I want to convert dataframe to Array of Json using Java and Spark version 1.6, for which am converting the data from Dataframe -> Json -> RDD -> Array where the data looks like this.
[
{
"prtdy_pgm_x":"P818_C",
"prtdy_pgm_x":"P818",
"prtdy_attr_c":"Cost",
"prtdy_integer_r":0,
"prtdy_cds_d":"prxm",
"prtdy_created_s":"2018-05-12 04:12:19.0",
"prtdy_created_by_c":"brq",
"prtdy_create_proc_x":"w_pprtdy_security_t",
"snapshot_d":"2018-05-12-000018"
},
{
"prtdy_pgm_x":"P818_I",
"prtdy_pgm_x":"P818",
"prtdy_attr_c":"Tooling",
"prtdy_integer_r":0,
"prtdy_cds_d":"prxm",
"prtdy_created_s":"2018-05-12 04:12:20.0",
"prtdy_created_by_c":"brq",
"prtdy_create_proc_x":"w_pprtdy_security_t",
"snapshot_d":"2018-05-12-000018"
},
{
"prtdy_pgm_x":"P818_W",
"prtdy_pgm_x":"P818",
"prtdy_attr_c":"Weight",
"prtdy_integer_r":0,
"prtdy_cds_d":"prxm",
"prtdy_created_s":"2018-05-12 04:12:20.0",
"prtdy_created_by_c":"brq",
"prtdy_create_proc_x":"w_pprtdy_security_t",
"snapshot_d":"2018-05-12-000018"
},
......
]
so I wrote my code something like this.
if(cmnTableNames != null && cmnTableNames.length > 0)
{
for(int i=0; i < cmnTableNames.length; i++)
{
String cmnTableName = cmnTableNames[i];
DataFrame cmnTableContent = null;
if(cmnTableName.contains("PTR_security_t"))
{
cmnTableContent = hiveContext.sql("SELECT * FROM " + cmnTableName + " where fbrn04_snapshot_d = '" + snapshotId + "'");
}
else
{
cmnTableContent = hiveContext.sql("SELECT * FROM " + cmnTableName);
}
String cmnTable = cmnTableName.substring(cmnTableName.lastIndexOf(".") + 1);
if (cmnTableContent.count() > 0)
{
String cmnStgTblDir = hdfsPath + "/staging/" + rptName + "/common/" + cmnTable;
JavaRDD<String> cmnTblCntJson = cmnTableContent.toJSON().toJavaRDD();
String result = cmnTblCntJson.reduce((ob1, ob2) -> (String)ob1+","+(String)ob2); //This Part, takes more time than usual contains large set of data.
String output = "["+result+"]";
ArrayList<String> outputList = new ArrayList<String>();
outputList.add(output);
JavaRDD<String> finalOutputRDD = sc.parallelize(outputList);
String cmnStgMrgdDir = cmnStgTblDir + "/mergedfile";
if(dfs.exists(new Path(cmnStgTblDir + "/mergedfile"))) dfs.delete(new Path(cmnStgTblDir + "/mergedfile"), true);
finalOutputRDD.coalesce(1).saveAsTextFile(cmnStgMrgdDir, GzipCodec.class);
fileStatus = dfs.getFileStatus(new Path(cmnStgMrgdDir + "/part-00000.gz"));
dfs.setPermission(fileStatus.getPath(),FsPermission.createImmutable((short) 0770));
dfs.rename(new Path(cmnStgMrgdDir + "/part-00000.gz"), new Path(CommonPath + "/" + cmnTable + ".json.gz"));
}
else
{
System.out.println("There are no records in " + cmnTableName);
}
}
}
else
{
System.out.println("The common table lists are null.");
}
sc.stop();
but while reduce function is applied it's taking more time
JavaRDD<String> cmnTblCntJson = cmnTableContent.toJSON().toJavaRDD();
String result = cmnTblCntJson.reduce((ob1, ob2) -> (String)ob1+","+(String)ob2); //This Part, takes more time than usual contains large set of data.
the table with the partition "PTR_security_t" is huge and takes a lot of time compared to other tables which don't have partitions (40-50 mins odd for 588kb)
I Tried Applying Lambda but i ended up with Task not serializable error. Check the code below.
if(cmnTableNames != null && cmnTableNames.length > 0)
{
List<String> commonTableList = Arrays.asList(cmnTableNames);
DataFrame commonTableDF = sqc.createDataset(commonTableList,Encoders.STRING()).toDF();
commonTableDF.toJavaRDD().foreach(cmnTableNameRDD -> {
DataFrame cmnTableContent = null;
String cmnTableName = cmnTableNameRDD.mkString();
if(cmnTableName.contains("PTR_security_t"))
{
cmnTableContent = hiveContext.sql("SELECT * FROM " + cmnTableName + " where fbrn04_snapshot_d = '" + snapshotId + "'");
}
else
{
cmnTableContent = hiveContext.sql("SELECT * FROM " + cmnTableName);
}
String cmnTable = cmnTableName.substring(cmnTableName.lastIndexOf(".") + 1);
if (cmnTableContent.count() > 0)
{
String cmnStgTblDir = hdfsPath + "/staging/" + rptName + "/common/" + cmnTable;
JavaRDD<String> cmnTblCntJson = cmnTableContent.toJSON().toJavaRDD();
String result = cmnTblCntJson.reduce((ob1, ob2) -> (String)ob1+","+(String)ob2);
String output = "["+result+"]";
ArrayList<String> outputList = new ArrayList<String>();
outputList.add(output);
JavaRDD<String> finalOutputRDD = sc.parallelize(outputList);
String cmnStgMrgdDir = cmnStgTblDir + "/mergedfile";
if(dfs.exists(new Path(cmnStgTblDir + "/mergedfile"))) dfs.delete(new Path(cmnStgTblDir + "/mergedfile"), true);
finalOutputRDD.coalesce(1).saveAsTextFile(cmnStgMrgdDir, GzipCodec.class);
fileStatus = dfs.getFileStatus(new Path(cmnStgMrgdDir + "/part-00000.gz"));
dfs.setPermission(fileStatus.getPath(),FsPermission.createImmutable((short) 0770));
dfs.rename(new Path(cmnStgMrgdDir + "/part-00000.gz"), new Path(CommonPath + "/" + cmnTable + ".json.gz"));
}
else
{
System.out.println("There are no records in " + cmnTableName);
}
});
}
else
{
System.out.println("The common table lists are null.");
}
sc.stop();
is there any efficient way where i can enhance my Performance ?
