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JavaFX Table Cells: Interdependence and Dynamic Editability
The standard usage of JavaFX TableView with currently available set of cell controls, with all its indisputable merits, fails to meet a fairly important requirement, which usually arises when editable fields are mutually dependent. Generally it is easy enough to change values of all dependent fields when value of some field had been changed. The real problem emerges when a cell control, which is connected with a single data field (property), should become editable or not editable (having a fixed value) depending on values of some other fields (properties) of the same record (row data object). Besides, to spare user some useless clicks and confusion, it is important to make appearance of the cell reflect at least two conditions: editable/not editable and within/outside of the selected row. To make it work, there is no need to subclass anything but table-cell controls. While it is possible to subclass TableCell and recreate all the specific cell controls, it is much easier to subclass each particular cell control, although it does lead to some minor duplication of code. To insure that all our custom cells refer to the same style definitions in a CSS stylesheet, let's share style constants in an interface: public interface IDeCell { public static final String CLASS_DE_CELL = "de-cell"; public static final String PSEUDO_CLASS_NOT_EDITABLE = "not-editable"; public static final String PSEUDO_CLASS_ROW_SELECTED = "row-selected"; } Prefix "de" stands for Dynamic Editability. We are going to prefix our custom cell extensions with "De" and refer to such cells as "de-cells". Here is source code of custom TextFieldTableCell extension: public class DeTextFieldTableCell extends TextFieldTableCell implements IDeCell { private static final PseudoClass NOT_EDITABLE_PSEUDO_CLASS = PseudoClass.getPseudoClass(PSEUDO_CLASS_NOT_EDITABLE); private static final PseudoClass ROW_SELECTED_PSEUDO_CLASS = PseudoClass.getPseudoClass(PSEUDO_CLASS_ROW_SELECTED); public BooleanProperty notEditableProperty() { return notEditable; } public final boolean isNotEditable() { return notEditableProperty().get(); } private final BooleanProperty notEditable = new SimpleBooleanProperty(this, PSEUDO_CLASS_NOT_EDITABLE, false) { @Override protected void invalidated() { pseudoClassStateChanged(NOT_EDITABLE_PSEUDO_CLASS, get()); } }; public final BooleanProperty rowSelectedProperty() { return rowSelected; } public final boolean isRowSelected() { return rowSelectedProperty().get(); } private final BooleanProperty rowSelected = new SimpleBooleanProperty(this, PSEUDO_CLASS_ROW_SELECTED, false) { @Override protected void invalidated() { pseudoClassStateChanged(ROW_SELECTED_PSEUDO_CLASS, get()); } }; public SimpleObjectProperty recordProperty() { return record; } private SimpleObjectProperty record = new SimpleObjectProperty<>(); public DeTextFieldTableCell(StringConverter converter) { super(converter); getStyleClass().add(CLASS_DE_CELL); notEditable.bind(editableProperty().not()); tableRowProperty().addListener((ov, vOld, vNew)-> { record.unbind(); rowSelected.unbind(); if (vNew != null) { record.bind(vNew.itemProperty()); rowSelectedProperty().bind(vNew.selectedProperty()); } }); } } DeTextFieldTableCell and all other de-cell controls share identical lines of code which define Boolean properties "rowSelected" and "notEditable" and respective custom CSS pseudo-classes "row-selected" and "not-editable". Additionally, de-cells expose row object (record) with "record" property. Code, which makes editability and appearance of a cell depend on values of one or more properties of the "record" has to look like this: cell.recordProperty().addListener((ov, r0, r) -> { cell.editableProperty().unbind(); if (r != null) { cell.editableProperty().bind(r.someProperty().and(r.otherProperty())); } }); Here is sample CSS for de-cells: .de-cell:filled:not-editable { -fx-background-color: #cccccc; -fx-text-fill: #0000ff; -fx-border-width: 1px 0px 0px 1px; -fx-border-color: #eeeeee } .de-cell:filled:row-selected { -fx-background-color: skyblue; -fx-text-fill: black; -fx-border-width: 1px 0px 0px 1px; -fx-border-color: #eeeeee } .de-cell:filled:not-editable:row-selected { -fx-background-color: #999999; -fx-text-fill: #0000ff } The example below is a taken (with some simplification) from the existing working application. There is a system, which allows customers to buy online prints and file downloads. All general, non-specific files, which are accessible by general customers, have preassigned General Prices: price of 1 printed copy for printable and download price for not printable ones. Besides, there are special, tailor-made files, which are not accessible by general customers and don't have General Prices. A customer can fetch products himself (with General Price only), or some products can be pushed to him by a sales manager - with General Price or (relatively reduced) Special Price. In the first case number of the printed copies to be chosen by the customer, whereas in the last case number of printed copies has to be limited by the sales manager. In case of special, tailor-made files Special Price has to be assigned. Number of copies (quantity) for the file download is always equal 1. At some point a sales manager collects all products (both general and tailor-made) to be pushed to a customer and has to edit collected entries. He/She can change any general price to a special one, allow download of a printable product, edit Special prices and printed copies (quantities). There are following dependencies: 1. General/Special choice is enabled for the general products only. 2. Print/Download choice is enabled for printable products when Special Price is selected. 3. Price is editable when Special Price is selected, otherwise it has to be equal to preassigned General Price. 4. Quantity is editable when Special Price and Print Service are selected. When Download selected quantity is fixed and equals 1, when General and Print selected quantity is null (to be chosen by customer). For example, for a sample product "*A: General Print" the only editable dynamic field is "Price Type". If a user changes Price Type to Special then Service Type, Item Price and Quantity become editable. If a user changes Service Type to Download then Quantity become not editable (and set to 1). Here is source code for data object Entry: public class Entry { private final IntegerProperty entryId = new SimpleIntegerProperty(); private final StringProperty name = new SimpleStringProperty(); private final BooleanProperty printable = new SimpleBooleanProperty(); private final BooleanProperty useGeneralPrice = new SimpleBooleanProperty(); private final BooleanProperty usePrintService = new SimpleBooleanProperty(); private final ObjectProperty generalPrice = new SimpleObjectProperty<>(); private final ObjectProperty price = new SimpleObjectProperty<>(); private final ObjectProperty quantity = new SimpleObjectProperty<>(); private final ObjectProperty totalPrice = new SimpleObjectProperty<>(); public IntegerProperty entryIdProperty() { return entryId; } public StringProperty nameProperty() { return name; } public BooleanProperty printableProperty() { return printable; } public BooleanProperty useGeneralPriceProperty() { return useGeneralPrice; } public BooleanProperty usePrintServiceProperty() { return usePrintService; } public ObjectProperty generalPriceProperty() { return generalPrice; } public ObjectProperty priceProperty() { return price; } public ObjectProperty quantityProperty() { return quantity; } public ObjectProperty totalPriceProperty() { return totalPrice; } public Entry(int aEntryId, String aName, BigDecimal aGeneralPrice, boolean aPrintable) { entryId.set(aEntryId); name.set(aName); printable.set(aPrintable); useGeneralPrice.set(aGeneralPrice != null); usePrintService.set(aPrintable); generalPrice.set(aGeneralPrice); price.set(aGeneralPrice); quantity.set((aPrintable) ? null : 1); totalPrice.bind(new ObjectBinding() { { super.bind(price, quantity); } @Override protected Number computeValue() { return (price.get() == null || quantity.get() == null) ? null : price.get().doubleValue()*quantity.get(); } }); } } Here is an excerpt from the sample app - creation of table columns with de-cells: TableColumn priceTypeCol = new TableColumn<>("Price Type"); priceTypeCol.setPrefWidth(60); priceTypeCol.setCellValueFactory(new PropertyValueFactory<>("useGeneralPrice")); priceTypeCol.setCellFactory((tableColumn) -> { DeComboBoxTableCell cell = new DeComboBoxTableCell<>( new ABooleanConverter(PRICE_TYPES[0], PRICE_TYPES[1]), true, false); cell.recordProperty().addListener((ov, vOld, vNew) -> { cell.editableProperty().unbind(); if (vNew != null) cell.editableProperty().bind(vNew.generalPriceProperty().isNotNull()); }); return cell; }); priceTypeCol.setOnEditCommit((ev) -> { Entry entry = ev.getRowValue(); Boolean value = ev.getNewValue(); entry.useGeneralPriceProperty().set(value); if (value) { entry.priceProperty().set(entry.generalPriceProperty().get()); entry.usePrintServiceProperty().set(entry.printableProperty().get()); } entry.quantityProperty().set( (value && entry.usePrintServiceProperty().get()) ? null : 1); }); //============================================================== TableColumn serviceTypeCol = new TableColumn<>("Service Type"); serviceTypeCol.setPrefWidth(60); serviceTypeCol.setCellValueFactory(new PropertyValueFactory<>("usePrintService")); serviceTypeCol.setCellFactory((tableColumn) -> { DeComboBoxTableCell cell = new DeComboBoxTableCell<>( new ABooleanConverter(SERVICE_TYPES[0], SERVICE_TYPES[1]), true, false); cell.recordProperty().addListener((ov, vOld, vNew) -> { cell.editableProperty().unbind(); if (vNew != null) { cell.editableProperty().bind(vNew.useGeneralPriceProperty().not() .and(vNew.printableProperty())); } }); return cell; }); serviceTypeCol.setOnEditCommit((ev) -> { Entry entry = ev.getRowValue(); Boolean value = ev.getNewValue(); entry.usePrintServiceProperty().set(value); entry.quantityProperty().set(1); }); //============================================================== TableColumn priceCol = new TableColumn<>("Item Price"); priceCol.setPrefWidth(50); priceCol.setCellValueFactory(new PropertyValueFactory<>("price")); priceCol.setCellFactory((tableColumn) -> { DeTextFieldTableCell cell = new DeTextFieldTableCell<>( new AMoneyConverter()); cell.setAlignment(Pos.CENTER_RIGHT); cell.recordProperty().addListener((ov, vOld, vNew) -> { cell.editableProperty().unbind(); if (vNew != null) cell.editableProperty().bind(vNew.useGeneralPriceProperty().not()); }); return cell; }); priceCol.setOnEditCommit((ev) -> { ev.getRowValue().priceProperty().set(ev.getNewValue()); }); //============================================================== TableColumn quantityCol = new TableColumn<>("Quantity"); quantityCol.setPrefWidth(40); quantityCol.setCellValueFactory(new PropertyValueFactory<>("quantity")); quantityCol.setCellFactory((tableColumn) -> { DeTextFieldTableCell cell = new DeTextFieldTableCell<>( new IntegerStringConverter()); cell.setAlignment(Pos.CENTER); cell.recordProperty().addListener((ov, vOld, vNew) -> { cell.editableProperty().unbind(); if (vNew != null) { cell.editableProperty().bind(vNew.useGeneralPriceProperty().not() .and(vNew.usePrintServiceProperty())); } }); return cell; }); priceCol.setOnEditCommit((ev) -> { ev.getRowValue().priceProperty().set(ev.getNewValue()); }); //============================================================== TableColumn totalPriceCol = new TableColumn<>("Total Price"); totalPriceCol.setPrefWidth(50); totalPriceCol.setCellValueFactory(new PropertyValueFactory<>("totalPrice")); totalPriceCol.setCellFactory((tableColumn) -> { DeTextFieldTableCell cell = new DeTextFieldTableCell<>( new AMoneyConverter()); cell.setAlignment(Pos.CENTER_RIGHT); cell.setEditable(false); return cell; }); totalPriceCol.setEditable(false); --
July 2, 2015
by Felix Golubov
· 13,044 Views
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Using Liquibase Without a Database Connection
There are many, many different processes and requirements companies have for managing their database schemas. Some allow the application to directly manage them on startup, some require SQL scripts be executed by hand. Some have schemas that can differ across customers, some have only one database to deal with. For people who prefer to execute SQL themselves, Liquibase has always supported an “updateSQL” mode which does not update the database but instead outputs what would be run. This allows developers and DBAs to know exactly what will be ran and even make modifications as needed before actually executing the script. Before version 3.2, however, Liquibase required an active database connection for updateSQL. It used that connection to determine the SQL dialect to use and to query the DATABASECHANGELOG table to learn what changeSets have already been executed. Controlling updateSql SQL Syntax With version 3.2, Liquibase added a new “offline” mode. Instead of specifying a jdbc url such as “jdbc:mysql://localhost/lbcat” you can use “offline:mysql” or “offline:postgresql” which lets Liquibase know what dialect to use. For finer dialect control, you can specify parameters like “offline:mysql?version=3.4&caseSensitive=false Available dialect parameters: version: Standard X.Y.Z version of the database productName: String description of the database, like the JDBC driver would return catalog: String containing the name of the default top-level container ('database' in some databases 'schema' in others) caseSensitive: Boolean value specifying if the database is case sensitive or not Tracking History With CSV These parameters let Liquibase know what SQL to generate for each changeSet, but without an active database connection you cannot rely on the DATABASECHANGELOG table to track what changeSets have already been ran. Instead, offline mode uses a CSV file which mimics the structure of the DATABASECHANGELOG table. By default, Liquibase will use a file called “databasechangelog.csv” in the working directory, but it can be specified with a “changeLogFile” parameter such as “offline:mssql?changeLogFile=path/to/file.csv” It is up to you to ensure that the contents of the csv file match what is in the database. Running updateSQL automatically appends to the CSV file under the assumption that you will apply the SQL to the database. Since the csv file matches a particular database, it isn’t something you normally would store or share under version control because every database can (and probably will) be in a different state. If you do store the files in a central location, you will probably want to at least have a separate file for each database. By default, the SQL generated by updateSql in offline mode will still contain the standard DATABASECHANGELOG insert statements, so each database that you apply the SQL to will still have a correct DATABASECHANGELOG table. This means that you can switch between a direct-connection update and offline updateSQL as needed. It also means that you can also extract the current contents of the DATABASECHANGELOG table to a CSV file and use that as the file passed to the offline connection to ensure you have the right contents in the file. If you do not want the DATABASECHANGELOG table SQL included in updateSQL output, there is an “outputLiquibaseSql” parameter which can be passed in your offline url. Possible outputLiquibaseSql values: "none" will output no DATABASECHANGELOG statements "data_only" will output only INSERT INTO DATABASECHANGELOG statements "all" will output CREATE TABLE DATABASECHANGELOG if the csv file does not exist as well as INSERT statements (default value) Offline Snapshots The new 3.4.0 release of Liquibase expands offline support with a new “snapshot” parameter which can be passed to the offline url pointing to a saved database structure. Liquibase will use the snapshot anywhere it would have normally needed to read the current database state. This allows you to use preconditions and perform diff and diffChangeLog operations without an active connection and even between snapshots of the same database from different points in time. To create a snapshot of your live databases, use the “—snapshotFormat=json” parameter on the “snapshot” command. Command line example: $ liquibase --url=jdbc:mysql://localhost/lbcat snapshot --snapshotFormat=json > snapshot.json or $ liquibase --url=jdbc:mysql://localhost/lbcat –outputFile=path/to/output.json snapshot --snapshotFormat=json NOTE: currently only “json” is supported as a snapshotFormat. You can then use that file with your offline url and any snapshot operations will use it as the database state. liquibase –url=jdbc:mysql://localhost/lbcat –referenceUrl=offline:mysql?snapshot=path/to/snapshot.json diff will compare the stored snapshot with the current database state liquibase –url=offline:mysql?snapshot=path/to/snapshot.json diff –referenceUrl=offline:mysql?snapshot=path/to/older-snapshot.json diff will compare two snapshots liquibase –url=offline:mysql?snapshot=path/to/snapshot.json generateChangeLog will generate a changelog based on what is in the snapshot liquibase –url=jdbc:mysql://localhost/lbcat –referenceUrl=offline:mysql?snapshot=path/to/snapshot.json diffChangeLog will generate a changelog based on what is new in the real database compared to what is in the snapshot.
July 2, 2015
by Nathan Voxland
· 10,862 Views
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Interoute Virtual Data Centre is the fastest transatlantic cloud service
Double the throughput and lower latency than the leading global cloud providers between the US and Europe in independent comparison research London & New York, 1 July, 2015. Interoute has today announced that its global cloud platform Interoute Virtual Data Centre (VDC), has been proven to deliver nearly double the throughput across the Atlantic than the next best cloud provider in comparison research conducted by Cloud Spectator. The research from March 2015 compared Interoute VDC with three leading cloud providers (Amazon AWS, Rackspace and Microsoft Azure), testing network throughput and latency between Europe and USA and between providers' European data centres. In all of the comparisons, Interoute VDC demonstrated the highest throughputs and lowest latencies. Cloud Spectator's full research report, and more information about Interoute VDC's performance and features, can be viewed here: http://bit.ly/1GHyzwJ Network performance is a significant factor in cloud computing for business services requiring the highest network capacity (throughput) and the shortest possible time from the server to the client (latency), to meet the needs of the businesses and their users. Innovating new applications and business services in the cloud needs network performance to match and this report shows the advantages of building the cloud into a huge global high performance network. Key research findings: Transatlantic: Interoute VDC delivered 1.1 Gbit/s throughput, which was 96% better than Amazon AWS, 141% better than Rackspace, and 195% better than Microsoft Azure. Interoute VDC had the lowest latency, between its London and New York data centres. Interoute was the only provider in the comparison with both of its transatlantic data centres located in key business cities, meaning that VDC users can access compute and storage resources, and deliver data to their customers, from two centres of European and US business activity. Within Europe: Interoute VDC achieved 1.3 Gbit/s throughput between its London and Amsterdam data centres. This was 52% better than Amazon AWS (Dublin - Frankfurt) and 73% better than Microsoft Azure (Dublin - Amsterdam) Interoute VDC achieved a latency of 6 milliseconds between London and Amsterdam, over three times better than the inter-data centre latency of the comparison providers. Matthew Finnie, CTO of Interoute, commented: "This independent report confirms and validates our networked cloud strategy. Building cloud into a world class network provides our customers with significantly better performance when compared with the traditional cloud models. Businesses looking to grow between Europe and US should definitely be looking at the importance of these network characteristics for their ability to shift workloads into the cloud. Interoute's fourteen global zones are all built into high performance network with over 300 interconnects in Europe alone. So wherever you choose to put your data and connect to us, your services are typically going to perform faster on Interoute than on many other global providers." Danny Gee, Senior Analyst, Cloud Spectator: "Users want to transfer large amounts of data between data centres quickly. Our study revealed that for a trans-Atlantic connection between cloud data centers, Interoute provided the highest throughput and lowest latency out of AWS, Rackspace and Azure. Interoute also had the higher network throughput and lowest latency in European testing compared to Azure and AWS (Rackspace was excluded, having only one location in Europe), making it a good option for users operating servers within this region. Interoute also provided the best latency, ideal for real-time communications. Users running geographically dispersed environments for such things as geo-redundancy would benefit from Interoute's high performance cloud connectivity."
July 1, 2015
by Fran Cator
· 1,154 Views
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Learning Spring-Cloud - Writing a Microservice
Continuing my Spring-Cloud learning journey, earlier I had covered how to write the infrastructure components of a typical Spring-Cloud and Netflix OSS based micro-services environment - in this specific instance two critical components, Eureka to register and discover services and Spring Cloud Configuration to maintain a centralized repository of configuration for a service. Here I will be showing how I developed two dummy micro-services, one a simple "pong" service and a "ping" service which uses the "pong" service. Sample-Pong microservice The endpoint handling the "ping" requests is a typical Spring MVC based endpoint: @RestController public class PongController { @Value("${reply.message}") private String message; @RequestMapping(value = "/message", method = RequestMethod.POST) public Resource pongMessage(@RequestBody Message input) { return new Resource<>( new MessageAcknowledgement(input.getId(), input.getPayload(), message)); } } It gets a message and responds with an acknowledgement. Here the service utilizes the Configuration server in sourcing the "reply.message" property. So how does the "pong" service find the configuration server, there are potentially two ways - directly by specifying the location of the configuration server, or by finding the Configuration server via Eureka. I am used to an approach where Eureka is considered a source of truth, so in this spirit I am using Eureka to find the Configuration server. Spring Cloud makes this entire flow very simple, all it requires is a "bootstrap.yml" property file with entries along these lines: --- spring: application: name: sample-pong cloud: config: discovery: enabled: true serviceId: SAMPLE-CONFIG eureka: instance: nonSecurePort: ${server.port:8082} client: serviceUrl: defaultZone: http://${eureka.host:localhost}:${eureka.port:8761}/eureka/ The location of Eureka is specified through the "eureka.client.serviceUrl" property and the "spring.cloud.config.discovery.enabled" is set to "true" to specify that the configuration server is discovered via the specified Eureka server. Just a note, this means that the Eureka and the Configuration server have to be completely up before trying to bring up the actual services, they are the pre-requisites and the underlying assumption is that the Infrastructure components are available at the application boot time. The Configuration server has the properties for the "sample-pong" service, this can be validated by using the Config-servers endpoint - http://localhost:8888/sample-pong/default, 8888 is the port where I had specified for the server endpoint, and should respond with a content along these lines: "name": "sample-pong", "profiles": [ "default" ], "label": "master", "propertySources": [ { "name": "classpath:/config/sample-pong.yml", "source": { "reply.message": "Pong" } } ] } As can be seen the "reply.message" property from this central configuration server will be used by the pong service as the acknowledgement message Now to set up this endpoint as a service, all that is required is a Spring-boot based entry point along these lines: @SpringBootApplication @EnableDiscoveryClient public class PongApplication { public static void main(String[] args) { SpringApplication.run(PongApplication.class, args); } } and that completes the code for the "pong" service. Sample-ping micro-service So now onto a consumer of the "pong" micro-service, very imaginatively named the "ping" micro-service. Spring-Cloud and Netflix OSS offer a lot of options to invoke endpoints on Eureka registered services, to summarize the options that I had: 1. Use raw Eureka DiscoveryClient to find the instances hosting a service and make calls using Spring's RestTemplate. 2. Use Ribbon, a client side load balancing solution which can use Eureka to find service instances 3. Use Feign, which provides a declarative way to invoke a service call. It internally uses Ribbon. I went with Feign. All that is required is an interface which shows the contract to invoke the service: package org.bk.consumer.feign; import org.bk.consumer.domain.Message; import org.bk.consumer.domain.MessageAcknowledgement; import org.springframework.cloud.netflix.feign.FeignClient; import org.springframework.http.MediaType; import org.springframework.web.bind.annotation.RequestBody; import org.springframework.web.bind.annotation.RequestMapping; import org.springframework.web.bind.annotation.RequestMethod; import org.springframework.web.bind.annotation.ResponseBody; @FeignClient("samplepong") public interface PongClient { @RequestMapping(method = RequestMethod.POST, value = "/message", produces = MediaType.APPLICATION_JSON_VALUE, consumes = MediaType.APPLICATION_JSON_VALUE) @ResponseBody MessageAcknowledgement sendMessage(@RequestBody Message message); } The annotation @FeignClient("samplepong") internally points to a Ribbon "named" client called "samplepong". This means that there has to be an entry in the property files for this named client, in my case I have these entries in my application.yml file: samplepong: ribbon: DeploymentContextBasedVipAddresses: sample-pong NIWSServerListClassName: com.netflix.niws.loadbalancer.DiscoveryEnabledNIWSServerList ReadTimeout: 5000 MaxAutoRetries: 2 The most important entry here is the "samplepong.ribbon.DeploymentContextBasedVipAddresses" which points to the "pong" services Eureka registration address using which the service instance will be discovered by Ribbon. The rest of the application is a routine Spring Boot application. I have exposed this service call behind Hystrix which guards against service call failures and essentially wraps around this FeignClient: package org.bk.consumer.service; import com.netflix.hystrix.contrib.javanica.annotation.HystrixCommand; import org.bk.consumer.domain.Message; import org.bk.consumer.domain.MessageAcknowledgement; import org.bk.consumer.feign.PongClient; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.beans.factory.annotation.Qualifier; import org.springframework.stereotype.Service; @Service("hystrixPongClient") public class HystrixWrappedPongClient implements PongClient { @Autowired @Qualifier("pongClient") private PongClient feignPongClient; @Override @HystrixCommand(fallbackMethod = "fallBackCall") public MessageAcknowledgement sendMessage(Message message) { return this.feignPongClient.sendMessage(message); } public MessageAcknowledgement fallBackCall(Message message) { MessageAcknowledgement fallback = new MessageAcknowledgement(message.getId(), message.getPayload(), "FAILED SERVICE CALL! - FALLING BACK"); return fallback; } } Boot"ing up I have dockerized my entire set-up, so the simplest way to start up the set of applications is to first build the docker images for all of the artifacts this way: mvn clean package docker:build -DskipTests and bring all of them up using the following command, the assumption being that both docker and docker-compose are available locally: docker-compose up Assuming everything comes up cleanly, Eureka should show all the registered services, at http://dockerhost:8761 url - The UI of the ping application should be available at http://dockerhost:8080 url - Additionally a Hystrix dashboard should be available to monitor the requests to the "pong" app at this url http://dockerhost:8989/hystrix/monitor?stream=http%3A%2F%2Fsampleping%3A8080%2Fhystrix.stream: References 1. The code is available at my github location - https://github.com/bijukunjummen/spring-cloud-ping-pong-sample 2. Most of the code is heavily borrowed from the spring-cloud-samples repository - https://github.com/spring-cloud-samples
July 1, 2015
by Biju Kunjummen
· 13,685 Views · 4 Likes
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Gene Kim Explains ‘Why DevOps Matters’
Ever wonder why DevOps gets so much attention these days? The answer is simple: “DevOps solves the most important business problem of our generation, [which is] how organizations make the transition from good to great.” That’s according to Gene Kim, co-author of The Phoenix Project, founder of Tripwire, and a DevOps advocate. Gene headlined a New Relic DevOps roadshow with stops in Chicago, Dallas, and Houston last month, regaling attendees with the inside scoop of what DevOps really is, what it does, and how to make it work (more on that in upcoming blog posts). But perhaps his most important point was the overwhelming importance of the effort. Traditional IT leads to “hopelessness and despair” According to Gene, the opportunity cost of wasted IT spending is some $2.6 trillion. These days, he says, “every company is an IT company”—we like to say “every company is a software company,” but you get the message. Gene observes that 95% of all capital projects have an IT component and 50% of all capital spending is technology related. And every IT organization is pressured to simultaneously respond more quickly to urgent business needs while also providing stable, secure, and predictable IT service. That chronic conflict created what Gene described as “a horrible downward spiral that leads to horrendous outcomes. Every time we cut corners, or manually deploy code, or write code that doesn’t have automated testing, it all leads to the accumulation of technical debt.” And the ever-increasing amount of technical debt sets the stage for intertribal warfare that can exist between dev and ops. Those wars mean that “Devs submit code at 5 p.m. on Friday, and ops then works all weekend to deploy it by 9 a.m. Monday. Everyone becomes buried in unplanned work, and this deprives our ability to pay down the technical debt being created. This led to hopelessness and despair, with everyone doomed to repeat the same mistakes.” DevOps offers a better way Fortunately, Gene explained, “We know now there is a better way. The DevOps exemplars have shown us that we can have incredibly fast flow from dev to ops to deployment while preserving world-class quality and security.” According to Gene, the top predictors of IT performance are all associated with DevOps: Version control of all production artifacts Continuous integration and deployment Automated acceptance testing Peer-review of production changes (vs. external change approval) High-trust culture Proactive monitoring of the production environment Win-win relationship between dev and ops Lead time is the key metric Lead time from raw material to finished product is the key metric in manufacturing, “and that’s true for software, too,” Gene said. “How long does it take to go from code committed to code successfully running in production?” The standard 9-month software lead time common in waterfall development projects is “highly correlated with catastrophic deployment errors,” Gene warned. The key, he said, is to have smaller deployments, and to do them more frequently. That approach is already working for high-performing organizations, he added, who are accelerating away from the herd. “Ten deploys a day used to be startling,” Gene noted. “Now it’s probably considered merely average among high performers.” Amazon Web Services deploys every 11.6 seconds! That kind of speed is possible only by doing small deployments more frequently, Gene said. “The bigger the change, the bigger the crater when it hits.” DevOps correlates with business success! IT high-performers who incorporate DevOps are much more agile and more reliable, Gene said. Critically, he added, “They are more likely to win in the marketplace!” The common reaction to that statement is shock. Gene noted he often hears: “That’s absurd! How can IT ops practices be visible on the bottom line or in the stock price?” But the Puppet Labs 2014 State of DevOps report noted that IT high-performers are twice as likely to exceed profitability, market share, and productivity goals as well as enjoy 50% higher market capitalization growth over three years. Of course, that doesn’t mean all those good things will happen to your company just by moving to DevOps. But do you really want to risk the “horrendous outcomes” of staying with outmoded models that lead to excruciatingly long deployment cycles?
July 1, 2015
by Fredric Paul
· 1,929 Views
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DevOps Tools for Continuous Delivery: Workloads Distribution and Jenkins Installation
the vast majority of software development companies have to place a great emphasis on the process of continuous integration and rapid delivery of new versions of their product. obviously, when supplying enterprise-level projects, such processes need to be automated as much as possible. and this is when the cloud devops tools come in handy. thus, in today’s article we’d like to pay a special attention to the devops tools that automate the continuous integration and delivery within the jelastic paas that can be installed on any bare metal or cloud infrastructure as virtual private cloud or hybrid cloud. this is a pretty complex example of enterprise application life cycle with continuous integration and seamless migration throughout devops pipeline from development to several productions (you can use simplified process if you have less complex project ). the instruction below will be useful for jelastic cluster administrators such as systems integrators, hosting service providers, enterprises, and isv customers, who can easily implement it at their jelastic cloud installations. nevertheless, this guide contains plenty of features and continuous integration tips described, which can be interesting for different developers. so, let’s get started with the first part of the instruction! setting up dedicated user groups first of all, you need to allocate separate hardware sets for all your project teams (one per each development phase, i.e. development > testing > production ) and adjust the access permissions to make them completely isolated and not influenced by others. the multi-regions for a hybrid cloud option, that became available within the recently released jelastic 3.3 version , is optimally suited for this task. to start with, create three hardware node groups (within one region) and name them after the corresponding stages for more convenience (e.g. dev , test , production ). the next step is to prepare three user groups and attach them to the corresponding hardware – in our case the dev group has access to the dev hardware node group only, qa – to the test one, and ops should work specifically with the production set. in such a way, users from the appropriate groups can use the specified sets of hardware only, but at the same time – they have a possibility to transfer their environments throughout the whole platform, between different teams’ accounts. jenkins continuous integration server configuration now we need the integration tool, that will control and perform all of the required operations automatically, i.e. build the cloud devops pipeline. our choice fell on jenkins as one of the most popular solutions used for this goal – it can be easily installed from our marketplace either at the corresponding site page or directly via the dashboard . as a result, you’ll get the pure jenkins installed, which should be properly adjusted before you start organizing your application life cycle. thus, select the open in browser button and proceed with the following configurations steps: while at the home page, click on the manage jenkins option at the left-hand menu and select the manage plugins link within the appeared list. after you’ve been redirected to the plugin manager, switch to the available tab, find the following plugins using the search filter field above and tick them for installation: git plugin – is required for building our project’s source (stored at the github repository) envfile plugin – is used for storing system environment variables (its necessity is driven by security restrictions, implemented at jelastic, which forbid the direct exporting of environment variables from the tomcat server) click install without restart when ready. during the installation process, tick the restart jenkins when installation is complete and no jobs are running option to automatically restart jenkins for enabling the chosen plugins. then, you also need to install maven, which will be used for building the project. for that, navigate to the manage jenkins > configure system menu, scroll down to the maven section and click add maven. within the expanded section, type the desired name for your maven installation (e.g. maven ) and save the changes using the same-named button at the bottom of the page. in such a way, this tool will be also automatically installed when required (i.e. during the first app build). now your jenkins server is well-staffed for the further work. add deployment process scripts to the jenkins container the next step is to upload the scripts that you are going to use for automating different organizational actions, required to be applied to your application at the intermediate development life cycle phases (like deploying, placing it to the appropriate hardware according to the stage, running auto-test, etc). the easiest way to do this is to access your jenkins container via the jelastic ssh-gateway. in the case you haven’t performed similar operations before, you need to: generate an ssh keypair add your public ssh key to the dashboard access your account via ssh protocol once inside, create a new folder for your project (we’ll use demo ) and move in there: mkdir /opt/tomcat/demo cd /opt/tomcat/demo this location can be used for storing your scripts, variables, logs etc. here, you can upload the required scripts using the command of the following type: curl -fssl {link_to_script} -o {file_name} we also provide the set of script examples, which can be used as templates for your own ones: install.sh – gets a user session and creates a new environment via the jelastic api according to the specified manifest file. it also defines, that the name of this environment will be equal to its creation date and time (as a unique name is required for every script execution, but you won’t be able to set it manually as this operation would be run automatically). however, you can set your own dynamic name pattern to be used here transfer.sh – changes the environment ownership based on the jelastic environment transferring feature migrate.sh – physically moves an environment to another hardware set (hardnode group) note: that before the appliance, each of the script templates, presented above, have to be additionally adjusted to make them work properly within a particular jelastic installation. thus, the list of parameters below should be obligatory substituted according to your platform’s settings: /path/to/scripts/ – the full path to your scripts folder (created in the previous step) {cloud_domain} – your jelastic platform domain name {jca_dashboard_appid} – your dashboard id, that could be seen within the platform.dashboard_appid parameter at the jca > about section {jca_appstore_appid} – appstore id, listed within the same section (at the platform.appstore_appid parameter) {url_to_manifest} – link to the manifest file created according to our documentation (you may also use this one as an example – it sets up two tomcat application servers with the nginx load-balancer in front of them) note: above you can see one more runtest.sh script uploaded – it simulates the testing activities for demonstration purposes, thus we don’t provide its code in this tutorial. if required, create your own one according the specifics of your application and upload it alongside the rest of the scripts. in addition, you need to create a separate file for storing the variable with environment name (as it needs to be dynamically changed each time a new environment is created): echo env_name= > /opt/tomcat/demo/variables these are the main steps of preparation to achieve automatic continuous integration and delivery of your web application with a help of jenkins within jelastic cloud platform. in the second part of these blog series, we’ll configure the set of jobs at the jenkins server, which represents the core of our automation. each of them will be devoted for a particular operation, required to be run at the corresponding application life cycle phase: create environment > build and deploy > dev tests > migrate to qa > qa tests > migrate to production stay tuned to see the next steps. if you still don’t have jelastic installation, contact us to get access to our free demo for cloud platform evaluation or just start with trial registration at one of our hosting partners .
June 30, 2015
by Tetiana Markova
· 3,159 Views · 1 Like
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Enterprises Signing Up For More Than One New Cloud Service Every Day
No weekend downtime for cloud risk in Europe warns Skyhigh Networks in new report Report: info.skyhighnetworks.com/rs/274-AUP-214/images/WP-Cloud-Adoption-and-Risk-Report-Q2-EU.pdf LONDON - 30 JUNE 2015 - European enterprises are adding new cloud services at a rapid rate, finds a new report from Skyhigh Networks, the cloud security and enablement company. According to The Cloud Adoption and Risk Report for Q2 2015 - based on real data from over 2.5 million European employees across 12,000 cloud services - the average number of services has increased by more than 365 in a year. More than ten percent of cloud activity takes place outside the Monday-Friday working week, as overall cloud use continues to surge. Key findings of the report include: The average European enterprise now uses 897 cloud services (and a minimum of 507 services), up 61% from a year ago. Cloud usage never stops - with over 14% of traffic taking place at weekends 72.1% of European organisations have exposure to compromised credentials and 8.5% of employees have at least one compromised credential for sale on the darknet Security measure adoption: Only 15.4% of cloud services support multi-factor authentication, 9.4% encrypt data at rest and 2.8% support ISO 27001 64.9% of cloud services are not safe for EU data Enabled by cloud computing, flexible working has had a huge impact on the enterprise and the amount of work conducted out of office hours and on weekends. Skyhigh Networks analysed cloud usage by day of the week and found that weekend usage did not drop to zero, with Saturdays and Sundays representing 6.8% and 7.8% of weekly cloud usage respectively. IT departments therefore need to consider that enterprise exposure to cloud-related security risks is not tied to traditional office hours. The report also found that cloud adoption in Europe continues to grow, with the average European enterprise now using 897 services, 61% more than the same quarter in 2014. Indeed, from its database, Skyhigh found that the minimum number of services in use by a single organisation is 507 (for a company with less than 200 employees) and the highest is more than 3,000. "European business use of cloud is at an all-time high," said Nigel Hawthorn, European spokesperson for Skyhigh Networks. "Companies are adding a new cloud service to their network each day and it won't be long until the average organisation is using well over 1,000 distinct services. While cloud services offer clear agility gains for the enterprise, the situation is by no means perfect. Too few cloud services are suited to enterprise use. The vast majority fall at the first hurdle: failure to adopt the right IT security features or adhere to the EU Data Protection regulations." The report investigated current cloud service security capabilities. Just 7% of the 12,000 cloud services analysed meet enterprise security and compliance requirements, as rated by Skyhigh's CloudTrust Program. Only 15.4% support multi-factor authentication, 2.8% have ISO 27001 certification, and 9.4% encrypt data stored at rest. More worrying still, Skyhigh founds that 72.1% of European organisations have exposure to compromised credentials and 8.5% of employees at European companies have at least one compromised credential for sale on the darknet. "While security measures are increasingly being introduced by CSPs, our findings that no user is the same, combined with the fact that each enterprise is using an additional cloud service each day, demonstrates the sheer complexity of the issue. IT departments need to get their heads out of the clouds - or perhaps in it - and take an active role in ensuring their enterprise isn't weighed down by risks, poorly thought out cloud usage policies, and blanket bans. In a nutshell, the potential rewards of a cloud-enabled business are just too good for an apathetic approach to cloud," concluded Hawthorn.
June 30, 2015
by Fran Cator
· 835 Views
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Cornerstone OnDemand and TED join forces to spark innovation in professional learning and development
Curated TED Talk playlists integrated within Cornerstone Learning enable organisations to instantly access new, innovative ideas and share knowledge across their workforce June 30, 2015 - Cornerstone OnDemand, a global leader in cloud-based talent management software solutions, today announced the company is teaming with TED, the non-profit global community devoted to spreading ideas, to deliver curated TED Talks to Cornerstone clients for a new, innovative approach to professional learning and development. The first and only collaboration of its kind, Cornerstone clients now have the ability to provide their workforce with modern, mobile-enabled TED Talks from world-class leaders at the forefront of their fields from within Cornerstone Learning. Cornerstone's collaborative learning functionality also allows organisations to enable peer-to-peer knowledge capture and discussions that can extend the learning impact of TED Talks. Watched and listened to more than 1 billion times this year, TED Talks introduce ideas that can help companies transform how their people think and work. Cornerstone clients will have access to a series of curated TED Talk playlists designed to address key business challenges in an innovative format that is unique, powerful and inspiring. With curated TED Talk playlists through Cornerstone: Inspire your workforce. TED brings together the world's most inspiring and ingenious people whose ideas can strengthen how professionals understand and think about the world around them. TED's curation of talks on behalf of Cornerstone can help organisations generate excitement and engagement among employees, help management crystallise goals, start important conversations, and spark collaborations. Provide the best, most relevant content. Organisations will gain access to the very best collections of TED Talks across a wide range of topics that are central to innovation and talent development, including change management, culture building, leadership, technology, globalisation, diversity and design. Playlists have been curated to reflect talks from visionary leaders across the most influential industries, such as healthcare, education, technology, manufacturing, finance and more. Amplify the value of your learning and development strategy. Employees can view TED Talks from within Cornerstone Learning, the global learning management system (LMS) for over 1,800 leading organisations. Integrating TED Talks into professional development curriculum allows organisations to inspire each individual employee at any stage in their career. Organisations can easily target and deliver learning and development to groups or individuals with the support of TED Talks and measure impact on workforce development from within Cornerstone. Watch and Share Instantly on Mobile: As smartphones emerge as the leading platform for watching video and Web content among busy professionals, TED Talks allow employees to consume and share content on their mobile devices while on the go. Comments on the News "TED Talks are brilliantly crafted and make an emotional connection with viewers. Their ability to convey innovative and complex ideas through powerful, first-person stories is the type of talent management content that can inspire and drive real change in the workforce," said Kirsten Helvey, senior vice president, client success, Cornerstone OnDemand. "We are dedicated to helping people reach their potential by providing our clients with the most innovative talent management solutions that support their professional development and training initiatives." "With the growing demand from companies for TED Talks, Cornerstone provides TED with the expertise and efficiency in reaching millions of learners in organisations across the globe that can benefit from our content," said Deron Triff, TED's director of global distribution and licensing. "This collaboration also provides TED with an important opportunity to understand how the talks can be utilised for professional development to strengthen how we collaborate with the business community. Cornerstone will be a great alliance for bringing TED Talks to companies and sparking innovation among their employees." Additional Resources Learn more about curated TED Talk playlists for Cornerstone via the Cornerstone Marketplace: marketplace.csod.com/#/content/90 Read additional commentary by Cornerstone's director of talent management, Jeff Miller, on the value and influence of TED Talks for empowering today's workforce via the Cornerstone blog: www.cornerstoneondemand.com/blog/how-ted-gets-your-workforce-talking
June 30, 2015
by Fran Cator
· 985 Views
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DBmaestro is the first 3rd party vendor to release extension for Oracle SQL Developer 4.x
DBmaestro, the pioneer and leading provider of DevOps for database solutions, has announced that TeamWork’s extension for Oracle SQL Developer 4.1 has been released, just weeks after Oracle released its latest version. DBmaestro TeamWork is now the only tool with an external extension that supports version 4.x of Oracle’s database development tool. TeamWork, DBmaestro’s flagship product, enables agile development and continuous integration & delivery for the database. TeamWork supports streamlining of development process management and enforcing change policy practices. Many leading enterprises use DBmaestro to facilitate DevOps for their database by executing deployment automation, enhancing and reinforcing security, and mitigating risk. DBmaestro’s extension for SQL Developer 4.x helps Oracle developers and DBAs streamline database development, collaborate across database teams, and allows for agile database development in an efficient and reliable way. Upon the release of SQL Developer 4.0, Oracle required that all extensions be updated to be compatible with the new version’s API. This is a result of the drastic changes made to JDeveloper, on which SQL Developer is built. “The SQL Developer 4.1 extension represents an important achievement for DBmaestro,” said Yaniv Yehuda, co-founder and CTO of DBmaestro. “Oracle SQL Developer has over 4 million active users and is the de facto standard database IDE tool out there. Oracle has drastically changed their API on the 4.0 version, which presented a challenge to those seeking to update third-party extensions. This integration is a statement of our commitment to our customers, and we will continue to lead the way to achieve DevOps for the databases.”
June 30, 2015
by Jeremy Tess
· 910 Views
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Restify Commands with the Request Pattern
REST is all the rage. It provides a networked approach to exposing data and services, both internally within a company and to partners. Here, we explore a novel approach to providing a RESTful API to a set of existing commands. Business Problem: expose Commands via REST Many organizations have made considerable software available through Unix-like commands. Imagine an Enterprise, well, the Enterprise, that wishes to provide RESTful APIs to commands like: OrderSickLeave , IncreaseSpeed , SelfDestruct … hundreds of them… Each of these commands is backed by some software, say a Java Class of some sort. They’d like to publish this functionality, as follows: Restify – provide a RESTful API for each command. The REST attributes are the command arguments, above Validate – before the commands is issued, validate the arguments (does exist, is the a positive number, etc) Secure – ensure the commands are available as appropriate Background: Espresso creates RESTful API for SQL data Espresso creates RESTful APIs, principally for SQL data. Some key functionality is described below. API Creation You connect Espresso to a database, and it creates a RESTful endpoint for each table, view and Stored Procedure. The API supports the usual RESTful operations of Get (including filters based on HTTP arguments), and update (Put, Post and Delete). Validation Rules You can specify validation rule expressions that are automatically executed on update requests. For example, you might specify a rule that balance <= creditLimit. Validation rules are part of the larger notion of business logic, which includes computations (the balance is the sum of the unpaid order amounts). Computations can chain, and validations work over computed data, so you can solve remarkably complicated problems with a few simple rules. Extensibility Espresso creates a server-side JavaScript Domain Object for each table, providing access to attributes (customer.balance), and persistence (customer.save()). The objects encapsulate their integrity in two ways: Rule Invocation – update requests trigger the relevant validation / computation rules Events – you can also associate server-side JavaScript event handlers with a table. These fire as update requests are processed The Event handlers can invoke anything in the JVM, such as jar file code you can load into Espresso, other RESTful services, etc. Background: Command Pattern Imagine a word processor, providing functionality to make a string of selected text bold, another bit of functionality for italics, and so forth. Now imagine we wish to provide Undo functionality. Not possible unless we save the commands. So, the Command Pattern emerged: create a class for each Command creating an instance “does” the command (e.g., make text bold), where the constructor arguments provide the necessary information (text start, length) maintain an ordered list of commands (object instances) require the Command Classes to provide an Undo method to reverse the effects of the constructor (e.g., remove the bold) Database Adaption: insert logic on a Request Table We can see this same pattern in database applications, where we wish to keep a record of transactions. For example, instead of just changing a salary (and forgetting who, when, old values etc), we create a SalaryAction(employeeId, raisePercent). We can think of this as a Request Table. The logic on the SalaryAction table might give the raise, but also validate (is the percent too big? is the resultant salary out of range?), and record who, when etc. Restify Commands with Request Tables So we can combine these notions: Create a database with table for each Command (OrderSickLeave, IncreaseSpeed, etc), with columns for command arguments. We can also provide columns for admin data (date executed, etc) Leverage Espresso to Restify these Declare Validation Logic JavaScript table events execute the command We can now use this approach to Restify our Enterprise commands, as described below. Command Requests Database The lower 3 tables are the Request Tables. They each have Foreign Keys to the Crew table, simply identifying the source of the request (this is optional). Load into Espresso We next connect Espresso, which discovers these tables and creates a Default RESTful API to them. We can view it in a test tool immediately: We can also define a Custom API, exposing just the elements we wish: Validate Command Parameters The following rules apply to Posts against our Command Request tables. Here we ensure that the IncreaseSpeed request has a argument (attribute) for warpFactor, that it be between 0 and 10, and that the optional emergencyReason parameter is supplied if the warpFactorexceeds 8 Live Browser – view, post data You can use the API tool, or this automatically constructed app: Invoke existing code Finally, we add server-side JavaScript logic (stubbed here) to execute our command. This would execute existing code in a loadable jar. Conclusion: 4 hour project This project was completed in about a half a day.
June 30, 2015
by Val Huber DZone Core CORE
· 5,729 Views
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Instant Enterprise REST Accelerates the Software Driven Business
Software Driven Business is a consensus goal. But real challenges exist: the time, cost and complexity of building such apps is substantial. Business Agility – and strategic business advantage – is lost. We need another revolution – Instant Enterprise REST – that provides Business Agility using business-level specifications rather than low-level code, and delivers Enterprise-class scalability, integration, enforcement and extensibility. It’s now a reality with Instant Enterprise REST. Software Driven Business: Consensus Vision Businesses have seen the value in providing mobile and tablet apps that bring the business into the hands of customers and employees. They provide information at their finger tips – wherever they are. Industry Leaders like CA have pioneered the vision of a Software Driven Business. They argue persuasively that strategic business advantage lies in Time to Market and Time to Decision: “reveal the need for speed in the application economy. As companies transform into software-driven enterprises, bringing high-quality applications to market faster becomes one of the most critical differentiators.” The Business Agility Gap While there is consensus around this vision, there is a substantial gap in realizing the Software Driven Business. It centers around Agility – time to market. As CA argues, this drives strategic business advantage. This problem manifests both to Business Users and IT, although differently. You might have been party to a discussion like this: Business Users are frustrated about how long it takes to create systems, and revise them. They see problems that look nearly as simple as a spreadsheet take weeks… to months. How can it months for IT to build a system that takes days on a spreadsheet? IT is no less frustrated. They understand the deep technology it takes to build Enterprise-class systems: We’re working 90 hours a week. And falling behind. Gap Analysis For apps about critical corporate data, there’s general consensus that the time and cost for such systems are about evenly split between backends and front ends. And there’s nearly universal consensus that, independent of the UI technology, that RESTful APIs deliver the backend data. But the backend is far more than basic data access. A “SQL Pass-through” – simply restifying SQL data – does not meet Enterprise-class requirements to scale, integrate and enforce: Scale – APIs require Pagination to address large result sets, Nested Documents to reduce latency, Optimistic Locking to ensure concurrency. These are not provided in a simple SQL Pass-through – you must program them, by hand. Integrate – a wizard can produce an API from schema objects, but it cannot address multiple databases, or integrate non-SQL data sources such as ERP, other RESTful services, or NoSQL. Enforce – an API needs to enforce our security (down to the row level), and the integrity of the data. These are significant tasks, which are sadly often placed in client buttons where they cannot be shared. Providing these Enterprise class services takes significant time, expertise and expense. Business Agility is reduced. IT is essentially being forced to cover inadequate technology infrastructure. The Business Users are right: if the Business Specification is clear, then that ought to be enough: A clear business specification should be sufficient. Everything else is just friction. The vision of the Software Driven Business requires Business Driven Software that pre-supplies the infrastructure. We are not seeking 10 or 15%. We are looking for orders of magnitude. Our vision must be: We should be able to create RESTful APIs (mainly) from business specifications, not low level code. It should be no more difficult to create a system than it is toimagine it. Business-Driven Software: Instant Enterprise REST Business Driven Software is more than just a clever play on words. It’s a real implementation that delivers this vision, and we call it Instant Enterprise REST. It consists of 3 core technologies: Enterprise Pattern Automation – creates APIs that with Enterprise-class scalability built-in (pagination, nested documents, optimistic locking, etc) Declarative – specify your API, integration and enforcement policies with spreadsheet-like rules in a simple point-and-click UI Extensibility – enables the RESTful APIs to invoke your existing logic, inside or outside the JVM, via standard server-side JavaScript. The combination of these 3 technologies enables you to create RESTful APIs for database backends – half your system – 10 times faster. Let’s briefly examine them below. Technology 1: Enterprise Pattern Automation There are well known patterns in the data domain, describing data structure and access via SQL. There are also well-known patterns for managing SQL data in the context of RESTful services. Well known patterns can be automated. Let’s imagine a service (say, a server accessed via a browser) that automates these patterns, as described below, just by connecting the service to a database: Schema Discovery – tables, views, stored procedures: The system creates a complete (default) API for each schema object. Note this includes Stored Procedures, which often represent a significant investment. Enterprise Pattern Automation: the resultant API provides well-known services for Filter, Sort, Pagination, Optimistic Locking, handling Generated Keys and so forth. So, the service has provided a default Enterprise-class API, instantly. So, literally seconds into your project, you can test your running API: Not enough, not done, but a great start. Technology 2: Declarative Declarative is the key (“what, not how”). It has had striking impacts on domains where there are well-understood underlying patterns. Max Tardiveau has put it well: Whatever can be declarative, will be declarative. For example, spreadsheets are declarative – and they gave birth to the PC industry. And SQL is declarative – itself an industry. Two game-changers. So, the challenge is to apply the spirit of declarative to REST integration and enforcement. The stakes are high – success can deliver breathtaking agility. Declarative Integration: Multi-Database Custom API, Point and Click Enterprise Pattern Automation provides a good start, but the API is not rich. It is a flat, single-table API, really just “restified” SQL. What we really need is Nested Documents – returning multiple types (e.g., an Order, a list of Items, and a list of contact names) in a single call can reduce latency (vs. a separate call for each type). REST is perfect for this. Multi-database APIs – a RESTful server provides the opportunity to integrate multiple databases in single call, shielding clients from underlying complexity. Nested Documents are easy: define them by simply selecting tables (via a User Interface or Command Line). Foreign Keys are used to default the joins. Add the ability to choose / alias columns, and we’re on the way to a pretty good API. But what about databases that have no Foreign Keys? Or multi-database APIs? Leveraging the schema does not mean we are limited to it. All we need to do is: Provide a means to define “Virtual” Foreign Keys for the service (i.e., stored outside the schema) Extend this to Foreign Keys between databases We now have a rich, multi-database API. Defined declaratively as shown below, no code required, running in minutes, ready for client development: Declarative Enforcement: Integrity Logic, with spreadsheet-like rules So now consider enforcement, specifically database integrity. A very significant portion of any project is the multi-table validations and computations that define how the data is processed. “Your code goes here” means, well, a lot of code. We need a more powerful, more declarative, paradigm. In a spreadsheet, you assign expressions to cells. Whenever the referenced data is changed, the cell is updated. Since the cells references can chain, a series of simple expressions can solve remarkably complex problems. What if we did the same for database data? We could assign derivation expressions to columns, and validation expressions to tables. Then, the API could “watch” for requests that change the referenced column, and recompute (efficiently) the calculated column. Just as in a spreadsheet, support for chaining and proper ordering is required and implicit. To address multi-table logic, such expressions would need to address references to related tables. It’s only at this point that the logic becomes seriously powerful. Let’s take an example. To check credit in a Customer / Purchaseorder / Lineitem application, we could define spreadsheet-like expressions such as: There is actually a sub-branch of declarative that addresses this: Reactive Programming. Here it’s declarative,since you don’t need to code a Observer handler. The result is that the logic above can be fully executable. No need to code Change Detection / Change Dependency – it’s invoked and enforced automatically by the API in reaction to RESTful updates. SQL handling is also implicit, including underlying optimizations (caching, pruning etc). The impact is massive – the 5 expressions above express the same logic as hundreds of lines of code. That’s a massive 40X more concise. Game changer. And quality goes up, since the rules are applied automatically. Declarative Enforcement: Security, filter expressions for role/table We can provide an analogous approach to security: define filter expressions for roles (like SalesRep), so that when a table is accessed by the role, the API adds the filter. That way, a user with that role sees only the rows for which they are authorized. Technology 3: Standards-based Extensibility Declarative is great, but you’re probably thinking “ok, but you can’t solve every problem declaratively”. And you’re dead right. Business Value requires that we integrate a declarative approach with a procedural one that is familiar, standards-based, and enables us to integrate existing software. Automatic JavaScript Object Model The first phase of many projects is to build an ORM for natural programmatic access to data: JPA, Hibernate, Entity Framework. It’s not a small project, and cumbersome to maintain as changes occur. In fact, the Object Model can be created directly from the schema. So, you’d have an object type for Purchaseorder, for Lineitem, and so forth. The model provides access to attributes and related data, and persistence services. You could then use it as shown below. JavaScript seems like the best language choice: reasonable across technology bases (everybody uses JavaScript), and its dynamic nature eliminates code generation hassles. JavaScript Events In addition to accessors and persistence, the JavaScript objects are Logic Aware. That is, the save operation above executes any rules associated with OrderAudit (e.g., updated-by), and JavaScript Events. Here is a sample event for the PurchaseOrder object, where you access the JavaScript Object Model via the system-supplied row variable: Extensible Logic Auditing is a common pattern. It should be possible to solve this once in a genericmanner, then re-use it (e.g, to audit employees, orders and so forth). So, Instant Enterprise REST should enable you to provide Extensible Logic – load your own JavaScript code, and invoke it. So, the code above could become: MyLibrary.auditFromTo(orderRow,"OrderAudit"); where auditFromTo creates an instance of OrderAudit, sets the foreign key, sets like-named attributes, and saves it. Pluggable Authentication Most organizations have existing data stores that identify users and their roles, such as Active Directory, LDAP, OAuth, etc. Security should integrate with such systems as a function of enforcing row/column access. Standard deployment Finally, the system should deploy in a familiar manner: available on the cloud, or an on-premise virtual appliance or war file. Standards also enable integration with related critical infrastructure, such as API Management, ERP Systems, etc. See a project in 3 minutes To see how it all fits together, you can view this video to see a full project built: from concept, through initial implementation, and an iteration cycle. Actual project time was about half an hour. Instant Enterprise REST: Business Agility Instant Enterprise REST enables us to close the Agility Gap in realizing the Software Driven Business vision. We can now create important portions of our software in largely business terms, rather than technical terms. This offers major advantages: Time to Market: spreadsheet-like rules are 40X more concise. Instant REST eliminates all the SQL / REST / JSON boilerplate. Simplicity: team members can learn the basics of Espresso in days, and be as productive as rocket scientists using alternative technologies Leverage Expertise and Software: Espresso is built on standards like REST, JavaScript, and Event Oriented Programming. You can call out to existing software, and extend the rule types by identifying your own patterns and loading their implementations into Espresso. Quality: at the defect level, automatic invocation and ordering eliminate large classes of bugs. At the architectural level, centralized enforcement factors logic out of the client buttons where it can be shared, audited for compliances, etc
June 30, 2015
by Val Huber DZone Core CORE
· 1,398 Views
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Azure Service Bus – As I Understand It: Part I (Overview)
Recently we started working on including support for Azure Service Bus in Cloud Portam. Prior to this, I had no experience with this service though it has been around for quite some time and I always wanted to try this out but one thing or another (oh, my stupid excuses :)!) prevented me from doing so. I learned a lot (and I am still learning) about this service while including support for it in Cloud Portam and this blog post talks about my learning. Please note that at the time of writing of all in all I have about a week of learning about this service so it is quite possible that I may be wrong about certain things. If that’s the case, please let me know and I will fix them ASAP. Now that the tone is set, let’s start! Azure Service Bus Offering The way I understand is that “Azure Service Bus” is a cloud-based messaging service that enables you to connect virtually anything – be it applications, services or devices. The beauty of Service Bus is that these things need not be in the cloud. They can run anywhere even inside the firewalled networks! Another thing I learned is that “Azure Service Bus” is essentially an umbrella service. At the time of writing of this post, there are actually four distinct services that are collectively offered under “Service Bus” umbrella – Queues, Topics & Subscriptions, Relays and Notification Hubs. Each service serves a different purpose yet the common theme is that all of them provide rich messaging infrastructure. To give you an analogy, if you have used Azure Storage Service you may already know that it offers four distinct services – Blobs, Files, Queues and Tables. It is the same with Service Bus as well. Queues Queues is the simplest of the service and kind of compares with Azure Storage Queue Service in the sense that it provides a unidirectional messaging infrastructure where a publisher publishes a message and the message is received by a receiver. There can be many receivers ready to receive the messages however one receiver can only receive a message. No two receivers can receive a single message simultaneously. For an in-depth comparison of Service Bus Queue and Storage Queues, please see this link: https://msdn.microsoft.com/en-us/library/azure/hh767287.aspx. Topics Topics are like queues in the sense that it also provides a unidirectional messaging infrastructure where a publisher publishes a message and receivers receive the message. The key difference is that same message can be received by multiple receivers (subscribers). Each subscriber can optionally specify a filter criteria so that they only receive the messages matching that criteria. To understand the difference between the two, let’s consider an example. Let’s say you run an e-commerce site and on successful completion of order, you have two tasks: 1) Send an email to customer about the order and 2) Notify the warehouse. If you were using Queues, you would either create 2 queues and put email notification message in one queue and warehouse notification message in another queue or build a workflow where you would send order confirmation message to a queue. Receiver would take that message and send out an email and then put warehouse notification message in the same queue (or other queue) and then another receiver would receive the message and notify the warehouse. However if you were using Topics, things would be much simpler logistically speaking. Essentially you would have just one message (order confirmation) but there will be two subscribers – one will be responsible for sending the email confirmation and the other will be responsible for notifying the warehouse. Relays Unlike Queues and Topics, which provide unidirectional flow of messages a Relay provides bi-directional flow. Using Relays, two disparate applications, services or devices can exchange messages. Other key difference is that a Relay doesn’t store the message like Queues and Topics. It just passes the messages from source to destination. Event Hubs Event Hubs service is meant for ingesting events and telemetry data in the cloud at massive scale (millions of events / second). Event Hubs are now more than important considering the push for connected devices (Internet-of-Things). Azure Service Bus Tiers Azure Service Bus is offered under two tiers (or SKUs if you would like): Basic and Standard. The difference is the level of functionality offered in each tier and the pricing. For example, Topics, Relays and Notification Hubs are only offered under Standard tier. Even with Queues, a limited set of functionality is exposed under Basic tier. For a list of features offered under each tier, please see this link: http://azure.microsoft.com/en-in/pricing/details/service-bus/. Summary That’s it for this post. In the next posts in this series, I will share my learnings about Queues and other Service Bus services. So stay tuned for that! Again, if you think that I have provided some incorrect information, please let me know and I will fix them ASAP.
June 30, 2015
by Gaurav Mantri
· 1,262 Views
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Using Parameterized Query to Avoid SQL Injection
introduction to explain why you have to use parameterized query to avoid sql injection over concatenated inline query it needs to know about sql injection. what does sql injection mean? it means when any end user send some invalid inputs to perform any crud operation or forcibly execute the wrong query into the database, those can be harmful for the database. harmful means ‘data loss’ or ‘get the data with invalid inputs. to know more, follow the below steps. step 1: create a table named ‘login’ in any database. create table user_login ( userid varchar(20), pwd varchar(20) ) now save some user credentials into the database for login purpose and select the table. insert into user_login values('rahul','bansal@123') insert into user_login values('bansal','rahul@123') step 2: create a website named ‘website1’. now i will create a login page named ‘default.aspx’ to validate the credentials from the ‘login’ table and if user is valid then redirect to it to the next page named ‘home.aspx’. add 2 textboxes for userid & password respectively and a button for login. add 2 namespaces in the .cs file of the ‘default.aspx’. using system.data.sqlclient; using system.data; now add the following code to validate the credentials from the database on click event of login button. protected void btn_login_click(object sender, eventargs e) { string constr = system.configuration.configurationmanager.connectionstrings["constr"].connectionstring; sqlconnection con = new sqlconnection(constr); string sql = "select count(userid) from user_login where userid='" + txtuserid.text + "' and pwd='" + txtpwd.text + "'"; sqlcommand cmd = new sqlcommand(sql, con); con.open(); object res = cmd.executescalar(); con.close(); if (convert.toint32(res) > 0) response.redirect("home.aspx"); else { response.write("invalid credentials"); return; } } add a new page named ‘home.aspx’. where any valid user will get welcome message. step 3: now run the ‘default’ page and log in with valid credentials. it will redirect to next page ‘home.aspx’ for valid user. note: here i have not used the textmode="password" property in password textbox to show the password. i have not used any input validations to explain my example. problem: now i will perform the sql injection with some invalid credentials with successful query execution and after that i will redirect to the next page ‘home.aspx’ as a valid user. i will enter a string in both textboxes like the following: ‘ or ‘1’=’1 now run the page and login with above string in both textboxes. it will redirect to next page name ‘home.aspx’ for valid user. see what happened. this is called sql injection in the hacking world. reason: it happened just because of the string and after filling this string in both textboxes orur sql query became like the following: select count(userid) from user_login where userid='' or '1'='1' and pwd='' or '1'='1' which will give the userid count and that is 2 in the table because 2 users are in ‘user_login’ table. it can be used in more ways like just fill the following string only in user id textbox and you will go the next page as valid user. or 1=1 - - and it will also give users count 2 because sqlquery will become like the following: select count(userid) from user_login where userid='' or 1=1 --' and pwd='' or '1'='1' note: the sign -- are for commenting the preceding text in sql. it can be more harmful or dangerous when the invalid user/hacker executes a script to drop all tables in the database or drop whole database. solution: to resolve this issue you have to do 2 things: always use parameterized query. input validations on client and server both side. sometimes if your input validation fail, then parameterized will not execute any scripted value. let’s see the example. protected void btn_login_click(object sender, eventargs e) { string constr = system.configuration.configurationmanager.connectionstrings["constr"].connectionstring; sqlconnection con = new sqlconnection(constr); string sql = "select count(userid) from user_login where userid=@userid and pwd=@pwd"; sqlcommand cmd = new sqlcommand(sql, con); sqlparameter[] param = new sqlparameter[2]; param[0] = new sqlparameter("@userid", txtuserid.text); param[1] = new sqlparameter("@pwd", txtpwd.text); cmd.parameters.add(param[0]); cmd.parameters.add(param[1]); con.open(); object res = cmd.executescalar(); con.close(); if (convert.toint32(res) > 0) response.redirect("home.aspx"); else { response.write("invalid credentials"); return; } } now if i run the page and try to login with sql scripts as done earlier. with ‘ or ‘1’=’1 with ' or 1=1 - - as you have seen parameterized didn’t execute the sql script but why? reason: the reason behind this the parameterized query would not be vulnerable and would instead look for a user id or password which literally matched the entire string. in other words ‘the sql engine checks each parameter to ensure that it is correct for its column and are treated literally, and not as part of the sql to be executed’. conclusion: always use parameterized query and input validations on client and server both side.
June 30, 2015
by Rahul Bansal
· 11,809 Views
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The Secret to More Efficient Data Science with Neo4j and R [OSCON Preview]
It’s a sad but true fact: Most data scientists spend 50-80% of their time cleaning and munging data and only a fraction of their time actually building predictive models. This is most often true in a traditional stack, where most of this data munging consists of writing lines upon lines of some flavor of SQL, leaving little time for model-building code in statistical programming languages such as R. These long, cryptic SQL queries not only slow development time but also prevent useful collaboration on analytics projects, as contributors struggle to understand each others’ SQL code. For example, in graduate school, I was on a project team where we used Oracle to store Twitter data. The kinds of queries my classmates and I were writing were unmaintainable and impossible to understand unless the author was sitting next to you. No one worked on the same queries together because they were so unwieldy. This not only hindered our collaboration efforts but also slowed our progress on the project. If we had been using an appropriate data store (like a graph database) we would have spent significantly less time pulling our hair out over the queries. Why Today’s Data Is Different This data-munging problem has persisted in the data science field because data is becoming increasingly social and highly-connected. Forcing this kind of interconnected data into an inherently tabular SQL database, where relationships are only abstract, leads to complicated schemas and overly complex queries. Yet, several NoSQL solutions – specifically in the graph database space – exist to store today’s highly-connected data. That is, data where relationships matter. A lot of data analysis today is performed in the context of better understanding people’s behavior or needs, such as: How likely is this visitor to click on advertisement X? Which products should I recommend to this user? How are User A and User B connected? Written by Nicole White People, as we know, are inherently social, so most of these questions can be answered by understanding the connections between people: User A is similar to User B, and we already know that User B likes this product, so let’s recommend this product to User A. The Good News: Data-Munging No More Data science doesn’t have to be 80% data munging. With the appropriate technology stack, a data scientist’s development process is seamless and short. It’s time to spend less time writing queries and more time building models by combining the flexibility of an open-source, NoSQL graph database with the maturity and breadth of R – an open-source statistical programming language. The combination of Neo4j’s ability to store highly-connected, possibly-unstructured data and R’s functional, ad-hoc nature creates the ideal data analysis environment. You don’t have to spend an hour writing CREATE TABLE statements. You don’t have to spend all day on StackOverflow figuring out how to traverse a tree in SQL. Just Cypher and go. Learn More at OSCON 2015 At my upcoming OSCON session we will walk through a project in which we analyze #OSCON Twitter data in a reproducible, low-effort workflow without writing a single line of SQL. For this highly-connected dataset we will use Neo4j, an open-source graph database, to store and query the data while highlighting the advantages of storing such data in a graph versus a relational schema. Finally, we will cover how to connect to Neo4j from an R environment for the purposes of performing common data science tasks, such as analysis, prediction and visualization.
June 30, 2015
by Mark Needham
· 1,668 Views
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The Philosophy of the CUBA Platform
A huge amount has happened recently. Following the official launch of CUBA on 1st of June, we have rolled out a new release, published our first article on a few Java sites and presented the platform at the Devoxx UK сonference in London. But before the rush continues, about it is an apt time to articulate the philosophy behind CUBA. The first words associated with enterprise software development will probably be: slow, routine, complex and convoluted - nothing exciting at all! A common approach to combat these challenges is raising the level of abstraction - so that developers can operate with interfaces and tools encapsulating internal mechanisms. This enables the focus on high-level business requirements without the need to reinvent common processes for every project. Such a concept is typically implemented in frameworks, or platforms. The previous CUBA article explained why CUBA is more than just a bunch of well-known open-source frameworks comprehensively integrated together. In brief, it brings declarative UI with data aware visual components, out-of-the-box features starting from sophisticated security model to BPM, and awesome development tools to complement your chosen IDE. You can easily find more details on our Learn page, so instead of listing all of them I'll try to "raise the abstraction level" and explain the fundamental principles of CUBA. Practical The platform is a living organism, and its evolution is mostly driven by specific requests from developers. Of course, we constantly keep track of emerging technologies, but we are rather conservative and employ them only when we see that they can bring tangible value to the enterprise software development. As a result, CUBA is extremely practical; every part of it has been created to solve some real problem. Integral Apart from the obvious material features, the visual development environment provided by CUBA Studio greatly reduces the learning curve for newcomers and juniors. It is even more important that the platform brings a unified structure to your applications. When you open a CUBA-based project, you will always know where to find a screen, or a component inside of it; where the business logic is located and how is it invoked. Such an ability to quickly understand and change the code written by other developers cannot be underestimated as a significant benefit to continual enterprise development. An enterprise application lifecycle may last tens of years and your solution must constantly evolve with the business environment, regardless of any changes in your team. For this reason, the flexibility to rotate, or scale up or down the team when needed, is one of the major concerns for the companies, especially those who outsource development or have distributed teams. Open One of the key principles of CUBA is openness. This starts with the full platform source code, which you have at hand when you work on a CUBA-based project. In addition, the platform is also open in the sense that you can change almost any part of it to suit your needs. You don't need to fork it to customize some parts of the platform - it is possible to extend and modify the platform functionality right in your project. To achieve this, we usually follow the open inheritance pattern, providing access to the platform internals. We understand that this can cause issues when the project is upgraded to a newer platform version. However, from our experience, this is far less evil than maintaining a fork, or accepting the inability to adapt the tool for a particular task. We could also make a number of specific extension points, but in such case we would have to anticipate how application developers will use the platform. Such predictions always fail, sooner or later. So instead we have made the whole platform extension-friendly: you can inherit and override platform Java code including the object model, XML screens layout and configuration parameters. Transitively, this remains true for CUBA-based projects. If you follow a few simple conventions, your application becomes open for extension, allowing you to adapt the single product for many customers. Symbiotic CUBA is not positioned as a “thing-in-itself”. When a suitable and well-supported instrument already exists and we can integrate with it without sacrificing platform usability, we will integrate with it. An illustration of such integrations is full-text search and BPM engines, JavaScript charts and Google Maps API. At the same time, we have had to implement our ownreport generator from scratch, because we could not find a suitable tool (technology and license wise). The CUBA Studio follows this principle too. It is a standalone web application and it doesn't replace your preferred IDE. You can use Studio and the IDE in parallel, switching between them to accomplish different tasks. WYSIWYG approach, implemented in Studio, is great for designing the data model and screens layout, while the classic Java IDE is the best for writing code. You can change any part of your project right in the IDE, even things created by Studio. When you return to Studio, it will instantly parse all changes, allowing you to keep on developing visually. As you see, instead of competing with the power of Java IDEs, we follow a symbiotic approach. Moreover, to raise coding efficiency, we’ve developed plugins for the most popular IDEs. When we integrate with a third-party framework, we always wrap it in a higher level API. This enables replacing the underlying implementation if needed and makes the whole platform API more stable long term and less dependent on the constant changes in the integrated third-party frameworks. However, we don't restrict the direct use of underlying frameworks and libraries. It makes sense if CUBA API does not fit a particular use case. For example, if you can't do something via Generic UI, you can unwrap a visual component and get direct access to Vaadin (or Swing). The same applies for data access; if some operation is slow or not supported by ORM, just write SQL and run it via JDBC or MyBatis. Of course, such “hacks” lead to more complex and less portable application code, but they are typically very rare compared to the use of standard platform API. This knowledge of inherent flexibility and a sense of “Yes you can” adds a lot of confidence to developers. Wide use area We recommend using CUBA if you need to create an application with anything starting from 5-10 screens, as long as they consist of standard components like fields, forms, and tables. The effect from using CUBA grows exponentially with the complexity of your application, independent of the domain. We have delivered complex projects in financial, manufacturing, logistics and other areas. As an example, a non-obvious, but popular use case is using CUBA as the backend and admin UI, while creating the end-user interface with another, lighter or more customizable web technology. I hope you will see some use cases of the platform for yourself, so in the next articles we’ll focus on “what's under the hood” – as we provide a detailed overview of the different CUBA parts.
June 29, 2015
by Aleksey Stukalov
· 12,046 Views · 6 Likes
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Persistence and DAO Testing Made Simple (with Exparity-Stub and Hamcrest-Bean)
Persistence of model objects is a part of many Java projects and a part which deserves, and often gets, high test coverage as one of the key layer integration points in the code. However, I've often felt the testing paradigms for this can be cumbersome, often involving a large amount of setup with an equivalent amount of validation. This can be tedious to both create and maintain. As a solution to this I've been testing persistence with a different pattern; by combining both the exparity-stub and the hamcrest-bean library you can thoroughly test model persistence in a few lines of test code as per the snippet below; .. User user = aRandomInstanceOf(User.class); User saved = dao.save(user); assertThat(dao.getUserById(saved.getId()), theSameBeanAs(saved)); The test snippet above is small but in those few lines will thoroughly test that all fields in a graph can be persisted and retrieved without loss, that any JPA or other mapping is valid, and that your queries are valid. For a complete example we'll work through testing a simple DAO for storing and retrieving User objects using the in-memory H2 database for simplicity. The same example will work for any persistence mechanism. Before we get started with an example lets briefly outline what the libraries are and what they do. The Exparity-Stub Library The exparity-stub libraries provides a set of static methods for creating stubs of model objects, object graphs, collections, types, and primitive types. For our example we'll be creating random stubs because we want to completely fill the graph with junk data and check it can be written down. exparity-stub offers two approaches to this, the RandomBuilder or the BeanBuilder. The RandomBuilder provides a terser notation to create random objects with less code. For example: User user = RandomBuilder.aRandomInstanceOf(User.class); List users = RandomBuilder.aRandomListOf(User.class); String anyString = RandomBuilder.aRandomString(); Whereas the BeanBuilder provides a fluent interface with finer control for building individual objects and graphs, for example; User user = BeanBuilder.aRandomInstanceOf(User.class) .excludeProperty("Id").build(); For this example i'm going to use the BeanBuilder so I can exclude the User.Id property from being populated by the random builder. The Hamcrest-Bean Library The hamcrest-bean library is an extension library to the Java Hamcrest library. The hamcrest-bean library provides a set of matchers specifically for testing Java objects and object graphs and performs deep inspections of those objects. It supports exclusions and overrides to allow fine control, if required, of how matching of any property, path, or type is handled, for example: User expected = new User("Jane", "Doe"); assertThat(new User("John", "Doe"), BeanMatchers.theSameAs(expected).excludeProperty("FirstName")); A Sample Project The sample project I'll work through is persistence of a simple User object with a child list of UserComment objects. This simple graph will be persisted to a H2 database with hibernate handling the Object-Relational Mapping (ORM) mapping, and Java Persistence Annotation (JPA) used to mark-up the model. The Model Below are the two model classes; first the User class. package org.exparity.hamcrest.bean.sample.dao; import java.util.*; import javax.persistence.*; @Entity @Table public class User { @Id @GeneratedValue(strategy = GenerationType.SEQUENCE) private Long id; private Date createTs; private String username, firstName, surname; @OneToMany(cascade = CascadeType.ALL, fetch = FetchType.EAGER) private List comments = new ArrayList<>(); public Long getId() { return id; } public void setId(Long id) { this.id = id; } public Date getCreateTs() { return createTs; } public void setCreateTs(Date createTs) { this.createTs = createTs; } public String getUsername() { return username; } public void setUsername(String username) { this.username = username; } public String getFirstName() { return firstName; } public void setFirstName(String firstName) { this.firstName = firstName; } public String getSurname() { return surname; } public void setSurname(String surname) { this.surname = surname; } public List getComments() { return comments; } public void setComments(List comments) { this.comments = comments; } } Followed by the UserComment class. package org.exparity.hamcrest.bean.sample.dao; import java.util.Date; import javax.persistence.*; @Table @Entity public class UserComment { private Long id; private Date timestamp; @Transient private String text; private String title; public Date getTimestamp() { return timestamp; } public void setTimestamp(Date timestamp) { this.timestamp = timestamp; } public String getText() { return text; } public void setText(String text) { this.text = text; } public String getTitle() { return title; } public void setTitle(String title) { this.title = title; } } Followed by the UserComment class. package org.exparity.hamcrest.bean.sample.dao; import java.util.Date; import javax.persistence.*; @Table @Entity public class UserComment { private Long id; private Date timestamp; @Transient private String text; private String title; public Date getTimestamp() { return timestamp; } public void setTimestamp(Date timestamp) { this.timestamp = timestamp; } public String getText() { return text; } public void setText(String text) { this.text = text; } public String getTitle() { return title; } public void setTitle(String title) { this.title = title; } } The Data Access Object (DAO) Next up we write our DAO layer. I've excluded the UserDAO interface from this post but it is available in the sample project ongithub .The full, if somewhat crude, implementation of the UserDAO is below. package org.exparity.hamcrest.bean.sample.dao; import org.hibernate.boot.registry.StandardServiceRegistryBuilder; import org.hibernate.cfg.Configuration; import org.hibernate.*; public class UserDAOHibernateImpl implements UserDAO { private final SessionFactory factory; public UserDAOHibernateImpl(final String resourceFile) { this.factory = new Configuration() .addAnnotatedClass(User.class) .addAnnotatedClass(UserComment.class) .buildSessionFactory( new StandardServiceRegistryBuilder() .loadProperties(resourceFile) .build()); } @Override public User save(final User user) { Session session = factory.getCurrentSession(); Transaction txn = session.beginTransaction(); try { session.save(user); txn.commit(); } catch (final Exception e) { txn.rollback(); } return user; } @Override public User getUserById(Long userId) { Session session = factory.getCurrentSession(); Transaction txn = session.beginTransaction(); try { return (User) session.get(User.class, userId); } finally { txn.rollback(); } } } Integration Test And finally, onto our integration test. The hibernate.properties will create an instance of an in-memory database and create the necessary tables on instantiation of the DAO. hibernate.dialect=org.hibernate.dialect.H2Dialect hibernate.connection.username=sa hibernate.connection.password= hibernate.connection.driver_class=org.h2.Driver hibernate.connection.url=jdbc:h2:mem:test hibernate.current_session_context_class=thread hibernate.cache.provider_class=org.hibernate.cache.internal.NoCacheProvider hibernate.show_sql=true hibernate.hbm2ddl.auto=update The integration test is below. package org.exparity.hamcrest.bean.sample.dao; import static org.exparity.hamcrest.BeanMatchers.theSameBeanAs; import static org.exparity.stub.bean.BeanBuilder.aRandomInstanceOf; import static org.hamcrest.MatcherAssert.assertThat; import static org.hamcrest.Matchers.*; import org.junit.Test; public class UserDAOHibernateImplTest { @Test public void canSaveAUser() { User user = aRandomInstanceOf(User.class).excludeProperty("Id").build(); UserDAOHibernateImpl dao = new UserDAOHibernateImpl("hibernate.properties"); User saved = dao.save(user); User loaded = dao.getUserById(saved.getId()); assertThat(loaded, not(sameInstance(user))); assertThat(loaded, theSameBeanAs(user)); } } Let's break the test down step by step to see what each step is doing and why the test is put together this way. 1) Model Setup User user = aRandomInstanceOf(User.class).excludeProperty("Id").build(); Create a random instance of the User class and it's associates using exparity-stub. The instance will be populated with random data with the exception of the Id property. I've excluded the Id property so that is left null to test that the id is being generated in the database. 2) DAO Setup UserDAOHibernateImpl dao = new UserDAOHibernateImpl("hibernate.properties") Instantiate the DAO ready to be tested, passing in the property file to use for the test. The hibernate properties used will configure an in-memory instance of H2 and create the schema automatically. 3) Exercise the DAO User saved = dao.save(user); User loaded = dao.getUserById(saved.getId()); Save the random instance of the model set up in step (1) and then query the object back out again. 4) Verify the results assertThat(loaded, not(sameInstance(user))); assertThat(loaded, theSameBeanAs(user)); The first line verifies that the loaded User instance is not the same instance as the originally saved User. This prevents false positive results when the loaded instance is returned directly from a cache. The second line uses hamcrest-bean to perform a deep comparison of the loaded User instance against the original user instance. Running the Test The first run of the test yields an error; specifically a hibernate warning because a @Id annotation has been missed on UserComment. org.hibernate.AnnotationException: No identifier specified for entity: org.exparity.hamcrest.bean.sample.dao.UserComment at org.hibernate.cfg.InheritanceState.determineDefaultAccessType(InheritanceState.java:277) at org.hibernate.cfg.InheritanceState.getElementsToProcess(InheritanceState.java:224) at org.hibernate.cfg.AnnotationBinder.bindClass(AnnotationBinder.java:775) at org.hibernate.cfg.Configuration$MetadataSourceQueue.processAnnotatedClassesQueue(Configuration.java:3845) at org.hibernate.cfg.Configuration$MetadataSourceQueue.processMetadata(Configuration.java:3799) at org.hibernate.cfg.Configuration.secondPassCompile(Configuration.java:1412) at org.hibernate.cfg.Configuration.buildSessionFactory(Configuration.java:1846) at org.exparity.hamcrest.bean.sample.dao.UserDAOHibernateImpl.(UserDAOHibernateImpl.java:15) at org.exparity.hamcrest.bean.sample.dao.UserDAOHibernateImplTest.canSaveAUser(UserDAOHibernateImplTest.java:18) A fix to the UserComment object and we can run the test again. @Table @Entity public class UserComment { @Id @GeneratedValue(strategy = GenerationType.SEQUENCE) private Long id; private Date timestamp; @Transient private String text; private String title; ... After running the test again we get another failure. The presence of the @Transient annotation on the UserComment.text property is preventing the value being persisted java.lang.AssertionError: Expected: the same as but: User.Comments[0].Text is null instead of "mDAWDJXbheIHbbHLR1NNVJqAki49RvaVwQtKD38r79u0y3MTDD" at org.hamcrest.MatcherAssert.assertThat(MatcherAssert.java:20) at org.hamcrest.MatcherAssert.assertThat(MatcherAssert.java:8) at org.exparity.hamcrest.bean.sample.dao.UserDAOHibernateImplTest.canSaveAUser(UserDAOHibernateImplTest.java:19) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:483) at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:47) Another change to the UserComment object to remove the @Transient annotation and we can run the test again. @Table @Entity public class UserComment { @Id @GeneratedValue(strategy = GenerationType.SEQUENCE) private Long id; private Date timestamp; private String text; private String title; ... After running the test again it all passes. Try It Out To try hamcrest-bean and exparity-stub out for yourself include the dependency in your maven pom or other dependency manager. org.exparity hamcrest-bean 1.0.10 test org.exparity exparity-stub 1.1.5 test
June 29, 2015
by Stewart Bissett
· 3,245 Views
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The Cloudcast #198 - Architecting Cloud Foundry
Download the MP3 Date: June 19, 2015 By: Aaron Delp and Brian Gracely Description: Aaron and Brian talk to Chip Childers (@chipchilders, VP of Technology @CloudFoundryOrg) about the current status of Cloud Foundry projects, how Microsoft .NET will be integrated, IaaS vs. PaaS, and the CF.org thinking about overall interoperability Interested in the O'Reilly OSCON? Want to register for OSCON now? Use promo code 20CLOUD for 20% off Details to win an OSCON pass coming soon! Check out the OSCON Schedule Free eBook from O'Reilly Media for Cloudcast Listeners! Check out an excerpt from the upcoming Docker Cookbook Topic 1 - From an overall project perspective, what grades would you give Cloud Foundry in terms of stability, core functionality, security, operations, etc? Topic 2 - You were previously involved (directly/indirectly)with CloudStack. As you talk to people in the marketplace, how is it different discussing IaaS vs. PaaS. Topic 3 - How much ability will you have to drive prioritization within sub-projects or new projects? (eg. Security vs. new Languages vs. Interop, etc.) Topic 4 - What’s the CF.org way of thinking about interoperability? Topic 5 - What guidance are you giving the teams in terms of expandability of Cloud Foundry? Architecturally, are there certain places you recommend over other places? Topic 6 - Is there a place for integrating SaaS applications (monitoring, logging, etc.) into Cloud Foundry?
June 29, 2015
by Brian Gracely
· 1,153 Views
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Building an App with MongoDB: Creating a REST API Using the MEAN Stack Part 2
Written by Norberto Leite In the first part of this blog series, we covered the basic mechanics of our application and undertook some data modeling. In this second part, we will create tests that validate the behavior of our application and then describe how to set-up and run the application. Write the tests first Let’s begin by defining some small configuration libraries. file name: test/config/test_config.js Our server will be running on port 8000 on localhost. This will be fine for initial testing purposes. Later, if we change the location or port number for a production system, it would be very easy to just edit this file. To prepare for our test cases, we need to ensure that we have a good test environment. The following code achieves this for us. First, we connect to the database. file name: test/setup_tests.js Next, we drop the user collection. This ensures that our database is in a known starting state. Next, we will drop the user feed entry collection. Next, we will connect to Stormpath and delete all the users in our test application. Next, we close the database. Finally, we call async.series to ensure that all the functions run in the correct order. Frisby was briefly mentioned earlier. We will use this to define our test cases, as follows. file name: test/create_accounts_error_spec.js We will start with the enroll route in the following code. In this case we are deliberately missing the first name field, so we expect a status reply of 400 with a JSON error that we forgot to define the first name. Let’s “toss that frisby”: In the following example, we are testing a password that does not have any lower-case letters. This would actually result in an error being returned by Stormpath, and we would expect a status reply of 400. In the following example, we are testing an invalid email address. So, we can see that there is no @ sign and no domain name in the email address we are passing, and we would expect a status reply of 400. Now, let’s look at some examples of test cases that should work. Let’s start by defining 3 users. file name: test/create_accounts_spec.js In the following example, we are sending the array of the 3 users we defined above and are expecting a success status of 201. The JSON document returned would show the user object created, so we can verify that what was created matched our test data. Next, we will test for a duplicate user. In the following example, we will try to create a user where the email address already exists. One important issue is that we don’t know what API key will be returned by Stormpath a priori. So, we need to create a file dynamically that looks like the following. We can then use this file to define test cases that require us to authenticate a user. file name: /tmp/readerTestCreds.js In order to create the temporary file above, we need to connect to MongoDB and retrieve user information. This is achieved by the following code. file name: tests/writeCreds.js In the following code, we can see that the first line uses the temporary file that we created with the user information. We have also defined several feeds, such as Dilbert and the Eater Blog. file name: tests/feed_spec.js Previously, we defined some users but none of them had subscribed to any feeds. In the following code we test feed subscription. Note that authentication is required now and this is achieved using .auth with the Stormpath API keys. Our first test is to check for an empty feed list. In our next test case, we will subscribe our first test user to the Dilbert feed. In our next test case, we will try to subscribe our first test user to a feed that they are already subscribed-to. Next, we will subscribe our test user to a new feed. The result returned should confirm that the user is subscribed now to 2 feeds. Next, we will use our second test user to subscribe to a feed. The REST API Before we begin writing our REST API code, we need to define some utility libraries. First, we need to define how our application will connect to the database. Putting this information into a file gives us the flexibility to add different database URLs for development or production systems. file name: config/db.js If we wanted to turn on database authentication we could put that information in a file, as shown below. This file should not be checked into source code control for obvious reasons. file name: config/security.js We can keep Stormpath API and Secret keys in a properties file, as follows, and need to carefully manage this file as well. file name: config/stormpath_apikey.properties Express.js overview In Express.js, we create an “application” (app). This application listens on a particular port for HTTP requests to come in. When requests come in, they pass through a middleware chain. Each link in the middleware chain is given a req (request) object and a res (results) object to store the results. Each link can choose to do work, or pass it to the next link. We add new middleware via app.use(). The main middleware is called our “router”, which looks at the URL and routes each different URL/verb combination to a specific handler function. Creating our application Now we can finally see our application code, which is quite small since we can embed handlers for various routes into separate files. file name: server.js We define our own middleware at the end of the chain to handle bad URLs. Now our server application is listening on port 8000. Let’s print a message on the console to the user. Defining our Mongoose data models We use Mongoose to map objects on the Node.js side to documents inside MongoDB. Recall that earlier, we defined 4 collections: Feed collection. Feed entry collection. User collection. User feed-entry-mapping collection. So we will now define schemas for these 4 collections. Let’s begin with the user schema. Notice that we can also format the data, such as converting strings to lowercase, and remove leading or trailing whitespace using trim. file name: app/routes.js In the following code, we can also tell Mongoose what indexes need to exist. Mongoose will also ensure that these indexes are created if they do not already exist in our MongoDB database. The unique constraint ensures that duplicates are not allowed. The “email : 1” maintains email addresses in ascending order. If we used “email : -1” it would be in descending order. We repeat the process for the other 3 collections. The following is an example of a compound index on 4 fields. Each index is maintained in ascending order. Every route that comes in for GET, POST, PUT and DELETE needs to have the correct content type, which is application/json. Then the next link in the chain is called. Now we need to define handlers for each combination of URL/verb. The link to the complete code is available in the resources section and we just show a few examples below. Note the ease with which we can use Stormpath. Furthermore, notice that we have defined /api/v1.0, so the client would actually call /api/v1.0/user/enroll, for example. In the future, if we changed the API, say to 2.0, we could use /api/v2.0. This would have its own router and code, so clients using the v1.0 API would still continue to work. Starting the server and running tests Finally, here is a summary of the steps we need to follow to start the server and run the tests. Ensure that the MongoDB instance is running mongod Install the Node libraries npm install Start the REST API server node server.js Run test cases node setup_tests.js jasmine-node create_accounts_error_spec.js jasmine-node create_accounts_spec.js node write_creds.js jasmine-node feed_spec.js MongoDB University provides excellent free training. There is a course specifically aimed at Node.js developers and the link can be found in the resources section below. The resources section also contains links to good MongoDB data modeling resources. Resources HTTP status code definitions Chad Tindel’s Github Repository M101JS: MongoDB for Node.js Developers Data Models Data Modeling Considerations for MongoDB Applications
June 29, 2015
by Dana Groce
· 2,272 Views
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Stackato on the Microsoft Azure Cloud
The growth of Azure has been outstanding--more than 90,000 new subscriptions every month. And the innovation is exponential with over 500 new features and services being added to the platform in the last 12 months. We're very excited to be part of this growth. As we announced yesterday, you can now access Stackato through Azure. We think it's a great way for Azure customers to get access to a Cloud Foundry and Docker based PaaS. With Azure, Microsoft provides an easy path to the cloud for their customers. All applications can be run on one cloud. Microsoft wants to dominate the cloud the same as it has with on-premise software and rarely does a day go by without reading an article about Azure. Whether it's their recent announcement to help encourage start-up's use of Azure by providing $120,000 worth of credits per year or their commitment to open source. Azure gives its customers a growing collection of integrated services that make it easier to build and manage enterprise, mobile, web and Internet of Things (IoT) apps faster. Enterprises face real complexities when building their cloud solution. Having a solid infrastructure is really just the first step in the process--companies also need the right platform to support the deployment and management of their cloud-native applications. The platform should give their developers the freedom to use the language best suited to build the application. In addition, enterprises are on more than one cloud. They need to have the versatility to scale out or move their applications to whatever cloud is appropriate in order to meet end user demand without any downtime. With Stackato, we help remove these complexities. We provide enterprises with a polyglot PaaS that supports the development of applications in virtually any language. We like to refer to Stackato as being "infrastructure-agnostic" and allow companies to deploy their applications to any cloud--private, public or hybrid--without the need to run new scripts or re-package the application in order for it to work in the new environment. The combination of Stackato on Azure gives enterprises the technology they need to streamline application delivery, drive innovation and meet the demands of their customers.
June 29, 2015
by Kathy Thomas
· 943 Views
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Building an App with MongoDB: Creating a REST API Using the MEAN Stack Part 1
Written by Norberto Leite Introduction In this 2-part blog series, you will learn how to use MongoDB, Mongoose Object Data Mapping (ODM) with Express.js and Node.js. These technologies use a uniform language - JavaScript - providing performance gains in the software and productivity gains for developers. In this first part, we will describe the basic mechanics of our application and undertake data modeling. In the second part, we will create tests that validate the behavior of our application and then describe how to set-up and run the application. No prior experience with these technologies is assumed and developers of all skill levels should benefit from this blog series. So, if you have no previous experience using MongoDB, JavaScript or building a REST API, don’t worry - we will cover these topics with enough detail to get you past the simplistic examples one tends to find online, including authentication, structuring code in multiple files, and writing test cases. Let’s begin by defining the MEAN stack. What is the MEAN stack? The MEAN stack can be summarized as follows: M = MongoDB/Mongoose.js: the popular database, and an elegant ODM for node.js. E = Express.js: a lightweight web application framework. A = Angular.js: a robust framework for creating HTML5 and JavaScript-rich web applications. N = Node.js: a server-side JavaScript interpreter. The MEAN stack is a modern replacement for the LAMP (Linux, Apache, MySQL, PHP/Python) stack that became the popular way for building web applications in the late 1990s. In our application, we won’t be using Angular.js, as we are not building an HTML user interface. Instead, we are building a REST API which has no user interface, but could instead serve as the basis for any kind of interface, such as a website, an Android application, or an iOS application. You might say we are building our REST API on the ME(a)N stack, but we have no idea how to pronounce that! What is a REST API? REST stands for Representational State Transfer. It is a lighter weight alternative to SOAP and WSDL XML-based API protocols. REST uses a client-server model, where the server is an HTTP server and the client sends HTTP verbs (GET, POST, PUT, DELETE), along with a URL and variable parameters that are URL-encoded. The URL describes the object to act upon and the server replies with a result code and valid JavaScript Object Notation (JSON). Because the server replies with JSON, it makes the MEAN stack particularly well suited for our application, as all the components are in JavaScript and MongoDB interacts well with JSON. We will see some JSON examples later, when we start defining our Data Models. The CRUD acronym is often used to describe database operations. CRUD stands for CREATE, READ, UPDATE, and DELETE. These database operations map very nicely to the HTTP verbs, as follows: POST: A client wants to insert or create an object. GET: A client wants to read an object. PUT: A client wants to update an object. DELETE: A client wants to delete an object. These operations will become clear later when define our API. Some of the common HTTP result codes that are often used inside REST APIs are as follows: 200 - “OK”. 201 - “Created” (Used with POST). 400 - “Bad Request” (Perhaps missing required parameters). 401 - “Unauthorized” (Missing authentication parameters). 403 - “Forbidden” (You were authenticated but lacking required privileges). 404 - “Not Found”. A complete description can be found in the RFC document, listed in the resources section at the end of this blog. We will use these result codes in our application and you will see some examples shortly. Why Are We Starting with a REST API? Developing a REST API enables us to create a foundation upon which we can build all other applications. As previously mentioned, these applications may be web-based or designed for specific platforms, such as Android or iOS. Today, there are also many companies that are building applications that do not use an HTTP or web interface, such as Uber, WhatsApp, Postmates, and Wash.io. A REST API also makes it easy to implement other interfaces or applications over time, turning the initial project from a single application into a powerful platform. Creating our REST API The application that we will be building will be an RSS Aggregator, similar to Google Reader. Our application will have two main components: The REST API Feed Grabber (similar to Google Reader) In this blog series we will focus on building the REST API, and we will not cover the intricacies of RSS feeds. However, code for Feed Grabber is available in a github repository, listed in the resources section of this blog. Let’s now describe the process we will follow in building our API. We will begin by defining the data model for the following requirements: Store user information in user accounts Track RSS feeds that need to be monitored Pull feed entries into the database Track user feed subscriptions Track which feed entry a user has already read Users will need to be able to do the following: Create an account Subscribe/unsubscribe to feeds Read feed entries Mark feeds/entries as read or unread Modeling Our Data An in-depth discussion on data modeling in MongoDB is beyond the scope of this article, so see the references section for good resources on this topic. We will need 4 collections to manage this information: Feed collection Feed entry collection User collection User-feed-entry mapping collection Let’s take a closer look at each of these collections. Feed Collection Lets now look at some code. To model a feed collection, we can use the following JSON document: If you are familiar with relational database technology, then you will know about databases, tables, rows and columns. In MongoDB, there is a mapping to most of these Relational concepts. At the highest level, a MongoDB deployment supports one or more databases. A database contains one or more collections, which are the similar to tables in a relational database. Collections hold documents. Each document in a collection is, at a highest level, similar to a row in a relational table. However, documents do not follow a fixed schema with pre-defined columns of simple values. Instead, each document consists of one or more key-value pairs where the value can be simple (e.g., a date), or more sophisticated (e.g., an array of address objects). Our JSON document above is an example of one RSS feed for the Eater Blog, which tracks information about restaurants in New York City. We can see that there are a number of different fields but the key ones that our client application may be interested in include the URL of the feed and the feed description. The description is important so that if we create a mobile application, it would show a nice summary of the feed. The remaining fields in our JSON document are for internal use. A very important field is _id. In MongoDB, every document must have a field called _id. If you create a document without this field, at the point where you save the document, MongoDB will create it for you. In MongoDB, this field is a primary key and MongoDB will guarantee that within a collection, this value is unique. Feed Entry Collection After feeds, we want to track feed entries. Here is an example of a document in the feed entry collection: Again, we can see that there is a _id field. There are also some other fields, such as description, title and summary. For the content field, note that we are using an array, and the array is also storing a document. MongoDB allows us to store sub-documents in this way and this can be very useful in some situations, where we want to hold all information together. The entryID field uses the tag format to avoid duplicate feed entries. Notice also the feedID field that is of type ObjectId - the value is the _id of the Eater Blog document, described earlier. This provides a referential model, similar to a foreign key in a relational database. So, if we were interested to see the feed document associated with this ObjectId, we could take the value 523b1153a2aa6a3233a913f8 and query the feed collection on _id, and it would return the Eater Blog document. User Collection Here is the document we could use to keep track of users: A user has an email address, first name and last name. There is also an sp_api_key_id and sp_api_key_secret - we will use these later with Stormpath, a user management API. The last field, called subs, is a subscription array. The subs field tells us which feeds this user is subscribed-to. User-Feed-Entry Mapping Collection The last collection allows us to map users to feeds and to track which feeds have been read. We use a Boolean (true/false) to mark the feed as read or unread. Functional Requirements for the REST API As previously mentioned, users need to be able to do the following: Create an account. Subscribe/unsubscribe to feeds. Read feed entries. Mark feeds/entries as read or unread. Additionally, a user should be able to reset their password. The following table shows how these operations can be mapped to HTTP routes and verbs. Route Verb Description Variables /user/enroll POST Register a new user firstName lastName email password /user/resetPassword PUT Password Reset email /feeds GET Get feed subscriptions for each user with description and unread count /feeds/subscribe PUT Subscribe to a new feed feedURL /feeds/entries GET Get all entries for feeds the user is subscribed to /feeds/&ltfeedid>/entries GET Get all entries for a specific feed /feeds/&ltfeedid> PUT Mark all entries for a specific feed as read or unread read = &lttrue | false> /feeds/&ltfeedid>/entries/&ltentryid> PUT Mark a specific entry as either read or unread read = &lttrue | false> /feeds/&ltfeedid> DELETE Unsubscribe from this particular feed In a production environment, the use of secure HTTP (HTTPS) would be the standard approach when sending sensitive details, such as passwords. Real World Authentication with Stormpath In robust real-world applications it is important to provide user authentication. We need a secure approach to manage users, passwords, and password resets. There are a number of ways we could authenticate users for our application. One possibility is to use Node.js with the Passport Plugin, which could be useful if we wanted to authenticate with social media accounts, such as Facebook or Twitter. However, another possibility is to use Stormpath. Stormpath provides User Management as a Service and supports authentication and authorization through API keys. Basically, Stormpath maintains a database of user details and passwords and a client application REST API would call the Stormpath REST API to perform user authentication. The following diagram shows the flow of requests and responses using Stormpath. In detail, Stormpath will provide a secret key for each “Application” that is defined with their service. For example, we could define an application as “Reader Production” or “Reader Test”. This could be very useful when we are still developing and testing our application, as we may be frequently adding and deleting test users. Stormpath will also provide an API Key Properties file. Stormpath also allows us to define password strength requirements for each application, such as: Must have >= 8 characters. Must include lowercase and uppercase. Must include a number. Must include a non-alphabetic character Stormpath keeps track of all of our users and assigns them API keys, which we can use for our REST API authentication. This greatly simplifies the task of building our application, as we don’t have to focus on writing code for authenticating users. Node.js Node.js is a runtime environment for server-side and network applications. Node.js uses JavaScript and it is available for many different platforms, such as Linux, Microsoft Windows and Apple OS X. Node.js applications are built using many library modules and there is a very rich ecosystem of libraries available, some of which we will use to build our application. To start using Node.js, we need to define a package.json file describing our application and all of its library dependencies. The Node.js Package Manager installs copies of the libraries in a subdirectory, called node_modules/, in the application directory. This has benefits, as it isolates the library versions for each application and so avoids code compatibility problems if the libraries were to be installed in a standard system location, such as /usr/lib, for example. The command npm install will create the node_modules/ directory, with all of the required libraries. Here is the JavaScript from our package.json file: Our application is called reader-api. The main file is called server.js. Then we have a list of the dependent libraries and their versions. Some of these libraries are designed for parsing the HTTP queries. The test harness we will use is called frisby. The jasmine-node is used to run frisby scripts. One library that is particularly important is async. If you have never used node.js, it is important to understand that node.js is designed to be asynchronous. So, any function which does blocking input/output (I/O), such as reading from a socket or querying a database, will take a callback function as the last parameter, and then continue with the control flow, only returning to that callback function once the blocking operation has completed. Let’s look at the following simple example to demonstrate this. In the above example, we may think that the output would be: one two but in fact it might be: two one because the line that prints “one” might happen later, asynchronously, in the callback. We say “might” because if conditions are just right, “one” might print before “two”. This element of uncertainty in asynchronous programming is called non-deterministic execution. For many programming tasks, this is actually desirable and permits high performance, but clearly there are times when we want to execute functions in a particular order. The following example shows how we could use the async library to achieve the desired result of printing the numbers in the correct order: In the above code, we are guaranteed that function two will only be called after function one has completed. Wrapping Up Part 1 Now that we have seen the basic mechanics of node.js and async function setup, we are ready to move on. Rather than move into creating the application, we will instead start by creating tests that validate the behavior of the application. This approach is called test-driven development and has two very good features: It helps the developer really understand how data and functions are consumed and often exposes subtle needs like the ability to return 2 or more things in an array instead of just one thing. By writing tests before building the application, the paradigm becomes “broken / unimplemented until proven tested OK” instead of “assumed to be working until a test fails.” The former is a “safer” way to keep the code healthy.
June 28, 2015
by Dana Groce
· 10,912 Views · 2 Likes
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