Kusto Query Language (KQL) Plugin Integrations for Custom Computations

Explores how to leverage the python() and R() plugins for custom computations

Kusto Query Language (KQL) is a powerful tool for querying and analyzing large datasets in Azure Data Explorer. While KQL excels at filtering, aggregating, and joining data, its plugin integrations, such as python() and R(), unlock advanced capabilities by embedding external programming languages for custom computations. These plugins enable sophisticated tasks like machine learning scoring and statistical analysis, which are not commonly covered in standard KQL tutorials. This blog post explores how to leverage the python() and R() plugins for custom computations, focusing on practical applications like anomaly detection and regression analysis.

The Power of KQL Plugins

KQL plugins allow users to extend KQL’s functionality by embedding code in languages like Python and R directly within queries. This is particularly useful for scenarios requiring advanced computations that go beyond KQL’s built-in operators. The python() and R() plugins enable seamless integration of these languages, allowing users to process query results with custom logic while maintaining the efficiency of KQL’s data processing engine.

Key Benefits of Plugins

• Flexibility: Run Python or R code inline with KQL queries.

• Advanced Analytics: Perform machine learning, statistical modeling, or custom computations on query results.

• Data Serialization: Handle data exchange between KQL and external languages efficiently using formats like JSON or tabular data.

• Scalability: Leverage Azure Data Explorer’s distributed computing for large-scale data processing.

Using the python() Plugin for Machine Learning Scoring

The python() plugin allows users to embed Python code within KQL queries, making it ideal for tasks like machine learning scoring. For example, you can apply anomaly detection models to query results to identify unusual patterns in data, such as system logs or IoT telemetry.

Example: Anomaly Detection with python()

Suppose you have a dataset of system performance metrics (e.g., CPU usage) and want to detect anomalies using a Python-based isolation forest model. The python() plugin can process the query results and return scores indicating potential anomalies.

Sample KQL Query with python()

let data = MyTable
| where Timestamp > ago(1h)
| project CpuUsage, Timestamp;
data
| evaluate python(
    typeof(*, AnomalyScore:double),
    ```
    from sklearn.ensemble import IsolationForest
    import pandas as pd
    def evaluate_anomaly(df):
        model = IsolationForest(contamination=0.1)
        scores = model.fit_predict(df[[’CpuUsage’]])
        df[’AnomalyScore’] = scores
        return df
    result = evaluate_anomaly(df)
    ```
)
| where AnomalyScore == -1

Explanation

1. Data Preparation: The query filters MyTable for the last hour of data and projects the CpuUsage and Timestamp columns.

2. Python Plugin: The evaluate python() operator invokes the Python runtime. The typeof(*, AnomalyScore:double) specifies that the output includes all input columns (*) plus a new column AnomalyScore of type double.

3. Python Code: Inside the plugin, a Python script uses scikit-learn’s IsolationForest to compute anomaly scores. The input data is automatically passed as a pandas DataFrame (df), and the script returns the DataFrame with an additional AnomalyScore column.

4. Filtering Anomalies: The final where clause filters for rows where AnomalyScore == -1, indicating anomalies.

Use Case

This approach is valuable for real-time monitoring in scenarios like DevOps, where detecting unusual spikes in CPU or memory usage can trigger alerts. The python() plugin enables seamless integration of machine learning models without leaving the KQL environment.

Notes

• Dependencies: Ensure required Python libraries (e.g., scikit-learn) are available in the Azure Data Explorer Python sandbox.

• Performance: The python() plugin runs in a sandboxed environment, so large datasets may require optimization to avoid memory or timeout issues.

• Serialization: Input data is serialized as a pandas DataFrame, and the output must match the specified schema.

Using the R() Plugin for Statistical Analysis

The R() plugin enables embedding R code within KQL queries, making it ideal for statistical analysis tasks like regression modeling. R is particularly suited for scenarios requiring advanced statistical techniques, such as linear regression or time-series analysis.

Example: Linear Regression with R()

Suppose you have a dataset of sales data and want to perform a linear regression to predict sales based on advertising spend. The R() plugin can process the query results and return regression coefficients or predictions.

Sample KQL Query with R()

let data = SalesTable
| where Date > ago(30d)
| project AdvertisingSpend, Sales;
data
| evaluate R(
    typeof(*, PredictedSales:double, Coefficient:double),
    ```
    df <- data.frame(df)
    model <- lm(Sales ~ AdvertisingSpend, data=df)
    df$PredictedSales <- predict(model, df)
    df$Coefficient <- coef(model)[”AdvertisingSpend”]
    df
    ```
)
| project Date, AdvertisingSpend, Sales, PredictedSales, Coefficient

Explanation

1. Data Preparation: The query filters SalesTable for the last 30 days and projects the AdvertisingSpend and Sales columns.

2. R Plugin: The evaluate R() operator invokes the R runtime. The typeof(*, PredictedSales:double, Coefficient:double) specifies the output schema, including all input columns plus PredictedSales and Coefficient.

3. R Code: The R script converts the input data to a data frame, fits a linear regression model using lm(), and computes predicted sales and the regression coefficient for AdvertisingSpend. The output is returned as a data frame.

4. Output: The final project clause selects relevant columns for further analysis or visualization.

Use Case

This approach is useful for business analytics, such as understanding the relationship between marketing spend and sales outcomes. The R() plugin allows analysts to leverage R’s statistical capabilities directly within KQL.

Notes

• Dependencies: Ensure R libraries are available in the Azure Data Explorer R sandbox.

• Data Serialization: Input data is serialized as a data frame, and the output must conform to the specified schema.

• Performance: Similar to the python() plugin, large datasets may require optimization to manage memory and computation time.

Handling Data Serialization

Both the python() and R() plugins handle data serialization automatically, converting KQL query results into formats compatible with Python (pandas DataFrame) or R (data frame). However, users must ensure the output schema matches the typeof specification to avoid errors. Key considerations include:

• Input Format: KQL query results are passed as tabular data, which is converted to a DataFrame/data frame.

• Output Format: The plugin’s output must be a DataFrame/data frame with columns matching the typeof declaration.

• Data Types: Ensure compatibility between KQL data types (e.g., double, string) and Python/R data types to prevent serialization issues.

• Large Datasets: For large datasets, consider sampling or aggregating data in KQL before passing it to the plugin to reduce memory usage.

Best Practices for KQL Plugin Integrations

1. Optimize Data Size: Use KQL’s filtering, aggregation, and sampling operators (e.g., take, summarize) to reduce the dataset size before passing it to the plugin.

2. Validate Output Schema: Always define the output schema using typeof to ensure compatibility with downstream KQL operations.

3. Test Incrementally: Start with small datasets to debug Python or R code before scaling to larger datasets.

4. Leverage Sandbox Libraries: Use pre-installed libraries in the Azure Data Explorer sandbox to avoid dependency issues.

5. Monitor Performance: Be mindful of timeouts and memory limits in the sandboxed environment, especially for complex computations.

TLDR

The python() and R() plugins in KQL open up a world of possibilities for advanced analytics, from machine learning scoring to statistical modeling. By embedding Python or R code within KQL queries, users can perform custom computations like anomaly detection and regression analysis without leaving the Azure Data Explorer environment. These plugins combine the scalability of KQL with the flexibility of Python and R, making them powerful tools for data scientists and analysts. Whether you’re detecting anomalies in system metrics or modeling business trends, KQL plugin integrations provide a seamless way to extend your analytics capabilities.

For more details, check the Azure Data Explorer documentation or experiment with these plugins in your own KQL queries!