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Kuri"},"license":"LGPL-2.1-or-later","homepage":"https://getdtl.org/","keywords":["dtl","dtl-js","json templates","data templates","data transformation","data","transform","etl","csv","extract","json","yaml","transformation","transform","translate","translate data","dataops","grep","api","templates","validation","arbitrary","precision","arithmetic","bignumber"],"repository":{"type":"git","url":"git+https://gitlab.com/jk0ne/DTL.git"},"description":"Data Transformation Language - JSON templates and data transformation","maintainers":[{"name":"jayk","email":"jayk@ionzero.com"}],"readme":"# DTL\n\n[WebSite](https://getdtl.org) | [Repository](https://gitlab.com/jk0ne/DTL) | \n[Wiki](https://gitlab.com/jk0ne/DTL/-/wikis/home) | [Bug Reports](https://gitlab.com/jk0ne/DTL/-/issues) | \n[Live Help (discord)](https://discord.gg/ECA4H3PuAE) | By [Jay Kuri](https://gitlab.com/jk0ne/)\n\nDTL, short for Data Transformation Language, is a versatile tool ideal for\nmanaging and transforming structured data, especially from sources like\nNoSQL databases and APIs. It seamlessly integrates into your JavaScript\nprojects, whether you're working in a Node.js environment or directly in the\nbrowser. Additionally, DTL offers powerful command-line tools that excel in\nhandling various formats, including JSON, YAML, CSV, and plain text. These\ntools are perfect for processing and exploring existing data sets. \n\nWhether you need something as simple as **JSON templating**, more complex tasks\nlike **format conversion**, or **intricate data restructuring**, DTL stands out as\n**an invaluable resource for developers** dealing with any form of structured\ndata manipulation.\n\nIf your work involves manipulating structured data, and what development\ndoesn't, DTL offers an efficient and easy to understand toolset to help you.\n\n## What DTL looks like\n\nDTL looks like JSON and has a very familiar syntax:\n\n```json\n{\n    \"out\": {\n        \"name\": \"(: &( $first_name ' ' $last_name ) :)\",\n        \"order_total\": \"(: $order_subtotal + $tax_amt :)\",\n        \"origin\": \"Import from legacy database\",\n        \"address\": {\n            \"street\": \"(: $address1 :)\",\n            \"unit\": \"(: $address2 :)\",\n            \"city\": \"(: $addr_city :)\",\n            \"state\": \"(: $addr_state :)\",\n            \"postal\": \"(: $addr_zip :)\"\n        }\n    }\n}\n```\n\nWe'll explore this example in detail a bit later.\n\n## Installing DTL\n\nDTL can be installed within your project, or globally for command-line use.\n\n### Global Installation\n\nFor global access to DTL's command-line tools, install DTL globally using npm or yarn:\n\n- **Using npm**\n  ```bash\n  npm install -g dtl-js\n  ```\n\n- **Using yarn**\n  ```bash\n  yarn global add dtl-js\n  ```\n\n### Local Installation\n\nTo install DTL as part of your project, navigate to your project directory and run:\n\n- **Using npm**\n  ```bash\n  npm install dtl-js\n  ```\n\n- **Using yarn**\n  ```bash\n  yarn add dtl-js\n  ```\n\n### Verifying the Installation\n\nAfter installation, you can verify the installation of DTL:\n\n- For global installations, check the version to ensure it's installed correctly:\n  ```bash\n  dtl --version\n  ```\n\n- For local installations, you can check your `package.json` to see if `dtl-js` is listed under dependencies.\n\n\n## What DTL is for...\n\nDTL is built for transforming and manipulating data structures. At its most\nbasic level, **DTL can act as a templating tool for JSON or YAML**, enabling\nstraightforward data reformatting and restructuring.  However, **DTL's\ncapabilities extend far beyond simple templating**. DTL is ideal for complex\ntasks such as data **extraction, conversion, and preparation for analysis or\nreporting**. Its simple syntax and robust functions facilitate the clear and\nconcise expression of complex data transformations, making DTL an\n**extremely useful tool** for a wide range of data processing needs.\n\n## Basic Structure\n\nIn DTL, transforms are generally defined within a **JSON object**, which\nforms the *transform library*. Each key in this JSON object is a distinct\ntransform, functioning like a template or function for how input data should\nbe outputted. The `out` key is special as it's the default transform used by\n`DTL.apply()`, akin to the `main` function in other languages. \n\nIn most cases, the value of `out` will be an object that looks like your\ndesired output structure, guiding the transformation of input data to this\nformat.  This library can have multiple transforms, each capable of\nreferencing and calling one another. When a transform is executed through\n`DTL.apply()`, the provided data is represented as `$.` within the\ntransform.\n\n## Syntax Essentials\n\n### 1. Happy Tags\n\nEvery **DTL expression must be enclosed in happy tags**: `(: :)`. Happy tags\ntell DTL to do something. Anything not in Happy tags is passed through\nuntouched. Happy tags are called that because they resemble happy emoji.\n\n### 2. Accessing data\n\nReference data using **dot notation**, with the root represented as `$.`\nFor nested data access, use dot notation, e.g., `$user.address.street` \nData can be referenced with or without a leading `.`, in other words, \n`$.user` and `$user` are equivalent.\n\nAnd here's a bit of good news: in DTL, trying to access a property of a\nnon-existent or non-object value won't send you into the dreaded JavaScript\nspiral of `Cannot read properties of undefined`. In DTL, it just sensibly\nreturns `undefined`. No drama, no crash – just the peace of mind knowing DTL\nhas got your back in these scenarios.\n\n### 3. No Commas \n\nIn DTL expressions, **commas are treated like whitespace** and are not necessary.\n\n### 4. Helper Functions and Transform Calls\n\nUse **built-in helper functions** for complex operations, e.g.,\n`math.round($number)`. To **call another transform** in the library, use\n`($input_data -> transform_name)`. In this example, `$input_data` would be\nsent to `transform_name` and the result would be the new data.\n\n**Operations look like this**: `$data op $otherdata`, e.g., `$number1 + $number2`.\n\n### 5. Parentheses for Order\n\nUse **parentheses** to control the order that things should be done, e.g.,\n`($number1 + $number2) * $number3`. DTL understands mathematical order of\noperations, but there's no PEMDAS for data types, so parenthesis will help\nmake your intentions clearer.\n\n### 6. Conditional Syntax\n\nFor conditionals, use `?($condition $truevalue $falsevalue)`. You can use\nthis to send data to one or another transform depending on some condition:\n`?(!empty($address) ($. -> process_address) ($. -> no_address))`\n\nIn this example, `?(!empty($address) ($. -> process_address) ($. -> no_address))`, \nDTL checks if `$address` is not empty. If it's not, the data\nis processed with the `process_address` transform; if `$address` is empty,\nthe `no_address` transform is used instead. This showcases DTL's ability to\nelegantly handle conditional data routing based on the presence or absence\nof data.\n\n### 7. Static and Dynamic Values\n\nA mix of **static (literal values) and dynamic (DTL expressions)** is common\nin transforms. Only dynamic expressions use happy tags.\n\n### 8. Iteration\n\nYou can loop or iterate over lists of data. In all helpers that iterate,\nlike `map` or `grep`, use **`$item`, `$index`, and `$all`** for referencing.\n`$item` is the current item.  `$index` is the index of the current item and\n`$all` is the entire list or object.\n\n### 9. Scope\n\nTransforms only have access to what was given to them (from `DTL.apply()` or\nanother transform in the library) and have no way to access information\nother than that. This is a good thing, it means your transform will always\ndo exactly what you want. Within a transform, `$.` refers to the input data \nof that transform.\n\n### 10. DTL Math is Real Math\n\nIn DTL, mathematical operations are carried out using Arbitrary Precision\nMathematics. This approach is a game-changer, especially when you compare it\nto JavaScript, where 0.1 + 0.2 equals 0.30000000000000004 (seriously, try it\nin your web or Node.js console). In DTL, it simply equals 0.3, just as you\nwould logically expect. This means that when you're doing calculations in\nDTL, you're getting the exactly correct answer every time. DTL's precision\nmakes it ideal for scenarios where mathematical accuracy isn't just a\npreference, it's a necessity.\n\n### 11. The concept of empty\n\nIn DTL, the concept of **empty** is key. Empty in DTL means the data has no\nmeaningful value, e.g. undefined, null, an empty string, array, or object.\nThe `empty()` function returns true if a value is 'empty' \n\nIf you've ever wondered why `[]` evaluates to true or been bitten by\n`if(formfield)` evaluating to false if someone entered 0, we feel your pain.\nIn most cases, you really want to know if a variable has a meaningful\nvalue. `empty()` is here to help. \n\nComplementing this, the `fne()` (**First Non-Empty**) function scans a list of\nvalues and returns the first one that is not considered empty, streamlining\nthe selection of valid data from multiple sources. It looks like this:\n`fne($first_place_to_look $second_place_to_look defaultvalue)` \n\n\n## Example in JSON\n\nHere's an example of DTL:\n\n```json\n{\n    \"out\": {\n        \"name\": \"(: &( $first_name ' ' $last_name ) :)\",\n        \"order_total\": \"(: $order_subtotal + $tax_amt :)\",\n        \"origin\": \"Import from legacy database\",\n        \"address\": {\n            \"street\": \"(: $address1 :)\",\n            \"unit\": \"(: $address2 :)\",\n            \"city\": \"(: $addr_city :)\",\n            \"state\": \"(: $addr_state :)\",\n            \"postal\": \"(: $addr_zip :)\"\n        }\n    }\n}\n```\n\nIn this JSON transformation example:\n  \n- The `name` field uses the `&()` helper to concatenate `$first_name` and\n `$last_name` with a space in between. \n- The `order_total` field does some simple math, adding `$order_subtotal` \n  and `$tax_amt` together. \n- `origin` is a static string, \"Import from legacy database\". \n- The `address` field is an object containing multiple expressions. This shows\n  how your transforms mirror your desired output format.\n\n\n\n### There is no step 3.\n\nUsing DTL is as straightforward as it gets: \n\n1. **Create Your Transform**: Defining how you want your output data to\n   look. This step involves crafting the template or transformation logic in\n   a JSON object, specifying how the input data should be processed and\n   structured.\n\n2. **Call `DTL.apply()`**: Once your transform is ready, the next step is to\n   apply it to your data. This is done using the `DTL.apply()` method, which\n   takes your input data and the transform, performs the transformation as defined,\n   and returns the result.\n\nAnd that's it — there is no step 3. DTL's design is centered around this\nsimplicity, making data transformations easy and accessible.\n\nBelow is a practical example demonstrating these two steps in a Node.js script:\n\n### Putting It All Together: Example Node.js Script\n\nNow that we've covered the basics of DTL, let's see it in action with a\nsimple Node.js script. This example demonstrates how to use DTL to\ntransform some sample data and output the result.\n\n```javascript\n// Import the DTL module\nconst DTL = require('dtl-js');\n\n// Example input data\nconst inputData = {\n    first_name: \"John\",\n    last_name: \"Doe\",\n    order_subtotal: 100,\n    tax_amt: 20,\n    address1: \"123 Main St\",\n    address2: \"Apt 4\",\n    addr_city: \"Springfield\",\n    addr_state: \"IL\",\n    addr_zip: \"62704\"\n};\n\n// Example DTL transform\nconst transformLibrary = {\n    \"out\": {\n        \"name\": \"(: &( $first_name ' ' $last_name ) :)\",\n        \"order_total\": \"(: $order_subtotal + $tax_amt :)\",\n        \"origin\": \"Import from legacy database\",\n        \"address\": \"(: $. -> formatAddress :)\"\n    },\n    \"formatAddress\": {\n        \"street\": \"(: $address1 :)\",\n        \"unit\": \"(: $address2 :)\",\n        \"city\": \"(: $addr_city :)\",\n        \"state\": \"(: $addr_state :)\",\n        \"postal\": \"(: $addr_zip :)\"\n    }\n};\n\n// Applying the transform to the input data\nconst transformedData = DTL.apply(inputData, transformLibrary);\n\nconsole.log(\"Transformed Data:\", JSON.stringify(transformedData, undefined, 4));\n```\n\nThis script demonstrates how to import DTL, define a transform library,\napply it to some sample data, and output the transformed result. It's a\npractical example of how DTL can be utilized in a typical Node.js\nenvironment to manipulate and transform data structures.\n\nAs you can see, integrating DTL into your project is super simple. With its\nsimple syntax and powerful capabilities, DTL is all set to make your job\neasier. Now, go use it to streamline your code, make your workflows more\nefficient or just do some awesome stuff!\n\n## Full Editor support\n\nWorking with DTL is made significantly easier thanks to comprehensive editor\nsupport. Full syntax highlighting in the editor bundles not only clarifies\nDTL syntax but also enhances your coding efficiency. With snippets for all\nbuilt-in functions, these bundles facilitate a more intuitive and error-free\ncoding experience. We encourage you to download the appropriate bundle for\nyour editor of choice. \n\nLook for DTL in the VSCode extension marketplace, or if you use Vim, Sublime\nText or other Textmate compatible editors, you can download the language\nsupport files from the \n[Releases page of the DTL Repository](https://gitlab.com/jk0ne/DTL/-/releases)\nStreamline your coding with these powerful features for an improved,\nerror-free DTL coding experience.\n\n## Common Problems That DTL Solves\n\nDTL is adept at addressing a wide range of frequently encountered data\ntransformation challenges. Here are some common problems where DTL can be\nparticularly beneficial:\n\n1. **Creating Data Templates**: Effortlessly generate structured data from\n   templates. DTL excels in scenarios like auto-generating JSON configurations\n   or preparing data payloads for APIs, making these tasks quick and error-free.\n\n2. **Simple Data Transformations**: Whether it's renaming keys in JSON objects,\n   restructuring data (such as flattening nested structures), or performing\n   basic aggregations and calculations, DTL simplifies these common tasks.\n\n3. **Format Conversion**: DTL streamlines the conversion between various data\n   formats. Transforming data from JSON to CSV, XML to JSON, or YAML to JSON\n   becomes a hassle-free process.\n\n4. **Data Cleaning and Normalization**: Clean or normalize data from diverse\n   sources to fit specific standards. Tasks like removing unwanted fields,\n   standardizing date formats, or converting data types are made straightforward\n   with DTL.\n\n5. **Data Validation**: Ensure that incoming data conforms to specified\n   criteria or formats. DTL's data validation capabilities are particularly\n   useful for data from external sources or user inputs.\n\n6. **Extracting Specific Data from Complex Structures**: Extracting necessary\n   information from complex or nested data structures, such as certain fields\n   from deeply nested JSON objects, is made efficient and simple.\n\n7. **Automating Repetitive Data Tasks**: DTL helps automate repetitive data\n   manipulation tasks, saving time and minimizing the risk of errors in routine\n   operations.\n\nUsing DTL, you can address these common data transformation challenges\nmore efficiently, allowing you to focus on the more intricate aspects of your\nprojects.\n\n## TL;DR\n\n![demonstration of DTL REPL tool](https://gitlab.com/jk0ne/DTL/-/raw/master/docs/assets/dtlr-demo.gif)*DTL Repl demo* \n\n## MOAR Usage\n\nTo open the `dtlr` data exploration environment:\n\n```\n$ dtlr filename.json\n```\n\nTo do bulk data manipulation on the command line:\n\nSee available command line options:\n\n```\n$ dtl -h\n```\n\n(Movie examples below use [sample movie data](https://raw.githubusercontent.com/prust/wikipedia-movie-data/master/movies.json) from [Awesome JSON Datasets](https://github.com/jdorfman/awesome-json-datasets) )\n\n```\n# Convert movies.json to yaml\n$ dtl -o movies.yaml movies.json\n\n# How many movies are listed?\n$ dtl -e 'length($.)' movies.json`\n\n# Get all the movies where Bill Murray was in the cast\n$ dtl -e 'grep($. \"(: member($item.cast `Bill Murray`) :)\")' movies.json\n\n#  Extract the `name` field in the JSON file in uppercase.\n$ dtl -e 'uc($name)' data.json\n\n# Apply the transform expression specified in the `transform.dtl` file to the CSV input file.\n$ dtl -f transform.dtl input.csv\n\n# Process `input.json`, output CSV data in `output.csv` including all fields found in input.json.\n$ dtl -o output.csv input.json`\n\n# Process `input.json`, output CSV data on stdout using the fields `first_name`, `last_name` and `primary_email` \ndtl -O csv -C first_name,last_name,primary_email input.json\n```\n\n## Is DTL safe?\n\nUnlike regular code, the output of DTL can only include the information\nprovided to the DTL call, so DTL transforms are much safer to use than\nthe code that would be required to produce the same output. They're also\na heck of a lot easier to read... AND since they are self-contained and\ndon't refer to your code, they are safe and easy to share.\n\nDTL can be used within javascript code (node.js and browser, and even inside\nMongoDB) or it can be used on the command line with the DTL cli tools.\n\n## Why should I care?\n\nDTL is interesting for several reasons:\n\n * Clarity - DTL is purpose-built for data transformation and only data\n   transformation. It is not intended to be a general-purpose programming\n   language and is therefore simple to learn and free of unnecessary\n   components.\n\n * Portable - DTL transforms are self-contained and transferrable between\n   applications. Since they can be stored as JSON, they can even be\n   kept in your database.\n\n * Security - DTL transforms only have access to the data\n   that was provided as input. DTL transforms have no other variable or\n   system access, so they are much safer to use than custom code.\n\n * Stateless - DTL transforms have no access to previous state, only\n   to the data provided and therefore avoid bugs related to bleed\n   over or inadvertant modification, one of the most common sources of\n   bugs.\n\n * Provable - It is trivial to create a DTL transform to verify the\n   output of another. This obviously allows for simple test-creation.\n   What may not be obvious is that these verification transforms can\n   be used to check data at run-time.\n\n * Non-linear - DTL transforms define how to arrive at the desired\n   data. They do not define a sequence of steps. This means that\n   each expression is independent and not subject to bugs due to\n   issues that occurred in other expressions.\n\n * Stable - DTL has been in use in production since 2013 and has\n   been its own separate project since 2015. It is being used in\n   many production applications, handling many millions of\n   transformations every day.\n\n * DTL is a language with an implementation. The DTL npm module is only\n   one implementation of the DTL language. The DTL module contains\n   hundreds of tests that verify the language is behaving properly.\n   This allows DTL to be implemented and verified in any programming\n   language.\n\n## Where did DTL come from?\n\nTruth be told, DTL was not originally intended to be it's own thing. \nDTL began as an expression language inside a meta-programming engine built\nby [Jay Kuri (me)](https://gitlab.com/jk0ne/) during my work at Ionzero, a\ncompany I founded. One of the first applications of this engine was a system\nbuilt to handle linking other systems together. I created the language out\nof the need for a way to define how to map data from one system to another\nwithout resorting to hard-coded custom code.\n\nI also realized during the course of this work that DTL could be used\nfor far more than I ever had originally envisioned.  As a result of this\nrealization, over time, I refined the DTL language and eventually\nextracted it into a self-contained module that could be used in any\nsystem and proceeded to do so.\n\nI decided to release DTL as open source in the hopes that others would\nfind it as useful and as powerful as I have.\n\n## The DTL command line tools\n\nAs mentioned earlier in this document, if you have installed the DTL \npackage globally with npm or yarn, you will have two command line tools for\nworking with DTL. The `dtl` cli tool works on bulk data\n\nIf you want to just take DTL for a spin without coding you can use the\n`dtlr` tool. The `dtlr` cli tool is an interactive REPL (Read\nExecute Print Loop) tool you can use to test out expressions and get\nhelp.\n\nThe `dtl` cli tool works on bulk data and is designed to process CSV, yaml\nand JSON, as well as JSONLines data. It can produce CSV, yaml, JSON and\nJSONLines data as well, regardless of whether the input data was the\nsame type. You can learn more about how to use it by using the\n`dtl -h` command. Note that by default it sends its output to stdout.\nIf you'd rather have the output go into a file, use the `-o filename.json`\noption.\n\n## Feedback and where to get help\n\nWe are always looking for constructive feedback. If you have ideas on\nhow we might improve DTL, please reach out. If you are looking for\nhelp on how to use DTL, we also want to hear from you.\n\nFor help learning DTL, the ``dtlr`` tool has help built in by\nusing the ``.help`` command.  You can also You can visit the\n[docs](https://gitlab.com/jk0ne/DTL/-/tree/master/docs) or look at the\n[DTL Expression Syntax](https://gitlab.com/jk0ne/DTL/-/blob/master/docs/DTL-Expressions.md).\nYou can also view all the [helper function docs here](https://gitlab.com/jk0ne/DTL/-/blob/master/docs/DTL-Helpers.md).\n\nIf you want to see examples of DTL or try it out, head to the [DTL\nwebsite](https://getdtl.org/) and click the 'Try DTL' button to explore\nDTL right in your browser.\n\nIf you want to see more examples, you can take a look at the [Test\nSuite](https://gitlab.com/jk0ne/DTL/-/tree/master/src/js/test) where you can\nfind an example of just about anything DTL can do.\n\nIf you have ChatGPT Plus, you can talk to the [DTL\nHelper](https://chat.openai.com/g/g-GCFlnJHoY-dtl-helper) to get help with\nDTL.\n\nIf you want something a bit more real-time and real-person, you can talk with us on the\n[DTL discord](https://discord.gg/ECA4H3PuAE).\n\nAnd, if you encounter a bug, please don't hesitate to file an\n[Issue](https://gitlab.com/jk0ne/DTL/-/issues).\n\n\n","readmeFilename":"README.md"}