Jun 19, 2014
In Apache CXF, one can generate a Wadl for any registered resource by appending ?_wadl to the resource url. This Wadl provides an excellent source of real-time REST API documentation, but the output format is not reader-friendly.
I extended the default CXF WadlGenerator to support text/html mediaType using .
Here is the code for HTMLWadlGenerator, which can be registered as a jaxrs:provider with the jaxrs:server. To see the output, append ?_wadl&_type=text/html to the resource url.
This would give a nice looking HTML page of REST API documentation for the registered resources.
Jun 8, 2014
Solr includes an autosuggest component, Suggestor. From Solr 4.7 onwards, the implementation of this Suggestor is changed. The old SpellChecker based search component is replaced with a new suggester that utilizes Lucene suggester module. The latest Solr download is preconfigured with this new suggester, but the documentation on the Solr wiki is still of the previous SpellCheck version.
It took me sometime to understand the new suggester and get it working.
There are two configurations for suggester, a search component and a request handler:
<searchComponent name="suggest" class="solr.SuggestComponent">
<str name="lookupImpl">FuzzyLookupFactory</str> <!-- org.apache.solr.spelling.suggest.fst -->
<str name="dictionaryImpl">DocumentDictionaryFactory</str> <!-- org.apache.solr.spelling.suggest.HighFrequencyDictionaryFactory -->
<requestHandler name="/suggest" class="solr.SearchHandler" startup="lazy">
To check thesuggester, index few documents with good test values for cat field, which is set as the suggestion field.
The url for getting suggestions
(use suggest.build=true for the first time)
In my case this returns
<str name="term">A Clash of Kings</str>
<str name="term">A Game of Thrones</str>
Since a default suggester is not configured, suggest.dictionary is required, without it, you will get an exception: No suggester named default was configured
You can configure default suggestor in the SolrConfig.xml
<requestHandler name="/suggest" class="solr.SearchHandler" startup="lazy">
Now you should be able to get suggeston, without having to specify dictionary in the URL.
Apr 13, 2014
We are designing a large scale distributed event-driven system for real-time data replication across transactional databases. The data(messages) from the source system undergoes a series of transformations and routing-logic before reaching its destination. These transformations are multi-process and multi-threaded operations, comprising of smaller stateless steps and tasks that can be performed concurrently. There is no shared state across processes instead, the state transformations are persisted in the database, and each process pulls its work-queue directly from the database.
Based on this, we needed a technology that supported distributed event processing, routing and concurrency on the Java + Spring platform, the three options considered were, MessageBroker (RabbitMQ), Spring Integration and Akka
RabitMQ: MQ was the first choice because it is the traditional and proven solution for messaging/event-processing. RabbitMQ, because it is popular light-weight open source option with commercial support from a vendor we already use. I was pretty impressed with RabbitMQ, it was easy to use, lean, yet supported advance distribution and messaging features. The only thing that it lacked for us, was the ability to persist messages in Oracle.
Even though RabbitMQ is Open Source (free), for enterprise use, there is a substantial cost factor to it. As MQ is an additional component in the middleware stack, it requires dedicated staff for administration and maintenance, and a commercial support for the product. Also, setup and configuration of MesageBroker has its own complexity and involves cross-team coordination.
MQs are primarily EAI products and provide cross-platform (multi-language, multi-protocol) support. They might be too bulky and expensive when used just as asynchronous concurrency and parallelism solution.
Spring Integration: Spring has a few modules that provide scalable asynchronous execution.
Spring TaskExecutor provides asynchronous processing with lightweight thread pool options.
Spring Batch allows distributed asynchronous processing via the Job Launcher and Job Repository.
Spring Integration extends it further by providing EAI features, messaging, routing and mediation capabilities.
While all three Spring modules have some of the required feature, it was difficult to get everything together. Like this user, I was expecting Spring Integration would have RMI-like remoting capability.
Akka Java: Akka is a toolkit and runtime for building highly concurrent, distributed, and fault tolerant event-driven applications on the JVM. It has a Java API and I decided to give it a try.
Akka was easy to get started, I found Activator quite helpful. Akka is based on Actor Model, which is a message-passing paradigm of achieving concurrency without shared-objects and blocking. In Akka, rather than invoking an object directly, a message is constructed and send it to the object (called an actor) by way of an actor reference. This design greatly simplifies concurrency management.
However, the simplicity does not mean that a traditional lock-based concurrent program (thread/synchronization) can be converted into Akka with few code changes. One needs to design their Actor System by defining smaller tasks, messages and communication between the them. There is a learning curve for Akka’s concepts and Actor Model paradigm. It is comparatively small, given the complexity of concurrency and parallelism that it abstracts.
Akka offers the right level of abstraction, where you do not have to worry about thread and synchronization of shared-state, yet you get full flexibility and control to write your custom concurrency solution.
Besides simplicity, I thought the real power of Akka is, remoting and its ability to distribute actors across multiple nodes for high scalability. Akka's Location Transparency and Fault Tolerance make it easy to scale and distribute application without code changes.
I was able to build a PoC for my multi-process and multi-threading use-case, fairly easily. I still need to work out Spring injection in Actors.
A few words of caution, Akka’s Java code has a lot of typecasting due to Scala’s type system and achieving object mutability could be tricky. I am tempted to reuse my existing JPA entities (mutable) as messages for reduced database calls.
Also, Akka community, is geared towards Scala and there is less material on Akka Java.
In spite of all this, Akka Java seems cheaper, faster and efficient option out of the three.
Feb 13, 2014
There is an excellent tutorial on RabbitMQ, however, I thought it lacked detailed steps on installation and setup of RabbitMQ server and Pika Python client. I would like to share the steps on MacOS.
Installing and Running RabbitMQ
RabbitMQ is available in homebrew
brew install rabbitmq
once installed, run the server
verify RabbitMQ is running
http://localhost:15672/ should give a login prompt, login using guest/guest
Install python if you don't already have it,
brew install python
The Python formula comes with Pip and setuptools, check
sudo pip install pika=0.9.8
I also ran
sudo easy_install pika
it pulls-in additional packages for pika.
That is it, you should be able to use Pika in your python programs.
You should be all set now and can start with the RabbitMQ tutorial.