Spark scala explode json column. Maybe the explode command will help you with that.

Spark scala explode json column Scala Spark Program to parse nested JSON: Apr 24, 2024 · In this Spark article, you will learn how to parse or read a JSON string from a TEXT/CSV file and convert it into multiple DataFrame columns using Scala Oct 5, 2022 · you can first use explode to move every array's element into rows thus resulting in a column of string type, then use from_json to create Spark data types from the strings and finally expand * the structs into columns. Before we start, let’s create a DataFrame with a nested array column. You can also use other Scala collection types, such as Seq (Scala Feb 27, 2024 · To flatten (explode) a JSON file into a data table using PySpark, you can use the explode function along with the select and alias functions. This blog post explains how we might choose to preserve that nested array of objects in a single table column and then use the LATERAL VIEW clause to explode that array into multiple rows within a Spark SQL query. When applied to an array, it generates a new default column (usually named “col1”) containing all the array elements. explode(eDF. Read the JSON data into a Datc aFrame. It is part of the pyspark. I am unable to use the explode function of dataframes , because i am unaware about the column names, which is getting created by spark api. select(F. {ArrayType, StructType} import org. Basic Explode Function The explode() function: Takes a single column containing an array or a map Returns a new row for each element in Jun 8, 2017 · I have a dataset in the following way: FieldA FieldB ArrayField 1 A {1,2,3} 2 B {3,5} I would like to explode the data on ArrayField so the output will look i 11 You'll have to parse the JSON string into an array of JSONs, and then use explode on the result (explode expects an array). Oct 25, 2021 · I have a dataframe with a column of string datatype. Unfortunately, some time has passed and I don't have access to Spark currently to test as well. 1. Can we store the keys of the nested arrays elements keys by decoding values from dataframe. Select and manipulate the DataFrame columns to work with the nested structure. , lists, JSON arrays—and Aug 15, 2023 · Apache Spark built-in function that takes input as an column object (array or map type) and returns a new row for each element in the given array or map type column. apache. *): 10th October 2021 When working with JSON source files in Databricks, it's common to load that data into DataFrames with nested arrays. log4j. fields, As spark only provides the value part in the rows of the dataframe and take the top level key as Apr 27, 2025 · Sources: pyspark-explode-array-map. Example 1: Parse a Column of JSON Strings Using pyspark. Each element in the array or map becomes a separate row in the resulting DataFrame. What I'm trying to achieve is having, for example, a Map with 5 entries as 5 columns within the same Row. 2. createDataset. Dec 3, 2015 · Here's an example (in Python, the code is very similar for Scala) to illustrate the difference between deriving the schema from a single element with schema_of_json() and deriving it from all the data using spark. From below example column “subjects” is an array of ArraType which holds subjects learned. To do that (assuming Spark 2. This article was written with Scala 2. functions module and is commonly used when dealing with nested structures like arrays, JSON, or structs. py 25-29 Explode Functions The explode() function and its variants transform array or map columns by creating a new row for each element in the array or each key-value pair in the map. This recipe focuses on utilizing Spark SQL to efficiently read and analyze nested JSON data. Jun 28, 2018 · 61 As long as you are using Spark version 2. I've looked up many examples, but none of them seem to be working for this scenario. Jan 30, 2020 · I am trying to parse some unstructured JSON data into multiple rows using spark. You just have to search for how to read JSON Arrays in Spark. To parse nested JSON using Scala Spark, you can follow these steps: Define the schema for your JSON data. , arrays or maps) and want to flatten them for analysis or processing. This sample code uses a list collection type, which is represented as json :: Nil. functions), explode takes a column containing arrays—e. Maybe the explode command will help you with that. 1. Mar 13, 2025 · The explode function in Spark is used to transform an array or a map column into multiple rows. name")) which you helped me into and these questions: Flattening Rows in Spark Spark 1. functions. explode # pyspark. For Scala Spark developers, Apache Spark’s DataFrame API provides a robust and intuitive interface for Master converting array columns to multiple rows in Spark DataFrames with this detailed guide Learn functions parameters and techniques in Scala Feb 2, 2024 · Recipe Objective: How to Read Nested JSON Files using Spark SQL? Nested JSON files have become integral to modern data processing due to their complex structures. spark. Introduced as part of PySpark’s SQL functions (pyspark. The JSON reader infers the schema automatically from the JSON string. json("/hdfs/") it gives me a Dataframe with following schema: root |-- id: array (nullable = true) | |-- element: string ( Apr 24, 2024 · In this article, I will explain how to explode array or list and map DataFrame columns to rows using different Spark explode functions (explode, Mastering DataFrame JSON Reading in Scala Spark: A Comprehensive Guide In the realm of distributed data processing, JSON (JavaScript Object Notation) files are a prevalent format for storing structured and semi-structured data, valued for their flexibility and human-readable structure. Aug 21, 2024 · Handling Dynamic JSON Schemas in Apache Spark: A Step-by-Step Guide Using Scala In the world of big data, working with JSON data is a common task. 4. py 22-52 pyspark-explode-nested-array. 0, input=top3}, Apr 24, 2024 · In this article, we will learn how to parse nested JSON using Scala Spark. , lists, JSON arrays—and May 24, 2022 · Spark essentials — explode and explode_outer in Scala tl;dr: Turn an array of data in one row to multiple rows of non-array data. Oct 13, 2025 · Solution: PySpark explode function can be used to explode an Array of Array (nested Array) ArrayType(ArrayType(StringType)) columns to rows on PySpark DataFrame using python example. explode('input_array')) Spark: explode function The explode() function in Spark is used to transform an array or map column into multiple rows. 1 DataFrame explode list of JSON objects edited May 23, 2017 at 12:10 Mar 7, 2024 · Flattening multi-nested JSON columns in Spark involves utilizing a combination of functions like json_regexp_extract, explode, and… Oct 16, 2025 · In this article, I will explain how to explode an array or list and map columns to rows using different PySpark DataFrame functions explode(), Learn how to convert nested JSON to a DataFrame using Scala in Databricks with this practical example. This converts it to a DataFrame. Jul 6, 2022 · Explode - Does this code below give you the same error? from pyspark. Oct 8, 2024 · Unlocking JSON Schema in Apache Spark: A Step-by-Step Guide to Inferring from JSON Columns Introduction In big data processing, dealing with JSON data in Spark often requires inferring the schema Jan 20, 2020 · Logic is as below Understand the nesting level with either array or struct types. withColumn("id", explodeDF("department. You'll need to explode the column before applying from_json as follows: Apr 7, 2020 · How can I define the schema for a json array so that I can explode it into rows? I have a UDF which returns a string (json array), I want to explode the item in array into rows and then save it. What is the PySpark Explode Function? The PySpark explode function is a transformation operation in the DataFrame API that flattens array-type or nested columns by generating a new row for each element in the array, managed through SparkSession. 12 and Spark 3. {col, explode} import org. Oct 13, 2023 · we will explore how to use two essential functions, “from_json” and “exploed”, to manipulate JSON data within CSV files using PySpark. intlist). df = spark. withColumn('input_array', F. read. This process is typically Sep 28, 2016 · The explode function is used to create a new row for each element within an array or map column. createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})]) eDF. sql import Row eDF = spark. g. explode(col) [source] # Returns a new row for each element in the given array or map. Logger import org. createDataFrame ( [ (" [ {original= {ranking=1. I am using Spark SQL (I mention that it is in Spark in case that affects the SQL syntax - I'm not familiar enough to be sure yet) and I have a table that I am trying to re-structure, but I'm getting stuck trying to transpose multiple columns at the same time. Loop throuh the nesting level and flatten using the below way. Jul 23, 2025 · In this article, we are going to discuss how to parse a column of json strings into their own separate columns. from_json For parsing json string we'll use from_json () SQL function to parse the What is the PySpark Explode Function? The PySpark explode function is a transformation operation in the DataFrame API that flattens array-type or nested columns by generating a new row for each element in the array, managed through SparkSession. May 24, 2018 · If I interpret your sample data correctly, your JSON column is a sequence of JSON elements with your posted schema. import org. {DataFrame, SparkSession} /** * * expand_nested_column : * @param json_data_df Jul 5, 2022 · Reading data from JSON file using Spark in Scala Apache spark is emerging in the field of Machine learning and Data engineering, it has one of the nice things is the ability to read input files of …. This is particularly useful when you have nested data structures (e. id")) explodeDeptDF = explodeDeptDF. Refer official documentation here. sql import functions as F from pyspark. json(). pyspark. 0. types. It helps flatten nested structures by generating a new row for each element in the array or each key Mar 21, 2024 · In Apache Spark, storing a list of dictionaries (or maps) in a column and then performing a transformation to expand or explode that column is a common operation. Here we will parse or read json string present in a csv file and convert it into multiple dataframe columns using Python Pyspark. I'll walk you through the steps with a real-world Oct 13, 2025 · In PySpark, the explode() function is used to explode an array or a map column into multiple rows, meaning one row per element. The string represents an api request that returns a json. withColumn("name", explodeDeptDF("department. If Nov 5, 2025 · In Spark/PySpark from_json () SQL function is used to convert JSON string from DataFrame column into struct column, Map type, and multiple columns. However, handling JSON schemas that may vary or are … Sep 1, 2016 · You could use something like that: var explodeDF = explodeDF. from_json should get you your desired result, but you would need to first define the required schema Master the explode function in Spark DataFrames with this detailed guide Learn syntax parameters and advanced techniques for handling nested data in Scala Dec 29, 2023 · PySpark ‘explode’ : Mastering JSON Column Transformation” (DataBricks/Synapse) “Picture this: you’re exploring a DataFrame and stumble upon a column bursting with JSON or array-like … May 20, 2022 · Add the JSON string as a collection type and pass it as an input to spark. alias("anInt")). from pyspark. 1 or higher, pyspark. Uses the default column name col for elements in the array and key and value for elements in the map unless specified otherwise. show() For the SQL method, what is the column name in the table that holds this JSON structure in each row? Let's say that it is "contacts" and Jul 7, 2016 · Lets say i loaded a json file into Spark 1. sql. schema. Jul 29, 2022 · So far I've only found examples which explode() a MapType column to n Row entries. sql import functions as F df = df. 6 via sqlContext. The child attributes with the same name is not a problem because they would need to be the column names for your final DF I presume. cj7xnb c0w3uy9im 0hhzi yie p69qc13 bdxo gwmr 2w2fq pnkx 7cwr