Know the Top 6 Enterprise Database Performance Metrics

Know the Top 6 Enterprise Database Performance Metrics

This article expands on these themes by examining six of the foremost important database performance metrics to watch so as to assess the health of your enterprise application’s database.

This article focuses on the subsequent topics:

• Transactions in Business
• Performance of Query
• Conflicts between the user and also the query
• Capacity
• Configuration
• NoSQL databases

Transactions in Business

The first database performance metrics is Business Transactions. It captures real-time database performance that real users are experiencing as they interact together with your application, providing insight into real-user behavior. As mentioned in the previous article, capturing the time interval of a business transaction holistically also with measuring the response times of its constituent tiers constitutes measuring the performance of a business transaction. The normalcy of those response times can then be determined by comparing them to the baseline that best meets your business needs.

If you merely have time to live one aspect of your app, I like to recommend tracking the behavior of your business transactions. While container metrics can provide a wealth of data and aid in determining when to auto-scale your environment, the performance of your application is decided by your business transactions. Rather than asking about your application server’s CPU usage, you must inquire about whether or not your users can complete their business transactions and whether or not those transactions are performing optimally.

To provide some context, business transactions are distinguished by their entry-point, which is the interaction together with your application that initiates the transaction.

The performance of a business transaction is measured across your entire application ecosystem once it’s defined. To work out normalcy, each business transaction’s performance is compared to its baseline.

Performance of Query

The query itself is the most blatant place to see poor query performance. Queries that take too long to spot the desired data or return the info can cause issues. Search for the subsequent issues in your queries:

Choosing more data than is required

Writing queries that return the acceptable rows isn’t enough; queries that return too many columns can prevent both the choice and retrieval of knowledge. Instead of writing SELECT*, it’s preferable to list the desired columns. The plan may identify a covering index when the query relies on selecting specific fields, which might speed up the results. All of the fields employed in the query are included during a covering index. This suggests that the index is enough for the database to get the results. To construct the result, it’s not necessary to travel to the underlying table. Additionally, listing the specified columns within the result reduces the number of knowledge sent, which improves performance.

Table joins that are inefficient

Joins cause the database to load multiple sets of knowledge into memory and compare values, leading to an outsized number of database reads and a major amount of CPU usage. The join may necessitate scanning all of the rows of both tables, betting on how the tables are indexed. A badly written join on two large tables that necessitates a full scan of everyone is computationally expensive. Joining columns of various data types, requiring conversions, or a join condition that has LIKE, which prevents the utilization of indexes, are all factors that abate joins. When possible, avoid employing a full outer join and instead use inner joins to retrieve only the information you would like.

There are either too few or too many indexes

When the query optimizer can’t find any indexes to use, the database must resort to table scans to provide query results, which generate an oversized amount of disc I/O. additionally; proper indexes reduce the requirement to sort results. Indexes on non-unique values aren’t as helpful in generating results as indexes on unique values. When the keys are large, the indexes grow in size additionally, leading to more disc I/O. Most indexes are designed to boost data retrieval performance, but it is vital to recollect that indexes also affect data inserts and updates because all associated indexes must be updated.

Parse contention because of an excessive amount of literal SQL

Any SQL query must be parsed before it will be executed, which involves checking syntax and permissions before generating the execution plan. Because parsing is expensive, databases save the SQL they’ve parsed so that they can reuse it and save time. Because the WHERE clauses differ, queries that use literal values cannot be shared. Each query is parsed and added to the shared pool as a result of this. Because of an absence of space within the pool, some saved queries are discarded to form a room. If those queries come up again, you will have to parse them again.

Conflicts between the User and the Query

Multi-user databases are designed to be multi-user, but multiple users’ activities can cause conflicts.

Due to slow queries, page/row locking is required

Databases must lock tables to forestall inserts and updates while a read query is running to make sure that queries produce accurate results. Users who have to change database values may experience slowness and delays if a report or query is running slowly. Lock hints assist the database in selecting the smallest amount of inconvenient locks. it is also a decent idea to stay reporting and transactional databases separate.

Deadlocks and transactional locks

When two transactions are blocked because all require a resource held by the opposite, a deadlock occurs. A transaction is blocked until a resource is released when there’s a standard lock. There’s no reply of a deadlock. Deadlocks are detected by databases, which then opt to terminate one in all the blocked transactions, freeing up the resource and allowing the opposite transaction to continue. The opposite transaction has been reversed.

Batch operations are causing resource conflicts for online users

Batch processes are wont to perform large-scale tasks like loading large amounts of knowledge or creating complex analytical reports. These operations consume lots of resources and may impede online users’ experience. The simplest solution for this problem is to run batch operations when online usage is low, like in the dark, or to separate transactional processing and analytical reporting databases.

Capacity

Database problems aren’t always database problems. Some issues arise as a result of the database being run on insufficient hardware.

There aren’t enough CPUs, or the speed of the CPUs is simply too slow

The workload on the server may be shared among more CPUs, leading to improved database performance. The database’s performance is affected not only by the database, but also by other processes running on the server, so it is vital to appear at the general load likewise as database usage. Because CPU utilization varies throughout the day, metrics for low, average, and peak usage should be examined to see whether additional CPU resources are going to be beneficial.

IOPS are insufficient on a slow disc

Input/output operations per second are a measure of disc performance (IOPS). When combined with the I/O size, this offers you a metric for the disk’s throughput in megabytes per second. The latency of the disc, or how long it takes for the missive of invitation to finish, has sway on throughput. These metrics are specific to disc storage technology. Traditional disk drives (HDDs) have a rotating disc and are slower than solid-state drives (SSDs) or non-volatile storage, which don’t have any moving parts. Until recently, SSDs were dearer than HDDs, but prices have dropped, making them a viable option.

Disks that are full or are misconfigured

Because databases necessitate extensive disc access, improperly configured discs have a major performance impact. System data, like catalogs and logs, should be partitioned separately from user data on discs. To avoid contention, highly active tables should be separated. Place databases and indexes on different discs to extend parallelism. On the identical disc because of the database, don’t arrange the software system and swap files.

Insufficient memory

Database performance is hampered by a scarcity of or improperly allocated physical memory. The more memory available, the higher the performance is probably going to be. Keep a watch on the paging and swapping. Create multiple page spaces on several non-busy discs. Confirm the paging space allotted is adequate for the database’s needs; each database vendor can advise you on this.

The network is slow

The speed with which retrieved data is returned to the top user or calling process is littered with network speeds. When connecting to remote databases, use broadband. In some cases, using TCP/IP instead of named pipes because the connection protocol can improve performance significantly.

Configuration

Each database includes a plethora of configuration options. The default values might not be sufficient to produce your database with the performance it requires. Examine all parameter settings, paying special attention to the subsequent issues:

Too small a buffer cache

By storing data in kernel memory and eliminating disc I/O, the buffer cache improves database performance. Data is flushed from the cache more frequently when the cache is simply too small. It must be reread from the disc if it’s required again. Apart from the slowness of the disc read, this adds to the workload on I/O devices, potentially creating a bottleneck. Tuning SQL queries can help them use the buffer cache more efficiently, additionally allocating enough space to the buffer cache.

There is no query caching

Both database queries and their result sets are cached by query caching. When a question with identical parameters is run, the information is quickly retrieved from memory instead of having to run the query again. Because data changes invalidate the results, query caching is merely useful for static data. a question cache can, in some cases, become a bottleneck instead of a performance boost. When large caches are locked for updates, they’ll cause contention.

Due to the creation of temporary tables on disc, there’s I/O contention

When performing certain query operations, like executing a gaggle BY clause, databases must create temporary tables. Temporary tables are created in memory whenever possible. However, in some cases, like when the info contains BLOB or TEXT objects, creating the temporary table in memory isn’t possible. Temporary tables are created on disc in those cases. To make the temporary table, populate it with records, select the needed data from it, and drop the table when the query is finished, lots of disc I/O is required. The temporary database should be kept and cut loose the most database space to avoid performance issues. By creating derived tables rather than temporary tables, rewriting queries reduces the necessity for temporary tables. Data is joined in memory instead of on disc by employing a derived table, which selects directly from the results of another SELECT statement.

NoSQL Databases

Because of its ability to handle large amounts of information quickly, NoSQL contains a lot of appeals. However, there are some drawbacks to contemplate when deciding whether NoSQL is true for your use-case scenario. As a result, it is a good idea to recollect that NoSQL stands for “Not Only SQL.” This more precise definition acknowledges that NoSQL isn’t the most effective solution, and it doesn’t always replace SQL — here are five reasons why:

Transactions that are risky

With NoSQL, it’s difficult to keep entries consistent. When working with structured data, it’s not always possible to form changes to multiple tables at identical times. Tables may become inconsistent if a process crashes. Double-entry accounting is an example of consistent transactions. Every debit must be balanced by a corresponding credit and the other way around. The entry can’t be made if the information on either side is inconsistent. NoSQL might not be able to properly “balance the books.”

Databases That Are Complicated

The efficient code, simplicity, and speed of NoSQL are often cited by proponents. When database tasks are simple, all of those factors align. When databases become more complex, however, NoSQL begins to fail. Because SQL has mature, industry-standard interfaces, it’s more potential than NoSQL when database demands are complex. Each NoSQL database has its own interface.

JOINS that are consistent

Because the system must pull data from multiple tables and align them with keys when performing a take-part SQL, there’s a major amount of overhead. Because there aren’t any JOINS in NoSQL, it appears to be a dream. Everything is gathered at the identical table in one location. When data is retrieved, all key-value pairs are retrieved at an identical time. The difficulty is that this will lead to multiple copies of the identical data being created. These copies must be updated, and NoSQL lacks the functionality to help during this situation.

Schema Design Flexibility

When it first came out, NoSQL was unique therein it didn’t require a schema. Previously, programmers had to think about which columns they might accommodate all of the potential and data entries in each row. Entries in NoSQL can have any number of strings or none in the slightest degree. This flexibility enables programmers to quickly work up applications. It is an issue, however, when multiple groups are performing on the identical program or when new development teams take over a project. There could also be a large sort of key pair implementations after some developers have modified the database using the liberty of NoSQL.

Using a lot of resources

NoSQL databases typically consume plenty more resources than relational databases. They necessitate significantly more CPU and RAM resources. As a result, most shared hosting providers don’t provide NoSQL. You will need to urge a VPS or find your own dedicated server. SQL, on the opposite hand, is meant to run on one server. This works fine initially, but as database demands grow, the hardware must grow likewise. The problem is that one large-capacity server is way costlier than a set of smaller servers. The worth rises at an exponential rate. One of the explanations NoSQL has found a range in enterprise computing scenarios like those employed by Google and Facebook is due to this.

About Enteros

Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.