Spark Read Json Example

The json library in python can parse JSON from strings or files. json is the example JSON file to represent our object. By default, this is equivalent to float(num_str). 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. You should see an output similar to the following:. You can use Spark to build real-time and near-real-time streaming applications that transform or react to the streams of data. This library is built on top of Jerkson, which is a Scala wrapper around the super-fast Java based JSON library, Jackson. It was originally developed in 2009 in UC Berkeley’s AMPLab, and open. json, spark. Following R code is reading small JSON file but when I am applying huge JSON data (3 GB, 5,51,367 records, and 341 features), the reading process continues and does not end. Featuring push-to-deploy, Redis, queues, and everything else you could. Contribute to apache/spark development by creating an account on GitHub. The Spark cluster I had access to made working with large data sets responsive and even pleasant. Everyone who has read the seminal book Learning Spark has encountered this example in chapter 9 – Spark SQL on how to ingest JSON data from a file using the Hive context to produce a resulting Spark SQL DataFrame:. There two ways to create Datasets: dynamically and by reading from a JSON file using SparkSession. Reading JSON file & Distributed processing using Spark-RDD map transformation. So, the difference is that $. get specific row from spark dataframe; What is Azure Service Level Agreement (SLA)? How to sort a collection by date in MongoDB ? mongodb find by multiple array items; RELATED QUESTIONS. Programmatically, by creating a ConfigurationFactory and Configuration implementation. Seems like your json is not valid. 1 & Python 3. It was introduced in Spark 1. Underlying processing of dataframes is done by RDD’s , Below are the most used ways to create the dataframe. baahu June 16, 2018 No Comments on SPARK : How to generate Nested Json using Dataset Tweet I have come across requirements where in I am supposed to generate the output in nested Json format. This library is built on top of Jerkson, which is a Scala wrapper around the super-fast Java based JSON library, Jackson. Amazon Athena uses Presto with ANSI SQL support and works with a variety of standard data formats, including CSV, JSON, ORC, Avro, and Parquet. Notice that 'overwrite' will also change the column structure. Cloudera has been named as a Strong Performer in the Forrester Wave for Streaming Analytics, Q3 2019. json' has the following content: "Category": "Category A",. by reading it in as an RDD and converting it to a dataframe after pre-processing it Let's specify schema for the ratings dataset. We'll start with a simple, trivial example and then move to an analysis of more realistic JSON example. SparkSession spark = SparkSession. Optional arguments; currently unused. CouchDB is a terrific single-node database that works just like any other database behind an application server of your choice. val path = "/tmp/people. The OMDb API is a RESTful web service to obtain movie information, all content and images on the site are contributed and maintained by our users. Distributed collection of data ordered into named columns is known as a DataFrame in Spark. If your data is in another format, you are free to write your own implementation of the Record Reader and/or Record Writer Controller Service. An R interface to Spark. The code snippet loads JSON data from a JSON file into a column table and executes the query against it. Towards a folder with JSON object, you can use that with JSON method. If you try to read a partitioned json table, spark automatically tries to read figure out if the partition column is a timestamp based on the first value it sees. val dataFrame = spark. json ) In this blog, all the above JSONs will be referred to as "Raw JSONs" (dealer, employee & car_servicing_details). jsoup is designed to deal with all varieties of HTML found in the wild; from pristine and validating, to invalid tag-soup; jsoup will create a sensible parse tree. Jupyter Notebooks are a fantastic environment in which to prototype code, and for a local environment providing both Jupyter and Spark it all you can't beat the Docker image all-spark-notebook. 8 Direct Stream approach. Examples >>>. GSON Streaming api provide facility to read and write large json objects using JsonReader and JsonWriter classes which is available from GSON version 1. So if you really partitioned by a string, and the first value happens to look like a timestamp, then you'll run into errors. I suggest you take the NetworkWordCount example as starting point. json) used to demonstrate example of UDF in Apache Spark. Vega-Lite is a high-level grammar of interactive graphics. Introduction Overview. Before you get a hands-on experience on how to run your first spark program, you should have-Understanding of the entire Apache Spark Ecosystem; Read the Introduction to Apache Spark tutorial; Modes of Apache Spark. You can directly run SQL queries on supported files (JSON, CSV, parquet). My JSON data file is of proper format which is required for stream_in() function. Specifies the behavior when data or table already exists. NET for Apache Spark anywhere you write. This tutorial teaches you how to run a. For example, using a pattern of "MM/dd/yy" and a SimpleDateFormat instance created on Jan 1, 1997, the string "01/11/12" would be interpreted as Jan 11, 2012 while the string "05/04/64" would be interpreted as May 4, 1964. But JSON can get messy and parsing it can get tricky. json is not a Parquet file (too small) spark. Simple example of processing twitter JSON payload from a Kafka stream with Spark Streaming in Python - 01_Spark+Streaming+Kafka+Twitter. Java JSON Parser Example. Read from MongoDB. How to load JSON data in hive non-partitioned table using spark with the description of code and sample data. Data lineage, or data tracking, is generally defined as a type of data lifecycle that includes data origins and data movement over time. This is because index is also used by DataFrame. I want to write csv file. WhileStatementConditional - How to use a while loop to calibrate a sensor while a button is being read. This is because, even though the from_json() function relies on Jackson, there is no way to specify the format of the date to read at that time (we used an ISO-8601 format). This conversion can be done using SQLContext. Data files. Lets begin the tutorial and discuss about the SparkSQL and DataFrames Operations using Spark 1. This conversion can be done using SparkSession. Reading JSON string with Nested array of elements | SQL Server 2016 - Part 3 November 1, 2015 Leave a comment Go to comments In my [ previous post ] I discussed about how to Import or Read a JSON string and convert it in relational/tabular format in row/column from. Contribute to apache/spark development by creating an account on GitHub. Apache Spark Streaming is a scalable, high-throughput, fault-tolerant streaming processing system that supports both batch and streaming workloads. Go to the directory where you unzipped the file i. Recently, we have been interested on transforming of XML dataset to something easier to be queried. - raj kumar Sep 7 '16 at 0:35 add a comment |. This tutorial teaches you how to run a. Enter your template below and press the Convert button below. Spark does not support conversion of nested json to csv as its unable to figure out how to convert complex structure of json into a simple CSV format. Create a SparkSession. With Gson, you can read JSON dataset and map them to a custom class MyClass. json is the example JSON file to represent our object. Apache Spark is a fast and general engine for large-scale data processing. By default, this is equivalent to float(num_str). txt") A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. Because I selected a JSON file for my example, I did not need to name the. Use Dataset. Solr is the popular, blazing-fast, open source enterprise search platform built on Apache Lucene ™. You can vote up the examples you like and your votes will be used in our system to product more good examples. 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. html is the HTML page to call the JavaScript and display the data. 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. In this blog post, I'll walk you through how to use an Apache Spark package from the community to read any XML file into a DataFrame. Following R code is reading small JSON file but when I am applying huge JSON data (3 GB, 5,51,367 records, and 341 features), the reading process continues and does not end. Your help would be appreciated. Start your free trial today!. - raj kumar Sep 7 '16 at 0:35 add a comment |. For example, using a pattern of "MM/dd/yy" and a SimpleDateFormat instance created on Jan 1, 1997, the string "01/11/12" would be interpreted as Jan 11, 2012 while the string "05/04/64" would be interpreted as May 4, 1964. In fact, it even automatically infers the JSON schema for you. This makes parsing JSON files significantly easier than before. Spark DataFrames makes it easy to read from a variety of data formats, including JSON. Combine the two to parse all the lines of the RDD. json() on either a Dataset[String], or a JSON file. JSON: Ideal when records are stored across a number of small files; By choosing the optimal HDFS file format for your Spark jobs, you can ensure they will efficiently utilize data center resources and best meet the needs of downstream consumers. 0+ with python 3. Use Dataset. Parquet is a columnar storage format for Hadoop. A large Health payment dataset, JSON, Apache Spark, and MapR Database are an interesting combination for a health analytics workshop because: JSON is an open-standard and efficient format that uses human-readable text to represent, transmit, and interpret data objects consisting of attribute-value pairs. And we have provided running example of each functionality for better support. Using commas (,) within decimals is not supported. Who is using Apache Phoenix? Read more here. Once you have created a Jackson JsonParser you can use it to parse JSON. I have written this code to convert JSON to CSV. Because I selected a JSON file for my example, I did not need to name the. A character element. Formats may range the formats from being the unstructured, like text, to semi structured way, like JSON, to structured, like Sequence Files. This article describes and provides an example of how to continuously stream or read a JSON file source from a folder, process it and write the data to another source. Manning is an independent publisher of computer books for all who are professionally involved with the computer business. It allows to transform RDDs using SQL (Structured Query Language). The master element specifies the URL of the Spark Master; for example, spark://host:port, mesos://host:port, yarn-cluster, yarn-master, or local. Spark working with Unstructured data; Spark to Connect with Azure SQL DB and read Table; SSIS Folder Traversing in SPARK SQL; SSIS Conditional Split with SPARK SQL; Download JSON file from Azure Storage and Read it Spark SQL to join Flat File and JSON File; Twitter Live Streaming with Spark Streaming (Using April (1) March (1). jQuery getJSON, getJSON() is equivalent to a Ajax function with 'json' datatype, which can load JSON format data from server. Why does JSON. jar OR by importing javax. Basically, JSON (JavaScript Object Notation) is a lightweight data-interchange format. The requirement is to process these data using the Spark data frame. How to Store and Query JSON Objects. Update (18. 4 in Windows ). Specifies the behavior when data or table already exists. SchemaBuilder // When reading the key and value of a Kafka topic, decode the // binary (Avro) data into structured data. JSON objects are easy to read and write and most of the technologies provide support for JSON objects. I was having some trouble reading columns that are arrays or maps (i. Since Gson is not serializable, each executor needs its own Gson object. Steps to read JSON file to Dataset in Spark Create a Bean Class (a simple class with properties that represents an object in the JSON file). Basically, JSON (JavaScript Object Notation) is a lightweight data-interchange format. In such a happy path JSON can be read using context. * in Java 7 or above. For example, { "name":"mkyong" } Token 1 = {Token 2 = name; Token 3 = mkyong; Token 4 = } 3. simple example-read and write JSON GSON example-read and write JSON Jackson example – read and write JSON Jackson Streaming API – read and write JSON reading and writing JSON using json-simple. Amazon Athena uses Presto with ANSI SQL support and works with a variety of standard data formats, including CSV, JSON, ORC, Avro, and Parquet. json(facebookJSON); frame. Check post for java object to yaml examplejava to yaml. Gain new skills and earn a certificate of completion. Laravel is a web application framework with expressive, elegant syntax. Implicitly, a logical AND conjunction connects the clauses of a compound query so that the query selects the documents in the collection that match all the conditions. 6 and above. Logstash (part of the Elastic Stack) integrates data from any source, in any format with this flexible, open source collection, parsing, and enrichment pipeline. JSON Lines' biggest strength is in handling lots of similar nested data structures. Spark reduce operation is an action kind of operation and it triggers a full DAG execution for all pipelined lazy instructions. In this chapter, we will walk you through using Spark Streaming to process live data streams. ) however it does require you to specify the schema which is good practice for JSON anyways. In our last python tutorial, we studied How to Work with Relational Database with Python. json file in HDFS. Online tool to convert your CSV or TSV formatted data to JSON. With Spark, you can have a REST API ready to serve JSON in less than ten lines of code. After IntelliJ IDEA has indexed your source code, it offers a blazing fast and intelligent experience by giving relevant suggestions in every context: instant and clever code completion, on-the-fly code analysis, and reliable refactoring tools. 11 to use and retain the type information from the table definition. JavaScript provides methods JSON. 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. Apr 30, 2018 · 1 min read This is a quick step by step tutorial on how to read JSON files from S3. JSON data (JavaScript object notation) is represented as key-value pairs in a partially structured format. 6 we need to use separate packages for CSV and XML but in latest release of Spark 2. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large. How to convert Java object to JSON string? This page shows how to convert java object to JSON string using Jackson's data binding. A Simple Spark Structured Streaming Example Recently, I had the opportunity to learn about Apache Spark, write a few batch jobs and run them on a pretty impressive cluster. Configuration of Log4j 2 can be accomplished in 1 of 4 ways: Through a configuration file written in XML, JSON, YAML, or properties format. Introduction Following R code is written to read JSON file. databricks:spark-xml_2. Converting a nested JSON document to CSV using Scala, Hadoop, and Apache Spark Posted on Feb 13, 2017 at 6:48 pm Usually when I want to convert a JSON file to a CSV I will write a simple script in PHP. JSON (JavaScript Object Notation) is a lightweight data-interchange format. If you do this you will see changes instantly when you refresh, but if you build a jar file it will only work on your computer (because of the absolute path). Below is a sample code which helps to do the same. zip files, or the higher-level functions in shutil. json (String jsonFilePath) to read the contents of JSON to Dataset. I want to write csv file. While XML is a first-class citizen in Scala, there's no "default" way to parse JSON. The json library in python can parse JSON from strings or files. Issue - How to read\write different file format in HDFS by using pyspark. It's machine-to-machine (M2M) for the Internet Of Things (IOT) the way it was meant to be. Sometimes, your infrastructure may generate a volume of log events that is too large or has significant fluctuations. Building a simple RESTful API with Spark Disclaimer : This post is about the Java micro web framework named Spark and not about the data processing engine Apache Spark. It provides a concise JSON syntax for rapidly generating visualizations to support analysis. To use the Apache Spark DataFrame API, it is necessary to create an entry point for programming with Spark. JsonParser is the jackson json streaming API to read json data, we are using it to read data from the file and then parseJSON() method is used to loop through the tokens and process them to create our java object. Part 1 So lets say we have data coming in JSON format and below is an example of such data: We then read in the. It supports running pure Julia scripts on Julia data structures, while utilising the data and code distribution capabalities of Apache Spark. io Find an R package R language docs Run R in your browser R Notebooks. json file in HDFS. In addition, Spark greatly simplifies the query syntax required to access fields in complex JSON data structures. Most people start with a single node CouchDB instance. Spark SQL essentially tries to bridge the gap between the two models we mentioned previously — the relational and procedural models by two major components. Contribute to apache/spark development by creating an account on GitHub. This library is built on top of Jerkson, which is a Scala wrapper around the super-fast Java based JSON library, Jackson. My JSON data file is of proper format which is required for stream_in() function. @ Kalyan @: How To Stream JSON Data Into Hive Using Apache Flume, hadoop training in hyderabad, spark training in hyderabad, big data training in hyderabad, kalyan hadoop, kalyan spark, kalyan hadoop training, kalyan spark training, best hadoop training in hyderabad, best spark training in hyderabad, orien it hadoop training, orien it spark. Vega-Lite is a high-level grammar of interactive graphics. RuntimeException: file:/temp/path/c000. So, the difference is that $. Read a table serialized in the JavaScript Object Notation format into a Spark DataFrame. 0 and above. Spark runtime Architecture – How Spark Jobs are executed; Deep dive into Partitioning in Spark – Hash Partitioning and Range Partitioning; Ways to create DataFrame in Apache Spark [Examples with Code] Steps for creating DataFrames, SchemaRDD and performing operations using SparkSQL. Lets take an example and convert the below json to csv. json() on either an RDD of String or a JSON file. Redis as a JSON store. Loads a JSON file into a Spark data frame; Examines the contents of the data frame and displays the apparent schema; Like the other preceding data frames, moves the data frame into the context for direct access by the Spark session; Shows an example of accessing the data frame in the Spark context. And now you check its first rows. stringsdict formatting; JSON sample files; PHP sample files; PO file features; QT Linguist Format (. Save the code as file parse_json. NET for Apache Spark. 4 in Windows ). Here are some samples of parsing nested data structures in JSON Spark DataFrames (examples here finished Spark one. That being said, I think the key to your solution is with org. Use Dataset. publish() tutorial, but you need to send data that needs more processing once it gets to its destination on the web. The main reason for this is because XML allows inline metadata using tag attributes and there is no standard way of representing this metadata in JSON. simple example-read and write JSON GSON example-read and write JSON Jackson example – read and write JSON Jackson Streaming API – read and write JSON reading and writing JSON using json-simple. It uses the Apache Spark SparkPi example. Let's now try to read some data from Amazon S3 using the Spark SQL Context. sql and we want to import SparkSession … and then we want to create a spark context … which is the variable again that gives us a reference point. One of the examples in repository accompanying the Learning Spark book I'm working through is a JSON payload of a tweet by the author. JSON: Ideal when records are stored across a number of small files; By choosing the optimal HDFS file format for your Spark jobs, you can ensure they will efficiently utilize data center resources and best meet the needs of downstream consumers. 10/04/2019; 2 minutes to read; In this article. Processing JSON data using Spark SQL Engine: DataFrame API October 21 2015 Written By: Poonam Ligade In the previous blog we played around actual data using Spark core API and understood basic building blocks of Spark i. ===== Spark sql provides, two types of contexts. Apr 30, 2018 · 1 min read This is a quick step by step tutorial on how to read JSON files from S3. --Spark website Spark provides fast iterative/functional-like capabilities over large data sets, typically by. if you want to register as the table you can register like below and print the schema. Always prefer to use XML, whenever document markup and meta-data is essential part of data and cannot be taken away. 1 Connecting to MySQL Using Connector/Python The connect() constructor creates a connection to the MySQL server and returns a MySQLConnection object. Here in spark reduce example, we'll understand how reduce operation works in Spark with examples in languages like Scala, Java and Python. Spark SQL understands the nested fields in JSON data and allows users to directly access these fields without any explicit transformations. Many spark-with-scala examples are available on github (see here). Spark Streaming uses readStream to monitors the folder and process files that arrive in the directory real-time and uses writeStream to write DataFrame or Dataset. The spark session read table will create a data frame from the whole table that was stored in a disk. It uses the Apache Spark SparkPi example. Then use sql statements to query , if in case age field is in table - for example val age = spark. This post is the follow-up to the previous one, but a little bit more advanced and up to date. json() を使うと、データを各ファイルの各行がJSONオブジェクトであるJSONファイルのディレクトリからデータをロードします。. json")); Using the fromJson() method of the gson object, parse the JSON to Java Object (Employee. jl is the package that allows the execution of Julia programs on the Apache Spark™ platform. Sadly, it's not as easy in other languages. This works very good when the JSON strings are each in line, where typically each line represented a JSON object. Logstash (part of the Elastic Stack) integrates data from any source, in any format with this flexible, open source collection, parsing, and enrichment pipeline. Last revision 2015/07/28 by SM. 2015): added spray-json-shapeless library Update (06. Token In Jackson streaming mode, it splits JSON string into a list of tokens, and each token will be processed incremental. Importing Data into Hive Tables Using Spark. If you click "Upload", JSON will be stored on the server and you can download generated file by clicking "Download" button or access it via ajax-request by URL that will be copied to clipboard after clicking "Copy URL" button. This means you can use. parse() function later in this tutorial. The ETL Tool (Deprecated)¶ When working with GeoTrellis, often the first task is to load a set of rasters to perform reprojection, mosaicing and pyramiding before saving them as a GeoTrellis layer. Below is a sample code which helps to do the same. Displaying actual schema of JSON file stored in json_guru tables; Step 2) Using get_json_object() Method we can able to fetch the Data values stored in JSON hierarchy. Reading nested JSON data with Spark SQL. Before Spark v2. 0 release there is an option to switch between micro-batching and experimental continuous streaming mode. Analytics are nothing but jobs as a service. Underlying processing of dataframes is done by RDD's , Below are the most used ways to create the dataframe. §The Play JSON library §Overview The recommend way of dealing with JSON is using Play's typeclass based JSON library, located at play. Learn how to integrate Spark Structured Streaming and. Ignite provides its own implementation of this catalog, called IgniteExternalCatalog. Using SparkSession, read JSON file with schema defined by. Here are a few examples of parsing nested data structures in JSON using Spark DataFrames (examples here done with Spark 1. Data files. Java JSON Parser Example. From that point we can use spark. When we planned to write this I was ready to the unavoidable Javaesque avalanche of interfaces, boilerplate code and deep hierarchies. Guide to Using HDFS and Spark. 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. For more details. Always prefer to use XML, whenever document markup and meta-data is essential part of data and cannot be taken away. This conversion can be done using SQLContext. Part 1 focus is the “happy path” when using JSON with Spark SQL. sql and we want to import SparkSession … and then we want to create a spark context … which is the variable again that gives us a reference point. The tarfile module makes it possible to read and write tar archives, including those using gzip or bz2 compression. json' has the following content: "Category": "Category A",. The tutorial is organized in two parts, one for each deep learning framework, specifically TensorFlow and Keras with TensorFlow backend. The default parser implementation for JsonSlurper is JsonParserCharArray. Processing JSON in Spark. It's been 2 years since I wrote first tutorial on how to setup local docker environment for running Spark Streaming jobs with Kafka. Akka is the implementation of the Actor Model on the JVM. To populate the tree with a JSON object you need to use the $. From the above screen shot we can observe the following. We’ve already laid the foundation — freeing you to create without sweating the small things. What is Spark? Apache Spark Tutorial Guide for Beginner, Apache Spark Ecosystem Components, Spark Features, Evolution of Apache Spark, Reason for Spark Popularity, Apache Spark Data Frames, Operations offered by Spark, spark vs hadoop, spark wiki, what is spark software, spark scala. Line 16) I save data as CSV files in "users_csv" directory. Learn Spark use case and manage data in Nosql Cassandra, MongoDB, Hbase, Kafka, Streaming data processing and analytics. Go through the complete video and learn how to work on nested JSON using spark and parsing the nested JSON files in integration and become a data scientist by enrolling the course. Enter your template below and press the Convert button below. However, BOM is not mandatory by Unicode standard and prohibited by RFC 7159 for example, section 8. We are pleased to announce the release of our new Apache Spark Streaming Example Project!. §The Play JSON library §Overview The recommend way of dealing with JSON is using Play's typeclass based JSON library, located at play. I want to write csv file. Converting a nested JSON document to CSV using Scala, Hadoop, and Apache Spark Posted on Feb 13, 2017 at 6:48 pm Usually when I want to convert a JSON file to a CSV I will write a simple script in PHP. Each line must contain a separate, self-contained valid JSON object. Formats may range the formats from being the unstructured, like text, to semi structured way, like JSON, to structured, like Sequence Files. ecode) it can fetch ecode values from table json_guru. Processing JSON data using Spark SQL Engine: DataFrame API October 21 2015 Written By: Poonam Ligade In the previous blog we played around actual data using Spark core API and understood basic building blocks of Spark i. Spark SQL provides a natural syntax for querying JSON data along with automatic inference of JSON schemas for both reading and writing data. This article describes Spark Streaming example on Consuming messages from Kafa and Producing messages to Kafka in JSON format using from_json and to_json Spark functions respectively. Let's take a look at the real-life example and review it step-by-step. publish() tutorial, but you need to send data that needs more processing once it gets to its destination on the web. Spark Packages, from Xml to Json. Below is a sample code which helps to do the same. Spark SQL, part of Apache Spark big data framework, is used for structured data processing and allows running SQL like queries on Spark data. Working with JSON in Scala using the Json4s library (part two) Working with JSON in Scala using the json4s library (Part one). Apache Spark. 1 Connecting to MySQL Using Connector/Python The connect() constructor creates a connection to the MySQL server and returns a MySQLConnection object. Create a SparkSession. JSON is a very common way to store data. The JSON output from different Server APIs can range from simple to highly nested and complex. It is easy for humans to read and write. Spark examples: how to work with CSV / TSV files (performing selection and projection operation) Hadoop MapReduce wordcount example in Java. Sample data which you have shown is basically a multiline JSON and Spark method provides you option to indicate whether the JSON data is single line or multiline JSON with a boolean input multiline: true or flase Next you can check the schema of JSON before doing any transformation or action if Spark is properly reading. Spark can read/write data to Apache Hadoop using Hadoop {Input,Output}Formats. To get started with Spark we need the following Maven dependencies: In this example we use the. json) used to demonstrate example of UDF in Apache Spark. Transform models to and from json strings using read and write; Custom serializer; Json4s DSL; I've previously used the Play 2 Json library and I was reasonably satisfied with it but I was asked to start using json4s since it's bundled by default in Akka, Spray and Spark and we would rather not pull in any extra dependencies right now. other JSON objects). And also let us see how to read and parse the same Json file using Java. JSON could be a quite common way to store information. Commons Configuration provides typed access to single, and multi-valued configuration parameters as demonstrated by the following code:. 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. One of the examples in repository accompanying the Learning Spark book I’m working through is a JSON payload of a tweet by the author. --Spark website Spark provides fast iterative/functional-like capabilities over large data sets, typically by. You will learn more about the JSON. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: