When I use the BigQuery console manually, I can see that the 3 options when exporting a table to GCS are CSV, JSON (Newline delimited), and Avro.
With Airflow, when using the BigQueryToCloudStorageOperator operator, what is the correct value to pass to export_format in order to transfer the data to GCS as JSON (Newline delimited)? Is it simply JSON? All examples I've seen online for BigQueryToCloudStorageOperator use export_format='CSV', never for JSON, so I'm not sure what the correct value here is. Our use case needs JSON, since the 2nd task in our DAG (after transferring data to GCS) is to then load that data from GCS into our MongoDB Cluster with mongoimport.
I found that the value export_format='NEWLINE_DELIMITED_JSON' was required after finding the documentation https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#jobconfigurationextract and refering to the values for destinationFormat
According to the BigQuery documentation the three possible formats to which you can export BigQuery query results are: CSV, JSON, and Avro (and this is compatible with the UI drop-down menu).
I would try with export_format='JSON' as you already proposed.
Related
I have a CSV Data stored at some location. I want to read this data and store it in some PCollection but I am unable to specify the type of PCollection or the way it should be stored in a PCollection
As a quite straitforward way - you can read it as just strings (e.g. TextIO for Java SDK will return a PCollection<String>) and then parse it with your own PTransform into required format that returns a PCollection<YourPOJO> (see an example).
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
I'm trying to understand the code for reading JSON file in Synapse Analytics. And here's the code provided by Microsoft documentation:
Query JSON files using serverless SQL pool in Azure Synapse Analytics
select top 10 *
from openrowset(
bulk 'https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.jsonl',
format = 'csv',
fieldterminator ='0x0b',
fieldquote = '0x0b'
) with (doc nvarchar(max)) as rows
go
I wonder why the format = 'csv'. Is it trying to convert JSON to CSV to flatten the file?
Why they didn't just read the file as a SINGLE_CLOB I don't know
When you use SINGLE_CLOB then the entire file is important as one value and the content of the file in the doc is not well formatted as a single JSON. Using SINGLE_CLOB will make us do more work after using the openrowset, before we can use the content as JSON (since it is not valid JSON we will need to parse the value). It can be done but will require more work probably.
The format of the file is multiple JSON's like strings, each in separate line. "line-delimited JSON", as the document call it.
By the way, If you will check the history of the document at GitHub, then you will find that originally this was not the case. As much as I remember, originally the file included a single JSON document with an array of objects (was wrapped with [] after loaded). Someone named "Ronen Ariely" in fact found this issue in the document, which is why you can see my name in the list if the Authors of the document :-)
I wonder why the format = 'csv'. Is it trying to convert json to csv to flatten the hierarchy?
(1) JSON is not a data type in SQL Server. There is no data type name JSON. What we have in SQL Server are tools like functions which work on text and provide support for strings which are JSON's like format. Therefore, we do not CONVERT to JSON or from JSON.
(2) The format parameter has nothing to do with JSON. It specifies that the content of the file is a comma separated values file. You can (and should) use it whenever your file is well formatted as comma separated values file (also commonly known as csv file).
In this specific sample in the document, the values in the csv file are strings, which each one of them has a valid JSON format. Only after you read the file using the openrowset, we start to parse the content of the text as JSON.
Notice that only after the title "Parse JSON documents" in the document, the document starts to speak about parsing the text as JSON.
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.
Using this DevCenter, how to save result of query to json (I actually get JSON table) to file on disk? I know there is "COPY All as CSV" when right click on any row, however I dont see copy as JSON. If I just use copy as csv, the original format is modified and therefore of no use for further access in any other tool.
In short, there should be some way to export as whatever type. But it seems this visual tool only has export as csv.
DevCenter 1.4 was recently released (Jul 20, '15) and has support for Cassandra 2.2 INSERT JSON, SELECT JSON statements and fromJson and toJson functions. Included in this support, DevCenter can display JSON for entire result sets or individual cell contents.
So with the combination of Cassandra 2.2 and DevCenter 1.4 you can query your data using either SELECT JSON or toJson (for a single column) and then copy/paste the JSON data as you wish.