Wednesday, December 19, 2018

What's the case for your API?

Disclaimer: In pure REST, API's are opaque and the URL should be whatever what was sent as a link in the response to a previous request. But, I'm not talking pure REST, I'm talking more pragmatic APIs which involve some concepts from REST and as well as general API best practices. 

When writing an API, it starts simple. You identify the obvious resources and end up with endpoints such as:
/api.mycompany.com/tweet

Eventually, your API will have to capture more sophisticated concepts and model more complex resources that cannot be expressed in short single nouns.  Some real world examples include:
  • Enabling request validation via a Request Validator resource (AWS API Gateway API)
  • Performing a customer search via a Customer Search resource (Google Customer Search API)
  • Running powerful checks against code via a Check Runs resource (Github API)
In English grammar, nouns that are really two nouns joined in some way are called compound nouns and in English grammar, compound nouns follow one of three patterns:
  1. All the one word: haircut, toothpaste
  2. Two words: rain forest, ice cream
  3. Hyphenated: self-esteem, brother-in-law
In the API world there are different options to choose from but it is better for consistency that your APIs just pick one approach and stick to that it. So firstly, what are the options for compound nouns from an API perspective?

Camel Case


Camel case is the practise of writing each word in the phrase with a capital letter.  There are two variations:
  1. Initial upper case (also know as Pascal's case) is where the first letter is also a capital, for example: IceCream.  Pascal's case is popular in programming languages for naming classes e.g. Java. 
  2. Initial lower case is where the initial letter is always lower case, for example: iceCream.  This approach is popular in programming languages (again Java is a good example)  for naming variables.  When people say camel case, they are usually referring to the initial lower case format.

Kebab Case

In Kebab Case, the individual words are separated by hyphens. Ice cream is expressed as ice-cream.  This approach is used in the Lisp programming language, in lots of URLs (for example, every blog post in www.blogger.com e.g. http://dublintech.blogspot.com/2018/08/oauth-20-authorisation-code-grant.html).  The observant amongst you will note sometimes the word "dash" is sometimes used in technical references instead of "hyphen".  So, what's the difference?  In English grammar, hyphen is the thing  used to join two words to make one whereas the dash is the thing used to usually add some sort stylistic emphasis to the end of a sentence such as: "I might have an interesting point here - you never know".

In programming we don't care whether the term is hyphen and dash. They are used interchangeably and mean the same thing.

The kebab case approach became popular in Web URIs because search engines knew that the hyphen meant separate words and could index the URI properly.  This convention used by search engines meant hyphens became a de facto standard for URIs.

Snake Case

In this approach, an underscore is used to separate words.  Ice cream becomes ice_cream. This approach is used in Python and Ruby for anything other than a class name or static constant.

Join words

In this approach the words are just joined. There is no -, no _ and no capitalisation of anything. This is not a popular approach with developers because it's difficult to read.

APIs

Should we use camelCase, kebab-case or snake_case in an API?  Well unfortunately, Mr. Fielding's dissertation did not go into such detail.  So what are people actually doing?  And is the approach used consistent across the API's URL and the JSON Body.  Let's take a look.

AWS

AWS have different API styles for different services.  The API Gateway REST API reference shows that JSON payload uses camel case but the URL uses nothing, it's just:
/restapis/{id}/requestvalidators/{requestvalidatorId}

Google

Surprise, surprise Google also have lots of APIs. The Google
Custom Search API is similar to the AWS API Gateway API.  The compound noun in the URL is just the one word and the JSON body is camel case.  

The Google Gmail API has camel case in request body and in some URLs, for example the forwarding addresses API.  

The Google youtube API sometimes will use kebab case in the URL e.g. yt-analytics but in other cases will use single word e.g. youtubepartner.   But the JSON payload is camel case.

Github

The Github API is a good example where we get a reminder that if possible, you should try to avoid this issue by trying to avoid compound nouns as it avoids them by using some creative name spacing.

However, some more rooting around and you'll find a compound noun such as check run expressed using kebab case in the URL and he JSON body using snake case. 

Stripe

Stripe use snake case in the URL and in the JSON body.  For example the PaymentsIntents API

 https://api.stripe.com/v1/payment_intents 

and JSON body...
{
  "id": "pi_Aabcxyz01aDfoo",
  "object": "payment_intent",
  "allowed_source_types": [
    "card"
  ],
  "amount": 1099,
  "amount_capturable": 1000,

Paypal

Paypal have more compound nouns than the other APIs checked. APIs for resources such as billing agreement  the API will use kebab case in the URL but then use snake case in the JSON payloads.

Twitter

Twitter use snake case in the URL e.g. /saved_searches/ and snake case in the JSON payloads.

Facebook

Facebook's Graph API tends to avoid resource naming in URLs and in JSON bodies it is snake case.

By this stage, you should be getting a little but confused. So let's recap via the table below.

APIURLJSON Body
AWS API GatewayNo separatorcamelCase
Facebook Graph API N/Asnake_case
Github Snake and Kebabsnake_case
Google custom search No separatorcamelCase
Google Gmail camelCasecamelCase
LinkedIn camelCasecamelCase
Pay pal kebab-casesnake_case
Stripe snake_casesnake_case
Twitter snake_casesnake_case


Everyone is different, what should I do?

So there is a lack of consistency across the industry.  However there are point worth making:
  1. In general compound nouns are best avoided.  In all the APIs checked (except PayPal), they appear in under 5% of the APIs.  This means developers don't get upset when their favourite approach is not used.
  2. The only Web API in the selection above that had more than 5% of its APIs using compound nouns was PayPal and they went for kebab-case in URIs.
  3. kebab-case is never used in any JSON body.  The syntax is allowed.  So what drives this trend? It's more than likely because JavaScript Web UIs are possibly the mos popular client invoking API and the similarly the most popular back end language serving the API is Java and both of those dudes don't allow the - in any of their declarations.

Is there anyone else saying anything?


In the excellent REST API Design Cookbook, industry expert Mark Masse suggests:
  1. Make your APIs lower case, which rules out camel case
  2. Use kebab case when expressing compound terms in your URIs
  3. Avoid using underscores as they may not display well since some browser render hyperlinks with an underline added

Make a decision

  1. Avoid compound nouns if you can.  This isn't always possible. Sticking to a ubiquitous language is important and helpful.  If you have a complex business application you will have lots of compound nouns. 
  2. If you can't avoid compound nounds and more than 5% of the APIs are going to involve compound nouns use kebab case for your URIs. Why?  Because if you have a complex business domain it's not only developers you need to think about.  Lots of BA's, Product Architects, curious Managers will also be looking at your APIs. Kebab-case is the easiest to read for everyone.
  3. For JSON body, I think it is okay to use camelCase because this is the easiest to map back to JavaScript and Java code.  It is also a recommendation from Google to use camelCase in JSON.
  4. If you have to use camelCase in your URI's, consider using the first letter capital approach for the URIs as the URIs are supposed to marking resources not attributes. Resources are more analogous to Java Classes which also use initial letter capital format; whereas the JSON payload attributes are analogous to Java attributes which use initial lower case.
Until the next time, take care of yourselves.



Monday, November 19, 2018

Technical Debt will kill your Agile Dreams

Warning this blog post is a bit of a rant, if you want something more technical there's plenty of other articles on this blog and many others!
Technical debt is a concept used in software engineering to express the additional complexity that is added to a project due to technical decisions that result in inferior solutions being chosen because they can be delivered quicker. It is analogous to financial debt. Borrow money for some gain, incur debt but -and there's always abut - you have to pay the debt off. Otherwise, with interest, the debt will grow and you will eventually be bankrupt.  Similarly, it is ok to incur some technical debt.  If you spend too long looking for the perfect solution your customers will have moved over to someone else. At the same time, if you incur too much technical debt and you are not meeting your repayments, just as the interest on the financial debt will compound repayments, if you don't meet your technical debt repyaments the complexity and entropy increases with time and eventually it will stagnate your product. Something that should really only take one month to deliver, suddenly takes 3 months, then 6 months, then there's only a few people in the entire company who can do it in 6 months, then a few of them leave, then...
Where the analogy breaks down is that all too often technical debt is also used to express that there has just been some bad engineering. Bad engineering decisions are in a different category to ones that were tactically made with full knowledge that the short-term priority was worth it. When it's clear that such a decision was, in fact, a tactical decision, it is much easier to convince people that refactoring needs to happen and the debt has to be paid off. Unfortunately, when the term is used as a polite way of saying bad engineering, it's unlikely there is any repayment strategy in place and it is even harder to create one because first, you need to convince people there is some bad engineering, then you need to convince people it is causing problems, then you have to have the ability to think of a better approach, cost it and then convince the various stakeholders the investment is worth it.  It is like trying to win 5 matches in a row away from home when the odds are against you.  
So, what are the signs that your technical debt (irrespective of whether it is intentional or accidental) is just too high and you could be filing for bankruptcy soon?
Well, think about this. In the Agile world, we want to develop, build, release in quick cycles and get rapid feedback from the customer and go again. This is only possible if there is an abundance of high quality and well engineered automated tests that run fast and provide confidence that a change no matter what it is has not broken anything. It doesn't matter if the breakdown of the tests are: 68% unit tests and 32% are integration tests or 91% are unit tests and 9% integration tests, the tests have to run fast and they have to provide confidence to all stakeholders. Otherwise releasing will be a pain and it will not be possible to release regularly. That means being Agile and getting all the benefits of it will be very difficult — no matter how good your backlog grooming sessions are.
What usually makes it difficult for developers to write good tests? Well, it's usually technical debt. It doesn't matter if it is intentional or accidental.
Now, there are all sorts of tools that will measure technical debt and put it up on a nice looking SonarQube board but usually these tools only pick up the trivial stuff — removing an unused import etc. Who cares?  Such trivial stuff isn't going to slow anyone down.   The real technical debt problems are the ones that slow people down, they make it harder to make changes, fix bugs, add functionality and do it all quickly with confidence - why because they have made things much harder to test.  Sadly, these are technical problems are usually not just something an IDE, a PMD or Checkstyle will prompt you to do.  They are generally much deeper in nature - towards the architectural end of specturm. Some examples:
  • Lack of data encapsulation and immutability leading to huge cyclomatic complexity, unpredictable code paths and difficulty in predicting impacts.
  • Impedence mismatches
  • Lack of modularity and too much coupling
  • Lack of or bad application of patterns
  • A proprietary language introduced with no IDE support, no mechanism to easily unit test or debug code. Zero support from Stackoverflow.
  • Spawning of threads and asynchronous call paths when are better approaches which would be much easier to test
This is where the analogy of technical debt and financial debt breaks down.   When you have architectural debt you have big problems. When you have a HashMap used when an ArrayList would have made more sense, you don't. Financial debt doesn't have such a critical distinction.  Unless we say, the debt is due to a friendly and sympathetic bank you have a good relationship with or its debt due to some lunatic load shark who will call around to your house with a baseball bat.  
So, if you realize you are approaching your credit limit, what do you do? Firstly, you need to get confidence in your tests. They need to test key functionality and they need to be maintainable. That is more important than speed. If you can't get confidence, you can't ship. There is not much use with tests that run in 5 minutes if no-one has any confidence the functionality you deliver will actually work. Secondly, once you have confidence in the tests (even if they are ugly end-to-end tests), you need to get them to run fast. This is where you can start refactoring — the end to end tests should facilitate refactoring of parts of the call paths; there may be obvious sprouts for example and this should help to move towards a classical test pyramid
Thirdly, you now need to understand why the code is so difficult to achieve high-quality tests. Too much coupling, bad exception handling, bad decompositions it's probably a long list. Understanding why your code is difficult to test is a key architectural and engineering skill as it requires the ability to not just understand the complexity but the ability to be able to know how to reduce it.   Reducing the debt should then provide a pathway to make the code easier to test and thus make good tests run fast.   Achieving this means you are winning the battle against eventual project stagnation. 
So, the last part of the rant.  There is a growing problem with the application of Agile which means we end very easily end up with faux Agile. The various Agile books, courses, blogs will detail things like story points, burn downs, stand ups. That stuff is all good but there is not enough focus on technical excellence. Without the technical excellence you get inevitable architectural problems and code that is difficult to test irrespective of your best efforts at story pointing, backlog grooming, sticking yellow post-its up on walls, having arguments over the definition of done, doing your show and tells. All that stuff is nice and beneficial but in comparison to technical excellence it is almost superficial. It is something people can easily see but on its own it never captures the complexity of technical debt.  It is the ratio of technical debt to technical excellence that determines whether you can write testable code easily and thus be able to deliver in regular short iteration which is the goal of Agile. Isn't it? 
Lastly, something we all say to our kids when they try any sport: "If you are losing kido, never give up, play to the end of the match". 
Technical Debt is natural, it happens in every project in the world.  Your job is to keep it in check and when it gets too high to be able innovate your way work around it and eventually lesson it.  That's such an essential characteristic of a good Architect, it is also a handy interview question. 
"Mr. Candidate, describe some Technical Debt you have experienced, describe the effects of it and what strategies you put in place to either work around it or deal with it?"
Or even, 
"Mr. Candidate here are some examples of Technical Debt, how would you prioritise which ones to deal with? Could you describe some better solutions and how would you convince people they are worth it?"
The quality of detail in the answer you get back is very likely to indicate the level of pragmatism, experience and the innovation skills of the Architect /  Engineer.
Until the next take care of yourselves.

Sunday, August 5, 2018

OAUTH 2.0 Authorisation Code Grant

OAuth 2.0 provides a number of security flows (or grant types) to allow an application access user's data in another application.  In this blog, we will look at the
OAuth 2.0 grant: Authorisation Code Grant.

Firstly, a number of definitions:
  • Client: The application the user is currently interacting with.  For example, let's assume a fictitious funky blogging site: www.myfunkyblog.com.  The client wants to communicate with another application and retrieve something about the user from there.  For example, their favourite photo!  Let's assume the fictitious megaphotosharing.com as the service the client wishes to access. 
  • Client ID: this is an ID which identifies the client.  It can be passed around openly in Web URLs etc
  • Client secret ID:  A secret ID that only the client knows. This is kept server side and will be used in requests to the application for which access is sought.   It cannot be passed around in Web URLs. 
  • Resource owner: This is usually the human, who is using the client application. The resource owner has data in another application (e.g. megaphotosharing.com) that the client (myfunkyblog.com) wishes to access. The goal is to facilitate that sharing without the need for the Resource owner aka the human to ever pass their megaphotosharing.com password to myfunkyblog.com.  Note: the resource owner doesn't have to be a human but interestingly according to the OAuth spec, when it is a human it can also be referred to as the end - user. 
  • Resource Server: hosts the protected resources of the resource owner that the client is interested in. So this is the megaphotosharing.com server that has the Resource owner photos the myfunkyblog.com is interested in.
  • Authorisation Server: the server who issues a token to myfunkyblog.com after the resource owner has successfully authenticated and allowed myfunkyblog.com obtain some of its megaphotosharing.com.  Sometimes the Authorisation Server and the Resource server are effectively the same but they don't have to be. 
  • Access token: a special type of token the myfunkyblog.com authorisation server gives megaphotosharing.com to access the protected resources. It will contain scope, lifetime and other access attributes.

Use case

So the use case is that the client (myfunkyblog.com) wants to access information about
the resource owner (the human) from another application: megaphotosharing.com.

Client Registration

The first the client must do is register with the service (megaphotosharing.com)
providing its name, website etc.  The service will return a secret client code.
The client keeps this private and is responsible for ensuring only it knows it.  Usually,
it will encrypt and persist it in the client somewhere on the backend.  The service will
also receive a client id.  Unlike the client secret, this is public and can be passed around
in URLs etc.

Flow

Ok so now the actual flow.    The user is browsing around myfunkyblog.com and accesses a part of the site where myfunkyblog.com wants to know what the end-user's favourite photo is.

A pop-up screen appears to end-user.

This has URL:

https://megaphotosharing.com/oauth/authorize?response_type=code&client_id=CLIENT_ID&redirect_uri=CALLBACK_URL&scope=read

Key parts of this URL:
  • megaphotosharing.com: This is the domain for the authorisation server
  • response_type=code: Required parameter to enable the client informs the authorization server the desired grant type. An alternative value would be the "token", this is for the implicit flow.
    "code" means the client wants an authorization code which will be returned after
    resource owner logs in.  This authorization code will be used in a subsequent request by the Client. 
  • client_id: Required parameter, to identify the client.  Remember this is public and
    can be passed to and from a web browser.
  • redirect_uri: This is an optional parameter.  It enables the client to dynamically specify the URL the auth server should redirect to.  In some flows, this isn't needed as there is only one redirect URI and this is registered by the client with the service during client registration.
  • scope:  This is an optional parameter.  It specifies the level of access that the application is requesting. In this case it is just a read.  The auth server uses this to inform the user / resource owner what the client is trying to do.
The user then logs into megaphotosharing.com who tells the user what the Client wants to do.  If the user selects ok, megaphotosharing.com redirects to the redirect URI passed up.

https://myfunkyblog.com/callback?code=212132kjhkhj

Notice how the client id is passed over the web in a URL and the authorisation code is passed back over the web.

The client, then uses the returned authorization code, its client id, client secret and the grant type to make a POST request Server to Server to get an Access Token.  This happens all on the back end.

https://megaphotosharing.com/v1/oauth/token?client_id=CLIENT_ID&client_secret=CLIENT_SECRET&grant_type=authorization_code&code= 212132kjhkhj&redirect_uri=CALLBACK_URL

Notes:

  • client id and client secret identify the client.  This is a back-end request and hence it is okay to pass the client_secret (which would obviously never pass to or from the browser).
  • grant_type: This must be set to authorisation_code. As it indicates the Authorisation Code Grant.  Remember, the grant is used to indicate the flow the client is using (it can also be used by the server what types of flows are available). If the client was using the Client Credentials Grant, this value would be: "client_credentials".  If the client was using "Resource Owner Password Credentials Grant" the value would be "password".
  • code: 212132kjhkhj - The actual authorisation code what was returned from initial authorisation request from the authorisation server. This is required.
  • redirect_uri: if the redirect_uri was included in the authorisation request this value must be the same as the value used in that request.


The client then receives back an access token. Something like this:

{"access_token":"ACCESS_TOKEN","token_type":"bearer","expires_in":2592000,"refresh_token":"REFRESH_TOKEN","scope":"read","uid":1001013121222}

It will now use this to access some of the resource owner's resource data.

So what's the big deal?

  • There are obviously big advantages for users not having to tell one website its password for another site. 
  • Reduces the number of passwords a user needs to remember
  • Allows richer websites by allowing disparate applications to talk to each other. 

Why do people find it confusing?

There are a number of reasons why people find OAuth 2.0 confusing. 
  • There are a few different flows or grants.  The Authorisation Code Grant is just one.  Sometimes when you google explanations for OAuth 2.0 you get explanations for different grants without making it clear what is and isn't being explained. Hence why I put Authorisation Code Grant in the title.  
  • Terminology.  I'll just speak for myself.  But if I am reading quickly, I am likely to:
    •  confuse "Client" with the end-user
    • get confused between the Resource Server and Authorisation Server
  • Consistenty.  A lot of places implement OAuth 2.0 or something very similar to OAuth but will refer to things differently along the way.  For example, go to quora.com and try to login to google.  You are taken to: 
    https://accounts.google.com/signin/oauth/oauthchooseaccount?client_id=917071888555.apps.googleusercontent.com&as=rdWeinbqWJbt6ChoW2f3Fg&destination=https%3A%2F%2Fwww.quora.com&approval_state=!ChRyQlhnbEYzai1xQTliNlNmTEVmNRIfZ3doM2hlRVIycGdiMEVBN1JaNXdOM085MERXLVVCWQ%E2%88%99ANKMe1QAAAAAW2i2to0SOyO2_w3k3O4gjwUKQLGNmZ2h&oauthgdpr=1&xsrfsig=AHgIfE8EzSxvWfzyxou0dwLDxv4GhD6e5g&flowName=GeneralOAuthFlow
    
    There's no response_type in that URL.
  • OAuth is an authorisation spec.  It is usually used with Authentication spec like Open Connect but that is actually a separate spec. 

Friday, July 27, 2018

Java Lambda Streams and Groovy Clouses Comparisons

This Blog post will look at some proverbial operations on List data structure and make some comparison between Java 8/9 and Groovy syntax.  So firstly, the data structure.  It's just a simple Rugby player who has name and a rating.

Java

class RugbyPlayer {
    private String name;
    private Integer rating;
    
    RugbyPlayer(String name, Integer rating) {
        this.name = name;
        this.rating = rating;
    }

    public String toString() {
        return name + "," + rating;
    }
        
    public String getName() {
        return name;
    }
        
    public Integer getRating() {
        return rating;
    }
}

//...
//...
List<RugbyPlayer> players = Arrays.asList(
    new RugbyPlayer("Tadgh Furlong", 9),
    new RugbyPlayer("Bundee AKi", 7),
    new RugbyPlayer("Rory Best", 8),
    new RugbyPlayer("Jacob StockDale", 8)
);

Groovy

@ToString
class RugbyPlayer {
    String name
    Integer rating
}
//...
//...
List<RugbyPlayer> players = [
    new RugbyPlayer(name: "Tadgh Furlong", rating: 9),
    new RugbyPlayer(name: "Bundee AKi", rating: 7),
    new RugbyPlayer(name: "Rory Best", rating: 8),
    new RugbyPlayer(name: "Jacob StockDale", rating: 8)
]

Find a specific record

Java

// Find Tadgh Furlong
Optional<RugbyPlayer> result = players.stream()
    .filter(player -> player.getName().indexOf("Tadgh")  >= 0)
    .findFirst();      
String outputMessage = result.isPresent() ? result.get().toString() : "not found";
System.out.println(outputMessage);

Groovy

println players.find{it.name.indexOf("Tadgh") >= 0}

Comments

  • The Java lambda has just one parameter: player.  This doesn't need to be typed as its type can be inferred.  Note: If there were two parameters in the parameter list, parenthesis would be needed around the parameter list.
  • In Java, a stream must be created from the List first before the functional operation can be applied.  
  • A lambda is then used to before performing a function which returns an Optional
  • The lambda definition doesn't need a return statement.  It also doesn't need {} braces or one of those semi-colons to complete a Java statement.  However, you can use {} if you want to and if you want to, you must include the ; and the return statement.  Note: if you lambda is more than one line, you don't get a choice, you must use {}.   It is recommended best practise to keep Lambda's short and to just one line. 
  • Java 8 supports fluent APIs for pipeline Stream operations.  This is also supported in Groovy Collection operations.
  • In Java a player variable is specified for the Lambda.  The Groovy closure doesn't need to specify a variable.  It can just use "it" which is the implicit reference to the parameter (similar to _ in Scala).  
  • The Java filter API takes a parameters of type Predicate.   A Functional Interface means: can be used as the assignment target for a lambda expression or method reference.  Predicate, is type of Functional interface.  It's one abstract method is: boolean test(T t).    In this case, in the lamda, the player corresponds to t.  The body definition should evaluate to a true or a false, in our case player.getName().indexOf("Tadgh") will always evaluate to true or false.  True corresponds to a match. 
  • Java 8 has other types of Functional Interfaces:
    • Function – it takes one argument and returns a result
    • Consumer – it takes one argument and returns no result (represents a side effect)
    • Supplier – it takes not argument and returns a result
    • Predicate – it takes one argument and returns a boolean
    • BiFunction – it takes two arguments and returns a result
    • BinaryOperator – it is similar to a BiFunction, taking two arguments and returning a result. The two arguments and the result are all of the same types
    • UnaryOperator – it is similar to a Function, taking a single argument and returning a result of the same type
  • Java 8 can infer the type for the lambda input parameters. Note if you have to specify the parameter type,  the declaration must be in brackets which adds further verbosity.
  • Groovy can println directly.  No System.out needed, and no need for subsequent braces.
  • Like Java, Groovy doesn't need the return statement.  However, this isn't just for closures, in Groovy it extends to every method.    Whatever is evaluated as the last line is automatically returned. 
  • Groovy has no concept of a Functional interface.  This can mean if you forget to ensure your last expression is an appropriate boolean expression, you get unexpected results and bugs at runtime.
  • The arrow operator is used in both Groovy and Java to mean effectively the same thing - separating parameter list from body definition. In Groovy it is only needed it you need to declare the parameters (the default it, doesn't suffice). Note: In Scala, => is used.

 

Find specific records

 

Java

// Find all players with a rating over 8
List<RugbyPlayer> ratedPlayers = players.stream()
    .filter(player -> player.getRating() >= 8)
    .collect(Collectors.toList());
ratedPlayers.forEach(System.out::println);

Groovy

println players.findAll{it.rating >= 8}

Comments

  • In the Java version, the iterable object ratedPlayers has its forEach method invoked.   This method takes a FunctionalInterface of type Consumer (see Jdoc here).  Consumer, methods a function which takes an input parameter but returns nothing, it is void.  
  • In Java, the stream.filter() will return another stream. Stream.collect() is one of Java 8's Stream terminal methods. It performs mutable fold operations on the data elements held inside the stream instance return by the filter method.  
  • Collectors.toList () returns a Collector which collects all Stream elements into a List.
  • When using the toList() collector, you can't assume the type of List that will be used.  If you want more control you need to use the toCollection().  For example: 
    • .collect(toCollection(LinkedList::new)
  • Note: We could have omitted the .collect() operation and invoked forEach straight on the stream.   This would make the Java code shorter.  
players.stream()
   .filter(player -> player.getRating() >= 8)
   .forEach(System.out::println);
  • System.out::println is a method reference - a new feature in Java 8.   It is syntactic sugar to reduce the verbosity of some lambdas.  This is essentially saying, for every element in ratedPlayers, execute, System.out.println, passing in the the current element as a parameter.
  • Again less syntax from Groovy.  The function can operate on the collection, there is no need to create a Stream.  
  • We could have just printed the entire list in the Java sample, but heck I wanted to demo forEach and method reference.

 

Map from object type to another

Java

// Map the Rugby players to just names. 
// Note, the way we convert the list to a stream and then back again to a to a list using the collect API. 
System.out.println("Names only...");
List<String> playerNames = players
    .stream()
    .map(player -> player.getName())
    .collect(Collectors.toList());
playerNames.forEach(System.out::println);

Groovy:

println players.collect{it.name}

Comments

  • A stream is needed to be created first before executing the Lambda.  Then the collect() method is invoked on the Stream - this is needed to convert it back to a List. This makes code more verbose. 
  • That said, if all you are doing is printing the list, you can just do...
    players.stream()
       .map(player -> player.getName())
       .forEach(System.out::println);
    

 

Perform a Reduction calculation

Java

System.out.println("Max player rating only...");
Optional<Integer> maxRatingOptional = players.
   stream()
   .map(RugbyPlayer::getRating)
   .reduce(Integer::max);
String maxRating = maxRatingOptional.isPresent() ? maxRatingOptional.get().toString() : "No max";
System.out.println("Max rating=" + maxRating);

Groovy

def here = players.inject(null){ 
    max, it -> 
        it.rating > max?.rating ? it : max
} 

Comments

  • In the Java version, the reduce operation is invoked on the Stream.  There are three different versions of this method.   In this version, no initial value is specified meaning and an Optional type is returned.  The input parameter of type BinaryOperator.  Because BinaryOperator is a functional interface it means a lamda expression or method reference can be used to specify its value.  In this case, the method reference Integer.max() is used.
  • The null safe operator is used in the Groovy inject closure - so that the first comparsion will work 
  • In Java, it is possible to avoid the isPresent check on the optional by just doing...
    players.stream()
       .map(RugbyPlayer::getRating)
       .reduce(Integer::max);
       .map(Objects::toString).orElse("No max")
    

Summary

  • Groovy is still far more terse
  • However, some of the function operations in Java are lazily run.  For example map(), filter() which are considered intermediate.  Intermediate operations produce antoher Stream.  They won't execute unless a terminal function e.g. forEach, collect, reduce is invoked on the stream.  Terminal functions are value or side-effect producing. 
  • Intermediate operations can either be stateless or stateful.  Stateless operations like map() or filter() can operate on elements independently. Stateful operations like distinct() or sorted() may incorporate data from previously seen elements.  
  • The elements of a stream are only visited once during the life of a stream. Like an Iterator, a new stream must be generated to revisit the same elements of the source.
  • In Java processing streams lazily allows for two performances efficiencies:
    • fusing of multiple operations to minimise passes of the data
    • avoiding examination of the data unless it is necessary.  A stream() may have an infinite number of elements, but a findFirst() or limit() operation will mean that only data that needs to checked will be.
  • This may the code more verbose in cases, but it also means it can be more performant.
  • Groovy also offers some lazy functions. 
Full Java code here. Full Groovy code here.