SmartFD

SmartFD is an efficient and scalable algorithm for discovering all minimal, non-trival functional dependencies (FDs) from large-scale distributed datasets. Furthermore, SmartFD is built on the widely-used distributed data-parallel platform Apache Spark.

News

SmartFD won the first place in the 4th National University Cloud Computing Application Innovation Competition Big Data Skills Challenge held in 2017-2018, China.

Prerequisites

• Apache Spark: As SmartFD is built on top of Spark, you need to get the Spark installed first. If you are not clear how to setup Spark, please refer to the guidelines here. Currently, SmartFD is developed on the APIs of Spark 2.1.0 version.
• Apache HDFS: SmartFD uses HDFS as the distributed file system. If you are not clear how to setup HDFS, please refer to the guidelines here. The HDFS version is 2.7.3.
• Java: The JDK version is 1.7.

Compile

SmartFD is built using sbt. We have offered a default build.sbt file to manage the whole project. Make sure you have installed sbt and you can just run sbt assembly in the root directory to get an assembly jar in the target/scala-2.10 directory. Note that the default Spark version and Hadoop version are defined in the file build.sbt, you can modify it if necessary.

The compiled jar is available at target/scala-2.10/SparkFD-assembly-1.0.jar

Note: A precompiled jar is provided in the lib directory.

Run

The entry of SmartFD is defined in the scala class pasa.bigdata.nju.smartfd.main.Main.

The parameters of SmartFD can be configured by appending --paramKey paramVaule to the run command.

For instance, run SmartFD with the following command:

spark-submit --master [SPARK_MASTER_ADDRESS] \
--executor-memory 20G \
--driver-memory 20G \
--executor-cores [Total cores in each executor] \ 
--conf spark.driver.maxResultSize=20g \
--conf spark.memory.fraction=0.3 \
--conf spark.memory.storageFraction=0.5 \
--conf spark.shuffle.spill.compress=true \
--class pasa.bigdata.nju.smartfd.main.Main \
SparkFD-assembly-1.0.jar \
--inputFilePath ${inputPath} --tempFilePath ${tempPath} --numAttributes ${numAttributes} --numPartition ${numPartitions} --outputFilePath ${inputPath}_results

The run command contains the following parameters:

• <inputFilePath>: The input data path on HDFS.
• <tempFilePath>: The temporary data path on HDFS..
• <numAttributes>: The number of attributes of the input dataset.
• <numPartition>: The computation parallelism on Spark.
• <outputFilePath>: The output data path on HDFS.

Note: The parameters configured in the run command have higher priority than those in the configuration file. To find more information about the configuration file, please refer to the Configuration File.

Demo

The demo directory contais 11 datasets (the input file and the output file).

The format of the input file is csv, the default column seperator is comma(,).

All minimal, non-trival functional dependencies are in the output file.

Output format

Each FD is represented by the Lhs and Rhs.

For example, [column1,column5]:column3 indicates the FD {column1,column5}-> column3.

In addition, the FDs that have the same Lhs can be combined.

Given the FDs [column1,column5]:column3 and [column1,column5]:column4, the combined result is [column1,column5]:column3,column4.

Licence

Please contact the authors for the licence info of the source code.