka as well.

Performance


The consumer and producer performance is one of the fundamental performance concerns for a producer and consumer.
 

Stream performance


The Kafka data flow diagram



Kafka data flow diagram


What is lag


For producers, we care mostly about how fast the producer is sending messages to the broker. Obviously, the higher the throughput, the better.

For consumers, we’re also concerned with performance, or how fast we can read messages from a broker.

we care about how much and how fast our producers can publish to a broker, and we simultaneously care about how quickly our consumers can read those messages from the broker. The difference between how fast the producers place records on the broker and when consumers read those messages is called consumer lag


How to check consumer lag


To check for consumer lag, Kafka provides a convenient command-line tool, kafka-consumer-groups.sh, found in the <kafka-install-dir>/bin directory. The script has a few options, but here we’ll focus on the list and describe options. These two options will give you the information you need about consumer group performance.

List command

<kafka-install-dir>/bin/kafka-consumer-groups.sh \ --bootstrap-server localhost:9092 \ --list


Describe command

<kafka-install-dir>/bin/kafka-consumer-groups.sh \ --bootstrap-server localhost:9092 \ --group <GROUP-NAME> \ --describe


How to trace problem

  • A small lag or one that stays constant is OK, but a lag that continues to grow over time is an indication you’ll need to give your consumer more resources. 
  • For example, you might need to increase the partition count and hence increase the number of threads consuming from the topic. Or maybe your processing after reading the message is too heavyweight. After consuming a message, you could hand it off to an async queue, where another thread can pick up the message and do the processing.

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