Historical Resource Consumption Metrics on Dataproc Ephemeral Clusters

[sqoor]

Hello,

I am currently tasked with analyzing the historical resource consumption of our Dataproc ephemeral clusters on Google Cloud Platform. The goal is to make informed decisions about configuring an on-premises cluster equivalent to our cloud setup.

Given that our Dataproc clusters are ephemeral and get deleted upon job completion, I am looking for a retrospective analysis of resource metrics such as CPU usage, memory consumption, machine count, and core utilization during the lifespan of these clusters.

How to achieve it in the simplest way?

[ms4446]

Analyzing the historical resource consumption of Dataproc ephemeral clusters can be partially achieved using the Dataproc History Server, but it's important to note its limitations. The History Server primarily provides insights into completed Hadoop and Spark jobs, focusing more on job execution details rather than detailed cluster-level metrics like CPU usage, memory consumption, machine count, and core utilization.

Here’s how you can proceed, with these points in mind:

1. **Enable the Dataproc History Server**: If not already done, enable the Dataproc History Server for your project from the Dataproc console or using the gcloud command-line tool.

2. **Collect and Access History Server Logs**: The History Server will collect logs for completed jobs. Access these logs through the History Server web UI or by using the `gcloud dataproc jobs describe` command. However, be aware that these logs might not provide comprehensive cluster-level resource metrics.

3. **Supplement with Additional Metrics Sources**: Since the History Server may not capture detailed resource metrics, consider integrating with Stackdriver Monitoring or other GCP tools for more in-depth data.

4. **Analyze Resource Metrics**: Use Python scripts or other data analysis tools to parse the logs. Here's an example script, assuming you have the necessary detailed metrics in your logs:

import pandas as pd
# Load History Server logs into a DataFrame
data = {'cpuUsage': [10, 20, 30], 'memoryConsumption': [40, 50, 60], 'machineCount': [70, 80, 90], 'coreUtilization': [100, 110, 120]}
data = pd.DataFrame(data)
# Calculate average resource consumption
average_cpu_usage = data['cpuUsage'].mean() if 'cpuUsage' in data else "Not Available"
average_memory_consumption = data['memoryConsumption'].mean() if 'memoryConsumption' in data else "Not Available"
average_machine_count = data['machineCount'].mean() if 'machineCount' in data else "Not Available"
average_core_utilization = data['coreUtilization'].mean() if 'coreUtilization' in data else "Not Available"
# Print average resource consumption metrics
print("Average CPU Usage:", average_cpu_usage)
print("Average Memory Consumption:", average_memory_consumption)
print("Average Machine Count:", average_machine_count)
print("Average Core Utilization:", average_core_utilization)

5. **Compare with On-Premises Requirements**: Use the analyzed data to inform the configuration of your on-premises cluster. Ensure that the comparison takes into account the different environments and scalability aspects between cloud and on-premises infrastructure.

By combining data from the Dataproc History Server with other GCP monitoring tools, you can get a more comprehensive view of your clusters' resource usage, aiding in making informed decisions for your on-premises setup.

[waynez]

ms4446,

I am working on Dataproc Serverless with the same goal to get resource usage metrics for the past batches. Metrics Explorer by default shows the metrics only "active" for the last 25 hr.  How to get the raw data from the same source used by Metrics Explorer via API?