1. Identify SayPro’s Data Sources

Let’s assume that SayPro’s data is stored in the following systems:

• MySQL Database: A performance log database stores application metrics (e.g., execution times, errors, CPU, memory usage).
• System Logs: SayPro’s internal application logs track errors, execution times, and system resource usage.
• Cloud Monitoring (e.g., AWS CloudWatch): If SayPro hosts their applications in the cloud, AWS CloudWatch could have historical performance metrics.

2. Define the Key Performance Indicators (KPIs)

We are interested in the following KPIs for SayPro:

• Execution time: How long the programs took to run.
• CPU and Memory Usage: Resource consumption by the programs.
• Error count: How many errors occurred during program execution.
• Uptime/Downtime: Availability of the application or service.

3. Example Data Sources

Let’s assume the data is organized as follows:

• MySQL Table performance_logs with columns: timestamp, execution_time, cpu_usage, memory_usage, error_count.
• System Log File with entries similar to:pgsqlCopy2024-01-01 12:00:00 Execution time: 150ms Memory usage: 200MB Error count: 1 2024-01-01 12:01:00 Execution time: 120ms Memory usage: 190MB Error count: 0
• CloudWatch logs (if applicable) for cloud-hosted applications.

4. Fetching Data from MySQL Database

Assume you have a MySQL database with performance logs. We can use Python to extract this data.

Python Script to Extract Data from SayPro’s MySQL Database:

pythonCopyimport mysql.connector
import pandas as pd

# Connect to SayPro's MySQL database
db_connection = mysql.connector.connect(
    host="saypro-db-host",  # SayPro's database host
    user="your_username",
    password="your_password",
    database="saypro_database"  # Database name where performance logs are stored
)

# Create a cursor to execute SQL queries
cursor = db_connection.cursor()

# Query to collect performance logs for a specific time range
query = """
SELECT timestamp, execution_time, cpu_usage, memory_usage, error_count
FROM performance_logs
WHERE timestamp BETWEEN '2024-01-01' AND '2024-12-31'
"""

# Execute the query
cursor.execute(query)

# Fetch all results
results = cursor.fetchall()

# Convert results into a pandas DataFrame for easy manipulation
columns = ['timestamp', 'execution_time', 'cpu_usage', 'memory_usage', 'error_count']
df_mysql = pd.DataFrame(results, columns=columns)

# Close cursor and connection
cursor.close()
db_connection.close()

# Preview the fetched data
print(df_mysql.head())

5. Parsing System Log Files

Let’s assume SayPro also logs application performance in text files.

Python Script to Parse Log Files for SayPro:

pythonCopyimport re
import pandas as pd

# Path to SayPro's log file
log_file_path = '/path/to/saypro/logfile.log'

# Regex pattern to extract performance data (execution time, memory usage, errors)
pattern = r'(?P<timestamp>\S+ \S+)\s+.*?Execution time: (?P<execution_time>\d+)\s+.*?Memory usage: (?P<memory_usage>\d+)\s+.*?Error count: (?P<error_count>\d+)'

# Open and read the log file
with open(log_file_path, 'r') as file:
    logs = file.readlines()

# Extract data using regex
log_data = []
for line in logs:
    match = re.search(pattern, line)
    if match:
        log_data.append(match.groupdict())

# Convert to pandas DataFrame
df_logs = pd.DataFrame(log_data)

# Preview the parsed log data
print(df_logs.head())

6. Combining Data from MySQL and Log Files

Now, let’s combine the data from both the MySQL database and log files.

pythonCopy# Combine both MySQL data and log data (assuming both are DataFrames)
df_combined = pd.concat([df_mysql, df_logs])

# Convert timestamps to datetime format
df_combined['timestamp'] = pd.to_datetime(df_combined['timestamp'])

# Drop duplicates if any
df_combined.drop_duplicates(inplace=True)

# Preview the combined data
print(df_combined.head())

7. Data Analysis

You can now analyze the collected data. For instance, calculate average execution time, total errors, and memory usage.

pythonCopy# Calculate average execution time
avg_execution_time = df_combined['execution_time'].mean()

# Calculate average memory usage
avg_memory_usage = df_combined['memory_usage'].mean()

# Calculate total errors
total_errors = df_combined['error_count'].sum()

# Print results
print(f"Average Execution Time: {avg_execution_time} ms")
print(f"Average Memory Usage: {avg_memory_usage} MB")
print(f"Total Errors: {total_errors}")

8. Visualization of Data

You can use Python’s matplotlib or seaborn to visualize the performance data.

pythonCopyimport matplotlib.pyplot as plt

# Plot execution time over time
plt.figure(figsize=(10, 6))
plt.plot(df_combined['timestamp'], df_combined['execution_time'], label='Execution Time (ms)')
plt.xlabel('Timestamp')
plt.ylabel('Execution Time (ms)')
plt.title('Execution Time Over Time')
plt.xticks(rotation=45)
plt.legend()
plt.show()

# Plot CPU and memory usage over time
plt.figure(figsize=(10, 6))
plt.plot(df_combined['timestamp'], df_combined['cpu_usage'], label='CPU Usage (%)', color='red')
plt.plot(df_combined['timestamp'], df_combined['memory_usage'], label='Memory Usage (MB)', color='blue')
plt.xlabel('Timestamp')
plt.ylabel('Usage')
plt.title('CPU and Memory Usage Over Time')
plt.xticks(rotation=45)
plt.legend()
plt.show()

9. Automating the Process (Optional)

If you need to run this process periodically (e.g., every night to update the data), you can set up a cron job (Linux) or Task Scheduler (Windows) to run the script.

Example Cron Job:

bashCopy0 0 * * * /usr/bin/python3 /path/to/saypro_data_collection_script.py

This cron job runs the script at midnight every day.

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Final Thoughts

• Security: Ensure that sensitive information (like database credentials) is stored securely (e.g., in environment variables or a secure vault).
• Scalability: For large datasets, consider using databases or cloud services to store and analyze data (e.g., AWS Redshift or Google BigQuery).
• Backup: Always have a backup mechanism in place to store historical logs and d

Steps for Data Cleaning, Completeness, and Readiness

1. Handling Missing Data
   • Identify missing data: You need to identify if any of your key performance indicators (KPIs) are missing.
   • Decide how to handle missing data: You can either fill in missing values (e.g., using mean, median, or forward-fill methods) or remove rows/columns with missing data, depending on the context.
2. Removing Duplicate Entries
   • If there are any duplicate entries in the data (e.g., the same log entry or database entry repeated), you need to remove them to avoid biasing the analysis.
3. Correcting Data Types
   • Ensure that each column has the correct data type (e.g., timestamp should be in datetime, numerical values should be integers or floats).
4. Handling Outliers
   • Check for any extreme values in your data that may skew the analysis, especially for performance metrics like execution time, memory usage, and CPU usage.
   • You can remove or adjust these outliers depending on the context.
5. Ensuring Consistency Across Data Sources
   • If you’re combining data from multiple sources (e.g., MySQL database and log files), ensure that the columns are consistent in terms of naming, units, and formats.
6. Filtering Out Irrelevant Data
   • Filter out irrelevant records or columns that do not contribute to the analysis.

Python Code for Data Cleaning

1. Handle Missing Data

You can use pandas to identify missing data and decide how to handle it.

pythonCopy# Check for missing values in the DataFrame
print(df_combined.isnull().sum())  # This will show the number of missing values per column

# Handle missing data - Here, let's fill missing values for execution_time, cpu_usage, and memory_usage
# with the mean of each column
df_combined['execution_time'].fillna(df_combined['execution_time'].mean(), inplace=True)
df_combined['cpu_usage'].fillna(df_combined['cpu_usage'].mean(), inplace=True)
df_combined['memory_usage'].fillna(df_combined['memory_usage'].mean(), inplace=True)
df_combined['error_count'].fillna(0, inplace=True)  # Assuming no errors is the default value

# If you prefer to remove rows with missing data (e.g., if it's critical), use:
# df_combined.dropna(inplace=True)

# Check if any missing values remain
print(df_combined.isnull().sum())

2. Remove Duplicate Entries

You can remove duplicate rows using drop_duplicates.

pythonCopy# Remove duplicates based on all columns (or specify columns)
df_combined.drop_duplicates(inplace=True)

# Check if duplicates were removed
print(f"Remaining rows after removing duplicates: {df_combined.shape[0]}")

3. Correct Data Types

Ensure that columns have the correct data types, particularly the timestamp column.

pythonCopy# Convert 'timestamp' to a datetime format
df_combined['timestamp'] = pd.to_datetime(df_combined['timestamp'])

# Convert numeric columns to appropriate types
df_combined['execution_time'] = pd.to_numeric(df_combined['execution_time'], errors='coerce')
df_combined['cpu_usage'] = pd.to_numeric(df_combined['cpu_usage'], errors='coerce')
df_combined['memory_usage'] = pd.to_numeric(df_combined['memory_usage'], errors='coerce')
df_combined['error_count'] = pd.to_numeric(df_combined['error_count'], errors='coerce')

# Check the data types after conversion
print(df_combined.dtypes)

4. Handle Outliers

You can remove or handle outliers using a method such as Z-score or IQR (Interquartile Range) method. Here’s an example using the IQR method:

pythonCopy# Calculate IQR for numerical columns
Q1 = df_combined[['execution_time', 'cpu_usage', 'memory_usage']].quantile(0.25)
Q3 = df_combined[['execution_time', 'cpu_usage', 'memory_usage']].quantile(0.75)
IQR = Q3 - Q1

# Define lower and upper bounds for acceptable values
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR

# Remove rows with outliers
df_combined = df_combined[
    (df_combined['execution_time'] >= lower_bound['execution_time']) & 
    (df_combined['execution_time'] <= upper_bound['execution_time']) &
    (df_combined['cpu_usage'] >= lower_bound['cpu_usage']) & 
    (df_combined['cpu_usage'] <= upper_bound['cpu_usage']) &
    (df_combined['memory_usage'] >= lower_bound['memory_usage']) & 
    (df_combined['memory_usage'] <= upper_bound['memory_usage'])
]

# Check shape of the dataset after removing outliers
print(f"Remaining rows after removing outliers: {df_combined.shape[0]}")

5. Ensuring Consistency Across Data Sources

When combining data from multiple sources, ensure that column names, formats, and units are consistent. For example, if one source uses “Memory (MB)” and another uses “Memory Usage (MB)”, rename them to a common name: