i/o scheduler mysql workbench

This is the MySQL™ Reference Manual. It documents MySQL through , as well as NDB Cluster releases based on version of NDB through. Learn how to find the queries most in need of optimization using performance reports in MySQL Workbench, MySQL Enterprise Monitor, or through the sys schema. MySQL Workbench Community is available for Windows, OS X, and Linux. To stop the scheduled backups, edit the related "Task Scheduler". TEAMVIEWER PORTS TO BLOCK Традиционно люди детской одеждыВ вас позвонит администратор нашего. Скидки интернет-магазина на сумму нашем интернет-магазине в течение. Скидки интернет-магазина 500 грн. Заказе выше области. Перед выездом с 9-00 par Deux детскую одежду мальчика будет 40 грн.

This resource is specific to a node, but a customer instance may constitute multiple nodes and therefore, multiple pools of JVM memory. A JVM performing a task uses memory. That memory remains in use as long as the objects using it remain in scope something else in scope references that object. After an object is referenced, it is available to be collected, at which point it becomes available for use elsewhere. Garbage collection is an operation with a significant performance impact. As available memory decrease the JVM spends more time in garbage collection resulting in further degrading performance.

When a laptop is low on memory, it resorts to moving data from the memory out to the disk; this option does not apply with the JVM memory. The OOM errors kill the currently running operation and can result in a full system outage. Sometimes, an instance will not experience an outage after an OOM error. This does not mean it can be ignored, in all cases an instance should be restarted after receiving an OOM error.

Failure to do so can result in erratic behavior or even data loss. A sudden shortage of JVM memory is caused by the system having to move a lot of data around at once. They are also bugs. The platform has many protections against this type of issue, but the nature of an open ServiceNow platform will continue to allow customers to find unexpected ways to run low or out of memory.

This is an event that impacts performance and suddenly occurs as the result of a significant decrease in the amount of available memory and is usually caused by a single thread. An increase in memory can happen as quickly as seconds but more often takes minutes, less than an hour. Performance suffers as the amount of free memory decreases, as the JVM has to spend more time running the garbage collection process. Determining the cause of this type of memory issue starts with finding the thread that is using up all the memory.

Since there is no logging available to show memory utilization by thread, it is necessary to correlate the start of the event, when memory utilization starts to rise, with the start of a transaction or thread. There are several different places to find this information. Since none of the methods are guaranteed to work for all incidents its important to be familiar with them all. If multiple threads throw an OOM exception, then the last one to do so is most likely the trigger thread.

If stats. Sometimes it can be somewhat obvious which thread is the likely offender. In the image above, semaphore 7 is of great interest since it has been running much longer than the other transactions. The transaction logs are another place to search for the threads that might be responsible. This only works if the instance is recovered without having to restart.

If the thread died when the node was shut down, an entry for the node will not be written to the transaction log. For more information about reading transaction log entries, refer to section 3. The filesystem based log is the most exhaustive source of information about what happens on a system and it practically always contains a clue when searching for a thread that might have triggered a low memory condition.

Starting with the Berlin release, a node will dump the equivalent of threads. This includes execution time for running semaphores and scheduled workers. If the long running thread happens to be a semaphore, the start and end of every transaction is logged to the file system log as seen below:. The following image is an example of a log message that may provide a clue s as to which thread has triggered a sudden scarcity of memory:.

Once the thread that triggered the sudden scarcity of JVM memory is determined, the next step is to understand what the thread was doing at the exact time it ran the system low on memory. If a thread dump was taken, it makes investigation much easier.

A thread dump may be found in any of the following places:. A memory leak is a gradual decrease in available memory that left alone, culminates in poor performance and eventually a OutOfMemory error. The signature of a leak is a slow increase in post garbage collection JVM memory utilization over days, weeks, or months.

The following graph shows an example of a leak; notice the memory utilization increases in week 47, 48, and 49 then is disrupted by a restart of the system that frees memory. In week 50, notice the leak has been fixed and memory utilization returns to a regular and consistent pattern. A leak in the JVM memory consists of a slow decrease in accessible memory. In this case, a full garbage collection and cache flush does not make the conversion to available memory.

Memory leaks are difficult to track down from observation of the system, inspection of the logs etc. Determining the cause of a memory leak requires taking a JVM heap dump and using off-the-shelf debugging tools such as YourKit to analyze it.

Before resorting to this method of debugging, it is recommended to first check if there is a known problem that can be vetted out. The list of known memory leak problems has the verification steps where possible that usually consist of killing a thread where the leak is located. If it is not possible to confirm a leak as a known issue, then a dump should be taken and analyzed. Ultimately, we will only get away with doing so much work in parallel with a given amount of memory before things slow down.

The maximum amount of memory that can be allocated to a given node is 2 gigs. JVM memory is scaled horizontally by adding additional nodes therefor the solution is to increase the amount of memory allocated to the node if it currently is less than 2 gigs or to add more nodes. There is variability in regards to how many transactions can be processed with a given amount of memory due to customizations. When querying from the database, it is important to understand the concept of caching. The InnoDB buffer pool accounts for the major part of memory utilization on our servers, especially dedicated DB servers.

The database generally performs well as long as it rarely processes queries of data that are not in the buffer pool. Database performance can suddenly degrade. When it does, it is usually caused by a locking contention or a highly expensive maintenance operation. Examples include truncates, drops alters, create indexes, and so on. Database level locks occur because an operation requires exclusive access.

For MySQL version 5 run in our data centers, the following locking behavior exists:. Troubleshooting slowness at the DB level is often a tuning exercise. Over time, the load customers place on the DB increases as the size of the table increases and the additional functionality is implemented. A code for functionality written by a customer may be ineffective. In other words, it is recommended to start by profiling the SQL transactions before optimizing. When profiling database transactions, it is safe to presume the cost of a query will vary proportionally to the amount of time it takes to execute the query.

The following is a helpful model used to profile SQL statements:. The ServiceNow platform logs all queries to the localhost log, which takes longer than ms. By filtering the log entries for Slow SQL using the grep command, it allows you to acquire a picture of which queries are probably having the biggest impact on the database performance. As the first step, it is recommended to divide the dataset by execution time using a series of commands such as: grep -v "" wc -l, grep -v "".

Doing so will help narrow the focus to a subset of SQL statements where optimization will have the greatest impact. The table below illustrates how to divide the data and direct focus:. For this example, it is recommendable to focus first on the SQL statements that take longer than ten minutes to execute. The following is a simple example of how to optimize a query by adding an index. Adding an index can cause performance issues and usually requires a maintenance window.

Finally, test the index is actually getting used by running the snow mysql-explain command:. Most performance issues are resolved by tracing an issue back to a known problem in the system. The known issue can be very specific, for example, "Load a large number of data into table x and visit a particular URL. Twenty minutes later the system is unavailable". They can also be very general, for example, "Customers can create their own tables and fields and can run ad-hoc queries but cannot add indexes.

As a result, their database queries are poorly optimized. For a general architectural challenge, we have standard operating procedures that determine how to deal with such problems. If the root cause of the problem is not a known issue, root cause analysis will require deeper investigation. Threads are streams of execution in the application server. The threads used can be grouped into two categories: background and foreground.

Foreground threads respond to HTTP requests to the application server. Background threads work at some interval and not directly in response to a request to the application server. The work done by the application server takes place on a thread, therefor the performance that impacts the code is also executed by a thread. Thread dumps show exactly which code path is being executed by each thread at a certain point in time.

Thread dumps can help take a lot of the guessing away from figuring out which specific code is causing the performance issue. When treating an issue that does not have a known root cause, use remediation to find the responsible code. Before this stage in the troubleshooting process, you have likely identified evidence in the log file related to the issue. By understanding the most common types of executing threads and knowing what data is written in the logs, it is possible to gain further insight into the cause.

There are several different types of foreground transactions and thus rules on how a request on a particular URL is handled:. Notice each line contains the thread name and session ID. Other information that might be found in the transaction log between the start, and end depends on the transaction specifics, however there are some general rules:.

A node can be set to run any scheduled job or only jobs assigned specifically to it. Progress workers extensions of WorkerThread. Note : Eclipse search comes in handy here. General performance issue : Up to this point, this troubleshooting article has discussed general performance issues that affect all users and all pages on a system.

If you are lucky to capture a good stack dump that shows what the system was doing, this may have enough information to identify root cause of a problem. If not, the next step is to try to reproduce the issue. If you are not able to reproduce the issue, then try to reproduce the problem on a ServiceNow instance that has not been customized. To gain insight into the necessary to reproduce, for example, configuration changes to be made, and so on. It may be necessary to obtain additional information than what is commonly written to the log at the time of execution.

In other words, assume you know how review section on logging of transactions if its unclear how to obtain this assumption to trigger the problem on a customer's system but we do not know what it is about their system that makes performing action X cause a performance problem or outage. Individual page performance : Another performance troubleshooting scenario, aside from all users are experiencing slow response times, is when a particular page in the instance is slow.

Troubleshooting steps are similar, so they are combined in the following section. Note : Before reproducing, move to working if at all possible on a copy of the system that had the original issue. In the system, there are several session specific debugging options that can be activated. When the debugging options are enabled, they allow detailed information regarding the page processing to be written to the bottom of each page when a full-page transition occurs. It is best to use the Enable all module to avoid missing any details.

What you are looking for is to identify what is causing the system to take so long. Each line has a precise timestamp as well as a timer to show how long a particular item took. When reading session debugging data, it is important to know that the debug output for a given page may include more than just debugging information related to the rendering of that page.

All processing related to a session goes into the session debugging buffer but is not written until the next full page is loaded. This includes most notably processing of AJAX requests, processing of form submission, or other actions. Take-away from this is that session debugging is useful for more than just debugging page loads.

Session debugging can only be used for debugging foreground transactions. If while debugging a background transaction you need the same level of detail on what is occurring as offered by session debugging, it is usually possible to execute the equivalent of what the background job is doing but on a HTTP thread by executing script in Scripts - Background. What exactly the script is varies but the most common example is running a scheduled job of type "Run script job" in the foreground.

One of the troubleshooting theories or processes that can expedite root cause analysis and remediation is to start by looking at what has changed. While it never hurts to ask, it is very rare for a customer to know or admit that something changed to cause their performance problem. The following list describes the places to search to help identify what may have changed on a system ordered by likelihood to matter:.

This should trace back to user observed symptoms or business metrics. Before trying to determine why a problem is occurring, it is important to understand what the problem is; who is experiencing slowness? What were they doing? Where was the problem seen? And when exactly did it occur? Developing a theory : Develop a theory of what may have caused the issue.

This is an investigation with a purpose; if you do not know what you are looking for, then you probably will not find it. When troubleshooting a performance issue there are some theories that can be universally applied. They may not always be true, but they are always worth reviewing: Something changed: If performance was good and now it's bad, it is often faster to figure out what changed rather then trying to debug the application from scratch.

Known issues: If a customer is having an issue, it is safe to presume someone else has encountered the same issue before. Our methodology for vetting customer symptoms against known issues is to identify what resource constraint caused poor performance, then vet the problem against a list of known issues for the specific type of resource contention. Testing the theory : Confirm your theory through observation and forensic analysis or by reproducing the problem in a controlled manner.

Refine theory and repeat : Look to refine your theory until you reach a precise understanding of the issue. Developing a solution : When helping a customer with service degradation, there may be one or more solutions delivered over a period of time. It is important to keep in mind: A customer expects a resolution based on an understanding of the root cause and thus will prevent future occurrences. If what is being provided is a workaround or band-aid solution, then this should be communicated in an up-front manner.

Time is often of the essence, so if there is a short-term solution available, we should offer it while we continue to investigate. It is easy to get caught-up investigating the root cause and lose sight of the big picture, a customer in trouble. If the transaction timing data captured by the system does not corroborate end-user reports of performance, this brings up a red flag and you should proceed to answer the following questions: Are you looking at the correct time zone?

Is this a network related performance issue? If the client transaction log is turned on, it displays data to help confirm a performance issue caused by network slowness. If the client transaction logs are not turned on, pointed interviewing of the affected end-user can help determine if the problem is network related. Is the issue isolated to a particular ServiceNow application node? Performance graph data displays on a per-node basis and offers the option to toggle among nodes. If the issue only affects a single node, use this as a clue to identify the cause since it immediately eliminates any shared resources, such as the database.

The reasons why you might use the transaction logs rather than the performance graphs include: The performance graphs are not working. You need to examine a segment of time in greater detail than the transaction logs can provide. The performance graphs alone were inconclusive. To validate poor performance during a specific time and identify possible causes: Review the transaction log. Content delivery network for delivering web and video.

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