Spark flatten nested json pyspark This article is code-oriented and self-explanatory. alias("phone"), col("contact. However, when dealing with nested JSON files, data scientists often face challenges. 3. columns: array_cols = [ c[0] for c in Jun 21, 2020 · Implementation steps: Load JSON/XML to a spark data frame. Sep 19, 2024 · Flattening a struct in a Spark DataFrame refers to converting the nested fields of a struct into individual columns. json", multiLine=True) from pyspark. You signed out in another tab or window. sql import SparkSession # Initialize Spark session spark = SparkSession. You switched accounts on another tab or window. 1. For the python part, you just need to loop through the different columns and let the magic appen : May 5, 2023 · I am trying to process json files with PySpark that contain a struct column with dynamic keys. Column¶ Collection function: creates a single array from an array of arrays. The problem is with the exponential growth of records due to exploding the Array type inside the nested json. parallelize for parsing the JSON data. Loop until the nested element flag is set to false. Mar 27, 2024 · Problem: How to explode & flatten nested array (Array of Array) DataFrame columns into rows using PySpark. '+c). alias("city"), col("contact. evry time json file structure will change in pyspark how we handle flatten any kind of json file. Assume we have a JSON file named data. json(spark. getOrCreate() Load JSON data. json") When dealing with nested JSON structures in PySpark and needing to flatten Jul 8, 2022 · In Spark, we can create user defined functions to convert a column to a StructType. Jun 30, 2024 · Flattening nested rows in PySpark involves converting complex structures like arrays of arrays or structures within structures into a more straightforward, flat format. dtypes if c[1][:6] != 'struct'] nested_cols = [c[0] for c in nested_df. functions import explode Initialize Spark Session. flatten (col: ColumnOrName) → pyspark. In this post we’re going to read a directory of JSON files and enforce a schema on load to make sure each file has all of the columns that we’re expecting. 0. I don’t know who this might offend, but I have to say it Aug 27, 2021 · you need to explode each array individually, use probably an UDF to complete the missing values and unionAll each newly created dataframes. _ val DF= spark. %scala import org. Spark SQL: A Love-Hate Relationship. json") May 8, 2024 · Here is the Json Flattener class that can help transform the nested JSON into Spark data frames. master("local[1]") \ . flatten nested json scala code in pyspark. SparkContext. _ import spark. PySpark, the Python API for Apache Spark, simplifies big data processing with distributed Oct 4, 2024 · Here’s the complete code: from pyspark. implicits. Below, I will show you how to flatten a struct in a Spark DataFrame using PySpark. Solution: Spark SQL provides flatten Mar 17, 2025 · In this article, we will explore how to flatten JSON using PySpark in a Databricks notebook, leveraging Spark SQL functions. email"). Nov 14, 2023 · As you can see there are nested objects within the json array. appName("jsonFlatten"). I want to return a single dataframe which contains all the fields from the json response. Modified 2 years, 5 months ago. select(flat_cols + [F. How to flatten the sparkPlanInfo struct into an array of the same struct, then later explode it. read. types as T from pyspark. types but not of struct-type or array-type) order of exploding (provides the sequence in which columns are to be exploded, in case of array-type). apache. createDataset(json :: Nil)) Extract and flatten All, Is there an elegant and accepted way to flatten a Spark SQL table (Parquet) with columns that are of nested StructType For example If my schema is: foo |_bar |_baz x y z How do I select it Sep 4, 2022 · df = spark. city"). The endgoal is to get a dataframe looking like this: I hope you can give me some advice on how to approach and handle these files in databricks. Directly connect with me on:- https://topmate. To flatten (explode) a JSON file into a data table using PySpark, you can use the function along with the and Feb 4, 2022 · All source code in this article is written in Python and Spark(Pyspark ver 3. We’ll walk through the process step by step, from reading the JSON file to transforming it into a flat table, making it easier to analyze and work with. Mar 5, 2025 · You can use pyspark. In such setting, data types limit how data can be flattened from JSON structures to Aug 23, 2024 · In this blog, we'll explore how to flatten a nested JSON structure into a tabular format using PySpark. createDataset. getOrCreate() df = spark. 4. , “Create” a “New Array Column” in a “Row” of a “DataFrame”, having “All” the “Inner Elements” of “All” the “Nested Array Elements” as the “Value” of that Nov 30, 2023 · This is from Spark Event log on Event SparkListenerSQLExecutionStart. getOrCreate() # Load the nested JSON file May 20, 2022 · This sample code uses a list collection type, which is represented as json :: Nil. Aug 8, 2023 · In this video I have talked about how you can flatten your nested json in spark. json Fetching values from nested JSON column in pyspark. dtypes if c[1][:6] == 'struct'] flat_df = nested_df. That's for the pyspark part. io/manish_kumar25Download data So we decided to flatten the nested json using spark into a flat table and as expected it flattened all the struct type to columns and array type to rows. When a spark RDD reads a dataframe using json function it identifies the top level keys of json and converts them to dataframe columns. This sample code uses a list collection type, which is represented as json :: Nil. Apr 13, 2022 · nested json flattening spark dataframe. flatten¶ pyspark. Let's first create a DataFrame using the following script: Add the JSON string as a collection type and pass it as an input to spark. from pyspark. sql. Here are different methods Jul 17, 2023 · “Flatten” the “Array of Array” Using “flatten ()” Method on “Array of Array” It is possible to “Flatten” an “Array of Array Type Column” in a “Row” of a “DataFrame”, i. Below is my JSON schema looks like: I am always getting nulls while converting to spark dataframe for products struct which i Apr 23, 2024 · In this post, we will see how to flatten the JSON file’s column/s where the column has nested value to the depth of 25(it is just for an example) while ensuring good performance in PySpark can be… Flatten nested json using pyspark The following repo is about to unnest all the fields of json and make them as top level dataframe Columns using pyspark in aws glue Job. Nov 19, 2023 · My goal today is to show how to dynamically transform JSON data into tabular data, in the context of PySpark. col(nc+'. option("multiline", "true"). Feb 18, 2024 · The above code reads a JSON string into a Spark DataFrame. Here is function that is doing what you want and that can deal with multiple nested columns containing columns with same name: import pyspark. You can follow the entire playlist in which I covered the Spark concepts from Jan 18, 2023 · Solved: Hi All, I have a deeply nested spark dataframe struct something similar to below |-- id: integer (nullable = true) |-- lower: struct - 11424. 4 df = spark. This article shows you how to flatten or explode a *StructType *column to multiple columns using Spark SQL. Why Flatten JSON? JSON data often contains arrays and structs, which Is there any optimal way to flatten the json by using the dataframe methods via determining the schema at the run time. I have read in the JSON as follows: df = spark. alias(nc+ The corresponding PySpark code to flatten this would be: from pyspark. json(r"input. If a structure of nested arrays is deeper than two levels, only one level of nesting is removed. In your case I would also define the schema explicitly as shown next: In your case I would also define the schema explicitly as shown next: Mar 13, 2025 · explode is commonly used in PySpark (Python API for Spark) When dealing with nested JSON structures, Flattening JSON Data: Learn how to efficiently flatten nested JSON structures in PySpark with this comprehensive guide. Reload to refresh your session. You can also use other Scala collection types, such as Seq (Scala Sequence). appName("Flatten JSON") \. Loop through the schema fields — set the flag to true when we find ArrayType and Sep 5, 2020 · I have json file structure as shown below. parallelize() to create an RDD Aug 23, 2024 · Step 2: Reading the Nested JSON File Let's start by reading the nested JSON file into a PySpark DataFrame. column. Parameters col Column or str. alias("street"), col("address. Pyspark exploding nested JSON into multiple columns and rows. functions import col df_flattened = df. functions as F def flatten_df(nested_df): flat_cols = [c[0] for c in nested_df. sql import SparkSession from array import array from functools import singledispatch Jun 28, 2018 · As long as you are using Spark version 2. This blog post aims to guide you through reading nested JSON files using PySpark, a Python library for Apache Spark. 2). The JSON reader infers the schema automatically from the JSON string. street"). To achieve this elegantly, we can use the PySpark and Scala APIs to recursively flatten the DataFrame. Oct 10, 2022 · I am trying to Convert a nested JSON to a flattened DataFrame. Ask Question Asked 2 years, 5 months ago. spark = SparkSession. json") Flatten struct Dec 14, 2022 · Pyspark - Flatten nested json. import pyspark. Download a Sample nested Json file for flattening logic Jul 21, 2023 · In the world of big data, JSON (JavaScript Object Notation) has become a popular format for data interchange due to its simplicity and readability. functions import * from pyspark. You can also use other Scala collection types, such as Seq (Scala Apr 7, 2022 · So I have tried using standard functions in spark with json_normalize or explode but it doesnt seem to work with this particular json format. appName("PySpark Read JSON") \ . Create a DataFrame with complex data type. builder \ . In our input directory we have a list of JSON files that have sensor readings that we want to read in. #import the spark libraries import re from pyspark. The name of the column or expression to be flattened. json("test. sql import DataFrame import pyspark from pyspark import SQLContext from pyspark import SparkContext from pyspark. The string is parallelized using sc. Mar 1, 2022 · Step1:Download a Sample nested Json file for flattening logic. Mar 7, 2024 · Flattening multi-nested JSON columns in Spark involves utilizing a combination of functions like json_regexp_extract, explode, and potentially struct depending on the specific JSON structure. Create Python function to do the magic # Python function to flatten the data dynamically from pyspark. May 1, 2021 · The key to flattening these JSON records is to obtain: the path to every leaf node (these nodes could be of string or bigint or timestamp etc. json("/mnt/ins/duedil/combined. Jul 1, 2020 · I have a nested JSON that Im able to fully flatten by using the below function # Flatten nested df def flatten_df(nested_df): for col in nested_df. This can be particularly useful when dealing with deeply nested JSON data, where you want to work with a flat schema. Code formatting is a bit annoying on reddit, and I didn't trim the imports, but this is the gist. 0: Supports Spark Connect. functions import explode, posexplode, col,concat_ws spark = SparkSession. Apr 25, 2024 · Problem: How to flatten the Array of Array or Nested Array DataFrame column into a single array column using Spark. functions as F import pyspark. e. name of column or expression. types import * import re def get_array_of_struct_field_names(df): """ Returns dictionary with column name as key Feb 27, 2024 · This is often necessary to make the data easier to analyze within the Spark framework. df = spark. PySpark vs. Can u help me on this. 1 or higher, pyspark. The structure of raw data is NOT fixed, Aug 23, 2021 · Ask questions, find answers and collaborate at work with Stack Overflow for Teams. sql the flattening the json spark df list Hello Everyone,This series is for beginners and intermediate level candidates who wants to crack PySpark interviewsHere is the link to the course : https://w Sep 5, 2019 · Then, I read this file using pyspark 2. json("path_to_your_json_file. The schema of the struct column looks like this: { "UUID_KEY": { "time": ST Jun 27, 2023 · 🚀 Using notebooks in #MicrosoftFabric: Whenever technology advances for citizen #PowerBI developers, it's impossible for me not to pay attention! I'm thrilled to share how recent improvements in Microsoft Fabric have broadened my horizons by empowering me to create tangible solutions and acquire new skills. sql import DataFrame # Create outer method to return the flattened Data Frame def flatten_json_df(_df: DataFrame) -> DataFrame: # List to hold the dynamically generated column names flattened_col_list = [] # Inner method to iterate over Data Frame to generate the Jan 29, 2021 · Using PySpark to Read and Flatten JSON data with an enforced schema. Examples Apr 8, 2021 · Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand What is Reading JSON Files in PySpark? Reading JSON files in PySpark means using the spark. Solution: PySpark explode function can be Aug 8, 2023 · from pyspark. Note that the element children is an array containing the parent struct, and the level of nesting could be 0 to any random number. 1. Explore Teams Sep 23, 2021 · I am trying to convert 2 levels of nested json into pyspark dataframe. json containing the above JSON object. builder \. types import * def flatten_test(df, sep="_"): """Returns a Oct 7, 2022 · Create Example Data Frame. json() method to load JavaScript Object Notation (JSON) data into a DataFrame, converting this versatile text format into a structured, queryable entity within Spark’s distributed environment. Oct 12, 2024 · Flattening the Nested JSON, use PySpark’s select and explode functions to flatten the structure. sql import SparkSession from pyspark. select( col("name"), col("age"), col("address. The JSON string is provided as a single string variable called example. flattening complex data types in pyspark You signed in with another tab or window. A new column that contains the flattened array. from_json should get you your desired result, but you would need to first define the required schema pyspark. spark. Aug 24, 2024 · Flattening of Nested JSON Array depends on the use case, here we kept as it is, we can also explode. phone"). functions. Reading nested Json structure in PySpark. Dec 14, 2022 · Pyspark - Flatten nested json. root |-- student: struct (nullabl Mar 27, 2024 · I have a scenario where I want to completely flatten string payload JSON data into separate columns and load it in a pyspark dataframe for further processing. Viewed 219 times nested json flattening spark dataframe. This converts it to a DataFrame. builder. alias("email") ) df Changed in version 3. Follow our step-by-step solution to transform your data sea May 8, 2024 · In this video I have talked about how you can flatten your nested json in spark. This is the output I need: Sep 19, 2024 · In Apache Spark, flattening nested DataFrames can be a common task, particularly when dealing with complex data structures like JSON. JSON Data Set Sample. namsm owmps mvwy rmbqxa rky rjiykv mmlg rvxkhgs iqy tkx