jOOQ: code generation in Eclipse

jOOQ allows code generation from a database schema through ANT tasks, Maven and shell command tools. But if you're working with Eclipse it's easier to create a new Run Configuration to perform this operation.
First of all you have to write the usual XML configuration file for the code generation starting from the database:

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<configuration xmlns="http://www.jooq.org/xsd/jooq-codegen-2.0.4.xsd">
<jdbc>
<driver>oracle.jdbc.driver.OracleDriver</driver>
<url>jdbc:oracle:thin:@dbhost:1700:DBSID</url>
<user>DB_FTRS</user>
<password>password</password>
</jdbc>

<generator>
<name>org.jooq.util.DefaultGenerator</name>

<database>
<name>org.jooq.util.oracle.OracleDatabase</name>

<inputSchema>DB_FTRS</inputSchema>

<includes>.*</includes>

<excludes></excludes>
</database>

<generate>
<relations>true</relations>
<deprecated>false</deprecated>

<pojos>true</pojos>

<daos>true</daos>
</generate>

<target>
<packageName>jooqbench.generated</packageName>

<directory>src</directory>
</target>
</generator>
</configuration>

Then you have to be sure to have in your project classpath the following libraries:

jooq-2.6.1.jar
jooq-codegen-2.6.1.jar
jooq-meta-2.6.1.jar
log4j-1.2.16.jar
slf4j-log4j12-1.7.2.jar

the log4j.xml configuration file and your database JDBC driver. Then create a new Run Configuration. Select your application project and the jOOQ generation class as main class:

Move to the Arguments tab and add as Program argument your jOOQ configuration file for code generation putting a slash before its name:

Click on the Run button and the generation will start. Each time you need to refresh your code, just run this configuration.

Comments

Streamsets Data Collector log shipping and analysis using ElasticSearch, Kibana and... the Streamsets Data Collector

One common use case scenario for the Streamsets Data Collector (SDC) is the log shipping to some system, like ElasticSearch, for real-time analysis. To build a pipeline for this particular purpose in SDC is really simple and fast and doesn't require coding at all. For this quick tutorial I will use the SDC logs as example. The log data will be shipped to Elasticsearch and then visualized through a Kibana dashboard. Basic knowledge of SDC, Elasticsearch and Kibana is required for a better understanding of this post. These are the releases I am referring to for each system involved in this tutorial: JDK 8 Streamsets Data Collector 1.4.0 ElasticSearch 2.3.3 Kibana 4.5.1 Elasticsearch and Kibana installation You should have your Elasticsearch cluster installed and configured and a Kibana instance pointing to that cluster in order to go on with this tutorial. Please refer to the official documentation for these two products in order to complete their installation (if you do

Exporting InfluxDB data to a CVS file

Sometimes you would need to export a sample of the data from an InfluxDB table to a CSV file (for example to allow a data scientist to do some offline analysis using a tool like Jupyter, Zeppelin or Spark Notebook). It is possible to perform this operation through the influx command line client. This is the general syntax: sudo /usr/bin/influx -database '<database_name>' -host '<hostname>' -username '<username>' -password '<password>' -execute 'select_statement' -format '<format>' > <file_path>/<file_name>.csv where the format could be csv , json or column . Example: sudo /usr/bin/influx -database 'telegraf' -host 'localhost' -username 'admin' -password '123456789' -execute 'select * from mem' -format 'csv' > /home/googlielmo/influxdb-export/mem-export.csv

Using Rapids cuDF in a Colab notebook

During last Spark+AI Summit Europe 2019 I had a chance to attend a talk from Miguel Martinez who was presenting Rapids , the new Open Source framework from NVIDIA for GPU accelerated end-to-end Data Science and Analytics. Fig. 1 - Overview of the Rapids eco-system Rapids is a suite of Open Source libraries: cuDF cuML cuGraph cuXFilter I enjoied the presentation and liked the idea of this initiative, so I wanted to start playing with the Rapids libraries in Python on Colab , starting from cuDF, but the first attempt came with an issue that I eventually solved. So in this post I am going to share how I fixed it, with the hope it would be useful to someone else running into the same blocker. I am assuming here you are already familiar with Google Colab. I am using Python 3.x as Python 2 isn't supported by Rapids. Once you have created a new notebook in Colab, you need to check if the runtime for it is set to use Python 3 and uses a GPU as hardware accelerator. You

Googlielmo's blog

Search This Blog