Spark Row To Json

===== Spark sql provides, two types of contexts. Show some samples:. Apache Spark Streaming is a scalable, high-throughput, fault-tolerant streaming processing system that supports both batch and streaming workloads. json column is no longer a StringType, but the correctly decoded json structure, i. A foldLeft or a map (passing a RowEncoder). Since JSON is semi-structured and different elements might have different schemas, Spark SQL will also resolve conflicts on data types of a field. It is by Encoders that you can bridge JVM objects to data sources (CSV, JDBC, Parquet, Avro, JSON, Cassandra, Elasticsearch, memsql) and vice versa. Same time, there are a number of tricky aspects that might lead to unexpected results. js Pandas PHP PostgreSQL Python Qt R Programming Regex Ruby Ruby on. Data Source Libraries Users can use libraries based on Data Source API to read/write DataFrames from/to a variety of formats/systems. Then you may flatten the struct as described above to have individual columns. Starting in the MEP 4. send(message). In JSON, values must be one of the following data types: a string; a number; an object (JSON object) an array; a boolean; null. The first part shows examples of JSON input sources with a specific structure. Change #3 (applies to ReadWriteExampleKMeans only) The data source was replaced so that the application writes data to a Db2 Warehouse table instead of to a JSON file. 2: JSON Support. Various storage systems can implement uniform standard interfaces to connect to Spark. We examine how Structured Streaming in Apache Spark 2. 0 release, the connector introduces support for saving Apache Spark DataFrames and DStreams to MapR Database JSON tables. As an example, we will look at Durham police crime reports from the Durham Open Data website. sqlContext. Here is an example of a JSON document:. Ways to create DataFrame in Apache Spark - DATAFRAME is the representation of a matrix but we can have columns of different datatypes or similar table with different rows and having different types of columns (values of each column will be same data type). city)) For every row custom function is applied of the dataframe. How to parse Json formatted Kafka message in spark streaming You could dig into the json structure more with both spark sql and / or json4s (for example. json(jsonRDD); I've attached the explicit schema, and example of one of the erroneous json objects that gets converted into a Null row / record. The following are top voted examples for showing how to use org. With CSVJSON you can transpose the csv before conversion. loads methods, which help in serializing and deserializing JSON strings. Step 1: Choose the JSON file you want to convert to SQL. Load data from JSON data source and execute Spark SQL query. But JSON can get messy and parsing it can get tricky. escapedStringLiterals' that can be used to fallback to the Spark 1. withColumn('json', from_json(col('json'), json_schema)) Now, just let Spark derive the schema of the json string column. DataFrame First Row. At Databricks, we have continued to push Spark’s usability and performance envelope through the introduction of DataFrames and Spark SQL. ETL Pipeline to Analyze Healthcare Data With Spark SQL. json() on either an RDD of String or a JSON file. With Apache Spark you can easily read semi-structured files like JSON, CSV using standard library and XML files with spark-xml package. , nested StrucType and all the other columns of df are preserved as-is. We are going to load a JSON input source to Spark SQL’s SQLContext. Spark SQL supports many built-in transformation functions in the module org. It is by Encoders that you can bridge JVM objects to data sources (CSV, JDBC, Parquet, Avro, JSON, Cassandra, Elasticsearch, memsql) and vice versa. Dataframes is a buzzword in the Industry nowadays. In this video, we will also see a pivot() that allows you to translate rows into columns while performing aggregation on some of the columns. I originally used the following code. For example,. getOrCreate op = Optimus (spark) Loading data. So, converting to ORC will reduce the storage cost. com DataCamp Learn Python for Data Science Interactively. Here, the hive table will be a non-partitioned table and will store the data in ORC format. DataFrame from JSON files¶ It is easier to read in JSON than CSV files because JSON is self-describing, allowing Spark SQL to infer the appropriate schema without additional hints. Spark SQL is a Spark module for structured data processing. Existing practices In practice, users often face difficulty in manipulating JSON data with modern analytical systems. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. This post shows how to derive new column in a Spark data frame from a JSON array string column. Each line must contain a separate, self-contained valid JSON object. json(json_rdd) event_df. The actual method is spark. In this article we introduced you to the json. A very quick and easy alternative (especially over smaller bad data sets) is to download the bad rows locally (e. Spark - HiveContext - Unstructured Json: Date: Tue, 21 Oct 2014 21:56:52 GMT: Hi, I have unstructured JSON as my input which may have extra columns row to row. 6 behavior regarding string literal parsing. 0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. SparkR also supports distributed machine learning using MLlib. DataFrame is based on RDD, it translates SQL code and domain-specific language (DSL) expressions into optimized low-level RDD operations. The file may contain data either in a single line or in a multi-line. 0 release, the connector introduces support for saving Apache Spark DataFrames and DStreams to MapR Database JSON tables. Generally speaking, Spark provides 3 main abstractions to work with it. A JSON File can be read using a simple dataframe json reader method. How to process and work with JSON Data using Apache Spark Scala language on REPL. If the code uses sparklyr, You must specify the Spark master URL in spark_connect. Learn how to ETL Open Payments CSV file data to JSON, explore with SQL, and store in a document database using Spark Datasets and MapR-DB. It is by Encoders that you can bridge JVM objects to data sources (CSV, JDBC, Parquet, Avro, JSON, Cassandra, Elasticsearch, memsql) and vice versa. This Spark SQL JSON with Python tutorial has two parts. Loading GeoJSON data in Apache Spark. I need to convert the dataframe into a JSON formatted string for each row then publish the string to a Kafka topic. toJson() method in the Row class. hiveContent. spark_write_json (x, path, mode = NULL, options. 1 and enhanced in Apache Spark 1. Dataset Union can only be performed on Datasets with the same number of columns. conf stanza specifying INDEXED_EXTRACTIONS and all parsing options should live on the originating Splunk instance instead of the usual parsing Splunk instance. Randomly Sample Rows from a Spark DataFrame. In this tutorial, we shall learn how to read JSON file to an RDD with the help of SparkSession, DataFrameReader and DataSet. 2, vastly simplifies the end-to-end-experience of working with JSON data. 1> RDD Creation a) From existing collection using parallelize meth. 4, the community has extended this powerful functionality of pivoting data to SQL users. Underlying processing of dataframes is done by RDD's , Below are the most used ways to create the dataframe. spark sql中对json的支持spark sql提供了内置的语法来查询这些json数据,并且在读写过程中自动地推断出json数据的模式。 spark sql可以解析出json数据中嵌套的字段,并且允许用户直接访问这些字段,而不需要任何显示的转换操作。. Conceptually, it is equivalent to relational tables with good optimizati. Together, you can use Apache Spark and Kafka to transform and augment real-time data read from Apache Kafka and integrate data read from Kafka with information stored in other systems. I am running the code in Spark 2. They are extracted from open source Python projects. When reading text-based files from HDFS, Spark can split the files into multiple partitions for processing, depending on the underlying file system. These behave the same as INSERT JSON and SELECT JSON , but are limited to a single value or column. The library parses JSON into a Python dictionary or list. Spark File Format Showdown - CSV vs JSON vs Parquet Posted by Garren on 2017/10/09 Apache Spark supports many different data sources, such as the ubiquitous Comma Separated Value (CSV) format and web API friendly JavaScript Object Notation (JSON) format. Each line must contain a separate, self-contained valid JSON object. Create RDD from Text file Create RDD from JSON file Example - Create RDD from List Example - Create RDD from Text file Example - Create RDD from JSON file Conclusion In this Spark Tutorial, we have learnt to create Spark RDD from a List, reading a. A very quick and easy alternative (especially over smaller bad data sets) is to download the bad rows locally (e. 2, SparkR provides a distributed data frame implementation that supports operations like selection, filtering, aggregation etc. The foldLeft way is quite popular (and elegant) but recently I came across an issue regarding its performance when the number of columns to add is not trivial. However, my problem looks a bit different. , nested StrucType and all the other columns of df are preserved as-is. 4, the community has extended this powerful functionality of pivoting data to SQL users. Spark SQL is a part of Apache Spark big data framework designed for processing structured and semi-structured data. format[csv/json]. Importing data from a CSV file. Loading and Saving Data in Spark. Learn how to ETL Open Payments CSV file data to JSON, explore with SQL, and store in a document database using Spark Datasets and MapR-DB. Custom serializers. Spark SQL provides an option for querying JSON data along with auto-capturing of JSON schemas for both. The requirement is to load JSON. It represents rows, each of which consists of a number of observations. After the ingestion, Spark displays some records and the schema. Spark SQL can automatically capture the schema of a JSON dataset and load it as a DataFrame. You can vote up the examples you like or vote down the ones you don't like. Here, the hive table will be a non-partitioned table and will store the data in ORC format. Data Access. show() The output of the dataframe having a single column is something like this: { " e. runCommand is used when DataFrameWriter is requested to save the rows of a structured query (a DataFrame) to a data source (and indirectly executing a logical command for writing to a data source V1), insert the rows of a structured streaming (a DataFrame) into a table and create a table (that is used exclusively for saveAsTable). Spark - Read JSON file to RDD JSON has become one of the most common data format that is being exchanged between nodes in internet and applications. Thankfully this is very easy to do in Spark using Spark SQL DataFrames. This is the second post in the series in which we discuss how to handle csv data in spark. A Request object is comprised of the HTTP method, a set of HTTP headers, and a set of query parameters. when running the following command. Inserting JSON formatted values. The spark session read table will create a data frame from the whole table that was stored in a disk. Introduction In a previous article, I described how a data ingestion solution based on Kafka, Parquet, MongoDB and Spark Structured Streaming could have the following capabilities: Stream processing of data as it arrives. twitter parquet-avro 1. setConf("spark. json datasets. Importing Data into Hive Tables Using Spark. how to convert json string to dataframe on spark. Reason for this failure is that spark does parallel processing by splitting the file into RDDs and does processing. Starting in the MEP 4. Here are a few examples of parsing nested data structures in JSON using Spark DataFrames (examples here done with Spark 1. Below is a sample code which helps to do the same. Part 2 covers a “gotcha” or something you might not expect when using Spark SQL JSON data source. Function tFileInputJSON extracts JSON data from a file. In this article we introduced you to the json. Load JSON Data in Hive non-partitioned table using Spark Requirement Suppose there is a source data which is in JSON format. A Fusion Query Request organizes the contents of each query submitted to a Fusion query pipeline. 0 release, you can use the insertToMapRDB API to insert an Apache Spark DataFrame into a MapR Database JSON table in Python. Instead since I already know the data, I would prefer to provide the schema myself with. However, my problem looks a bit different. codec","snappy"); or sqlContext. For this go-around, we'll touch on the basics of how to build a structured stream in Spark. JSON to Spark KNIME Extension for Apache Spark core infrastructure version 4. For doing more complex computations, map is needed. JSON stands for JavaScript Object Notation and is an open standard file format. spark dataset api with examples – tutorial 20. The MapR Database OJAI Connector for Apache Spark provides an API to save an Apache Spark RDD to a MapR Database JSON table. SparkSession (sparkContext, jsparkSession=None) [source] ¶. How to parse Json formatted Kafka message in spark streaming You could dig into the json structure more with both spark sql and / or json4s (for example. Spark – Write Dataset to JSON file Dataset class provides an interface for saving the content of the non-streaming Dataset out into external storage. Show some samples:. JSON(JavaScript Object Notation) is a minimal, readable format for structuring data. Python For Data Science Cheat Sheet PySpark - SQL Basics Learn Python for data science Interactively at www. Plus, with the evident need for handling complex analysis and munging tasks for Big Data, Python for Spark or PySpark Certification has become one of the most sought-after skills in the industry today. Is there a simple way to converting a given Row object to json? Found this about converting a whole Dataframe to json output: Spark Row to JSON. Note that the ansi sql standard defines "timestamp" as equivalent to "timestamp without time zone". Working in pyspark we often need to create DataFrame directly from python lists and objects. Spark DataFrames are also compatible with R's built-in data frame support. 11, so we have to stick with 2. Data Source API in Spark. Learn how to use Spark & Hive Tools for Visual Studio Code to create and submit PySpark scripts for Apache Spark, first we'll describe how to install the Spark & Hive tools in Visual Studio Code and then we'll walk through how to submit jobs to Spark. In addition to this, we will also see how to compare two data frame and other transformations. Spark SQL – It is used to load the JSON data, process and store into the hive table. I am running the code in Spark 2. They are extracted from open source Python projects. When a field is JSON object or array, Spark SQL will use STRUCT type and ARRAY type to represent the type of this field. This block of code is really plug and play, and will work for any spark dataframe (python). Note that the file that is offered as a json file is not a typical JSON file. It also allows us to store the data in compressed format, still quarriable of data. We'll convert the above object your_list to a JSON object, and then coerce it back into a list, this is done with jsonlite::toJSON() and jsonlite::fromJSON(). Create RDD from Text file Create RDD from JSON file Example - Create RDD from List Example - Create RDD from Text file Example - Create RDD from JSON file Conclusion In this Spark Tutorial, we have learnt to create Spark RDD from a List, reading a. toJson() method in the Row class. This post shows how to derive new column in a Spark data frame from a JSON array string column. Thankfully this is very easy to do in Spark using Spark SQL DataFrames. So, with a very simple test we have seen how easy it is to load an external custom Serde JAR into Azure HDInsight!. row of the eventual data frame) to be serialized on one line. I would like to create a JSON from a Spark v. In this tutorial, you will learn how to query two database tables, join the data using a conditional expression, and write the data. json(jsonFilePath) however the problem is the json data format is kind of weird. Note In Spark SQL 2. Inserting JSON data with the INSERT command for testing queries. setConf("spark. Or if there is a library which can load nested json into a spark dataframe. Spark JDBC DataFrame Example. Refer to the following post to install Spark in Windows. The entry point to programming Spark with the Dataset and DataFrame API. Deleting values from a column or entire row Use the DELETE command to replace the value in a column with null or to remove an entire row of data. Spark SQL is a Spark module for structured data processing. For example, consider below simple example to extract name from json string using get_json_object function. The loaded data will be present as a single row which represents the JSON root node. We are going to load a JSON input source to Spark SQL’s SQLContext. On clicking on Open, the JSON data will be loaded into the Query Editor window. Learn more about JSON and RESTful Web Services. spark sql can automatically infer the schema of a json dataset and load it as a dataframe. Please make sure that each line of the file (or each string in the RDD) is a valid JSON object or an array of JSON objects. Use the SELECT command to return JSON data. By the way, If you are not familiar with Spark SQL, a couple of references include a summary of Spark SQL chapter post and the. If the code uses sparklyr, You must specify the Spark master URL in spark_connect. Figure 1 Spark is ingesting a JSON Lines file. 1, "How to create a JSON string from a Scala object. Step 4: Convert your file! Here’s a video showing the exact steps to convert JSON to SQL in 30 seconds. The command to ingest this data is similar to that of the CSV, substituting table and column names where appropriate: cat data. I have been following this steps: https://update. Is there a simple way to converting a given Row object to json? Found this about converting a whole Dataframe to json output: Spark Row to JSON. Part 1 focus is the "happy path" when using JSON with Spark SQL. A JSON File can be read using a simple dataframe json reader method. Sadly, the process of loading files may be long, as Spark needs to infer schema of underlying records by reading them. How to convert Row to JSON in Java?. city) sample2 = sample. I have been researching with Apache Spark currently and had to query complex nested JSON data set, encountered some challenges and ended up learning currently the best way to query nested structure as of writing this blog is to use HiveContext with Spark. Spark SQL can convert an RDD of Row objects to a DataFrame, inferring the datatypes. In order to access the text field in each row, you would have to use. This step returns a spark data frame where each entry is a Row object. The mapping will be done by name. The file Babynames. This block of code is really plug and play, and will work for any spark dataframe (python). Inserting an Apache Spark DataFrame into a MapR-DB JSON Table. I want to store these json rows using HiveContext so that it can be accessed from the JDBC Thrift Server. With CSVJSON you can parse values as numbers or JSON. Spark - Create RDD To create RDD in Spark, following are some of the possible ways : Create RDD from List using Spark Parallelize. Format query results as JSON, or export data from SQL Server as JSON, by adding the FOR JSON clause to a SELECT statement. 0 release, the connector introduces support for saving Apache Spark DataFrames and DStreams to MapR Database JSON tables. Saving an Apache Spark DStream to a MapR. Function tFileInputJSON extracts JSON data from a file. Here is the Spark Code: StructType orderSchemaStruct = orderSchema. A foldLeft or a map (passing a RowEncoder). spark sql can automatically infer the schema of a json dataset and load it as a dataframe. Dataset Union can only be performed on Datasets with the same number of columns. js Pandas PHP PostgreSQL Python Qt R Programming Regex Ruby Ruby on. JSON has become one of the most common data format that is being exchanged between nodes in internet and applications. Here we are using the spark library to convert the json data to parquet format, the main advantage of using the library is that provide any form of complex json format, it will convert it to parquet, however there are other library which do the same thing like avro-parquet library but in that case, if the json structure is generic or if it. Sprite sheets - either add a JSON file to tell Spark AR Studio how the frames should be arranged or add the information yourself. This post explains different approaches to create DataFrame ( createDataFrame()) in Spark using Scala example, for e. It attempts to infer the schema from the JSON file and creates a DataFrame = Dataset[Row] of generic Row objects. At the time of reading the JSON file, Spark does not know the structure of your data. In my case the json data is very very large and I want to save by avoiding Spark to make scan over data to infer the schema. Online tool to convert your CSV or TSV formatted data to JSON. Adding to the previous answer, If you are on spark 2+, you can pass the column names instead of defining schema //define column names as a Seq of string val columnNames= Seq("col1", "col2") //read file with format csv. Since JSON is semi-structured and different elements might have different schemas, Spark SQL will also resolve conflicts on data types of a field. In particular this process requires two steps where data is first converted from external type to row, and then from row to internal representation using generic RowEncoder. Existing practices In practice, users often face difficulty in manipulating JSON data with modern analytical systems. Spark can also access JSON data for manipulation. Click on 'Record' to drill down to the list of records. I am using spark 1. Spark Blog 3 - Simplify joining DB2 data and JSON data with Spark Spark SQL gives powerful API to work with data across different data sources using Python, Scala and Java. The foldLeft way is quite popular (and elegant) but recently I came across an issue regarding its performance when the number of columns to add is not trivial. You have a JSON string that represents an array of objects, and you need to deserialize it into objects you can use in your Scala application. Internally, Spark SQL uses this extra information to perform extra optimizations. Inserting JSON formatted values. Optimized Row Columnar (ORC) file format is a highly efficient columnar format to store Hive data with more than 1,000 columns and improve performance. Spark SQL JSON Overview. The following are top voted examples for showing how to use org. JSON is one of the many formats it provides. def customFunction(row): return (row. spark sql can automatically infer the schema of a json dataset and load it as a dataframe. Append or Concatenate Datasets Spark provides union() method in Dataset class to concatenate or append a Dataset to another. I also try json-serde in HiveContext, i can parse table, but can't querry although the querry work fine in Hive. 1 RowEncoder — Encoder for DataFrames 2. As an example, we will look at Durham police crime reports from the Dhrahm Open Data website. Converts a JSON column to multiple columns, whereby the column list is heuristically extracted from the JSON structure. Row; import org. Serialize a Spark DataFrame to the JavaScript Object Notation format. sqlContext. 1, "How to create a JSON string from a Scala object. Here you will learn the follwing. There are generally two ways to dynamically add columns to a dataframe in Spark. ETL Pipeline to Analyze Healthcare Data With Spark SQL. Or if there is a library which can load nested json into a spark dataframe. But I just want to convert a one Row to json. Importing Data into Hive Tables Using Spark. StructType objects contain a list of StructField objects that define the name, type, and nullable flag for each column in a DataFrame. We examine how Structured Streaming in Apache Spark 2. " If you're using the Play Framework, you can use its library to work with JSON, as shown in Recipes 15. 11 to use and retain the type information from the table definition. In order to access the text field in each row, you would have to use. Make sure that sample2 will be a RDD, not a dataframe. You can use Preview rows to display the rows generated by this step. Now that I am more familiar with the API, I can describe an easier way to access such data, using the explode() function. 0, Spark SQL is now de facto the primary and feature-rich interface to Spark’s underlying in-memory…. The JSON file format required by Spark is not a typical JSON file. These behave the same as INSERT JSON and SELECT JSON , but are limited to a single value or column. Learn Apache Spark Tutorials and know how to filter DataFrame based on keys in Scala List using Spark UDF with code snippets example. Dynamic Row Adding and Inserting using JSON Objective: To achieve, Dynamically adding or removing parent rows and include or exclude ‘ n ’ number child rows for the particular parent in the form. Row; import org. If you wish to learn Spark and build a career in domain of Spark and build expertise to perform large-scale Data Processing using RDD, Spark Streaming, SparkSQL, MLlib, GraphX and Scala with Real Life use-cases, check out our interactive, live-online Apache Spark Certification Training here, that comes with 24*7 support to guide you throughout. For this go-around, we'll touch on the basics of how to build a structured stream in Spark. This post provides information on easy ways to use JSON with DSE Search. SparkConf; import org. Spark makes pure simplicity of request handling, and it supports a variety of view templates. This Spark SQL JSON with Python tutorial has two parts. What, exactly, is Spark SQL? Spark SQL allows you to manipulate distributed data with SQL queries. json(rdd) to create a dataframe but that is having one character at a time in rows: import json json_rdd=sc. The following code examples show how to use org. Consider for instance a dataframe with the following columns: where C is a JSON containing C1, C2, C3. Scenarios include, but not limited to: fixtures for Spark unit testing, creating DataFrame from data. json() on either a Dataset[String], or a JSON file. It enables easy submission of Spark jobs or snippets of Spark code, synchronous or asynchronous result retrieval, as well as Spark Context management, all via a simple REST interface or an RPC client library. Posts about Json written by kalyanhadooptraining Skip to content Spark Training in Hyderabad | Hadoop Training in Hyderabad | ORIEN IT @ 040 65142345 , 9703202345. 0 and above, you can read JSON files in single-line or multi-line mode. Sprite sheets - either add a JSON file to tell Spark AR Studio how the frames should be arranged or add the information yourself. How to convert Row to JSON in Java?. The Apache Spark Dataset API provides a type-safe, object-oriented programming interface. People tend to use it with popular languages used for Data Analysis like Python, Scala and R. One file contains JSON row arrays, and the other JSON key-value objects. Part 2 covers a "gotcha" or something you might not expect when using Spark SQL JSON data source. Apache Spark Streaming is a scalable, high-throughput, fault-tolerant streaming processing system that supports both batch and streaming workloads. Dataset Union can only be performed on Datasets with the same number of columns. JSON has become one of the most common data format that is being exchanged between nodes in internet and applications. StructType is a collection of StructField's that defines column name, column data type, boolean to specify if the field can be nullable or not and metadata. json to a Person class. Note that the file that is offered as a json file is not a typical JSON file. We examine how Structured Streaming in Apache Spark 2. I have the following Java code that read a JSON file from HDFS and output it as a HIVE view using Spark. Spark SQL - DataFrames - A DataFrame is a distributed collection of data, which is organized into named columns. com @owen_omalley June 2018. class pyspark. Load data from JSON data source and execute Spark SQL query. The hive table will be partitioned by some column(s). How to Change Schema of a Spark SQL DataFrame? For the reason that I want to insert rows selected from a table (df_rows) to another table, I need to make sure that. We are going to load a JSON input source to Spark SQL’s SQLContext. Append or Concatenate Datasets Spark provides union() method in Dataset class to concatenate or append a Dataset to another. 08/21/2019; 6 minutes to read +1; In this article. By default, left unset. Databricks provides a unified interface for handling bad records and files without interrupting Spark jobs. Learn how to ETL Open Payments CSV file data to JSON, explore with SQL, and store in a document database using Spark Datasets and MapR-DB. We can go ahead and invoke the complex_test. In this post I'll show how to use Spark SQL to deal with JSON. You can vote up the examples you like or vote down the ones you don't like. Internally, Spark SQL uses this extra information to perform extra optimizations. Spark makes pure simplicity of request handling, and it supports a variety of view templates. 0 DataFrame type is a mere type alias for Dataset[Row] with RowEncoder being the encoder. I have extracted and explained each of them in the section below it. " If you're using the Play Framework, you can use its library to work with JSON, as shown in Recipes 15. how to convert json string to dataframe on spark. In Part 1 you set up a Spark project in your Eclipse. Parse JSON data and read it. rdd_json = df. Root Cause: As mentioned in Spark Documentation:Note that the file that is offered as a json file is not a typical JSON file. 15, but if you're using. SparkSession spark = SparkSession. How to convert Row to JSON in Java?. Each new release of Spark contains enhancements that make use of DataFrames API with JSON data more convenient. runCommand is used when DataFrameWriter is requested to save the rows of a structured query (a DataFrame) to a data source (and indirectly executing a logical command for writing to a data source V1), insert the rows of a structured streaming (a DataFrame) into a table and create a table (that is used exclusively for saveAsTable). There is an underlying toJSON() function that returns an RDD of JSON strings using the column names and schema to produce the JSON records. Note In Spark SQL 2. HyukjinKwon changed the title [SPARK-17764][SQL] Add `to_json` supporting to convert nested struct column to JSON string [SPARK-17764][SQL][WIP] Add `to_json` supporting to convert nested struct column to JSON string Oct 5, 2016. parallelize(json. You can use Preview rows to display the rows generated by this step. withColumn('json', from_json(col('json'), json_schema)) Now, just let Spark derive the schema of the json string column. 2 Builder — Building SparkSession using Fluent API 2.