I have been using Spark in Scala for a while. I am now looking into pySpark and SparkR. I don't see streaming mentioned for PySpark and SparkR. Does any one know if you can do Spark streaming when using Python and R?
Spark is now supporting pySpark streaming in 1.3. And an implementation of SparkR streaming can be found in https://github.com/hlin09/spark/tree/SparkR-streaming.
Currently (as of Spark 1.1), Spark Streaming is only supported in Scala & Java. If you have a specific R program or Python program you want to use you can take a look at the pipe interface on RDDs along with the transform function on DStreams. This is a bit awkward but its probably the easiest way to use Python or R code in Spark Streaming currently.
sparkR streaming is not available till latest version apache spark 2.1.1
but we can use sparkR streaming from github
https://github.com/hlin09/spark/tree/SparkR-streaming
build spark using mvn then you can be able to do sparkR streaming.
Related
Looking for kedro+ airflow implementation on spark. Is the plugin now available for spark ?
Looked at PipelineX but couldn't find relevant examples on spark ?
I haven't prepared or seen an example to use Spark with PipelineX or Airflow, but it should be possible to use kedro-airflow to run tasks on Spark.
The following document and DataEngineerOne's video might be helpful.
https://kedro.readthedocs.io/en/stable/10_tools_integration/01_pyspark.html?highlight=Spark
https://www.youtube.com/watch?v=vYBMpPZep6E
How can I create a custom write format for Spark Dataframe to use it like df.write.format("com.mycompany.mydb").save()? I've tried reading through Datastax Cassandra connector code but still couldn't figure it out
Spark 3.0 completely changes the API. Some new interfaces e.g. TableProvider and SupportsWrite have been added.
You might find this guide helpful.
Using Spark's DataSourceV2.
If your are using Spark version < 2.3, then you can use Spark Data Source API V1.
I am only trying to read a textfile into a pyspark RDD, and I am noticing huge differences between sqlContext.read.load and sqlContext.read.text.
s3_single_file_inpath='s3a://bucket-name/file_name'
indata = sqlContext.read.load(s3_single_file_inpath, format='com.databricks.spark.csv', header='true', inferSchema='false',sep=',')
indata = sqlContext.read.text(s3_single_file_inpath)
The sqlContext.read.load command above fails with
Py4JJavaError: An error occurred while calling o227.load.
: java.lang.ClassNotFoundException: Failed to find data source: com.databricks.spark.csv. Please find packages at http://spark-packages.org
But the second one succeeds?
Now, I am confused by this because all of the resources I see online say to use sqlContext.read.load including this one: https://spark.apache.org/docs/1.6.1/sql-programming-guide.html.
It is not clear to me when to use which of these to use when. Is there a clear distinction between these?
Why is difference between sqlContext.read.load and sqlContext.read.text?
sqlContext.read.load assumes parquet as the data source format while sqlContext.read.text assumes text format.
With sqlContext.read.load you can define the data source format using format parameter.
Depending on the version of Spark 1.6 vs 2.x you may or may not load an external Spark package to have support for csv format.
As of Spark 2.0 you no longer have to load spark-csv Spark package since (quoting the official documentation):
NOTE: This functionality has been inlined in Apache Spark 2.x. This package is in maintenance mode and we only accept critical bug fixes.
That would explain why you got confused as you may have been using Spark 1.6.x and have not loaded the Spark package to have csv support.
Now, I am confused by this because all of the resources I see online say to use sqlContext.read.load including this one: https://spark.apache.org/docs/1.6.1/sql-programming-guide.html.
https://spark.apache.org/docs/1.6.1/sql-programming-guide.html is for Spark 1.6.1 when spark-csv Spark package was not part of Spark. It happened in Spark 2.0.
It is not clear to me when to use which of these to use when. Is there a clear distinction between these?
There's none actually iff you use Spark 2.x.
If however you use Spark 1.6.x, spark-csv has to be loaded separately using --packages option (as described in Using with Spark shell):
This package can be added to Spark using the --packages command line option. For example, to include it when starting the spark shell
As a matter of fact, you can still use com.databricks.spark.csv format explicitly in Spark 2.x as it's recognized internally.
The difference is:
text is a built-in input format in Spark 1.6
com.databricks.spark.csv is a third party package in Spark 1.6
To use third party Spark CSV (no longer needed in Spark 2.0) you have to follow the instructions on spark-csv site, for example provide
--packages com.databricks:spark-csv_2.10:1.5.0
argument with spark-submit / pyspark commands.
Beyond that sqlContext.read.formatName(...) is a syntactic sugar for sqlContext.read.format("formatName") and sqlContext.read.load(..., format=formatName).
I'm wondering how to create an RDD reading data from Kinesis with a specific offset, with a non-streaming Spark job.
For Kafka I know this is possible with KafkaUtils.createRDD.
But I don't find the same library for Kinesis. Any suggestion or workaround?
Thanks!
As of Spark 1.5.0 it seems possible to write your own UDAF's for custom aggregations on DataFrames:
Spark 1.5 DataFrame API Highlights: Date/Time/String Handling, Time Intervals, and UDAFs
It is however unclear to me if this functionality is supported in the Python API?
You cannot defined Python UDAF in Spark 1.5.0-2.0.0. There is a JIRA tracking this feature request:
https://issues.apache.org/jira/browse/SPARK-10915
resolved with goal "later" so it probably won't happen anytime soon.
You can use Scala UDAF from PySpark - it is described Spark: How to map Python with Scala or Java User Defined Functions?