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Refactor your applications to Kubernetes | Opensource.com
Refactor your applications to Kubernetes
Tackle-DiVA helps developers understand database operations and transaction processes inside applications.
Application modernization developers must be able to understand database operations and transaction processes inside applications precisely. Tackle-DiVA (Data-intensive Validity Analyzer) is an open source data-centric Java application analysis tool in the Konveyor Tackle project that aims at refactoring applications to Kubernetes.
This article gives an overview of Tackle-DiVA and presents example instructions and analysis results.
What is Tackle-DiVA?
Tackle-DiVA is built using Java and Python and operated using a command-line interface. It imports target Java application source files and provides analysis results as files.
- Service entry inventory analysis extracts a list of Java classes for implementing public APIs.
- Database inventory analysis exports a list of database tables operated by an application.
- Transaction inventory extracts a set of transaction processes.
- Code-to-database dependency analyzes which Java class operates which database table.
- The database-to-database and transaction-to-transaction dependency analyses find clues for transforming parallel executions.
- Finally, transaction refactoring recommendation analysis shows parallel executable transactions from original sequential executions.
Try it out!
It is easy to get started with Tackle-DiVA. It makes full use of Docker containers, and the only prerequisite is a Docker-runnable environment, such as RedHat Enterprise Linux, Ubuntu, or macOS.
Once you have Docker available on your machine, run:
$ cd /tmp
$ git clone https://github.com/konveyor/tackle-diva.git && tackle-diva
$ docker build . -t diva
This builds Tackle-DiVA and packs it as a Docker image. Tackle-DiVA is now ready to use on your machine.
The next step is to prepare source codes of your target Java applications. I'll use the DayTrader application as an example:
$ cd /tmp
$ git clone https://github.com/WASdev/sample.daytrader7.git
The final step is to execute the
diva_docker command by attaching the full directory path:
$ cd /tmp/tackle-diva/distrib/bin/
$ ./diva_docker /tmp/sample.daytrader7/
This creates the
tackle-diva/distrib/output directory and stores the analysis result files:
$ ls /tmp/tackle-diva/distrib/output
contexts.yml transaction.json transaction_summary.dot
database.json transaction.yml transaction_summary.pdf
Explore the analysis results
Take a look at some analysis results for the DayTrader application.
The service entry inventory result is stored in the
contexts.yml file. It finds that the
TradeAppServlet.init class/method plays a key role in service entries for the
The database inventory analysis exports six database tables in the
database.json file. These tables are used in the DayTrader application:
The transaction inventory analysis result is dumped into the
.yml files, but it's better to check the
transaction_summary.pdf file when looking through transactions. The following transaction consists of six SQL operations to two database tables:
.yml files also contain code-to-database dependency analysis results. The following shows how the TradeDirect class invokes query operations to two database tables,
"stacktrace" : [
"method" : "<src-method: < Source,
"file" : "/app/daytrader-ee7-ejb/src/
"position" : "TradeDirect.java [1935:15] -> [1935:41]"
"sql" : "select * from accountprofileejb ap where ap.userid = (
select profile_userid from accountejb a where a.profile_userid=?)"
The database-to-database dependency analysis result is located in the
accoutejb database tables have a mutual-query relationship:
The transaction-to-transaction dependency analysis results are found in the
orderejb database table. The upper transaction updates the table, and the lower transaction queries it:
Finally, parallel executable transactions are shown in the