Bark

Bark is a Data Quality solution for distributed data systems at any scale in both streaming or batch data context. It provides a framework process for defining data quality model, executing data quality measurement, automating data profiling and validation, as well as a unified data quality visualization across multiple data systems. You can access our home page here.

Contact us

Google Groups

CI

https://travis-ci.org/eBay/DQSolution

Repository

Snapshot: https://oss.sonatype.org/content/repositories/snapshots

Release: https://oss.sonatype.org/service/local/staging/deploy/maven2

How to build

1. git clone the repository of https://github.com/eBay/DQSolution
2. run "mvn install"

How to run in docker

1. Install docker.

2. Download our docker folder to your work path.

3. Enter docker directory and build images.
   The first step is to build bark-base-env, which prepares the environment for bark.

   cd <your work path>/docker/bark-base
   docker build -t bark-base-env .
   

   The second step is to build bark-env, which contains examples for bark demo.

   cd <your work path>/docker/bark
   docker build --no-cache -t bark-env .
   

4. Run docker image bark-env, then the backend is ready.

   docker run -it -h sandbox --name bark -m 8G --memory-swap -1 \
   -p 2122:2122 -p 47077:7077 -p 46066:6066 -p 48088:8088 -p 48040:8040 \
   -p 48042:8042 -p 48080:8080 -p 47017:27017 bark-env bash
   

   You can also drop the tail "bash" of the command above, then you will get tomcat service log printing in docker only.

5. Now you can visit UI through your browser, and follow the next steps on web UI here.

   http://<your local IP address>:48080/
   

   And you can also ssh to the docker container using account "bark" with password "bark".

   ssh bark@<your local IP address> -p 2122
   

How to deploy and run at local

1. Install jdk (1.7 or later versions)

2. Install Tomcat (7.0 or later versions)

3. Install MongoDB and import the collections

   mongorestore /db:unitdb0 /dir:<dir of bark-doc>/db/unitdb0
   

4. Install Hadoop (2.7 or later versions), you can get some help here.
   Make sure you have the permission to use command "hadoop".
   Create an empty directory in hdfs as your hdfs path, and then create running and history directory in it

   hadoop fs -mkdir <your hdfs path>
   hadoop fs -mkdir <your hdfs path>/running
   hadoop fs -mkdir <your hdfs path>/history
   

5. Install Spark (version 2.0.0), if you want to install Pseudo Distributed/Single Node Cluster, you can get some help here.
   Make sure you have the permission to use command "spark-shell".

6. Install Hive (version 2.1.0), you can get some help here.
   Make sure you have the permission to use command "hive".

7. Create a working directory, and it will be your local path now.

8. In your local path, put your data into Hive.
   First, you need to create some directories in hdfs

   hadoop fs -mkdir /tmp
   hadoop fs -mkdir /user/hive/warehouse
   hadoop fs -chmod g+w /tmp
   hadoop fs -chmod g+w /user/hive/warehouse
   

Then, run the following command in your local path schematool -dbType derby -initSchema Now you can put your data into Hive by running "hive" here. You can get sample data here, then put into hive as following commands

```
CREATE TABLE movie_source (
  movieid STRING,
  title STRING,
  genres STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\;'
STORED AS TEXTFILE;

LOAD DATA LOCAL INPATH '<your data path>/MovieLensSample_Source.dat' OVERWRITE INTO TABLE movie_source;

CREATE TABLE movie_target (
  movieid STRING,
  title STRING,
  genres STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\;'
STORED AS TEXTFILE;

LOAD DATA LOCAL INPATH '<your data path>/MovieLensSample_Target.dat' OVERWRITE INTO TABLE movie_target;
```

If you use hive command mode to input data, remember to create _SUCCESS file in hdfs table path as following

```
hadoop fs -touchz /user/hive/warehouse/movie_source/_SUCCESS
hadoop fs -touchz /user/hive/warehouse/movie_target/_SUCCESS
```

9. You can create your own model, build your jar file, and put it in your local path.
   (If you want to use our default models, please skip this step)

10. Currently we need to run the jobs automatically by script files, you need to set your own parameters in the script files and run it. You can edit the demo script files as following

    env.sh

    HDFS_WORKDIR=<your hdfs path>/running
    

    bark_jobs.sh

    spark-submit --class com.ebay.bark.Accu33 --master yarn --queue default --executor-memory 512m --num-executors 10 bark-models-0.0.1-SNAPSHOT.jar  $lv1dir/cmd.txt $lv1dir/
    spark-submit --class com.ebay.bark.Vali3 --master yarn --queue default --executor-memory 512m --num-executors 10 bark-models-0.0.1-SNAPSHOT.jar  $lv1dir/cmd.txt $lv1dir/
    

    These commands submit the jobs to spark, if you want to try your own model or modify some parameters, please edit it. If you want to use your own model, change "bark-models-0.0.1-SNAPSHOT.jar" to "your path/your model.jar", and change the class name.

    Put these script files in your local path, run bark_regular_run.sh as following

    nohup ./bark_regular_run.sh &
    

11. Open application.properties file, read the comments and specify the properties correctly. Or you can edit it as following

    env=prod
    job.local.folder=<your local path>/tmp
    job.hdfs.folder=<your hdfs path>
    job.hdfs.runningfoldername=running
    job.hdfs.historyfoldername=history
    

    If you set the properties as above, you need to make sure the directory "tmp" exists in your local path

12. Build the whole project and deploy bark-core/target/ROOT.war to tomcat

    mvn install -DskipTests
    

13. Then you can review the RESTful APIs through http://localhost:8080/api/v1/application.wadl

How to develop

In dev environment, you can run backend REST service and frontend UI seperately. The majority of the backend code logics are in the bark-core project. So, to start backend, please import maven project Bark into eclipse, right click bark-core->Run As->Run On Server

To start frontend, please follow up the below steps.

1. Open bark-ui/js/services/services.js file

2. Specify BACKEND_SERVER to your real backend server address, below is an example

   var BACKEND_SERVER = 'http://localhost:8080'; //dev env
   //var BACKEND_SERVER = 'http://localhost:8080/ROOT'; //dev env
   

3. Open a command line, run the below commands in root directory of bark-ui

   • npm install
   • bower install
   • npm start

4. Then the UI will be opened in browser automatically, please follow the User Guide, enjoy your journey!

Note: The front-end UI is still under development, you can only access some basic features currently.

Contributing

See CONTRIBUTING.md for details on how to contribute code, documentation, etc.