8 Crucial Database Performance Metrics
8 Crucial Database Performance Metrics
What are the most important database performance metrics, and how do you monitor them?
This is a question many IT professionals would like the answer to. We can collect and use a wide range of database metrics to analyze database and server resource consumption, not to mention overall usage. You are probably wondering why this is essential for business, so let’s explore this next.
Key database performance metrics are useful for keeping track of database performance and resources and optimizing them for your organization. Doing so enables you to create and maintain quality and high availability (HA) enterprise application infrastructure that reaps tangible results. Let’s consider some well-known examples. They are the categories for SQL Server, MySQL, and Oracle database monitoring that consist of query details, session information, scheduled jobs, replication data, and database performance.
We analyze data from each of these groups to reduce and realistically, prevent database slowdowns as well as outages. The data points we choose and how we evaluate them will vary depending on the type of database.
What to Monitor - And Why
When we talk about instrumentation, we monitor infrastructure, applications, and even user performance, but sometimes overlook database monitoring. This is primarily because most databases demonstrate high performance, and we assume they always do the job well. The important thing is to confirm that our assumptions are correct in every scenario, but why?
When we monitor our databases, we do it for more or less the same reasons we would monitor any other component of our IT systems. Being aware of the proceedings in the components of our enterprise applications makes us well-informed junior and senior developers with smarter decision-making capabilities.
Let’s dig deeper into the subject. Databases are dynamic indicators of system health and behavior. Unusual performance in a specific database can direct us to problem areas in enterprise applications. When we identify the bottlenecks, we can then leverage database metrics to accelerate the debugging process.
Here's the challenge: databases have several metrics that can overwhelm even the most experienced IT specialists. This means monitoring all of them is not viable, so it comes down to deciding the metrics we must monitor. The easiest approach is to start with the basics. Point out some database-agonistic metrics as these offer valuable insights into the health and behavior of our databases.
Next, let’s look at the metrics to monitor, and why.
Database response is a crucial database performance metric for every business. It represents the standard response time per query for your database server. You not only wish to know the number of requests the database receives but also how long it spends on each request.
There are a few ways to approach the response time. Firstly, you can assess the live database response as a solo number on a dashboard that you can then share with other developers in your team. You can also utilize a line chart or column graph to measure the overall average response time for all the queries your database server receives.
What should you do if your database shows a greater average response time? In this case, the best solution is to optimize your queries or increase concurrency using program analysis. This is because data warehouses have concurrency limitations or the maximum number of queries one can run at the same time without compromising on the speed of creating interactive reports. As a developer, you must prepare yourself to handle issues surrounding concurrency when it comes to data democratization for users. Such an approach is effective for those who go through data within their dashboards through Business Intelligence tools or mechanisms.
Why It Matters
Analyzing response time is a useful method for boosting database performance. It encourages developers to focus on what causes applications to wait. This enables IT departments to synchronize their efforts with service level delivery for customers. Good response time implementations do not simply evaluate server health statistics and make guesses concerning performance impact. Response time methods are crucial because they break down the time into discrete and separate, measurable blocks. Next, they identify the stages in which operations may lead to app delays. Essentially, its goal is to respond with an actionable result that allows developers to make better performance decisions.
What To Look For
Be on the lookout for wait types and events. It is essential to identify the steps accumulating time individually if you wish to accurately measure a database’s response time. This is where database vendors can be of help as they instrument the steps equivalent to I/O operations, manipulating buffers, waiting on locks, and similar database processes. When we account for wait types and events, we can identify how much time sessions spend while waiting for database resources. If we can correctly monitor and evaluate wait types and events, we can determine the exact queries as well as bottlenecks that result in delays.
What accounts for substandard query performance? The answer lies in the query itself. We can discover issues in queries that take too long to identify the necessary data or retrieve time and mission-critical information. So, the objective for developers is to track slow queries that impede performance.
Let’s assume a scenario in which you are running an enterprise app with a MySQL server. Your database receives a query each time a user logs in after which it assesses the login credentials. In the event of a slow query, it can negatively affect how a database performs. This specific query can also have a significant impact on the overall performance of an enterprise app if users refer to it frequently.
What if everything is online and resources are not overwhelmed, and yet, we receive poor database performance? More often than not, inefficient query plans, database schema modifications, and unmanaged database stats are the reasons behind this. To troubleshoot these issues, we must be equipped with knowledge related to database internals. This means referring to query plans, filters, and joins that the database query optimizer utilizes.
The N+1 query problem is a performance antipattern that demonstrates an inefficient query. What do you do when you encounter performance discrepancies in an app that affects end UX? Chances are you refer to the timeline of the requests in your APM. You might find that a single, repeated query utilizes a significant portion of the timeline.
How can you tell when an N+1 antipattern appears? This occurs when the system executes a query for every result of an initial query. N+1 is the query count, with N denoting the number of outcomes for every result of the previous query. If the initial query has a single result, then N+1 = 2. If it has 1000 results, this means N + 1 = 1001 queries.
The good news is that N+1 queries are easy to detect and fix with reliable solutions. Let’s consider a scenario. Developers use Rail to effortlessly bootstrap, prototype, and release enterprise apps. The issue is that while developing prototypes, some developers neglect performance indexes at the initial stages. This can present complications when the time comes for them to scale and optimize an app. Developers then find themselves in a difficult position when they must figure out how to refactor an app for maximum speed and performance.
One area that developers can improve is the queries the app forwards to the database. It is possible to boost app performance by minimizing the number of queries. The majority of Rails apps distribute data over several models and use ORMs to provide access. Even then, knowing the pitfalls associated with such a scenario is crucial, and the N+1 query problem is an example. So, the solution is to avoid performing N+1 queries instead of a single query if you do not want to burden your database with queries.
Why It Matters
After collecting the necessary information, we can touch upon the areas that require query optimization. Doing so will allow us to determine the most efficient way to execute a query with various strategies. This involves choosing the best query evaluation plan from among numerous options if the query turns out to be complicated. Developers not only benefit from rapid query processing and less cost per query. It contributes to high system performance and minimal memory consumption, and there is less burden on the database.
What To Look For
Begin by identifying queries that take too long to run. You can extract them from database logs if you have configured the database to capture time-consuming queries. It becomes possible to conduct further analysis once you find slow queries. Ideally, track the top ten most frequent queries that your database server receives, along with their average latency. Then, optimize these queries to give your database a significant performance boost.
An event is a task that a developer can run according to a schedule, and many well-known DBMSes integrate database event support. One leverages database events for tasks including optimizing database tables, archiving information, accumulating data for complex reports during off-peak hours, cleaning logs, etc. Refer to database classes consisting of events that you can tap into. This will allow you to gather data for analysis and reporting that can be useful for business-critical decision-making processes.
Events are temporal triggers because they are scheduled according to time and not a table update. As a developer, you can schedule events to run once or more during certain times. Notably, not all databases support events. Moreover, they are not simple to implement within a typical web app, so you should refer to your documentation and work accordingly.
Why It Matters
A database event represents any occurrence that an app program is engineered to manage. Database events enable an app to notify other apps that a certain event has taken place so that developers can proactively pinpoint it. How does it work? Different apps or database processes raise database events, and the Database Management System informs an app programmed to receive these events. This is where the role of the monitor app is crucial. It actively responds to the database events by implementing the actions an app designer specified when writing the monitor app.
Keep a check on event statements that declare the events an app will be notified of. Examples include adding a new object, updating an object, deleting an object. With all this information, developers can go beyond identifying the name of the database on which an event occurred and the text of the executed Data Definition Language (DDL) They can detect the client app that issued it and the related database principal.
What To Look For
When should we avoid events or temporal triggers? We should not use them as an alternative for foreign key constraints. This is because foreign keys enforce referential integrity, making it impossible to add, edit, or delete information that would exit orphan records. Also, triggers lead to performance overhead. Although they are capable of executing faster than the SQL commands a back-end code passes, developers should not use complex triggers on frequently modified tables. Only consider triggers when they automate changes relevant to a database or the management of its data.
Think about a CRM system that features WordPress with a blog table consisting of titles and other content. An audit log can record the date and time a user adds, edits, or removes an article. However, a web system may not present this data or even be aware that it has been recorded. So, a database event or trigger would fit perfectly.
We define throughput as the speed at which a data warehouse is capable of implementing queries. It is an essential aspect to consider when assessing a data warehouse. This is because customers want to invest time and resources into a flexible system that can easily adapt as per their unique requirements.
The simplest method is to track the number of requests a database receives. We expect databases to store information securely and manage all the queries we feed them. This can be anything from a single, massive query to hundreds of queries from users throughout the day. The purpose of query throughput is to notify which of those is true.
Why It Matters
We want to calculate the amount of work a database performs under normal circumstances, and this is what throughput does for us. Some examples of throughput metrics are the queries per second, transactions per second, replication latency, queries waiting for disk IO per second, etc. These are useful in helping us determine how a database system performs. The data we derive can give us a good idea about the ability of an IT system to support multi-user workloads without any technical gaps.
This is an integral consideration for many enterprise apps. The ability to scale query throughput with ease makes it affordable to expand an IT system as one’s user-base increases.
What To Look For
Our main concern in monitoring any system is to ensure that work is being done as expected. Taking this into account, let’s say you are operating in a MySQL environment. The function of a database is to run queries, so your priority should be to ensure that MySQL executes queries according to expectations. It is also possible to track the breakdown of read and write commands. This offers an in-depth understanding of your database’s workload and the underlying bottlenecks if any.
Database connections are network connections that facilitate communication between client software and database software. Open connections are necessary for sending commands and receiving responses in result sets. With these connections, we can access database data sources and read data from tables, perform SQL queries, or insert records into tables.
The concept of a connection pool matters here. It refers to a cache of database connections that a system maintains so IT can share and reuse them to boost connection latency and performance. If an app attempts to connect to a database and is unsuccessful, the database can come temporarily unavailable. Sending excessive connection requests all at once wastes extra database resources and increases the time the system needs to recover.
It is also expensive to open and maintain a database connection for each user. This especially includes requests that we forward to a dynamic data-oriented website app. To address this, we must go for connecting pooling.
What happens in connection pooling is simple. After creating a connection, it is placed in the pool for repeat use so that IT need not establish a new connection. If all connections are being utilized, the system makes a new connection and adds it to the pool. The purpose of connection pooling is to reduce the amount of time a user has to wait to establish a connection to the database.
Why It Matters
Database connections offer a reliable and convenient way to store and reuse connection parameters. Let’s say you need to utilize the same connection in a workspace. It is a simple matter of using the saved connection rather than re-entering the connection parameters. In the event that connection parameters change, you can edit them in one location. There is no need to perform this in each workspace that utilizes the connection.
It is possible for a database server to cease functioning because of excessive open connections or long-running queries. You must individually track the number of open database connections and check if they are burdening your server.
What To Look For
What if you only have a few users and too many open connections? Chances are your app or website is not closing database connections once it retrieves query results. If this happens, you should review the app or website code and verify that it is closing unwanted database connections. Also, opt for low-code business process management software that easily builds automated workflows by establishing connections to MySQL, PostgreSQL, Oracle, and SQL Server/Sybase databases.
A database connection error can cause a site to be temporarily inaccessible. We come across this error when we enter a site URL and the message ‘error establishing a database connection’ appears. What this means is the PHP code failed to connect to the MySQL database so it could recover the necessary information to build that page.
An error is always displayed on a blank page. This is because there is no information about the site for a clear reason. It is not connected to a database. Errors in database connectivity can be a result of incorrect login credentials or a corrupt database.
Why It Matters
A database returns an error response code when an SQL query fails to run successfully. If the error persists, a website or app remains down which could translate into low productivity or lost revenue. To prevent this, it is essential to troubleshoot and resolve the problem.
What To Look For
Examine each error code and the number of queries associated with it. This will enable you to easily identify the most frequently occurring errors and take proactive measures to resolve them.
Buffer Pool Usage
The buffer pool or cache utilizes as much memory as we allocate to it so that it accommodates maximum pages of data. When the pool fills up, it eliminates older and less used data to make room for newer information. It is possible to access information related to the buffer pool. Doing so enables us to know everything about the data that the SQL Server stores in memory. Using the dynamic management view, we can identify a single row for each buffer descriptor. This finds and provides relevant information related to each page in memory. It may take some time to query this view on a server with large databases.
So, how does a buffer pool work? To understand this, let’s take the example of a buffer pool engine in MySQL that prevents the system from querying data on disk I/O operation each time. InnoDB is a storage mechanism for MySQL’s database management system. Now, let’s move on to InnoDB’s buffer pool cache and its usage.
You will come across cache table data and index data. Load the data on the disk into the buffer pool so you can avoid disk I/O on each access, and to speed up the access itself. Why not put all the data in the buffer pool if performance is fast? Note that with fast access comes small capacity. So, assume that cache access is fast but the capacity is small. The cache capacity is 64G, and the database stores 200G of data. To reduce disk access, you can only place the ‘hottest’ data in the ‘most recent’ place. Now, you are probably wondering how you can manage the buffer pool and boost performance. This is where you must understand the concept of Read-Ahead.
A Read-Ahead is an I/O request that prefetches several pages in the buffer pool asynchronously. It anticipates that you will require these pages soon. When it comes to disk reading and writing, the platform will not read on demand. Instead, it will read in advance by page or at least one page of data at a time. You can enhance efficiency by saving the disk I/O if the data you need to read in the future is in the page.
Let’s approach the above example from another perspective. When you first access a row of data in a table, your database places the page that contains the information into a buffer pool. Pages reside in the buffer pool until the database is shut down or if another page requires the space occupied by the current page.
Pages in the buffer pool can be in-use or idle, and clean or dirty. In-use pages are those that the system currently reads or updates. To ensure data consistency, the database manager allows a single agent to update a specific page in a buffer pool at one time. An agent accessing one page exclusively will update the page. If the page is being read, it means multiple agents are using it at the same time. Dirty pages consist of data that has been changed but not yet written to disk. A changed page is clean after being written to disk and it may remain in the buffer pool.
Why are these concepts essential? This is where the process of tuning a database comes in. A large portion involves setting configuration parameters that supervise data movement into the buffer pool, not to mention the writing of data from the buffer to the disk. If a recent agent does not require a page space, the page space can be useful for new page requests from new apps. On the other hand, additional disk I/O degrades database manager performance.
Why It Matters
The buffer pool has an important role in database performance metrics. It serves as a place in system memory that developers use to cache table and index data pages. This happens as they are modified or read from disk. Essentially, the buffer cache enhances performance by storing information in kernel memory and eliminating disk I/O. This improves the response time for data retrieval. Data is flushed from the cache if it is too small, and IT must re-read it from disk when necessary. The slowness of the disk read puts a strain on I/O devices, leading to a bottleneck.
Taking this into account, here are some points to consider:
- Designing Buffer Pools
Buffer pool sizes can have a direct impact on database performance.
- Creating Buffer Pools
You use a statement to define a new buffer pool that the database manager will utilize.
- Modifying Buffer Pools
For some reason or the other, you might wish to modify buffer pools. For instance, to facilitate self-tuning memory. To perform this, there will be a statement related to altering buffer pools that you have to use.
- Dropping Buffer Pools
What if you want to drop buffer pools? It is as simple as ensuring that you have not assigned any table spaces to those buffer pools.
- Buffer Pool Memory Protection
The database manager has an important function here. It leverages the buffer pool to implement additions, changes, and deletions to the majority of the database data.
We can see that the buffer pool and the buffer management system within the storage layer of a database management platform not only offers quick access. It also uses in-memory images of pages to enable fast recall of on-disk pages. To achieve this, the size of individual pages and the complete buffer pool are not the only critical factors here. Some key considerations are page replacement in view of buffer faults, page retention, and update as a response to database recovery.
What To Look For
What we understand so far is that a buffer pool provides working memory and cache for database pages. Its function is to improve database system performance by allowing the system to access data from memory instead of disk. Configuring buffer pools is the most important tuning area as the majority of data manipulation occurs in there. When an app accesses a table row, the database manager reads the page from disk and puts it in the buffer pool. IT can then use the data to process a query. To be on the safe side, ensure that a suitable buffer pool is available in every scenario. Each should have page sizes ranging from 4 KB, 8 KB, and 16 KB to 32 KB.
A feasible solution is to extend the buffer pool and benefit from performance gains, such as managing read-intensive OLTP workloads. Also, by allocating sufficient space to the buffer pool, developers can tune SQL queries to utilize the cache more resourcefully.
Take the Guesswork out of Performance Monitoring
Every business needs performance insights that allow them to code efficiently. The real challenge appears when we have to proactively track performance bottlenecks and regressions in real-time. This is because there is no one-size-fits-all database performance testing metrics solution. Moreover, with so many options available in the market, making a practical decision can be difficult. This is where Scout APM comes in.
Scout APM is an app performance monitoring add-on solution that examines your Ruby, Python, PHP, NodeJS, Elixir, and Phoenix app behavior. We help your business detect and resolve critical performance as well as stability issues, including:
- Slow SQL queries
- Slow requests such as HTTP and Database Calls
- Expensive N+1 logic
- Throughput and response time
- Transaction and response time traces
- Capacity bottlenecks
- Sources of memory bloat and leaks
Scout APM is designed to help modern dev teams monitor problems down to a single line-of-code. Apart from combining deep transaction analysis with an intuitive UI, Scout APM automatically tracks major performance indicators in enterprise apps. This enables companies to proactively gauge the overall health of their apps. The following are the main services:
- View response times according to tier with the purpose of identifying issues in back-end resources. These tiers include database, HTTP calls, and more.
- Memory and CPU usage of app processes.
Scout APM also offers a Database Add-on module that enables access to the Database monitoring page and Slow Query Insights. Consistent monitoring features analyze improved response time with dynamic dashboards. These deliver visibility into unique endpoints, paths, requests, and users that contribute to bottlenecks. You can even gain access to Time Selections that follow you around the entire web app. This helps when you investigate certain time periods for performance spikes and understand what is happening in the database. Not to mention the opportunity to access Database Events that indicates a jump in database activity,
Want to take Scout APM services for a spin? Try the 14-day free trial with no obligations and no credit card required, or request a demo to understand how it can fulfill your tech requirements!