In Scala how do you efficiently (memory consumption + performance) read very large csv file? is it fast enough to just stream it line by line and process each line at each iteration?
What i need to do with CSV data :->
In my application Single line in CSV file is treated as an one single record and all the records of the CSV file are to be converted into XML elements and JSON format and save it into another file in xml and json formats.
So here question is while reading the file from csv is it a good idea to read the file in chunks and provide that chunk to another thread which will convert that CSV records into an xml/json and write that xml/json to file? If yes how?
Data of the CSV can be anything, there is no restriction on the type of the data it can be numeric, big decimal, string or date. Any easy way to handle this different data types before saving it to xml? or we don't need to take care of types?
Many Thanks
If this is not a one time task, create a program that will break this 1GB file to small size files. Then provide those new files as a input to separate futures.
Each future will read one file and resolve in the order of file content. File4 resolves after File3, which resolves after File2, which resolves after File1.
As the file has no key-value pair or hierarchical data structure, so I will suggest, just read as a string.
Hope this helps.
Related
I'm working on a document-based SwiftUI application, and my file contents includes large arrays of doubles. The application involves importing some data from CSV, and then the file saves this data in a data structure.
However, when looking at the file sizes, the custom document type I declare in my application is 2-3 times larger than the CSV file. The other bits of metadata could not possibly be that large.
Here's the JSON output that is saved to file:
Here's the CSV file that was imported:
On looking at the raw text of the JSON output and comparing it with the original CSV, it became obvious that the file format I declare was using a lot of unnecessary precision. How do I make Swift's JSONEncoder only use, for example, 4 decimal places of precision?
There is a nested json with very deep structure. File is of the format json.gz size 3.5GB. Once this file is uncompressed it is of size 100GB.
This json file is of the format, where Multiline = True (if this condition is used to read the file via spark.read_json then only we get to see the proper json schema).
Also, this file has a single record, in which it has two columns of Struct type array, with multilevel nesting.
How should I read this file and extract information. What kind of cluster / technique to use to extract relevant data from this file.
Structure of the JSON (multiline)
This is a single record. and the entire data is present in 2 columns - in_netxxxx and provider_xxxxx
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I was able to achieve this in a bit different way.
Use the utility - Big Text File Splitter -
BigTextFileSplitter - Withdata Softwarehttps://www.withdata.com › big-text-file-splitter ( as the file was huge and multiple level nested) the split record size I kept was 500. This generated around 24 split files of around 3gb each. Entire process took 30 -40 mins.
Processed the _corrupt_record seperately - and populated the required information.
Read the each split file in a using - this option removes the _corrupt_record and also removes the null rows.
spark.read.option("mode", "DROPMALFORMED").json(file_path)
Once the information is fetched form each file, we can merge all the files into a single file, as per standard process.
I basically have a procedure where I make multiple calls to an API and using a token within the JSON return pass that pack to a function top call the API again to get a "paginated" file.
In total I have to call and download 88 JSON files that total 758mb. The JSON files are all formatted the same way and have the same "schema" or at least should do. I have tried reading each JSON file after it has been downloaded into a data frame, and then attempted to union that dataframe to a master dataframe so essentially I'll have one big data frame with all 88 JSON files read into.
However the problem I encounter is roughly on file 66 the system (Python/Databricks/Spark) decides to change the file type of a field. It is always a string and then I'm guessing when a value actually appears in that field it changes to a boolean. The problem is then that the unionbyName fails because of different datatypes.
What is the best way for me to resolve this? I thought about reading using "extend" to merge all the JSON files into one big file however a 758mb JSON file would be a huge read and undertaking.
Could the other solution be to explicitly set the schema that the JSON file is read into so that it is always the same type?
If you know the attributes of those files, you can define the schema before reading them and create an empty df with that schema so you can to a unionByName with the allowMissingColumns=True:
something like:
from pyspark.sql.types import *
my_schema = StructType([
StructField('file_name',StringType(),True),
StructField('id',LongType(),True),
StructField('dataset_name',StringType(),True),
StructField('snapshotdate',TimestampType(),True)
])
output = sqlContext.createDataFrame(sc.emptyRDD(), my_schema)
df_json = spark.read.[...your JSON file...]
output.unionByName(df_json, allowMissingColumns=True)
I'm not sure this is what you are looking for. I hope it helps
Snowflake supports multiple file types via creation FILE_FORMAT (avro, json, csv etc).
Now I have tested SELECTing from snowflake stage (s3) both:
*.avro files (generated from nifi processor batching 10k source oracle table).
*.json files (json per line).
And when Select $1 from #myStg, snowflake expands as many rows as records on avro or json files (cool), but.. the $1 variant is both json format and now i wonder if whatever snowflake file_format we use do records always arrive as json on the variant $1 ?
I haven't tested csv or others snowflake file_formats.
Or i wonder if i get json from the avros (from oracle table) because maybe NiFi processor creates avro files (with internally uses json format).
Maybe im making some confusion here.. i know avro files contain both:
avro schema - language similar to json key/value.
compressed data (binary).
Thanks,
Emanuel O.
I tried with CSV, When Its came to CSV its parsing each records in the file like below
So when its came to JSON it will treat one complete JSON as one records so its displaying in JSON format.
I am interested in data mining and I am writing my thesis about it. For my thesis I want to use yelp's data challenge's data set, however i can not open it since it is in json format and almost 2 gb. In its website its been said that the dataset can be opened in phyton using mrjob, but I am also not very good with programming. I searched online and looked some of the codes yelp provided in github however I couldn't seem to find an article or something which explains how to open the dataset, clearly.
Can you please tell me step by step how to open this file and maybe how to convert it to csv?
https://www.yelp.com.tr/dataset_challenge
https://github.com/Yelp/dataset-examples
data is in .tar format when u extract it again it has another file,rename it to .tar and then extract it.you will get all the json files
yes you can use pandas. Take a look:
import pandas as pd
# read the entire file into a python array
with open('yelp_academic_dataset_review.json', 'rb') as f:
data = f.readlines()
# remove the trailing "\n" from each line
data = map(lambda x: x.rstrip(), data)
data_json_str = "[" + ','.join(data) + "]"
# now, load it into pandas
data_df = pd.read_json(data_json_str)
Now 'data_df' contains the yelp data ;)
Case, you want convert it directly to csv, you can use this script
https://github.com/Yelp/dataset-examples/blob/master/json_to_csv_converter.py
I hope it can help you
To process huge json files, use a streaming parser.
Many of these files aren't a single json, but a stream of jsons (known as "jsons format"). Then a regular json parser will consider everything but the first entry to be junk.
With a streaming parser, you can start reading the file, process parts, and wrote them to the desired output; then continue writing.
There is no single json-to-csv conversion.
Thus, you will not find a general conversion utility, you have to customize the conversion for your needs.
The reason is that a JSON is a tree but a CSV is not. There exists no ultimative and efficient conversion from trees to table rows. I'd stick with JSON unless you are always extracting only the same x attributes from the tree.
Start coding, to become a better programmer. To succeed with such amounts of data, you need to become a better programmer.