Aprenda Aggregating and Summarizing Metrics | Data-Driven DevOps Decisions

Understanding system health at a glance is a crucial part of DevOps, especially when you are dealing with large volumes of metrics data. Aggregation helps you transform raw, granular data into meaningful summaries, making it easier to spot trends, identify bottlenecks, and prioritize issues. Without aggregation, you might miss the bigger picture and struggle to make informed decisions about infrastructure and service reliability.


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import pandas as pd

# Simulated metrics data
data = {
    "timestamp": ["2024-06-01 10:00", "2024-06-01 10:05", "2024-06-01 10:10", "2024-06-01 10:15"],
    "cpu_usage": [55.2, 61.4, 58.9, 63.1],
    "memory_usage": [70.1, 68.4, 72.3, 69.8]
}

df = pd.DataFrame(data)

# Calculate average CPU and memory usage
avg_cpu = df["cpu_usage"].mean()
avg_memory = df["memory_usage"].mean()

print(f"Average CPU usage: {avg_cpu:.2f}%")
print(f"Average memory usage: {avg_memory:.2f}%")

Aggregation functions in pandas, such as mean() and sum(), allow you to quickly summarize large datasets. The mean() function calculates the average value of a column, which is useful for understanding typical resource usage, while sum() provides the total, helping you track cumulative consumption. These functions, along with others like min(), max(), and count(), are essential tools for distilling complex metrics into actionable insights.


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import pandas as pd

# Simulated server metrics data
data = {
    "server_type": ["web", "db", "web", "cache", "db", "cache", "web"],
    "cpu_usage": [60, 80, 55, 40, 75, 35, 65],
    "memory_usage": [70, 90, 68, 50, 88, 45, 72]
}

df = pd.DataFrame(data)

# Group by server type and calculate average CPU and memory usage
summary = df.groupby("server_type").mean(numeric_only=True)

print(summary)

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 3. Capítulo 2

Pergunte à IA

Pergunte o que quiser ou experimente uma das perguntas sugeridas para iniciar nosso bate-papo

Suggested prompts:

Can you explain how the groupby function works in this context?

What other aggregation functions can I use with pandas?

How can I visualize these aggregated metrics?

Deslize para mostrar o menu


              1234567891011121314151617
            
import pandas as pd

# Simulated metrics data
data = {
    "timestamp": ["2024-06-01 10:00", "2024-06-01 10:05", "2024-06-01 10:10", "2024-06-01 10:15"],
    "cpu_usage": [55.2, 61.4, 58.9, 63.1],
    "memory_usage": [70.1, 68.4, 72.3, 69.8]
}

df = pd.DataFrame(data)

# Calculate average CPU and memory usage
avg_cpu = df["cpu_usage"].mean()
avg_memory = df["memory_usage"].mean()

print(f"Average CPU usage: {avg_cpu:.2f}%")
print(f"Average memory usage: {avg_memory:.2f}%")


              123456789101112131415
            
import pandas as pd

# Simulated server metrics data
data = {
    "server_type": ["web", "db", "web", "cache", "db", "cache", "web"],
    "cpu_usage": [60, 80, 55, 40, 75, 35, 65],
    "memory_usage": [70, 90, 68, 50, 88, 45, 72]
}

df = pd.DataFrame(data)

# Group by server type and calculate average CPU and memory usage
summary = df.groupby("server_type").mean(numeric_only=True)

print(summary)

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 3. Capítulo 2

Aggregating and Summarizing Metrics

1. Why is aggregation important in DevOps?

2. Which pandas function calculates the average of a column?

3. How can grouping data help in analysis?

Aggregating and Summarizing Metrics

1. Why is aggregation important in DevOps?

2. Which pandas function calculates the average of a column?

3. How can grouping data help in analysis?