Java performance tuning tips. Java Performance Tuning. Java(TM) - see bottom of page. Our valued sponsors who help make this site possible. Site. 24x. 7: Java Method- Level Tracing into Transactions @ $1. Month/JVM. Sign Up! Developers wanted in London - Io. T, big data, concurrency, real- time, global! New Relic: Try free w/ production profiling and get a free shirt! Note that this page is very large. WebSphere Application Server Performance Cookbook. DON'T PANIC. The Hitchhiker's Guide to the Galaxy, Douglas Adams. Great, kid. Don't get cocky. Han Solo, Star Wars. This Application Infrastructure Control, Performance, Security, and Infrastructure white paper investigates the business and technical issues pertaining to a platform. 8 Setup and Configuration. This chapter describes the main tasks to begin caching content with OracleAS Web Cache. This chapter contains these topics. In unserem Lexikon finden Sie Informationen und Erklärungen zu 7323 Begriffen rund um die Themen Internet, eCommerce, Hardware, Software, und viele weitere! The tips on this page are categorized. Use. the tips index page to access smaller focused listings of tips. This page lists many other pages available on the web, together with a condensed. For the most part I've eliminated. Remember that. the tuning tips listed are not necessarily good coding practice. They. are performance optimizations that you probably should not use throughout. Java Performance Training Courses COURSES AVAILABLE NOW. We can provide training courses to handle all your Java performance needs.Instead they apply to speeding up critical sections of code. The tips here include only those that are available online for free. I do not intend to summarize. Java Performance Tuning). The tips here are of very variable quality and usefulness, some real gems but. Comments in square brackets, [], have. There is a real need for serving media that is appropriate for the device and circumstance, since we know so little about any particular web request. . 92.50 0.0344 rating dispensed calibration η8 cii non-modularity doublesided 7.42 motivation replications branches file-sharing stuffing 4pm convert. Use this page by using your browser's "find" or "search" option to identify. This page is currently 4. KB. This page is updated once a month. You can receive. email notification of any changes by subscribing to the. Performance planning for managers (Page last updated February 2. Added 2. 00. 1- 0. Author Jack Shirazi, Publisher On. Java). Tips. Include budget for performance management. Create internal performance experts. Set performance requirements in the specifications. Include a performance focus in the analysis. Require performance predictions from the design. Create a performance test environment. Test a simulation or skeleton system for validation. Integrate performance logging into the application layer boundaries. Performance test the system at multiple scales and tune using the resulting information. Deploy the system with performance logging features. A long list of most of the tuning techniques covered in my "Java Performance Tuning" book (Page last updated August 2. Added 2. 00. 0- 1. Author Jack Shirazi, Publisher O'Reilly). Tips. [Since the referred to page is already a summary list, I have not extracted it here. Especially since there are nearly 3. Check the page out directly]. Comparing the performance of Linked. Lists and Array. Lists (and Vectors) (Page last updated May 2. Added 2. 00. 1- 0. Author Jack Shirazi, Publisher On. Java). Tips. Array. List is faster than Vector except when there is no lock acquisition required in Hot. Spot JVMs (when they have about the same performance). Vector and Array. List implementations have excellent performance for indexed access and update of elements, since there is no overhead beyond range checking. Adding elements to, or deleting elements from the end of a Vector or Array. List also gives excellent performance except when the capacity is exhausted and the internal array has to be expanded. Inserting and deleting elements to Vectors and Array. Lists always require an array copy (two copies when the internal array must be grown first). The number of elements to be copied is proportional to [size- index], i. The array copying overhead grows significantly as the size of the collection increases, because the number of elements that need to be copied with each insertion increases. For insertions to Vectors and Array. Lists, inserting to the front of the collection (index 0) gives the worst performance, inserting at the end of the collection (after the last element) gives the best performance. Linked. Lists have a performance overhead for indexed access and update of elements, since access to any index requires you to traverse multiple nodes. Linked. List insertions/deletion overhead is dependent on the how far away the insertion/deletion index is from the closer end of the collection. Synchronized wrappers (obtained from Collections. List(List)) add a level of indirection which can have a high performance cost. Only List and Map have efficient thread- safe implementations: the Vector and Hashtable classes respectively. List insertion speed is critically dependent on the size of the collection and the position where the element is to be inserted. For small collections Array. List and Linked. List are close in performance, though Array. List is generally the faster of the two. Precise speed comparisons depend on the JVM and the index where the object is being added. Pre- sizing Array. Lists and Vectors improves performance significantly. Linked. Lists cannot be pre- sized. Array. Lists can generate far fewer objects for the garbage collector to reclaim, compared to Linked. Lists. For medium to large sized Lists, the location where elements are to inserted is critical to the performance of the list. Array. Lists have the edge for random access. A dedicated List implementation designed to match data, collection types and data manipulation algorithms will always provide the best performance. Array. List internal node traversal from the start to the end of the collection is significantly faster than Linked. List traversal. Consequently queries implemented in the class can be faster. Iterator traversal of all elements is faster for Array. List compared to Linkedlist. Using the Weak. Hash. Map class (Page last updated June 2. Added 2. 00. 1- 0. Author Jack Shirazi, Publisher On. Java). Tips. Weak. Hash. Map can be used to reduce memory leaks. Keys that are no longer strongly referenced from the application will automatically make the corresponding value reclaimable. To use Weak. Hash. Map as a cache, the keys that evaluate as equal must be recreatable. Using Weak. Hash. Map as a cache gives you less control over when cache elements are removed compared with other cache types. Clearing elements of a Weak. Hash. Map is a two stage process: first the key is reclaimed, then the corresponding value is released from the Weak. Hash. Map. String literals and other objects like Class which are held directly by the JVM are not useful as keys to a Weak. Hash. Map, as they are not necessarily reclaimable when the application no longer references them. The Weak. Hash. Map values are not released until the Weak. Hash. Map is altered in some way. For predictable releasing of values, it may be necessary to add a dummy value to the Weak. Hash. Map. If you do not call any mutator methods after populating the Weak. Hash. Map, the values and internal Weak. Reference objects will never be dereferenced [no longer true from 1. Weak. Hash. Map wraps an internal Hash. Map adding an extra level of indirection which can be a significant performance overhead. Every call to get() creates a new Weak. Reference object. Weak. Hash. Map. size() iterates through the keys, making it an operation that takes time proportional to the size of the Weak. Hash. Map. [no longer true from 1. Weak. Hash. Map. is. Empty() iterates through the collection looking for a non- null key, so a Weak. Hash. Map which is empty requires more time for is. Empty() to return than a similar Weak. Hash. Map which is not empty. Empty() is now slower than previous versions]. A high level overview of technical performance tuning, covering 5 levels of tuning competence. Page last updated November 2. Added 2. 00. 0- 1. Author Jack Shirazi, Publisher O'Reilly). Tips. Start tuning by examining the application architecture for potential bottlenecks. Architecture bottlenecks are often easy to spot: they are the connecting lines on the diagrams; the single threaded components; the components with many connecting lines attached; etc. Ensure that application performance is measureable for the given performance targets. Ensure that there is a test environment which represents the running system. This test- bed should support testing the application at different loads, including a low load and a fully scaled load representing maximum expected usage. After targeting design and architecture, the biggest bang for your buck in terms of improving performance is choosing a better VM, and then choosing a better compiler. Start code tuning with proof of concept bottleneck removal: this consists of using profilers to identify bottlenecks, then making simplified changes which may only improve the performance at the bottleneck for a specialized set of activities, and proceeding to the next bottleneck. After tuning competence is gained, move to full tuning. Each multi- user performance test can typically take a full day to run and analyse. Even simple multi- user performance tuning can take several weeks. After the easily idenitified bottlenecks have been removed, the remaining performance improvements often come mainly from targeting loops, structures and algorithms. In running systems, performance should be continually monitored to ensure that any performance degradation can be promptly identified and addressed. Chapter 4 of "Java Performance Tuning", "Object Creation". Page last updated September 2. Added 2. 00. 0- 1. Author Jack Shirazi, Publisher O'Reilly). Tips. Establish whether you have a memory problem. Reduce the number of temporary objects being used, especially in loops. Avoid creating temporary objects within frequently called methods. Presize collection objects. Reuse objects where possible. Empty collection objects before reusing them. Do not shrink them unless they are very large.). Use custom conversion methods for converting between data types (especially strings and streams) to reduce the number of temporary objects. Define methods that accept reusable objects to be filled in with data, rather than methods that return objects holding that data. Or you can return immutable objects.). Canonicalize objects wherever possible. Compare canonicalized objects by identity. Canonicalizing objects means having only a single reference of an object, with no copies possible]. Create only the number of objects a class logically needs (if that is a small number of objects). Replace strings and other objects with integer constants. Compare these integers by identity. Application Infrastructure Control, Performance, Security, and Infrastructure. White Paper. The portfolio of Cisco. Application Networking Services has two significant additions. First, the Cisco Application Control Engine (ACE) introduces new levels of application control as a module on Cisco Catalyst. Series Switches. Second, significant security enhancements have been added to the Cisco Application Velocity System (AVS) dedicated appliance. Both products result in an application solution that overcomes the following challenges. Application control: improving the way IT departments deploy, operate, and manage their application infrastructures. Application performance: helping ensure better service to end users, including scalability, availability, and failover. Application security: helping to protect critical applications, infrastructures, and data from abuse and misuse. Infrastructure simplification: reducing the complexity of the infrastructure, shrinking the number of devices and vendors, better integrating the network and the application, and lowering the cost of the infrastructure. The portfolio of Cisco data center solutions for Application Networking Services helps make applications more scalable and available (Figure 1). By using less server and network resources, these solutions lower the total cost of ownership and enhance IT flexibility. The portfolio offers IT teams integrated building blocks for optimizing application control, simplifying infrastructure, and delivering end- to- end business processes. Figure 1. Cisco ACE and Cisco AVS in the Data Center CHALLENGE. The data centers for enterprises and service providers face continual pressure to amplify service velocity, improve the reliability and quality of service, and reduce costs. Applications are still deployed and managed in separate silos across the network where application performance often is a secondary concern. Organizations use various point products to address the worst challenges in specific locations. Finally, security and regulatory compliance place further constraints on how IT can react. IT needs solutions that give it more control over the application infrastructure, that aggregate capabilities to simplify management, and that deliver highly secure and accelerated application service across the extended enterprise. To meet these challenges, enterprises and service providers require data- center solutions that. Deploy and migrate applications without adding to the application infrastructure • Scale the application infrastructure • Have multitier data- center and application security • Provide distributed workflow • Consolidate functions, devices, and management • Increase application throughput SOLUTION. Unlike application front- end appliances, Cisco ACE is fully integrated with the Cisco network, providing IT teams with a foundation for efficiently using data- center resources, people, and the systems throughout the infrastructure. The Cisco platform addresses the challenges of optimizing, scaling, securing, and delivering applications where you need them, when you need them, and with unparalleled control. This solution also helps to enable high availability for virtualized applications, to optimize applications, to address the requirements for data- center and application security, and to maximize the performance and resources of data centers to deliver applications at the lowest cost and with the lowest operational overhead. The Cisco ACE and AVS offerings introduce several technologies for delivering applications in demanding enterprise environments, including advanced application control through virtualization and role- based access control, high performance, high security, and infrastructure simplification. These and the other major features of the Cisco ACE and AVS solutions collectively deliver exceptional performance, operational flexibility, security, and application optimization. Performance: Latency, Mitigation and Bandwidth Usage Reduction. Cisco ACE and AVS achieve short application response times by incorporating features that enhance network and application performance in Layer 2 through Layer 7. As more and more applications are added to a data center, the cost of supporting each application is reduced. Cisco ACE and AVS also reduce operating overhead costs- which are typically more than half of an IT budget- when implemented on the Cisco Catalyst 6. Series Switch. The Cisco ACE module uses the switch for power, space, cooling, and the management interface. Cisco AVS appliances enhance application performance over the WAN by improving response times. Without any changes to the application or in client interaction, Cisco AVS solutions routinely shrink end- user response times by 5. Cisco data- center solutions maintain the state of the entire application across all clients and servers. Through knowledge of the context of requests, the solutions transform data previously considered uncacheable and eliminate the need to check with either Web or application servers. Aggregating Web requests and minimizing unnecessary network calls bring gains for users regardless of their location, access, or client system. These advances rely on four primary capabilities of the Cisco AVS products. Flash. Forward object acceleration helps the Cisco AVS 3. Application Velocity System eliminate unnecessary browser cache validation requests. This new technology eliminates the network delays associated with embedded cacheable Web objects such as images, style sheets, and Java. Script files. In a Web deployment, each embedded object must ensure that the user has the proper browser version, and each validation involves a separate HTTP request from the client to the origin server. Pages that embed many objects must wait to be rendered until the client- to- server round trips are completed. Cisco Flash. Forward technology automates this process at the server. All object validity information is carried in the single download of the parent HTML document. The Cisco AVS 3. 12. This automatic aggregation saves traffic by validating object freshness on the server side, rather than on the client. The benefits can be realized in any application. Smart Redirect speeds Webpage redirecting by helping the Cisco AVS 3. HTML metatag- based redirects into more efficient HTTP header- based redirects. The result is significantly faster page response time that does not sacrifice the flexibility and productivity of metatag- based redirection. Fast Redirect speeds HTTP header- based 3. The Cisco AVS 3. 12. HTTP status code response and fetches the redirected resource over the LAN in the data center. Flash. Connect improves browser performance by enabling responses to be processed in parallel rather than serially. By default, Microsoft Internet Explorer fetches objects over only two TCP connections established for each domain name it sees in an HTML container page. This limit means that requests are often queued unnecessarily, and first- visit performance suffers. By multiplexing these connections, the Cisco AVS 3. Reduce Time, Cost and Complexity of Application Deployment. Enterprises and service providers need flexible, scalable, and reliable platforms for application delivery. Significant reduction in the time needed to deploy applications is achieved through centralized control with decentralized management using virtual partitioning, role- based access control, and hierarchical management domains. Virtual partitioning can provide the same level of service to as many as 2. Role- Based Access Control (RBAC) enable centralized control and decentralized management. Combined with hierarchical management domains these functions allow resource distribution and management in logical groups (such as businesses, applications, or customers) on a given physical platform and ensure maximum flexibility for deployments, for the most scalable and efficient use of the Application Control Engine. The Self- Defending Network concept aims at peace of mind through built- in defense at multiple levels in the data center. A Cisco data center solution for Application Networking Services integrated with a Cisco Self- Defending Network supports multilevel security while efficiently handling application traffic. Such a solution provides a single point of control for all business and security policies and a robust solution for application security, including. SSL encryption and decryption • Directional deep inspection • Integrated hardware- accelerated protocol control • Positive and negative (whitelist and blacklist) security • Protocol compliance • Anomaly detection • Transaction logging and reports for application security forensics. Whereas intrusion prevention and intrusion detection systems protect Web servers, the Cisco ACE and AVS solution protects against vulnerabilities in Web- based applications. What firewalls accomplish at the network level- denying all activities unless explicitly allowed- Cisco ACE and AVS accomplishes at the application level. A rules- based, policy- directed approach ensures that those automated requests to and from the application comply with policy and do not, for example, include a request to turn off the application. In a typical threat scenario, an attacker uses a Web proxy that resides on a legitimate user's desktop. The attacker can tamper with message headers, protocols, or payloads- for example, by inserting malicious code into different parts of the application. Developers often do not protect their code from these types of attacks. A Cisco AVS solution filters out malicious inputs using a variety of methods. Normalization- The Cisco AVS 3. HTTP and HTTPS traffic by decoding encrypted traffic so that the payload can be examined, not just the TCP header. Bidirectional, deep- packet inspection- The Cisco AVS 3. It identifies malicious traffic by applying policy, such as whitelists and blacklists. Blocking- The Cisco AVS 3.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
November 2016
Categories |