CryTek just released its CryEngine 3.8.1, an update packed with features. The CryEngine website dubs this the heftiest upgrade since debuting their Engine-as-a-Service in May. Among the many new features is virtual reality (VR) support, OpenGL compatibility, and Linux support. One of the biggest trends in gaming is VR, and CryEngine 3.8.1 adds VR support. Initially, it’s limited to the Oculus Rift, but chances are as more headsets emerge and see adoption among both developers and gamers, compatibility will expand to support these as well. Epic Games’ Unreal Engine also added VR support, along with Unity3D’s most recent release. There’s a neat VR demo on the CryTek website, which developers will surely want to check out. Another significant change is the addition of Linux support. While Wine and Playonlinux have both helped many games to run on a variety of Linux-based operating systems (OSes). However, native Linux support means easier use for developers. As more games add Linux compatibility, spearheaded by Steam’s SteamOS, the CryEngine itself can now be run on Linux. This latest update means that the CryEngine will join Unity3D, the Unreal Engine, and Source as a powerful game development engine with Linux and VR support. Virtual reality is seeing widespread adoption among the developer community, and Linux compatibility in gaming is a huge trend. While CryEngine 3.8.1’s ability to run on Linux won’t necessarily mean games developed will be compatible on the popular open source OS, it certainly makes it easier to ensure Linux support. There’s also OpenGL support, which will also aid cross-platform development. The CryEngine has been used to create many games known for their gorgeous eye-candy. Notably, the CryTek’s aptly names Crysis series is built with the CryEngine, as is Ryse: Son of Rome, State of Decay, and Kingdom Come: Deliverance.

[This article was written by Matt Kiernan] We are excited to announce that Logentries’ new SQL-like query language, LEQL, is now available for more advanced analytics and easy extraction of valuable insights from your log data. A SQL-Like Query Language If you’ve ever used SQL, LEQL should feel familiar. In fact, Logentries already supports a number of SQL-like search functions, including: SUM: Sums a set of values COUNT: Counts the number of times a value occurs GROUPBY: Groups values by a unique key UNIQUE: Enables the count of only unique values With the rollout of LEQL, we’ll be introducing four new query functions: MIN: Calculate the minimum value of a specified key MAX: Calculate the maximum value of a specified key SORT: Display results sorted either ascending or descending TIMESLICE: Specifies how to group by time (e.g. by specific number of minutes, hours or days) A Consistent Yet Expressive Syntax We believe a reliable query language depends on a consistently enforced syntax. For this reason, we’ll be enforcing how queries are structured. Here’s an example of how an old query would change with LEQL: Old pages>0 | GroupBY(dbName) | SUM(pages) New where(pages>0) groupby (dbName) calculate(SUM:pages) *In this example, pages & dbName are Key names in log events Notice how the search logic gets wrapped in a where() clause, used for refining your search to return only results that match your search criteria (i.e. where events include the text or Key “pages”.) groupBy() is an optional clause that enables you to organize your search results into groups by specifying a Key from a Key-Value Pair (i.e. key: value). Calculations made within your query get utilized in the calculate()clause. When building your query, you no longer need to separate sections with pipes “|”. Though we believe in the value of a consistent query syntax, we also believe in the importance of giving users an expressive language that is easy to use and delivers expected results. We’re taking the following steps to make LEQL easy to use: Outdated saved queries will automatically be converted into LEQL – no effort required where clauses will automatically be added to any new query you write LEQL terms will not be case sensitive An updated search bar will provide a query builder and validator An updated search bar & query builder An Updated Search Bar & Query Builder As we rollout LEQL, we’ll be introducing a new search bar, allowing users to switch between a simple & advanced modes based on their preference. Simple mode “Simple mode” provides an easy way to build queries by providing a list of the available functions. Type-Assist will show a list of keys to associate with each functions, or new keys can be typed manually. Advanced mode “Advanced mode” will allow users to type their queries manually. Type-Assist will autocomplete key names while the new search bar will automatically validate query syntax. July 1st Rollout The LEQL rollout will take place in phases, beginning July 1st and will continue over the next few weeks to update all plans. If you’d like early beta access to LEQL, or have any questions, feel free to reach us at [email protected].

Occasionally we are asked about JBoss BPM Suite integration with other products and layers in an enterprises architecture. We have published articles talking about how to achieve this with various aspects such as: Microservices integration Data integration Articles are one thing, but seeing is believing, so we have done a few webinars to show you live how to tackle integration: Data integration webinar PEX webinar Along with these articles we have always published demo projects that give you a closer look and chance to get hands on with these integration strategies: JBoss BPM Suite & JBoss Fuse Travel microservices story JBoss BPM Suite & JBoss Data Virtualization integration Imported Signavio Process Editor mortgage workflow. There is another integration story yet to be told about how one can leverage other tooling together with JBoss BPM Suite. This article will introduce one such company,Signavio, that provides a Signavio Process Editor so"...you can start modeling and engaging your organization in improving operational efficiency through the development of optimal models..." The following demo project provides a working example of how you can model an example mortgage process in Signavio Process Editor and then bring it into JBoss BPM Suite where you can add implementation details, integration details and other implementation details to finally execute the mortgage process end-to-end. Demo project As always we bring you not only a story, but a reusable demo project you can easily spin up yourself to explore the details around how a JBoss BPM project would integrate with the model designed in Signavio Process Editor. The project is called the JBoss BPM Suite & Signavio Process Editor Integration Demo. The project installs JBoss BPM Suite 6.1 with an example mortgage project with rules, process, forms and other artifacts. It also includes a copy of an exported Signavio Process Editor mortgage process that we then show how to import. Final mortgage workflow project with implementation details and integration details completed. Ready to run! This gives you the initial starting point after importing the Signavio process and the completely integrated final mortgage project that you can run side-by-side. To setup this project there are just a few simple steps to get going and will be up and running minutes: Installation Download and unzip. Add products to installs directory. Run 'init.sh' or 'init.bat' file. 'init.bat' must be run with Administrative privileges. Start JBoss BPMS Server by running 'standalone.sh' or 'standalone.bat' in the /target/jboss-eap-6.1/bin directory. Login to http://localhost:8080/business-central - login for admin, appraisor, broker, and manager roles (u:erics / p:bpmsuite1!) Mortgage Loan demo pre-installed as project. Using process designer, import the Signavio process that was exported to the file found in: support/MortgageDemoSignavio.bpmn Looking to Automate your business? See screenshots provided in project for how this should look and note that the JBoss BPM Suite process designer included validation that puts messages about tasks not specified, this is correct as at this point you need to start implementing the process tasks. You can examine the imported process and note the various details captured during initial workshops have been put into the process details for each step in the workflow. After implementing these steps you will find the final process ready to run. You can now explore the final project by deploying it and starting a new instance. We hope you enjoy this example project and feel free to browse for more at JBoss Demo Central.

The use of OpenStack in the private cloud is invaluable for DevOps. It provides engineers the ability to innovate quickly and deal with uncertainty. It also maximizes existing infrastructure and provides a programmable, software-defined IaC. Openstack in the private cloud = agile development OpenStack has emerged as the de facto standard for IaaS in the private cloud. It gives engineers a vital self-service capability to provision (and de-provision) environments, allowing them to act autonomously, in the moment. This helps to eliminate the downstream bottleneck caused by waiting for operations staff to find time to do the provisioning. As OpenStack is open source it is vendor agnostic, allowing you to take advantage of competitive pricing rather than suffering from vendor lock-in. A private cloud means lower cost for the same capacity in a public cloud, which is especially useful for enterprises with high data needs. For security reasons, OpenStack is still mainly used in the private cloud by developers and QA, i.e. in a non-production context. However, OpenStack gives an ability to optimize application performance and/or security by having more control compared to public cloud. The software is increasingly backed by the critical mass of leading IT infrastructure vendors such as IBM, CICSO and HP. Gartner assumes that “by 2019, OpenStack enterprise deployments will grow tenfold, up from just hundreds of production deployments today, due to increased maturity and growing ecosystem support.”1 Challenges to consider OpenStack implementation skills are still rare in the market, so experimentation and self-learning is necessary. Although this takes time, it is offset by the fact the software is free and represents a good opportunity to gain internal expertise. This is particularly valid if you class infrastructure as a core competence. The maturity and functionality of OpenStack projects vary widely - while it covers storage, network and compute, the main adoption currently happens around compute (Nova) and block storage (Cinder), with object storage and network (Neutron) lacking significantly behind. However, without leveraging virtualized network services as part of a private cloud, full-stack environment provisioning is not possible, so don’t forget to add necessary network services to your private cloud. Where to begin Integrating OpenStack clouds with existing infrastructure can be a challenge. It is hardly plug and play. At first, it is best to focus on relatively isolated DevOps environments, such as Gartner’s “mode two”2 applications rather than introducing open stack across the board straight away, (Bimodal IT “refers to having two modes of IT, each designed to develop and deliver information – and technology – intensive services in its own way. Mode 1 is traditional, emphasizing scalability, efficiency, safety and accuracy. Mode 2 is nonsequential, emphasizing agility and speed.”3) As with any open source software, new functions and upgrades are frequently released. This means keeping up with changes in functionality and filling gaps with customizations or third-party products. Upgrades are complex and typically require planned downtime. For these reasons, we recommend choosing a hardened distribution and sticking with it. Openstack is the most complete vendor agnostic solution for storage, network and compute services. The ability for developers to instantly spin up environments at any time is invaluable for a fully agile DevOps environment, and is well worth the effort it takes to acclimatize to Openstack. 1 http://www.prnewswire.com/news-releases/suse-openstack-cloud-5-to-simplify-private-cloud-management-300048721.html 2 http://www.gartner.com/it-glossary/bimodal 3 http://www.gartner.com/it-glossary/bimodal

Written by Yves Trudeau Ever since MySQL replication has existed, people have dreamed of a good solution to automatically split read from write operations, sending the writes to the MySQL master and load balancing the reads over a set of MySQL slaves. While if at first it seems easy to solve, the reality is far more complex. First, the tool needs to make sure it parses and analyses correctly all the forms of SQL MySQL supports in order to sort writes from reads, something that is not as easy as it seems. Second, it needs to take into account if a session is in a transaction or not. While in a transaction, the default transaction isolation level in InnoDB, Repeatable-read, and the MVCC framework insure that you’ll get a consistent view for the duration of the transaction. That means all statements executed inside a transaction must run on the master but, when the transaction commits or rollbacks, the following select statements on the session can be again load balanced to the slaves, if the session is in autocommit mode of course. Then, what do you do with sessions that set variables? Do you restrict those sessions to the master or you replay them to the slave? If you replay the set variable commands, you need to associate the client connection to a set of MySQL backend connections, made of at least a master and a slave. What about temporary objects like with “create temporary table…”? How do you deal when a slave lags behind or what if worse, replication is broken? Those are just a few of the challenges you face when you want to build a tool to perform read/write splitting. Over the last few years, a few products have tried to tackle the read/write split challenge. The MySQL_proxy was the first attempt I am aware of at solving this problem but it ended up with many limitations. ScaleARC does a much better job and is very usable but it stills has some limitations. The latest contender is MaxScale from MariaDB and this post is a road story of my first implementation of MaxScale for a customer. Let me first introduce what is MaxScale exactly. MaxScale is an open source project, developed by MariaDB, that aims to be a modular proxy for MySQL. Most of the functionality in MaxScale is implemented as modules, which includes for example, modules for the MySQL protocol, client side and server side. Other families of available modules are routers, monitors and filters. Routers are used to determine where to send a query, Read/Write splitting is accomplished by the readwritesplit router. The readwritesplit router uses an embedded MySQL server to parse the queries… quite clever and hard to beat in term of query parsing. There are other routers available, the readconnrouter is basically a round-robin load balancer with optional weights, the schemarouter is a way to shard your data by schema and the binlog router is useful to manage a large number of slaves (have a look at Booking.com’s Jean-François Gagné’s talk at PLMCE15 to see how it can be used). Monitors are modules that maintain information about the backend MySQL servers. There are monitors for a replicating setup, for Galera and for NDB cluster. Finally, the filters are modules that can be inserted in the software stack to manipulate the queries and the resultsets. All those modules have well defined APIs and thus, writing a custom module is rather easy, even for a non-developer like me, basic C skills are needed though. All event handling in MaxScale uses epoll and it supports multiple threads. Over the last few months I worked with a customer having a challenging problem. On a PXC cluster, they have more than 30k queries/s and because of their write pattern and to avoid certification issues, they want to have the possibility to write to a single node and to load balance the reads. The application is not able to do the Read/Write splitting so, without a tool to do the splitting, only one node can be used for all the traffic. Of course, to make things easy, they use a lot of Java code that set tons of sessions variables. Furthermore, for ISO 27001 compliance, they want to be able to log all the queries for security analysis (and also for performance analysis, why not?). So, high query rate, Read/Write splitting and full query logging, like I said a challenging problem. We experimented with a few solutions. One was a hardware load balancer that failed miserably – the implementation was just too simple, using only regular expressions. Another solution we tried was ScaleArc but it needed many rules to whitelist the set session variables and to repeat them to multiple servers. ScaleArc could have done the job but all the rules increases the CPU load and the cost is per CPU. The queries could have been sent to rsyslog and aggregated for analysis. Finally, the HA implementation is rather minimalist and we had some issues with it. Then, we tried MaxScale. At the time, it was not GA and was (is still) young. Nevertheless, I wrote a query logging filter module to send all the queries to a Kafka cluster and we gave it a try. Kafka is extremely well suited to record a large flow of queries like that. In fact, at 30k qps, the 3 Kafka nodes are barely moving with cpu under 5% of one core. Although we encountered some issues, remember MaxScale is very young, it appeared to be the solution with the best potential and so we moved forward. The folks at MariaDB behind MaxScale have been very responsive to the problems we encountered and we finally got to a very usable point and the test in the pilot environment was successful. The solution is now been deployed in the staging environment and if all goes well, it will be in production soon. The following figure is simplified view of the internals of MaxScale as configured for the customer: The blocks in the figure are nearly all defined in the configuration file. We define a TCP listener using the MySQL protocol (client side) which is linked with a router, either the readwritesplit router or the readconn router. The first step when routing a query is to assign the backends. This is where the read/write splitting decision is made. Also, as part of the steps required to route a query, 2 filters are called, regexp (optional) and Genlog. The regexp filter may be used to hot patch a query and the Genlog filter is the logging filter I wrote for them. The Genlog filter will send a json string containing about what can be found in the MySQL general query log plus the execution time. Authentication attempts are also logged but the process is not illustrated in the figure. A key point to note, the authentication information is cached by MaxScale and is refreshed upon authentication failure, the refresh process is throttled to avoid overloading the backend servers. The servers are continuously monitored, the interval is adjustable, and the server status are used when the decision to assign a backend for a query is done. In term of HA, I wrote a simple Pacemaker resource agent for MaxScale that does a few fancy things like load balancing with IPTables (I’ll talk about that in future post). With Pacemaker, we have a full fledge HA solution with quorum and fencing on which we can rely. Performance wise, it is very good – a single core in a virtual environment was able to read/write split and log to Kafka about 10k queries per second. Although MaxScale supports multiple threads, we are still using a single thread per process, simply because it yields a slightly higher throughput and the custom Pacemaker agent deals with the use of a clone set of MaxScale instances. Remember we started early using MaxScale and the beta versions were not dealing gracefully with threads so we built around multiple single threaded instances. So, since a conclusion is needed, MaxScale has proven to be a very useful and flexible tool that allows to elaborate solutions to problems that were very hard to tackle before. In particular, if you need to perform read/write splitting, then, try MaxScale, it is best solution for that purpose I have found so far. Keep in touch, I’ll surely write other posts about MaxScale in the near future.

I have project where I need to output some reports as CSV-files. I found a good library called CsvHelper from NuGet and it works perfect for me. After some playing with it I was able to generate CSV-files that were shown correctly in Excel. Here is some sample code and also extensions that make it easier to work with DataTables. Simple report Here’s the simple fragment of code that illustrates how to use CsvHelper. using (var writer = new StreamWriter(Response.OutputStream)) using (var csvWriter = new CsvWriter(writer)) { csvWriter.Configuration.Delimiter = ";"; csvWriter.WriteField("Task No"); csvWriter.WriteField("Customer"); csvWriter.WriteField("Title"); csvWriter.WriteField("Manager"); csvWriter.NextRecord(); foreach (var project in data) { csvWriter.WriteField(project.Code); csvWriter.WriteField(project.CustomerName); csvWriter.WriteField(project.Name); csvWriter.WriteField(project.ProjectManagerName); csvWriter.NextRecord(); } } Of course, you can use other methods to output whole object or object list with one shot. I just needed here custom headers that doesn’t match property names 1:1. Generic helper for DataTable Some of my projects come from service layer as DataTable. I don’t want to add new models or Data Transfer Objects (DTO) with no good reason and DataTable is actually flexible enough if you need to add new fields to report and you want to do it fast. As DataTables are not supported by default (yet?), I wrote simple extension methods that work on DataTable views. When called on DataTable it selects default view automatically. The idea is – you can set filter on default data view and leave out the rows you don’t need. If you just want to show DataTable to screen as table then check out my posting Simple view to display contents of DataTable. public static class CsvHelperExtensions { public static void WriteDataTable(this CsvWriter csvWriter, DataTable table) { WriteDataView(csvWriter, table.DefaultView); } public static void WriteDataView(this CsvWriter csvWriter, DataView view) { foreach (DataColumn col in view.Table.Columns) { csvWriter.WriteField(col.ColumnName); } csvWriter.NextRecord(); foreach (DataRowView row in view) { foreach (DataColumn col in view.Table.Columns) { csvWriter.WriteField(row[col.ColumnName]); } csvWriter.NextRecord(); } } } And here is simple MVC controller action that gets data as DataTable and returns it as CSV-file. The result is CSV-file that opens correctly in Excel. [HttpPost] public void ExportIncomesReport() { var data = // Get DataTable here Response.ContentType = "text/csv"; Response.AddHeader("Content-disposition", "attachment;filename=IncomesReport.csv"); var preamble = Encoding.UTF8.GetPreamble(); Response.OutputStream.Write(preamble, 0, preamble.Length); using (var writer = new StreamWriter(Response.OutputStream)) using (var csvWriter = new CsvWriter(writer)) { csvWriter.Configuration.Delimiter = ";"; csvWriter.WriteDataTable(data); } } One thing to notice – with CsvHelper we have full control over a stream where we write data and this way we can write more performant code. Related Posts .Net Framework 4.0: string.IsNullOrWhiteSpace() method Exporting GridView Data to Excel Code Contracts: Hiding ContractException How to dump object properties My object to object mapper source released The post Generating CSV-files on .NET appeared first on Gunnar Peipman - Programming Blog.

BQ deploys its call centre and telephony solution on Interoute Virtual Data Centre to improve international customer and employee communications Madrid, June 25th, 2015 - Interoute, owner operator of Europe's largest cloud services platform has announced that BQ, a leading European technology company, has chosen Interoute Virtual Data Centre (VDC), to host its new customer and employee unified communications solution. BQ has deployed a new telephony and call centre solution on Interoute VDC, leveraging the throughput, flexibility and scalability provided by this cloud platform. The BQ solution supports its 1,000 employees across different international offices, using Interoute VDC to provide the global reach they need. The solution is complemented with telephony services and worldwide DDIs from Interoute with great cost savings thanks to the economies of scale provided by Interoute's global infrastructure. Since it was founded in Spain, BQ has grown its business inside and outside the country thanks to its latest generation technology devices catalogue and highly competitive prices, as well as its full commitment to its users through a comprehensive support service. Mario Fernández, IT Manager at BQ, has said: "One of the main BQ objectives is to give the best user support. So, we chose Interoute to provide and guarantee the performance of our telephony service. The VoIP solution provided by Interoute meets all our needs: hosted private cloud, high availability and the ability to quickly scale and expand when needed." Interoute Virtual Data Centre is Interoute's scalable, fully automated Infrastructure as a Service (IaaS) solution. Interoute VDC provides on-demand computing, storage and applications integrated into the heart of its customers' IT infrastructure. This networked cloud replaces the need to buy, manage and maintain physical IT infrastructure and is built into Interoute's fibre connected physical Data Centres world-wide. It's simple to provision, scalable, compliant and cost effective. Diego Matas, General Manager at Interoute Iberia, has added: "We are proud that a Spanish company such as BQ, committed to education and pioneering innovation in exciting fields like robotics and 3D printing, has chosen our cloud platform for its networked communications. Interoute's networked cloud will enable BQ to continue to build upon its excellent reputation for high quality service. We look forward to working with this innovative company to support its future ICT needs."

- Now available with limited-time pricing, package brings together InfinityQS’ cloud-based enterprise quality hub, ProFicient on Demand, with training and ongoing services to ensure a successful deployment - InfinityQS International, Inc., the global authority on real-time quality and Manufacturing Intelligence, announces the launch of ProFicient Now!, a program that blends InfinityQS’ cloud-based enterprise quality hub, ProFicient on Demand, with training and ongoing services to ensure a successful deployment. Available with limited-time pricing, ProFicient Now! aims to give manufacturers the knowledge, tools and continued guidance needed to realise the benefits of a cloud-based quality management program and quickly gain a competitive advantage. “In today’s fast-paced market, manufacturers are looking for ways to better align their quality systems with overall manufacturing excellence goals,” said Doug Fair, Chief Operating Officer, InfinityQS. “By combining the power of the cloud with ongoing expert guidance from our engineering team, ProFicient Now! helps both new and existing clients track towards their goals and achieve a competitive edge through their quality initiatives.” With ProFicient Now!, manufacturers receive expert engineering guidance that leads them through their deployment. Included in the ProFicient Now! package is: Training: Administrators obtain comprehensive skills for building and maintaining the system. Solution Design: The client works closely with InfinityQS to examine the current environment and establish goals for the deployment. Onsite Services: An InfinityQS engineer creates the initial system configurations. Quarterly Consultations: InfinityQS experts guide clients in data analysis and help uncover opportunities for improvement and cost reduction. Executive Review: The InfinityQS engineer leads a review with the client senior management team to review successes, quality enhancements and opportunities for improvement that were uncovered during the use of ProFicient Now! InfinityQS ProFicient is a proven enterprise quality hub powered by a robust, centralised Statistical Process Control (SPC) software engine. ProFicient enables global manufacturers to proactively monitor, analyse and report on Manufacturing Intelligence to improve quality, decrease costs and make smarter business decisions. With a cloud-based deployment option, ProFicient streamlines global data collection and analysis with a unified data archive. For more information about ProFicient Now, including its limited-time pricing, visit here: http://www.infinityqs.com/ProFicient-Now-EMEA

[This article was written by Laura Strassman] AlertSite is now integrated with Ready! API. This means that developers, testers and operations teams can collaborate together on API quality using the same tests and metrics, simplifying configuration of monitoring assets and ultimately turning around performance problems in real time. There are several advantages to this approach: You should be monitoring your APIs in production. When your API moves into production from test, the environment changes – there is no way to know if theAPI performanceis compromised unless you look. Furthermore, you can find problems that may be a result of the location or variance in traffic. There is no easier method. You can simply click a button from right in the Ready! API interface and see the status of your APIs in production it can’t get any easier. You take your already created test cases and turn them into monitors whenever you have a new API or test you want to keep an eye on. Troubleshooting is like shooting fish in a barrel. You wrote the functional test, you know it works, and when something comes back as not working you can isolate it quickly. All of this makes it easy to be ahead of problems, solve them quickly when they happen and keep customers happy. You can be monitoring your APIs in less than 3 minutes: <br>

When running a Maven build with many plugins (e.g. the jOOQ or Flyway plugins), you may want to have a closer look under the hood to see what’s going on internally in those plugins, or in your extensions of those plugins. This may not appear obvious when you’re running Maven from the command line, e.g. via: C:\Users\jOOQ\workspace>mvn clean install Luckily, it is rather easy to debug Maven. In order to do so, just create the following batch file on Windows: @ECHO OFF IF "%1" == "off" ( SET MAVEN_OPTS= ) ELSE ( SET MAVEN_OPTS=-Xdebug -Xnoagent -Djava.compile=NONE -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=5005 ) Of course, you can do the same also on a MacOS X or Linux box, by usingexport intead of SET. Now, run the above batch file and proceed again with building: C:\Users\jOOQ\workspace>mvn_debug C:\Users\jOOQ\workspace>mvn clean install Listening for transport dt_socket at address: 5005 Your Maven build will now wait for a debugger client to connect to your JVM on port 5005 (change to any other suitable port). We’ll do that now with Eclipse. Just add a new Remote Java Application that connects on a socket, and hit “Debug”: That’s it. We can now set breakpoints and debug through our Maven process like through any other similar kind of server process. Of course, things work exactly the same way with IntelliJ or NetBeans. Once you’re done debugging your Maven process, simply call the batch again with parameter off: C:\Users\jOOQ\workspace>mvn_debug off C:\Users\jOOQ\workspace>mvn clean install And your Maven builds will no longer be debugged. Happy debugging!

Spring Integration Kafka 1.2 is out with a major performance overhaul.

Last week’s Gartner IT Operations Strategies & Solutions Summit in Orlando, Fla., was exactly what you’d expect—a place to talk about the IT operations issues impacting some of the largest companies in the world. Even so, there were a few interesting surprises. Among them: 1. Bi-modal is big. Not everyone will succeed. Gartner continued to tell its customers to employ two modes of IT—a traditional, slower moving capability for older, typically internal systems of record; and a high-speed, experimental one for new, typically customer-facing Web and mobile apps. “This is a time of experimentation and innovation,” said Gartner VP and distinguished analyst Chris Howard in his opening keynote. Organizations can’t ignore that there are multiple speeds and they should participate in all. Gartner managing VPRonni Colville added that by 2017, 75% of IT orgs will have this “bi-modal” IT capability. See also: Bi-Modal IT: Gartner Endorses Both Disruptive and Conservative Approaches to Technology However, “50% will make a mess of it,” Colville said. Why? Not necessarily because of technology failings, but more often because of a lack of people skills. 2. IT success is all about people. Donna Scott, also a Gartner VP and distinguished analyst, told her keynote audience that “you will be judged on agility, speed, and innovation.” However, the biggest problems Gartner sees for infrastructure and operations team engagement and innovation are lack of time, company culture that’s not conducive to these approaches, and a lack of business skills in IT. More than half of the people responding to an in-room poll said “people” are the part of IT ops that must change first. Not technology. Gartner research director George Spafford underscored similar issues in large organizations trying to use DevOps at scale: people and “human factors” are the biggest concerns from his in-room poll. All these probably contributed to hiring best-selling author Daniel Pink as a keynote speaker on the opening day of the conference. His focus? Not IT or architecture. Instead, he pounded home the importance of influencing people and selling internally. 3. Big orgs are trying DevOps. But the issues are different at scale. In numerous sessions I saw many hands go up when analysts asked, “Who here is trying DevOps?” Clearly, the approach is getting traction in large companies. But there’s lots of learning still to do. In fact, that was Spafford’s biggest bit of advice. “Always be learning,” he said, “trying to see what works and what breaks, especially at scale.” And, even once you’ve had some initial success, keep learning. “If you’ve done ‪DevOps, stay humble,” he advised. 4. Looking to innovative organizations for ideas … analytics on the rise. Many sessions addressed how large organizations are taking on ideas fostered by smaller, more risk-tolerant companies, and offered advice for doing so successfully. In addition to multiple discussions of DevOps, an entire session was devoted to establishing your own “Genius Bar®—a “walk-up IT support center” as explained in this CIO article. As at previous conferences, Gartner research VP Cameron Haight ran several sessions on lessons learned from firms running massive, Web-scale IT systems. “You need lots of data … and access to it inexpensively,” he said. Some commercial monitoring companies (New Relic included!) got a shout out for taking the lessons of Web scale IT to heart in their offerings. In addition, Haight said, “Analytics are increasingly important for application performance monitoring given the huge amount of data now available.” 5. Cloud: Enterprises want it, but aren’t very good at it yet. Gartner research director Dennis Smith talked through the enterprise’s interest in cloud computing. A huge majority of his in-room poll wanted some mix of both public and private cloud, while only 9% wanted to use only a private cloud environment and a measly 4% were looking to move entirely to the public cloud. The most popular choice (41%) was an 80/20 split between private and public cloud infrastructure. “Enterprises don’t make the dean’s list,” for cloud usage, Smith said, earning no more than a C average in his opinion. Large organizations are doing well at visibility, governance, and delivering standardized stacks, he said, but are less skilled at optimizing for these new environments. Still, Smith said the trends point toward enterprises improving on all fronts. 6. Cloud security can be better than yours. Importantly, Gartner VP and distinguished analyst Neil MacDonald gave the cloud a vote of confidence: noting that, for a variety of reasons, “Well-managed public cloud can be more secure than your own data center.” For example, on-premise software can pose serious security risks, he said, because of “deployment lag” where customers are stuck using software releases with unpatched security vulnerabilities. With a cloud-based Software-as-a-Service (SaaS), security updates can be more quickly rolled out to all customers. But cloud security can be different, requiring a shift to information-level security from OS-level security. Best practices include doing away with a huge pool of all-powerful sysadmins in favor of JEA, or “just enough administration,” where sysadmins have just enough privileges to do their job, and no more. An analogous security practice for compute resources is “least privilege,” where apps and microservices can’t talk to each other unless they specifically need to do so. Audience polling supported MacDonald’s optimistic view of cloud security, which suggests that large enterprises may struggle less with their cloud policies moving forward. 7. Containers: Try ’em! Ahead of this week’s DockerCon in San Francisco, Gartner devoted significant airtime to educating the audience on containers and microservices. My summary of ‪Gartner VP and distinguished analyst Tom Bittman’s advice on containers was simple: Try ’em. Now. Complement them with VMs. ‪And Docker (the company) is important, but not the be-all and end-all in this space. Bittman (copping to some deja vu from Gartner presentations he made on server virtualization 13 years ago) noted that while virtualization has been focused on admin and ops functions, containers are focused on value for developers. But because containers are well suited for driving up VM utilization for workloads that share the same OS, we can expect to see more combinations of containers and server virtualization. Finally, Bittman underscored that Gartner doesn’t see containers having much impact on premise, but making a huge difference in the cloud. That doesn’t necessarily fit with what’s been shown in other research, such as this 2015 State of Containers Survey sponsored by VMblog.com and StackEngine, so we’ll want to watch how this plays out. This is all a lot to digest. The Gartner IT Operations Strategies & Solutions Summitacknowledges the importance of dealing with existing IT systems and practices as well as promising new technologies and thinking, and tries to point a way forward. In fact, Haight had a very good quote about microservices that I thought also served to wrap up the entire event: “If you want to run with the big dogs, you need to rethink application architecture,” he said. That can be very difficult for an enterprise to fully implement … but also very appealing. Note: Al Sargent contributed to this post. All product and company names herein may be trademarks of their registered owners. Server, tortoise and hare, business team, and cloud security images courtesy ofShutterstock.com.

New Integration Provides Seamless Connections Between Perforce Helix and Leading Application Lifecycle Management Systems WOKINGHAM, UK. (June 24, 2015) – Perforce Software, the leader in software configuration management (SCM) and collaboration, and Go2Group, an Atlassian Platinum and Enterprise Expert, today announced the Perforce ConnectALL Adapter. The new adapter for Go2Group’s ConnectALL ALM Router connects Perforce Helix to Application Lifecycle Management (ALM) systems supported by ConnectALL. The companies also announced that they have expanded their partnership, which first began in 2002. “Very few SCMs can handle binary data, and no other SCM solution supports large file formats that scale across globally distributed enterprises like Helix,” said Brett Taylor, president of Go2Group. “Our customers demand future-proof solutions, and with Perforce we know they don’t have to worry about outgrowing their systems—it will serve them well whether they’re a team of 50 or 50,000.” With the Perforce adapter, ConnectALL automatically synchronises data and workflow with other ALM systems and integrates ALM systems components within minutes. “We’re excited to be a part of the ConnectALL ecosystem of adapters and to enable companies to more easily design, configure, synchronise, manage, and monitor their integrations with Perforce,” said Dave Robertson, vice president of Channels at Perforce. “We’re glad to extend our partnership with Go2Group to new technologies and markets.” Go2Group is part of Perforce’s network of sales partners across Europe, the Middle East, Africa, Asia Pacific and India. Perforce partners serve customers in more than 100 countries worldwide. The Perforce ConnectALL Adapter is available for purchase from the Go2Group website.

Combining BioConnect with Digitus server cabinet access control makes biometric identity management more effective and easier ENTERTECH SYSTEMSandDigitus Biometricshave just announced a new technology partnership between ENTERTECH’S BioConnect identity management platform and the Digitus db Bus and db Cabinet Sentry for server cabinet access control. The result is a new, fully-integrated solution called db BioConnect. The newdb BioConnectlets company data centres, co-location data centres and IT room customers simplify biometric implementation, enrollment and management for access control of perimeter doors, interior rooms, cages and now server racks all integrated into one identity platform. This game-changing solution now extends enterprise access security platforms into the data centre, making investments in Access Control Management Systems far more economical and effective. “Digitus Biometrics is the leader in providing biometric access control to the critical infrastructure market," said Rob Douglas, ENTERTECH SYSTEMS CEO. "With Digitus technology integrated to BioConnect, all 15 of our certified access control partners will now be able to offer it to their customers. For the first time, end users will have access to an integrated biometric solution that secures access control from the data centre entrance to the server cabinet instead of having to deal with stand-alone systems.” The list of BioConnect certified access control partners can be found atwww.bioconnect.com/partners. ENTERTECH SYSTEMS’BioConnectis the most advanced identity management platform on the global market today. Simple, secure and scalable, it provides Suprema biometric authentication across leading access control systems. As an application for security professionals, BioConnect helps enterprises successfully implement identity solutions by making deployment and use of biometrics easier than ever. BioConnect addresses deployment challenges by reducing costs, overcoming complexity and making it easier to on-board users. The platform provides seamless synchronization of data such as new cardholders, changes and deletions, and is tailor-made for enterprises where true identity is critical for secure access to physical facilities and software applications. “Our biometric access control solutions are designed to meet the needs of a diverse range of clients," said David Orischak, Digitus Biometrics CEO. "This technology integration to create db BioConnect will let us offer a single, centralised, highly advanced access control solution that's easy to deploy and use. An industry first, our customers will be able to use one centralised system facility-wide to secure a company’s critical infrastructure and data." db Bus ServerRack Access Controlis designed for data centres needing to secure large volumes of server cabinets with only one small component per cabinet. A single db Bus controller allows the user to secure up to 32 racks with a single 48V power supply and may be infinitely scaled.db Cabinet Sentryis designed for data centres with a structured cabling scheme. It is Digitus’ most compact, cost-effective and energy-efficient means of securing server cabinets. This extremely versatile unit can be deployed in networked or stand-alone environments, using power over Ethernet (PoE) or an external power supply.db BioLockis a server cabinet lock that uses biometric identification with prints for up to 10 fingers per user. Through this new technology partnership, these db products will be integrated with BioConnect to create the new integrated db BioConnect solution. “Digitus’ use of leading Suprema biometric technology in their server cabinet access control solutions is a natural fit for ENTERTECH SYSTEMS as the operating partner for Suprema in the US, Canada, UK, Ireland and Puerto Rico,” added Douglas. “The implications to the market are significant," added Orischak. "The integrated db BioConnect solution can be used to manage any cabinet system where biometric access control is warranted – even SCIF’s and locked areas housing sensitive assets such as pharmaceuticals, hazardous materials, intelligence archives, customer and patient records, as well as critical IP.” For more information on the db BioConnect integrated solution, visitwww.bioconnect.com/db.

We're pleased to announce the release of Vert.x 3! This is the culmination of over a year's work to bring you what we hope is the most compelling way to write scalable modern applications and services on the JVM using Java, Groovy, Ruby or JavaScript. Explore the new web-site to find out more. Please see the official release announcement for full details.

Downloading various files (either text or binary) is a bread and butter of every enterprise application. PDF documents, attachments, media, executables, CSV, very large files, etc. Almost every application, sooner or later, will have to provide some form of download. Downloading is implemented in terms of HTTP, so it's important to fully embrace this protocol and take full advantage of it. Especially in Internet facing applications features like caching or user experience are worth considering. This series of articles provides a list of aspects that you might want to consider when implementing all sorts of download servers. Note that I avoid "best practices" term, these are just guidelines that I find useful but are not necessarily always applicable. One of the biggest scalability issues is loading whole file into memory before streaming it. Loading full file into byte[] to later return it e.g. from Spring MVC controller is unpredictable and doesn't scale. The amount of memory your server will consume depends linearly on number of concurrent connections times average file size - factors you don't really want to depend on so much. It's extremely easy to stream contents of a file directly from your server to the client byte-by-byte (with buffering), there are actually many techniques to achieve that. The easiest one is to copy bytes manually: @RequestMapping(method = GET) public void download(OutputStream output) throws IOException { try(final InputStream myFile = openFile()) { IOUtils.copy(myFile, output); } } Your InputStream doesn't even have to be buffered, IOUtils.copy() will take care of that. However this implementation is rather low-level and hard to unit test. Instead I suggest returning Resource: @RestController @RequestMapping("/download") public class DownloadController { private final FileStorage storage; @Autowired public DownloadController(FileStorage storage) { this.storage = storage; } @RequestMapping(method = GET, value = "/{uuid}") public Resource download(@PathVariable UUID uuid) { return storage .findFile(uuid) .map(this::prepareResponse) .orElseGet(this::notFound); } private Resource prepareResponse(FilePointer filePointer) { final InputStream inputStream = filePointer.open(); return new InputStreamResource(inputStream); } private Resource notFound() { throw new NotFoundException(); } } @ResponseStatus(value= HttpStatus.NOT_FOUND) public class NotFoundException extends RuntimeException { } Two abstractions were created to decouple Spring controller from file storage mechanism.FilePointer is a file descriptor, irrespective to where that file was taken. Currently we use one method from it: public interface FilePointer { InputStream open(); //more to come } open() allows reading the actual file, no matter where it comes from (file system, database BLOB, Amazon S3, etc.) We will gradually extend FilePointer to support more advanced features, like file size and MIME type. The process of finding and creatingFilePointers is governed by FileStorage abstraction: public interface FileStorage { Optional findFile(UUID uuid); } Streaming allows us to handle hundreds of concurrent requests without significant impact on memory and GC (only a small buffer is allocated in IOUtils). BTW I am using UUID to identify files rather than names or other form of sequence number. This makes it harder to guess individual resource names, thus more secure (obscure). More on that in next articles. Having this basic setup we can reliably serve lots of concurrent connections with minimal impact on memory. Remember that many components in Spring framework and other libraries (e.g. servlet filters) may buffer full response before returning it. Therefore it's really important to have an integration test trying to download huge file (in tens of GiB) and making sure the application doesn't crash. Writing a download server Part I: Always stream, never keep fully in memory Part II: headers: Last-Modified, ETag and If-None-Match Part III: headers: Content-length and Range Part IV: Implement HEAD operation (efficiently) Part V: Throttle download speed Part VI: Describe what you send (Content-type, et.al.) The sample application developed throughout these articles is available on GitHub.

Originally written by David Brimley. The Death Spiral. A new feature in the 3.5 release of Hazelcast is the Cluster Quorum. In this instance we’re not talking about a Quorum in its traditional distributed systems sense, think of a Cluster Quorum as a kind of gatekeeper, protecting your cluster during times of unexpected member loss. You can use Cluster Quorums to restrict operations on Maps or indeed the entire cluster based upon environmental criteria. This sounds great you say, but I’m still not sure how this can help me? OK. Let's take a look at a scenario… Imagine a cluster that has a very high number of writes to a certain map. We also have other maps that are not updated quite so frequently and all the while we have hundreds of clients all reading from the cluster but not at the same frequency as the data that is entering the system. In normal circumstances if a machine or a number of machines were to die in the cluster we may still have enough memory available to store our data, but the amount of threads available to process requests would be reduced. We now have less cores available and the partition threads in the cluster could quickly become overwhelmed by the one map that is updated rapidly. This could mean other clients becoming starved of threads, unable to service requests. It’s also possible that the remaining members would become so consumed that they’re unable to respond to membership pings, the knock on effect could result in the member being forced out of the cluster on the assumption that it is dead. To protect the rest of the cluster in the event of member loss we need a way to stop the writes to the high frequency map whilst allowing operations to the other data structures. We can then continue to provide a good service to our other users whilst the crashed machines are restored to the cluster. Bring on the Quorum! As of Hazelcast 3.5 we now have the ability to restrict operations on distinct data structures. We do this via a Quorum configuration. We observed that other IMDG products provide Quorums that have protection at a cluster level,we decided to go one step further and provide Quorum protection around data structures as well. In the example below we create a very simple Quorum on the default map. The ‘default’ map in Hazelcast is the configuration used if no other match is found. In this instance no operations will be allowed unless the cluster has a minimum of 3 members. You’ll also note that the Quorum configuration is separate from the Map. This means that you can have multiple Quorums in a cluster attached to many different structures. If the Quorum thresholds are not satisfied then a QuorumException is thrown when we try to interact with the default map in any way. Be it from a client or another member. 3 quorumRuleWithThreeNodes Quorum Functions It’s simple to set up a Quorum check based on cluster size as we’ve seen above, but if you want to make a slightly more complex check you can do this by applying a Quorum Function. QuorumConfig quorumConfig = new QuorumConfig(); quorumConfig.setName("MyQuorum"); quorumConfig.setEnabled(true); quorumConfig.setType(QuorumType.WRITE); quorumConfig.setQuorumFunctionImplementation(new QuorumFunction() { @Override public boolean apply(Collection members) { return (members.size() >= 3) && (someOtherExternalClusterState); } }); In the example above we use Configuration API to set-up the Quorum to disallowwrites if the boolean returned from the QuorumFunction is false. In the function we test if the size of the cluster is greater than 3 and also if a variable namedsomeOtherExternalClusterState is equal true. You now get the idea that by using a function you can test for other state and not just cluster member. Listen In. Another nice feature of Quorums is the ability to listen in to Quorum Events. You can register a new callback interface called not surprisingly a QuorumListener. Quorum listeners are local to the node that they are registered, so they receive only events occurred on that local node. 3 com.company.quorum.ThreeNodeQuorumListener quorumRuleWithThreeNodes The QuorumListener has just one method that is called passing you aQuorumEvent. package com.hazelcast.quorum; import java.util.EventListener; /** * Listener to get notified when a quorum state is changed */ public interface QuorumListener extends EventListener { /** * Called when quorum presence state is changed. * * @param quorumEvent provides information about quorum presence and current member list. */ void onChange(QuorumEvent quorumEvent); } The QuorumEvent itself allows you to determine if a Quorum has been established or if it has been lost via its isPresent() method call. Additionally it provides the required cluster members to form a quorum and also the current membership list. Query the Quorums. Above we saw how we could receive callbacks, but in some cases we may just wish to make an immediate check to see if the Quorum is established or not. We can do this via the QuorumService. HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance(config); QuorumService quorumService = hazelcastInstance.getQuorumService(); Quorum quorum = quorumService.getQuorum(quorumName); boolean quorumPresence = quorum.isPresent(); In Conclusion The Cluster Quorum feature is another important tool for you to manage your cluster. In future versions of Hazelcast there are plans to add other data structures, for example you’ll be able to protect operations against Topics or Queues.

[This article was written by Ophir Prusak] Guest author Ophir Prusak is chief evangelist atBlazeMeter. To learn more about load and performance testing automation, he invites readers toattend a meetupthis Wednesday, June 24, at New Relic’s San Francisco offices. Performance and load testing are kind of like flossing your teeth. You know you need to do it, but you might not be doing it as much as you should. When your site goes down because it couldn’t handle the load, you look back and realize you might have easily prevented it with a little more testing in advance. That’s why companies are automating their application testing in an effort to lower costs, increase efficiency, and reduce the time needed to release new features. The importance of automated testing in a continuous delivery era Continuous Delivery (CD) is rapidly emerging as the “new normal” in software development, as Perforce discovered in an independent survey, with an estimated 80% of SaaS companies and 51% of non-SaaS companies adopting this practice. Companies that provide Software-as-a-Service know they need to be continuously creating new features, updating their websites, and optimizing their backend. But while software development has adapted nicely in terms of automation, the testing side has moved more slowly. For a fully Continuous Delivery and Integration process to be realized, performance testing must be automated. As the need for testing increases, doing it manually can dramatically increase your time to release. Automating testing throughout the CD process can help detect errors instantly and deliver software faster. Making it work JMeter is the de facto standard in open source load testing. It’s the most widely used open source tool for performance testing for a good reason. There’s virtually nothing it can’t test (websites, native mobile applications, APIs, and Web applications) and it’s extremely powerful and fully featured. Yet there are challenges. JMeter poses a steep learning curve in terms of integration and ease of use. Additionally, it doesn’t integrate easily with APM and Continuous Integration (CI) tools. Many developers have been looking for a way to conduct performance testing with less time and effort—and fewer hiccups along the way. Taurus: An effort to simplify test automation A new open source project called Taurus (Test AUtomation Running Smoothly) is designed to provide exactly that—a way to remove most of the pain of using JMeter on its own. Taurus can give you the ability to Create and define a load test even without using JMeter. Override existing JMeter files or tests configurations. Create human-readable configuration files and testing scripts that are easily added to source control systems like GitHub. Integrate into CI tools like Jenkins. Run multiple tests in parallel. Provide pass/fail criteria back into the CI tool for easier automation of test-results analysis. Make analysis of test results easier and more intuitive. Taurus still uses JMeter under the hood, but is designed to have a much easier learning curve, especially for simple tests. Taurus also offers a built-in result analysis engine that provides both console-based reporting features and result analysis. Performance testing and optimizing your applications is not simple, yet there are solutions available that make the process easier and more successful. I’m looking forward to seeing how the technology evolves even further in the near future. If you want to learn more about Taurus, check out the project on GitHub. Better yet, you are invited to come to a meetup this Wednesday, June 24, at New Relic’s San Francisco offices. You can learn a lot more about Taurus and how you can use it to help scale load and performance testing automation.

[This article was written by Andrew Marshall] We’ve been talking a lot about Docker over the past few weeks—with good reason. Docker’s explosive growth in popularity within the enterprise has enabled new distributed application architectures and with it a need for app-centric monitoring of your Docker containers within the context of the rest of your infrastructure. We’re thrilled to announce today that New Relic’s Docker monitoring is now generally available to New Relic customers, just in time for DockerCon 2015! (And as we noted last week, New Relic’s Docker monitoring solution has been selected by Docker for its Ecosystem Technology Partner program as a proven container monitoring solution.) Why app-centric monitoring? If you’re a software business using Docker containers, chances are you’ve done so to gain efficiencies from your system resources or portability across environments to shorten the cycle between writing and running code. Either way, adding Docker containers to your app development meant a new tier of infrastructure to monitor, which equated to a “black box” in your data—one that you had no visibility into from a monitoring perspective, Docker monitoring with New Relic is designed to “fix” this lack of monitoring visibility by adding an app-centric view of Docker containers to the existing New Relic Servers interface you already use. Now, instead of having a gap between the application and server monitoring views, we’ve added the ability to see containers with the same “first-class“ experience as you would with virtual machines and servers. You can now drill down from the application (which is really what you care about) to the individual Docker container, and then to the physical server. No more blind spots! As we strive to do with all of our products, we took the approach of “important” over “impressive” when it comes to the container information we provide to users. Based on direct feedback from customers, we’ve tried to take the mystery out of finding the right container to help you get back to developing your applications. As the way people use containers changes over time, we plan to continue to listen to our customers to help shape how we approach Docker container monitoring. Restoring 360-degree view of your application environment One example of how app-centric monitoring can impact a team moving to microservices or distributed application environments is Motus, a mobile workforce management company. Motus has been a New Relic customer for more than four years and recently has been shifting to a microservices architecture with approximately 95% of its production workload now running in Docker containers. While Docker helpd Motus gain speed and agility while reducing infrastructure complexity, the link between the application and what was happening with the container it was running on was broken. During the trial of New Relic’s Docker monitoring, Motus was able to more easily identify which container an app was running on, all the way down to the node. That was a big help when they needed to investigate an issue and determine if a new container was required.. During the beta alone, Motus estimates that using New Relic helped them to reduce the time to investigate and fix problems with its Docker containers by 30%! Motus isn’t just using New Relic to diagnose when a problem occurs. Docker monitoring with New Relic has helped Motus analyze and “right size” its containers for the application to better allocate resources for performance and budget. Get started with New Relic’s Docker monitoring today, for more information, please stop by our booth at DockerCon, June 22-23 in San Francisco! Resources: Motus Docker Monitoring Case Study Docker Monitoring with New Relic Enabling Docker Monitoring with New Relic Docker in the New Relic Community Forum

Written by Stephane Combaudon. A question I often hear when customers want to set up a production PXC cluster is: “How many nodes should we use?” Three nodes is the most common deployment, but when are more nodes needed? They also ask: “Do we always need to use an even number of nodes?” This is what we’ll clarify in this post. This is all about quorum I explained in a previous post that a quorum vote is held each time one node becomes unreachable. With this vote, the remaining nodes will estimate whether it is safe to keep on serving queries. If quorum is not reached, all remaining nodes will set themselves in a state where they cannot process any query (even reads). To get the right size for you cluster, the only question you should answer is: how many nodes can simultaneously fail while leaving the cluster operational? If the answer is 1 node, then you need 3 nodes: when 1 node fails, the two remaining nodes have quorum. If the answer is 2 nodes, then you need 5 nodes. If the answer is 3 nodes, then you need 7 nodes. And so on and so forth. Remember that group communication is not free, so the more nodes in the cluster, the more expensive group communication will be. That’s why it would be a bad idea to have a cluster with 15 nodes for instance. In general we recommend that you talk to us if you think you need more than 10 nodes. What about an even number of nodes? The recommendation above always specifies odd number of nodes, so is there anything bad with an even number of nodes? Let’s take a 4-node cluster and see what happens if nodes fail: If 1 node fails, 3 nodes are remaining: they have quorum. If 2 nodes fail, 2 nodes are remaining: they no longer have quorum (remember 50% is NOT quorum). Conclusion: availability of a 4-node cluster is no better than the availability of a 3-node cluster, so why bother with a 4th node? The next question is: is a 4-node cluster less available than a 3-node cluster? Many people think so, specifically after reading this sentence from the manual: Clusters that have an even number of nodes risk split-brain conditions. Many people read this as “as soon as one node fails, this is a split-brain condition and the whole cluster stop working”. This is not correct! In a 4-node cluster, you can lose 1 node without any problem, exactly like in a 3-node cluster. This is not better but not worse. By the way the manual is not wrong! The sentence makes sense with its context. There could actually reasons why you might want to have an even number of nodes, but we will discuss that topic in the next section. Quorum with multiple data centers To provide more availability, spreading nodes in several datacenters is a common practice: if power fails in one DC, nodes are available elsewhere. The typical implementation is 3 nodes in 2 DCs: Notice that while this setup can handle any single node failure, it can’t handle all single DC failures: if we lose DC1, 2 nodes leave the cluster and the remaining node has not quorum. You can try with 4, 5 or any number of nodes and it will be easy to convince yourself that in all cases, losing one DC can make the whole cluster stop operating. If you want to be resilient to a single DC failure, you must have 3 DCs, for instance like this: Other considerations Sometimes other factors will make you choose a higher number of nodes. For instance, look at these requirements: All traffic is directed to a single node. The application should be able to fail over to another node in the same datacenter if possible. The cluster must keep operating even if one datacenter fails. The following architecture is an option (and yes, it has an even number of nodes!): Conclusion Regarding availability, it is easy to estimate the number of nodes you need for your PXC cluster. But node failures are not the only aspect to consider: Resilience to a datacenter failure can, for instance, influence the number of nodes you will be using.