The best way to optimize JVM throughput and avoid runtime bottlenecks is to make Java performance profiling a key part of your team’s continuous development and deployment (CI/CD) strategy. Here are five Java performance profiling tips and best practices that will help you get the most out of both your local hardware and your cloud-hosted runtime environments.
1. Never turn off Java performance profiling
Java Flight Recorder is an incredibly efficient Java performance profiling tool built directly into the JVM. It’s lightweight and has a minimal impact on running applications.

The default setting has less than a 1% impact on performance, while even the most extreme data gathering settings consume little more than 2%. Given the fact that no production server should ever experience a 50% load for any extended period of time, there should always be enough resources available to keep Java Flight Recorder running in the background.

The benefit to this configuration is that when a JVM crash occurs, or an application encounters a catastrophic runtime failure, a recording of all the events that led up to the disaster is available for inspection. Even on a production system under heavy load, a Java performance profiler should always be running. In fact, the times when a JVM is under extreme load is when the metrics garnered from a Java performance profiling tool are most useful.

2. Know when to choose sampling over instrumentation
There are two different approaches to monitoring and profiling the JVM: sampling and instrumentation.

A Java performance profiler that uses a sampling approach will place a more predictable load on the JVM at runtime. As a result, a sampling-based profiler is best to use when searching for patterns in the JVM’s bytecode invocations.

Instrumentation profilers are more exact and can be more surgical in their application. With instrumentation, software architects add additional code or components into the system. They add these instruments into spots they believe are likely to be sources of performance problems. Because instruments are inserted into application code or embedded within the JVM, they get triggered every time they are encountered.

When you have a good understanding of what the problem is and where the problem lives in the codebase, instrumentation-based profilers tend to be more effective.

A strategic use of both sampling and instrumentation-based profilers will lead to the best Java performance optimization outcomes.
3. Augment JVM performance problem analysis with AI
Be it a memory leak or a thread deadlock, most Java performance problems follow well-known patterns of behavior. Before investing a great deal of time henpecking a JVM heap histogram or inspecting Java GC pause times, run your Java performance profiler data through an analytics tool that can apply some degree of AI to the file.

Most Java performance profiling tools will be able to quickly categorize the type of bottleneck that plagues your application, going so far as to identify the actual line of code that is slowing throughput and latency down.

4. Keep performance benchmarks in version control
A history of performance data will help you identify patterns in your Java performance metrics.

Create a performance test suite for your applications, run it as part of every continuous integration build, and ensure you have Java Flight Recorder turned on in the background. Then save the generated Flight Recorder data file in Git or GitHub as part of the CI/CD build.

This Java performance profiling strategy not only creates a performance benchmark for your applications, but it also gives you a history over which you can view how trends emerge over time. You might not be able to identify a looming performance issue from one sprint to the next, but a comparison of JVM profiler data over a series of weeks or months will help to unearth patterns.

Furthermore, tell developers that if performance metrics fall out of acceptable ranges, then it’s considered a broken build. Get developers thinking about nonfunctional requirements like performance and security early on in the development lifecycle.

5. Modernize your Java profiling tools
Don’t get set in your ways when it comes to Java performance profiling tools.

The tools that may have been effective at troubleshooting applications deployed to WebSphere or Tomcat servers 10 years ago aren’t going to be as effective at troubleshooting microservices-based applications deployed to Docker containers. The world of application development, design and deployment has changed greatly over the last 10 years, and the Java performance profiling tools you use need to change with it.

VisualVM remains a popular Java performance profiling and monitoring tool, but new analysis tools like Java Mission Control provide updated ways to visualize and inspect JVM metrics. And many vendors specialize in the analysis of JVM runtimes when bytecode is packaged inside of Docker containers and deployed to Kubernetes clusters. Always make sure the analytics tool you use matches your Java performance profiling needs.

Java performance profiling tips and tricks
In review, the five tips to help you get the most out of your Java performance profiling tools and routines are:

Never turn off Java performance profiling.
Know when to choose sampling over instrumentation.
Augment JVM performance problem analysis with AI.
Keep benchmarks in version control.
Modernize your Java profiling tools.
Stick with these Java performance profiling tips and stay one step ahead of impending JVM bottlenecks that threaten to slow your application’s throughput down.

The cloud computing landscape appears to be in rude health after new analysis found that the three industry heavyweights were all showing ongoing performance improvements.

That’s according to the 2021 Cloud Report from Cockroach Labs, which claimed that Google Cloud has made up ground in a number of areas on rivals AWS and Microsoft Azure.

“Working with each of the three major cloud providers, we worked to optimize all microbenchmarks to be more accurate and representative of real-world performance,” the report read. “We evaluated 54 machine configurations and conducted nearly 1,000 benchmark test runs including CPU, Network Throughput, Network Latency, Storage Read Performance, Storage Write Performance, and Cockroach Labs Derivative of TPC-C, the industry-standard OLTP benchmark.”

We’ve assembled a list of the best cloud computing services around
And these are the best cloud hosting providers on the market
Also check out our roundup of the best cloud storage solutions
Back in 2018, when Cockroach Labs first issued a comparative cloud report, AWS outperformed Google Cloud across almost every metric. By 2020, all three major cloud providers showed relatively equal performance, while 2021’s report reveals that each provider comes out on top for at least some of the benchmark criteria tested.

Cloud comparisons
Looking more closely at Google Cloud, Cockroach Labs praised the level of network throughput delivered, which bettered that being offered by both AWS and Azure. Google Cloud also had the best single-core CPU performance and outperformed its rivals in terms of network and storage I/O throughput.

Despite its impressive gains, Google Cloud was not the only provider to receive praise in the report. AWS was judged to be the most cost-effective over a three-year period and delivered the best latency figures, while Azure beat its competitors on storage I/O read IOPS, write IOPS, and write latency when provisioned with ultra disks.

Given the respective strengths of the three cloud providers, declaring an overall winner was more difficult this year than it has been previously. While Google Cloud delivered the best overall performance, AWS boasted the best cost-efficiency, and Azure scored highly when its Ultra Disk storage was employed. Promisingly, all three cloud providers seem to be offering customers improved performance year-on-year.

Datadog, (DDOG) observed an average volume with 4969.74K shares and exchanged 598325 shares on hands in most recent trading session. The stock acknowledged active focus on shares in the current trading session. DDOG disclosed a change of -0.96% and its listed share value at $101.1 in the recent trade transaction. This company belong to USA Country and part of Technology sector. It has a market capitalization of $30011.52M. Using market capitalization to display company size because this is basic factor of different characteristics to develop the investors’ interests including risk.

PERFORMANCE REVIEW:

DDOG revealed weekly performance of -9.15% and shown yearly performance of 214.09%. The stock moved to 136.13% in last six months and it maintained for the month at 15.51%. The stock noted year to date performance at 170.20% and changed about 16.22% over the last three months. The stock’s price situated at -14.42% from 52 week high and stock price seen at 250.07% when looking at 52 week low price.

According to data provided by FINVIZ, the current analyst consensus rating supposed at 2.4 on company shares (1.0 Strong Buy, 2.0 Buy, 3.0 Hold, 4.0 Sell, 5.0 Strong Sell). Looking ahead of 52-week period, the stock will achieve $97.67 price. This mean target price is expected by analysts.

TECHNICAL INTERPRETATION:

A Simple moving average is one of the most flexible as well as most-commonly used technical analysis indicators. It is highly popular among traders, mostly because of its simplicity. It works best in a trending environment. Simple Moving Average calculated as an average of the last N-periods (20-Day, 50-Day, 200-Day).The share price is a downward from its 20 days moving average with -4.34% and trading from 50 days moving average with 8.47%. The stock price is performing along up drift from its 200 days moving average with 49.16%.Any type of moving average can be used to generate buy or sell signals and this process is very simple. The charting software plots the moving average as a line directly into the price chart.

Currently Relative Strength Index (RSI-14) reading of stock is 49.03. RSI reading varies between 0 and 100. Speed and change of stock price movement is measured by the RSI momentum oscillator. J. Welles Wilder developed Relative Strength Index (RSI) that is used most commonly as momentum indicator. Commonly when RSI goes below 30 then stock is overload and stock is overbought when it goes above 70. 30 to 70 are considered a normal reading of a stock. General trend, finding deviations and failure swings can be detected by using RSI.

VOLATILITY ANALYSIS:

Volatility remained at 5.71% over last week and shows 6.39% volatility in last month. VOLATILITY is also measured by ATR an exponential moving average (14-days) of the True Ranges. ATR value stands at 6.18.

PROFITABILITY SNAP:

Net profit margin of the company is 0.70% that shows the company earning by every dollar of sales. Gross margin is detected at 78.30% and Operating Margin is noted at -0.40%. Return on Assets (ROA) shows that the company is profitable as compared to its total assets which is 0.30%. Return on Investment (ROI) is -2.70%. Return on Equity (ROE) is 0.40%.

6 Main Things to Consider Before Migrating to Cloud
Cloud computing, popularly referred to as ‘cloud,’ simply refers to the simple storage and access of data via the internet as opposed to the traditional storage that only utilized the computer hard drive. Over the internet, countless data centers together form what is unanimously referred to as the cloud.

Cloud computing is the basis of data science, a very significant pinnacle of the growth of organizations in today’s economy. Cloud is benefitting business with barely any IT infrastructure in place. The existence of cloud computing has led to the creation of so many cloud-based solutions such as Microsoft Azure and Office 365, which have helped businesses improve their productivity.

Cloud-based solutions for businesses help in enhancing accessibility, flexibility, and cybersecurity in a cost-effective manner. Instead of spending loads of money buying needless machines, contact organizations such as Alpacked.io for cloud consulting and take a step into cloud transforming your organization.

What is Cloud Migration?
The term cloud computing is not so alien to most people. As more people get acquainted with technology, they have slowly turned to more advanced solutions to their different activities. Business operations have benefited massively by the wonders of cloud computing and cloud-based solutions. Turning to the cloud for business solutions has been tested and proven by almost every facet of the business world. This evidenced success has triggered the need for existing and founding businesses to base their solutions to cloud-based solutions. As a result, more and more businesses are migrating to the cloud to answer some of the most intricate business matters.

This processing of moving operations to, from, or across the cloud is referred to as cloud migration. Cloud migration involves the transference of applications, databases, and IT processes into the cloud, from the cloud, or from one cloud to another. However, despite it being referred to as a move, the actual migration does not involve the physical hauling pieces of equipment from one data center to another.

The actual transfer, however, is not as simple as it sounds as it requires extensive planning. There are several types of cloud migrations that an organization can carry out. The first one is the movement of data from a local, on-premise data center to the public cloud. This migration could also be the movement of applications and data from one cloud provider or platform to another. This is the movement across the cloud, referred to as cloud-to-cloud transference. The third mode of migration is where an organization exits the cloud. This can also be referred to as cloud repatriation or reverse cloud migration. This is basically the opposite of the first type of migration. In this mode of migration, the applications, the data, and the IT operations are transferred from the cloud back to the local center.

Why do Businesses need to Migrate to Cloud?
Cloud computing is all about access to data. Amazon and Microsoft and some of the leading companies in the arena of cloud computing and delivery of cloud services. Without the cloud, businesses would have to buy, run, and maintain very sophisticated machines. The cost of such, not to forget the amount of skilled labor it demanded, was shocking. This was going to be a massive challenge for small and medium businesses to store, manage, and cost-effectively access their data. With cloud computing, this cost has been cut down and in its place access to raw and massive data that can be cost-effectively stored and retrieved at any time.

The data is easy to group as well. This is because businesses can now have all the teams from sales, accounts, marketing, and customer services gathering their related data into the saver reservoir that can be retrieved in an organized manner. This allows the different segments of the business’s stakeholders to access the same data in a coordinated and less cumbersome way. Cloud computing has contributed to this so much that one does not have to be on-site to access the information they need. Cloud computing in today’s business world is no longer a luxury but pretty much a necessity for competition and success. Below are some of the significant reasons for businesses to migrate to the cloud.

Scalability
Just as cloud computing is useful to small and medium businesses, it is equally applicable to large organizations. This adjustment to the size of data being handled is easy and less costly.

Cost
As soon as a company migrates to the cloud, their IT operations’ budget reduces since they no longer have to provide the storage machines, maintain them, or even upgrade them. This surplus cash can be channeled into other business needs.

Performance
Moving to the cloud will help the business perform better and even improve your customer experience. As a result of reduced travel of data, the business websites are going to experience lowered latency.

Flexibility
This is the benefit of allowing all business stakeholders to access data from anywhere. As a result, businesses have a bigger chance of growing and increasing the flexibility of their employees.

Below are ten practical performance tuning tips for AWS EC2 that every DevOps engineer and solutions architect should keep in mind.

1. Plan Your Architecture Before You Deploy

One of the most common pitfalls in cloud deployments is what practitioners sometimes call “cloud hangover” — the unexpected operational costs and complexity that emerge after a rushed migration or launch. Before provisioning instances, define your traffic patterns, fault-tolerance requirements, and latency budgets. Decide upfront whether you need multi-AZ redundancy, what your data-transfer costs will look like, and which EC2 instance family best matches your workload (compute-optimised, memory-optimised, storage-optimised, etc.). AWS Enhanced Networking, available on supported instance types, uses single-root I/O virtualisation (SR-IOV) to deliver higher packet-per-second throughput and lower inter-instance latency — enabling it from the start is much easier than retrofitting it later.

2. Implement Load Balancing

Distributing traffic evenly across multiple EC2 instances is critical for both performance and availability. AWS Elastic Load Balancing (ELB) offers three load balancer types: Application Load Balancer (ALB) for HTTP/HTTPS traffic with content-based routing, Network Load Balancer (NLB) for ultra-low latency TCP/UDP traffic, and the classic Gateway Load Balancer for inline virtual appliances. ELB automatically scales to handle sudden traffic increases, performs health checks on registered targets, and integrates with Auto Scaling to add or remove instances based on demand. Without load balancing, a single overloaded instance becomes a bottleneck — or a single point of failure.

3. Cache Static Content and Serve Dynamic Content Efficiently

Offloading static assets (images, CSS, JavaScript) to Amazon CloudFront or an S3-backed CDN dramatically reduces the load on your EC2 instances and cuts latency for end users. For application-layer caching, ElastiCache (Redis or Memcached) lets you store frequently requested database query results or session data in memory, avoiding repeated round-trips to your database. Caching is one of the highest-leverage performance improvements available in a cloud architecture — implement it as early as possible rather than as a late-stage fix.

4. Monitor and Benchmark Application Performance

You cannot tune what you do not measure. Establish baselines for the metrics that matter to your application: page load time, request throughput, error rate, CPU utilisation, memory usage, and disk I/O. AWS CloudWatch provides built-in metrics for EC2 instances (CPU, network, disk) and supports custom metrics for OS-level and application-level data. Pair CloudWatch with a tool like Prometheus, Datadog, or Grafana to build dashboards that make anomalies immediately visible. Set alarms on threshold breaches so your team is alerted before users notice degradation.

5. Adopt a DevOps Approach to Operations

Performance tuning is not a one-time event — it is an ongoing practice. A DevOps culture that bridges development and operations ensures that performance requirements are treated as first-class concerns alongside security and functionality. Infrastructure-as-Code tools (Terraform, AWS CloudFormation, CDK) let you version-control your EC2 configurations and reproduce environments consistently. CI/CD pipelines enable rapid, low-risk deployment of performance improvements. Shorter feedback loops between code change and production observation make it easier to correlate a deployment with a performance regression or gain.

6. Partition Your Data Strategically

Data layout has a direct impact on query performance, especially at scale. For relational databases running on EC2, ensure tables are properly indexed and that frequently joined columns are co-located. For Amazon Athena or other query-on-S3 workloads, partitioning your data by high-cardinality dimensions (date, region, customer) means each query scans only the relevant subset of data rather than the full dataset — significantly reducing both cost and latency. Even for application-level data stores, grouping related data together minimises the number of I/O operations required to fulfil a request.

7. Optimise Database Joins and Query Patterns

Inefficient queries are a frequent source of application-level latency. For distributed query engines (such as Amazon EMR running Hive or Presto), place the larger table on the left side of a join so the engine can stream the smaller table across worker nodes efficiently. Use broadcast joins when one table is small enough to fit in memory on each node. Avoid unnecessary cross joins, which produce a Cartesian product and can exponentially increase processing time. For traditional RDBMS workloads on EC2, use EXPLAIN plans to identify missing indexes, full table scans, and inefficient join orders before they reach production.

8. Tune EBS Volume Configuration for Your I/O Profile

Amazon EBS is network-attached block storage, which means it has different performance characteristics than locally attached disks. Each EBS volume type has a defined IOPS ceiling: gp3 volumes allow you to independently provision IOPS and throughput, while io2 Block Express volumes support the highest IOPS for latency-sensitive workloads. A common problem is hitting the provisioned IOPS limit under burst traffic — monitor the VolumeReadOps, VolumeWriteOps, BurstBalance, and VolumeQueueLength CloudWatch metrics. If your queue depth rises consistently, you are likely volume-bound and should either upgrade your volume type, increase provisioned IOPS, or stripe multiple volumes using RAID 0 to increase aggregate throughput.

9. Manage EC2 Memory Proactively

Unlike CPU, EC2 memory utilisation is not reported as a default CloudWatch metric — you must install the CloudWatch agent and configure it to emit memory and swap metrics from within the operating system. Running out of RAM is one of the most severe performance problems an EC2 instance can encounter: the OS will begin swapping to disk (orders of magnitude slower than RAM), and if swap is also exhausted, the OOM (Out of Memory) killer will terminate processes. To mitigate this: right-size your instance class for your workload’s memory requirements, add a swap volume as a safety net, set up CloudWatch alarms on memory utilisation, and review application logs regularly to catch memory-leaking processes before they cause an outage.

10. Prepare for Scheduled Maintenance and Service Events

AWS occasionally schedules maintenance events that require instance reboots or host retirements. If your application is not designed to handle these gracefully, they can cause unexpected downtime. Ensure that AWS notification emails are not filtered into spam — these alerts are sent to the email address associated with your AWS account. Regularly review the AWS Health Dashboard (formerly Personal Health Dashboard) in the console, which provides personalised alerts about events affecting your specific resources. Architect your workloads across multiple Availability Zones so that a maintenance event on one host does not take down your entire application.

Summary

EC2 performance tuning is a multi-layered discipline that spans architecture decisions, storage configuration, caching strategy, query optimisation, and operational vigilance. No single tip will solve all performance problems, but applying these practices systematically — and measuring the impact of each change — will give your team a much stronger foundation for running reliable, cost-effective workloads on AWS.