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Live Activity Monitoring of NGINX Plus in 3 Simple Steps
[This article was written by Nick Shadrin] One of the most popular features in NGINX Plus is live activity monitoring, also known as extended status reporting. The live activity monitor reports real-time statistics for your NGINX Plus installation, which is essential for troubleshooting, error reporting, monitoring new installations and updates to a production environment, and cache management. We often get questions from DevOps engineers – experienced and new to NGINX Plus alike – about the best way to configure live activity monitoring. In this post, we’ll describe a sample configuration file that will have you viewing real-time statistics on the NGINX Plus dashboard in just a few minutes. The file is a preview of the set of sample configuration files that we’re introducing in NGINX Plus R6 to make it even easier to set up many of NGINX Plus’ advanced features in your environment. The set of examples will grow over time, and the NGINX Plus packages available at cs.nginx.com will include the latest versions available when the packages are created. You can download the first sample configuration file, for live activity monitoring, in advance of the release of NGINX Plus R6 next week (the file works with NGINX Plus R5 too). Here we’ll review the instructions for installing and customizing the file. Note: These instructions assume that you use the conventional NGINX Plus configuration scheme (in which configuration files are stored in the /etc/nginx/conf.d directory), which is set up automatically when you install an NGINX Plus package. If you use a different scheme, adjust the commands accordingly. Installating the Configuration File The commands do not include prompts or other extraneous characters, so you can cut and paste them directly into your terminal window. Download the sample configuration file and rename it to status.conf. cd /etc/nginx/conf.d/ curl http://nginx.com/resources/conf/status.txt > /etc/nginx/conf.d/status.conf Customize your configuration files as instructed in Customizing the Configuration. Test the configuration file for syntactic validity and reload NGINX Plus. nginx -t && nginx -s reload The NGINX Plus status dashboard is available immediately at http://nginx-server-address:8080/ (or the alternate port number you configure as described in Changing the Port for the Status Dashboard.). Customizing the Configuration To get the most out of live activity monitoring, make the changes described in this section to both the sample configuration file and your existing configuration files. Monitoring Servers and Upstream Server Groups For statistics about virtual servers and upstream groups to appear on the dashboard, you must enable a shared memory zone in the configuration block for each server and group. The shared memory is used to store configuration and run-time state information referenced by the NGINX Plus worker processes. If you don’t configure shared memory, the dashboard reports only basic information about the number of connections and requests, plus caching statistics. In the figure in Preview of the NGINX Plus R6 Dashboard, this corresponds to the first line (to the right of the NGINX+ logo) and the finalCaches section. Edit your existing configuration files to add the status_zone directive to the server configuration block for each server you want to appear on the dashboard. (You can specify the same zone name in multiple server blocks, in which case the statistics for those servers are aggregated together in the dashboard.) server { listen 80; status_zone backend-servers; location / { proxy_pass http://backend; } } Similarly, you must add the zone directive to the upstream configuration block for each upstream group you want to appear on the dashboard. The following example allocates 64 KB of shared memory for the two servers in the upstream-backend group. The zone name for each upstream group must be unique. upstream backend { zone upstream-backend 64k; server 10.2.3.5; server 10.2.3.6; } Restricting Access to the Dashboard The default settings in the sample configuration file allow anyone on any network to access the dashboard. We strongly recommend that you configure at least one of the following security measures: IP address-based access control lists (ACLs). In the sample configuration file, uncomment theallow and deny directives, and substitute the address of your administrative network for10.0.0.0/8. Only users on the specified network can access the status page. allow 10.0.0.0/8; deny all; HTTP basic authentication. In the sample configuration file, uncomment the auth_basic andauth_basic_user_file directives and add user entries to the /etc/nginx/users file (for example, by using an htpasswd generator). If you have an Apache installation, another option is to reuse an existing htpasswd file. auth_basic on; auth_basic_user_file /etc/nginx/users; Client certificates, which are part of a complete configuration of SSL or TLS. For more information, see NGINX SSL Termination in the NGINX Plus Admin Guide and the documentation for the HTTP SSL module. Firewall. Configure your firewall to disallow outside access to the port for the dashboard (8080 in the sample configuration file). Changing the Port for the Status Dashboard To set the port number for the dashboard to a value other than the default 8080, edit the followinglisten directive in the sample configuration file. listen 8080; Limiting the Monitored IP Addresses If your NGINX Plus server has several IP addresses and you want the dashboard to display tatistics for only some of them, create a listen directive for each one that specifies its address and port. The sample configuration script includes the following example that you can uncomment and change to set the correct IP address. You also need to comment out the listen directive (with a port number only) discussed in Changing the Port for the Status Dashboard. listen 10.2.3.4:8080; Preview of the NGINX Plus R6 Dashboard Here’s a sneak peek at the new NGINX Plus dashboard, which we’re unveiling next week in NGINX Plus R6. More Information about Live Activity Monitoring Live Activity Monitoring of NGINX Plus in the NGINX Plus Admin Guide HTTP Status module documentation
April 10, 2015
by Patrick Nommensen
· 6,948 Views
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Currency Format Validation and Parsing
In Java, formatting a number according to a locale-specific currency format is pretty simple. You use an instance of java.text.NumberFormat class by instantiating it through NumberFormat.getCurrencyInstance() and invoke one of the format() methods. Following is a code-snippet from https://docs.oracle.com/javase/tutorial/i18n/format/numberFormat.html static public void displayCurrency( Locale currentLocale) { Double currencyAmount = new Double(9876543.21); Currency currentCurrency = Currency.getInstance(currentLocale); NumberFormat currencyFormatter = NumberFormat.getCurrencyInstance(currentLocale); System.out.println( currentLocale.getDisplayName() + ", " + currentCurrency.getDisplayName() + ": " + currencyFormatter.format(currencyAmount)); } However, if an application allows users to enter an amount as a string using separators and currency symbols, there is quite a possibility that currency format may not have been followed according to the locale. For example, user can use a wrong thousand or decimal separator, or a wrong currency symbol altogether. In that case, the application should incorporate a mechanism to first ensure that format is followed and then parse that string to convert it into a proper number in order to perform several mathematical calculations in a currency format independent manner. It is important to note that parsing a string using java.text.NumberFormat#public Number parse(String source) method requires that the currency string must contain a value following the locale-specific pattern defined in java.text.DecimalFormat and symbols defined in java.text.DecimalFormatSymbols. If the pattern and/or symbols are not followed, program will throw java.text.ParseException. For example, currency pattern for it_CH = Italian (Switzerland) is ¤ #,##0.00 and grouping separator is ', hence SFr. 1'234.56 is valid while 1,234.56 SFr. is invalid In order to check the validity of a currency amount before actually parsing it, Apache Commons Validator project's org.apache.commons.validator.routines.CurrencyValidator comes in real handy. All you have to do is to construct its object and call public boolean isValid(String value,Locale locale) method to check whether locale-specific format is followed or not. Once you are done with validation and found that number is valid, you can then parse the number by calling the parse() method. Following code snippet shows how to validate and parse. Note that the validation is lenient and if currency symbol is not already present in the string, it appends appropriate currency symbol according to the pattern and then validate and parse it. /** * Converts given item price into a number based on given * currency code. * * It works generically for all currencies supported by Java * * * @param itemPrice currency amount * @param currencyCode 3-letter ISO country code * @return {@link String} containing stripped price or same as given price * if parsing failed or formatter couldn't be constructed * @author Muhammad Haris */ public static Double convertPrice(String itemPrice, String currencyCode) { Double itemPriceConverted = null; Locale currencyLocale = LocaleUtility .getLocaleAgainstCurrency(currencyCode); DecimalFormat currencyFormatter = getCurrencyFormatter(currencyLocale); if (currencyFormatter != null) { itemPrice = appendCurrencySymbol(itemPrice, currencyFormatter); try { Number number = currencyFormatter.parse(itemPrice); itemPriceConverted = number.doubleValue(); } catch (ParseException e) { LOG.error("Failed to parse currency: " + currencyCode + ", value: " + itemPrice + ". " + e.getMessage(), e); } } else { LOG.error("No appropriate formatter found for currency: " + currencyCode + ", value: " + itemPrice + ". "); } return itemPriceConverted; } /** * Gets currency formatter against given currency locale * * @param currencyCode * {@link String} containing 3 letter ISO currency code * @return {@link NumberFormat} object specialized for the currency or null * if it couldn't be composed * @author Muhammad Haris */ public static DecimalFormat getCurrencyFormatter(Locale currencyLocale) { if (currencyLocale != null) { return (DecimalFormat) NumberFormat .getCurrencyInstance(currencyLocale); } return null; } /** * Appends appropriate currency symbol to the given price using the pattern * defined in the given currency formatter * * @param itemPrice * {@link String} containing price of the item in locale specific * format * @param currencyFormatter * {@link DecimalFormat} object containing currency locale * specific formatting info * @author Muhammad Haris */ public static String appendCurrencySymbol(String itemPrice, DecimalFormat currencyFormatter) { String currencySymbol = currencyFormatter.getDecimalFormatSymbols() .getCurrencySymbol(); String pattern = currencyFormatter.toPattern(); if (!itemPrice.contains(currencySymbol)) { if (pattern.startsWith("¤ ")) { itemPrice = currencySymbol + " " + itemPrice; } else if (pattern.endsWith(" ¤")) { itemPrice = itemPrice + " " + currencySymbol; } else if (pattern.startsWith("¤")) { itemPrice = currencySymbol + itemPrice; } else if (pattern.endsWith("¤")) { itemPrice = itemPrice + currencySymbol; } } return itemPrice; }
April 10, 2015
by Muhammad Haris
· 15,010 Views
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Parse an XML Response with Java and Dom4J
Previously we’ve explored how to parse XML data using NodeJS as well as PHP. Continuing on the trend of parsing data using various programming languages, this time we’re going to take a look at parsing XML data using the dom4j library with Java. Now dom4j, is not the only way to parse XML data in Java. There are many other ways including using the SAX parser. Everyone will have their own opinions on which of the many to use. To keep up with my previous two XML tutorials, we’re going to use the following XML data saved in a file called data.xml at the root of the project: Code Blog Nic Raboy Nic Raboy Maria Campos With our XML content figured out, let’s make sure we structure our project like the following: project root src xmlparser MainDriver.java libs dom4j-1.6.1.jar build.xml data.xml Based on our project structure, you can probably tell that we’re going to be using Apache Ant for building. Say what you want about using Ant, but I’m still one of many who still uses it. Feel free to make changes to Apache Maven or other to better meet your needs. We’re now ready to crack open our src/xmlparser/MainDriver.java to start adding our parse logic. package xmlparser; import java.io.*; import java.util.*; import org.dom4j.*; import org.dom4j.io.*; public class MainDriver { public static void main(String[] args) { } public static void printRecursive(Element element) { } public static Document readFile(String filename) throws Exception { } } To further explain our intentions, the readFile(String filename) function will load the data.xmlfile and return it as a Document object for further parsing. The printRecursive(Element element)function will iterate through each node of the XML and print it out if it contains text. All levels of the XML will be iterated through. So let’s start with readFile(String filename): public static Document readFile(String filename) throws Exception { SAXReader reader = new SAXReader(); Document document = reader.read(new File(filename)); return document; } Nothing really to the above code. In fact, I pulled most of it from the dom4j quick-start code. The printRecursive(Element element) function is where things get more complex: public static void printRecursive(Element element) { for(int i = 0, size = element.nodeCount(); i < size; i++) { Node node = element.node(i); if(node instanceof Element) { Element currentNode = (Element) node; if(currentNode.isTextOnly()) { System.out.println(currentNode.getText()); } printRecursive(currentNode); } } } Some of the above code was taken from the dom4j quick-start, but the rest is some custom work. We are basically looking at each node and trying to visit any available children. If none exist, bail out. We also only want to print if there is text. Finally, we’re looking at the main(String[] args) function to bring it all together: public static void main(String[] args) { try { Element root = readFile("data.xml").getRootElement(); printRecursive(root); } catch (Exception e) { e.printStackTrace(); } } Just like that we’ve printed our each node of our XML document. In case you’re interested in the build.xml code, it can be seen below: To test the project you’d just run ant buildandrun from your command prompt or Terminal. Assuming of course you have Apache Ant configured correctly. The dom4j library is very thorough so I recommend have a look at the Javadocs that go with it.
April 7, 2015
by Nic Raboy
· 15,972 Views
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To Shard, or Not to Shard
When I talk with customers about sharding decisions I often start by telling the following true story… A couple of years ago, a customer came to me looking for advice on how to shard his system. He told me he was already convinced he needed to do that since he read that some smart people at MySQL giants like Facebook and Twitter were sharding—so naturally this was something he should be doing, too. I paused for a moment and then I asked him what the size of his database was. “10GB,” he said. I nodded and asked if he handles many queries or if they were very complicated. “No,” he said. “Just a few hundred queries per second, and they have not been loading down the system by more than a few percent.” I asked him whether he was expecting exponential growth in the near future—looking to double every week or something like that. “No, our load and data size grew about 7 percent last year and we expect about the same growth this year and for the foreseeable future.” My recommendation to him was not to waste time and effort on sharding because it is just not needed in his company’s case. Before you decide how to shard, you’d best understand whether or not you really need to shard to begin with. Yes, on the extremely large-scale side of database demands, sharding is the only game in town. And not just for MySQL, but for pretty much any technology out there. Yet thanks to emerging technologies there is an increasing amount of applications that can run databases without sharding. Today we can easily run with terabytes of data per MySQL instance and serve tens of thousands of queries in many OLTP environments. This allows organizations to build very large applications without needing to shard. And keep this in mind: Sharding is a pain under all circumstances. Even if you have sharding provided out of the box by the database system, it is a pain because it introduces more components and complexity. Creating good distributed query execution plans is a very complicated task that needs to take network topology and load into account in addition to the data distribution and load of individual nodes. Before you decide if you need to shard, you should look at alternatives to scale your application. In the MySQL world, the solutions are typically as follows: Alternatives to Sharding Functional Partitioning: In many environments a single MySQL instance becomes a dumping ground for all kinds of databases—you might end up having your main application share a database instance with Drupal, which powers your website, with WordPress, which powers your blog, and with vBulletin, which powers your forums. Splitting those pieces into different database instances is something you should consider before you look into sharding. Custom-made systems will often have many applications using different data sets that can be easily split out. Replication: Many applications are read-heavy, so scaling reads becomes the issue earlier than it does with scaling writes. Replication is a great solution for this. MySQL’s built-in replication is very robust, though due to its asynchronous nature it adds complexity to the application. The developer must decide which of the reads can be done from the replica servers and which can’t, because you must be absolutely certain that you’re reading the most recent, actual data. This is the reason that alternative, synchronous replication technologies for MySQL like Percona XtraDB Cluster, are gaining popularity: They provide single database-like behavior from the cluster in most cases. Caching and Queueing: Caching is a great technology for reducing the amount of reads that hit the database. There are many applications that have reduced read load on the database by 80-95% using this technology. Queueing, in contrast, optimizes writes. It does this by merging multiple write operations together so they hit the database efficiently. Most large-scale applications should rely heavily on both of these technologies. Memcached and Redis are two popular caching technologies in the MySQL space. For queueing, the most popular technologies are ActiveMQ and RabbitMQ [1]. Supplemental Technologies: MySQL is great at many things but not at everything. If you’re looking for high-performance full-text search, consider ElasticSearch, Sphinx, or Lucene. If you’re looking at large-scale data analytics, a Hadoop-based infrastructure or Vertica might work well for you. You should let MySQL handle the things it is good at, and leave the rest to supporting tools. Optimizations to Make Before Sharding Scaling isn’t just about architecture either. You also need to make sure your system is reasonably optimized. Many people decide sharding is inevitable for them even though there are much easier and more cost-effective ways to get the performance and scale they are looking for. All of which, I might add, are also going to be valuable if sharding is indeed eventually needed. Hardware: Are you using the right hardware? I’ve seen many people looking into sharding when in fact simply purchasing decent hardware would solve their problems for years to come. Make sure you have plenty of memory and high-performance flash storage if you’re working with a large database. In many cases it can transform your system so much it will look like magic. MySQL version and Configuration: Use a recent MySQL version. By that I mean the latest GA version (MySQL 5.6 at the time of this article’s publication). Percona Server, which is free, often offers additional performance improvements for demanding workloads. Use the most recent operating system too, especially if you’re using modern hardware. Finally, make sure MySQL is configured properly. The difference in MySQL performance between poorly configured MySQL and well-tuned MySQL can be 10x or more. Schema and Queries: The same application logic can be expressed using a variety of schema and queries. I’ve seen a lot of similar applications approaching things differently, and the difference in the performance between an optimal approach and a poor one (but still used in production) can be 100x or more. Many of the changes can be retrofitted to existing schema—such as minor query changes and changes to the index structure—however, if your schema doesn’t fit your application needs well, then you might be looking at a complete redesign. So it is a good idea to think things through early. When to Shard So when should you start thinking about sharding? Basically, if none of the measures listed above have given you the performance you need, it might be time to consider sharding. Sharding does have the advantage of allowing you to potentially use lower-cost hardware or cheaper cloud instances. Most developers are using agile development methods these days and there is a common term, “Architectural Runway,” which defines how far the application can go with its current architecture. If you’ve already found success using replication in particular, it might be a bad decision to add sharding because it will force your developers to deal with the complexity of sharding and asynchronous replication. However, replication is still typically used to achieve high availability even if you’re already sharding, but in this case it’s not for scaling reads. If you’ve come to the point where you’re sure you need to shard, here are some of the questions you need to ask about how you’ll implement your sharding strategy: Shard Level: At which level should we shard? It does not have to be at the database level. Many applications, SaaS in particular, often “shard” on higher levels, deploying multiple copies of their full stack to offer complete isolation for availability, performance, security etc. In many large scale applications you will see multiple copies of a full stack deployed, each having its own sharded MySQL environment. Shard Key: How do we shard? In many cases the choice depends on whether you’re authenticating for user accounts or your organization, but in other cases it is not so obvious. When making a sharding choice, you need to think about two things: 1) as many data access points as possible should go into a single shard, because cross-shard access is expensive if supported at all, and 2) making sure such sharding does not produce a shard that is too large to handle either in terms of data size or traffic. For example, sharding by country is a poor idea because the requirements to handle Belgium traffic won’t be the same for the United States or China, which require a lot more resources. Shard by Schema or Instance: What is the unit of your shard? The typical choices are MySQL instance or database (schema). I like the shard = database approach, which doesn’t limit you to a single MySQL instance per physical box. That way you do not have to run too many MySQL instances, but you can run more than one if the application works better that way. Shard Unit: If you shard by a single MySQL server, you will run into a problem with high availability very soon. When you have 100 MySQL servers there are roughly 100 more chances for one of them to crash compared with having only one, so ensuring there is a high availability solution becomes critical. Instead of sharing across MySQL servers you will usually be sharding across “Replication Clusters,” such as one MySQL primary node and one or several replica or PXC (Percona XtraDB Cluster) nodes. Shard Technology: What technology can you use to assist you with sharding? Within the MySQL world there is no standard sharding technology as of yet that everyone uses. Most of the large web properties have implemented something in-house for their sharding needs, and some have released their solutions as open source projects. One example is Vitess, contributed by Google, and another is JetPants, contributed by Tumblr. Rolling out your own simple sharding framework might look easy for some developers until you have to deal with operational issues like balancing the shards, resharding, etc., on a large scale. There are a number of purpose-built technologies that can help you with sharding if this doesn’t sound like something your team can manage. Sharding Technologies Here are technologies that you should consider: MySQL Fabric: This is the sharding technology being developed by the MySQL team at Oracle. MySQL Fabric is GA, but its functionality right now is rather limited, especially in terms of their support for multi-sharded queries. Given more time however, it has the potential to become the standard sharding technology for MySQL. Tesora: Tesora has a proxy-based solution for MySQL sharding that became open source some time ago. I would be especially looking at Tesora if you’re also looking at deploying OpenStack, as they’ve invested a lot into the integration. ScaleArc: ScaleArc is a commercial database proxy solution that can do caching, filtering, routing, and sharding. It is a pretty mature solution that handles multiple database technologies and not just MySQL. ScaleBase: ScaleBase is a sharding solution designed specifically for MySQL and the cloud, which similarly to MySQL, operates at the proxy level. There are many technologies in the MySQL space that can help you scale your application without sharding. If you’re going to build the next “Facebook,” however, you will surely need to shard, and there are a number of technologies that can help you do it as painlessly as possible. Large-scale applications on large-scale databases will always introduce complexity, which makes them more complicated to develop against and manage. Success comes with cost. [1] http://dzone.com/research/guide-to-enterprise-integration Peter Zaitsev co-founded Percona in 2006, assuming the role of CEO. Percona helps companies of all sizes maximize their success with MySQL. Peter enjoys mixing business leadership with hands on technical expertise. Peter is also the co-author of O’Reilly’s High Performance MySQL, one of the most popular books on MySQL performance.
April 2, 2015
by Peter Zaitsev
· 21,030 Views · 1 Like
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Dismantling invokedynamic
Many Java developers regarded the JDK's version seven release as somewhat a disappointment. On the surface, merely a few language and library extensions made it into the release, namely Project Coin and NIO2. But under the covers, the seventh version of the platform shipped the single biggest extension to the JVM's type system ever introduced after its initial release. Adding the invokedynamic instruction did not only lay the foundation for implementing lambda expressions in Java 8, it also was a game changer for translating dynamic languages into the Java byte code format. While the invokedynamic instruction is an implementation detail for executing a language on the Java virtual machine, understanding the functioning of this instruction gives true insights into the inner workings of executing a Java program. This article gives a beginner's view on what problem the invokedynamic instruction solves and how it solves it. Method handles Method handles are often described as a retrofitted version of Java's reflection API, but this is not what they are meant to represent. While method handles can represent a method, constructor or field, they are not intended to describe properties of these class members. It is for example not possible to directly extract metadata from a method handle such as modifiers or annotation values of the represented method. And while method handles allow for the invocation of a referenced method, their main purpose is to be used together with an invokedynamic call site. For gaining a better understanding of method handles, looking at them as an imperfect replacement for the reflection API is however a reasonable starting point. Method handles cannot be instantiated. Instead, method handles are created by using a designated lookup object. These objects are themselves created by using a factory method that is provided by the MethodHandles class. Whenever this factory is invoked, it first creates a security context which ensures that the resulting lookup object can only locate methods that are also visible to the class from which the factory method was invoked. A lookup object can then be created as follows: class Example { void doSomething() { MethodHandles.Lookup lookup = MethodHandles.lookup(); } private void foo() { /* ... */ } } As argued before, the above lookup object could only be used to locate methods that are also visible to the Example class such asfoo. It would for example be impossible to look up a private method of another class. This is a first major difference to using the reflection API where private methods of outside classes can be located just as any other method and where these methods can even be invoked after marking such a method as accessible. Method handles are therefore sensible of their creation context which is a first major difference to the reflection API. Apart from that, a method handle is more specific than the reflection API by describing a specific type of method rather than representing just any method. In a Java program, a method's type is a composite of both the method's return type and the types of its parameters. For example, the only method of the following Counter class returns an int representing the number of characters of the only String-typed argument: class Counter { static int count(String name) { return name.length(); } } A representation of this method's type can be created by using another factory. This factory is found in the MethodType class which also represents instances of created method types. Using this factory, the method type for Counter::count can be created by handing over the method's return type and its parameter types bundled as an array: MethodType methodType = MethodType.methodType(int.class, new Class[] {String.class}); By using the lookup object that was created before and the above method type, it is now possible to locate a method handle that represents the Counter::count method as depicted in the following code: MethodType methodType = MethodType.methodType(int.class, new Class[] {String.class}); MethodHandles.Lookup lookup = MethodHandles.lookup(); MethodHandle methodHandle = lookup.findStatic(Counter.class, "count", methodType); int count = methodHandle.invokeExact("foo"); assertThat(count, is(3)); At first glance, using a method handle might seem like an overly complex version of using the reflection API. However, keep in mind that the direct invocation of a method using a handle is not the main intent of its use. The main difference of the above example code and of invoking a method via the reflection API is only revealed when looking into the differences of how the Java compiler translates both invocations into Java byte code. When a Java program invokes a method, this method is uniquely identified by its name and by its (non-generic) parameter types and even by its return type. It is for this reason that it is possible to overload methods in Java. And even though the Java programming language does not allow it, the JVM does in theory allow to overload a method by its return type. Following this principle, a reflective method call is executed as a common method call of the Method::invoke method. This method is identified by its two parameters which are of the types Object and Object[]. In addition to this, the method is identified by its Object return type. Because of this signature, all arguments to this method need to always be boxed and enclosed in an array. Similarly, the return value needs to be boxed if it was primitive or null is returned if the method was void. Method handles are the exception to this rule. Instead of invoking a method handle by referring to the signature ofMethodHandle::invokeExact signature which takes an Object[] as its single argument and returns Object, method handles are invoked by using a so-called polymorphic signature. A polymorphic signature is created by the Java compiler dependant on the types of the actual arguments and the expected return type at a call site. For example, when invoking the method handle as above with int count = methodHandle.invokeExact("foo"); the Java compiler translates this invocation as if the invokeExact method was defined to accept a single single argument of typeString and returning an int type. Obviously, such a method does not exist and for (almost) any other method, this would result in a linkage error at runtime. For method handles, the Java Virtual Machine does however recognize this signature to be polymorphic and treats the invocation of the method handle as if the Counter::count method that the handle refers to was inset directly into the call site. Thus, the method can be invoked without the overhead of boxing primitive values or the return type and without placing the argument values inside an array. At the same time, when using the invokeExact invocation, it is guaranteed to the Java virtual machine that the method handle always references a method at runtime that is compatible to the polymorphic signature. For the example, the JVM expected that the referenced method actually accepts a String as its only argument and that it returns a primitive int. If this constraint was not fulfilled, the execution would instead result in a runtime error. However, any other method that accepts a single String and that returns a primitive int could be successfully filled into the method handle's call site to replace Counter::count. In contrast, using the Counter::count method handle at the following three invocations would result in runtime errors, even though the code compiles successfully: int count1 = methodHandle.invokeExact((Object) "foo"); int count2 = (Integer) methodHandle.invokeExact("foo"); methodHandle.invokeExact("foo"); The first statement results in an error because the argument that is handed to the handle is too general. While the JVM expected a String as an argument to the method, the Java compiler suggested that the argument would be an Object type. It is important to understand that the Java compiler took the casting as a hint for creating a different polymorphic signature with anObject type as a single parameter type while the JVM expected a String at runtime. Note that this restriction also holds for handing too specific arguments, for example when casting an argument to an Integer where the method handle required aNumber type as its argument. In the second statement, the Java compiler suggested to the runtime that the handle's method would return an Integer wrapper type instead of the primitive int. And without suggesting a return type at all in the third statement, the Java compiler implicitly translated the invocation into a void method call. Hence, invokeExact really does mean exact. This restriction can sometimes be too harsh. For this reason, instead of requiring an exact invocation, the method handle also allows for a more forgiving invocation where conversions such as type castings and boxings are applied. This sort of invocation can be applied by using the MethodHandle::invoke method. Using this method, the Java compiler still creates a polymorphic signature. This time, the Java virtual machine does however test the actual arguments and the return type for compatibility at run time and converts them by applying boxings or castings, if appropriate. Obviously, these transformations can sometimes add a runtime overhead. Fields, methods and constructors: handles as a unified interface Other than Method instances of the reflection API, method handles can equally reference fields or constructors. The name of theMethodHandle type could therefore be seen as too narrow. Effectively, it does not matter what class member is referenced via a method handle at runtime as long as its MethodType, another type with a misleading name, matches the arguments that are passed at the associated call site. Using the appropriate factories of a MethodHandles.Lookup object, a field can be looked up to represent a getter or a setter. Using getters or setters in this context does not refer to invoking an actual method that follows the Java bean specification. Instead, the field-based method handle directly reads from or writes to the field but in shape of a method call via invoking the method handle. By representing such field access via method handles, field access or method invocations can be used interchangeably. As an example for such interchange, take the following class: class Bean { String value; void print(String x) { System.out.println(x); } } For the above Bean class, the following method handles can be used for either writing a string to the value field or for invoking the print method with the same string as an argument: MethodHandle fieldHandle = lookup.findSetter(Bean.class, "value", String.class); MethodType methodType = MethodType.methodType(void.class, new Class[] {String.class}); MethodHandle methodHandle = lookup.findVirtual(Bean.class, "print", methodType); As long as the method handle call site is handed an instance of Bean together with a String while returning void, both method handles could be used interchangeably as shown here: anyHandle.invokeExact((Bean) mybean, (String) myString); Note that the polymorphic signature of the above call site does not match the method type of the above handle. However, within Java byte code, non-static methods are invoked as if they were static methods with where the this reference is handed as a first, implicit argument. A non-static method's nominal type does therefore diverge from its actual runtime type. Similarly, access to a non-static field requires an instance to be access. Similarly to fields and methods, it is possible to locate and invoke constructors which are considered as methods with a voidreturn value for their nominal type. Furthermore, one can not only invoke a method directly but even invoke a super method as long as this super method is reachable for the class from which the lookup factory was created. In contrast, invoking a super method is not possible at all when relying on the reflection API. If required, it is even possible to return a constant value from a handle. Performance metrics Method handles are often described as being a more performant as the Java reflection API. At least for recent releases of the HotSpot virtual machine, this is not true. The simplest way of proving this is writing an appropriate benchmark. Then again, is not all too simple to write a benchmark for a Java program which is optimized while it is executed. The de facto standard for writing a benchmark has become using JMH, a harness that ships under the OpenJDK umbrella. The full benchmark can be found as a gist in my GitHub profile. In this article, only the most important aspects of this benchmark are covered. From the benchmark, it becomes obvious that reflection is already implemented quite efficiently. Modern JVMs know a concept named inflation where a frequently invoked reflective method call is replaced with runtime generated Java byte code. What remains is the overhead of applying the boxing for passing arguments and receiving a return values. These boxings can sometimes be eliminated by the JVM's Just-in-time compiler but this is not always possible. For this reason, using method handles can be more performant than using the reflection API if method calls involve a significant amount of primitive values. This does however require that the exact method signatures are already known at compile time such that the appropriate polymorphic signature can be created. For most use cases of the reflection API, this guarantee can however not be given because the invoked method's types are not known at compile time. In this case, using method handles does not offer any performance benefits and should not be used to replace it. Creating an invokedynamic call site Normally, invokedynamic call sites are created by the Java compiler only when it needs to translate a lambda expression into byte code. It is worthwhile to note that lambda expressions could have been implemented without invokedynamic call sites altogether, for example by converting them into anonymous inner classes. As a main difference to the suggested approach, using invokedynamic delays the creation of a similar class to runtime. We are looking into class creation in the next section. For now, bear however in mind that invokedynamic does not have anything to do with class creation, it only allows to delay the decision of how to dispatch a method until runtime. For a better understanding of invokedynamic call sites, it helps to create such call sites explicitly in order to look at the mechanic in isolation. To do so, the following example makes use of my code generation framework Byte Buddy which provides explicit byte code generation of invokedynamic call sites without requiring a any knowledge of the byte code format. Any invokedynamic call site eventually yields a MethodHandle that references the method to be invoked. Instead of invoking this method handle manually, it is however up to the Java runtime to do so. Because method handles have become a known concept to the Java virtual machine, these invocations are then optimized similarly to a common method call. Any such method handle is received from a so-called bootstrap method which is nothing more than a plain Java method that fulfills a specific signature. For a trivial example of a bootstrap method, look at the following code: class Bootstrapper { public static CallSite bootstrap(Object... args) throws Throwable { MethodType methodType = MethodType.methodType(int.class, new Class[] {String.class}) MethodHandles.Lookup lookup = MethodHandles.lookup(); MethodHandle methodHandle = lookup.findStatic(Counter.class, "count", methodType); return new ConstantCallSite(methodHandle); } } For now, we do not care much about the arguments of the method. Instead, notice that the method is static what is as a matter of fact a requirement. Within Java byte code, an invokedynamic call site references the full signature of a bootstrap method but not a specific object which could have a state and a life cycle. Once the invokedynamic call site is invoked, control flow is handed to the referenced bootstrap method which is now responsible for identifying a method handle. Once this method handle is returned from the bootstrap method, it is invoked by the Java runtime. As obvious from the above example, a MethodHandle is not returned directly from a bootstrap method. Instead, the handle is wrapped inside of a CallSite object. Whenever a bootstrap method is invoked, the invokedynamic call site is later permanently bound to the CallSite object that is returned from this method. Consequently, a bootstrap method is only invoked a single time for any call site. Thanks to this intermediate CallSite object, it is however possible to exchange the referenced MethodHandle at a later point. For this purpose, the Java class library already offers different implementations of CallSite. We have already seen a ConstantCallSite in the example code above. As the name suggests, a ConstantCallSite always references the same method handle without a possibility of a later exchange. Alternatively, it is however also possible to for example use aMutableCallSite which allows to change the referenced MethodHandle at a later point in time or it is even possible to implement a custom CallSite class. With the above bootstrap method and Byte Buddy, we can now implement a custom invokedynamic instruction. For this, Byte Buddy offers the InvokeDynamic instrumentation that accepts a bootstrap method as its only mandatory argument. Such instrumentations are then fed to Byte Buddy. Assuming the following class: abstract class Example { abstract int method(); } we can use Byte Buddy to subclass Example in order to override method. We are then going to implement this method to contain a single invokedynamic call site. Without any further configuration, Byte Buddy creates a polymorphic signature that resembles the method type of the overridden method. However, for non-static methods, the this reference is set as a first, implicit argument. Assuming that we want to bind the Counter::count method which expects a String as a single argument, we could not bind this handle to Example::method because of this type mismatch. Therefore, we need to create a different call site without the implicit argument but with an String in its place. This can be achieved by using Byte Buddy's domain specific language: Instrumentation invokeDynamic = InvokeDynamic .bootstrap(Bootstrapper.class.getDeclaredMethod(“bootstrap”, Object[].class)) .withoutImplicitArguments() .withValue("foo"); With this instrumentation in place, we can finally extend the Example class and override method to implement the invokedynamic call site as in the following code snippet: Example example = new ByteBuddy() .subclass(Example.class) .method(named(“method”)).intercept(invokeDynamic) .make() .load(Example.class.getClassLoader(), ClassLoadingStrategy.Default.INJECTION) .getLoaded() .newInstance(); int result = example.method(); assertThat(result, is(3)); As obvious from the above assertion, the characters of the "foo" string were counted correctly. By setting appropriate break points in the code, it is further possible to validate that the bootstrap method is called and that control flow further reaches theCounter::count method. So far, we did not gain much from using an invokedynamic call site. The above bootstrap method would always bindCounter::count and can therefore only produce a valid result if the invokedynamic call site really wanted to transform a Stringinto an int. Obviously, bootstrap methods can however be more flexible thanks to the arguments they receive from the invokedynamic call site. Any bootstrap method receives at least three arguments: As a first argument, the bootstrap method receives a MethodHandles.Lookup object. The security context of this object is that of the class that contains the invokedynamic call site that triggered the bootstrapping. As discussed before, this implies that private methods of the defining class could be bound to the invokedynamic call site using this lookup instance. The second argument is a String representing a method name. This string serves as a hint to indicate from the call site which method should be bound to it. Strictly speaking, this argument is not required as it is perfectly legal to bind a method with another name. Byte Buddy simply serves the the name of the overridden method as this argument, if not specified differently. Finally, the MethodType of the method handle that is expected to be returned is served as a third argument. For the example above, we specified explicitly that we expect a String as a single parameter. At the same time, Byte Buddy derived that we require an int as a return value from looking at the overridden method, as we again did not specify any explicit return type. It is up to the implementor of a bootstrap method what exact signature this method should portray as long as it can at least accept these three arguments. If the last parameter of a bootstrap method represents an Object array, this last parameter is treated as a varargs and can therefore accept any excess arguments. This is also the reason why the above example bootstrap method is valid. Additionally, a bootstrap method can receive several arguments from an invokedynamic call site as long as these arguments can be stored in a class's constant pool. For any Java class, a constant pool stores values that are used inside of a class, largely numbers or string values. As of today, such constants can be primitive values of at least 32 bit size, Strings, Classes,MethodHandles and MethodTypes. This allows bootstrap methods to be used more flexible, if locating a suitable method handle requires additional information in form of such arguments. Lambda expressions Whenever the Java compiler translates a lambda expression into byte code, it copies the lambda's body into a private method inside of the class in which the expression is defined. These methods are named lambda$X$Y with X being the name of the method that contains the lambda expression and with Y being a zero-based sequence number. The parameters of such a method are those of the functional interface that the lambda expression implements. Given that the lambda expression makes no use of non-static fields or methods of the enclosing class, the method is also defined to be static. For compensation, the lambda expression is itself substituted by an invokedynamic call site. On its invocation, this call site requests the binding of a factory for an instance of the functional interface. As arguments to this factory, the call site supplies any values of the lambda expression's enclosing method which are used inside of the expression and a reference to the enclosing instance, if required. As a return type, the factory is required to provide an instance of the functional interface. For bootstrapping a call site, any invokedynamic instruction currently delegates to the LambdaMetafactory class which is included in the Java class library. This factory is then responsible for creating a class that implements the functional interface and which invokes the appropriate method that contains the lambda's body which, as described before, is stored in the original class. In the future, this bootstrapping process might however change which is one of the major advantages of using invokedynamic for implementing lambda expressions. If one day, a better suited language feature was available for implementing lambda expressions, the current implementation could simply be swapped out. In order to being able to create a class that implements the functional interface, any call site representing a lambda expression provides additional arguments to the bootstrap method. For the obligatory arguments, it already provides the name of the functional interface's method. Also, it provides a MethodType of the factory method that the bootstrapping is supposed to yield as a result. Additionally, the bootstrap method is supplied another MethodType that describes the signature of the functional interface's method. To that, it receives a MethodHandle referencing the method that contains the lambda's method body. Finally, the call site provides a MethodType of the generic signature of the functional interface's method, i.e. the signature of the method at the call site before type-erasure was applied. When invoked, the bootstrap method looks at these arguments and creates an appropriate implementation of a class that implements the functional interface. This class is created using the ASM library, a low-level byte code parser and writer that has become the de facto standard for direct Java byte code manipulation. Besides implementing the functional interface's method, the bootstrap method also adds an appropriate constructor and a static factory method for creating instances of the class. It is this factory method that is later bound to the invokedyanmic call site. As arguments, the factory receives an instance to the lambda method's enclosing instance, in case it is accessed and also any values that are read from the enclosing method. As an example, consider the following lambda expression: class Foo { int i; void bar(int j) { Consumer consumer = k -> System.out.println(i + j + k); } } In order to be executed, the lambda expression requires access to both the enclosing instance of Foo and to the value j of its enclosing method. Therefore, the desugared version of the above class looks something like the following where the invokedynamic instruction is represented by some pseudo-code: class Foo { int i; void bar(int j) { Consumer consumer = ; } private /* non-static */ void lambda$foo$0(int j, int k) { System.out.println(this.i + j + k); } } In order to being able to invoke lambda$foo$0, both the enclosing Foo instance and the j variable are handed to the factory that is bound by the invokedyanmic instruction. This factory then receives the variables it requires in order to create an instance of the generated class. This generated class would then look something like the following: class Foo$$Lambda$0 implements Consumer { private final Foo _this; private final int j; private Foo$$Lambda$0(Foo _this, int j) { this._this = _this; this.j = j; } private static Consumer get$Lambda(Foo _this, int j) { return new Foo$$Lambda$0(_this, j); } public void accept(Object value) { // type erasure _this.lambda$foo$0(_this, j, (Integer) value); } } Eventually, the factory method of the generated class is bound to the invokedynamic call site via a method handle that is contained by a ConstantCallSite. However, if the lambda expression is fully stateless, i.e. it does not require access to the instance or method in which it is enclosed, the LambdaMetafactory returns a so-called constant method handle that references an eagerly created instance of the generated class. Hence, this instance serves as a singleton to be used for every time that the lambda expression's call site is reached. Obviously, this optimization decision affects your application's memory footprint and is something to keep in mind when writing lambda expressions. Also, no factory method is added to a class of a stateless lambda expression. You might have noticed that the lambda expression's method body is contained in a private method which is now invoked from another class. Normally, this would result in an illegal access error. To overcome this limitation, the generated classes are loaded using so-called anonymous class loading. Anonymous class loading can only be applied when a class is loaded explicitly by handing a byte array. Also, it is not normally possible to apply anonymous class loading in user code as it is hidden away in the internal classes of the Java class library. When a class is loaded using anonymous class loading, it receives a host class of which it inherits its full security context. This involves both method and field access rights and the protection domain such that a lambda expression can also be generated for signed jar files. Using this approch, lambda expression can be considered more secure than anonymous inner classes because private methods are never reachable from outside of a class. Under the covers: lambda forms Lambda forms are an implementation detail of how MethodHandles are executed by the virtual machine. Because of their name, lambda forms are however often confused with lambda expressions. Instead, lambda forms are inspired by lambda calculus and received their name for that reason, not for their actual usage to implement lambda expressions in the OpenJDK. In earlier versions of the OpenJDK 7, method handles could be executed in one of two modes. Method handles were either directly rendered as byte code or they were dispatched using explicit assembly code that was supplied by the Java runtime. The byte code rendering was applied to any method handle that was considered to be fully constant throughout the lifetime of a Java class. If the JVM could however not prove this property, the method handle was instead executed by dispatching it to the supplied assembly code. Unfortunately, because assembly code cannot be optimized by Java's JIT-compiler, this lead to non-constant method handle invocations to "fall off the performance cliff". As this also affected the lazily bound lambda expressions, this was obviously not a satisfactory solution. LambdaForms were introduced to solve this problem. Roughly speaking, lambda forms represent byte code instructions which, as stated before, can be optimized by a JIT-compiler. In the OpenJDK, a MethodHandle's invocation semantics are today represented by a LambdaForm to which the handle carries a reference. With this optimizable intermediate representation, the use of non-constant MethodHandles has become significantly more performant. As a matter of fact, it is even possible to see a byte-code compiled LambdaForm in action. Simply place a break point inside of a bootstrap method or inside of a method that is invoked via a MethodHandle. Once the break point kicks it, the byte code-translated LambdaForms can be found on the call stack. Why this matters for dynamic languages Any language that should be executed on the Java virtual machine needs to be translated to Java byte code. And as the name suggests, Java byte code aligns rather close to the Java programming language. This includes the requirement to define a strict type for any value and before invokedynamic was introduced, a method call required to specify an explicit target class for dispatching a method. Looking at the following JavaScript code, specifying either information is however not possible when translating the method into byte code: 1 2 3 function (foo) { foo.bar(); } Using an invokedynamic call site, it has become possible to delay the identification of the method's dispatcher until runtime and furthermore, to rebind the invocation target, in case that a previous decision needs to be corrected. Before, using the reflection API with all of its performance drawbacks was the only real alternative to implementing a dynamic language. The real profiteer of the invokedynamic instruction are therefore dynamic programming languages. Adding the instruction was a first step away from aligning the byte code format to the Java programming language, making the JVM a powerful runtime even for dynamic languages. And as lambda expressions proved, this stronger focus on hosting dynamic languages on the JVM does neither interfere with evolving the Java language. In contrast, the Java programming languages gained from these efforts.
April 2, 2015
by Rafael Winterhalter
· 13,801 Views · 7 Likes
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How CAS (Compare And Swap) in Java works
Before we dig into CAS (Compare And Swap) strategy and how is it used by atomic constructs like AtomicInteger, first consider this code: public class MyApp { private volatile int count = 0; public void upateVisitors() { ++count; //increment the visitors count } } This sample code is tracking the count of visitors to the application. Is there anything wrong with this code? What will happen if multiple threads try to update count? Actually the problem is simply marking count as volatile does not guarantee atomicity and ++count is not an atomic operations. To read more check this. Can we solve this problem if we mark the method itself synchronized as shown below: public class MyApp { private int count = 0; public synchronized void upateVisitors() { ++count; //increment the visitors count } } Will this work? If yes then what changes have we made actually? Does this code guarantee atomicity? Yes. Does this code guarantee visibility? Yes. Then what is the problem? It makes use of locking and that introduces lot of delay and overhead. Check this article. This is very expensive way of making things work. To overcome these problems atomic constructs were introduced. If we make use of an AtomicInteger to track the count it will work. public class MyApp { private AtomicInteger count = new AtomicInteger(0); public void upateVisitors() { count.incrementAndGet(); //increment the visitors count } } The classes that support atomic operations e.g. AtomicInteger, AtomicLong etc. makes use of CAS. CAS does not make use of locking rather it is very optimistic in nature. It follows these steps: Compare the value of the primitive to the value we have got in hand. If the values do not match it means some thread in between has changed the value. Else it will go ahead and swap the value with new value. Check the following code in AtomicLong class: public final long incrementAndGet() { for (;;) { long current = get(); long next = current + 1; if (compareAndSet(current, next)) return next; } } In JDK 8 the above code has been changed to a single intrinsic: public final long incrementAndGet() { return unsafe.getAndAddLong(this, valueOffset, 1L) + 1L; } What advantage this single intrinsic have? Actually this single line is JVM intrinsic which is translated by JIT into an optimized instruction sequence. In case of x86 architecture it is just a single CPU instruction LOCK XADD which might yield better performance than classic load CAS loop. Now think about the possibility when we have high contention and a number of threads want to update the same atomic variable. In that case there is a possibility that locking will outperform the atomic variables but in realistic contention levels atomic variables outperform lock. There is one more construct introduced in Java 8, LongAdder. As per the documentation: This class is usually preferable to AtomicLong when multiple threads update a common sum that is used for purposes such as collecting statistics, not for fine-grained synchronization control. Under low update contention, the two classes have similar characteristics. But under high contention, expected throughput of this class is significantly higher, at the expense of higher space consumption. So LongAdder is not always a replacement for AtomicLong. We need to consider the following aspects: When no contention is present AtomicLong performs better. LongAdder will allocate Cells (a final class declared in abstract class Striped64) to avoid contention which consumes memory. So in case we have a tight memory budget we should prefer AtomicLong. That's all folks. Hope you enjoyed it.
April 1, 2015
by Akhil Mittal
· 71,225 Views · 2 Likes
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Fork/Join Framework vs. Parallel Streams vs. ExecutorService: The Ultimate Fork/Join Benchmark
How does the Fork/Join framework act under different configurations? Just like the upcoming episode of Star Wars, there has been a lot of excitement mixed with criticism around Java 8 parallelism. The syntactic sugar of parallel streams brought some hype almost like the new lightsaber we’ve seen in the trailer. With many ways now to do parallelism in Java, we wanted to get a sense of the performance benefits and the dangers of parallel processing. After over 260 test runs, some new insights rose from the data and we wanted to share these with you in this post. Fork/Join Framework vs. Parallel Streams vs. ExecutorService: The Ultimate Fork/Join Benchmark http://t.co/CMNfYZe58Z pic.twitter.com/6WExlmbyo6 — Takipi (@takipid) January 20, 2015 ExecutorService vs. Fork/Join Framework vs. Parallel Streams A long time ago, in a galaxy far, far away.... I mean, some 10 years ago concurrency was available in Java only through 3rd party libraries. Then came Java 5 and introduced the java.util.concurrent library as part of the language, strongly influenced by Doug Lea. The ExecutorService became available and provided us a straightforward way to handle thread pools. Of course java.util.concurrent keeps evolving and in Java 7 the Fork/Join framework was introduced, building on top of the ExecutorService thread pools. With Java 8 streams, we’ve been provided an easy way to use Fork/Join that remains a bit enigmatic for many developers. Let’s find out how they compare to one another. We’ve taken 2 tasks, one CPU-intensive and the other IO-intensive, and tested 4 different scenarios with the same basic functionality. Another important factor is the number of threads we use for each implementation, so we tested that as well. The machine we used had 8 cores available so we had variations of 4, 8, 16 and 32 threads to get a sense of the general direction the results are going. For each of the tasks, we’ve also tried a single threaded solution, which you’ll not see in the graphs since, well, it took much much longer to execute. To learn more about exactly how the tests ran you can check out the groundwork section below. Now, let’s get to it. Indexing a 6GB file with 5.8M lines of text In this test, we’ve generated a huge text file, and created similar implementations for the indexing procedure. Here’s what the results looked like: ** Single threaded execution: 176,267msec, or almost 3 minutes. ** Notice the graph starts at 20000 milliseconds. 1. Fewer threads will leave CPUs unutilized, too many will add overhead The first thing you notice in the graph is the shape the results are starting to take - you can get an impression of how each implementation behaves from only these 4 data points. The tipping point here is between 8 and 16 threads, since some threads are blocking in file IO, and adding more threads than cores helped utilize them better. When 32 threads are in, performance got worse because of the additional overhead. 2. Parallel Streams are the best! Almost 1 second better than the runner up: using Fork/Join directly Syntactic sugar aside (lambdas! we didn’t mention lambdas), we’ve seen parallel streams perform better than the Fork/Join and the ExecutorService implementations. 6GB of text indexed in 24.33 seconds. You can trust Java here to deliver the best result. 3. But… Parallel Streams also performed the worst: The only variation that went over 30 seconds This is another reminder of how parallel streams can slow you down. Let’s say this happens on machines that already run multithreaded applications. With a smaller number of threads available, using Fork/Join directly could actually be better than going through parallel streams - a 5 second difference, which makes for about an 18% penalty when comparing these 2 together. 4. Don’t go for the default pool size with IO in the picture When using the default pool size for Parallel Streams, the same number of cores on the machine (which is 8 here), performed almost 2 seconds worse than the 16 threads version. That’s a 7% penalty for going with the default pool size. The reason this happens is related with blocking IO threads. There’s more waiting going on, so introducing more threads lets us get more out of the CPU cores involved while other threads wait to be scheduled instead of being idle. How do you change the default Fork/Join pool size for parallel streams? You can either change the common Fork/Join pool size using a JVM argument: [java] -Djava.util.concurrent.ForkJoinPool.common.parallelism=16 [/java] (All Fork/Join tasks are using a common static pool the size of the number of your cores by default. The benefit here is reducing resource usage by reclaiming the threads for other tasks during periods of no use.) Or... You can use this trick and run Parallel Streams within a custom Fork/Join pool. This overrides the default use of the common Fork/Join pool and lets you use a pool you’ve set up yourself. Pretty sneaky. In the tests, we’ve used the common pool. 5. Single threaded performance was 7.25x worse than the best result Parallelism provided a 7.25x improvement, and considering the machine had 8 cores, it got pretty close to the theoretic 8x prediction! We can attribute the rest to overhead. With that being said, even the slowest parallelism implementation we tested, which this time was parallel streams with 4 threads (30.24sec), performed 5.8x better than the single threaded solution (176.27sec). What happens when you take IO out of the equation? Checking if a number is prime For the next round of tests, we’ve eliminated IO altogether and examined how long it would take to determine if some really big number is prime or not. How big? 19 digits. 1,530,692,068,127,007,263, or in other words: one quintillion seventy nine quadrillion three hundred sixty four trillion thirty eight billion forty eight million three hundred five thousand thirty three. Argh, let me get some air. Anyhow, we haven’t used any optimization other than running to its square root, so we checked all even numbers even though our big number doesn’t divide by 2 just to make it process longer. Spoiler alert: it’s a prime, so each implementation ran the same number of calculations. Here’s how it turned out: ** Single threaded execution: 118,127msec, or almost 2 minutes. ** Notice the graph starts at 20000 milliseconds 1. Smaller differences between 8 and 16 threads Unlike the IO test, we don’t have IO calls here so the performance of 8 and 16 threads was mostly similar, except for the Fork/Join solution. We’ve actually ran a few more sets of tests to make sure we’re getting good results here because of this “anomaly” but it turned out very similar time after time. We’d be glad to hear your thoughts about this in the comment section below. 2. The best results are similar for all methods We see that all implementations share a similar best result of around 28 seconds. No matter which way we tried to approach it, the results came out the same. This doesn’t mean that we’re indifferent to which method to use. Check out the next insight. 3. Parallel streams handle the thread overload better than other implementations This is the more interesting part. With this test, we see again that the the top results for running 16 threads are coming from using parallel streams. Moreover, in this version, using parallel streams was a good call for all variations of thread numbers. 4. Single threaded performance was 4.2x worse than the best result In addition, the benefit of using parallelism when running computationally intensive tasks is almost 2 times worse than the IO test with file IO. This makes sense since it’s a CPU intensive test, unlike the previous one where we could get an extra benefit from cutting down the time our cores were waiting on threads stuck with IO. Conclusion I’d recommend going to the source to learn more about when to use parallel streams and applying careful judgement anytime you do parallelism in Java. The best path to take would be running similar tests to these in a staging environment where you can try and get a better sense of what you’re up against. The factors you have to be mindful of are of course the hardware you’re running on (and the hardware you’re testing on), and the total number of threads in your application. This includes the common Fork/Join pool and code other developers on your team are working on. So try to keep those in check and get a full view of your application before adding parallelism of your own. Groundwork To run this test we’ve used an EC2 c3.2xlarge instance with 8 vCPUs and 15GB of RAM. A vCPU means there’s hyperthreading in place so in fact we have here 4 physical cores that each act as if it were 2. As far as the OS scheduler is concerned, we have 8 cores here. To try and make it as fair as we could, each implementation ran 10 times and we’ve taken the average run time of runs 2 through 9. That’s 260 test runs, phew! Another thing that was important is the processing time. We’ve chosen tasks that would take well over 20 seconds to process so the differences will be easier to spot and less affected by external factors. What’s next? The raw results are available right here, and the code is on GitHub. Please feel free to tinker around with it and let us know what kind of results you’re getting. If you have any more interesting insights or explanations for the results that we’ve missed, we’d be happy to read them and add it to the post. Originally posted on Takipi's blog
April 1, 2015
by Chen Harel
· 16,758 Views
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Get Client (Browser) timezone and maintain it in cookie
Recently, I came with requirement where we need to get browser timezone and maintain it so our Spring MVC application can use it. Our application need to convert date and time from server timezone to client timezone. Below is overall idea of implementation: Get Browser timezone by javascript. We can use opensource 'jstz.min.js' file for getting this. We can find this from ‘http://pellepim.bitbucket.org/jstz/’. We need to maintain this timezone. For same, we will store this timezone in cookie. This can be done by creating one jsp 'findTimeZonePage.jsp'. This page will store timezone in cookie and again redirect to original page. Every method of Spring MVC controller will check whether cookie is available, If not then it will redirect to findTimeZonePage.jsp. While doing this we will also pass current Url(will set in model) so that findTimeZonePage jsp can redirect to same page again. Code: 1. findTimeZonePage.jsp loading the page... 2. Add below Methods in Util class: public static TimeZonegetBrowserTimeZone(HttpServletRequest request){ Cookie[] cookieArray = request.getCookies(); if(cookieArray != null){ for(Cookie cookie : cookieArray){ if("CalenderAppTimeZone".equals(cookie.getName())){ String timeZoneId = cookie.getValue(); return TimeZone.getTimeZone(timeZoneId); } } } return null; } public static StringgetFullURL(HttpServletRequest request) { StringBuffer requestURL = request.getRequestURL(); String queryString = request.getQueryString(); if (queryString == null) { return requestURL.toString(); } else { return requestURL.append('?').append(queryString).toString(); } } 3. In each method of MVC Controller class, Add below code at start of method: TimeZone currentTimeZone = MyUtil.getBrowserTimeZone(request); if(currentTimeZone == null){ String url = MyUtil.getFullURL(request); System.out.println("Url="+url); model.addAttribute("redirectUrl", url); //Redirect to 'findTimeZone' for setting timezone. System.out.println("####Timezone is not set. Redirecting to findTimeZone.jsp for setting timezone."); return "findTimeZonePage"; } System.out.println("####Current TimeZone="+currentTimeZone.getID()); Hope this will help.
March 28, 2015
by Rajeshkumar Dave
· 12,877 Views
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6 Python Performance Tips
[this post was written by john paul mueller] python is such a cool language because you can do so much with it in such a short time with so little code. not only that, it supports many tasks, such as multiprocessing, with ease. python detractors sometimes claim python is slow. but it doesn’t have to be that way: try these six tips to speed up your python applications. 1. rely on an external package for critical code python makes many programming tasks easy, but it may not always provide the best performance with time-critical tasks. using a c, c++, or machine language external package for time-critical tasks can improve application performance. these packages are platform-specific, which means that you need the appropriate package for the platform you’re using. in short, this solution gives up some application portability in exchange for performance that you can obtain only by programming directly to the underlying host. here are some packages you should consider adding to your performance arsenal: cython pyinlne pypy pyrex the packages act in different ways. for example, pyrex makes it possible to extend python to do things like use c data types to make memory tasks more efficient or straightforward. pyinline lets you use c code directly in your python application. the inline code is compiled separately, but it keeps everything in one place while making use of the efficiencies that c can provide. 2. use keys for sorts there is a lot of really old python sorting code out there that will cost you time in creating a custom sort and speed in actually performing the sort during runtime. the best way to sort items is to use keys and the default sort() method whenever possible. for example, consider the following code: import operator somelist = [(1, 5, 8), (6, 2, 4), (9, 7, 5)] somelist.sort(key=operator.itemgetter(0)) somelist #output = [(1, 5, 8), (6, 2, 4), (9, 7, 5)] somelist.sort(key=operator.itemgetter(1)) somelist #output = [(6, 2, 4), (1, 5, 8), (9, 7, 5)] somelist.sort(key=operator.itemgetter(2)) somelist #output = [(6, 2, 4), (9, 7, 5), (1, 5, 8)], in each case the list is sorted according to the index you select as part of the key argument. this approach works just as well with strings as it does with numbers. 3. optimizing loops every programming language emphasizes the need to optimize loops. when working with python, you can rely on a wealth of techniques for making loops run faster. however, one method developers often miss is to avoid the use of dots within a loop. for example, consider the following code: lowerlist = ['this', 'is', 'lowercase'] upper = str.upper upperlist = [] append = upperlist.append for word in lowerlist: append(upper(word)) print(upperlist) #output = ['this', 'is', 'lowercase'] every time you make a call to str.upper, python evaluates the method. however, if you place the evaluation in a variable, the value is already known and python can perform tasks faster. the point is to reduce the amount of work that python performs within loops because the interpreted nature of python can really slow it down in those instances. ( note: there are many ways to optimize loops; this is only one of them. for example, many programmers would say that list comprehension is the best way to achieve speed benefits in loops. the key is that optimizing loops is one of the better way to achieve higher application speed.) 4. use a newer version anyone who searches python information online will find countless messages asking about moving from one version of python to another. in general, every version of python included optimizations that make it faster than the previous version. the limiting factor is whether your favorite libraries have also made the move to the newer version of python. rather than asking whether the move should be made, the key question is determine when a new version has sufficient support to make a move viable. you need to verify that your code still runs. you need to use the new libraries you obtained to use with the new version of python and then check your application for breaking changes. only after you make the required corrections will you notice any difference. however, if you just ensure your application runs with the new version, you could miss out on new features found in the update. once you make the move, profile your application under the new version, check for problem areas, and then update those areas to use new version features first. users will see a larger performance gain earlier in the upgrade process. 5. try multiple coding approaches using precisely the same coding approach every time you create an application will almost certainly result in some situations where the application runs slower than it might. try a little experimentation as part of the profiling process. for example, when managing items in a dictionary, you can take the safe approach of determining whether the item already exists and update it or you can add the item directly and then handle the situation where the item doesn’t exist as an exception. consider this first coding example: n = 16 mydict = {} for i in range(0, n): char = 'abcd'[i%4] if char not in mydict: mydict[char] = 0 mydict[char] += 1 print(mydict) this code will generally run faster when mydict is empty to start with. however, when mydict is usually filled (or at least mostly filled) with data, an alternative approach works better. n = 16 mydict = {} for i in range(0, n): char = 'abcd'[i%4] try: mydict[char] += 1 except keyerror: mydict[char] = 1 print(mydict) the output of {'d': 4, 'c': 4, 'b': 4, 'a': 4} is the same in both cases. the only difference is how the output is obtained. thinking outside the box and creating new coding techniques can help you obtain faster results with your applications. 6. cross-compile your application developers sometimes forget that computers don’t actually speak any of the languages used to create modern applications. computers speak machine code. in order to run the application, you use an application to convert the human readable code you use into something the computer can understand. there are times when writing an application in one language, such as python, and running it in another language, such as c++, makes sense from a performance perspective. it depends on what you want the application to do and the resources that the host system can provide. one interesting cross-compiler, nuitka , converts your python code into c++ code. the result is that you can execute the application in native mode instead of relying on an interpreter. depending on the platform and task, you could see a significant performance increase. ( note: nuitka is currently in beta, so use it with care on production applications. in fact, it’s best used for experimentation right now. there is also some discussion as to whether cross-compilation is the best way to achieve better performance. developers have used cross-compilation for years to achieve specific goals, such as better application speed. just remember that every solution comes with trade-offs and you should consider them before using the solution in a production environment.) when working with a cross-compiler, be sure it supports the version of python you work with. nuitka supports python 2.6, 2.7, 3.2, and 3.3. to make this solution work, you need both a python interpreter and a c++ compiler. nuitka supports a number of c++ compilers, including microsoft visual studio , mingw , and clang/llvm . cross-compilation can bring some serious downsides. for example, when working with nuitka, you find that even a small program can consume major drive space because nuitka implements python functionality using a number of dynamic link libraries (dlls). so this solution may not work well if you’re dealing with a resource-constrained system. bottom line each of the six tips in this article can help you create faster python applications. but there are no silver bullets. none of the tips will work every time. some work better than others with specific versions of python—even the platform can make a difference. you need to profile your application to determine where it works slowly and then try the tips that appear to best address those issues.
March 27, 2015
by Fredric Paul
· 8,373 Views
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Java 8 Functional Interfaces and Checked Exceptions
The Java 8 lambda syntax and functional interfaces have been a productivity boost for Java developers. But there is one drawback to functional interfaces. None of the them as currently defined in Java 8 declare any checked exceptions. This leaves the developer at odds on how best to handle checked exceptions. This post will present one option for handling checked exceptions in functional interfaces. We will use use the Function in our example, but the pattern should apply to any of the functional interfaces. Example of a Function with a Checked Exception Here’s an example from a recent side-project using a Function to open a directory in Lucene. As expected, opening a directory for writing/searching throws an IOException: Create a Lucene Directory private Function createDirectory = path -> { try{ return FSDirectory.open(path); }catch (IOException e){ throw new RuntimeException(e); } }; While this example works, it feels a bit awkward with the try/catch block. It’s adding to the boilerplate that functional interfaces are trying to reduce. Also, we are just re-throwing the exception up the call stack. If this how we are going to handle exceptions, is there something we can do to make our code a little bit cleaner? A Proposed Solution The solution is straight forward. We are going to extend the Function interface and add a method called throwsApply. The throwsApply method declares a throws clause of type Exception. Then we override the applymethod as a default method to handle the call to throwsApply in a try/catch block. Any exceptions caught are re-thrown as RuntimeExceptions Extending the Function Interface @FunctionalInterface public interface ThrowingFunction extends Function { @Override default R apply(T t){ try{ return applyThrows(t); }catch (Exception e){ throw new RuntimeException(e); } } R applyThrows(T t) throws Exception; } Here we are doing exactly what we did in the previous example, but from within a functional interface. Now we can rework our previous example to this: (made even more concise by using a method handle) using Function that handles checked Exceptions private ThrowingFunction createDirectory = FSDirectory::open; Composing Functions that have Checked Exceptions Now we have another issue to tackle. How do we compose two or more functions involving checked exceptions? The solution is to create two new default methods. We create andThen and compose methods allowing us to compose ThrowingFunction objects into one. (These methods have the same name as the default methods in the Function interface for consistency.) New Default method andThen default ThrowingFunction andThen(ThrowingFunction after){ Objects.requireNonNull(after); try{ return (T t) -> after.apply(apply(t)); }catch (Exception e){ throw new RuntimeException(e); } } default ThrowingFunction compose(ThrowingFunction before) { Objects.requireNonNull(before); try { return (V v) -> apply(before.apply(v)); }catch (Exception e){ throw new RuntimeException(e); } } The code for theandThen and compose is the same found in the original Function interface. We’ve just added try/catch blocks and re-throw any Exception as a RuntimeException. Now we are handling exceptions in the same manner as before with the added benefit of our code being a little more concise. Caveats This approach is not without its drawbacks. Brian Goetz spoke about this in his blog post ‘Exception transparency in Java’. Also, when composing functions we must use the type of ThrowingFunction for all parts. This is regardless if some of the functions don’t throw any checked exceptions. There is one exception, the last function added could be of type Function. Conclusion The purpose of this post is not to say this the best approach for handling checked exceptions, but to present one option. In a future post we will look at other ways of handling checked exceptions in functional interfaces. Resources Source for post Stackoverflow topic Functional Programming in Java presents good coverage of how to handle checked exceptions in functional interfaces. Java 8 Lambdas Throwing Checked Exceptions From Lambdas
March 24, 2015
by Bill Bejeck
· 19,194 Views · 2 Likes
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Spock 1.0 with Groovy 2.4 Configuration Comparison in Maven and Gradle
Spock 1.0 has been finally released. About new features and enhancements I already wrote two blog posts. One of the recent changes was a separation on artifacts designed for specific Groovy versions: 2.0, 2.2, 2.3 and 2.4 to minimize a chance to come across a binary incompatibility in runtime (in the past there were only versions for Groovy 1.8 and 2.0+). That was done suddenly and based on the messages on the mailing list it confused some people. After being twice asked to help properly configure two projects I decided to write a short post presenting how to configure Spock 1.0 with Groovy 2.4 in Maven and Gradle. It is also a great place to compare how much work is required to do it in those two very popular build systems. Maven Maven does not natively support other JVM languages (like Groovy or Scala). To use it in the Maven project it is required to use a third party plugin. For Groovy the best option seems to be GMavenPlus (a rewrite of no longer maintained GMaven plugin). An alternative is a plugin which allows to use Groovy-Eclipse compiler with Maven, but it is not using official groovyc and in the past there were problems with being up-to-date with the new releases/features of Groovy. Sample configuration of GMavenPlus plugin could look like: org.codehaus.gmavenplus gmavenplus-plugin 1.4 compile testCompile As we want to write tests in Spock which recommends to name files with Spec suffix (from specification) in addition it is required to tell Surefire to look for tests also in those files: maven-surefire-plugin ${surefire.version} **/*Spec.java **/*Test.java Please notice that it is needed to include **/*Spec.java not **/*Spec.groovy to make it work. Also dependencies have to be added: org.codehaus.groovy groovy-all 2.4.1 org.spockframework spock-core 1.0-groovy-2.4 test It is very important to take a proper version of Spock. For Groovy 2.4 version 1.0-groovy-2.4 is required. For Groovy 2.3 version 1.0-groovy-2.3. In case of mistake Spock protests with a clear error message: Could not instantiate global transform class org.spockframework.compiler.SpockTransform specified at jar:file:/home/foo/.../spock-core-1.0-groovy-2.3.jar!/META-INF/services/org.codehaus.groovy.transform.ASTTransformation because of exception org.spockframework.util.IncompatibleGroovyVersionException: The Spock compiler plugin cannot execute because Spock 1.0.0-groovy-2.3 is not compatible with Groovy 2.4.0. For more information, see http://versioninfo.spockframework.org Together with other mandatory pom.xml elements the file size increased to over 50 lines of XML. Quite much just for Groovy and Spock. Let’s see how complicated it is in Gradle. Gradle Gradle has built-in support for Groovy and Scala. Without further ado Groovy plugin just has to be applied. apply plugin: 'groovy' Next the dependencies has to be added: compile 'org.codehaus.groovy:groovy-all:2.4.1' testCompile 'org.spockframework:spock-core:1.0-groovy-2.4' and the information where Gradle should look for them: repositories { mavenCentral() } Together with defining package group and version it took 15 lines of code in Groovy-based DSL. Btw, in case of Gradle it is also very important to match Spock and Groovy version, e.g. Groovy 2.4.1 and Spock 1.0-groovy-2.4. Summary Thanks to embedded support for Groovy and compact DSL Gradle is preferred solution to start playing with Spock (and Groovy in general). Nevertheless if you prefer Apache Maven with a help of GMavenPlus (and XML) it is also possible to build project tested with Spock. The minimal working project with Spock 1.0 and Groovy 2.4 configured in Maven and Gradle can be cloned from my GitHub. Note 1. I haven’t been using Maven in my project for over 2 years (I prefer Gradle), so if there is a better/easier way to configure Groovy and Spock with Maven just let me know in the comments. Note 2. The configuration examples assume that Groovy is used only for tests and the production code is written in Java. It is possible to mix Groovy and Java code together, but then the configuration is a little more complicated.
March 19, 2015
by Marcin Zajączkowski
· 12,539 Views · 3 Likes
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Walking Recursive Data Structures Using Java 8 Streams
The Streams API is a real gem in Java 8, and I keep finding more or less unexpected uses for them. I recently wrote about using them as ForkJoinPool facade. Here’s another interesting example: Walking recursive data structures. Without much ado, have a look at the code: class Tree { private int value; private List children = new LinkedList<>(); public Tree(int value, List children) { super(); this.value = value; this.children.addAll(children); } public Tree(int value, Tree... children) { this(value, asList(children)); } public int getValue() { return value; } public List getChildren() { return Collections.unmodifiableList(children); } public Stream flattened() { return Stream.concat( Stream.of(this), children.stream().flatMap(Tree::flattened)); } } It’s pretty boring, except for the few highlighted lines. Let’s say we want to be able to find elements matching some criteria in the tree or find particular element. One typical way to do it is a recursive function – but that has some complexity and is likely to need a mutable argument (e.g. a set where you can append matching elements). Another approach is iteration with a stack or a queue. They work fine, but take a few lines of code and aren’t so easy to generalize. Here’s what we can do with this flattened function: // Get all values in the tree: t.flattened().map(Tree::getValue).collect(toList()); // Get even values: t.flattened().map(Tree::getValue).filter(v -> v % 2 == 0).collect(toList()); // Sum of even values: t.flattened().map(Tree::getValue).filter(v -> v % 2 == 0).reduce((a, b) -> a + b); // Does it contain 13? t.flattened().anyMatch(t -> t.getValue() == 13); I think this solution is pretty slick and versatile. One line of code (here split to 3 for readability on blog) is enough to flatten the tree to a straightforward stream that can be searched, filtered and whatnot. It’s not perfect though: It is not lazy and flattened is called for each and every node in the tree every time. It probably could be improved using a Supplier. Anyway, it doesn’t matter for typical, reasonably small trees, especially in a business application on a very tall stack of libraries. But for very large trees, very frequent execution and tight time constraints the overhead might cause some trouble.
March 18, 2015
by Konrad Garus
· 25,171 Views · 1 Like
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Java 8 Stream to Rx-Java Observable
I was recently looking at a way to convert a Java 8 Stream to Rx-JavaObservable. There is one api in Observable that appears to do this : public static final Observable from(java.lang.Iterable iterable) So now the question is how do we transform a Stream to an Iterable. Stream does not implement the Iterable interface, and there are good reasons for this. So to return an Iterable from a Stream, you can do the following: Iterable iterable = new Iterable() { @Override public Iterator iterator() { return aStream.iterator(); } }; Observable.from(iterable); Since Iterable is a Java 8 functional interface, this can be simplified to the following using Java 8 Lambda expressions!: Observable.from(aStream::iterator); First look it does appear cryptic, however if it is seen as a way to simplify the expanded form of Iterable then it slowly starts to make sense. Reference: This is entirely based on what I read on this Stackoverflow question.
March 12, 2015
by Biju Kunjummen
· 12,667 Views · 2 Likes
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Java Mapper and Model Testing Using eXpectamundo
As a long time Java application developer working in variety of corporate environments one of the common activities I have to perform is to write mappings to translate one Java model object into another. Regardless of the technology or library I use to write the mapper, the same question comes up. What is the best way to unit test it? I've been through various approaches, all with a variety of pros and cons related to the amount of time it takes to write what is essentially a pretty simple test. The tendency (I hate to admit) is to skimp on testing all fields and focus on what I deem to be the key fields in order to concentrate on, dare I say it, more interesting areas of the codebase. As any coder knows, this is the road to bugs and the time spent writing the test is repaid many times over in reduced debugging later. Enter eXpectamundo eXpectamundo is an open source Java library hosted on github that takes a new approach to testing model objects. It allows the Java developer to write a prototype object which has been set up with expectations. This prototype can then be used to test the actual output in a unit test. The snippet below illustrates the setup of the prototype. ... User expected = prototype(User.class); expect(expected.getCreateTs()).isWithin(1, TimeUnit.SECONDS, Moments.today()); expect(expected.getFirstName()).isEqualTo("John"); expect(expected.getUserId()).isNull(); expect(expected.getDateOfBirth()).isComparableTo(AUG(9, 1975)); expectThat(actual).matches(expected); .. For a complete example lets take a simple Data Transfer Object (DTO) which transfers the definition of a new user from a UI. package org.exparity.expectamundo.sample.mapper; import java.util.Date; public class UserDTO { private String username, firstName, surname; private Date dateOfBirth; public UserDTO(String username, String firstName, String surname, Date dateOfBirth) { this.username = username; this.firstName = firstName; this.surname = surname; this.dateOfBirth = dateOfBirth; } public String getUsername() { return username; } public String getFirstName() { return firstName; } public String getSurname() { return surname; } public Date getDateOfBirth() { return dateOfBirth; } } This DTO needs to mapped into the domain model User object which can then be manipulated, stored, etc by the service layer. The domain User object is defined as below: package org.exparity.expectamundo.sample.mapper; import java.util.Date; public class User { private Integer userId; private Date createTs = new Date(); private String username, firstName, surname; private Date dateOfBirth; public User(String username, String firstName, String surname, final Date dateOfBirth) { this.username = username; this.firstName = firstName; this.surname = surname; this.dateOfBirth = dateOfBirth; } public Integer getUserId() { return userId; } public Date getCreateTs() { return createTs; } public String getUsername() { return username; } public String getFirstName() { return firstName; } public String getSurname() { return surname; } public Date getDateOfBirth() { return dateOfBirth; } } The code for the mapper is simple so we'll use a simple hand coded mapping layer however I've introduced a bug into the mapper which we'll detect later with our unit test. package org.exparity.expectamundo.sample.mapper; public class UserDTOToUserMapper { public User map(final UserDTO userDTO) { return new User(userDTO.getUsername(), userDTO.getSurname(), userDTO.getFirstName(), userDTO.getDateOfBirth()); } } We then write a unit test for the mapper using eXpectamundo to test the expectation. package org.exparity.expectamundo.sample.mapper; import java.util.concurrent.TimeUnit; import org.junit.Test; import static org.exparity.dates.en.FluentDate.AUG; import static org.exparity.expectamundo.Expectamundo.*; import static org.exparity.hamcrest.date.Moments.now; public class UserDTOToUserMapperTest { @Test public void canMapUserDTOToUser() { UserDTO dto = new UserDTO("JohnSmith", "John", "Smith", AUG(9, 1975)); User actual = new UserDTOToUserMapper().map(dto); User expected = prototype(User.class); expect(expected.getCreateTs()).isWithin(1, TimeUnit.SECONDS, now()); expect(expected.getFirstName()).isEqualTo("John"); expect(expected.getSurname()).isEqualTo("Smith"); expect(expected.getUsername()).isEqualTo("JohnSmith"); expect(expected.getUserId()).isNull(); expect(expected.getDateOfBirth()).isSameDay(AUG(9, 1975)); expectThat(actual).matches(expected); } } The test shows how simple equality tests can be performed and also introduced some of the specialised tests which can be performed, such as testing for null, or testing the bounds of the create timestamp and performing a comparison check on the dateOfBirth property. Running the unit test reports the failure in the mapper where the firstname and surname properties have been transposed by the mapper. java.lang.AssertionError: Expected a User containing properties : getCreateTs() is expected within 1 seconds of Sun Jan 18 13:00:33 GMT 2015 getFirstName() is equal to John getSurname() is equal to Smith getUsername() is equal to JohnSmith getUserId() is null getDateOfBirth() is comparable to Sat Aug 09 00:00:00 BST 1975 But actual is a User containing properties : getFirstName() is Smith getSurname() is John A simple fix to the mapper resolves the issue: package org.exparity.expectamundo.sample.mapper; public class UserDTOToUserMapper { public User map(final UserDTO userDTO) { return new User(userDTO.getUsername(),userDTO.getFirstName(), userDTO.getSurname(), userDTO.getDateOfBirth()); } } But I can do this with hamcrest! The hamcrest equivalent to this test would follow one of two patterns; a custom implementation of org.hamcrest.Matcher for matching User objects, or a set of inline assertions as per the following example: package org.exparity.expectamundo.sample.mapper; import java.util.concurrent.TimeUnit; import org.junit.Test; import static org.exparity.dates.en.FluentDate.AUG; import static org.exparity.hamcrest.date.DateMatchers.within; import static org.exparity.hamcrest.date.Moments.now; import static org.hamcrest.MatcherAssert.assertThat; import static org.hamcrest.Matchers.*; public class UserDTOToUserMapperHamcrestTest { @Test public void canMapUserDTOToUser() { UserDTO dto = new UserDTO("JohnSmith", "John", "Smith", AUG(9, 1975)); User actual = new UserDTOToUserMapper().map(dto); assertThat(actual.getCreateTs(), within(1, TimeUnit.SECONDS, now())); assertThat(actual.getFirstName(), equalTo("John")); assertThat(actual.getSurname(), equalTo("Smith")); assertThat(actual.getUsername(), equalTo("JohnSmith")); assertThat(actual.getUserId(), nullValue()); assertThat(actual.getDateOfBirth(), comparesEqualTo(AUG(9, 1975))); } } In this example the only difference eXpectamundo offers over hamcrest is a different way of reporting mismatches. eXpectamundo will report all differences between the expected vs the actual whereas the hamcrest test will fail on the first difference. An improvement, but not really a reason to consider alternatives. Where the approach eXpectomundo offers starts to differentiate itself is when testing more complex object collections and graphs. Collection testing with eXpectamundo If we move our code forward and we create a repository to allow us to store and retrieve User instances. For the sake of simplicity I've used a basic HashMap backed repository. The code for the repository is as follows: package org.exparity.expectamundo.sample.mapper; import java.util.*; public class UserRepository { private Map userMap = new HashMap<>(); public List getAll() { return new ArrayList<>(userMap.values()); } public void addUser(final User user) { this.userMap.put(user.getUsername(), user); } public User getUserByUsername(final String username) { return userMap.get(username); } } We then write a unit test to confirm the behaviour of repository package org.exparity.expectamundo.sample.mapper; import java.util.Date; import java.util.concurrent.TimeUnit; import org.junit.Test; import static org.exparity.dates.en.FluentDate.AUG; import static org.exparity.expectamundo.Expectamundo.*; public class UserRepositoryTest { private static String FIRST_NAME = "John"; private static String SURNAME = "Smith"; private static String USERNAME = "JohnSmith"; private static Date DATE_OF_BIRTH = AUG(9, 1975); private static User EXPECTED_USER; static { EXPECTED_USER = prototype(User.class); expect(EXPECTED_USER.getCreateTs()).isWithin(1, TimeUnit.SECONDS, new Date()); expect(EXPECTED_USER.getFirstName()).isEqualTo(FIRST_NAME); expect(EXPECTED_USER.getSurname()).isEqualTo(SURNAME); expect(EXPECTED_USER.getUsername()).isEqualTo(USERNAME); expect(EXPECTED_USER.getUserId()).isNull(); expect(EXPECTED_USER.getDateOfBirth()).isComparableTo(DATE_OF_BIRTH); } @Test public void canGetAll() { User user = new User(USERNAME, FIRST_NAME, SURNAME, DATE_OF_BIRTH); UserRepository repos = new UserRepository(); repos.addUser(user); expectThat(repos.getAll()).contains(EXPECTED_USER); } @Test public void canGetByUsername() { User user = new User(USERNAME, FIRST_NAME, SURNAME, DATE_OF_BIRTH); UserRepository repos = new UserRepository(); repos.addUser(user); expectThat(repos.getUserByUsername(USERNAME)).matches(EXPECTED_USER); } } The test shows how the prototype, once constructed, can be used to perform a deep verification of an object and, if desired, can be re-used in multiple tests. The equivalent matcher in hamcrest is to write a custom matcher for the User object, or as below with flat objects using a multi matcher. (Note there are a number of ways to write the matcher, the one below I felt was the most terse example). package org.exparity.expectamundo.sample.mapper; import java.util.Date; import java.util.concurrent.TimeUnit; import org.hamcrest.*; import org.junit.Test; import static org.exparity.dates.en.FluentDate.AUG; import static org.exparity.hamcrest.BeanMatchers.hasProperty; import static org.exparity.hamcrest.date.DateMatchers.*; import static org.hamcrest.MatcherAssert.assertThat; import static org.hamcrest.Matchers.*; public class UserRepositoryHamcrestTest { private static String FIRST_NAME = "John"; private static String SURNAME = "Smith"; private static String USERNAME = "JohnSmith"; private static Date DATE_OF_BIRTH = AUG(9, 1975); private static final Matcher EXPECTED_USER = Matchers.allOf( hasProperty("CreateTs", within(1, TimeUnit.SECONDS, new Date())), hasProperty("FirstName", equalTo(FIRST_NAME)), hasProperty("Surname", equalTo(SURNAME)), hasProperty("Username", equalTo(USERNAME)), hasProperty("UserId", nullValue()), hasProperty("DateOfBirth", sameDay(DATE_OF_BIRTH))); @Test public void canGetAll() { User user = new User(USERNAME, FIRST_NAME, SURNAME, DATE_OF_BIRTH); UserRepository repos = new UserRepository(); repos.addUser(user); assertThat(repos.getAll(), hasItem(EXPECTED_USER)); } @Test public void canGetByUsername() { User user = new User(USERNAME, FIRST_NAME, SURNAME, DATE_OF_BIRTH); UserRepository repos = new UserRepository(); repos.addUser(user); assertThat(repos.getUserByUsername(USERNAME), is(EXPECTED_USER)); } } In comparison this hamcrest-based test matches the eXpectamundo test in compactness but not in type-safety. A type-safe matcher can be created which checks each property individual which would make considerably more code for no benefit over the eXpectamundo equivalent. The error reporting during failures is also clear and intuitive for the eXpectamundo test, less so for the hamcrest-equivalent. (Again an equivalent descriptive test can be written using hamcrest but will require much more code). An example of the error reporting is below where the surname is returned in place of the firstname: java.lang.AssertionError: Expected a list containing a User with properties: getCreateTs() is a expected within 1 seconds of Fri Mar 06 17:29:52 GMT 2015 getFirstName() is equal to John getSurname() is equal to Smith getUsername() is equal to JohnSmith getUserId() is is null getDateOfBirth() is is comparable to Sat Aug 09 00:00:00 BST 1975 but actual list contains: User containing properties getFirstName() is Smith Summary In summary eXpectamundo offers a new approach to perform verification of models during testing. It provides a type-safe interface to set expectations making creation of deep model tests, especially in an IDE with auto-complete, particularly simple. Failures are also reported with a clear to understand error trace. Full details of eXpectamundo and the other expectations and features it supports are available on the eXpectamundo page on github. The example code is also available on github. Try it out To try eXpectamundo out for yourself include the dependency in your maven pom or other dependency manager org.exparity expectamundo 0.9.15 test
March 12, 2015
by Stewart Bissett
· 8,104 Views
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Using Java 8 Lambda Expressions in Java 7 or Older
I think nobody declines the usefulness of Lambda expressions, introduced by Java 8. However, many projects are stuck with Java 7 or even older versions. Upgrading can be time consuming and costly. If third party components are incompatible with Java 8 upgrading might not be possible at all. Besides that, the whole Android platform is stuck on Java 6 and 7. Nevertheless, there is still hope for Lambda expressions! Retrolambda provides a backport of Lambda expressions for Java 5, 6 and 7. From the Retrolambda documentation: Retrolambda lets you run Java 8 code with lambda expressions and method references on Java 7 or lower. It does this by transforming your Java 8 compiled bytecode so that it can run on a Java 7 runtime. After the transformation they are just a bunch of normal .class files, without any additional runtime dependencies. To get Retrolambda running, you can use the Maven or Gradle plugin. If you want to use Lambda expressions on Android, you only have to add the following lines to your gradle build files: /build.gradle: buildscript { dependencies { classpath 'me.tatarka:gradle-retrolambda:2.4.0' } } /app/build.gradle: apply plugin: 'com.android.application' // Apply retro lambda plugin after the Android plugin apply plugin: 'retrolambda' android { compileOptions { // change compatibility to Java 8 to get Java 8 IDE support sourceCompatibility JavaVersion.VERSION_1_8 targetCompatibility JavaVersion.VERSION_1_8 } }
March 11, 2015
by Michael Scharhag
· 10,564 Views
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Minor GC vs Major GC vs Full GC
The post expects the reader to be familiar with generic garbage collection principles built into the JVM.
March 10, 2015
by Nikita Salnikov-Tarnovski
· 53,403 Views · 6 Likes
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ExecutorService vs ExecutorCompletionService in Java
Suppose we have list of four tasks: Task A, Task B, Task C and Task D which perform some complex computation and result into an integer value. These tasks may take random time depending upon various parameters. We can submit these tasks to executor as: ExecutorService executorService = Executors.newFixedThreadPool(4); List futures = new ArrayList>(); futures.add(executorService.submit(A)); futures.add(executorService.submit(B)); futures.add(executorService.submit(C)); futures.add(executorService.submit(D)); Then we can iterate over the list to get the computed result of each future: for (Future future:futures) { Integer result = future.get(); // rest of the code here. } Now the similar functionality can also be achieved using ExecutorCompletionService as: ExecutorService executorService = Executors.newFixedThreadPool(4); CompletionService executorCompletionService= new ExecutorCompletionService<>(executorService ); Then again we can submit the tasks and get the result like: List futures = new ArrayList>(); futures.add(executorCompletionService.submit(A)); futures.add(executorCompletionService.submit(B)); futures.add(executorCompletionService.submit(C)); futures.add(executorCompletionService.submit(D)); for (int i=0; i> solvers) throws InterruptedException { CompletionService ecs = new ExecutorCompletionService(e); int n = solvers.size(); List> futures = new ArrayList>(n); Result result = null; try { for (Callable s : solvers) futures.add(ecs.submit(s)); for (int i = 0; i < n; ++i) { try { Result r = ecs.take().get(); if (r != null) { result = r; break; } } catch(ExecutionException ignore) {} } } finally { for (Future f : futures) f.cancel(true); } if (result != null) use(result); } In the above example the moment we get the result we break out of the loop and cancel out all the other futures. One important thing to note is that the implementation ofExecutorCompletionService contains a queue of results. We need to remember the number of tasks we have added to the service and then should use take or poll to drain the queue otherwise a memory leak will occur. Some people use the Future returned by submit to process results and this is NOT correct usage. There is one interesting solution provided by Dr. Heinz M, Kabutz here. Peeking into ExecutorCompletionService When we look inside the code for this we observe that it makes use of Executor,AbstractExecutorService (default implementations of ExecutorService execution methods) and BlockingQueue (actual instance is of class LinkedBlockingQueue>). public class ExecutorCompletionService implements CompletionService { private final Executor executor; private final AbstractExecutorService aes; private final BlockingQueue> completionQueue; // Remaining code.. } Another important thing to observe is class QueueingFuture which extends FutureTaskand in the method done() the result is pushed to queue. private class QueueingFuture extends FutureTask { QueueingFuture(RunnableFuture task) { super(task, null); this.task = task; } protected void done() { completionQueue.add(task); } private final Future task; } For the curios ones, class FutureTask is base implementation of Future interface with methods to start and cancel a computation, query to see if the computation is complete, and retrieve the result of the computation. And constructor takes RunnableFuture as parameter which is again an interface which extends Future interface and adds only one method run as: public interface RunnableFuture extends Runnable, Future { /** * Sets this Future to the result of its computation * unless it has been cancelled. */ void run(); } That is all for now. Enjoy!!
March 8, 2015
by Akhil Mittal
· 40,452 Views · 6 Likes
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Introduction to Hypertext Application Language (HAL)
Principles of REST architectural style were put forth by Dr. Roy Fielding in his thesis “Architectural Styles and the Design of Network-based Software Architectures”. One of the main principles of the style is that REST applications should be hypermedia driven, that is the change of an application's state or, in other words, transition from one resource to another, should be done by following the links. The rationale behind this principle is that all possible operations with the resource can be discovered without the need of any out-of-band documentation and if some URI changes, there is no need to change the client as it is server's responsibility to generate URIs and insert them into representations. This principle is also called Hypermedia As The Engine Of An Application state (HATEOAS). While the Thesis gives the prescription to use hyperlinks in the representations of resources, Hypertext Application Language (HAL) is one possible recipe how to do design representations with links. In particular, it describes how to design JSON representations, although there is a XML counterpart too. Our discussion will be limited only to the JSON variety. As to to HAL media type, currently, according to the document, its media type is application/vnd.hal+json. It has something to do with the fact that there are several registration trees, that is buckets for media types, with different requirements. Examples include standards, vendor and personal trees. The standards tree has no prefix for a media type. The latter two do have vnd and prs respectively. When HAL moves to standards tree, its content type name will be application/hal+json. Currently, the example application provided by the author of HAL, Mike Kelly, produces application/json in its Content-Type header, so the exact media type is not that important for our discussion. There is a convenient way to tell whether an API is RESTful or not. A so-called Richardson Maturity Model (RMM) was introduced by Leonard Richardson in his 2008 QCon talk and later popularized by Martin Fowler. The model introduces four levels of API maturity starting from Level 0, so that at level two each resource is not only identified by its own URI, but all operations with resources are done using HTTP methods like GET, PUT etc. If an API is at Level 3 it can be considered as RESTful. From the point of view of the RMM HAL helps to upgrade a Level 2 API to Level 3 whereby hypermedia is used. To make our hands dirty with HAL let's discuss a simple book catalog API where a user can browse books; this API could be a part of a larger application like a bookstore, but its mission only to be a catalog of books. The data for our API could be scraped from amazon.com. Some URIs and methods are listed below. Method URI Description GET /books Show all books GET /books/{id} Show details of the book with identificator id GET /authors/{id} Show details for author with identificator id Let's start with the representation of a single book. A book has certain properties such as the title, the price, number of pages, language and so on. The simplest JSON representation of a book could be like the following.As we deal with representations, the methods to update or modify resources were omitted, although they could be the part of the administrative interface of the service.s { "Title":"RESTful Web APIs", "Price":"$31.92", "Paperback":"408 pages", "Language":"English" } It is high time to add some links. The first candidate is a link to the object itself, as it is recommended by the HAL specification. To add links there is a reserved keyword in HAL, _links. The first character of it is an underscore which was selected to make the reserved keywords different from the names of the properties of objects in representations, although not all names starting with an underscore are reserved. Actually, _links is the name of an object the properties of which describe the meaning of a particular link. The values of the properties should contain the URI as well as may contain some other goodies. Are the names of the properties fixed? Well, some are, and the list is here, but one can add her own as will be described later. Going back to our self link there is an eponymous relation type in the list and our book object can look like the snippet below. { "_links":{ "self":{ "href":"/book/123" } }, "Title":"RESTful Web APIs", "Price":"$31.92", "Paperback":"408 pages", "Language":"English" } The href property contains the URI by following which our resource can be accessed. That is nice, but most books should have authors. While it is possible to add an array of authors' names to our representation, it is more convenient to add links to authors due to the fact that somebody could wish to navigate to the representation of the author's resource and discover what other books a particular author could have written. While there is an author relation type, there is no keyword for the case when there are several authors, so we can add a relation type for this particular case. Although the relation type could be registered in IANA for public usage to be added to the list, it is important to know how to add one's own rel types. A common practice to do the job is to use a URI by navigating which a description of the relation type could be accessed. The description may contain possible actions, supported representation formats and the purpose of the resource. The first stab at adding authors to our book could look like this. { "_links":{ "self":{ "href":"/book/123" }, "http://booklistapi.com/rels/authors":[ { "href":"/author/4554", "title":"Leonard Richardson" }, { "href":"/author/5758", "title":"Mike Amundsen" }, { "href":"/author/6853", "title":"Sam Ruby" } ] }, "Title":"RESTful Web APIs", "Price":"$31.92", "Paperback":"408 pages", "Language":"English" } By navigating to http://booklistapi.com/rels/authors one can read additional information about this relation type. As it is seen from the snippet above, the properties of a link object are not limited to href, which is the only required one, there are several more properties including title, a human-readable identifier. Some other examples are type for media type and hreflang to hint about the language of the representation. One more notice concerning custom relations is that in its form used above it seems a little bit wordy. HAL has a way to cope with this concern and it is called Compact URI or CURIE. HAL adds to relation types its own type called curies, example usage of which is demonstrated by the following snippet. { "_links":{ "self":{ "href":"/book/123" }, "curries":[ { "name":"ns", "href":"http://booklistapi.com/rels/{rel}", "templated":true } ], "ns:authors":[ { "href":"/author/4554", "title":"Leonard Richardson" }, { "href":"/author/5758", "title":"Mike Amundsen" }, { "href":"/author/6853", "title":"Sam Ruby" } ] }, "Title":"RESTful Web APIs", "Price":"$31.92", "Paperback":"408 pages", "Language":"English" } We used the name property of the link object to provide a key to select an object, which is later used in the name of our custom relation type - ns:authors. Then, we used a templated URI which can be expanded to produce the full URI, same as in the previous example, to access documentation. One additional property is templated, which is false by default but should be set to true when using URI templates. Another place where templates can be used are links that enable search whereby search terms are added to the template to produce the full URI. How this representation can be further improved? Links in a representation could not only be used to add related data, but also to show what possible actions could be taken by the client. For example, if it is an administrative interface, links to edit or delete the book using eponymous relation types may be added. Otherwise, one can add links by following which the client may be able to add a review or rate the book. We have spent enough time with our book and now let's turn to authors. The example above shows that HAL representation may contain the properties of an object as well as some links. Links can help navigate to related objects, such as authors in our book example. Another way to add information about related objects to our representation is to embed some objects in our representation. For example, if one navigates to the representation of an author, a list of all books with some detail is shown. { "_links":{ "self":{ "href":"/author/4554" }, "curries":[ { "name":"ns", "href":"http://booklistapi.com/rels/{rel}", "templated":true } ] }, "_embedded":{ "ns:books":[ { "_links":{ "self":{ "href":"/books/123" } }, "Title":"RESTful Web APIs", "Price":"$31.92" }, { "_links":{ "self":{ "href":"/books/366" } }, "Title":"RESTful Web Services", "Price":"$79.78" }, { "_links":{ "self":{ "href":"/books/865" } }, "Title":"Ruby Cookbook", "Price":"$34.35" } ] } } Another keyword starting with an underscore, _embedded, is used to add data from another object to our representation. Embedded resources are the values of properties which are relation types. The difference from links is that instead of link objects resource objects are used as values. Each embedded object can have properties, links and its own embedded objects, although the latter option is not shown by the example above. The idea is that each representation can contain the trio, including embedded objects, so one has a repeating pattern in nested objects at all levels. Up to this moment we have dealt only with small lists which may not overwhelm a client if transmitted. A representation of the resource which is a list of books could be extremely demanding from the point of view of network bandwidth and memory consumption on the client side, so it may require pagination. For example, if we deal with the resource which is the list of all possible books, one can include some predefined amount of books along with links which enable the client to transition to the next and previous pages using next and previous relation types respectively. It should be noted that a representation can contain a link to some resource along with the same resource being embedded and both share the same relation type. This is done to reduce the number of trips to the server and client can extract the necessary information from representation by using relation type. We have just scratched the surface in learning HAL which is an invaluable tool in designing representations. By reading its specification and pocking around in the example application one can gain full grasp of the format. Two final notes: first, representations can be validated against schema using some on-line tool; second, list of libraries for working with HAL using various programming languages is here. References JSON Linking with HAL HAL – Hypertext Application Language JSON Hypertext Application Language An Interview with HAL Creator Mike Kelly
March 7, 2015
by Dmitry Noranovich
· 37,910 Views · 2 Likes
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Do it in Java 8: Recursive lambdas
Searching on the Internet about recursive lambdas, I found no relevant information. Only very old post talking about how to create recursive lambdas in ways that don't work with final version of Java8, or more recent (two years old only) post explaining this possibility had been remove from the JSR specification in October 2012, because “it was felt that there was too much work involved to be worth supporting a corner-case with a simple workaround.” Although it might have been removed from the spec, it is perfectly possible to create recursive lambdas. Lambdas are often used to create anonymous functions. This is not related to the fact that lambdas are implemented through anonymous classes. It is perfectly possible to create a named function, such as: UnaryOperator square = x -> x * x; We may then apply this function as: Integer x = square.apply(4); If we want to create a recursive factorial function, we might want to write: UnaryOperator factorial = x -> x == 0 ? 1 : x * factorial.apply(x - 1 ); But this won't compile because we can't use factorial while it is being defined. The compiler complains that “Cannot reference a field before it is defined”. One possible solution is to first declare the field, and then initialize it later, in the constructor, or in an initializer: UnaryOperator factorial; { factorial = i -> i == 0 ? 1 : i * factorial.apply( i - 1 ); } This works, but it is not very nice. There is however a much simpler way. Just add this. before the name of the function, as in: UnaryOperator factorial = x -> x == 0 ? 1 : x * this.factorial.apply(x - 1 ); Not only this works, but it even allows making the reference final: final UnaryOperator factorial = x -> x== 0 ? 1 : x * this.factorial.apply(x - 1 ); If you prefer a static field, just replace this with the name of the class: static final UnaryOperator factorial = x -> x== 0 ? 1 : x * MyClass.factorial.apply(x - 1 ); Interesting things to note: This function will silently produce an arithmetic overflow for factorial(26), producing a negative result. It will produce 0 for factorial(66) and over, until around 3000, where is will overflow the stack since recursion is implemented on the stack. If you try to find the exact limit, you may be surprised to see that it sometimes happens and sometimes not, for the same values. Try the following example: public class Test { static final UnaryOperator count = x -> x == 0 ? 0 : x + Test.count.apply(x - 1); public static void main(String[] args) { for (int i = 0;; i++) { System.out.println(i + ": " + count.apply(i)); } } } Here's the kind of result you might get: ... 18668: 174256446 18669: 174275115 18670: 174293785 18671: 174312456 Exception in thread "main" java.lang.StackOverflowError You could think that Java is able to handle 18671 level of recursion, but it is not. Try to call count(4000) and you will get a stack overflow. Obviously, Java is memoizing results on each loop execution, allowing to go much further than with a single call. Of course, it is possible to push the limits much beyond by implementing recursion on the heap through the use of trampolining. Another interesting point is that the same function implemented as a method uses much less stack space: static int count(int x) { return x == 0 ? 0 : x + count(x - 1); } This method overflows the stack only around count(6200).
March 6, 2015
by Pierre-Yves Saumont
· 35,568 Views · 3 Likes
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Why I Use OrientDB on Production Applications
Like many other Java developers, when i start a new Java development project that requires a database, i have hopes and dreams of what my database looks like: Java API (of course) Embeddable Pure Java Simple jar file for inclusion in my project Database stored in a directory on disk Faster than a rocket First I’m going to review these points, and then i’m going to talk about the database i chose for my latest project, which is in production now with hundreds of users accessing the web application each month. What I Want from My Database Here’s what i’m looking for in my database. These are the things that literally make me happy and joyous when writing code. Java API I code in Java. It’s natural for me to want to use a modern Java API for my database work. Embeddable My development productivity and programming enjoyment skyrocket when my database is embedded. The database starts and stops with my application. It’s easy to destroy my database and restart from scratch. I can upgrade my database by updating my database jar file. It’s easy to deploy my application into testing and production, because there’s no separate database server to startup and manage. (I know about the issue with clustering and an embedded database, but i’ll get to that.) Pure Java Back when i developed software that would be deployed on all manner of hardware, i was a stickler that all my code be pure Java, so that i could be confident that my code would run wherever customers and users deployed it. In this day of SaaS, i’m less picky. I develop on the Mac. I test and run in production on Linux. Those are the systems i care about, so if my database has some platform-specific code in it to make it run fast and well, i’m fine with that. Just as long as that platform-specific configuration is not exposed to me as the developer. Simple Jar File for Inclusion in My Project I really just want one database jar file to add to my project. And i don’t want that jar file messing with my code or the dependencies i include in my project. If the database uses Guava 1.2, and i’m using Guava 0.8, that can mess me up. I want my database to not interfere with jars that i use by introducing newer or older versions of class files that i already reference in my project’s jars. Database Stored in a Directory on Disk I like to destroy my database by deleting a directory. I like to run multiple, simultaneous databases by configuring each database to use a separate directory. That makes me super productive during development, and it makes it more fun for me to program to a database. Faster Than a Rocket I think that’s just a given. My Latest Project That Needs a Database My latest project is Floify.com. Floify is a Mortgage Borrower Portal, automating the process of collecting mortgage loan documents from borrowers and emailing milestone loan status updates to real estate agents and borrowers. Mortgage loan originators use Floify to automate the labor-intensive parts of their loan processes. The web application receives about 500 unique visitors per month. Floify experienced 28% growth in january 2015. Floify’s vital statistics are: 38,301 loan documents under management 3,619 registered users 3,113 loan packages under management The Database I Chose for My Latest Project When i started Floify, i looked for a database that met all the criteria i’ve described above. I decided against databases that were server-based (Postgres, etc). I decided against databases that weren’t Java-based (MongoDB, etc). I decided against databases that didn’t support ACID transactions. I narrowed my choices to OrientDB and Neo4j. It’s been a couple years since that decision process occurred, but i distinctly remember a few reasons why i ultimately chose OrientDB over Neo4j: Performance benchmarks for OrientDB were very impressive. The OrientDB development team was very active. Cost. OrientDB is free. Neo4j cost more than what i was willing to pay or what i could afford. I forget which it was. My Favourite OrientDB Features Here are some of my favourite features in OrientDB. These are not competitive advantages to OrientDB. It’s just some of the things that make me happy when coding against an embeddable database. I can create the database in code. I don’t have to use SQL for querying, but most of the time, i do. I already know SQL, and it’s just easy for me. I use the document database, and it’s very pleasant inserting new documents in Java. I can store multi-megabyte binary objects directly in the database. My database is stored in a directory on disk. When scalability demands it, i can upgrade to a two-server distributed database. I haven’t been there yet. Speed. For me, OrientDB is very fast, and in the few years i’ve been using it, it’s become faster. OrientDB doesn’t come in a single jar file, as would be my ideal. I have to include a few different jars, but that’s an easy tradeoff for me. Future In the future, as Floify’s performance and scalability needs demand it, i’ll investigate a multi-server database configuration on OrientDB. In the meantime, i’m preparing to upgrade to OrientDB 2.0, which was recently released and promises even more speed. Go speed. :-)
March 5, 2015
by Dave Sims
· 18,361 Views · 6 Likes
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