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Back to basics : anatomy of an ArrayList | Edgblog

https://edgblog.wordpress.com/2013/03/21/back-to-basics-anatomy-of-an-arraylist

Back to basics : anatomy of an ArrayList. The ArrayList is ubiquitous in Java programming and is often picked as the default List implementation. Is this always a good choice? Let’s take a peek at the source code for some of the key methods of ArrayList.java and find out. As the name implies an ArrayList is backed by an array, no surprises here. To retrieve an element:. Public E get(int index) { rangeCheck(index); return (E) elementData[index]; }. To add an element:. To remove an element. Removing is ano...

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JodaTime goodies – flexible parsing | Edgblog

https://edgblog.wordpress.com/2014/02/17/jodatime-goodies-flexible-parsing

JodaTime goodies – flexible parsing. Parsing dates in Java with JodaTime is easy. 1 instantiate a date time formatter. DateTimeFormatter dateTimeFormatter = DateTimeFormat.forPattern(yyyyMMddHH:mm:ss.SSS) ;. 2 …and parse the date time. DateTimeFormatter.parseDateTime(20120515 09:30:25.123) ;. 3 but when the date time format changes (say to include a timezone)…alarm bells. 5 now the same DateTimeFormatter handles different date time formats. This entry was posted in java. February 17, 2014. Fill in your d...

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Mining the codebase with bash and sed | Edgblog

https://edgblog.wordpress.com/2013/02/21/mining-the-codebase-with-bash-and-sed

Mining the codebase with bash and sed. Ever wondered what are the most commonly imported Apache classes in your projects? Here’s a possible way to do so, using bash and sed. Find -name '*.java' xargs grep apache grep import sed 's/.* (org .apache.*; ).*/ 1/p' sort uniq -c sort. Explanation of the piped expressions above (by order of appearance). Find all java files names recursively, starting from the current location. Search these files for the lines containing the word apache and the word import. Avoid...

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Performance gains from bitwise operations | Edgblog

https://edgblog.wordpress.com/2013/04/01/performance-gains-from-bitwise-operations

Performance gains from bitwise operations. An experiment to assess the performance gains from bitwise operations. Use the junit benchmark framework. To measure the time elapsed with and without bit-twiddling for four operations (each executed several million times): multiply, divide , modulo and finding the next power of two. JDK 8 running on MacOS 10.7, intel i5 processor. For each test two results are shown: time taken without bit-twiddling first, with bit-twiddling second. Round: 0.03 [ - 0.00]. Round...

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SparseArray vs HashMap | Edgblog

https://edgblog.wordpress.com/2013/05/07/sparsearray-vs-hashmap

A sparse array in Java is a data structure which maps keys to values. Same idea as a Map, but different implementation:. A Map is represented internally as an array of lists, where each element in these lists is a key,value pair. Both the key and value are object instances. A sparse array is simply made of two arrays: an arrays of (primitives) keys and an array of (objects) values. There can be gaps in these arrays indices, hence the term “sparse” array. Example source code. This entry was posted in java.

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Quick tip – link up tests and tracker issues | Edgblog

https://edgblog.wordpress.com/2013/03/15/quick-tip-link-up-tests-and-tracker-issues

Quick tip – link up tests and tracker issues. How do you associate a unit (or integration) test with the bug tracker issue it intends to fix? The usual approach is to use comments in the test code:. Fix issue raised by bug tracker item 2554 public void someTest(){ . }. This works… but can be fairly verbose. A cleaner approach is simply to annotate each test with the relevant tracker issue. First create the annotation:. And then use it to tag each test with the relevant tracker number:. March 15, 2013.

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Scoping JBehave tests with Spring | Edgblog

https://edgblog.wordpress.com/2013/12/01/scoping-jbehave-tests-with-spring

Scoping JBehave tests with Spring. Rule number 1 while running Jbehave tests (or any integration test really) is to execute the test scenarios independently from each other. Meaning the application state must be reset before each and every scenario run. The code below presents a generic way to accomplish this reset while using JBehave and Spring. This approach is articulated around 2 components:. This class is responsible for cleaning up the scenario context whenever a new test scenario is being run....

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Implementing the builder pattern with Jackson | Edgblog

https://edgblog.wordpress.com/2013/11/17/implementing-the-builder-pattern-with-jackson

Implementing the builder pattern with Jackson. The builder pattern allows for the construction of an object step by step (properties by properties). This comes handy while writing tests as this is when we want to instantiate the object being tested precisely in the state deemed useful for the test. When the object under test is a “Thing”, with 2 properties name and description:. Public class Builder { private String name; private String description; public Thing build(){ return new Thing(name, descriptio...

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The curious case of the Map key with a unique hashcode | Edgblog

https://edgblog.wordpress.com/2013/03/03/the-curious-case-of-the-map-key-with-a-unique-hashcode

The curious case of the Map key with a unique hashcode. What would happen if a HashMap used a key which always returned the same hashcode? The underlying structure of a HashMap is akin to an array, where each entry in the array is a linked list of key/value pairs. The hashcode of the key is used to quickly lookup one of the lists indexed by the array. When the key produces well distributed hashcodes, each entry in the array points to a list with a small number (ideally just one) of key,value pairs. Loadi...

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No such thing as average latency | Edgblog

https://edgblog.wordpress.com/2013/08/15/no-such-thing-as-average-latency

No such thing as average latency. Latency is the time taken for a message to travel from one system to another. Consequently the average latency is the sum of all latencies over the total number of messages processed (i.e the inverse of the throughput, which is total number of messages processed over total time taken to process these messages). Two competing systems process 200 messages each in 1000 ms. It takes 5 ms for System A. To process each of the 200 messages. Latency = 1/Throughput = 5 msec.