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I just wrote my first JSON schemas, great! However, I am now looking at how to structure multiple JSON schemas, both during development and later how to host them. There seems to be very limited guidance on this.
Of course, I looked up the official json-schema.org documentation on how to structure a complex scheme, and even got something to work. I now have three schemas, organized during development in a folder structure as follows:
json-schemas
- /common
- /common-schemas.json
- /data
- /data-schemas.json
- /DataStreamServiceRequest
- /DataStreamServiceRequest-schema.json
Out of these three, only DataStreamServiceRequest-schema.json contains a single schema (it is a schema for all possible requests to an application service endpoint). It refers to types defined in data-schemas.json and common-schemas.json by using relative references. The intent here was to have all types for a single subsystem (e.g., data) available in one file.
I assigned all three .json files with an $id containing the absolute URI, corresponding to the directory they are in.
For example, here is common-schemas.json:
{
"$schema": "https://json-schema.org/draft/2019-09/schema",
"$id": "https://carp.cachet.dk/schemas/common",
"NamespacedId": {
"type": "string",
"pattern": "^([a-z_0-9]+\\.?)+[a-z_0-9]$"
}
}
And, from data-schemas.json, I refer to NamespacedId in common-schemas.json using:
"dataType": { "$ref": "common#/NamespacedId" }
I'm glad this works, but, is this recommended? Am I overlooking something fundamental structuring my schemas as follows? Is there any particular reason to prefer "one schema per type"? Is there an idiomatic structure I am overlooking, e.g., an equivalent to Java's file location corresponding to namespace?
If there isn't (I would argue in Java there is) that would also constitute an answer.
Perhaps additional relevant context: I'm using networknt/json-schema-validator, and for development/testing purposes, it is very convenient to not have too many absolute URIs. I need to map each URI to a local file when initializing the validator.
Like #Ether said, there's no one right answer, but here are the guidelines I use.
The most important guide post is to treat your schemas like you would any other code in your system. Generally, that means each "thing" that you are describing ("Person", "Product", "Address", etc) should have it's own schema.
Definitions ($defs) should only be referenced ($ref) from within the same schema. Use definitions to improve readability or reduce duplication within a schema. Any reference to an external schema should not have a JSON Pointer URI fragment (e.g., the #/$defs/NamespacedId fragment you use). If you need to reference a definition in an external schema, it's probably a sign it should be in it's own schema.
An exception to this might be if you have a bunch of tiny schemas you want to put in a "common.schema.json" schema. In this case, all of your schemas should be defined as definitions, but have an anchor $anchor. It needs to be a definition so you can use standard tools to verify that your schemas are valid. Using an anchor allows you to reference it easier, but more importantly, it's a signal that some external schema might be depending on it. Definitions without an anchor are effectively private to the schema and definitions with an anchor are effectively public. You can freely refactor unanchored definitions, but you have to be wary of breaking other schemas if you refactor anchored definitions.
Don't use $ids. When a schema doesn't have an $id, it's identified by the URI that was used to retrieve the schema. That can be a file URI (file:///path/to/schemas/person.schema.json). Then all of your references can be relative meaning there are no absolute URIs in your schemas and you don't need configuration to map https URIs to file locations. Unfortunately, not all implementations support file based retrieval or even retrieval URI identification, so this isn't always possible.
Organize your schemas however makes sense. Follow the same instincts and guidelines you would for any other code. If you have to use $ids, make sure the path matches the path on the file system (Ex https://example.com/schemas/person => file:///local/path/to/app/schemas/person.schema.json). This just makes it easier to find schemas you're looking for.
Like code organization, there is no one right answer. If you are going to reuse a particular schema in a number of different environments, putting it in a separate file can make sense. But if you're only going to use it in one place, it may be more convenient to put it under a $defs in the same file. You could even give that schema a nicer name to reference it, via $anchor.
I am currently refactoring a project where so far a lot of data was kept as constants and arrays in the code. Also there are a lot of redundancies. Now I want to move all that data into the db, but I am not sure how I would do the mapping. The data is rarely dynamically selected based on user input but rather specifically selected in the code. It is used at a very core level of the application, but it is actually not THE core. Also a database is already being used, so there would be no real extra effort.
My idea would be to use a Mapping class in which I have constants pointing to the IDs of the respective rows. Is that a good idea?
Another idea would be to index the name row and just directly query for the names.
The database would probably have the following columns: id, name, polynom and params.
So, basically we are talking math data. For example: 1, "Price approximation", 20x^3 - 5x^2 + 11x", "non-cumulated".
I think this question is language-agnostic but since there might be a language-specific (or even framework-specific) best practice, here is what I use: PHP5 with the Yii Framework.
I don't have much experience with PHP nor Yii, but here is my 2 cents...
If these are constants and collections of constants that technically define your application (application architecture constants), but the end-user shouldn't have control over, I would put them in a configuration file instead of your database, unless you've built a module to easily access and modify them. Whether you implement a mapping class (or a configuration class) to retrieve them is not important, but be consistent in how you retrieve them. If you have too many to manage in a configuration file, then storing them in the database would be appropriate, but make sure you provide an easy way to modify them. To make your source code readable, I'd use descriptors that a human can understand and map those descriptors to the respective row like you mentioned.
If these are user defined constants, then you should definitely provide an interface. But keep the same architecture as the application architecture constants.
In a perfect program/application (or even better--an application framework), nothing is hard coded, and everything is controlled by constants (switches). If you're able to achieve this successfully without the need to maintain your source code, you will win the Nobel Peace Prize.
I have a class that contains a bunch of methods for checking data I scrape every week (for things like well-formedness and other errors in gathering the data). Each of these methods performs a test, and then prints out a summary of the test.
I want to print out the output from these tests to a file, but I'm not sure what the best way to do it is. For example...
Should the class hold an instance variable to the file, and each method open/appends/closes the file? (A problem is that methods sometimes call other methods, so this seems kinda messy?)
Should each method get passed the file as a parameter? (Seems messy as well.)
Should each method return a string, and a"central" method that calls all the other tests outputs all these strings to a file?
I'm not really familiar with using logger libraries -- would that be a solution?
My particular context
I have a scraper that pulls data from various websites and stores them in a database. Websites change all the time, so I'm writing a "scrape checker" program that checks my scrapes for various things, like:
number of empty results
length of results
weird characters in results
and so on
So I have methods like:
check_num_empty_results
check_weird_characters
check_scrape (calls a bunch of other checks)
check_scrape_pair (sometimes I want to check pairs of scrapes together, e.g., to match results against each other, so this is different checking each one in isolation)
etc.
I want my "scrape checker" program to print out a file that summarizes all the checks.
Separation of concerns. Write code the focuses on the scraping activity and return the value(s) scraped. Then use aspect oriented programming for logging, which can simplify the problem greatly as the aspect holds the reference to the file or logging API.
Ultimately, it depends on what language you're using.
The first solution makes the most sense if your language permits it. For each instance of the logging class, have a field for the file object that you're reading from/writing to. This is basically equivalent to passing the file object as a parameter to every method.
That said, most mature languages have modules that will do a lot of this work for you; off the top of my sh/awk, Perl, and Python all come to mind as being suited to this task (though if you want to, you could use Java or something else).
Seems like a logging framework would be a perfect solution for this. If you are using Java or .NET, log4j and log4net are pretty much the de-facto standards for that.
The problem:
You have some data and your program needs specified input. For example strings which are numbers. You are searching for a way to transform the original data in a format you need.
And the problem is: The source can be anything. It can be XML, property lists, binary which
contains the needed data deeply embedded in binary junk. And your output format may vary
also: It can be number strings, float, doubles....
You don't want to program. You want routines which gives you commands capable to transform the data in a form you wish. Surely it contains regular expressions, but it is very good designed and it offers capabilities which are sometimes much more easier and more powerful.
ADDITION:
Many users have this problem and hope that their programs can convert, read and write data which is given by other sources. If it can't, they are doomed or use programs like business
intelligence. That is NOT the problem.
I am talking of a tool for a developer who knows what is he doing, but who is also dissatisfied to write every time routines in a regular language. A professional data manipulation tool, something like a hex editor, regex, vi, grep, parser melted together
accessible by routines or a REPL.
If you have the spec of the data format, you can access and transform the data at once. No need to debug or plan meticulously how to program the transformation. I am searching for a solution because I don't believe the problem is new.
It allows:
joining/grouping/merging of results
inserting/deleting/finding/replacing
write macros which allows to execute a command chain repeatedly
meta-grouping (lists->tables->n-dimensional tables)
Example (No, I am not looking for a solution to this, it is just an example):
You want to read xml strings embedded in a binary file with variable length records. Your
tool reads the record length and deletes the junk surrounding your text. Now it splits open
the xml and extracts the strings. Being Indian number glyphs and containing decimal commas instead of decimal points, your tool transforms it into ASCII and replaces commas with points. Now the results must be stored into matrices of variable length....etc. etc.
I am searching for a good language / language-design and if possible, an implementation.
Which design do you like or even, if it does not fulfill the conditions, wouldn't you want to miss ?
EDIT: The question is if a solution for the problem exists and if yes, which implementations are available. You DO NOT implement your own sorting algorithm if Quicksort, Mergesort and Heapsort is available. You DO NOT invent your own text parsing
method if you have regular expressions. You DO NOT invent your own 3D language for graphics if OpenGL/Direct3D is available. There are existing solutions or at least papers describing the problem and giving suggestions. And there are people who may have worked and experienced such problems and who can give ideas and suggestions. The idea that this problem is totally new and I should work out and implement it myself without background
knowledge seems for me, I must admit, totally off the mark.
UPDATE:
Unfortunately I had less time than anticipated to delve in the subject because our development team is currently in a hot phase. But I have contacted the author of TextTransformer and he kindly answered my questions.
I have investigated TextTransformer (http://www.texttransformer.de) in the meantime and so far I can see it offers a complete and efficient solution if you are going to parse character data.
For anyone who will give it a try to implement a good parsing language, the smallest set of operators to directly transform any input data to any output data if (!) they were powerful enough seems to be:
Insert/Remove: Self-explaining
Group/Ungroup: Split the input data into a set of tokens and organize them into groups
and supergroups (datastructures, lists, tables etc.)
Transform
Substituition: Change the content of the tokens (special operation: replace)
Transposition: Change the order of tokens (swap,merge etc.)
Have you investigated TextTransformer?
I have no experience with this, but it sounds pretty good and the author makes quite competent posts in the comp.compilers newsgroup.
You still have to some programming work.
For a programmer, I would suggest:
Perl against a SQL backend.
For a non-programmer, what it sounds like you're looking for is some sort of business intelligence suite.
This suggestion may broaden the scope of your search too much... but here it is:
You could either reuse, as-is, or otherwise get "inspiration" from the [open source] code of the SnapLogic framework.
Edit (answering the comment on SnapLogic documentation etc.)
I agree, the SnapLogic documentation leaves a bit to be desired, in particular for people in your situation, i.e. when just trying to quickly get an overview of what SnapLogic can do, and if it would generally meet their needs, without investing much time and learn the system in earnest.
Also, I realize that the scope and typical uses of of SnapLogic differ, somewhat, from the requirements expressed in the question, and I should have taken the time to better articulate the possible connection.
So here goes...
A salient and powerful feature of SnapLogic is its ability to [virtually] codelessly create "pipelines" i.e. processes made from pre-built components;
Components addressing the most common needs of Data Integration tasks at-large are supplied with the SnapLogic framework. For example, there are components to
read in and/or write to files in CSV or XML or fixed length format
connect to various SQL backends (for either input, output or both)
transform/format [readily parsed] data fields
sort records
join records for lookup and general "denormalized" record building (akin to SQL joins but applicable to any input [of reasonnable size])
merge sources
Filter records within a source (to select and, at a later step, work on say only records with attribute "State" equal to "NY")
see this list of available components for more details
A relatively weak area of functionality of SnapLogic (for the described purpose of the OP) is with regards to parsing. Standard components will only read generic file formats (XML, RSS, CSV, Fixed Len, DBMSes...) therefore structured (or semi-structured?) files such as the one described in the question, with mixed binary and text and such are unlikely to ever be a standard component.
You'd therefore need to write your own parsing logic, in Python or Java, respecting the SnapLogic API of course so the module can later "play nice" with the other ones.
BTW, the task of parsing the files described could be done in one of two ways, with a "monolithic" reader component (i.e. one which takes in the whole file and produces an array of readily parsed records), or with a multi-component approach, whereby an input component reads in and parse the file at "record" level (or line level or block level whatever this may be), and other standard or custom SnapLogic components are used to create a pipeline which effectively expresses the logic of parsing a record (or block or...) into its individual fields/attributes.
The second approach is of course more modular and may be applicable if the goal is to process many different files format, whereby each new format requires piecing together components with no or little coding. Whatever the approach used for the input / parsing of the file(s), the SnapLogic framework remains available to create pipelines to then process the parsed input in various fashion.
My understanding of the question therefore prompted me to suggest SnapLogic as a possible framework for the problem at hand, because I understood the gap in feature concerning the "codeless" parsing of odd-formatted files, but also saw some commonality of features with regards to creating various processing pipelines.
I also edged my suggestion, with an expression like "inspire onself from", because of the possible feature gap, but also because of the relative lack of maturity of the SnapLogic offering and its apparent commercial/open-source ambivalence.
(Note: this statement is neither a critique of the technical maturity/value of the framework per-se, nor a critique of business-oriented use of open-source, but rather a warning that business/commercial pressures may shape the offering in various direction)
To summarize:
Depending on the specific details of the vision expressed in the question, SnapLogic may be worthy of consideration, provided one understands that "some-assembly-required" will apply, in particular in the area of file parsing, and that the specific shape and nature of the product may evolve (but then again it is open source so one can freeze it or bend it as needed).
A more generic remark is that SnapLogic is based on Python which is a very swell language for coding various connectors, convertion logic etc.
In reply to Paul Nathan you mentioned writing throwaway code as something rather unpleasant. I don't see why it should be so. After all, all of our code will be thrown away and replaced eventually, no matter how perfect we wrote it. So my opinion is that writing throwaway code is pretty much ok, if you don't spend too much time writing it.
So, it seems that there are two approaches to solving your solution: either a) find some specific tool intended for the purpose (parse data, perform some basic operations on it and storing it in some specific structure) or b) use some general purpose language with lots of libraries and code it yourself.
I don't think that approach a) is viable because sooner or later you'll bump into an obstacle not covered by the tool and you'll spend your time and nerves hacking the tool, or mailing the authors and waiting for them to implement what you need. I might as well be wrong, so please if you find a perfect tool, drop here a link (I myself am doing lots of data processing in my day job and I can't swear that I couldn't do it more efficiently).
Approach b) may at first seem "unpleasant", but given a nice high-level expressive language with bunch of useful libraries (regexps, XML manipulation, creating parsers...) it shouldn't be too hard, and may be gradually turned into a DSL for the very purpose. Beside Perl which was already mentioned, Python and Ruby sound like good candidates for these languages (I bet some Lisp derivatives too, but I have no experience there).
You might find AntlrWorks useful if you go so far as defining formal grammars for what you're parsing.
To take an example, consider a set of discounts available to a supermarket shopper.
We could define these rules as data in some standard fashion (lists of qualifying items, applicable dates, coupon codes) and write generic code to handle these. Or, we could write each as a chunk of code, which checks for the appropriate things given the customer's shopping list and returns any applicable discounts.
You could reasonably store the rules as objects, serialised into Blobs or stored in code files, so that each rule could choose its own division between data and code, to allow for future rules that wouldn't fit the type of generic processor considered above.
It's often easy to criticise code that mixes data in, via if statements that check for 6 different things that should be in a file or a database, but is there a rule that helps in the edge cases?
Or is this the point of Object Oriented design, to stop us worrying about the line between data and code?
To clarify, the underlying question is this: How would you code the above example? Is there a rule of thumb that made you decide what is data and what is code?
(Note: I know, code can be compiled, but in a world of dynamic languages and JIT compilation, even that is a blurry concept.)
Fundamentally, there is of course no difference between data and code, but for real software infrastructures, there can be a big difference. Apart from obvious things like, as you mentioned, compilation, the biggest issue is this:
Most sufficiently large projects are designed to produce "releases" that are one big bundle, produced in 3-month (or longer) cycles, tested extensively and cannot be changed afterwards except in tightly controlled ways. "Code" most definitely cannot be changed, so anything that does need to be changed has to be factored out and made "configuration data" so that changing it becomes palatable those whose job it is to ensure that a release works.
Of course, in most cases bad configuration data can break a release just as thoroughly as bad code, so the whole thing is largely an illusion - in reality it doesn't matter whether it's code or "configuration data" that changes, what matters is that the interface between the main system and the parts that change is narrow and well-defined enough to give you a good chance that the person who does the change understands all consequences of what he's doing.
This is already harder than most people think when it's really just a few strings and numbers that are configured (I've personally witnessed a production mainframe system crash because it had one boolean value set differently than another system it was talking to). When your "configuration data" contains complex logic, it's almost impossible to achieve. But the situation isn't going to be any better ust because you use a badly-designed ad hoc "rules configuration" language instead of "real" code.
This is a rather philosophical question (which I like) so I'll answer it in a philosophical way: with nothing much to back it up. ;)
Data is the part of a system that can change. Code defines behavior; the way in which data can change into new data.
To put it more accurately: Data can be described by two components: a description of what the datum is supposed to represent (for instance, a variable with a name and a type) and a value.
The value of the variable can change according to rules defined in code. The description does not change, of course, because if it does, we have a whole new piece of information.
The code itself does not change, unless requirements (what we expect of the system) change.
To a compiler (or a VM), code is actually the data on which it performs its operations. However, the to-be-compiled code does not specify behavior for the compiler, the compiler's own code does that.
It all depends on the requirement. If the data is like lookup data and changes frequently you dont really want to do it in code, but things like Day of the Week, should not chnage for the next 200 years or so, so code that.
You might consider changing your topic, as the first thing I thought of when I saw it, was the age old LISP discussion of code vs data. Lucky in Scheme code and data looks the same, but thats about it, you can never accidentally mix code with data as is very possible in LISP with unhygienic macros.
Data are information that are processed by instructions called Code. I'm not sure I feel there's a blurring in OOD, there are still properties (Data) and methods (Code). The OO theory encapsulates both into a gestalt entity called a Class but they are still discrete within the Class.
How flexible you want to make your code in a matter of choice. Including constant values (what you are doing by using if statements as described above) is inflexible without re-processing your source, whereas using dynamically sourced data is more flexible. Is either approach wrong? I would say it really depends on the circumstances. As Leppie said, there are certain 'data' points that are invariate, like the days of the week that can be hard coded but even there it may be advantageous to do it dynamically in certain circumstances.
In Lisp, your code is data, and your
data is code
In Prolog clauses are terms, and terms
are clauses.
The important note is that you want to separate out the part of your code that will execute the same every time, (i.e. applying a discount) from the part of your code which could change (i.e. the products to be discounted, or the % of the discount, etc.)
This is simply for safety. If a discount changes, you won't have to re-write your discount code, you'll only need to go into your discounts repository (DB, or app file, or xml file, or however you choose to implement it) and make a small change to a number.
Also, if the discount code is separated into an XML file, then you can give the entire application to a manager, and with sufficient instructions, they won't need to pester you whenever they want to change the discount rates.
When you mix in data and code, you are exponentially increasing the odds of breaking when anything changes. So, as leppie said, you need to extract the constantly changing parts, and put them in a separate place.
Huge difference. Data is a given to system while code is a part of system.
Wrong data is senseless: our code===handler is good and what you put that you take, it is not a trouble of system that you meant something else. But if code is bad - system is bad.
In example, let's consider some JSON, some bad code parser.js by me and let's say good V8. For my system bad parser.js is a code and my system works wrong. But for Google system my bad parser is data that no how says about quality of V8.
The question is very practical, no sophistic.
https://en.wikipedia.org/wiki/Systems_engineering tries to make good answer and money.
Data is information. It's not about where you decide to put it, be it a db, config file, config through code or inside the classes.
The same happens for behaviors / code. It's not about where you decide to put it or how you choose to represent it.
The line between data and code (program) is blurry. It's ultimately just a question of terminology - for example, you could say that data is everything that is not code. But, as you wrote, they can be happily mixed together (although usually it's better to keep them separate).
Code is any data which can be executed. Now since all data is used as input to some program at some point of time, it can be said that this data is executed by a program! Thus your program acts as a virtual machine for your data. Hence in theory there is no difference between data and code!
In the end what matters is software engineering/development considerations like performance, efficiency etc. For example data driven programs may not be as efficient as programs which have hard coded (and hence fragile) conditional statements. Hence I choose to define code as any data which can be efficiently executed and all else being plain data.
It's a tradeoff between flexibility and efficiency. Executable data (like XML rules) offers more flexibility (sometimes) while the same data/rules when coded as part of the application will run more efficiently but changing it frequently becomes cumbersome. In other words executable data is easy to deploy but is inefficient and vice-versa. So ultimately the decision rests with you - the software designer.
Please correct me if I wrong.
Relationship between code and data is as follows:
code after compiled to a program processes the data while execution
program can extract data, transform data, load data, generate data ...
Also
program can extract code, transform code, load code, generate code tooooooo...
Hence code without compiled or interperator is useless, data is always worth..., but code after compiled can do all the above activities....
For eg)
Sourcecontrolsystem process Sourcecodes
here source code itself is a code
Backupscripts process files
here files is a data and so on...
I would say that the distinction between data, code and configuration is something to be made within the context of a particular component. Sometimes it's obvious, sometimes less so.
For example, to a compiler, the source code it consumes and the object code it creates are both data - and should be separated from the compiler's own code.
In your case you seem to be describing the option of a particularly powerful configuration file, which can contain code. Much as, for example, the GIMP lets you 'configure' plugins using Scheme. As the developer of the component that reads this configuration, you would think of it as data. When working at a different level -- writing the configuration -- you would think of it as code.
This is a very powerful way of designing.
Applying this to the underlying question ("How would you code the above example?"), one option might be to adopt or design a high level Domain Specific Language (DSL) for specifying rules. At startup, or when first required, the server reads the rule and executes it.
Provide an admin interface allowing the administrator to
test a new rule file
replace the current configuration with that from a new rule file
... all of which would happen at runtime.
A DSL might be something as simple as a table parser or an XML parser, or it could be something as sophisticated as a scripting language. From C, it's easy to embed Python or Lua. From Java it's easy to embed Groovy or Clojure.
You could switch in compiled code at runtime, with clever linking or classloader tricks. This seems more difficult and less valuable than the embedded DSL option, in my opinion.
The best practical answer to this question I found is this:
Any class that needs to be serialized, now or in any foreseeable future, is data.
Everything else is code.
That's why, for example, Java's HashMap is data - although it has a lot of code, API methods and specific implementation (i.e., it might look as code at first glance).