Trying to save the spark dataframe(python) in .tde format. Will including these 4 jars in jars folder of spark will work?
jna.jar; tableauextract.jar; tableaucommon.jar; tableauserver.jar.If so how to get these jars?Could not find it on google search
You can get a Pandas dataframe using the .toPandas() method available on any Spark dataframe. From there, some options exist to get to a Tableau .tde file; check out this link:
https://github.com/chinchon/python-tableau-tde
Related
Is there any approach to read hdfs data to spark df without explicitly mentioning file type.
spark.read.format("auto_detect").option("header", "true").load(inputPath)
We can achieve above requirement by using scala.sys.process_ or python subprocess(cmd). and splitting the extension of any part file. But without using any subprocess or sys.process, can we achieve this ..?
I am trying to identify a solution to read data from HBASE table using spark streaming and write the data to another HBASE table.
I found numerous samples in internet which asks to create a DSTREAM to get the data from HDFS files and all.But I was unable to find any examples to get data from HBASE tables
For e.g, if I have a HBASE table 'SAMPLE' with columns as 'name' and 'activeStatus'. How can I retrieve the data from the table SAMPLE based on activeStatus column using spark streaming (New data?
Any examples to retrieve the data from HBASE table using spark streaming is welcome.
Regards,
Adarsh K S
You can connect to hbase from spark multiple ways
Hortonwork Spark hbase connector:
https://github.com/hortonworks-spark/shc
Unicredit hbase rdd : https://github.com/unicredit/hbase-rdd
Hortonworks SHC read hbase directly to dataframe using user defined
catalog whereas hbase-rdd read it as rdd and can be converted to DF
using toDF method. hbase-rdd has bulk write option (direct write HFiles) preferred for massive data write.
What you need is a library that enables spark to interact with hbase. Horton Works' shc is such an extension:
https://github.com/hortonworks-spark/shc
I am trying to find a way to interpret the table names from spark sql.
The answer given here is in Scala How to get table names from SQL query?
I want to change this into pyspark.
For that I want to import the library
org.apache.spark.sql.catalyst.analysis.UnresolvedRelation (or its equivalent) into pyspark.
Can this be done?
We can read avro file using the below code,
val df = spark.read.format("com.databricks.spark.avro").load(path)
is it possible to read pdf files using Spark dataframes?
You cannot read a pdf and store in a df as it will cannot interrupt the columns of the dataframe(basically it doens't have a standard schema), so if you want to get some data from a pdf first convert that to csv or parquet and then you can read from that file and then create a dataframe as it has a defined schema
visit this gitbook to understand more on what are the available read formats which you can use to get the data as a Dataframe
DataFrameReader — Loading Data From External Data Sources
I am new to Scala, and i have to use Scala and Spark's SQL, Mllib and GraphX in order to perform some analysis on huge data set. The analyses i want to do are:
Customer life cycle Value (CLV)
Centrality measures (degree, Eigenvector, edge-betweenness,
closeness) The data is in a CSV file (60GB (3 years transnational data))
located in Hadoop cluster.
My question is about the optimal approach to access the data and perform the above calculations?
Should i load the data from the CSV file into dataframe and work on
the dataframe? or
Should i load the data from the CSV file and convert it into RDD and
then work on the RDD? or
Are there any other approach to access the data and perform the analyses?
Thank you so much in advance for your help..
Dataframe gives you sql like syntax to work with the data where as RDD gives Scala collection like methods for data manipulation.
One extra benefit with Dataframes is underlying spark system will optimise your queries just like sql query optimisation. This is not available in case of RDD's.
As you are new to Scala its highly recommended to use Dataframes API initially and then Pick up RDD API later based on requirement.
You can use Databricks CSV reader api, which is easy to use and returns DataFrame. It automatically infer data types. If you pass the file with header it can automatically use that as Schema, otherwise you can construct schema using StructType.
https://github.com/databricks/spark-csv
Update:
If you are using Spark 2.0 Version , by default it support CSV datasource, please see the below link.
https://spark.apache.org/releases/spark-release-2-0-0.html#new-features
See this link for how to use.
https://github.com/databricks/spark-csv/issues/367