Saturday, January 23, 2016

Ext JS - Changing the "Today" button in a date field

The datefield in Ext JS has a date picker dropdown containing a handy "Today" button, which allows you to quickly select today's date. But what if you're not interested in today's date? This post shows how you can change the text and the handler of the "Today" button in order to select a different date e.g. yesterday.

My jsfiddle for changing the "Today" button is embedded below. After the date field has been rendered, it gets a reference to the Today button via the date field's picker object and then changes its handler to select yesterday's date instead.

Friday, January 01, 2016 in 2015

Happy 2016, everyone!

I'd like to wish everyone a great start to an even greater new year!

In keeping with tradition, here's one last look back at in 2015.

During 2015, I posted 13 new entries on I am also thrilled that I have more readers from all over the world! Thanks for reading and especially for giving feedback.

Top 5 posts of 2015:

I'm going to be writing a lot more this year, so stay tuned for more great techie tips, tricks and hacks! :)

Related posts:

Monday, December 28, 2015

Java 8: Convert a String to a Stream of Characters

I find it a bit strange that the Java API does not provide a method to convert a String to a Stream<Character>, but this is how you can do it:

Stream<Character> stream = string.chars().mapToObj(i -> (char)i);

// or:

Stream<Character> stream = IntStream.range(0, string.length())

Sunday, November 29, 2015

Java 8 Streams API: Grouping and Partitioning a Stream

This post shows how you can use the Collectors available in the Streams API to group elements of a stream with groupingBy and partition elements of a stream with partitioningBy.

Consider a stream of Employee objects, each with a name, city and number of sales, as shown in the table below:

| Name     | City       | Number of Sales |
| Alice    | London     | 200             |
| Bob      | London     | 150             |
| Charles  | New York   | 160             |
| Dorothy  | Hong Kong  | 190             |


Let's start by grouping employees by city using imperative style (pre-lamba) Java:

Map<String, List<Employee>> result = new HashMap<>();
for (Employee e : employees) {
  String city = e.getCity();
  List<Employee> empsInCity = result.get(city);
  if (empsInCity == null) {
    empsInCity = new ArrayList<>();
    result.put(city, empsInCity);

You're probably familiar with writing code like this, and as you can see, it's a lot of code for such a simple task!

In Java 8, you can do the same thing with a single statement using a groupingBy collector, like this:

Map<String, List<Employee>> employeesByCity =;

This results in the following map:

{New York=[Charles], Hong Kong=[Dorothy], London=[Alice, Bob]}

It's also possible to count the number of employees in each city, by passing a counting collector to the groupingBy collector. The second collector performs a further reduction operation on all the elements in the stream classified into the same group.

Map<String, Long> numEmployeesByCity =, counting()));

The result is the following map:

{New York=1, Hong Kong=1, London=2}

Just as an aside, this is equivalent to the following SQL statement:

select city, count(*) from Employee group by city

Another example is calculating the average number of sales in each city, which can be done using the averagingInt collector in conjuction with the groupingBy collector:

Map<String, Double> avgSalesByCity =,

The result is the following map:

{New York=160.0, Hong Kong=190.0, London=175.0}


Partitioning is a special kind of grouping, in which the resultant map contains at most two different groups - one for true and one for false. For instance, if you want to find out who your best employees are, you can partition them into those who made more than N sales and those who didn't, using the partitioningBy collector:

Map<Boolean, List<Employee>> partitioned = -> e.getNumSales() > 150));

This will produce the following result:

{false=[Bob], true=[Alice, Charles, Dorothy]}

You can also combine partitioning and grouping by passing a groupingBy collector to the partitioningBy collector. For example, you could count the number of employees in each city within each partition:

Map<Boolean, Map<String, Long>> result = -> e.getNumSales() > 150,
                               groupingBy(Employee::getCity, counting())));

This will produce a two-level Map:

{false={London=1}, true={New York=1, Hong Kong=1, London=1}}

Saturday, October 31, 2015

Java 8 Streams API: Finding and matching

The Streams API provides some useful methods to determine whether elements in a stream match a given condition.

The anyMatch method can be used to check if there exists an element in the stream that matches a given predicate. For example, to find out whether a stream of random numbers has a number greater than 5:

IntStream randomStream = new Random(100, 1, 11);
if (randomStream.anyMatch(i -> i > 5)) {
 System.out.println("The stream has a number greater than 5");

The allMatch method can be used to check if all elements in the stream match a given predicate. For example, to find out whether a stream of random numbers only contains positive numbers:

boolean isPositive = randomStream.allMatch(i -> i > 0);

noneMatch is the opposite of allMatch and can be used to check that no elements in the stream match a given predicate. The previous example could be rewritten using noneMatch as follows:

boolean isPositive = randomStream.noneMatch(i -> i <= 0);

The findAny method returns an arbitrary element of the stream. It returns an Optional because it's possible that no element might be returned by findAny. For example, to find a number greater than 5 in our random number stream:

OptionalInt number = randomStream.filter(i -> i > 5)

findFirst is similar to findAny but returns the first element in the stream. For example, to find the first number greater than 5 in our random number stream:

OptionalInt number = randomStream.filter(i -> i > 5)

The difference between findAny and findFirst arises when using parallel streams. Finding an arbitrary element in a stream is less constraining than finding the first element, when running in parallel mode, so findAny may perform better. So, if you don't care about which element is returned, use findAny.

An interesting thing to note is that the operations described above use short-circuiting i.e. they don't need to process the entire stream to produce a result. As soon as an appropriate element is found, a result is returned.