I want to know if there is a possibility to charge HFile in RDD or Dataframe in PySPark ?
In order to charge each HFile as csv file for instance.
Thanks for your help !
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We have some tables present on hbase (in TB's) which we have to migrate.
However, Hbase is fully utilize and we cannot run Export as it put too much pressure on hbase. As hbase use , HFile as its data. Can i directly read HFiles as data and export it to some commonly used format(Parquet/orc).
I followed some blogs/stackoverflow questions like How to directly edit HBase HFile with Spark without HBase API and https://programmer.group/hbase-operation-spark-read-hbase-snapshot-demo-share.html but these are using hbase to read snapshots .
Is there a way to directly read Hfiles directly?
i would like to be able to overwrite my output path with parquet format,
but it's not among available actions (append, complete, update),
Is there another solution here ?
val streamDF = sparkSession.readStream.schema(schema).option("header","true").parquet(rawData)
val query = streamDF.writeStream.outputMode("overwrite").format("parquet").option("checkpointLocation",checkpoint).start(target)
query.awaitTermination()
Apache Spark only support Append mode for File Sink. Check out here
You need to write code to delete path/folder/files from file system before writing a data.
Check out this stackoverflow link for ForeachWriter. This will help you to achieve your case.
I am currently in the process of designing AWS backed Data Lake.
What I have right now:
XML files uploaded to s3
AWS Glue crawler buids catalogue
AWS ETL job transforms data and saves it in the parquet format.
Each time etl jobs transforms the data it creates new parquet files. I assume that the most efficient way to store my data would be a single parquet file. Is it the case? If so how to achieve this.
Auto generated job code: https://gist.github.com/jkornata/b36c3fa18ae04820c7461adb52dcc1a1
You can do that by 'overwrite'. Glue doesn`t support 'overwrite' mode. But you can convert DynamicFrame object to spark's DataFrame and write it using spark instead of Glue:
dropnullfields3.toDF()
.write
.mode("overwrite")
.format("parquet")
.save(s3//output-bucket/[nameOfyourFile].parquet)
After I sorted all the entries and use write() function to S3, I want to re-load the data with exactly the same order and same partitions.
I tried to use read() and load() function but none of these work. Do we have a way to load the partitioned parquet files with same order and partitions?
if read() and load() did not help i would suggest to read file names from S3 and order it in fashion you need and then read back those files in the order in the spark. You can always build up your DataFrame (if you are and keep adding data to it from these partitions that you just read)
I have a DStream which is type [String , ArrayList[String]] , and I want to convert this DStream to avro format and save that to hdfs. How can I accomplish that?
You can convert your stream to JavaRDD or convert it to DataFrame and write it to a file and provide format as Avro.
// Apply a schema to an RDD
DataFrame booksDF = sqlContext.createDataFrame(books, Books.class);
booksDF.write()
.format("com.databricks.spark.avro")
.save("/output");
Please visit Accessing Avro Data Files From Spark SQL for more examples.
Hoping this helps.