Introduction:
Scalability is a critical aspect of database management, especially in Oracle databases, where performance significantly impacts overall system efficiency. This post explores a comprehensive approach to solving, monitoring, and testing for scalability, with a focus on feature engineering and feature selection. By leveraging key metrics and performance indicators, administrators can proactively address potential bottlenecks and optimize performance.
In this installment of our "Catching Fire" series, we delve into a data science-inspired feature selection approach to Oracle performance monitoring. This approach not only involves monitoring how the system performs under increasing loads to ensure effective scalability but also considers common problem areas. By identifying key metrics that serve as reliable indicators of typical performance problem areas, we aim to focus the evaluation on familiar potential problem areas, thereby navigating through some of the "noise" metrics. This enables us to identify potential issues before they impact the live production system's performance.
Problem Statement:
Solving, Monitoring, and Testing for Scalability in an Oracle Database presents several challenges:
1. Small model approaches, which rely on a few hand-picked metrics, risk overlooking important observations.
2. Database workloads can be unpredictable, making it difficult to anticipate their impacts on the database management system and where problems are likely to arise.
3. There are tens of thousands of historic metrics persisted in the AWR, scattered across many tables and columns, adding complexity to the monitoring process. The metrics fall into many categories, such as CPU, IO, memory utilization, network, data growth, transaction growth, … so settling on the right set of metrics for a particular environment is essential.
4. A significant amount of performance domain knowledge is required to identify the metrics and where to find them in the database.
5. Some metrics require transformations to make them meaningful for analysis.
6. When multiple metrics are identified as anomalies, it is crucial to have mechanisms in place to prioritize which metrics are the most important to focus on for modeling the system behavior, such as removing irrelevant, redundant, or noisy features.
Applying disciplined data science approaches, such as feature engineering and feature selection, can help reduce the complexity of determining which metrics are relevant to focus on for any given workload. Here are some of the benefits to taking this kind of approach:
Comprehensive Metrics: Inclusion of diverse metrics.
Feature engineering allows for the inclusion of a wide range of metrics beyond traditional ones, addressing the challenge of overlooking key observations.Adaptability: Dynamic selection of relevant features.
The approach can adapt to unpredictable workloads by dynamically selecting relevant features, mitigating the impact of workload unpredictability. Further, the approach is modular and is easy to engineer in new features.Efficient Monitoring: Streamlined monitoring of AWR metrics.
By selecting and transforming metrics, the approach streamlines monitoring of the vast number of AWR metrics, overcoming complexity.Knowledge Amplification: Amplified performance domain knowledge.
Feature engineering and selection amplify performance domain knowledge by guiding the selection and transformation of metrics. By encapsulating and making "permanent" knowledge of these metrics within the analysis code, you're effectively amplifying your understanding and insight into the performance of the system. This not only helps with the current analysis but also builds a knowledge base that can inform future monitoring and decision-making processes.Enhanced Analysis: Meaningful metric transformation.
Transformation makes metrics more meaningful, enhancing analysis capabilities.Focus on Essentials: Prioritization of important anomalies.
Prioritization mechanisms help focus on the most important anomalies, aligning with the objective of prioritizing relevant metrics for modeling.
Feature Engineering:
In the context of database scalability and performance optimization, feature engineering refers to the process of selecting, extracting, and transforming relevant metrics and performance indicators from the database system. These features are then used to build models, analyze trends, and make informed decisions regarding system scalability and performance.
In this process, the analyst uses their domain knowledge to create new features or transform existing features into a format that is more suitable for modeling. This can include deriving new features from existing ones, scaling or normalizing features, handling missing values, and encoding categorical variables. Feature engineering is crucial for improving the effectiveness of the analytical models by providing them with more informative and relevant features. The features are extracted from the database using queries against the various sources of information (e.g. dba_hist_sysmetric_summary, dba_hist_osstat, dba_hist_system_event, …). In the context of system scalability and performance we are looking for the features that represent areas of concern common to database scalability, such as application efficiency (locking, query response time, …), CPU utilization, I/O performance, wait events, connections, transactions, memory usage, swapping, REDO (impacts Data Guard Replication), UNDO, and TEMP. Sometimes these features need to be transformed into a format that is suitable for analysis and modeling. This may include aggregating data over time intervals, normalizing values, handling missing or outlier data points, and computing delta values for a time interval.
Feature Selection:
Feature selection involves choosing a subset of relevant features from the original set. This step identifies metrics and performance indicators indicative of scalability and performance issues. Techniques include removing irrelevant, redundant, or noisy features. Anomaly detection is a kind of feature selection, where anomalies are used to filter out noise from the many data points.
Feature Analysis and Model Building:
After feature engineering and selection, the next step is analyzing the selected features to gain insights and build models. The goal is to identify patterns, trends, and anomalies indicating scalability or performance bottlenecks. This analysis uses statistical techniques, data visualization, and trend analysis to optimize system performance.
Separating these steps ensures each aspect is handled effectively, leading to better model performance and more meaningful insights. Feature engineering is crucial for optimizing scalability and performance, providing insights, identifying issues, and guiding decision-making.
Key Areas of Focus:
Contention Issues:
Identify contention issues that can impact database performance and scalability.
· Monitoring locks and other wait events to identify the impact of contention to database scalability.
· Time can be estimated in various groupings from dba_hist_active_sess_history where blocking_session is not null
Application Efficiency
· query response time - Optimizing SQL queries and query execution plans to improve database performance and scalability. Sources include:
o dba_hist_active_sess_history
o dba_his_sqlstat
CPU Usage:
Managing CPU utilization to ensure optimal performance and scalability under varying workloads.
· CPU Utilization (dba_hist_osstat, dba_hist_sysmetric_summary)
I/O Performance:
Analyzing IO statistics to identify and address I/O bottlenecks that can impact database scalability.
· IO Statistics (dba_hist_sysmetric_summary, dba_hist_service_stat)
Wait Events:
Identifying and addressing top wait events to minimize performance bottlenecks and enhance database scalability.
· Top Wait Events (dba_hist_active_sess_history, dba_hist_system_event, dba_hist_bg_event)
Connections/Session Management:
Managing sessions and connections to prevent resource contention and improve database scalability.
· Sessions (dba_hist_active_sess_history; dba_hist_sysmetric_summary ; dba_audit_trail)
Transaction Management:
Monitoring transactions to ensure efficient processing and scalability of the database.
· Transactions (dba_hist_sysmetric_summary where metric_name like ‘%Per Txn’)
Memory Management:
Analyzing paging/swapping and PGA memory utilization to optimize memory allocation and avoid performance degradation.
· PGA Memory (dba_hist_pgastat, dba_hist_active_sess_history.pga_allocated)
· Swapping (dba_hist_osstat VM_IN/OUT_BYTES)
REDO (impacts Data Guard Replication)
Monitor redo metrics found in:
· dba_hist_sysstat and dba_hist_sysmetric_summary
Undo Management:
Managing undo to optimize database performance and scalability.
· Undo (dba_hist_undostat)
Temporary Tablespace Space Usage:
Monitoring TEMP space utilization to prevent temporary space-related issues that can impact scalability.
· TEMP Space (dba_hist_active_sess_history.temp_space_allocated, dba_hist_tbspc_space_usage)
Data Size and Growth:
Managing data file and table/index sizes to optimize storage and enhance database scalability.
· Datafile Sizes (dba_data_files, dba_temp_files)
· Table/Index Sizes (dba_segments, dba_indexes)
Network Performance:
Monitoring network latency and throughput to optimize network performance and improve database scalability.
· Network metrics in dba_hist_sysmetric_summary and dba_hist_sysstat
· Network latency (OS-level monitoring)
· Network throughput (OS-level monitoring)
Database Growth Management:
Managing disk space utilization and file system fragmentation to optimize storage and improve database scalability.
· Disk space utilization (dba_hist_seg_stat)
· File system fragmentation (OS-level monitoring)
Conclusion:
A feature engineering approach to solving, monitoring, and testing for scalability in an Oracle database is essential for optimizing performance and ensuring scalability under varying workloads. By focusing on key metrics and performance indicators, database administrators can proactively address potential bottlenecks and optimize system performance.
As we journey through the intricate landscape of Oracle performance metrics, we stand at the cusp of a transformative exploration. In this ongoing series, "Catching Fire," I hope I have helped to ignite the flames of curiosity and experimentation. Heeding the wisdom of William Butler Yeats, who once said, "Education is not the filling of a bucket, but the lighting of a fire," be inspired to delve into using data science-oriented analytical techniques.