Azure Data Factory extract JSON column from CSV - arrays

I am using ADF to process a csv file which has in one of its columns an array of JSON Documents with same key:value pairs e.g.
Col1| Col2| Col3| Col4
Str1| Str2| Str3| [{"key1":"value1","key2":"value1"},{"key1":"value3","key2":"value4"}]
As you can see, the documents have the same structure and each array could have many documents in it.
I've tried to use the flatten and parse activities but without success. The flatten activity doesn't see it as an array to flatten and having difficulty having the parse give me anything at all.
Any help would be appreciated.

Related

Big query nested json strings to arrays then new tables

Bigquery Database
I've got a webhook that's pushing to my big query table. The problem is it has lots of nested json strings which are brought in as strings. I ultimately want to make each column with these json strings into their own tables but I'm getting stuck because I can't figure out how to get them unnested and into an array.
[{"id":"63bddc8cfe21ec002d26b7f4","description":"General Admission", "src_currency":"USD","src_price":50.0,"src_fee":0.0,"src_commission":1.79,"src_discount":0.0,"applicable_pass_id":null,"seats_label":null,"seats_section_label":null,"seats_parent_type":null,"seats_parent_label":null,"seats_self_type":null,
"seats_self_label":null,"rate_type":"Rate","option_name":null,"price_level_id":null,"src_discount_price":50.0,"rate_id":"636d6d5cea8c6000222c640d","cost_item_id":"63bddc8cfe21ec002d26b7f4"}]
Here's the sample return from the original source and below is a screenshot of what I'm working with.
[Current Database
I've tried a number of things but the multiple nestings and string to array issue are really hampering everything I've tried.
I'm honestly not sure exactly what output/structure is best for this data set. I assume that each of the json returns probably just needs to be its own table and I can reference or join them based off that first "id" value in the json strings but I'm wide open to suggestions.

You can use a combination of JSON functions, and array functions to manipulate this kind of data.
JSON_EXTRACT_ARRAY can convert the JSON formatted string into an array, UNNEST then can make each entry into rows, and finally JSON_EXTRACT_SCALAR can pull out individual columns.
So here's an example of what I think you're trying to accomplish:
with sampledata as (
select """[{"id":"63bddc8cfe21ec002d26b7f4","description":"General Admission", "src_currency":"USD","src_price":50.0,"src_fee":0.0,"src_commission":1.79,"src_discount":0.0,"applicable_pass_id":null,"seats_label":null,"seats_section_label":null,"seats_parent_type":null,"seats_parent_label":null,"seats_self_type":null,"seats_self_label":null,"rate_type":"Rate","option_name":null,"price_level_id":null,"src_discount_price":50.0,"rate_id":"636d6d5cea8c6000222c640d","cost_item_id":"63bddc8cfe21ec002d26b7f4"},{"id":"63bddc8cfe21ec002d26b7f4","description":"General Admission", "src_currency":"USD","src_price":50.0,"src_fee":0.0,"src_commission":1.79,"src_discount":0.0,"applicable_pass_id":null,"seats_label":null,"seats_section_label":null,"seats_parent_type":null,"seats_parent_label":null,"seats_self_type":null,"seats_self_label":null,"rate_type":"Rate","option_name":null,"price_level_id":null,"src_discount_price":50.0,"rate_id":"636d6d5cea8c6000222c640d","cost_item_id":"63bddc8cfe21ec002d26b7f4"}]""" as my_json_string
)
select JSON_EXTRACT_SCALAR(f,'$.id') as id, JSON_EXTRACT_SCALAR(f,'$.rate_type') as rate_type, JSON_EXTRACT_SCALAR(f,'$.cost_item_id') as cost_item_id
from sampledata, UNNEST(JSON_EXTRACT_ARRAY(my_json_string)) as f
Which creates rows with specific columns from that data, like this:

How to transform a list of numbers into seperate elements talend tmap

I have a list of codes for each state as an input stored in a table.
What I want as output is this , using tmap transformations
This is the job I made but it doesn't seem to work correctly like I want. The output should have 1000 rows.
Does anybody know how to solve this?

One way to do this is to convert your list to a string of comma-separated values (using tConvertType for instance) and then use tNormalize to split this string into individual rows.

Handling new line in column value of HIVE AVRO format table - contains complex data type & nested arrays

I have an HIVE table which is AVRO format and one of the column in the table contains complex data type (nested arrays). One of the element in that nested array contains data which contains new line characters. I am using multiple lateral view explode to flatten the data; but because of the new line character in one of the columns the output is not good (means it is mapping wrong value against wrong column). I tried to use regex_replace function in my query by it is told a invalid function. I am using "Hive 1.1.0-cdh5.12.2". Can you please tell me how could I handle this new line issue within the data while querying from the avro table using multiple laterview explode (as there are nested arrays).

Save Pandas data frame from list of dicts as hdf5 table

I have a large pandas data frame that I create from a list of dictionaries where the column names are the dict keys. The columns contain different types of data, but the datatype is consistent in any given column.
Example: one of my columns contains 28x28 numpy arrays and another contains strings...etc. I would like to save this out as an HDF5 file, having table format so I can query the data when reading it in later (these files are ~1-2 GB).
This is how I'm trying to save the hdf5 file:
df = pd.DataFrame(list_of_dicts)
df.convert_objects(convert_numeric=True) (**have also tried pd.to_numeric)**
df.to_hdf(path_to_save, 'df', format='table')
And I get the following error:
TypeError: Cannot serialize the column [image_dims] because
its data contents are [mixed] object dtype
The image_dims column in this case has a numpy array for each entry, and this happens on any column that has an object datatype in pandas, I am not sure how to change/set it. I can save it as fixed format but I'd really like to use tables to save on loading time, etc, with queries. I've seen some other questions similar to this, but not with regard to creating the data frame from a list of dictionaries, which may be causing the problem?
Thanks for any suggestions

you changed your DF in memory - df.convert_objects(convert_numeric=True) - will return a copy of converted DF, but it WON'T change the original DF. Do it this way instead:
df.apply(pd.to_numeric, errors='coerce').to_hdf(path_to_save, 'df', format='table')
or
df = df.apply(pd.to_numeric)
df['image_dims'] = df['image_dims'].astype(str) # in case it can't be converted to numeric
df.to_hdf(path_to_save, 'df', format='table')
PS you may want to use errors='coerce' or errors='ignore' parameter when calling pd.to_numeric(...) function, depending on what you want

After trying many things I finally found what seems to be a good solution for saving mixed datatype objects in a way that they can be loaded quickly and queried. I thought I'd post it in case someone else is having trouble with this:
Tried and failed: Pandas data frame save as hdf5 or json files. Hdf5 works but only in fixed format (no querying capability) when data types are mixed. Json works with mixed but is no faster to load for files of this size. Tried converting all numpy arrays, lists, etc to byte strings for saving: this was more trouble than it was worth with the number of columns and different data types I'm dealing with.
Solution: Use ZODB (https://pypi.python.org/pypi/ZODB). Object database that was very easy to implement, and is easy to integrate with pandas if data frames are your thing.
from ZODB.FileStorage import FileStorage
from ZODB.DB import DB
import transaction
storage = FileStorage('path_to_store.fs')
db = DB(storage)
connection = db.open()
root = connection.root()
# insert each column of the dataframe into the db
for col in df.columns:
root[col] = df[col]
# commit changes to the db
transaction.commit()
# works like a dictionary with key:value pairs that correspond to column names
print root.keys()
# close db and connection
db.close()
connection.close()
You can then read a database with the same syntax, and access data through the root variable.

Importing jSON Array into Hive/Hadoop

I'm using the HortonWorks Hadoop Sandbox and I have imported a jSON string into a table, as per this guide however I run into trouble as my string contains an array which is not handled well by json_tuple(). I have tried the expolode() function, but that returns the following error:
"Error occurred executing hive query: OK FAILED: UDFArgumentException explode() takes an array or a map as a parameter"
What does that mean exactly and how can I fix it? Would the problem be with the format of the table? I just followed as per the guide above and created the table with:
CREATE EXTERNAL TABLE games
(
value STRING
)
LOCATION '/user/hue/games'
The value I am trying to explode is an array like this:
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Any help very much appreciated!

The explode() hive function takes a hive array or map, and you gave it a String value. json_tuple() worked in the guide because it made your string into a map.
You'll want to convert your json array into a format that hive can accept, or use one of the JSON SerDes or something of that nature in order to query the way you want.
JSON SerDe for Hive that supports JSON arrays

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