I am working with Databricks on AWS. I have mounted an S3 bucket as /mnt/bucket-name/. This bucket contains json files under the prefix jsons. I create a Delta table from these json files as follows:
%python
df = spark.read.json('/mnt/bucket-name/jsons')
df.write.format('delta').save('/mnt/bucket-name/delta')
%sql
CREATE TABLE IF NOT EXISTS default.table_name
USING DELTA
LOCATION '/mnt/bucket-name/delta'
So far, so good. Then new json files arrive in the bucket. In order to update the Delta table, I run the following:
%sql
COPY INTO default.table_name
FROM '/mnt/bucket-name/jsons'
FILEFORMAT = JSON
This does indeed update the Delta table, but it duplicates the rows contained in the initial load, i.e. the rows in df are now contained in table_name twice. I have the following workaround, whereby I create an empty dataframe with the correct schema:
%python
df_schema = spark.read.json('/mnt/bucket-name/jsons').schema
df = spark.createDataFrame([], df_schema)
df.write.format('delta').save('/mnt/bucket-name/delta')
%sql
CREATE TABLE IF NOT EXISTS default.table_name
USING DELTA
LOCATION '/mnt/bucket-name/delta'
%sql
COPY INTO default.table_name
FROM '/mnt/bucket-name/jsons'
FILEFORMAT = JSON
This works and there is no duplication, but it seems neither elegant nor efficient, since spark.read.json('/mnt/bucket-name/jsons').schema reads all the json files, even though only the schema needs to be inferred. (The schema of the json files can be assumed to be stable.) Is there a way to tell COPY INTO to ignore the initial json files? There's the option modifiedAfter, but that would be cumbersome and doesn't sit well idempotently. I also considered recreating the dataframe and then running df.write.format('delta').mode('append').save('/mnt/bucket-name/delta') followed by REFRESH TABLE default.table_name, but this seems inefficient, since why should the initial json files be read again? Edit: This method also duplicates the initial load.
Or is there a way to circumvent using a Spark dataframe entirely and create a Delta table from the json files directly? I have searched for such a solution but to no avail.
One last point: Schema inference is crucial and so I do not want a solution that requires the schema of the json files to be written out manually.
So I have a .CSV file that contains dataset information, the data seems to be described in JSON. I want to read it with MatLab. One line example(7000 total) of the data:
imagename.jpg,"[[{""name"":""nose"",""position"":[2911.68,1537.92]},{""name"":""left eye"",""position"":[3101.76,544.32]},{""name"":""right eye"",""position"":[2488.32,544.32]},{""name"":""left ear"",""position"":null},{""name"":""right ear"",""position"":null},{""name"":""left shoulder"",""position"":null},{""name"":""right shoulder"",""position"":[190.08,1270.08]},{""name"":""left elbow"",""position"":null},{""name"":""right elbow"",""position"":[181.44,3231.36]},{""name"":""left wrist"",""position"":[2592,3093.12]},{""name"":""right wrist"",""position"":[2246.4,3965.76]},{""name"":""left hip"",""position"":[3006.72,3360.96]},{""name"":""right hip"",""position"":[155.52,3412.8]},{""name"":""left knee"",""position"":null},{""name"":""right knee"",""position"":null},{""name"":""left ankle"",""position"":[2350.08,4786.56]},{""name"":""right ankle"",""position"":[1460.16,5019.84]}]]","[[{""segment"":[[0,17.28],[933.12,5175.36],[0,5166.72],[0,2306.88]]}]]",https://imageurl.jpg,
If I use the Import functionlity/tool, I am able separate the data in four colums using the , as delimiter:
Image File Name,Key Points,Segmentation,Image URL,
imagename.jpg,
"[[{""name"":""nose"",""position"":[2911.68,1537.92]},{""name"":""left eye"",""position"":[3101.76,544.32]},{""name"":""right eye"",""position"":[2488.32,544.32]},{""name"":""left ear"",""position"":null},{""name"":""right ear"",""position"":null},{""name"":""left shoulder"",""position"":null},{""name"":""right shoulder"",""position"":[190.08,1270.08]},{""name"":""left elbow"",""position"":null},{""name"":""right elbow"",""position"":[181.44,3231.36]},{""name"":""left wrist"",""position"":[2592,3093.12]},{""name"":""right wrist"",""position"":[2246.4,3965.76]},{""name"":""left hip"",""position"":[3006.72,3360.96]},{""name"":""right hip"",""position"":[155.52,3412.8]},{""name"":""left knee"",""position"":null},{""name"":""right knee"",""position"":null},{""name"":""left ankle"",""position"":[2350.08,4786.56]},{""name"":""right ankle"",""position"":[1460.16,5019.84]}]]",
"[[{""segment"":[[0,17.28],[933.12,5175.36],[0,5166.72],[0,2306.88]]}]]",
https://imageurl.jpg,
But I have truble trying to use the tool to do further decomposition of the data. Of corse the ideal would be to separate the data in a code.
I hope someone can orientate me on how to or the tools I need to use. I have seen other questions, but they don't seem to fit my particular case.
Thank you very much!!
You can read a JSON file and store it in a MATLAB structure using the following command structure1 = matlab.internal.webservices.fromJSON(json_string)
You can create a JSON string from a MATLAB structure using the following command json_string= matlab.internal.webservices.toJSON(structure1)
JSONlab is what you want. It has a 'loadjson' function which inputs a char array of JSON data and returns a struct with all the data
Issue
I recently encountered a challenge in Azure Data Lake Analytics when I attempted to read in a Large UTF-8 JSON Array file and switched to HDInsight PySpark (v2.x, not 3) to process the file. The file is ~110G and has ~150m JSON Objects.
HDInsight PySpark does not appear to support Array of JSON file format for input, so I'm stuck. Also, I have "many" such files with different schemas in each containing hundred of columns each, so creating the schemas for those is not an option at this point.
Question
How do I use out-of-the-box functionality in PySpark 2 on HDInsight to enable these files to be read as JSON?
Thanks,
J
Things I tried
I used the approach at the bottom of this page:
from Databricks that supplied the below code snippet:
import json
df = sc.wholeTextFiles('/tmp/*.json').flatMap(lambda x: json.loads(x[1])).toDF()
display(df)
I tried the above, not understanding how "wholeTextFiles" works, and of course ran into OutOfMemory errors that killed my executors quickly.
I attempted loading to an RDD and other open methods, but PySpark appears to support only the JSONLines JSON file format, and I have the Array of JSON Objects due to ADLA's requirement for that file format.
I tried reading in as a text file, stripping Array characters, splitting on the JSON object boundaries and converting to JSON like the above, but that kept giving errors about being unable to convert unicode and/or str (ings).
I found a way through the above, and converted to a dataframe containing one column with Rows of strings that were the JSON Objects. However, I did not find a way to output only the JSON Strings from the data frame rows to an output file by themselves. The always came out as
{'dfColumnName':'{...json_string_as_value}'}
I also tried a map function that accepted the above rows, parsed as JSON, extracted the values (JSON I wanted), then parsed the values as JSON. This appeared to work, but when I would try to save, the RDD was type PipelineRDD and had no saveAsTextFile() method. I then tried the toJSON method, but kept getting errors about "found no valid JSON Object", which I did not understand admittedly, and of course other conversion errors.
I finally found a way forward. I learned that I could read json directly from an RDD, including a PipelineRDD. I found a way to remove the unicode byte order header, wrapping array square brackets, split the JSON Objects based on a fortunate delimiter, and have a distributed dataset for more efficient processing. The output dataframe now had columns named after the JSON elements, inferred the schema, and dynamically adapts for other file formats.
Here is the code - hope it helps!:
#...Spark considers arrays of Json objects to be an invalid format
# and unicode files are prefixed with a byteorder marker
#
thanksMoiraRDD = sc.textFile( '/a/valid/file/path', partitions ).map(
lambda x: x.encode('utf-8','ignore').strip(u",\r\n[]\ufeff")
)
df = sqlContext.read.json(thanksMoiraRDD)
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.
I'm trying to pass data from a FSharp.Data.CsvProvider to a Deedle.Frame.
I'm almost new in F#, and I need to convert some CSV files from culture "it-IT" to "en-US", so I can use the data.
I found Deedle, and I want to learn how to use it, but I was not able to directly convert the data from a CSV file in Deedle (at least is what is printed in F# interactive).
I noticed that the CsvProvider makes the conversion, but after some days of attempts I am not able to pass the data.
I believe that Deedle should be able to deal with CSV files that use non-US culture:
let frame = Frame.ReadCsv("C:\\test.csv", culture="it-IT")
That said, if you want to use the CSV type provider for some reason, you can use:
let cs = new CsvProvider<"C:/data/fb.csv">()
cs.Rows
|> Frame.ofRecords
|> Frame.indexColsWith cs.Headers.Value
This uses Frame.ofRecords which creates a data frame from any .NET collection and expands the properties of the objects as columns. The CSV provider represents data as tuples, so this does not name the headers correctly - but the Frame.indexColsWith function lets you name the headers explicity.