The following files exist:
compress.zip
compress.z01
compress.z02
compress.z03
compress.z04
compress.z05 (fake)
An error occurs in the above case.
Is there a way to get information about other partitioned compressed file fragments by reading compressed.zip?
For example, information about z01, z02, z03, and z04
Related
I'm using SPARK to read files in hdfs. There is a scenario, where we are getting files as chunks from legacy system in csv format.
ID1_FILENAMEA_1.csv
ID1_FILENAMEA_2.csv
ID1_FILENAMEA_3.csv
ID1_FILENAMEA_4.csv
ID2_FILENAMEA_1.csv
ID2_FILENAMEA_2.csv
ID2_FILENAMEA_3.csv
This files are loaded to FILENAMEA in HIVE using HiveWareHouse Connector, with few transformation like adding default values. Similarly we have around 70 tables. Hive tables are created in ORC format. Tables are partitioned on ID. Right now, I'm processing all these files one by one. It's taking much time.
I want to make this process much faster. Files will be in GBs.
Is there is any way to read all the FILENAMEA files at the same time and load it to HIVE tables.
You have two methods to read several CSV files in pyspark. If all CSV files are in the same directory and all have the same schema, you can read then at once by directly passing the path of directory as argument, as follow:
spark.read.csv('hdfs://path/to/directory')
If you have CSV files in different locations or CSV files in same directory but with other CSV/text files in it, you can pass them as string representing a list of path in .csv() method argument, as follow:
spark.read.csv('hdfs://path/to/filename1,hdfs://path/to/filename2')
You can have more information about how to read a CSV file with Spark here
If you need to build this list of paths from the list of files in HDFS directory, you can look at this answer, once you've created your list of paths, you can transform it to a string to pass to .csv() method with ','.join(your_file_list)
Using: spark.read.csv(["path1","path2","path3"...]) you can read multiple files from different paths. But that means you have first to make a list of the paths. A list not a string of comma-separated file paths
I have created multiple parquet files using pyspark and now I'm trying to merge all the parquet files to 1. I'm able to merge the files, but while reading in the resulting file, I'm getting an error. Have anyone faced this issue before?
You cannot simply concatenate Parquet files using cat as they are binary files with a "table of contents" in the footer. To merge two files, you would have to read them both in and write out a completely new file. This could be done easily using the merge command in the parquet-tools.
The technical background that merging two Parquet files using cat isn't working comes down to the fact that a Parquet file is useless without a footer. Every Parquet file is made up roughly by the following structure:
RowGroup(nrows=..)
Column with nrows
Column with nrows
..
RowGroup(nrows=..)
..
..
Footer
Schema (tells you the type of the columns)
total_nrows
Location of RowGroups in the file
If you cat two files together, you would only see the last footer of the two files. Additionally, if the library used to read the files does some integrity checks, it will realise that your file is broken in some fashion and completely error out.
Scala 2.12 and Spark 2.2.1 here. I used the following code to write the contents of a DataFrame to S3:
myDF.write.mode(SaveMode.Overwrite)
.parquet("s3n://com.example.mybucket/mydata.parquet")
When I go to com.example.mybucket on S3 I actually see a directory called "mydata.parquet", as well as file called "mydata.parquet_$folder$"!!! If I go into the mydata.parquet directory I see two files under it:
_SUCCESS; and
part-<big-UUID>.snappy.parquet
Whereas I was just expecting to see a single file called mydata.parquet living in the root of the bucket.
Is something wrong here (if so, what?!?) or is this expected with the Parquet file format? If its expected, which is the actual Parquet file that I should read from:
mydata.parquet directory?; or
mydata.parquet_$folder$ file?; or
mydata.parquet/part-<big-UUID>.snappy.parquet?
Thanks!
The mydata.parquet/part-<big-UUID>.snappy.parquet is the actual parquet data file. However, often tools like Spark break data sets into multiple part files, and expect to be pointed to a directory that contains multiple files. The _SUCCESS file is a simple flag indicating that the write operation has completed.
According to the api to save the parqueat file it saves inside the folder you provide. Sucess is incidation that the process is completed scuesffuly.
S3 create those $folder if you write directly commit to s3. What happens is it writes to temporory folders and copies to the final destination inside the s3. The reason is there no concept of rename.
Look at the s3-distcp and also DirectCommiter for performance issue.
The $folder$ marker is used by s3n/amazon's emrfs to indicate "empty directory". ignore.
The _SUCCESS file is, as the others note, a 0-byte file. ignore
all other .parquet files in the directory are the output; the number you end up with depends on the number of tasks executed on the input
When spark uses a directory (tree) as a source of data, all files beginning with _ or . are ignored; s3n will strip out those $folder$ things too. So if you use the path for a new query, it will only pick up that parquet file.
I need a microcontroller to read a data file that's been stored in a Zip file (actually a custom OPC-based file format, but that's irrelevant). The data file has a known name, path, and size, and is guaranteed to be stored uncompressed. What is the simplest & fastest way to find the start of the file data within the Zip file without writing a complete Zip parser?
NOTE: I'm aware of the Zip comment section, but the data file in question is too big to fit in it.
I ended up writing a simple parser that finds the file data in question using the central directory.
While reading a ZIP file, can we safely assume that all file structures (by that I mean Local File Header + file data (compressed or stored) + Data Descriptor) are exactly consecutive? Can there be any irrelevant data in between?
PkWare Appnote tells that
"Immediately following the local header for a file is the compressed
or stored data for the file. The series of [local file header][file
data][data descriptor] repeats for each file in the .ZIP archive."
So there should be no gaps between them.
However, I would recommend to parse and read central directory, not go through local file headers (except that you need streamed processing).