Is there a way that I can refresh a dataframe that is being created by appending to the parquets in pyspark?
Basically I am writing to parquet in append mode, data that I get daily.
If I want to check the parquet file created I load it up in pyspark and do a count of the data. However if new data is appended to the parquet and I try a count again on the dataframe without reloading the dataframe I do not get the updated count. Basically, I have to create a new dataframe everytime there are any changes to my parquet file. Is there a way in Spark, where the changes are automatically loaded once my parquet is updated?
Related
I am trying to read data from elastic, I could see Column is present as Array of string in elastic but while I am reading by Spark as Dataframe i am seeing as a Srting, how could I handle this data in Spark.
Note: I am trying to read with mode (sqlContext.read.format("org.elasticsearch.spark.sql") becuase i need to write it as CSV file in future.
I’m trying to create a hive table from external location on S3 from a CSV file.
CREATE EXTERNAL TABLE coder_bob_schema.my_table (column data type)
ROW DELIMITED
FIELDS TERMINATED BY ‘,’
LOCATION ‘s3://mybucket/path/file.CSV’
The resultant table has data from n-x fields spilling over to n which leads me to believe Hive doesn’t like the CSV. However, I downloaded the CSV from s3 and it opens and looks okay in excel. Is there a workaround like using a different delimiter?
I have a csv file that contains large number of columns. I want to load just one column from that file using spark.
I know that we can use select statement to filter a column. But what i want, while doing the read operation itself, it should load just one column.
In this way, i should be able to avoid extra memory getting used by other columns. Is there any way to do this?
Spark will load complete file and parse for columns. As you mentioned, you can use select to restrict columns in dataframe, so dataframe will have only one column.
Spark will load the complete file in memory and will filter down the column you want with the help of select statements which you have mentioned.
Because all the read operation in spark, reads and scans the whole file as a distributed stream reader gets created (the reader gets instantiated at every node where the data has been stored).
And if your problem is to read the data column-wise then you can store the file in parquet format and read that file. Indeed, parquet is columnar storage and it is exactly meant for this type of use case(you can verify it using the explain).
Data gets loaded into dataframe when an action is performed on it.
But before performing any action and after creating it from a hive table, if the data in the table is modified, will the changes be reflected in the dataframe?
The dataframe will not contain the old data, because the dataframe does not contain any data at all. a dataframe is nothing more than a "query plan", not materalized data.
In your case, I would say that you get your new data or alternatively a FileNotFoundException if spark has already cached hive table metadata and filenames and these things changed with the new data.
I have 1000+ parquet files in a folder, which is a partitioned folder.
Now we got a requirement to use those files to perform some transformations on it.
i need to create data frame using those parquet file. Any suggestions?
Try below code:
DF = sqlContext.read.parquet(r"<folderpath>/*")
* indicates all files present under the specified folder.
DF will be a dataframe that will contain the data from all the parquet files inside the <folderpath>. Then you can perform your transformation on DF.