What Optimization in SQL is, and Why It’s Necessary for DBAs

SQL statements or queries are designed to retrieve information from the database. A user can achieve the same results through optimization in SQL; using a tuned query is especially useful from an execution perspective. 

Tuning a database is a vital step in organizing and accessing the information in a database. Performance tuning in SQL requires streamlining and homogenizing the environment of a database and the files in it. This simplifies the way users access data in a big way. 

Why Companies Need to Consider Optimization in SQL

Several organizations own databases, but not all of them hire IT staff knowledgeable in the ways of optimization in SQL. Only professionals who have tuning skills and experience along with insider information about the working of databases should do this. 

In case your company has a database but it hasn’t undergone performance tuning, you might encounter inadequate responses to queries and face unnecessary complications when handling data. Don’t let your efficiency get affected because of something avoidable like this! 

Performance Tuning in SQL: What It Involves

Tasks related to performance tuning include optimization in SQL database, creating and managing indexes, and other related tasks to maintain or improve database performance. The goal of MySQL query optimization is to increase the speed and brevity of query responses and to simplify data retrieval. 

Let’s look at three major reasons why companies need to take performance tuning and seriously – 

1. To enhance the rate of data fetching options

If your database lacks optimization, then fetching data can get slower with increasing data loads. Optimizing queries enables users to create indexes and eradicate issues that may be slowing down data retrieval. After all, it can get quite frustrating for your employees to wait for the database to perform its operations, which can pass on to customers forced to wait for the same.

2. To refrain from coding loops

Making your database go through a coding loop is akin to hammering it repeatedly. That’s because the same query is executed several times when it is placed in a loop. However, once you remove the query from the loop, you will experience a definite surge in performance because the query is run only once rather than going through multiple iterations. 

3. To increase the performance of your SQL statements 

Query tuning in SQL includes changing previous query patterns and habits that were affecting the speed of data storage and retrieval. For example, the use of SELECT is reduced by opting for separate column declaration and eliminating correlated subqueries. Queries are also simplified by obviating temporary tables at times, aside from many other techniques of optimization in SQL

Your database will be able to manage much more data after the application of all these improvements as these will increase its efficiency, making it scalable as well. Once your database has scalability, it also overcomes lower performance and ensures user satisfaction in terms of experience. 
If you require professional tools to manage MySQL query optimization and tuning, then Tosska can help you. Tosska provides highly intuitive tools that can simplify query tuning beyond your imagination, and it does this with the help of innovative AI technologies. Contact us today to learn more about our range of query optimization products and services.

Improve Performance of SQL Query with these Great Techniques

SQL performance tuning can be an extremely complicated task, especially where data in huge quantities is concerned. When implementing queries to insert data in large quantities, even the tiniest of changes can have a major impact on performance – for better or for worse. 

If you are new to databases, you may be wondering what SQL performance tuning is and how you can use it with sound knowledge of the fundamentals and a few tricks up your sleeve. In this blog, you will find some fundamental techniques for SQL tuning to improve performance of SQL query being entered in the database. 

Techniques to Improve the Performance of SQL Queries

Consider these five tips and techniques to enhance database performance – 


Indexes are quite effective in SQL tuning but are often overlooked at the time of development. Basically, an index is a data structure that can boost data retrieval speeds in tables by supplying quick random lookups and prompt access to requested records. This implies that once you have made an index, selecting, SQL performance monitoring, and sorting operations are faster. 

They are also useful in defining a primary key that will prevent other columns from having the same values. Naturally, database indexing is a vast topic that deserves its own set of blogs, but for now, it is important to understand that the aim is to index the larger columns intended for searching and ordering.

  • Keep in mind, however, that indexes must be modified after INSERT, UPDATE, and DELETE operations, which means they could actually worsen the performance of the database if your tables are receiving a large number of these commands. 
  • Furthermore, Database Administrators usually discard their indexes before executing gigantic batch inserts involving millions of rows, to hasten the insertion process. Once the task is complete, they then create the indexes all over again. It is important to remember, in such cases, that when the indexes are dropped in this manner, it affects all the queries being executed in that table. Hence, to improve performance of SQL query, this approach is typically taken in certain situations that require a single sizable insertion.

Execution Plan Tool in SQL Server

This tool helps create indexes and it shows all the data retrieval techniques selected by the query optimizer. There are walkthroughs available that will help newcomers learn more about this tool.

  • If you are using the SQL Server Management Studio, you can fetch the execution plan by pressing on Ctrl+M to select the “Include Actual Execution Plan” option before executing your query. This leads to a third tab named “Execution Plan” that will show any missing indexes that it has detected.

Steer Clear of Coding Loops

Suppose you need to insert a thousand queries in your database in one go. In that case, you may be tempted to do it using a loop but you must, in fact, refrain from doing so. 

  • Instead, consider changing the snippets containing the loop in unique INSERT or UPDATE statements that have additional rows and values. 
  • Make sure that your WHERE clause avoids updating the stored value if it matches the existing value. Such a trivial optimization can dramatically improve performance of SQL query by updating only hundreds of rows instead of thousands.

Checking Whether a Record Exists 

This is a handy SQL optimization approach that concerns the use of EXISTS(). 

  • In case you want to know if a certain record is present in the database, make a preference for EXISTS() instead of COUNT(). That’s because EXISTS() can give you much better performance with more coherent code as it leaves the table the moment it gets the data it needs. On the other hand, COUNT() scans the whole table every single time, counting up each and every entry that matches your condition.

How You Need Tuning to Improve MySQL Database Performance

SQL performance tuning is about saving you a great deal of money by improving various aspects of database management. Be it changing inefficient queries or replacing unnecessarily large indexes, tuning your database can really help you regulate your expenses.  

SQL tuning will not only improve MySQL database performance, but it will also help you save multiple resources, whether you operate your database servers on-premises or migrate your data over to the cloud.

Performance Tuning – More Than Just Optimization of SQL Queries

A majority of database professionals mostly focus on ensuring uptime by keeping tabs on the allocation of resources such as storage and memory. These formed a sizable portion of database management until companies started moving their databases to enormous cloud resources like AWS and Azure. 

This gave importance to other aspects like performance tuning in SQL, which DBAs work on once they ensure that things are running somewhat smoothly. However, a lot of database professionals may have to tweak a SQL Server that they aren’t familiar with, and there’s not much help available for such instances. 

Identifying Problem Areas During Performance Tuning

It can get intensive trying to figure out all the issues and resolving them one after another. At first, your problems may not even involve the optimization of SQL queries, and you may begin your search at the user/session-level. 

What some professionals do at this point is, they listen to the users and ask questions such as – 

  • Are there any specific times when the application slows down, for example, when they create an extra ticket or open an active one?
  • Which data takes excessive time to render?
  • How long does saving a record take? (a specific answer may be preferable)

Improve MySQL Database Performance in SQL Server with These Tools

Fortunately, SQL Server has multiple features that can help you improve MySQL database performance with greater convenience. Some of them are – 

Plan Guides

Plan guides enable you to adjust the way SQL Server executes a query, affecting performance. These are especially useful in case of queries written by another vendor, where some experts may not be willing to change them right away, as you can add a query hint to influence its operation. 

However, plan guides may get obsolete over time because the scenario may change after a while but they cannot. To overcome this, make a note of reviewing them from time to time. 

Query Store

This feature helps in the optimization of SQL queries too. It allows the user to determine which queries are taking up the most resources, and then tune them. Apparently, the Query Store feature is not enabled in some SQL databases because the user rarely needs it at first, but enabling it is easy.

Some DBAs are not aware of the Query Store, while others know of it but haven’t explored it enough, so the feature may as well be disabled. They can begin its use once they know how it works so that they can analyze various performance fluctuations caused because of modifications in the query plan.

Database Engine Tuning Advisor

This function examines workflows and suggests indexes or other strategies to improve MySQL database performance. On the other hand, don’t run this tool until your database has sufficient data stored, as the recommendations are more productive at that point. 

It won’t be as useful for a newer application that has, say, only a thousand rows in its tables, than after the app has expanded a bit.

Why DBA Tuning for Oracle SQL Query is Crucial in Databases

SQL tuning is an extensive topic in itself that deserves coverage in books dedicated to stored programming. This is because it is considered as a vital skill for those who create stored programs in MySQL or Oracle.

There are several other reasons as well, which we will discuss in this blog. 

DBA Tuning of SQL Queries – Why They are Important

Take a look at some reasons why SQL tuning is so important – 

  • Those who are not convinced about the importance of DBA tuning need to remember a simple fact that SQL statement execution takes up the most time when stored program execution time is analyzed. 
  • Additionally, a database that is insufficiently tuned or not tuned at all is tremendously slow by comparison. 
  • An untuned database cannot scale well with the growth in data volumes. This means that although your program appears to perform acceptably well at the moment, chances are, it will face serious performance issues in the future without proper tuning.

Database Query Optimization – An Informative Example

Given below is an example that explains the necessity of SQL query tuning in overall system performance. Let us consider a query that performs a straightforward join between two tables:

SELECT sum(sale_value),x.sales_id

FROM tx_10000 x,ty_10000 y

 WHERE x.sales_id=y.sales_id

GROUP by x.sales_id;

As data gets collected every day, the size of the tables also increases. At first, there may not be any visible change in performance, but that will change just after some days. After about seven days, the application will become nearly inoperable if the query plan is not correctly selected by the database SQL optimizer, and that’s when the need for a DBA SQL tuning is realized. 

On closer analysis, the DBA shall uncover a proportional relationship between table size and elapsed time. It is also observed that along with the worsening query performance as the tables expand, there is also an acceleration in the rate of increase.

The DBA may project the estimated time to be around twenty hours as the size of the tables approaches their peak sizes, around a million rows, which is truly a poor performance trend. 

The issue and solution both seem obvious once the SQL statements used in the application are assessed, and the solution is to perform database query optimization by supporting the join with an index, which can be created in this manner: 

CREATE INDEX i_ty_1000 ON ty_1000 (sales_id);

This brings a remarkable improvement in the performance trend, with over 99 percent decrease in elapsed time for the mentioned query. Furthermore, the database is over a hundred times quicker and it can now scale well with the raise in table data volumes. 

In Conclusion

Such dramatic changes cannot be achieved through hardware upgrades, stored program tuning, or any amount of server enhancement. Even if any of these were to be carried out, it wouldn’t make much of a difference in the long run. 

This is because the rapid degradation would ultimately quash any performance improvements attained by other techniques. This is why DBA tuning is more important than any other mode of optimization and should be performed first. 

Oracle or MySQL SQL query tuning is the most essential aspect of application performance in general, so make sure that SQL is tuned before any other sort of optimization activities are carried out.

MySQL SQL Performance Tuning: Confusing MySQL Server Variables

MySQL Server contains a wide variety of variables that can be modified according to different uses or to enhance performance. However, despite their detailed documentation, there is plenty of confusion regarding which of these variables are suitable only for storage engines and which ones are utilized on SQL layer, and are applicable for all storage engines as well. 

An important factor to consider during MySQL SQL performance tuning is the storage engine in use. Keeping that in mind, this blog features a list of variables that are sometimes mistaken with similar variables.

MySQL Database and SQL: List of Confusing Variables

Let’s take a look at some variables that may seem confusing to users in terms of their applications (whether they are storage engine specific or used with the SQL layer) – 

  • Read_buffer_size/read_rnd_buffer_size – These variables are used in certain tables for full table scanning and for viewing rows in a sorted manner respectively. However, they are not compatible with all storage engines. 
  • Sort_buffer_size – This buffer is applied when the user needs the result set to be sorted, and is used on the SQL Layer, so it’s applicable for all storage engines and may even be helpful for performance tuning in SQL MySQL
  • Bulk_insert_buffer_size – This variable is only applicable for MyISAM tables for optimizing inserts in a large quantity or with numerous values. It is quite helpful when the user needs to insert hundreds or thousands of values in a single insert statement, and there are several such statements.
  • Join_buffer_size – This buffer is used for specific cases, such as joins that do not include indexes. It can be utilized for all the storage engines. 
  • Max_write_lock_count – A variable that is applicable across memory and MyISAM tables, it is suitable for table locks and prevents read starvation in case of numerous table writes. 
  • Key_buffer_size – This is applied in the case of Index Blocks solely for MyISAM tables or, in rare cases, restricted to a range of 4-32MB for temporary tables.
  • Delayed_insert_limit/delayed_insert_timeout, delayed_queue_size – These are used for inserting configuration and are not exactly dependent on any storage engine, yet it lacks support from some of them, such as InnoDB. 
  • Delay_key_write – This is used to improve MySQL database performance in MyISAM tables by extending the time for index updates. However, this variable may lead to table corruption, if a crash occurs. 
  • Low_priority_updates – This provides higher priority to select queries by putting the updates on low priority. It can be enabled when LOCK TABLES are in use, which is why it is storage engine-specific. 
  • Large_pages – This variable enables the utilization of large pages if big global areas need to be allocated, and is also storage engine-specific, such as Innodb and MyISAM. However, it can be used by certain SQL level components like Query Cache in MySQL database and SQL.
  • Key_cache_age_threshold/key_cache_block_size/key_cache_division_limit Key Cache/Key Buffer – The Key_Buffer variable is only applicable to MyISAM. It is used for making changes to algorithm configuration. 
  • Ft_boolean_syntax/ft_max_word_len/ft_min_word_len/ft_query_expansion_limit/ft_stopword_file – These are search variables related to FullTEXT search. Again, these are only useful for limited storage engines for MySQL database and SQL
  • Flush/flush_time – Initially designed for MyISAM tables, this variable impacts all the tables once the query is over or for certain flush_time set intervals. 
  • Preload_buffer_size – Another buffer that is only useful for MyISAM tables in key preloading. 
  • Timed_mutexes – This variable has been designed to use on all storage engines to show the mutex status. 
  • Tmp_table_size – It is used to specify the maximum limit in size for implicit temporary tables. These are tables that are created automatically at the time of each execution. 


These were some variables whose application is often confused by some users. Knowing more about these may turn out to be useful from the point of view of MySQL SQL performance tuning

On a related topic, if you are in search of tools for improving MySQL database performance that don’t require expert knowledge about the database, then Tosska’s tools for database tuning can be a great fit for your organization. Our tools have been designed with cutting edge AI technology to make query tuning as easy as pointing and clicking. 

So, make sure to explore all of our tools and find the right variant for your requirements, and if you aren’t sure about anything, just get in touch with our experts and have your SQL performance tuning related queries resolved today!

Improve MySQL Database Performance by Controlling Partitions

If you have been dealing with large MySQL data for a while, you may have faced an interesting case like this at some point – one which involves a table with data merged with INSERT IN DUPLICATE KEY queries. 

Even if you haven’t faced such a situation so far, you may be able to tell that such a table will show an extremely slow performance, and the culprit most definitely is the multitude of partitions that have been made every day for this table. In this blog, we will examine if changing the number of partitions can improve MySQL database performance.   

Improve MySQL Database Performance by Paying Attention to Partitions!

Surprisingly, the statements are also affected differently from each other, and this is something that also impacts performance.

To understand this problem a bit more clearly, let us consider an example where we create a test table – 

CREATE TABLE `Partition` (

    `number` int(10) unsigned NOT NULL,

    `name`  int(10) unsigned NOT NULL,

     PRIMARY KEY (`number`),


     ) ENGINE=InnoDB

     PARTITION BY RANGE(number) (

     PARTITION p100000 VALUES LESS THAN(100001),











In this example, we are varying the quantity of partitions between 1 and 1000. We are also loading the table with one million sequential values through bulk insert queries, where the number and name columns are set the same and there are a thousand rows in each table.

Time Taken to Load in Different Scenarios:

  • This data ends up taking around 10 seconds for loading a single partition, 11 seconds for ten partitions, 17 seconds for a hundred partitions and 24 seconds for a thousand partitions. 
  • Also, every time the number of partitions increases by a thousand, the loading speed decreases 2.5 times. 
  • Such regression is slightly unexpected, given the lack of data insertion into the partitions in every insert statement. Moreover, it worsens when the user tests an update statement with a condition – “set name=name+1”; the loss in performance is at least five times with a change in the pattern, and it is way more drastic as the user moves from a hundred to a thousand partitions than it was in the case where we only used the insert command. 
  • Finally, the difference magnifies when the user eliminates the index on column C. The UPDATE aspect of the INSERT ON DUPLICATE KEY UPDATE statement takes 23 seconds for a single partition and more than 240 seconds for a thousand partitions. This is more than ten times the original time!

This issue with partitions impacts both MyISAM and Innodb, where the update query in MyISAM without indices can take ten seconds for 1 partition and upwards of fifty seconds for 1000 partitions. 

Why is this Happening?

The dramatically increased load times may be caused by one of two suspects – either the overhead of statements creating the partitions for execution or the overhead created by executing each row. 

On testing the batch performance with varying numbers of rows in the batches, the batch performance was not found to be significantly different among batches with 100 rows and those with 10000, which is why the answer here is the per row overhead.

Additionally, this test reveals that update statements for CPU bound workload can prove to be about five times slower than insert statements. 

Final Thoughts

Users must certainly keep an eye on the number of partitions being used and think about future use while creating idle partitions. This simple step can improve MySQL database performance to a large extent, but if this is not the case with you, it might be wise to consider tuning tools for MySQL, such as Tosska SQL Tuning Expert (TSEM™) for MySQL. 

This is because load times and other such results are highly dependent on workloads and every case is different. Nevertheless, these tools will prove indispensable for improving your database’s performance.  

Analyzing the Inner Works of MySQL SQL Performance Tuning and Oracle Database

When it comes to MySQL, slow performance in large tables is one of the main sources of complaints. It is true that some users face problems as their database fails to sufficiently handle a more than a certain number of rows.

However, there also are many corporations that use MySQL for millions, even billions of rows of data and yet they successfully deliver excellent promise. So, why is there a contradiction between these two cases? The answer lies in understanding the intricacies of table designing in MySQL, and with the help of MySQL SQL performance tuning, how to make them work in your favour.    

What to Consider During MySQL SQL Performance Tuning

There are three major aspects of the database that can have an impact on databases with huge amounts of data. Let’s take a look at two of them now:


The first thing to consider with any database management system is that you must have an estimate of the memory, even as you progress in terms of data accumulation. It is important for the memory to be sufficient because performance suffers greatly if it isn’t, so don’t be surprised if a drop in performance is greater than you anticipated because you may have lost track of the growth in data size and subsequently, the need for more memory space. This applies to the other aspects covered in this blog as well. Once data outgrows the memory, everything can be expected to slow down, and MySQL database and SQL becomes a necessity.

One way to ensure the memory remains sufficient for your data is to practice data partitioning. In this process, old data that is no longer required as often as recent data, is separated and stored in other servers. There are various other ways of ensuring sufficient space which we will talk about in another blog.


Indices, or indexes, are known by most of us to be a useful tool in improving the accessing speed of the database. An important thing to remember is that their usefulness depends a lot on exclusivity, i.e., the ability to select a number of rows that match with specific index ranges or values. Also, the nature of the workload – specifically whether it is cached or not – determines how much it will benefit from the use of an index.

This is actually overlooked by even MySQL optimizer at present and may need to be checked by other MySQL SQL performance tuning tools. Workload indices have a chance of much quicker access even if the size of the data being accessed is as large as fifty percent of the entire number of rows, as long as they are in-memory. On the other hand, for disk IO bound access, you may have greater success in fetching data through a full table scan irrespective of the number of rows you are requesting access to.

Since indices can differ from each other in many ways, they need to be used differently in order to effectively use them. For instance, you can place them either in a well-organized manner or at random spots, resulting in significant changes in their speed. Innodb also includes clustered keys which work by merging data and index access – such keys end up conserving IO that will prove invaluable for workloads that are entirely disk-bound.

In Conclusion

Designing table structures smartly involves taking into consideration all the abilities and disabilities of MySQL. This is especially important if you have to handle different kinds of databases in your organization.

The main reason why your organization has different databases in the first place is because of their different capabilities and shortcomings. So, the same design concepts won’t bring the same results in say, MS SQL or Oracle that they did in MySQL and vice versa. The same is true for their storage engines – each can have a different effect on the performance.

Once you have applied the right application architecture to plan your tables, you will be able to create applications that can easily handle huge data sets on the basis of MySQL.

Proper MySQL SQL performance tuning involves optimizations that can greatly boost the rate at which indices are accessed or scanned. There already are tools by Tosska Technologies Limited for this purpose like Tosska SQL Tuning Expert (TSEM™) for MySQL which you can download and start using today. Contact our team for further information or enquiries.

Improve Performance Tuning in SQL MySQL Through Multiple Parameters

MySQL tuning is no trivial task – it takes some work. However, Database Administrators know that there are a few parameters through which they can greatly enhance the speed and output of the database.

If you are in search of ways to improve performance tuning in sql MySQL , you will find some of the best in this blog. Each of the parameters mentioned here contain important settings that you can make changes to without much effort. Keep in mind that default values may vary according to the version of MySQL on your system. 

Performance Tuning in SQL MySQL: Main Categories

Here are the three major types of performance tuning in SQL MySQL, one of which DBAs usually focus on:

  • Hardware-based performance tuning
  • Tuning through Optimum techniques and practices 
  • Workload-based tuning

Hardware-based Performance Tuning in MySQL

Certain variables can be set according to the hardware specifications of your device. These include:


For maximum durability, set it to “1”. If performance is your main concern, adjust this value to either “2” or “0”. However, doing so will result in lesser durability than if the value is set to “1”. 


If you want to improve MySQL database performance by preventing double buffering, make sure this setting is at O_DIRECT.


This size parameter is typically set within 50 to 70 percent of the overall RAM. You can proceed with tuning by checking on the buffer pool usage from time to time using a monitoring tool. 


The size of the file log is usually set in the 128M – 2G range. It is supposed to be sufficiently spacious to store approximately sixty minutes of logs and enable MySQL to flush processes, place checkpoints, and reorganize writes for sequential I/O. Again, refer to a tuning tool like Tosska SQL Tuning Expert (TSEM™) for MySQL® for further insight on whether or not the log file size needs to be adjusted. 

Tuning through Optimum Techniques and Practices

This category involves using the best MySQL practices for performance tuning in SQL MySQL: 


Keep this at “ON” in order to ensure a separate InnoDB table space for each table present in the database. 


Don’t want database statistics to update constantly, and consequently, slow down read speeds? Ensure this setting is turned off, in that case. 


The recommended value for this is “8”. On the other hand, if the buffer pool size is less than 1G, then set it to “1”.

query_cache_type & query_cache_size

Disabling the query cache is considered useful in improving MySQL database performance. You can disable it by setting both query_cache_type and query_cache_size to zero.

Workload-based Performance Tuning for MySQL

This kind of performance tuning in SQL MySQL is relative; it depends on the workload, which is why additional details regarding the specific workload are needed. Thankfully, gathering such information is much more convenient, thanks to reliable MySQL graphing and tuning tools like Tosska SQL Tuning Expert (TSEM™). Tosska’s tools are designed to display an extensive range of metrics and give users insights and allocate resources accordingly. 

Experts suggest making changes to the innodb_buffer_pool_size parameter first. Consider the following metrics to decide whether this setting has to be raised or lowered – 

  • Your device’s RAM
  • Buffer pool size
  • The number of free pages available

Once this is done, you can improve MySQL database performance be observing the InnoDB Log File usage metrics; as mentioned already, the log file settings are generally adjusted in order to store around an hour of log data. If the data written exceeds the originally set capacity, then this setting has to be increased and MySQL rebooted. The query “Show engine innodb status” is useful in assessing what size will be ideal for the InnoDB log file.

If it starts to get burdensome, you can rely on Tosska’s tuning tools for MySQL. Visit our website for our top-of-the-line tools and to get in touch with our experts to know more about them!

Oracle Database and SQL: Tips for MySQL Database Performance Tuning

Until now we have learned a lot about Oracle database and SQL performance tuning. Now, let’s discuss MySQL database performance tuning. Just like any other relational database, MySQL could also be a nightmare to many. It can crawl to a halt at a moment’s notice and in the next moment, you will find it leaving your apps in the lurch and your business on the line. The fact is, regular errors underlie most MySQL performance issues. 

To ensure your MySQL server bustles along at great speed, providing consistent and stable performance, it’s imperative to eradicate such mistakes that are often confused by some subtlety in your configuration trap or workload. Just like any other performance tuning tips for Oracle database and SQL, MySQL too has some of its own performance tuning techniques that we have described in this blog. Let’s get started without wasting more words-

Oracle Database and SQL- Know How to Tune MySQL Database Performance

Fortunately, many MySQL performance problems have similar solutions which make both tuning MySQL and troubleshooting a manageable task. 

Here are a few tips for getting great performance out of MySQL.

Tip 1# Profile Your Workload

The best way you can understand how your server invests its time is to profile its workload. By doing so you could expose the most expensive queries for further tuning. When you issue a query against the server, you don’t have to care much about anything else except how quickly it completes. Therefore, time is the most crucial metric to consider here. 

The most significant way of profiling your workload is using a tool such as MySQL Enterprise Monitor’s query analyzer. Such a tool is meant to capture queries that are executed by the server. Furthermore, it returns a table of tasks sorted by decreasing order of response time, quickly bubbling up the most time-consuming and expensive jobs to the top so that you can find where you need to put your efforts. Workload-profiling tools group identical queries together that allow you to find slow queries as well as the queries that are fast but executed multiple times. 

Tip 2# Recognize the Four Basic Resources

For functioning, a database server requires four basic resources- Network, CPU, Disk, and memory. If any of these resources are weak, overloaded, or erratic, then the database server is most likely to perform badly. Recognizing these fundamental resources is crucial in two specific areas: selecting troubleshooting and hardware issues. 

While you choose hardware for MySQL, you must ensure the components perform well all around. Just as essential, ensure to balance them reasonably well against each other. Often, businesses choose servers having fast CPUs and disks but that are starved for memory. In some cases, adding memory is a cheap way of enhancing performance by order of magnitude, specifically on workloads that are disk-bound. This might seem counterintuitive, but in various cases, disks are overused as there isn’t sufficient memory to hold the server’s working set of data. 

Another good example of this perspective relates to CPUs. In most conditions, MySQL performs well with fast CPUs as every query runs in a single thread and can’t be parallelized across CPUs. 

During troubleshooting, examine the performance and utilization of all the four resources with much care and determine whether they are performing badly or are simply being asked to execute excessive work. This knowledge can help solve issues instantly. 

Tip 3# Don’t use MySQL as a Queue

Queues and queue-like access patterns can sneak into your application without even letting you know. For instance, if you set the status of an item so that a specific work process can claim it before working on it, then you are ignorantly formulating a queue. Marking emails as unsent, sending them and then remarking them as sent in a common example. 

Queues create problems for two big reasons: they serialize your workload which prevents tasks from being done in parallel, and they mostly result in a table that includes work in process as well as earlier data from jobs that were executed long ago. Both add concealment to the application and load to MySQL. 

Tip 4# Firstly Filter Results by the Cheapest 

The best way of optimizing MySQL is to do imprecise, cheap work first, then the precise and hard work on the smaller that will result in a set of data. 

For instance, consider you are searching for something within an assigned radius of a geographical point. The initial tool in many programmers’ toolbox is the great-circle (Haversine) formula. This will compute the distance along the sphere’s surface. The trouble with this technique is that the formula needs a variety of operations related to trigonometry, which are very intensive toward the CPU. Great-circle calculations tend to perform slowly and make the machine’s CPU utilization skyrocket. 

Before you implement the great-circle formula, cut down your records to a small subset of the total, and scrape the resulting set to a precise circle. A square that comprises the circle either precisely or precisely is a simple way of doing this. That way, the world outside the square never gets hit with all those expensive trig functions. 

Tip 5# Understand the Two Scalability Death Traps

Scalability isn’t as fuzzy as you may think. In fact, there are explicit mathematical definitions of scalability that are defined as equations. Such equations emphasize why systems don’t scale and why they should. Consider the Universal Scalability Law, a definition that is convenient in expressing and quantifying a system’s scalability qualities. It explains scaling issues in terms of two elementary costs: crosstalk and serialization. 

Parallel processes that must hold for something serialized to take place are inherently limited in their scalability. Similarly, if such processes are required to chat with each other all the time for coordinating their work, they restrict each other. If you avoid crosstalk and serialization, your application could scale much better.

SQL Plan Management Oracle – All You Need to Know About

While upgrading the Oracle database or making any changes in the system parameters, you might have noticed that some SQLs’ performance or queries get highly regressed. If this happens, then don’t worry it’s quite obvious and will always happen whenever there are any changes in the plan. But you can prevent this regression if you use SQL Plan Management Oracle.

In case you aren’t aware of what it is and how it can help, then this blog is surely meant for you. In this blog, we are going to cover every basic aspect related to the SQL plan management and that how it will help in preventing query or performance regression.

An Overview of SQL Plan Management Oracle

SQL Plan Management is a deterrent tool that allows the optimizer to manage SQL execution plans automatically while ensuring that only verified and known plans are used by the database.

SQL plan management uses a mechanism that lets the optimizer to use it for a SQL statement. This mechanism is known as a SQL Plan baseline. It’s a set of accepted plans. A plan is typically known to hold all plan-related information such as a set of hints, SQL plan identifier, optimizer environment, and bind values.

The optimizer uses this information for reproducing an execution plan. Commonly, the database accepts a plan into the plan baseline only after it verifies and confirms that the plan performs absolutely well.

In short, a SQL Plan management Oracle is a tool used for mitigating the risk of query regression when you upgrade to Oracle Database11g or 12c.

Key Components of SQL Plan Management

Mainly, there are three essential elements of SQL Plan management. They are as follows:

  • SQL Plan Baseline Capture

The component creates a SQL plan baselines that describes the accepted or trusted execution plan for all relevant SQL statements. If you aren’t sure where you can find the SQL plan baselines, then let us tell you that you can find the plan baselines stored in a plan history in the SQL Management Base. The management base will be found in the SYSAUX tablespace.

  • SQL Plan Baseline Selection

SQL Plan baseline selection makes sure that the tool uses only the accepted execution plans for statements that have a SQL plan baseline. Furthermore, it ensures that it tracks every new execution plan in the plan history for a statement. The plan history comprises both unaccepted and accepted plans. An unaccepted plan can either be rejected (verified but not performant) or unverified (newly found but not verified).

  • SQL Plan Baseline Evolution

This component is meant to assess every unverified execution plan for a given statement in the plan history for either to be accepted or rejected.

What’s the Main Purpose of SQL Plan Management?

SQL plan management oracle restricts performance or query regression due to any plan changes in the database. Secondly, this tool aims at gracefully adapting to changes.

It must be noted that if an event has caused any irreversible execution plan changes such as dropping an index, SQL plan baseline cannot help in this case.

Advantages of SQL Plan Management Oracle

SQL plan management can preserve or improve SQL performance in database systems and upgrades and data changes.

Some more specific benefits comprise the following-

  • When a database upgrade causes the installation of a new optimizer version, it usually results in plan changes for a small part of SQL statements. Due to plan changes, either the performance of the system improves or there isn’t any change literally. However, in some cases, plan changes result in performance regression and here’s where SQL plan baselines come into play. It significantly lessens potential regressions that result from an upgrade.
  • Ongoing data and system changes can have an impact on plans for some SQL statements potentially creating performance regressions. Plan baselines assist in reducing this performance regression. Further, it stabilizes the SQL performance of the system as well as the database.
  • Employing new application modules brings in new SQL statements into the database. The application software is likely to use the proper SQL execution plan that’s developed in a standard test configuration for the new statements. In case the system configuration differs from the test configuration, then the database can develop SQL plan baselines over time to produce better performance.

In the Bottom Line

During an automated or manual updating of statistics for some objects or while changing some parameters related to the optimizer, or any changes made in the system cause a drastic change in the execution plan. Even more dramatic change is noticed while a database is upgraded. While most plans lead to improvement as they are made to adapt to the new system environment, there are some that lead to performance regression as well. In such cases, we need a SQL plan management mechanism whose main work is to reduce the regression risk.

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