I have a parquet file which I reading atleast 4-5 times within my application.
I was wondering what is most efficient thing to do.
Option 1. While writing parquet file, immediately read it back to dataset and
call cache. I am assuming by doing an immediate read I might use some existing
hdfs/sp
Hi, I am trying to process a very large comma delimited csv file and I am
running into problems.
The main problem is that some fields contain quoted strings with embedded
commas.
It seems as if PySpark is unable to properly parse lines containing such
fields like say Pandas does.
Here is the code
Hi Rohit
You can use accumulators and increase it on every record processing.
At last you can get the value of accumulator on driver , which will give
you the count.
HTH
Deepak
On Nov 5, 2016 20:09, "Rohit Verma" wrote:
> I am using spark to read from database and write in hdfs as parquet file.
I am using spark to read from database and write in hdfs as parquet file. Here
is code snippet.
private long etlFunction(SparkSession spark){
spark.sqlContext().setConf("spark.sql.parquet.compression.codec", “SNAPPY");
Properties properties = new Properties();
properties.put("driver”,”oracle.jdbc
why visitCreateFileFormat doesn`t support hive STORED BY ,just support story
as
when i update spark1.6.2 to spark2.0.1
so what i want to ask is .does it on plan to support hive stored by ? or never
support that ?
configureOutputJobProperties is quit important ,is there any other method to
i
Hi,
I'm running Spark 2.0.1 version with Spark Launcher 2.0.1 version on Yarn
cluster. I launch map task which spawns Spark job via
SparkLauncher#startApplication().
Deploy mode is yarn-client. I'm running in Mac laptop.
I have this snippet of code:
SparkAppHandle appHandle = sparkLauncher.star
