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1. Json Cheat Sheet Pdf
2. Python Json Cheat Sheet
3. Json Cheat Sheet Python
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6. Json Cheat Sheet

Parsing and emitting JSON is a necessity for basically any program that wants to talk to the internet. Haskell’s most widely used JSON package, aeson, is the de-facto standard choice here. Its basic usage is easy enough: define your Haskell types for some JSON data, derive FromJSON and ToJSON instances, and you’re ready to encode and decode. Of course, real-life JSON data and API returns are never quite that simple; work with aeson long enough, and you’ll likely need more complicated use cases. For instance, what if you need to:

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• parse JSON data where the attribute names are different from your Haskell property names? e.g. where the attribute names are snake_case and your code is camelCase?
• have several different JSON parsers for the same Haskell data? If you use typeclasses, there has to be a single ‘canonical’ parser.
• parse a string enum which can only be one of a few alternatives? e.g. your JSON 'user_type' can only be one of 'user', 'admin', or 'customer_support'.
• use one field to determine how to parse the rest of the document? e.g. for Slack’s Web API, you need to check the “ok” field first to determine whether the document contains data, or whether it only has an error message.
• deal with weird strange JSON formats, like if collections are sent as dictionaries with ordered keys instead of a list?
• parse JSON fields without needing to create a new datatype?

The autoderived parsers that aeson gives you won’t cut it for these sorts of situations. Fortunately, it is possible to use aeson for any complicated JSON parsing solution you need, including the ones listed above, but it can be surprisingly nonobvious how to do so. So here’s a cheatsheet for some common operations using aeson.

Note that the later examples will make heavy use of monadic code; for the more complicated use cases of aeson, there’s really no way around it. You should be able to understand the logic behind what’s going on even if you’re not 100% on monads, but if you’re not comfortable with reading monadic code and do-syntax, take a look at my introduction to monads.

All examples were tested on aeson 1.4.4.0.

Importing and using

Add to your package.yaml/cabal file:

In modules where you need to manipulate JSON:

While the import of Data.Aeson.Types isn’t strictly necessary, it’s so often useful that it’s usually worth importing.

Basics

Autoderiving parsers and serializers

This is the ‘preferred’ way to automatically derive JSON parsers and encoders for your types, using Generic. Essentially, this gives aeson enough information to introspect on the structure of Foo and figure out all the fields it needs and what they’re named, and thus how to construct one using a JSON string. Don’t forget to enable the language extensions.

Like I mentioned before, the autoderived instances aren’t particularly flexible, and won’t cut it for even slightly complicated formats. It will only parse JSON where the attribute names are exactly the same as the Haskell names, including case. In practice, I find that I never write my instances this way; I prefer manually implementing the ToJSON and FromJSON instances for more control.

Decoding from JSON strings

Aeson only seems to provide functionality to decode from lazy ByteStrings.

Encoding to JSON strings

Basically, if you’re working with aeson, use lazy ByteStrings.

Core JSON types

Text, Bool, HashMap and Vector are as you’d expect. Scientific is from the scientific package, and represents arbitrary-precision numbers.

Constructing JSON values directly

While the data definition above is already enough to construct any valid JSON value you want, there’s some convenience functions for constructing JSON objects. (By the way, pay attention to the difference between JSON values and JSON objects.)

Common use cases

Implementing a custom parser

Eventually the default parsers won’t cut it. If you need custom parsing behavior, you can always write your ToJSON and FromJSON values by hand.

For writing ToJSON instances, you can use the convenience functions from before to build aeson Values.

For writing FromJSON instances, the main functions you’ll want are withObject and (.:).

If you have an optional field, use (.:?) instead of (.:).

Note that Parser implements Monad and Alternative. So if you need to do more complex things like take in both snake_case and camelCase keys for the same field, or conditionally parse one field based on the value of another, you can use the normal applicative/monadic tools for doing so. We’ll see some examples of doing that later.

Json Cheat Sheet Pdf

Parsing enum datatypes

The autoderive works for simple enum types as well.

But the output is, once again, exactly the same case as the Haskell code. So if you’re trying to parse some API enum you’ll need to write custom instances once again.

The ToJSON instance should be fairly obvious. Writing the FromJSON instance is a little bit trickier.

Parsing weird JSON formats

Since the Parser type is a monad, we can write as complicated conditional logic as we want inside our parser code.

For instance, let’s say an API we’re working with can either send us some data or an error message; we need to check the 'ok' attribute first to see which way to parse it. We might represent this with a sum type on the Haskell side. How do we write our FromJSON instance?

Another annoying situation might be if collections are sent as dictionaries with ordered keys instead of as JSON lists. But again, we can handle this:

Parse a type directly from a Value

Right now we can parse from a ByteString, but what if we already have a Value?

The simplest way is to use fromJSON:

However, fromJSON returns aeson’s own custom Result type, which is all fine and dandy, but probably not what you’re passing around in the rest of your application.

Thankfully, Data.Aeson.Types provides the parseMaybe and parseEither functions, which return values of types rather more compatible with the rest of the Haskell ecosystem:

Since parseJSON from the FromJSON typeclass already has the type Value -> Parser a, we can use it to define useful utility functions. Plugging it into the first argument of parseMaybe and gives us:

Have multiple parsing functions for a single type

Sometimes you might have several different JSON formats for the same object, and then the typeclass solution won’t cut it. But we just saw that parseMaybe and parseEither take a parser function as their first argument. We used the function that the FromJSON typeclass provides before, but there’s nothing stopping us from putting something else there.

Parse a type directly from an Object

Sometimes we already know that we have a JSON Object and don’t need the full generality of Value. But all of our functions thus far either parse from a ByteString or a Value.

However, since withObject takes in a parser that takes in an Object and turns it into a parser that takes a Value, we can get to what we want by just removing the withObject wrapping and defining the parser separately. So instead of defining FromJSON instances the way we did above, we can do it like this:

Parsing without a new datatype

Since the parseX family of functions takes in a parser directly, there’s no need to define a new datatype for one-off or bespoke parses.

Putting it all together, we can go straight from a ByteString to parsed data without having to define a datatype at all.

Less common stuff

Parsing nested fields

While being able to write parsers to access nested fields is a natural consequence of the monad instance for Parser, it may not immediately spring to mind the first time you need to do it.

If you find yourself doing this a lot, it might even be worth it to define a new operator specifically for this use case:

Parsing multiple JSON values in the same string

Python Json Cheat Sheet

The default functions that aeson provides don’t allow you to inspect what’s left over in the input string after parsing a Value, so if you need to do things like parse a complicated file format where JSON is somewhere in the format, or parse multiple JSON values appended to the same file, the functions we’ve looked at up to now won’t cut it.

Thankfully, aeson also exposes its attoparsec parsers, so we can use all the tools we have for manipulating parser combinators to handle JSON input as well.

You’ll likely want to import parser-combinators as well.

Useful auxilliary libraries

Since aeson is so widely used, there are a fair amount of libraries in the ecosystem that provide extra functionality on top of what is provided in aeson itself. You don’t need any of these libraries to work with JSON, but you might find them useful.

Pretty-printing

Aeson doesn’t provide a way to pretty-print the encoded JSON strings by default, but the aeson-pretty package does.

If you need more control over how the output is formatted, aeson-pretty also provides encodePretty':

Embedding literal JSON values in code

The aeson-qq package provides a quasiquoter to allow you to directly write JSON strings into your code and have them converted into Values.

This quasiquoter also allows you interpolate in any values that implement ToJSON by enclosing them with #{...}.

Newer versions of aeson provide a simple version of this quasiquoter in Data.Aeson.QQ.Simple, but without the ability to interpolate values.

Doing data access directly on Values

If you need to directly grab data from within a Value, you can always just use pattern matching. However, this quickly gets pretty tedious if you need to do anything more complicated than grabbing a single, surface-depth attribute.

The lens-aeson provides (what else?) lenses for accessing JSON data.

A full explanation of lenses is outside the scope of this article. Here are some more examples of using lens-aeson, as well as exercises for learning lenses themselves.

JSON seems to be the de-facto standard interchange format for the internet for the time being. So if you’re doing anything online with Haskell, you’ll likely end up working with aeson, one way or another. With this, you should be equipped to handle most of the JSON-related situations you come across.

If there’s one thing to take away from this post, keep in mind that the Parser type (and its Applicative/Monad instances) is what drives all the fancy JSON ingesting. If you’re having trouble parsing something, it’s likely in how you’re constructing values of this type. Pay attention to all the functions that produce Parser values.

While aeson is the de-facto standard JSON library, waargonaut is a recent addition to the ecosystem with a focus on supporting JSON parsing through term-level parsers rather than typeclasses. I haven’t actually used it and can’t comment on its usefulness, but if you need more flexible parsing, it may be worth taking a look at.

Come across any particularly hairy JSON and having trouble wrangling it in Haskell? Got a comment? Talk to me!

This post is a reference of my examples for processing JSON data in SQL Server. For more detailed explanations of these functions, please see my post series on JSON in SQL Server 2016:

Additionally, the complete reference for SQL JSON handling can be found at MSDN: https://msdn.microsoft.com/en-us/library/dn921897.aspx

Parsing JSON

Json Cheat Sheet Python

Getting string JSON data into a SQL readable form.

ISJSON()

Checks to see if the input string is valid JSON.

JSON_VALUE()

Extracts a specific scalar string value from a JSON string using JSON path expressions.

Strict vs. Lax mode

If the JSON path cannot be found, determines if the function should return a NULL or an error message.

JSON_QUERY()

Returns a JSON fragment for the specified JSON path.

This is useful to help filter an array and then extract values with JSON_VALUE():

OPEN_JSON()

Returns a SQL result set for the specified JSON path. The result set includes columns identifying the datatypes of the parsed data.

Creating JSON

Creating JSON data from either strings or result sets.

FOR JSON AUTO

Automatically creates a JSON string from a SELECT statement. Quick and dirty.

FOR JSON PATH

Formats a SQL query into a JSON string, allowing the user to define structure and formatting.

Modifying JSON

Updating, adding to, and deleting from JSON data.

JSON_MODIFY()

Allows the user to update properties and values, add properties and values, and delete properties and values (the delete is unintuitive, see below).

Modify:

Add:

Delete property:

Delete from array (this is not intuitive, see my Microsoft Connect item to fix this: https://connect.microsoft.com/SQLServer/feedback/details/3120404/sql-modify-json-null-delete-is-not-consistent-between-properties-and-arrays )

SQL JSON Performance Tuning

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SQL JSON functions are already fast. Adding computed columns and indexes makes them extremely fast.

Computed Column JSON Indexes

JSON indexes are simply regular indexes on computed columns.

Json Cheat Sheet Pdf

Add a computed column:

Json Cheat Sheet

Add an index to our computed column:

Performance test: