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Large Json File Download

  1. Json File Free Download
  2. Json Viewer Large Files
  3. Large Json File Download Free

Another good tool for parsing large JSON files is the JSON Processing API. For an example of how to use it, see this Stack Overflow thread. To download the API itself, click here. Large JSON File Parsing for Python. One programmer friend who works in Python and handles large JSON files daily uses the Pandas Python Data Analysis Library. It can open files of 1.4 GB in size or even larger, as long as you have 7 times the amount of RAM on your machine. System requirements Windows 7 SP1 or higher.NET 4.5 64 bit recommended (unless you open 'small' files with less than 300 MB) Releases Huge JSON Viewer 0.4.12.19 Setup.exe.zip (13.8 MB). The initial file is in the.shp (shapefile) format and as the conversion process is quite cumbersome I uploaded the data as a.json file. Warning: size is 189,9 MB. The initial file can be downloaded from San Francisco Data. Converting.shp to.json. Download the.zip file from San Francisco Data; install gdal. JSON Data Set Sample. The JSON output from different Server APIs can range from simple to highly nested and complex. The examples on this page attempt to illustrate how the JSON Data Set treats specific formats, and gives examples of the different constructor options that allow the user to tweak its behavior. JSONBuddy provides support for huge JSON and text data (multi-GB) to view and edit those documents directly in the application. Regardless of the size of your input data, the application will only use a small amount of memory to view the file content.

I used to spend considerably more time begging and, sometimes, badgering government agencies for data. Thanks to the push for more open and transparent data I’m more and more likely to find data I need posted in a nice manageable format on a government website. This was true on a recent project where I needed the locations of food markets in New York State. A quick web search turned up a lovely dataset on the New York State Open Data Portal posted by the Department of Agriculture and Markets. Thank you Open Data!

If you visit this site you can browse and download the data in a variety of different formats. The interface looks like this:

If you’re using R or other data analysis software, often the most convenient format to work with is comma separated values. With this particular data, though, you’ll find that there are two reasons why CSV is not the best option. For one, if you look closely at the data you will see that the location field is not a traditional set of columns and when you download in CSV you’ll find some of that data missing. Secondly, CSV does not allow for easy inclusion of important metadata. JSON, on the other hand, can easily accommodate the detailed location data and integrate the metadata directly in the file. But working with JSON can be challenging so I’ve put together this post to help guide others through the process.

1) Grab the data

I’ll warn you that I sometimes found the Open Data Portal to be a little slow. You can view and download the data from this link — but if you’re having timeout issues, I’ve also posted a ZIP file with the JSON as well as a shapefile that I use below for mapping (warning, it’s 5mb).

In order to read the data I’m using the R package RJSONIO and I’m reading directly from the Open Data Portal — if this takes too long for you be sure to download the ZIP and save to your local machine.

Note that I was asked by Scott Chamberlain why I used the package RJSONIO rather than rjsonlite. My answer was “no good reason”. I followed up with a speed test on this file using the fromJSON function from each package. RJSONIO was 3x faster in this one case. At the suggestion of Jeroen Ooms, creator of the jsonlite package, I added the simplifyVector=FALSE argument to the jsonlite::fromJSON and found this allows jsonlite to read the data 2x faster than RJSONIO (though for a perfect time comparison I would need to change RJSONIO simplification settings also).

For more information on related packages, Gaston Sanchez has a really nice presentation on the different options for reading JSON data in R.

2) Extract the data from the JSON file

If you take a look at the file in the browser or in a text editor you'll see that the first big chunk of lines is devoted to the metadata – the source of the file etc. It is not until line 1229 that you see the data node which is all we need initially. If you type str(foodMarketsRaw) you’ll notice that the data has been read as an R list. So we will use double brackets to extract the data node:

The data (in the JSON file) looks something like this:

3) Orient yourself to the data

Working with JSON in R can be a bit disorienting because you end up with lists within lists within lists so let's break it down a bit. In this dataset we have an outer list where each list item is an individual food market (you can see this from the sample data above. So foodMarkets[[1]] would give you all the data for the first food market in the list. If you type length(foodMarkets[[1]]) you'll see that the first food market comes with 23 pieces of information. For example, if you explore the sample above you'll see that the 14th element is the food market name:

4a) Assemble the data frame: an example of extracting a variable

I’m going to extract the data variable-by-variable and then assemble them into a data frame. Since we have a list, a logical place to start is the lapply function which will operate on the list piece-by-piece or, even better sapply which does the same thing but will return an array instead of a list.

So, for example, if you want to extract the food market names from the list you can use this code which essentially cycles through each food market and extracts the name:

We could copy and paste this line of code 23 times but this is cumbersome and prone to error — let’s do it programatically.

4b) Assemble the data frame: extracting all the variables (except the geography)

There are a ton of ways to extract all the variables without hard coding. I can pretty much guarantee that this is not the best approach (and please do write to me with alternatives!). Originally I tested using two, nested sapply statements but ran into trouble when certain data values were missing. So, instead, I wrote two functions. I have a function that returns the data if it exists and an NA otherwise (this is returnData) and then I have a function that does the sapply. (Note that I'm only applying this to the first 22 variables, not the geographic infomation – this is next).

4c) Assemble the data frame: extracting the geographic information

Json File Free Download

There is one additional level of complication with the geographic information stored in element 23 of each market. This “variable” is, itself, a list.

The first piece is a JSON format of the address and then lat and long and then two administrative variables. We will use the same approach as before but hard code the variable number and extract the data one level deeper.

5) Add the names

The column names are in the metadata. If you review the metadata you can see that the columns are under meta:view. The column detail, then, can be extracted with:

If you look at any of the entries (try columns[[14]]) you'll see that there is a lot more than just column names. So, once again, we'll use sapply. We again have a complication related to geo where the column names are not under meta:view:columns but rather meta:view:columns:subColumnTypes so I'll extract with hard coding here (which is clearer) but I'll also give the function that can do it for you regardless of whether the variable is geo or not:

Here is the function approach instead which will extract names for a geo or non-geo field:

Now we add the names to the dataset and take a look:

And format the lat/long for future use:

6) Make a quick map with the R package ggplot2

I'm going to use a simple shapefile of New York State that is included in the zip you can download. I’ll go through the mapping quickly but if you want more detail, much of the code comes from a previous post on mapping with ggplot2.

You can't simply add the points because the state boundary has a projected coordinate system while the points are unprojected latitude and longitude. You can get the projection of the state boundaries with proj4string(state). So we need to project before we can add food markets (again, see the previous post, I know this code is tricky):

Now we're ready to combine them:

7) An interactive map with CartoDB

This data is better suited to an interactive map, so I manually added the data and created the map below:

By
Molly Galetto
Tips & Tricks

By: Bruno Dirkx, Team Leader Data Science, NGDATA

When parsing a JSON file, or an XML file for that matter, you have two options. You can read the file entirely in an in-memory data structure (a tree model), which allows for easy random access to all the data. Or you can process the file in a streaming manner. In this case, either the parser can be in control by pushing out events (as is the case with XML SAX parsers) or the application can pull the events from the parser. The first has the advantage that it’s easy to chain multiple processors but it’s quite hard to implement. The second has the advantage that it’s rather easy to program and that you can stop parsing when you have what you need.

I was working on a little import tool for Lily which would read a schema description and records from a JSON file and put them into Lily.

Since I did not want to spend hours on this, I thought it was best to go for the tree model, thus reading the entire JSON file into memory. Still, it seemed like the sort of tool which might be easily abused: generate a large JSON file, then use the tool to import it into Lily. In this case, reading the file entirely into memory might be impossible.

So I started using Jackson’s pull API, but quickly changed my mind, deciding it would be too much work. But then I looked a bit closer at the API and found out that it’s very easy to combine the streaming and tree-model parsing options: you can move through the file as a whole in a streaming way, and then read individual objects into a tree structure.

As an example, let’s take the following input:

For this simple example it would be better to use plain CSV, but just imagine the fields being sparse or the records having a more complex structure.

The following snippet illustrates how this file can be read using a combination of stream and tree-model parsing. Each individual record is read in a tree structure, but the file is never read in its entirety into memory, making it possible to process JSON files gigabytes in size while using minimal memory.

Json Viewer Large Files

As you can guess, the nextToken() call each time gives the next parsing event: start object, start field, start array, start object, …, end object, …, end array, …

The jp.readValueAsTree() call allows to read what is at the current parsing position, a JSON object or array, into Jackson’s generic JSON tree model. Once you have this, you can access the data randomly, regardless of the order in which things appear in the file (in the example field1 and field2 are not always in the same order). Jackson supports mapping onto your own Java objects too. The jp.skipChildren() is convenient: it allows to skip over a complete object tree or an array without having to run yourself over all the events contained in it.

Once again, this illustrates the great value there is in the open source libraries out there.

Parsing a Large JSON File for 2017 and Beyond

While the example above is quite popular, I wanted to update it with new methods and new libraries that have unfolded recently.

GSON

There are some excellent libraries for parsing large JSON files with minimal resources. One is the popular GSON library. It gets at the same effect of parsing the file as both stream and object. It handles each record as it passes, then discards the stream, keeping memory usage low.

Here’s a great example of using GSON in a “mixed reads” fashion (using both streaming and object model reading at the same time).

If you’re interested in using the GSON approach, there’s a great tutorial for that here.

.NET Processing of Large JSON Files

If you’re working in the .NET stack, Json.NET is a great tool for parsing large files. It’s fast, efficient, and it’s the most downloaded NuGet package out there.

JSON Processing API

Another good tool for parsing large JSON files is the JSON Processing API. For an example of how to use it, see this Stack Overflow thread. To download the API itself, click here.

Large JSON File Parsing for Python

One programmer friend who works in Python and handles large JSON files daily uses the Pandas Python Data Analysis Library. For Python and JSON, this library offers the best balance of speed and ease of use. For added functionality, pandas can be used together with the scikit-learn free Python machine learning tool.

Additional Material

Here’s some additional reading material to help zero in on the quest to process huge JSON files with minimal resources.

Large Json File Download Free

File
  • Stack Overflow thread on processing large JSON files.
  • Parsing JSON files for Android. (See answer by Genson author.)
  • Maven and parsing JSON files. (See answer about GSON, ORG.JSON, and Jackson.)
  • Stack Overflow GSON JSON large file parsing example.