It is observed that the supplied JSON file has an object array within the JSONdocument. An order has three attributes namely OrderID, CustomerID and OrderStatus.Developing Custom Data Source for JSON FileAs Microsoft has not supplied the default data source for JSON files, we haveto develop a custom/bespoke data source using the script component. In this tip,we will use a script component to build a data source for JSON.Let's add a script component to the data flow task. This script component willread the JSON file and generate the output records with the help of.Net libraries.So, let's configure this script component as a source.
Using System;using System.Data;using Microsoft.SqlServer.Dts.Pipeline.Wrapper;using Microsoft.SqlServer.Dts.Runtime.Wrapper;using System.Collections.Generic;using System.Text;using System.Web.Script.Serialization;using System.IO;using OrderNamespace;Before we start coding on the C#, let us learn some basics.DeserializationDeserialization is a process that helps to transform JSON document to a runtimeobject. Once the data is available as a runtime object, then it can be parsed byusing the.Net libraries.Now we need to read the JSON file content and deserialize it to convert intoruntime object.Creating an Order ClassWe need to create object that can hold the JSON content. So, let's create aclass in the C#. This class must have the same structure and properties as the JSONcontent.A C# class can be created by selecting the project and click Add SelectClass as mentioned in the image below.
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. My JSON data file is of proper format which is required for streamin function. Here is my R code, library(jsonlite)mainsample = jsonlite::streamin(file('sample.json'),pagesize = 100000) # reads line by line, pagesize size is given to break records into chunksdata = jsonlite::flatten(mainsample) # convert into more nested columnsi.
If you can provide a bit more information around the structure of your JSON file, that can be helpful as well. One thing I have had success with in the past (since it looks like you are running out of memory), is streaming the file.This is easiest if every line is a JSON object (that's a standard in some JSON implementations.
Read Json File Javascript
Although I forget what it is called). However, if that is not the case and you know a JSON object usually does not have more than 500 lines, let's say, then it is possible to determine the start / end of a JSON object and split the file up that way. It's a bit more work, but it gets around your limitationreadr has some really great streaming file support. If I get a chance, I would love to scrounge up the example that I did (working with 4 GB of memory and a 10 GB file or something like that. Way bigger than I could fit in memory).I want to say I used jsonlite with readr::readlineschunked or something along those lines.
Tidyjson is also a favorite of mine when parsing JSON data (if the data is very complex), but it is not on CRAN anymore (a working version can be had with devtools::installgithub('colearendt/tidyjson')).EDIT: try jsonlite directly first, as suggested. Streaming is much more complex and potentially painful, but it gets around a memory limitation if one does exist.
Dhanashreedeshpande:readlineschunked('sample.json', str, chunksize = 100000)This function is from readr right? So you are not reading your file as json but just line by line, as character vector. If you want to continue, you'll need some package to deal with string, like stringr.However, I would pursue in the json parsing solution.In fact, it is possible that your json file is not a 'perfect json' file, that is to say not a valid json structure in a whole but a compilation of valid json. Something like that.
This format is called ndjson, and it is possible you big file is that.To deal with such file, you can use several tools. See and its example, or the ndjson that sometimes is more efficient. (see and its example)Also, you can read lines as you have done and reconstruct valid json as strings to parse with jsonlite::fromJSON.Can you try those options on your big file? Conversion from JSON to CSV through R implies two steps: reading and writing.
Reading is generally the hard step, and the liked page shows one way to do this in the second half: library(sergeant)library(tidyverse)db ALTER SESSION SET `store.format`='json';0: jdbc:drill: CREATE TABLE dfs.tmp.`/1996-97` AS SELECT. FROM dfs.root.`/Users/bob/Data/CollegeScorecardRawData/MERGED199697PP.csv`;The advantage of this approach is that Drill is quite clever about memory management. You can still subset along the way, if you like.The disadvantage is that while the APIs are well-designed, this is beyond basic R and requires some system setup.
If you can get jsonlite to handle everything and this is a one-time job, Drill is overkill. Library(sparklyr)library(dplyr)library(jsonlite)Sys.setenv(SPARKHOME='/usr/lib/spark')# Configure cluster (c3.4xlarge 30G 16core 320disk)conf.