This user guide provides an introduction to OPENMARU Cloud APM and its main features, and is intended for users who want to monitor web application servers (WAS) using OPENMARU Cloud APM. It is also a guide for identifying and analyzing failure causes through monitoring of web applications.

OPENMARU Cloud APM is an application performance monitoring (APM) tool that provides real-time monitoring of Java-based web applications, which can be used to proactively prevent failures and continuously improve performance.

In addition to real-time behavioral monitoring, OPENMARU Cloud APM provides the ability to apply real-time statistical analysis techniques to proactively determine problems.

OPENMARU Cloud APM is a product for monitoring web application servers and provides the following features.

APM consists of WAS Agent to monitor Web Application Server (WAS) server and SYS Agent to monitor system status and web server status, APM Server to collect and process/store data, and HTML 5-based user interface (UI).

The web server includes the SYS Agent and the SYS Agent’s Apache web server plugin to monitor system information and web server status.

The WAS server installs the System agent for collecting system information and the WAS agent for monitoring WAS. Both the agent and the connection to the server use the WebSocket protocol.

Go to "https://cloud.openmaru.io/console/login" and enter your Enter your username and password to log in.

Click the 'Logout' button in the upper right corner to log out and re-login. Log out and log in again.

The charts that make up the dashboard of WAS are shown in the following figure. The dashboard is organized by business application.

The dashboard displays the Request Velocity Chart, which shows the currently executing requests of individual instances used by the business system, and the Transaction Heatmap (T-Map), User Satisfaction Index (APDEX), Transactions Per Second (TPS), Active Users, Average Response Time, and Error Rate for the entire business system, as well as the Information Widget, which outputs the information as text.

The Request Velocity and Transaction Heatmap (T-Map) information is updated every 2 seconds, and the APDEX, TPS, Active Users, Average Response Time, and Error Rates information is automatically updated every 5 seconds.

The Request Velocity chart in the dashboard animates the processed requests for each individual instance by displaying them as circles and animating them from top to bottom. It also displays the requests that are currently being processed in the form of a bar.

If Label is turned on, it displays the TPS and average response time per instance as text.

Processing requests are colored green, orange, and red based on the level of user satisfaction index (APDEX) set by WAS Agent: Satisfying, Tolerating, and Frustrating.

When you see the requests being processed, click the BAR to go to the Thread Dump analysis page to get information about the request currently in progress. See Thread Dump Analysis Methods for details on how to analyze thread dumps.

A Transaction Heatmap is a heatmap that shows the distribution of response times over time in a grid, with darker colors indicating more requests processed in a given time/response time grid.

This is shown for all instances belonging to the business application.

Cells with HTTP Status codes of 400 or 500 errors are colored with a red border.

By dragging the mouse to draw a rectangle on the Transaction Heatmap (T-Map), you can analyze the detailed transactions on a method-by-method basis for the requests processed in that area.

At the top, the requests executed in the selected area are displayed, and if you click Row at the top, the transaction details are displayed at the bottom.

If you click on the link of the Request URL, you can go to the URL that the user requested and see what page was actually requested. In addition, you can check the start time, execution time, sum of execution time related to the database, and CPU time.

Transaction details are displayed in the following format. If the execution time is slow, it is displayed in red.

The exclusive time is calculated and displayed by calculating the time spent executing only in that method. The execution relationship of methods is represented in the form of a tree. It also displays B-Gab (Before Gab) and A-Gab (After Gab), and the calculation method is as follows.

If the SQL query takes a long time to execute as shown above (set in WAS Agent), Stack Trace is output to identify the location of the application that executed the query.

Collect all HTTP Status Code 40x and 50x error information. Among the 500 errors, errors such as JSP compilation errors are collected and displayed as error messages like below.

In the case of PreparedStatement, it is executed by setting values for each parameter in the SQL statement written with '?'.

Displays the SQL statement of PreparedStatement or CallableStatement executed in Transaction Trace, replacing '?' with the value set by the program and outputting it to the 'Mapped:' item. item by replacing '?' with the value set by the program.

You can copy the Mapped SQL statement and run it in a database query tool.

The JDBC URL of the database where the query was executed is also displayed.

After executing the SQL statement, the ResultSet’s next() function is used to get the value, which displays the number of hits and the time it took.

Fetch Count means the number of times the next() function was called, and Gab Time means the time from the first next() function execution until the ResultSet close() function is called.

It collects the information of the web server through the Apache Plugin in the System Agent on the machine where the Apache web server is installed.

It monitors the number of processes per second, the amount of traffic transferred per second, and the status of worker threads of Apache web servers and displays a dashboard of web servers on the screen.

Among the many data collected by the System Agent, a dashboard is organized and provided so that you can understand the problems that may occur in the system at a glance.

OPENMARU APM calculates and displays the user satisfaction index (APDEX) by task and the user satisfaction index by instance.

It also displays pending requests according to the level of the user satisfaction index.

The response time to satisfy a user varies depending on the type and nature of the business system. You can specify the response time that satisfies the user in the WAS Agent’s configuration file.

Displays the instance-specific user satisfaction index (APDEX). For the calculation method, see "3.4 What is APDEX?".

A chart that displays a scatter plot of user requests by response time per instance. Similar to the Transaction Map (T-Map) in the dashboard, you can drag the mouse to pop up the transactions that were executed at that time and analyze the detailed execution time.

For more information, see "APM Dashboard Screen Configuration - Transaction Heatmap (T-Map)".

Displays a graph of the number of transactions per second per instance.

Displays the number of active users per instance in a graph. The number of active users uses cookies to identify unique users and is calculated by summing the number of users with unique cookie values who have sent requests within 5 minutes.

Displays the latency information of all WAS instances in the business system in the form of Satisfying, Tolerating, and Frustrating statistics.

Graphically displays the number of sessions created in WAS per WAS instance, by web application context. Sessions in WAS are set by the session-timeout value in the application web.xml configuration file, as follows. The time unit is minutes.

In the Sessions graph, you can see which applications are deployed in WAS.

Summarize the number of HTTP Status response codes by WAS instance and application context by 20x, 30x, 40x, and 50x numbers and display the number in a graph.

Graphs the average response time of the instance application.

Graphically displays the average response time of the instance application compared to the processing time related to the database. in the application.

Displays the number of requests per instance in a graph.

Graphs the maximum, minimum, average, and number of requests for the application response time per instance. The top shows the maximum, minimum, and average values in unit time, and the bottom shows the number of requests.

Displays the number of URL calls, SQL query executions, and external calls using HTTP Client by day and hour.

Retrieve a list of the slowest URLs on a per-instance, per-day basis. You can specify the time and number of results using the time slider. Click a URL to display detailed transaction information by method.

If you check the 'Group by URL' checkbox, the searched URLs will be displayed grouped by URL.

You can also download the search results as an Excel file.

Calculate the number of HTTP Status code 40x and 50x error codes returned by each application as a percentage (%) of the total number of URL requests and display the error rate in a graph.

Displays hourly and daily graphs of errors that occurred in the instance. The information is categorized by the domain name that the user accessed by typing in the browser, separating 400 errors from 500 errors.

For example, if your WAS instance serves more than one domain name, you can see which domain name users are accessing and using to get errors.

Select a date to view hourly error statistics for that day.

If you select the 'Daily' tab, you can see error statistics for each day of the month.

If you want to see the details of the specific error that occurred, you can click on the corresponding bar to see the detailed transaction information for the error.

Displays the WAS instance memory utilization (%). The top graph shows the Heap utilization of the JVM, and the bottom graph shows the utilization of the Perm area.

Displays the memory size of the WAS instance JVM. It shows the maximum value and the current usage in bytes. Heap usage is shown at the top and Perm usage at the bottom.

Displays the GC status of the WAS instance JVM. The top line shows the time spent on GC, and the bottom line shows the number of GCs. Minor GC and Full GC are displayed separately.

Displays a graph of the number of files opened in the WAS instance. It shows the maximum number of files that can be opened and the number of files that are currently open. To analyze which files are open, see Analyzing Open Files per WAS Instance.

Displays the status of the WAS instance’s threads in a graph. Expresses the status of Java threads such as New, Blocked, DeadLock, and Runnable. If a deadlock occurs, an Alert is raised to notify you that a deadlock has occurred.

Displays the number of class files used by the WAS instance. Displays the total number, the number of loaded classes, and the number of unloaded classes in a graph.

Graphs the CPU time used by the WAS instance JVM.

Displays the number of data source connection pools set by WAS in a graph.

Max displays the maximum number of connections set in the Connection Pool,

InUse shows the number of connections currently in use,

Active is the number of connections that are currently available,

Avail is the number of currently connected connections, which is displayed as Active + Idle.

Displays the size and utilization (Hit Ratio) of the Prepared Statement cache in the WAS instance’s database connection pool. WAS such as Apache Tomcat and JBoss EAP 5 do not provide Prepared Statement Cache information, so it is not displayed.

To see what is currently running in a WAS instance, you can analyze the thread dump.

For details on how to analyze a thread dump, see, How to analyze when the server is slow.

Active Thread Control allows you to view information about the currently running threads without requesting a thread dump.

In addition, you can additionally view detailed transaction information, such as viewing the transaction information of the currently running thread in the T-Map.

You can analyze the objects being used by a given WAS instance.

Click the Request button to generate a histogram. Select the histogram and click the Analyze button to view the results.

Select the histogram you want to analyze for differences. You can also use the 'Analyze Difference' button to see the change in objects before and after.

You can analyze the files currently being used by the WAS instance.

Click the 'Request' button on the left to get a list of files that the WAS instance is using at this point in time. Click the 'Analyze' button to display the information.

The network health analysis of a WAS displays information about the TCP connections used by that WAS instance (Linux-based operating systems only). It shows only the ports used by the WAS instance. The graph in the upper left corner is only visible if the System Agent is installed on the machine where the WAS instance is installed.

To view system-wide TCP connection status information, go to System > Network > TCP Connection Status.

If you see an excessive increase in a particular item, you need to check which port or IP address is causing it. In the network status analysis, click the 'Request' button on the left to analyze individual TCP connections at the current time.

Each time you click the 'Request' button, the server stores the data at the current time, and when you click the 'Analyze' button, the information is displayed on the right.

Displays a graph of the user satisfaction index (APDEX) for the application. Multiple instances used for a task are summed to get the average APDEX value and graphed.

Displays information requested by applications organized by task in real time. You can monitor the processing status of requests.

Displays the Transaction Heatmap (T-Map) for the job. For instructions, see APM Dashboard Screen Configuration - Transaction Heatmap (T-Map).

To see what is currently running in your WAS instance, you can analyze it using thread dumps.

For details on how to analyze a thread dump, see, How to Respond to Server Failures with APM.

Summarizes and graphically displays the transactions per second (TPS) for all WAS instances that comprise the business system.

Summarizes and graphs the number of active users (the only users who have made a request in the last 5 minutes) for all WAS instances that make up the business system.

Displays the latency information of all WAS instances that make up the business system in the form of Satisfying, Tolerating, and Frustrating statistics.

Summarizes the average response time of all WAS instances that make up the business system and displays it in a graph.

Displays the response time for all WAS instances that make up the business system in a graph with the number of calls, maximum, minimum, and average values.

Shows the JVM Heap utilization of all the WAS instances that make up this business system, overlaid for comparison. You can show or hide the graph by clicking on the name shown in the legend of the graph, making it easier to spot instances with memory issues. See "APM Screen Organization and Common Features - Selecting Chart Legends and Exporting Data".

Displays the number of URL calls, SQL query executions, and external calls using HTTP Client by day and hour.

You can search for slow URLs for a job to see which URLs are slow. The usage is the same as "Statistics by WAS Instance - Search for Slow URLs", but this will output information for all instances that make up the job.

Summarizes and graphs the error rate for all WAS instances that make up the job system.

The error rate is calculated and displayed as the sum of the number of requests with HTTP Status codes of 40x, 50x, and 100x out of the total number of requests.

Displays hourly and daily graphs of errors in the workgroup. The information is categorized by the domain name that the user accessed by typing in their browser, and shows 400 and 500 errors separately.

For example, if your WAS instance serves more than one domain name, you can see which domain name users are accessing and using to get errors.

You can select a date to view hourly error statistics for that day.

If you select the 'Daily' tab, you can see error statistics for each day of the month.

Also, if you want to see the details of the specific error that occurred, you can click on the bar to see the detailed transaction information for the error.

This is a graphical representation of real-time request monitoring by task, as shown in the dashboard. For detailed usage, see Request Velocity Chart.

Summarize and graph the number of active users (the only users who have made a request in the last 5 minutes) of all monitored WASs without categorizing them by task.

Graphs the Java Heap memory utilization of all monitored WASs, not separated by task.

When the System Agent starts, it collects and stores information about the web server. It collects and displays the installation location of Apache/Nginx, the location of configuration files, build options, and version information of subcomponents used.

This graph displays the number of bytes processed by the Apache web server per second.

This graph shows the number of requests the Apache web server serves per second.

This graph shows the number of workers in the Apache web server that are currently processing requests and the number of workers that are idle.

Displays a graph of the work currently being done by each of the connections on the Apache web server, categorized by the following items.

Analyze and display the response code based on the web server Access log file.

It collects information through MySQL (mysql, mariadb, percona) and CUBRID Plugin in System Agent on the machine where the database is installed or on an external machine. + MySQL How to Install System Agent MySQL

MySQL displays Connection Usage, QPS, Pool Usage, and Write Latency information, and CUBRID displays CAS, QPS, Long Transaction, and Error Queries on the dashboard screen. In the case of OS CPU and Memory, it is information of the machine where System Agent is installed, so it cannot be collected when monitoring a database in a remote location.

This dashboard displays the monitoring items of a MySQL (mysql, mariadb, percona) Database cluster. Clusters can be grouped in the System Agent Mysql Plugin settings or customized in Application Group Management. Displays database cluster information and various metrics such as total resources and QPS on one screen. How to set up a user cluster

Displays monitoring items for each MySQL (mysql, mariadb, percona) Database instance on the dashboard. It displays the basic information of the database instance machine and resources, and various indicators such as QPS on one screen.

MAX_CONNECTIONS: The maximum number of concurrent connections to the MySQL server allowed. When this limit is reached, all new incoming connections are rejected until some of the existing connections are closed. This value can be set in the my.cnf file, or by using the "SET GLOBAL max_connections" command: +. In practice, Max Connections + 1 client connections are allowed. Additional connections are reserved for use by accounts with SUPER privileges, such as root.

MAX_USED_CONNECTIONS: A variable that indicates the maximum number of connections used since the MySQL server was started.

THREADS_CONNECTED: A variable that indicates the current number of connections to the MySQL server. If the number of connections is equal to the maximum number of connections, new connections are denied until some of the existing connections are closed.

To summarize, MAX_CONNECTIONS sets the limit on the number of concurrent connections that can be made to the MySQL server, MAX_USED_CONNECTIONS indicates the maximum number of connections used so far, and THREADS_CONNECTED indicates the current number of connections to the server.

Queries per second (QPS) is a measure of the number of database queries running on the MySQL server in a given amount of time (typically one second). It is a useful metric for monitoring the performance and capacity of a MySQL database because it provides a way to track the workload and demand on the database server.

A high QPS indicates a high level of traffic to the database, which can affect the server’s performance and ability to handle workloads. On the other hand, a low QPS can indicate that the database is underutilized or that the application is not generating enough traffic.

The parameter information used is shown below.

The DML ratio, or data manipulation language ratio, indicates the ratio of data manipulation language (DML) statements to the total number of statements executed in a MySQL database. DML statements include INSERT, UPDATE, and DELETE statements, which are used to modify data stored in the database. The DML ratio is an important metric to monitor in a database because it provides a way to understand the types of statements being executed and the balance between reading and writing data. A high DML ratio can indicate that the database is being heavily modified, which can affect the performance and responsiveness of the database.

The DML ratio is an important metric to monitor in a database because it provides a way to understand the types of statements being executed and the balance between reading and writing data. A high DML ratio can indicate that the database is heavily modified, which can affect the performance and responsiveness of the database.

The parameter information used is shown below.

The number of running threads in MySQL indicates the number of active threads, or connections, that are currently executing queries on the database server. Each connection to the database server creates a new thread, and each thread is responsible for executing one or more queries. The Running Thread Count provides a way to monitor the number of concurrent connections to the database and the overall workload on the server.

A high Running Thread Count can indicate that the database is under heavy workload, which can lead to performance issues and slow query response times. A low Running Thread Count can indicate that the database is not being fully utilized or that the application is not generating enough traffic.

It’s important to monitor the Running Thread Count over time and track any changes or trends. This is because it provides a way to understand the overall demand and workload on the database. If the Running Thread Count is consistently high, it might indicate that the database needs to be optimized or scaled to handle increased workload.

The parameter information used is shown below.

Session analysis in MySQL refers to the process of monitoring and analyzing the behavior of individual user sessions within a MySQL database. This includes analyzing the queries executed in each session, the resources used, and the overall performance of the session. This information can help you identify performance bottlenecks and optimize database performance.

The parameter information used is shown below.

MySQL’s buffer pool allocation percentage indicates the amount of memory allocated to the buffer pool in relation to the total memory available on the system. The buffer pool is the area of memory in MySQL where pages of data are stored, allowing for faster access to data.

The buffer pool allocation percentage is set by the variable "innodb_buffer_pool_size". The default value is 128 MB, but you can change it to a higher value to allocate more memory to the buffer pool and improve performance. The buffer pool allocation percentage is calculated as follows

innodb_buffer_pool_size / total system memory

A higher buffer pool allocation percentage means that more memory is dedicated to the buffer pool and less memory is available for other tasks. A buffer pool that is too large can lead to swapping and poor performance. It is important to find the optimal value for the buffer pool allocation percentage by monitoring the performance of your system and adjusting the value accordingly.

In summary, MySQL’s buffer pool allocation ratio determines how much memory is allocated to the buffer pool to store data pages and improve performance. The optimal value for this ratio can vary depending on the database size and available system resources.

The parameter information used is shown below.

MySQL’s buffer pool hit ratio indicates the percentage of database page requests that are fulfilled in buffer pool memory, as opposed to being retrieved from disk. A buffer pool is an area of memory where frequently accessed data pages are stored for quick retrieval without accessing disk.

A high buffer pool hit ratio indicates that the buffer pool is effectively caching frequently accessed data, which improves performance. On the other hand, a low buffer pool hit ratio indicates that the buffer pool is not large enough to store all frequently accessed data pages, resulting in frequent disk accesses and poor performance.

To optimize the buffer pool hit ratio, administrators can resize the buffer pool to match the amount of available memory and workload requirements. You can also use the innodb_buffer_pool_instances configuration parameter to divide the buffer pool into multiple smaller pools to improve concurrency and avoid performance bottlenecks.

Keep this ratio high for good performance.

The parameter information used is shown below.

MySQL’s buffer pool is an area of memory used to cache frequently used pages of data to improve performance. When a query is executed, MySQL first checks the buffer pool to see if the required data pages are already in memory. If so, the query can execute much faster because the data pages don’t need to be read from disk. If the required data page is not in memory, it is read from disk and added to the buffer pool.

The buffer pool is divided into multiple buffer pool pages, each of which can store a single data page. You can configure the size of the buffer pool and the number of buffer pool pages to balance performance and memory usage. Larger buffer pools generally provide better performance but consume more memory.

In summary, MySQL’s buffer pool is an important component that helps improve performance by caching frequently used data pages in memory. By configuring the size and number of buffer pool pages, administrators can control the amount of memory used for caching and optimize performance for their specific needs.

In MySQL, the IO ratio can be measured using various performance metrics such as InnoDB buffer pool hit ratio, query response time, and disk I/O latency. The exact calculation of the IO ratio can vary depending on the specific metric used, but it generally involves measuring the number of disk reads and writes against the number of database operations performed.

In conclusion, it is essential to monitor the IO ratio in MySQL to maintain efficient use of disk resources and optimal database performance.

The parameter information used is shown below.

InnoDB wait represents the amount of time a thread in the InnoDB storage engine must wait for a resource to become available before continuing its work. This wait can be caused by a variety of factors, such as lock contention, disk I/O, or memory allocation.

InnoDB wait can affect the overall performance of the database because it delays query and transaction processing. It is important to monitor and identify the root cause of InnoDB wait and take appropriate action to reduce or eliminate it.

There are several common InnoDB waits that database administrators may encounter, including the following

The parameter information used is shown below.

MySQL’s table open cache ratio indicates the percentage of tables that the MySQL server opens and caches in memory, relative to the total number of tables in the database. The table open cache ratio is a measure of the MySQL server’s efficiency in using memory to store frequently accessed tables and can affect the overall performance of the database.

In general, a high table open cache ratio means that more tables are being stored in memory, which can reduce the amount of disk I/O required to access the tables and speed up database queries. However, a high table open cache ratio also means that the MySQL server is using more memory, which affects the overall performance of the system and can lead to memory constraints if not managed properly.

To optimize the table open cache ratio, it is important to monitor cache usage and adjust the table_cache system variable as needed to ensure that the most frequently accessed tables are stored in memory. It is also important to track the overall memory usage of the system and adjust the available memory as needed to ensure that the MySQL server has enough resources to operate efficiently.

The parameter information used is shown below.

MySQL’s thread cache hit ratio is a performance metric that shows how efficiently the server uses the thread cache. It is calculated as the ratio of successful thread reuse to total thread creation requests. A high thread cache hit ratio indicates that the server is able to reuse threads efficiently and minimize the overhead of creating a new thread for each connection request.

A thread cache hit ratio of 100% means that all connection requests are being processed by cached threads, while a low hit ratio might indicate that the server is not using the thread cache efficiently, resulting in new threads being created for each request. This can reduce server performance.

To optimize the thread cache hit ratio, you can increase the size of the thread cache by adjusting the thread_cache_size variable in the MySQL configuration file. The larger the cache size, the more threads the server can reuse and minimize the overhead of creating new threads, which improves performance.

The parameter information used is shown below.

Binlog cache disk usage indicates the amount of disk space used by the MySQL database system to store binary log files. Binary log files, also known as binary log cache, are used to track database changes such as data insertions, updates, and deletions.

In MySQL, binary log files are stored on disk, and the amount of disk space required to store these files depends on the amount of database changes and the frequency of database transactions. The Binlog cache disk usage metric is important because it can affect the performance and reliability of your database system. High disk usage can result in slow database performance and increased disk I/O operations.

To reduce Binlog cache disk usage, administrators can optimize database configuration, such as increasing the cache size and regularly removing old binary log files. They can also use the Binary Log Rotate feature to automatically rotate and archive old binary log files to free up disk space and improve performance.

In conclusion, monitoring and optimizing Binlog cache disk usage is an important aspect of maintaining a healthy and efficient MySQL database system.

The parameter information used is shown below.

Check the information of the entire table.

View detailed information by schema.

Check the engine information and number of tables by schema.

Check the number of tables without PKs.

Check the table information for non-numeric types.

MySQL’s full-scan ratio indicates the percentage of times the database server scans the entire table instead of using indexes to retrieve data. A high full scan ratio indicates that the database is not using indexes efficiently, resulting in slow query performance.

To minimize the overall scan ratio, we recommend creating indexes on frequently used columns and building queries using the indexes. Optimizing indexes can significantly improve query performance because the database server can quickly find the desired data in the index instead of searching the entire table.

The parameter information used is shown below.

Immediate and waited are two performance metrics in the MySQL performance schema that show the number of table lock requests that are immediately authorized and the number of table lock requests that must wait to be unlocked, respectively.

IMMEDIATE reflects the number of lock requests that were immediately granted because the table was not locked or the lock was immediately available. This metric indicates the performance of the locking mechanism and the overall load on the server. A large number of immediate lock grants can indicate a well-tuned server that handles a large number of requests.

On the other hand, waited reflects the number of lock requests that have to wait for a lock to be released. This metric can indicate the presence of lock contention, where multiple queries are trying to access the same table at the same time and one must wait for the others to complete. A high value of waited can be an indicator of poor performance and may indicate that the database is not optimized for high concurrent usage.

In summary, these two metrics provide important information about the performance of MySQL’s locking mechanism and can be used to identify potential bottlenecks or problems in the system.

The parameter information used is shown below.

The tmp disk percentage in MySQL indicates the percentage of disk space used for temporary storage compared to the total disk space available to the database server. This temporary storage is used to store intermediate results during complex queries and is also used to sort and group data.

CREATED_TMP_TABLES is a performance metric in MySQL that indicates the number of temporary tables created on disk rather than in memory. The value of this metric is an indicator of how much temporary disk space the MySQL server is using. A high value for CREATED_TMP_TABLES indicates that the MySQL server is running out of memory and needs to use temporary disk space to store temporary tables.

The parameter information used is shown below.

The slow_log table in MySQL is a log file that tracks slow running queries in the database. It is used to diagnose and improve database performance by identifying the source of slow-running queries.

The slow_log table stores information about each slow query, such as the start time, execution time, SQL statements, and number of rows processed. You can use this information to identify which queries are taking too long to execute and what changes you need to make to improve performance.

To enable the slow_log table in MySQL, you need to modify the my.cnf file or my.ini file on Windows and add the following line.

The "slow_query_log" parameter enables the slow query log, the "slow_query_log_file" parameter specifies the path to the slow query log file, and the "long_query_time" parameter sets the threshold time in seconds for defining a slow query.

The slow_log table is a useful tool for database administrators to optimize database performance and improve query performance.

MySQL’s "memory_summary_by_account_by_event_name" represents a query that summarizes the memory usage of different accounts in the database by event name. It provides a breakdown of how much memory is used by each account and what types of events (queries, table operations, etc.) consume the most memory.

This information can be useful for database administrators to identify areas where memory usage should be optimized or resources should be allocated differently. You can run the query by running a SELECT statement with a group by clause against relevant database tables and columns, such as the "performance_schema.memory_summary_by_thread_by_event_name" table.

The query output provides a summary of the total memory used by each account, categorized by event name. You can use this information to identify areas where you can improve performance, such as optimizing database queries, reducing the amount of data stored, or adding additional resources.

memory_summary_by_host_by_event_name is a performance schema table in MySQL that provides information about memory usage for various events grouped by host. This table is part of the performance schema, a feature of MySQL that provides real-time information about server performance.

The memory_summary_by_host_by_event_name table contains information about the amount of memory allocated and used by each event and the number of instances of the event that were executed. This information can be useful for identifying which events consume the most memory, which can be useful for identifying performance bottlenecks and improving the performance of your server.

Some of the columns in the memory_summary_by_host_by_event_name table are.

In summary, the memory_summary_by_host_by_event_name table provides important information about the memory usage of server events that can be useful for identifying performance bottlenecks and improving server performance.

memory_summary_by_thread_by_event_name is a table in MySQL’s performance_schema that provides memory usage statistics for each thread on the server, categorized by event name. The table shows the amount of memory used by each thread, grouped by the type of event it is running (for example, parsing SQL statements, executing stored procedures, and so on).

The columns in this table include

This table can be useful for determining which threads are using the most memory and which event types are contributing to that usage. Database administrators can analyze the data in this table to identify areas where memory can be over-allocated or freed to optimize memory usage.

Memory_summary_by_user_by_event_name is a metric in MySQL that provides a summary of memory usage by user and event name. It helps you monitor the amount of memory consumed by different users and events within your database. This information is useful for identifying performance issues and managing database resources efficiently.

The memory_summary_by_user_by_event_name metric provides information about the following aspects of memory usage.

By regularly monitoring this metric, database administrators can identify performance bottlenecks and optimize resource utilization by adjusting query execution plans or tuning database configuration.

MySQL’s memory_summary_global_by_event_name references a performance schema table that provides information about memory usage by various events on the MySQL server. This table stores data about memory usage for various events and categories of events, such as memory used for sorting, memory used for hash tables, memory used to store query results, and so on. The data in this table is aggregated at a global level and can be used to identify the largest memory consumers on your MySQL server. This information is useful for optimizing memory usage and improving performance.

On the first day of every month, it automatically generates a report for the previous month.

Alternatively, you can generate it manually using the "Generate report" button.

The report is organized by Connections, Storage Engines, InnoDB, Caches, Tables, Queries, I/O related, Monitoring, and Concurrency to give you a quick overview of whether your DBMS is running stably.

The simplified report shows the daily fluctuations in usage through graphs.

Each time the System Agent is run, it collects and stores OS basic information such as version, kernel version, architecture, and number of cores, OS network basic information such as netmask, gateway, and DNS, disk partition information, and network interface card information.

If you press 'Ctrl + F', you can search for the desired text.

You can check the current Docker basic information, Image list, and Container list information.

You can check the current container status.

Running, Stopped, Paused, and Number of Images

Displays a graph of the CPU utilization of the system, separated by containers.

Only the top 20 heavily used items are shown.

Displays a graph of the system’s memory utilization by container.

Only the TOP 20 heavily used items are shown.

Displays a graph of all items of the system’s CPU utilization.

The meaning of each item is as follows

Graphically displays the system’s memory usage and swap usage. It displays total memory and the amount of memory currently in use, as well as the total swap memory size and swap memory usage that you have set. Swap memory is a way to use the file system on disk as memory. Therefore, unless the system suddenly runs out of physical memory, using swap memory in the operating system will significantly reduce the overall performance of the system. It also has a significant impact on the performance of JVM > applications running on the OS, so it’s a good idea to check Swap usage.

Graphically display the average load of the system.

A high Load Average can indicate that the system is overloaded. If you have a system with 1 core, a load average of 1 means that it is using all of its cores. Most system administrators agree that a Load Average of 0.7 or higher will sooner or later lead to an overload situation, so it’s a good idea to find the cause and fix it. If it’s above 1.0, you need to find and fix the problem immediately. If it’s over 5.0, it’s really serious. If left unchecked, the system will either hang or slow down significantly.

On multi-core systems, the average load value is affected by the number of cores available. If it is 100% utilized, it will show 1.0 on a single core and 2.0 on a dual core. Naturally, it would be 4.0 on a quad-core system with four cores.

It also shows the 1, 5, and 15 minute averages, so if the 15 minute average is over 1.0 on a single core, it means you are in a constant overload situation.

Of course, the Current chart shows the 1-, 5-, and 15-minute averages better, but the History chart shows averaged data over hours and tens of minutes, so the 1-, 5-, and 15-minute averages are almost identical overall.

To check the current CPU and Core count of your system, go to System - Server Information and check the 'CPU' section.

Displays the system’s disk usage graphically for each partition. It shows the total size and usage. Because it calculates and displays usage in bytes, the size will be slightly different from the size output by df, which displays in blocks.

Displays the number of disk I/Os for each partition in the system, separated by Read / Write.

Displays the amount of disk I/O bytes for each partition in the system, separated by Read / Write.

Disk Average Service Time (ms) shows the average amount of time it took to process a disk read/write request. This time does not include the time spent waiting in the queue for processing. A service time of 10 ms or less is considered a good value. Anything above 20 ms is a potential bottleneck.

Graphically displays the average size of the queue of requests waiting for disk writes/reads.

File Node Count graphs the number of inodes. An inode is a data structure used by Unix-like file systems that holds information about the file system, such as files and directories. Each file has one inode, which contains information about the file, such as the owner group, access mode (read, write, execute), and file type. Files within a file system are identified by their unique inode number.

Typically, when a file system is created, about 1% of the total space is allocated for inodes. Once the number of available inodes is used up, no more files can be created, even if disk space is available. You can easily run out of inodes by creating too many files in one directory.

Displays the system-wide Network TCP connection status information in a graph.

Each item looks like this

Graphically displays the number of send and receive bytes per second of data for each network interface card in the system.

Graphs the number of send/receive packets per second of data for each network interface card in the system.

Graphs the number of packet errors per second of data for each network interface card in the system.

If you are experiencing packet errors or drops, it is likely that you are experiencing excessive network load. It is recommended to check the network or operating system kernel parameters (network related).

The dashboard displays information about real-time success rate, average response time, current day and previous day data.

Follow the steps below to register a Health Check item.

You can set it up similarly to Postman, a Rest API testing tool, or JMeter, a tool used for load testing.

Apply
After completing the settings and modifications, click the Apply button to send the contents of the entire Job to each Agent to be reflected.

Test
Runs a trial test for the selected Job and displays the result on the screen.

* Register Service
+]

You can set the alert policy for each WAS instance, Web server, and system.

Event messages are displayed in the upper right corner of the screen when they occur. The types are as follows

Provides the ability to search for events that have occurred on a daily basis.

It displays a graph of the number of events over time at the top and a list of events at the bottom.

You can change the date to search for events that occurred on that date, or you can search for events. You can click on a column name in the table to sort by that column to analyze the event data.

Refer to Event Setup Guide to set up event notifications.

The daily report is generated for each application group, and 'Report Date' means the date of the report contents, and 'Created' shows the date and time when the report excel file was created. Click the 'Download daily report' link to download the excel file.

The report generates hourly call count, hourly average response time, hourly APDEX, hourly TPS, hourly active users, hourly error rate, hourly JVM Heap utilization status, hourly system CPU utilization, hourly system memory utilization, top 50 slow URLs, and top 50 slow queries for each item.

The first tab, 'Report', displays a summary report for each item, and each tab shows hourly data items, hourly data items compared to yesterday, and 10-minute data items.

Each Tab item displays tabular data and a graph.

To change the automatically generated graph, select the item you want to graph from the "Select Instance: menu, select the item you want to graph, and click the 'Create Chart' button to create a graph with that item.

The weekly report is generated for each application group, and the 'Report Date' means the date of the report content, and 'Created' shows the date and time when the report excel file was created. Click the 'Download Weekly Report' link to download the excel file.

By clicking the 'Select Weekly Report Generation Day' button, you can select the day of the week you want to generate the weekly report. For example, if you select 'Sunday', the statistics from last Monday to midnight Sunday will be generated at 33:00 on Monday morning.

To generate the Excel file for the weekly report, download the Excel file and click the 'Generate Report' button, just like the daily report.

User Groups is a menu to manage the groups to which a user belongs.

Application group management allows you to manage a list of clustered instance groups and enable/disable specific instances as needed.

You can also group different clustered instances together by adding user application groups.

You can manage the license of OPENMARU APM as follows by selecting 'Settings' 'License Management' from the left menu. To enter the issued license, click the 'Add New' button.

Enter the issued license in the license field and click the 'Save' button.

The license has been entered into the server as shown below.

When a service is slow, a thread dump can help you determine the cause. When a service is slowing down, you can use APM’s metrics to understand what’s going on.

If you are curious about the execution speed of the application you are developing in production, simply add the @TraceMethod annotation to your code and it will be added to the Transaction Trace item in APM to check the execution time of the method.

$ git clone https://github.com/opennaru-dev/khan-monitoring-api

$ mvn install

Alternatively, you can download the compiled JAR from the following URL.

https://github.com/opennaru-dev/khan-monitoring-api/blob/master/dist/khan-monitoring-api.jar

Add the dependency as follows

Call a class named Test in the JAX-RS method like below.

For applications that have finished processing, use the Transaction Heatm