|By Dave Chappell, Andrew Gregory||
|June 3, 2009 08:30 AM EDT||
According to Moore's Law , processing speed and storage capacity have been doubling about every two years since the invention of the integrated circuit in 1958.
Yet it seems that our propensity for building larger more complex software systems that anticipate these improvements inevitably outpace the exponential growth in capacity to support these systems. SOA is becoming more broadly adopted, along with the practice of using XML as a means of communicating data between services and the more rapid adoption of applications to Internet scale. Staring you in the face of your application's success, the potential to overwhelm your systems has become very real, and may happen at times when you least expect it.
How do we get ahead of this trend? Given that memory and storage are always increasing in the realm of enterprise computing, software needs to keep up with the pace. We need to architect from the beginning using the proper approach toward achieving linear scalability with predictable latency. Data files and feeds are increasing in size, requiring more processing, and becoming more cumbersome to manage with software designed to materialize entire files before consuming them. In some cases, the operations that are to be performed require multiple input sources to be consumed before processing can begin.
Those who are building the eXtreme Transaction Processing (XTP) style of applications - such as Telco call setup and billing, online gaming, securities trading, risk management, and online travel booking - understand this challenge well. The broader use case that is applicable across more industries is web applications that need to scale up to Internet volumes, against backend systems that were never designed to handle that kind of traffic.
In discussions with customers about scaling a SOA with predictable latency, the term that often comes up is "Boundary Costs." To put this in context, consider the following scenario - an XML document that may have originated from an internal application, database, an external business partner, or perhaps converted from an EDI document, needs to be processed by a number of services, which are coordinated by a BPEL process or an ESB process pipeline. The common approach is to place the XML document on the bus and have the bus invoke the services in accordance with the process definition, passing the XML document as part of the service request payload. Each service that needs to process that data will access the XML accordingly. Interaction with a database may also occur. This approach, as illustrated in Figure 1, sounds simple enough.
Figure 1: Calling services using BPEL process or Service Bus pipeline
However, in practice there are challenges to scalability when using this approach. What is the cost of crossing the boundary from one service to the next? How many times does that cost get incurred in the context of invoking a simple business process? What if the XML document is really large in the multi-megabyte range, or there are lots of them numbering in the thousands, or both?
Compounding this challenge is the reality that most IT environments are a mixture of platforms and technologies. Regardless of how efficient your process engine or service bus might be, the processing at the service endpoint might still become a bottleneck. A recent conversation at a customer site revealed a 15-step business process that normally takes 15 seconds to run, but of late under peak loads it is violating its 30-second SLA. The developers had spent the better part of the past two years optimizing and tuning every last bit of performance out of each one of those 15 services, and the remaining culprit identified for the poor end-to-end latency is the boundary cost between the services. A detailed examination revealed that each of the 15 service calls was spending 1-2 seconds in an open source web service toolkit doing parsing and marshaling of the XML payload. This is not intended to be a disparaging comment about open source web services toolkits, but is simply illustrating the point that parsing and marshaling of XML at the endpoints can introduce latency that can add up pretty quickly.
As illustrated in Figure 2, each service invoked needs to read the XML payload from its on-the-wire serialization form, and parse the XML into a native Java or .NET object form to be processed by the business logic. In addition if database interaction is required, then there is an additional object to relational mapping that needs to occur. Finally, the inverse of those steps needs to occur in order to generate a response to the service request and send that along to the next downstream service in accordance with the business process that is coordinating the interaction between the services.
Figure 2: Service request boundary cost between XML to Object to Relational and back again for each invocation
A popular approach for dealing with XML in a SOA is to use web services and XMLBeans. Using XMLBeans, objects are typically created by fully materializing the inputs and outputs, as this allows for maximum usability and processing. In-memory processing may include sorting, filtering, or aggregation operations, all of which increase the overall memory required to deal with each call. This strategy is not scalable and cannot be applied to many of the use cases in this area. Many products support streaming of XML, but this may limit the ability to do anything meaningful without putting the data somewhere else first.
What if there was a way to take this information and store it in an application grid, a place where the size of the data and the processing capability can far eclipse that of any single machine or process? The application grid can utilize the combined memory and processing power of multiple machines in order to complete an operation, such as the application of a complex formula or filter across an enormous data set. The application grid also provides the ability to hold the data for longer periods of time beyond the cycle of a single service request, survive server restarts, and even work across network boundaries.
If we could combine the power of the grid for data storage and manipulation with the efficiency of streaming, the result would be a highly scalable system capable of processing much more information than before. Using a combination of complementary technologies here, we achieve our goal of spreading compute operations across a distributed network of machines, and we lessen the processing and memory requirements of our data consumers - SOA services, application servers, and client applications. We also remove the need to use a database for intermediate storage of data while it is (or simply so it can be) processed. By using an application grid we can also implement patterns where we pass around references to data, rather than the data, resulting in huge efficiency gains in the communications layer, and dramatically reducing or eliminating the boundary cost.
This article includes a code example that covers the use case of processing large XML files in an application grid. In a typical XML file, there are a usually elements that repeat without any pre-determined limit. Using a STAX parser to handle streaming XML, and JAXB to handle conversion between XML and Java objects, we can extract these repeating elements from the XML stream and put them on the application grid as individual objects. The implementation can populate the grid with these objects, and do so with a limited amount of memory consumption. Once populated, the grid can process the data across the multiple machines that constitute the grid. Each grid member processes an operation or a filter and passes intermediate results to the grid client, which then assembles them into a final result set.
What Is an Application Grid?
An application grid is a horizontally scalable agent based in an in-memory storage engine for application state data. This effectively provides a distributed shared memory pool that can be linearly scaled across a heterogeneous grid of machines that consists of any combination of high-end and lower-cost commodity hardware. Use of an application grid in an application simultaneously provides performance, scalability and reliability to in-memory data.
One way that an application utilizes an application grid is to use API-level interfaces that mimic the Java Hashmap, .NET Dictionary, or JPA interfaces. An alternate approach is to use a service-level interface from a SOA environment. As applications or services place data into the application grid, a group of constantly cooperating caching servers coordinate updates to data objects, as well as their backups, using cluster-wide concurrency control.
As shown in Figure 3, the request to put data to the map is taken over by the application grid and transported across a highly efficient networking protocol to the grid node P, which owns the primary instance data. The primary node in turn copies the updated value to the secondary node B for backup, and then returns control to the service.
Figure 3: Application grid clustering ensures primary / backup of in-memory data on separate machines.
The application grid stores data across multiple machines with complete location transparency as it sees fit. A unique hash key value is all that is necessary to retrieve the stored data at a future point, regardless of where the application grid chose to store the data. This prevents the application logic from dealing with complex location dependencies and manual partitioning schemes. If one or more nodes in the grid fails, or can't be reached due to network failure, the application grid will immediately react to the failure and rebalance the data across the remaining healthy nodes. This can happen even if the failing node had been participating in an autonomous update operation. In Figure 4, the primary owner ‘P' of a piece of data fails while in the midst of retrieving data for the service. The get() request is immediately routed to the backup node and a new primary / backup pair is allotted.
Figure 4: Application grid provides continual failover of in-memory state data
This data stored in the grid can be anything from simple variables to complex objects or even large XML documents. In our case we chose to fragment what would have been very large XML documents into smaller parts and store those XML fragments as Java objects in the application grid. This allows us to do parallel queries against the data using the Java APIs.
The application grid supports a range of operations including parallel processing of queries, events, and transactions. For large datasets, an entire collection of data may be put to the grid as a single operation, and the grid can disperse the contents of the collection across multiple primary and backup nodes in order to scale. In more advanced applications, the grid may even execute business logic directly and in parallel on data storage nodes, and do so with data and logic affinity such that the logic executes on the same machine that is storing the data that the logic is operating on.
|jhv1blz5 07/03/09 10:31:00 AM EDT|
The article validated SOA as an IT architecture paradigm that can be leveraged in many ways. Taking data storage, scalability and application performance to a nifty level using SOA Application Grid infrastructure will no doubt enhance data and application performance on Oracle architecture platforms, it also has the promise of a cost effective and efficient IT delivery model. The very benefits of SOA.
Ten years ago, there may have been only a single application that talked directly to the database and spit out HTML; customer service, sales - most of the organizations I work with have been moving toward a design philosophy more like unix, where each application consists of a series of small tools stitched together. In web example above, that likely means a login service combines with webpages that call other services - like enter and update record. That allows the customer service team to writ...
Oct. 10, 2015 02:45 AM EDT Reads: 443
As we increasingly rely on technology to improve the quality and efficiency of our personal and professional lives, software has become the key business differentiator. Organizations must release software faster, as well as ensure the safety, security, and reliability of their applications. The option to make trade-offs between time and quality no longer exists—software teams must deliver quality and speed. To meet these expectations, businesses have shifted from more traditional approaches of d...
Oct. 10, 2015 02:15 AM EDT Reads: 240
Between the compelling mockups and specs produced by analysts, and resulting applications built by developers, there exists a gulf where projects fail, costs spiral, and applications disappoint. Methodologies like Agile attempt to address this with intensified communication, with partial success but many limitations. In his session at DevOps Summit, Charles Kendrick, CTO and Chief Architect at Isomorphic Software, will present a revolutionary model enabled by new technologies. Learn how busine...
Oct. 10, 2015 02:00 AM EDT Reads: 306
If you are new to Python, you might be confused about the different versions that are available. Although Python 3 is the latest generation of the language, many programmers still use Python 2.7, the final update to Python 2, which was released in 2010. There is currently no clear-cut answer to the question of which version of Python you should use; the decision depends on what you want to achieve. While Python 3 is clearly the future of the language, some programmers choose to remain with Py...
Oct. 10, 2015 02:00 AM EDT Reads: 268
SYS-CON Events announced today that Dyn, the worldwide leader in Internet Performance, will exhibit at SYS-CON's 17th International Cloud Expo®, which will take place on November 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA. Dyn is a cloud-based Internet Performance company. Dyn helps companies monitor, control, and optimize online infrastructure for an exceptional end-user experience. Through a world-class network and unrivaled, objective intelligence into Internet condit...
Oct. 10, 2015 02:00 AM EDT Reads: 652
Achim Weiss is Chief Executive Officer and co-founder of ProfitBricks. In 1995, he broke off his studies to co-found the web hosting company "Schlund+Partner." The company "Schlund+Partner" later became the 1&1 web hosting product line. From 1995 to 2008, he was the technical director for several important projects: the largest web hosting platform in the world, the second largest DSL platform, a video on-demand delivery network, the largest eMail backend in Europe, and a universal billing syste...
Oct. 10, 2015 01:00 AM EDT Reads: 241
Containers have changed the mind of IT in DevOps. They enable developers to work with dev, test, stage and production environments identically. Containers provide the right abstraction for microservices and many cloud platforms have integrated them into deployment pipelines. DevOps and Containers together help companies to achieve their business goals faster and more effectively.
Oct. 10, 2015 12:00 AM EDT Reads: 221
Somebody call the buzzword police: we have a serious case of microservices-washing in progress. The term “microservices-washing” is derived from “whitewashing,” meaning to hide some inconvenient truth with bluster and nonsense. We saw plenty of cloudwashing a few years ago, as vendors and enterprises alike pretended what they were doing was cloud, even though it wasn’t. Today, the hype around microservices has led to the same kind of obfuscation, as vendors and enterprise technologists alike ar...
Oct. 10, 2015 12:00 AM EDT Reads: 494
Opinions on how best to package and deliver applications are legion and, like many other aspects of the software world, are subject to recurring trend cycles. On the server-side, the current favorite is container delivery: a “full stack” approach in which your application and everything it needs to run are specified in a container definition. That definition is then “compiled” down to a container image and deployed by retrieving the image and passing it to a container runtime to create a running...
Oct. 10, 2015 12:00 AM EDT Reads: 268
Containers are revolutionizing the way we deploy and maintain our infrastructures, but monitoring and troubleshooting in a containerized environment can still be painful and impractical. Understanding even basic resource usage is difficult - let alone tracking network connections or malicious activity. In his session at DevOps Summit, Gianluca Borello, Sr. Software Engineer at Sysdig, will cover the current state of the art for container monitoring and visibility, including pros / cons and li...
Oct. 10, 2015 12:00 AM EDT Reads: 266
The web app is agile. The REST API is agile. The testing and planning are agile. But alas, data infrastructures certainly are not. Once an application matures, changing the shape or indexing scheme of data often forces at best a top down planning exercise and at worst includes schema changes that force downtime. The time has come for a new approach that fundamentally advances the agility of distributed data infrastructures. Come learn about a new solution to the problems faced by software organ...
Oct. 9, 2015 08:00 PM EDT Reads: 937
Saviynt Inc. has announced the availability of the next release of Saviynt for AWS. The comprehensive security and compliance solution provides a Command-and-Control center to gain visibility into risks in AWS, enforce real-time protection of critical workloads as well as data and automate access life-cycle governance. The solution enables AWS customers to meet their compliance mandates such as ITAR, SOX, PCI, etc. by including an extensive risk and controls library to detect known threats and b...
Oct. 9, 2015 03:00 PM EDT Reads: 241
Docker is hot. However, as Docker container use spreads into more mature production pipelines, there can be issues about control of Docker images to ensure they are production-ready. Is a promotion-based model appropriate to control and track the flow of Docker images from development to production? In his session at DevOps Summit, Fred Simon, Co-founder and Chief Architect of JFrog, will demonstrate how to implement a promotion model for Docker images using a binary repository, and then show h...
Oct. 9, 2015 02:15 PM EDT Reads: 191
DevOps has often been described in terms of CAMS: Culture, Automation, Measuring, Sharing. While we’ve seen a lot of focus on the “A” and even on the “M”, there are very few examples of why the “C" is equally important in the DevOps equation. In her session at @DevOps Summit, Lori MacVittie, of F5 Networks, will explore HTTP/1 and HTTP/2 along with Microservices to illustrate why a collaborative culture between Dev, Ops, and the Network is critical to ensuring success.
Oct. 9, 2015 01:30 PM EDT Reads: 167
Overgrown applications have given way to modular applications, driven by the need to break larger problems into smaller problems. Similarly large monolithic development processes have been forced to be broken into smaller agile development cycles. Looking at trends in software development, microservices architectures meet the same demands. Additional benefits of microservices architectures are compartmentalization and a limited impact of service failure versus a complete software malfunction....
Oct. 9, 2015 01:15 PM EDT Reads: 263
Despite all the talk about public cloud services and DevOps, you would think the move to cloud for enterprises is clear and simple. But in a survey of almost 1,600 IT decision makers across the USA and Europe, the state of the cloud in enterprise today is still fraught with considerable frustration. The business case for apps in the real world cloud is hybrid, bimodal, multi-platform, and difficult. Download this report commissioned by NTT Communications to see the insightful findings – registra...
Oct. 9, 2015 01:00 PM EDT Reads: 304
Manufacturing has widely adopted standardized and automated processes to create designs, build them, and maintain them through their life cycle. However, many modern manufacturing systems go beyond mechanized workflows to introduce empowered workers, flexible collaboration, and rapid iteration. Such behaviors also characterize open source software development and are at the heart of DevOps culture, processes, and tooling.
Oct. 9, 2015 12:30 PM EDT Reads: 1,108
In a report titled “Forecast Analysis: Enterprise Application Software, Worldwide, 2Q15 Update,” Gartner analysts highlighted the increasing trend of application modernization among enterprises. According to a recent survey, 45% of respondents stated that modernization of installed on-premises core enterprise applications is one of the top five priorities. Gartner also predicted that by 2020, 75% of
Oct. 9, 2015 12:00 PM EDT Reads: 345
DevOps Summit at Cloud Expo 2014 Silicon Valley was a terrific event for us. The Qubell booth was crowded on all three days. We ran demos every 30 minutes with folks lining up to get a seat and usually standing around. It was great to meet and talk to over 500 people! My keynote was well received and so was Stan's joint presentation with RingCentral on Devops for BigData. I also participated in two Power Panels – ‘Women in Technology’ and ‘Why DevOps Is Even More Important than You Think,’ both ...
Oct. 9, 2015 12:00 PM EDT Reads: 8,679
As the world moves towards more DevOps and microservices, application deployment to the cloud ought to become a lot simpler. The microservices architecture, which is the basis of many new age distributed systems such as OpenStack, NetFlix and so on, is at the heart of Cloud Foundry - a complete developer-oriented Platform as a Service (PaaS) that is IaaS agnostic and supports vCloud, OpenStack and AWS. In his session at 17th Cloud Expo, Raghavan "Rags" Srinivas, an Architect/Developer Evangeli...
Oct. 9, 2015 11:45 AM EDT Reads: 185