The first step is to load the json file into a python object. We will see at the end of this tutorial how to convert this type of file to csv and see how to do it in python. More on some of the used functions (PySpark 2.3.This file represents the pokemons as well as the characteristics associated with each one of them, if you wish to recover the complete list of the Pokedex, you will find it at this address : Keep your eyes open for future Databricks related blogs, which will demonstrate more of the versatility of this great platform. We’ve seen here how we can use Databricks to flatten JSON with just a few lines of code. , explode("persons_cars_brands.models").alias("persons_cars_model") # Explode all car models into different rows , col("persons_cars_brands.name").alias("persons_cars_brand") , explode("persons.cars").alias("persons_cars_brands")
, col("persons.age").alias("persons_age") # Explode all car brands into different rowsĬol("persons.name").alias("persons_name") Persons = source_df.select(explode("persons").alias("persons")) """ dbutils.fs.put("/tmp/source.json", source_json, True) source_df = ("multiline", "true").json("/tmp/source.json") # Explode all persons into different rows Here you go: from import explode, col source_json = """ We want to flatten this result into a dataframe. The JSON sample consists of an imaginary JSON result set, which contains a list of car models within a list of car vendors within a list of people. The ability to explode nested lists into rows in a very easy way (see the Notebook below)įollowing is an example Databricks Notebook (Python) demonstrating the above claims.Subtle changes in the JSON schema won’t break things.An easy way to infer the JSON schema and avoid creating it manually.And they definitely don’t like even subtle source schema changes.
However, Logic Apps are not so good at parsing more complex nested structures. We have these wonderful Azure Logic Apps, which help us consistently get the JSON results from various sources. Things get even more complicated if the JSON schema changes over time, which is often a real-life scenario. Things get more complicated when your JSON source is a web service and the result consists of multiple nested objects including lists in lists and so on. When your destination is a database, what you expect naturally is a flattened result set. Parsing complex JSON structures is usually not a trivial task.
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