Such data that we will collect is the producer metrics. These seek advice from the varied measurements that are collected by a Kafka producer to watch and consider the efficiency and effectivity of its message manufacturing course of.
As soon as we collect these metrics, we will use them to establish the potential bottlenecks, perceive the speed and velocity at which the messages are written to a Kafka dealer, and so forth. We additionally use the producer metrics to trace the success or failure of the message supply.
Key Producer Metrics
A few of the key producer metrics embrace:
Report Ship Fee – This metric measures the speed at which data are despatched to the dealer. We are able to use this metric to find out the speed of message manufacturing.
Report Measurement – The second producer metric within the Kafka cluster is the report dimension. This metric permits us to measure the data which can be despatched to the dealer. If you have to decide the community and disk I/O, that is in all probability the metric that you’re on the lookout for.
Report Error Fee – The error fee metric measures the speed at which report ship operations lead to errors. An instance utilization of this metric is figuring out the success fee for the message supply. If there are any points between the dealer and the producer software, this can be a good metric to help with that.
Batch Measurement – This metric permits us to find out the dimensions of the batch data which can be despatched to the dealer.
Compression Ratio – Utilizing the compression ratio metric, we will calculate the compression ratio that’s achieved by the producer software.
Community I/O – This metric permits us to get the quantity of the community I/O which is generated by the producer. If you have to decide community bottlenecks, that is in all probability the place to start out.
Request Latency – We are able to use this producer metric to get the time that the producer takes to obtain a response from the dealer for a report ship request.
These metrics, together with others, present invaluable insights into the efficiency and effectivity of the Kafka producer. By monitoring these metrics, the organizations can be sure that their Kafka producer runs optimally and makes use of the sources effectively.
Utilizing this tutorial, we’ll construct a easy Python software to assist us collect the producer metrics utilizing the Kafka-Python library.
Kafka Producer Metrics
We are able to use the Kafka-Python library to collect the metrics of a producer and dump the consequence as JSON within the following supply code:
from kafka import KafkaProducer
def gather_producer_metrics(producer):
# Get the metrics for the producer
metrics = producer.metrics()
# Convert the metrics dictionary to a JSON string
metrics_json = json.dumps(metrics)
# Print the producer metrics as JSON
print(metrics_json)
# Utilization
producer = KafkaProducer(
bootstrap_servers=‘localhost:9092’,
client_id=‘my-producer-client’
)
gather_producer_metrics(producer)
This venture setups a easy Kafka producer and makes use of the metrics() operate to indicate the varied metrics of the producer consumer.
We are able to run the next code:
$ python producer_metrics.py
To view the leads to a human-readable format, we will pipe the output to JQ as proven within the following:
Conclusion
There you’ve it! A easy approach of utilizing Python’s Kafka-Python bundle to collect the metrics of a producer software in Python.
