We want to read the file in spark using Scala. Learning about Spark SQL, the context of SQL in Spark for providing structured data processing, JSON support in Spark SQL, working with XML data, parquet files, creating Hive context, writing Data Frame to Hive, reading JDBC files, understanding the Data Frames in Spark, creating Data Frames, manual inferring of schema, working with CSV files, reading JDBC tables, Data Frame to JDBC, user-defined functions in Spark SQL, shared variables and accumulators, learning to query and transform data. Basically, each line in the file must contain a separate, valid JSON object. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. CRT020: Databricks Certified Associate Developer for Apache Spark 2. In our next tutorial, we shall learn to Read multiple text files to single RDD. This is used when putting multiple files into a partition. I have tested reading CSV and JSON files in spark-shell, and it was fine! So I figured this is a similar issue as before with Zeppelin as the Zepplin 0. JSON Object consists of two primary elements, keys and values. save(“destination location”) That’s it, you changed the file from json to avro. Spark Streaming Sample program using scala; Saprk-Sql : How to query a csv file. json_tuple(string jsonStr, string k 1,, string k n) Takes JSON string and a set of n keys, and returns a tuple of n values. CLICK FOR BIGGER IMAGE. parse_url_tuple(string urlStr, string p 1,, string p n). See the DataFrameReader class for some of the natively supported formats and Spark Packages for packages available for other formats, such as CSV and many others. Now-a-days most of the time you will find files in either JSON format, XML or a flat file. We will show examples of JSON as input source to Spark SQL’s SQLContext. On the other end, reading JSON data from a file is just as easy as writing it to a file. By renaming the text file say (abc. My task is to create scala function that gets text file and creates new file that contains JSON which is a combination of the type,hour and some attributes from the JSON. 1, “How to open and read a text file in Scala. Scala JSON FAQ: How can I parse JSON text or a JSON document with Scala? As I continue to plug away on my computer voice control application (), last night I started working with JSON, specifically the Lift-JSON library (part of the Lift Framework), which seems to be the preferred JSON library of the Scala community. •The DataFrame data source APIis consistent, across data formats. JSON and SPARK processing, we always come across these two big words in Data processing and manipulation. Read JSON file to Dataset Spark Dataset is the latest API, after RDD and DataFrame, from Spark to work with data. First step is to read our newline separated json file and convert it to a DataFrame. The structure is a little bit complex and I wrote a spark program in scala to accomplish this task. In case it fails a file with the name _FAILURE is generated. Home › spark › spark read sequence file(csv or json in the value) from hadoop hdfs on yarn spark read sequence file(csv or json in the value) from hadoop hdfs on yarn Posted on September 27, 2017 by jinglucxo — 1 Comment. I am trying to parse a json file as csv file. JSON (1) K-MUG (1). OK, I Understand. This video demonstrates how to read in a json file as a Spark DataFrame To follow the video with notes, refer to this PDF: https://goo. json() on either an RDD of String or a JSON file. Or if there is a library which can load nested json into a spark dataframe. scala Find file Copy path HyukjinKwon [SPARK-26745][SQL] Revert count optimization in JSON datasource by SP… d4d6df2 Jan 31, 2019. jsonFile(“/path/to/myDir”) is deprecated from spark 1. parquet placed in the same directory where spark-shell is running. Processing multiline JSON file - Apache Spark August 31, 2017 svyas Apache Spark Comments Off on Processing multiline JSON file - Apache Spark Apache Spark is great for processing JSON files, you can right away create DataFrames and start issuing SQL queries agains them by registering them as temporary tables. Read the json file as : val df = spark. // Read in the parquet file created above // Parquet files are self-describing so the schema is preserved // The result of loading a Parquet file is also a DataFrame: val parquetFileDF = spark. 6 with scala. This short Spark tutorial shows analysis of World Cup player data using Spark SQL with a JSON file input data source from Python perspective. §JSON basics. The spark supports the csv as built in source. Though this is a nice to have feature, reading files in spark is not always consistent and seems to keep changing with different spark releases. Hi, Starting again to write simple blogs for apache spark with scala after 2 years , hope will keep continue Problem - Process a simple json file for emloyee and find all employees having age > 25 and sort them with descending order of their ages we are using - eclipse oxygen , Spark version…. We can make spark dataframes to read csv files in a few simple steps. In single-line mode, a file can be split into many parts and read in parallel. In Spark word count example, we find out the frequency of each word exists in a particular file. Jan 30, 2016. groupBy on Spark Data frame. At the core of working with large-scale datasets is a thorough knowledge of Big Data platforms like Apache Spark and Hadoop. In single-line mode, a file can be split into many parts and read in parallel. Same time, there are a number of tricky aspects that might lead to unexpected results. To load data from a MapR Database JSON table into an Apache Spark Dataset we first define the Scala class and Spark StructType matching the structure of the JSON objects in the MapR Database table. Blog has four sections: Spark read Text File Spark read CSV with schema/header Spark read JSON Spark read JDBC There are various methods to load a text file in Spark documentation. One way which is easy and comes in handy is with the Typesafe Config project which is also used in Akka. Does it contain enough information to be answered. In this post, we introduce the Snowflake Connector for Spark (package available from Maven Central or Spark Packages, source code in Github) and make the case for using it to bring Spark and Snowflake together to power your data-driven solutions. Second, even if the files are processable, some records may not be parsable (for example, due to syntax errors and schema mismatch). Spark DataFrames makes it easy to read from a variety of data formats, including JSON. A common way to develop applications is to start by creating code like this. In part 1 of this blog post we explained how to read Tweets streaming off Twitter into Apache Kafka. In this tutorial, we will discuss different types of Python Data File Formats: Python CSV, JSON, and XLS. Spark SQL, DataFrames and Datasets Guide. Dynamic cache which allows us to handle arbitrary method calls. textFile() method, with the help of Java and Python examples. groupBy on Spark Data frame. Hello, I am using the Spark library to convert JSON/Snappy files to ORC/ZLIB format. I am trying to parse a json file as csv file. We added dependencies for Spark SQL - necessary for Spark Structured Streaming - and for the Kafka connector. This is an excerpt from the Scala Cookbook (partially modified for the internet). We can use scala. the path at the end of this bit of scala. function that returns an RDD of JSON strings using the column names and schema to val adult_df = spark. 0 and above, you can read JSON files in single-line or multi-line mode. 4 server works with 3235 ms speed. Solved: I am using Spark 1. 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. Loading data. {a: '1'} is not valid JSON for a couple of reasons, from what I can tell: a needs to be a string ("a") and you need to use double quotes for "1". 1 (with Scala 2. JSON Parsing in Scala I've been writing mostly Ruby & Python the past few years, and have been spending a lot of time with Clojure as well since 2013. json() on either an RDD of String or a JSON file. save("destination location") That's it, you changed the file from json to avro. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Root Cause: As mentioned in Spark Documentation:Note that the file that is offered as a json file is not a typical JSON file. Read a table serialized in the JavaScript Object Notation format into a Spark DataFrame. We can treat that folder as stream and read that data into spark structured streaming. Reading csv in Spark with scala While spark data frames come with native support for a variety of standard and popular formats such as json,parquet and hive etc. In this code example, JSON file named 'example. This is Recipe 15. From the command line, let’s open the spark shell with spark-shell. It turns out it was the JSON parsing library. to run as scala application, you need to create Scala App and not class In eclipse, package explorer select project/src/package right click new>scala app inform Name e. Learn how to Read JSON as File in Scala. session(sparkPackages = "com. Apache Spark Streaming is a scalable, high-throughput, fault-tolerant streaming processing system that supports both batch and streaming workloads. import json from pyspark. Instantiate the spark session(let’s say as spark). At the bottom is the complete code and it is online here. In this recipe, we are going to take a look at how to read a JSON file from HDFS and process it. Luckily, it's easy to create a better and faster parser. 1, “How to open and read a text file in Scala. Learn how to work with complex and nested data using a notebook in Scala; Introduction to Datasets Apache, Apache Spark, Spark, and the Spark logo are. In the example below we create a mapping between a JSON object representing a Stripe charge and a Scala case class. This function goes through the input once to determine the input schema. Each line must contain a separate, self-contained valid JSON object. Its able to create a RDD but when we try to read the file its not able to recognize the JSON format. 3) First, we have to read the JSON document. Source to read data from a file. In previous version (1. Let's open the spark shell and then work locally. June 9, JSON File. io Find an R package R language docs Run R in your browser R Notebooks. Let's load a JSON input source to Spark SQL’s SQLContext. Spark SQl is a Spark module for structured data processing. This Spark SQL tutorial with JSON has two parts. parquet(" people. Your database documentation should describe the proper format of. setConf("spark. 3 cannot read json file that only with a record. Lets find the max salary using temp table, which has been derived from the above data frame called Employee_DataFrame. Apache spark - a very known in memory computing engine to process big data workloads. Once the data is loaded, however, figuring out how to access individual fields is not so straightforward. Effectively, my Java service starts up an embedded Spark. which is an alternative to spark. json("newFile") Exploring a DataFrame We have two main methods used in inspecting the contents and structure of a DataFrame (or any other Dataset ) - show and printSchema. A very important ingredient here is scala. There are two property files that you need to edit to include the database URL and password for your environment. How to add new column in Spark Dataframe; How to read JSON file in Spark; How to execute Scala script in Spark without creating Jar; Spark-Scala Quiz-1; Hive Quiz - 1; Join in hive with example; Trending now. Presequisites for this guide are pyspark and Jupyter installed on your system. When using textFile with compressed files (file. textFile() method, with the help of Java and Python examples. Spark DataFrames makes it easy to read from a variety of data formats, including JSON. spark-json-schema. Spark's primary data abstraction is an immutable distributed collection of items called a resilient distributed dataset (RDD). RuntimeException: Failed to parse record "array" : [ {. How to load (open and read) an XML file in Scala. You should specify the absolute path of the input file-scala> val inputfile = sc. (In Spark 2. jsonFile(“/path/to/myDir”) is deprecated from spark 1. This comprehensive course covers all aspects of the certification using Scala as programming language. This post will walk through reading top-level fields as well as JSON arrays and nested. from_json (creates a JsonToStructs that) uses a JSON parser in FAILFAST parsing mode that simply fails early when a corrupted/malformed record is found (and hence does not support columnNameOfCorruptRecord JSON option). The following code examples show how to use org. Spark/Scala: Convert or flatten a JSON having Nested data with Struct/Array to columns (Question) January 9, 2019 Leave a comment Go to comments The following JSON contains some attributes at root level, like ProductNum and unitCount. Any help would be greatly appreciated in processing the JSON zip files. Json file size will be 30-40 GG. Here we show how to use ElasticSearch Spark. If you were able to read Json file and write it to a Parquet file successfully then you should have a parquet folder created in your destination directory. runQuery is a Scala function in Spark connector and not the Spark Standerd API. Though this is a nice to have feature, reading files in spark is not always consistent and seems to keep changing with different spark releases. Spark SQL 1. Reading JSON from a File. Also you read JSON data from RDD[String] object like: The latter option is also useful for reading JSON messages with Spark Streaming. JSON Object consists of two primary elements, keys and values. Spark SQL is a Spark module for structured data processing. In part 1 we dealt with ingesting data from a CSV file, and in part 2 we ingested from a JSON file. Read once when you post a question. load by default assumes that data source is in parquet format so it is able to load it but we can use format function which can be used to specify the different format and use the load function to load the data. Happy learning. Spark’s primary data abstraction is an immutable distributed collection of items called a resilient distributed dataset (RDD). Main menu: Spark Scala Tutorial In this Apache Spark Tutorial - We will be loading a simple JSON file. Description. JsonReader is an input reader that can read a JSON stream. Load JSON data in spark data frame and read it; Store into hive non-partition table; Components Involved. The structure and test tools are mostly copied from CSV Data Source for Spark. This enables using Spark SQL to query the contents of the JSON directly. Figure: Runtime of Spark SQL vs Hadoop. Let’s write a Spark Streaming example which streams from Slack in Scala. Spark has easy fluent APIs that can be used to read data from JSON file as DataFrame object. In this example, I am using Spark SQLContext object to read and write parquet files. The streaming output has two modes (Append and Compete), and output into four types of sinks: File Sink (Parquet, JSON, ORC, text, and others), Foreach sink, Console sink and Memory sink (can be used as temporary tables). Working with JSON in Scala using the Json4s library (part two) Working with JSON in Scala using the json4s library (Part one). Loads a JSON file (one object per line) and returns the result as a DataFrame. 4, “How to parse JSON data into an array of Scala objects. Big Data & Hadoop COURSE CONTENT Hadoop Developer Tasks ∑ Hadoop Eclipse integration ∑ Reading and writing data using Java ∑ How to write a Map-Reduce Job ∑ Mapper/Reducer in details ∑ Searching in HDFS ∑ Sorting in HDFS HBase ∑ Introduction to HBase ∑ Installation of HBase on your system ∑ Exploring HBase Master & Regionservers. You can read and parse JSON to DataFrame directly from file: Please note Spark expects each line to be a separate JSON object, so it will fail if you’ll try to load a pretty formatted JSON file. We use cookies for various purposes including analytics. Source to read data from a file. 15): added circe library Some time ago I wrote a post on relational database access in Scala since I was looking for a library and there were many of them available, making it hard to make a choice. Apache Spark has various features that make it a perfect fit for processing XML files. However you can try this. Spark SQL 1. Let’s create a Spark RDD using the input file that we want to run our first Spark program on. We will understand Spark RDDs and 3 ways of creating RDDs in Spark - Using parallelized collection, from existing Apache Spark RDDs and from external datasets. 4 server works with 3235 ms speed. Spark has easy fluent APIs that can be used to read data from JSON file as DataFrame object. io Find an R package R language docs Run R in your browser R Notebooks. Before getting into the file formats in Spark, let us see what is Spark in brief. Spark Scala Tutorial: In this Spark Scala tutorial you will learn how to read data from a text file, CSV, JSON or JDBC source to dataframe. Lets find the max salary using temp table, which has been derived from the above data frame called Employee_DataFrame. Is there a way to tell spark to use only one line of a file to infer the schema ?. Spark examples: how to work with CSV / TSV files (performing selection and projection operation) Hadoop MapReduce wordcount example in Java. I have tested reading CSV and JSON files in spark-shell, and it was fine! So I figured this is a similar issue as before with Zeppelin as the Zepplin 0. ) Read the Data from a CSV File into a Dataframe. Spark SQL can automatically capture the schema of a JSON dataset and load it as a DataFrame. In the following code: The SparkSession read method loads a CSV file and returns the result as a Dataframe. 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. 15): added circe library Some time ago I wrote a post on relational database access in Scala since I was looking for a library and there were many of them available, making it hard to make a choice. ATTENTION: The Scala code above was hard earned and is REALLY VALUABLE! Specifically, the " val df2. txt) to json file (abc. Or if there is a library which can load nested json into a spark dataframe. This function goes through the input once to determine the input schema. Spark SQL is a Spark module for structured data processing. This enables using Spark SQL to query the contents of the JSON directly. I have load into HDFS the file using the next command: [[email protected] labfiles]$. Hope it will be interesting. Quick Reference to read and write in different file format in Spark. here is the code: import org. parquet ("people. 8 because I am going to execute this example on a Google Dataproc cluster that is built on Spark 2. This video demonstrates how to read in a json file as a Spark DataFrame To follow the video with notes, refer to this PDF: https://goo. Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. [1,2,3] {"extra_key":null,"key":"value1"} 1: string1 [2,4,6] {"extra_key":null,"key":"value2"} 2: string2 [3,6,9] {"extra_key":"extra_value3","key":"value3"}. 11), you've come to the right place. The below is the sample data from a file. The easiest way to start working with Datasets is to use an example Databricks dataset available in the /databricks-datasets folder accessible within the Databricks workspace. Great! So, we have a build file. There are a few variations to how this can be done, specifically if I am using the contents of the file as DataFrame in Spark. In this tutorial, you will learn about the various file formats in Spark and how to work on them. 0") Basically, we have seen how to use data sources using an example, JSON input file. Play supports this via its JSON library. After the ingestion, Spark displays some records and the schema. Our strategy is to write the data read from Kafka to files on Google Cloud Storage during a Spark window and then trigger a load job at the end of each window. Firstly, I am completely new to scala and spark Although bit famailiar with pyspark. You can extend the support for the other files using third party libraries. parquet") // Parquet files can also be used to create a temporary view and then used in SQL statements. Next we invoke the loadFromMapRDB method on a SparkSession object, providing the tableName , schema and case class. Apache Spark 2. JDK is required to run Scala in JVM. 11 validates your knowledge of the core components of the DataFrames API and confirms that you have a rudimentary understanding of the Spark Architecture. 3 cannot read json file that only with a record. Loads a JSON file (one object per line) and returns the result as a DataFrame. But JSON can get messy and parsing it can get tricky. We can treat that folder as stream and read that data into spark structured streaming. Basically, each line in the file must contain a separate, valid JSON object. There are multiple ways to read the configuration files in Scala but here are two of my most preferred approaches depending on the structure of the configurations: Reading configurations from JSON. The example provided here is also available at Github repository for reference. GSON Streaming api provide facility to read and write large json objects using JsonReader and JsonWriter classes which is available from GSON version 1. here is the code: import org. Just enter code fccperrin into the discount code box at checkout at manning. Spark's primary data abstraction is an immutable distributed collection of items called a resilient distributed dataset (RDD). sql/package. createOrReplaceTempView("saas_response_json") The above code transforms the JSON String into a Spark Dataframe. Loading and Saving Data in Spark. Let us quickly see how to set it up,. Hi, Starting again to write simple blogs for apache spark with scala after 2 years , hope will keep continue Problem - Process a simple json file for emloyee and find all employees having age > 25 and sort them with descending order of their ages we are using - eclipse oxygen , Spark version…. 0+ with python 3. How to add new column in Spark Dataframe; How to read JSON file in Spark; How to execute Scala script in Spark without creating Jar; Spark-Scala Quiz-1; Hive Quiz - 1; Join in hive with example; Trending now. Now-a-days most of the time you will find files in either JSON format, XML or a flat file. 6 scala> val colors =. JournalDev is a great platform for Java Developers. Each line in the file must contain a separate, self-contained valid JSON object. My task is to create scala function that gets text file and creates new file that contains JSON which is a combination of the type,hour and some attributes from the JSON. 4 & Scala 2. Spark SQL supports operating on a variety of data source through the DataFrame interface. 0, the DataFrame APIs merged with Datasets APIs. 2015): added spray-json-shapeless library Update (06. Requirement Let’s say we have a set of data which is in JSON format. parquet ") // Parquet files can also be used to create a temporary view and then used in SQL statements. I am currently using the lift library to read the json then will read it into a spark dataframe was wondering if there was a better way of doing this. Firstly, I am completely new to scala and spark Although bit famailiar with pyspark. Let's write a Spark Streaming example which streams from Slack in Scala. maxPartitionBytes - The maximum number of bytes to pack into a single partition when reading files. 2 Answers 2. Please help me - how I process this file in the most efficient way. • "Opening" a data source works pretty much the same way, no matter what. We will show examples of JSON as input source to Spark SQL's SQLContext. As a consequence, a regular multi-line JSON file will most often fail. The main agenda of this post is to setup development environment for spark application in scala IDE and run word count example. Jackson provides facility to improve application performance by using it’s streaming API which uses very less memory and low CPU overhead. Spark SQL can automatically capture the schema of a JSON dataset and load it as a DataFrame. This conversion can be done using SQLContext. > Dear all, > > > I'm trying to parse json formatted Kafka messages and then send back to cassandra. This Spark SQL tutorial with JSON has two parts. From Spark we can read and write to parquet files using the methods given in below link. Learn how to Read JSON as File in Scala. Spark SQL executes upto 100x times faster than Hadoop. I wanted to parse the file and filter out few records and write output back as file. Spark DataFrames makes it easy to read from a variety of data formats, including JSON. Spark SQL is faster. This package supports to process format-free XML files in a distributed way, unlike JSON datasource in Spark restricts in-line JSON format. This is a more efficient version of the get_json_object UDF because it can get multiple keys with just one call. JDK is required to run Scala in JVM. In this example, a new data frame is created, executed some basic SQL queries and store the data into JSON format. jsonFile("/path/to/myDir") is deprecated from spark 1. Firstly, I am completely new to scala and spark Although bit famailiar with pyspark. /spark-shell 2 scala> val sqlContext = new org. html 2019-10-25 19:10:02 -0500. Spark has easy fluent APIs that can be used to read data from JSON file as DataFrame object. So, here are some notes to help others navigate the Scala JSON parsing landscape, where there are at least 6 different libraries -- on both performance and correctness. db-properties. import json from pyspark. Main menu: Spark Scala Tutorial In this Spark Scala tutorial you will learn how to download and install, Apache Spark on Windows Java Development Kit (JDK) Eclipse Scala IDE By the end of this tutorial you will be able to run Apache Spark with Scala on Windows machine, and Eclispe Scala IDE. 3 cannot read json file that only with a record. This tutorial covers using Spark SQL with a JSON file input data source in Scala. tableExists("t1") res1: Boolean = true // t1 exists in the catalog // let's load it val t1 = spark. Reading resource files in Spark using Scala Often while coding up unit tests in Scala, I need to read from a file which is available in the resources folder. I also refer the below 2 links but not sure how o porcess. Create sample data. I am currently using the lift library to read the json then will read it into a spark dataframe was wondering if there was a better way of doing this. Let's open the spark shell and then work locally. { "id: { the rest of your json}} Below we show how to make that transformation. json("/path/to/myDir") or spark. This scenario based certification exam demands basic programming using Python or Scala along with Spark and other Big Data technologies. 0+ with python 3. Serialize a Spark DataFrame to the JavaScript Object Notation format. Defaults to 128 mb. Reason for this failure is that spark does parallel processing by splitting the file into RDDs and does processing. Note that Spark streaming can read data from HDFS but also from Flume, Kafka, Twitter and ZeroMQ. We want to read the file in spark using Scala. spark_write_json: Write a Spark DataFrame to a JSON file in sparklyr: R Interface to Apache Spark rdrr. 0 and above. 6 with scala. "Apache Spark, Spark SQL, DataFrame, Dataset" Jan 15, 2017. Using apache spark 1. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. ) however it does require you to specify the schema which is good practice for JSON anyways. spark-json-schema. To read a JSON data file, first use the SparkSession object as an entry point, and access its DataFrameReader to read data into a DataFrame: > val df = spark. So were going to join a structured file with a Semi Structured file. There are several ways of reading configuration files in Scala including the java properties way with the deprecated Configgy and quite some more. which is an alternative to spark. Reading csv in Spark with scala While spark data frames come with native support for a variety of standard and popular formats such as json,parquet and hive etc. Note that the file that is offered as a json file is not a typical JSON file. Play supports this via its JSON library. Spark SQL supports operating on a variety of data source through the DataFrame interface. Spark Streaming Sample program using scala; Saprk-Sql : How to query a csv file. This video demonstrates how to read in a json file as a Spark DataFrame To follow the video with notes, refer to this PDF: https://goo. 0 IntelliJ on a system with MapR Client and Spark installed. Also, MyClass must be serializable in order to pass it between executors. how to parse the json message from streams. Note that the file that is offered as a json file is not a typical JSON file. What Is Spark SQL? Structured data is considered any data that has a schema such as JSON, Hive Tables, Parquet. Support for Scala 2. 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. I am happy to say that for the last 2 year all my students 100% satisfied and implementing spark projects without depends on others. How to read and write JSON files with Spark I wanted to build a Spark program that would read text file where every line in the file was a Complex JSON object like this. Spark SQL JSON with Python Overview. 6 scala> val colors =. Python: Reading a JSON File. This Running Queries Using Apache Spark SQL tutorial provides in-depth knowledge about spark sql, spark query, dataframe, json data, parquet files, hive queries Running SQL Queries Using Spark SQL lesson provides you with in-depth tutorial online as a part of Apache Spark & Scala course. In single-line mode, a file can be split into many parts and read in parallel. The goal of this library is to support input data integrity when loading json data into Apache Spark. "Apache Spark, Spark SQL, DataFrame, Dataset" Jan 15, 2017. For this purpose the library: Reads in an existing json-schema file; Parses the json-schema and builds a Spark DataFrame schema; The generated schema can be used when loading json data into Spark. Further more please read my blog Spark SQL with JSON data Bonus Read: Big Data, Data Science Problems and Solutions by Jose Praveen on Big Data, Data Science Learning Hope this helps. GSON Streaming api provide facility to read and write large json objects using JsonReader and JsonWriter classes which is available from GSON version 1. These sources include Hive tables, JSON, and Parquet files. Learn how to work with complex and nested data using a notebook in Scala; Introduction to Datasets Apache, Apache Spark, Spark, and the Spark logo are. File formats. import json from pyspark.