I've created an ADF pipeline that converts a delimited file to parquet in our datalake. I've added an additional column and set the value using the following expression #convertfromutc(utcnow(),'GMT Standard Time','o'). The problem I am having is when I look at the parquet file it is coming back in the US format.
eg 11/25/2021 14:25:49
Even if I use #if(pipeline().parameters.LoadDate,json(concat('[{"name": "LoadDate" , "value": "',formatDateTime(convertfromutc(utcnow(),'GMT Standard Time','o')),'"}]')),NULL) to try to force the format on the extra column it still comes back in the parquet in the US format.
Any idea why this would be and how I can get this to output into parquet as a proper timestamp?
Mention the format pattern while using convertFromUtc function as shown below.
#convertFromUtc(utcnow(),’GMT Standard Time’,’yyyy-MM-dd HH:mm:ss’)
Added date1 column in additional columns under source to get the required date format.
Preview of source data in mappings. Here data is previewed as giving format in convertFromUtc function.
Output parquet file:
Data preview of the sink parquet file after copying data from the source.
Related
I'm doing a string search for certain values that I would expect to be in a parquet file. Some are found, others are not found.
Yet when I view the parquet file content from a databricks notebook, I can find a missing value within the data shown.
Approx 70% of the data I search for with the raw parquet file (on windows) is found. But spot checks for some of the remaining data is found via the notebook.
Why is some data present when viewing the raw parquet content and other data isn't? It's also the same data each time which is or isn't found.
Here's a screenshot example. It shows a case where a value from input json IS found in the raw parquet, and a case where a value from input json is not found in raw parquet (but IS found in a csv export of same data from databricks)
Why do I have to convert an RDD to DF in order to write it as parquet, avro or other types? I know writing RDD as these formats is not supported. I was actually trying to write a parquet file with first line containing only the header date and other lines containing the detail records. A sample file layout
2019-04-06
101,peter,20000
102,robin,25000
I want to create a parquet with the above contents. I already have a csv file sample.csv with above contents. The csv file when read as dataframe contains only the first field as the first row has only one column.
rdd = sc.textFile('hdfs://somepath/sample.csv')
df = rdd.toDF()
df.show()
o/p:
2019-04-06
101
102
Could someone please help me with converting the entire contents of rdd into dataframe. Even when i try reading the file directly as a df instead of converting from rdd same thing happens.
Your file only has "one column" in Spark's reader, so therefore the dataframe output will only be that.
You didn't necessarily do anything wrong, but your input file is malformed if you expect there to be more than one column, and if so, you should be using spark.csv() instead of sc.textFile()
Why do I have to convert an RDD to DF in order to write it as parquet, avro or other types?
Because those types need a schema, which RDD has none.
trying to write a parquet file with first line containing only the header date and other lines containing the detail records
CSV file headers need to describe all columns. There cannot be an isloated header above all rows.
Parqeut/Avro/ORC/JSON cannot do not have column headers like CSV, but the same applies.
My data is in the format of csv file (sam,1,34,there,hello). I want to add an image to each row in the csv file using hadoop. Does any body have any idea about it. I have seen about Hipi which process the image files and adds it also. But I want to add as a column to csv file.
If you have to use CSV file, consider using Base64 encoding over binary image data - it will give you a printable string. But in general I would recommend you to switch to sequence file, there you would be able to store the image directly in a binary format
I start to work with Hive.
I wanted to know what queries should to use for each table format among formats:
rcfile, orcfile, parquet, delimited text
when you have tables with very large number of columns and you tend to use specific columns frequently, RC file format would be a good choice. Rather than reading the entire row of data you would just retrieve the required columns, thus saving time. The data is divided into groups of rows, which are then divided into groups of columns.
Delimited text file is the general file format.
For ORC file format , have a look at the hive documentation which has a detailed description here : https://cwiki.apache.org/confluence/display/Hive/LanguageManual+ORC
Parquet file format stores data in column form.
eg:
Col1 Col2
A 1
B 2
C 3
Normal data is stored as A1B2C3. Using Parquet, data is stored as ABC123.
For parquet file format , have a read on https://blog.twitter.com/2013/dremel-made-simple-with-parquet
I see that there are a couple of answers but since your question didn't asked for any particular file formats, the answers addressed one or the other file format.
There are a bunch of file formats that you can use in Hive. Notable mentions are AVRO, Parquet. RCFile & ORC. There are some good documents available online that you may refer to if you want to compare the performance and space utilization of these file formats. Follows some useful links that will get you going.
This Blog Post
This link from MapR [They don't discuss Parquet though]
This link from Inquidia
The above given links will get you going. I hope this answer your query.
Thanks!
I am using Amazon EMR Hadoop Hive for big data processing. Current data in my log files is in CSV format. In order to make the table from log files, I wrote regex expression to parse the data and store into different columns of external table. I know that SerDe can be used to read data in JSON format and this means that each log file line could be as JSON object. Are there any Hadoop performance advantages if my log files are in JSON format comparing CSV format.
If you can process the output of the table (that you created with the regexp) why do another processing? Try to avoid unnecessary stuff.
I think the main issue here is which format is faster to read. I believe CSV will provide better speed over JSON but don't take my word. Hadoop really doesn't care. It's all byte arrays to him, once in memory.