The SiteGround Optimizer plugin is a great choice for an all-in-one optimization plugin, offering plenty of features for caching, image compression, frontend optimizations, and other features that can significantly speed up any WordPress website.

It's free and easy to use, and comes packed with premium features such as CSS, JavaScript and HTML minificaiton, GZIP compression, and several options for caching. Solve for errors by running free, external tools to track down outcomes and remove dead links.

According to data from Statista , the number of mobile internet users in the United States in amounted to That number is expected to grow to Not only are more people using mobile devices to access the internet — more people are using it as their preferred device type.

According to data from a HubSpot survey of over web traffic analysts in the U. As more users switch to mobile devices as their primary browsing and shopping mediums, speedy mobile sites are essential.

A website builder can be a great option for quickly building and customizing a site. But it can lack the infrastructure and features to help websites with complex issues like slow load times.

If you don't have the time, resources, or experience to employ all the optimization strategies in this guide, consider migrating to a CMS. The right content management system can significantly improve website performance by streamlining content retrieval and offering robust options to modify site operations.

Some even come with a built-in CDN. JavaScript and CSS files are among the largest files on a website. They also count as individual HTTP requests. So five JS files and five CSS files would require a total of 10 HTTP request. Another way you can reduce the size of your JavaScript and CSS files is minifying them.

This technique involves removing any unnecessary code like:. This will reduce file sizes and therefore load times. You can do this manually or use a minification tool. The longer it takes your domain name server DNS to respond, the longer your time to first byte TTFB and the slower your site loads.

Free online tools can determine where your DNS provider ranks compared to other offerings, which in turn helps pinpoint specific performance issues. Worth noting? In some cases, your hosting provider will also supply DNS services, while in others these two functions are separate.

The good news is many CMS platforms provide built-in features or plugins to enable asynchronous loading for CSS and JavaScript elements, which allows them to load simultaneously. To maximize page loading speed, use the fewest number of fonts possible, and focus on fonts that are optimized for speed.

For example, in a study of the top 10 Google Fonts by KeyCDN , Open Sans had the lowest load time at 0. Last but not least: identify performance-sapping plugins also called add-ons or extensions. Here, the speed rule is simple: Only keep the plugins you need and always deploy the latest, fastest versions.

This is particularly important if you run a WordPress website. For more advanced methods to optimize your WordPress website speed using Google PageSpeed Insights specifically, check out this video by Jack Cao:.

To speed up load times for return visitors, you can add expires headers to the. htaccess file of your site. Minimizes the amount of tracking required which can be expensive by registering only those objects that will change. Note : The unit of work supports marking classes as read-only, which allows the unit of work to avoid tracking changes of objects that will not be changed.

Parameterized SQL The session or an individual query can be configured to use a prepared statement and cache the statement, thus avoiding the SQL prepare call on subsequent executions of the query.

Performance improves in situations when the same SQL statement is executed many times. Batch writing Supported in both JDK 1. Allows for all of the insert, update, and delete commands from a transaction to be grouped into a single database call.

Performance improves dramatically because the number of calls to the database is reduced. Sequence number preallocation Sequence numbers are cached pre-allocated on the client side to dramatically improve insert performance. Does exist alternatives "Does exist" call on write object can be avoided in certain situations by checking the cache for "does exist" or assuming existence.

Writing Case 1: Batch writes TopLink also provides several write optimization features. The most common write performance problem is a batch job that inserts a large volume of data into the database.

Consider a batch job that requires to load a large amount of data from one database and migrate the data into another. Assume that the objects are simple employee objects that use generated sequence numbers as their primary key, and have an address that also uses a sequence number. The batch job requires to load 10, employees from the first database and insert them into the target database.

First lets approach the problem naively and have the batch job read all of the employees from the source database, and then acquire a unit of work from the target database, register all of the objects and commit the unit of work. This batch job would have extremely poor performance and would cause 60, SQL executions.

It also reads huge amounts of data into memory that can cause memory performance issues. There are a number of TopLink optimization that can be used to optimize this batch job.

The first performance problem is that loading from the source database may cause memory problems. To optimize the problem, a cursored stream should be used to read the employees from the source database.

Also, a cache identity map should be used in both the source and target databases, not a full identity map a weak identity map could be used in JDK 1. The cursor should be streamed in groups of using the releasePrevious method after each read. Each batch of employees should be registered in a new unit of work and committed.

Although this does not change the amount of SQL executed, it does fix the memory problems. You should be able to notice a memory problem in a batch job through noticing the performance degrading over time and possible disk swapping occurring.

SQL select calls are more expensive than SQL modify calls, so the biggest performance gain is in reducing any select being issued. In this example, selects are used for the sequence numbers. Using sequence number pre-allocation dramatically improves the performance. In TopLink, the sequence pre-allocation size can be configured on the login; it defaults to In the non-optimized example, we used a pre-allocation size of 1 to demonstrate this point.

Because batches of are used, a sequence pre-allocation size of should also be used. Because both employees and address use sequence number, we can get even better pre-allocation by having them share the same sequence.

In this case, we set the pre-allocation size to This optimization reduces the number of SQL execution from 60, to 20, TopLink supports batch writing on batch compliant databases in JDK 1.

Batch writing allows for a group of SQL statements to be batched together into a single statement and sent to the database as a single database execution. This reduces the communication time between the application and the server and can lead to huge performance increases.

Batch writing can be enabled on the login through the useBatchWriting method. In our example, each batch of employees can be batched into a single SQL execution.

This reduces the number of SQL execution from 20, to TopLink supports parameterized SQL and prepared statement caching. Using parameterized SQL can improve write performance by avoiding the prepare cost of a SQL execution through reusing the same prepared statement for multiple executions.

Batch writing and parameterized SQL cannot be used together, because batch writing does not use individual statements. The performance benefits of batch writing are much greater than parameterized SQL; therefore, if batch writing is supported by your database, it is strongly suggested that you use batch writing and not use parameterized SQL.

Parameterized SQL avoids only the prepare part of the SQL execution, not the execute; therefore, it normally does not give a huge performance gain. However, if your database does not support batch writing, parameterized SQL can improve performance. In this example, the number of SQL executions is still 20,, but the number of SQL prepares is reduced to 4.

Multiple processes and even multiple machines can be used to split the batch job into several smaller jobs. Splitting the batch job across ten threads leads to performance increases.

In this case, the read from the cursored stream could be synchronized and parallel units of work could be used on a single machine. Even if the machine has only a single processor, this can lead to a performance increase. During any SQL execution the thread must wait for a response from the server, but in this waiting time the other threads can be processing.

The final optimized example does not show multi-processing as normally the other features are enough to improve the performance.

In most client-server database applications, most of the performance problems come from the communications between the client and the server. This means that optimizing Java code is normally not as important as optimizing database interactions.

However, you should still try to write clean, optimized Java code, since very poorly optimized Java code does affect the performance of your application. The following is a general checklist to keep in mind when developing Java applications.

Write encapsulated code so that complex behavior can be easily optimized within the encapsulation. Use instance or static variables to cache the results of expensive computations. Use hash tables for large collections that are looked up by key.

Always provide default sizes to vectors and hash tables if only a few elements will be added to them. Postpone executing expensive tasks until absolutely necessary. Make use of multi-tasking to perform background jobs.

Consider lazy initialization in cases where the value's initialization in the constructor is normally not required. If using RMI or CORBA, avoid fine-grain remote message sends. Schema optimization When designing your database schema and object model, optimization is very important.

The key element to remember in the design of your object model and database schema is to avoid complexity. The most common object-relational performance problem is when the database schema is derived directly from a complex object model.

This normally produces an over-normalized database schema that can be slow and difficult to query. Although it is best to design the object model and database schema together, there should not be a direct one-to-one mapping between the two. A common schema optimization technique is to de normalize two tables into one.

This can improve read and write performance by requiring only one database operation instead of two. This technique is demonstrated through analyzing the ACME Member Location Tracking System.

Details Title ACME Member Location Tracking System Classes Member, Address Tables MEMBER, ADDRESS Relationships Source Instance Variable Mapping Target Member address one-to-one Address Table Optimized schema Elements Details Classes Member, Address Tables MEMBER Relationships Source Instance Variable Mapping Target Member address aggregate Address Domain In the ACME Member Location Tracking System, employees and addresses are always looked up together.

Querying a member based on address information requires an expensive database join. Reading a member and its address requires two read statements. Writing a member requires two write statements. This unnecessarily adds complexity to the system and results in poor performance.

Since members are always read and written with their address information, considerable performance can be gained through combining the MEMBER and ADDRESS tables into a single table, and changing the one-to-one relationship to an aggregate relationship.

This allows all of the information to be read in a single operation, and doubles the speed of updates and inserts as only one row from one table is modified. This example demonstrates how a table schema can be further normalized to provide performance optimization.

Machine Learning: Optimizing data processing for training and inference in machine learning models. Streaming Analytics: Achieving real-time analytics on streaming data for time-sensitive decision-making. Other Technologies or Terms Related to Performance Optimization Performance optimization is closely related to other technologies and terms in the data lakehouse ecosystem.

Some of these include: Data Warehousing : Traditional data warehousing involves optimizing data storage and retrieval for structured data. Data Pipelines : Efficiently processing and transforming data from various sources to the data lakehouse.

Data Governance: Ensuring data quality , security, and compliance while optimizing performance. Data Virtualization: Providing a unified view of data from disparate sources without physical data movement.

Why Dremio Users Would be Interested in Performance Optimization Dremio is a powerful data lakehouse platform that enables organizations to leverage their data for analytics and insights. Dremio users would be interested in performance optimization because it allows them to: Accelerate Query Performance: Optimize query execution to achieve faster insights.

Handle Large Datasets: Efficiently process and analyze large volumes of data in real-time. Improve Data Exploration: Facilitate interactive and exploratory data analysis for improved decision-making. Scale with Ease: Scale the Dremio platform to handle growing data volumes without sacrificing performance.

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