{"id":13412087,"url":"https://github.com/codingconcepts/dg","last_synced_at":"2025-12-27T04:54:06.588Z","repository":{"id":168261391,"uuid":"643931934","full_name":"codingconcepts/dg","owner":"codingconcepts","description":"A fast data generator that produces CSV files from generated relational data","archived":false,"fork":false,"pushed_at":"2024-08-21T01:50:06.000Z","size":2434,"stargazers_count":32,"open_issues_count":3,"forks_count":3,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-12-06T22:09:57.770Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Go","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/codingconcepts.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2023-05-22T13:04:05.000Z","updated_at":"2024-11-15T19:40:11.000Z","dependencies_parsed_at":"2024-01-07T21:54:01.361Z","dependency_job_id":"ad6bd807-7289-4cf3-83c8-6c6c7e59cd46","html_url":"https://github.com/codingconcepts/dg","commit_stats":null,"previous_names":["codingconcepts/dg"],"tags_count":25,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codingconcepts%2Fdg","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codingconcepts%2Fdg/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codingconcepts%2Fdg/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codingconcepts%2Fdg/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/codingconcepts","download_url":"https://codeload.github.com/codingconcepts/dg/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243618770,"owners_count":20320288,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-07-30T20:01:20.862Z","updated_at":"2025-12-27T04:54:06.532Z","avatar_url":"https://github.com/codingconcepts.png","language":"Go","readme":"\u003cp align=\"center\"\u003e\n \u003cimg src=\"assets/cover.png\" alt=\"drawing\" width=\"350\"/\u003e\n\u003c/p\u003e\n\nA fast data generator that produces CSV files from generated relational data.\n\n## Table of Contents\n\n1. [Installation](#installation)\n1. [Usage](#usage)\n - Import via [HTTP](#import-via-http)\n - Import via [psql](#import-via-psql)\n - Import via [nodelocal](#import-via-nodelocal)\n1. [Tables](#tables)\n - [gen](#gen)\n - [set](#set)\n - [inc](#inc)\n - [ref](#ref)\n - [each](#each)\n - [range](#range)\n - [match](#match)\n1. [Inputs](#inputs)\n - [csv](#csv)\n1. [Functions](#functions)\n1. [Thanks](#thanks)\n1. [Todos](#todos)\n\n### Installation\n\nFind the release that matches your architecture on the [releases](https://github.com/codingconcepts/dg/releases) page.\n\nDownload the tar, extract the executable, and move it into your PATH:\n\n```\n$ tar -xvf dg_[VERSION]-rc1_macOS.tar.gz\n```\n\n### Usage\n\n```\n$ dg\nUsage dg:\n -c string\n the absolute or relative path to the config file\n -cpuprofile string\n write cpu profile to file\n -i string\n write import statements to file\n -o string\n the absolute or relative path to the output dir (default \".\")\n -p int\n port to serve files from (omit to generate without serving)\n -version\n display the current version number\n```\n\nCreate a config file. In the following example, we create 10,000 people, 50 events, 5 person types, and then populate the many-to-many `person_event` resolver table with 500,000 rows that represent the Cartesian product between the person and event tables:\n\n```yaml\ntables:\n - name: person\n count: 10000\n columns:\n # Generate a random UUID for each person\n - name: id\n type: gen\n processor:\n value: ${uuid}\n\n - name: event\n count: 50\n columns:\n # Generate a random UUID for each event\n - name: id\n type: gen\n processor:\n value: ${uuid}\n\n - name: person_type\n count: 5\n columns:\n # Generate a random UUID for each person_type\n - name: id\n type: gen\n processor:\n value: ${uuid}\n\n # Generate a random 16 bit number and left-pad it to 5 digits\n - name: name\n type: gen\n processor:\n value: ${uint16}\n format: \"%05d\"\n\n - name: person_event\n columns:\n # Generate a random UUID for each person_event\n - name: id\n type: gen\n processor:\n value: ${uuid}\n\n # Select a random id from the person_type table\n - name: person_type\n type: ref\n processor:\n table: person_type\n column: id\n\n # Generate a person_id column for each id in the person table\n - name: person_id\n type: each\n processor:\n table: person\n column: id\n\n # Generate an event_id column for each id in the event table\n - name: event_id\n type: each\n processor:\n table: event\n column: id\n```\n\nRun the application:\n\n```\n$ dg -c your_config_file.yaml -o your_output_dir -p 3000\nloaded config file took: 428µs\ngenerated table: person took: 41ms\ngenerated table: event took: 159µs\ngenerated table: person_type took: 42µs\ngenerated table: person_event took: 1s\ngenerated all tables took: 1s\nwrote csv: person took: 1ms\nwrote csv: event took: 139µs\nwrote csv: person_type took: 110µs\nwrote csv: person_event took: 144ms\nwrote all csvs took: 145ms\n```\n\nThis will output and dg will then run an HTTP server allow you to import the files from localhost.\n\n```\nyour_output_dir\n├── event.csv\n├── person.csv\n├── person_event.csv\n└── person_type.csv\n```\n\n##### Import via HTTP\n\nThen import the files as you would any other; here's an example insert into CockroachDB:\n\n```sql\nIMPORT INTO \"person\" (\"id\")\nCSV DATA (\n 'http://localhost:3000/person.csv'\n)\nWITH skip='1', nullif = '', allow_quoted_null;\n\nIMPORT INTO \"event\" (\"id\")\nCSV DATA (\n 'http://localhost:3000/event.csv'\n)\nWITH skip='1', nullif = '', allow_quoted_null;\n\nIMPORT INTO \"person_type\" (\"id\", \"name\")\nCSV DATA (\n 'http://localhost:3000/person_type.csv'\n)\nWITH skip='1', nullif = '', allow_quoted_null;\n\nIMPORT INTO \"person_event\" (\"person_id\", \"event_id\", \"id\", \"person_type\")\nCSV DATA (\n 'http://localhost:3000/person_event.csv'\n)\nWITH skip='1', nullif = '', allow_quoted_null;\n```\n\n##### Import via psql\n\nIf you're working with a remote database and have access to the `psql` binary, try importing the CSV file as follows:\n\n```sh\npsql \"postgres://root@localhost:26257/defaultdb?sslmode=disable\" \\\n-c \"\\COPY public.person (id, full_name, date_of_birth, user_type, favourite_animal) FROM './csvs/person/person.csv' WITH DELIMITER ',' CSV HEADER NULL E''\"\n```\n\n##### Import via nodelocal\n\nIf you're working with a remote database and have access to the `cockroach` binary, try importing the CSV file as follows:\n\n```sh\ncockroach nodelocal upload ./csvs/person/person.csv imports/person.csv \\\n --url \"postgres://root@localhost:26257?sslmode=disable\"\n```\n\nThen importing the file as follows:\n\n```sql\nIMPORT INTO person (\"id\", \"full_name\", \"date_of_birth\", \"user_type\", \"favourite_animal\")\n CSV DATA (\n 'nodelocal://1/imports/person.csv'\n ) WITH skip = '1';\n```\n\n### Tables\n\nTable elements instruct dg to generate data for a single table and output it as a csv file. Here are the configuration options for a table:\n\n```yaml\ntables:\n - name: person\n unique_columns: [col_a, col_b]\n count: 10\n columns: ...\n```\n\nThis config generates 10 random rows for the person table. Here's a breakdown of the fields:\n\n| Field Name | Optional | Description |\n| -------------- | -------- | ---------------------------------------------------------------------------------------------------------------------------- |\n| name | No | Name of the table. Must be unique. |\n| unique_columns | Yes | Removes duplicates from the table based on the column names provided |\n| count | Yes | If provided, will determine the number of rows created. If not provided, will be calculated by the current table size. |\n| suppress | Yes | If `true` the table won't be written to a CSV. Useful when you need to generate intermediate tables to combine data locally. |\n| columns | No | A collection of columns to generate for the table. |\n\n#### Processors\n\ndg takes its configuration from a config file that is parsed in the form of an object containing arrays of objects; `tables` and `inputs`. Each object in the `tables` array represents a CSV file to be generated for a named table and contains a collection of columns to generate data for.\n\n##### gen\n\nGenerate a random value for the column. Here's an example:\n\n```yaml\n- name: sku\n type: gen\n processor:\n value: SKU${uint16}\n format: \"%05d\"\n```\n\nThis configuration will generate a random left-padded `uint16` with a prefix of \"SKU\" for a column called \"sku\". `value` contains zero or more function placeholders that can be used to generate data. A list of available functions can be found [here](https://github.com/codingconcepts/dg#functions).\n\nGenerate a pattern-based value for the column. Here's an example:\n\n```yaml\n- name: phone\n type: gen\n processor:\n pattern: \\d{3}-\\d{3}-\\d{4}\n```\n\nThis configuration will generate US-format phone number, like 123-456-7890.\n\n##### const\n\nProvide a constant set of values for a column. Here's an example:\n\n```yaml\n- name: options\n type: const\n processor:\n values: [bed_breakfast, bed]\n```\n\nThis configuration will create a column containing two rows.\n\n##### set\n\nSelect a value from a given set. Here's an example:\n\n```yaml\n- name: user_type\n type: set\n processor:\n values: [admin, regular, read-only]\n```\n\nThis configuration will select between the values \"admin\", \"regular\", and \"read-only\"; each with an equal probability of being selected.\n\nItems in a set can also be given a weight, which will affect their likelihood of being selected. Here's an example:\n\n```yaml\n- name: favourite_animal\n type: set\n processor:\n values: [rabbit, dog, cat]\n weights: [10, 60, 30]\n```\n\nThis configuration will select between the values \"rabbit\", \"dog\", and \"cat\"; each with different probabilities of being selected. Rabbits will be selected approximately 10% of the time, dogs 60%, and cats 30%. The total value doesn't have to be 100, however, you can use whichever numbers make most sense to you.\n\n##### inc\n\nGenerates an incrementing number. Here's an example:\n\n```yaml\n- name: id\n type: inc\n processor:\n start: 1\n format: \"P%03d\"\n```\n\nThis configuration will generate left-padded ids starting from 1, and format them with a prefix of \"P\".\n\n##### ref\n\nReferences a value from a previously generated table. Here's an example:\n\n```yaml\n- name: ptype\n type: ref\n processor:\n table: person_type\n column: id\n```\n\nThis configuration will choose a random id from the person_type table and create a `ptype` column to store the values.\n\nUse the `ref` type if you need to reference another table but don't need to generate a new row for _every_ instance of the referenced column.\n\n##### each\n\nCreates a row for each value in another table. If multiple `each` columns are provided, a Cartesian product of both columns will be generated.\n\nHere's an example of one `each` column:\n\n```yaml\n- name: person\n count: 3\n columns:\n - name: id\n type: gen\n processor:\n value: ${uuid}\n\n# person\n#\n# id\n# c40819f8-2c76-44dd-8c44-5eef6a0f2695\n# 58f42be2-6cc9-4a8c-b702-c72ab1decfea\n# ccbc2244-667b-4bb5-a5cd-a1e9626a90f9\n\n- name: pet\n columns:\n - name: person_id\n type: each\n processor:\n table: person\n column: id\n - name: name\n type: gen\n processor:\n value: first_name\n# pet\n#\n# person_id name\n# c40819f8-2c76-44dd-8c44-5eef6a0f2695 Carlo\n# 58f42be2-6cc9-4a8c-b702-c72ab1decfea Armando\n# ccbc2244-667b-4bb5-a5cd-a1e9626a90f9 Kailey\n```\n\nHere's an example of two `each` columns:\n\n```yaml\n- name: person\n count: 3\n columns:\n - name: id\n type: gen\n processor:\n value: ${uuid}\n\n# person\n#\n# id\n# c40819f8-2c76-44dd-8c44-5eef6a0f2695\n# 58f42be2-6cc9-4a8c-b702-c72ab1decfea\n# ccbc2244-667b-4bb5-a5cd-a1e9626a90f9\n\n- name: event\n count: 3\n columns:\n - name: id\n type: gen\n processor:\n value: ${uuid}\n\n# event\n#\n# id\n# 39faeb54-67d1-46db-a38b-825b41bfe919\n# 7be981a9-679b-432a-8a0f-4a0267170c68\n# 9954f321-8040-4cd7-96e6-248d03ee9266\n\n- name: person_event\n columns:\n - name: person_id\n type: each\n processor:\n table: person\n column: id\n - name: event_id\n type: each\n processor:\n table: event\n column: id\n# person_event\n#\n# person_id\n# c40819f8-2c76-44dd-8c44-5eef6a0f2695 39faeb54-67d1-46db-a38b-825b41bfe919\n# c40819f8-2c76-44dd-8c44-5eef6a0f2695 7be981a9-679b-432a-8a0f-4a0267170c68\n# c40819f8-2c76-44dd-8c44-5eef6a0f2695 9954f321-8040-4cd7-96e6-248d03ee9266\n# 58f42be2-6cc9-4a8c-b702-c72ab1decfea 39faeb54-67d1-46db-a38b-825b41bfe919\n# 58f42be2-6cc9-4a8c-b702-c72ab1decfea 7be981a9-679b-432a-8a0f-4a0267170c68\n# 58f42be2-6cc9-4a8c-b702-c72ab1decfea 9954f321-8040-4cd7-96e6-248d03ee9266\n# ccbc2244-667b-4bb5-a5cd-a1e9626a90f9 39faeb54-67d1-46db-a38b-825b41bfe919\n# ccbc2244-667b-4bb5-a5cd-a1e9626a90f9 7be981a9-679b-432a-8a0f-4a0267170c68\n# ccbc2244-667b-4bb5-a5cd-a1e9626a90f9 9954f321-8040-4cd7-96e6-248d03ee9266\n```\n\nUse the `each` type if you need to reference another table and need to generate a new row for _every_ instance of the referenced column.\n\n##### range\n\nGenerates data within a given range. Note that a number of factors determine how this generator will behave. The step (and hence, number of rows) will be generated in the following priority order:\n\n1. If an `each` generator is being used, step will be derived from that\n1. If a `count` is provided, step will be derived from that\n1. Otherwise, `step` will be used\n\nHere's an example that generates monotonically increasing ids for a table, starting from 1:\n\n```yaml\n- name: users\n count: 10000\n columns:\n - name: id\n type: range\n processor:\n type: int\n from: 1\n step: 1\n```\n\nHere's an example that generates all dates between `2020-01-01` and `2023-01-01` at daily intervals:\n\n```yaml\n- name: event\n columns:\n - name: date\n type: range\n processor:\n type: date\n from: 2020-01-01\n to: 2023-01-01\n step: 24h\n format: 2006-01-02\n```\n\nHere's an example that generates 10 dates between `2020-01-01` and `2023-01-02`:\n\n```yaml\n- name: event\n count: 10\n columns:\n - name: date\n type: range\n processor:\n type: date\n from: 2020-01-01\n to: 2023-01-01\n format: 2006-01-02\n step: 24h # Ignored due to table count.\n```\n\nHere's an example that generates 20 dates (one for every row found from an `each` generator) between `2020-01-01` and `2023-01-02`:\n\n```yaml\n- name: person\n count: 20\n columns:\n - name: id\n type: gen\n processor:\n value: ${uuid}\n\n- name: event\n count: 10 # Ignored due to resulting count from \"each\" generator.\n columns:\n - name: person_id\n type: each\n processor:\n table: person\n column: id\n\n - name: date\n type: range\n processor:\n type: date\n from: 2020-01-01\n to: 2023-01-01\n format: 2006-01-02\n```\n\nThe range generate currently supports the following data types:\n\n- `date` - Generate dates between a from and to value\n- `int` - Generate integers between a from and to value\n\n##### match\n\nGenerates data by matching data in another table. In this example, we'll assume there's a CSV file for the `significant_event` input that generates the following table:\n\n| date | event |\n| ---------- | ----- |\n| 2023-01-10 | abc |\n| 2023-01-11 | |\n| 2023-01-12 | def |\n\n```yaml\ninputs:\n - name: significant_event\n type: csv\n source:\n file_name: significant_dates.csv\n\ntables:\n - name: events\n columns:\n - name: timeline_date\n type: range\n processor:\n type: date\n from: 2023-01-09\n to: 2023-01-13\n format: 2006-01-02\n step: 24h\n - name: timeline_event\n type: match\n processor:\n source_table: significant_event\n source_column: date\n source_value: events\n match_column: timeline_date\n```\n\ndg will match rows in the significant_event table with rows in the events table based on the match between `significant_event.date` and `events.timeline_date`, and take the value from the `significant_events.event` column where there's a match (otherwise leaving `NULL`). This will result in the following `events` table being generated:\n\n| timeline_date | timeline_event |\n| ------------- | -------------- |\n| 2023-01-09 | |\n| 2023-01-10 | abc |\n| 2023-01-11 | |\n| 2023-01-12 | def |\n| 2023-01-13 | |\n\n### Inputs\n\ndg takes its configuration from a config file that is parsed in the form of an object containing arrays of objects; `tables` and `inputs`. Each object in the `inputs` array represents a data source from which a table can be created. Tables created via inputs will not result in output CSVs.\n\n##### csv\n\nReads in a CSV file as a table that can be referenced from other tables. Here's an example:\n\n```yaml\n- name: significant_event\n type: csv\n source:\n file_name: significant_dates.csv\n```\n\nThis configuration will read from a file called significant_dates.csv and create a table from its contents. Note that the `file_name` should be relative to the config directory, so if your CSV file is in the same directory as your config file, just include the file name.\n\n### Functions\n\n| Name | Type | Example |\n| ------------------------------ | --------- | --------------------------------------------------------------------------------------------------------- |\n| ${ach_account} | string | 586981797546 |\n| ${ach_routing} | string | 441478502 |\n| ${adjective_demonstrative} | string | there |\n| ${adjective_descriptive} | string | eager |\n| ${adjective_indefinite} | string | several |\n| ${adjective_interrogative} | string | whose |\n| ${adjective_possessive} | string | her |\n| ${adjective_proper} | string | Iraqi |\n| ${adjective_quantitative} | string | sufficient |\n| ${adjective} | string | double |\n| ${adverb_degree} | string | far |\n| ${adverb_frequency_definite} | string | daily |\n| ${adverb_frequency_indefinite} | string | always |\n| ${adverb_manner} | string | unexpectedly |\n| ${adverb_place} | string | here |\n| ${adverb_time_definite} | string | yesterday |\n| ${adverb_time_indefinite} | string | just |\n| ${adverb} | string | far |\n| ${animal_type} | string | mammals |\n| ${animal} | string | ape |\n| ${app_author} | string | RedLaser |\n| ${app_name} | string | SlateBlueweek |\n| ${app_version} | string | 3.2.10 |\n| ${bitcoin_address} | string | 16YmZ5ol5aXKjilZT2c2nIeHpbq |\n| ${bitcoin_private_key} | string | 5JzwyfrpHRoiA59Y1Pd9yLq52cQrAXxSNK4QrGrRUxkak5Howhe |\n| ${bool} | bool | true |\n| ${breakfast} | string | Awesome orange chocolate muffins |\n| ${bs} | string | leading-edge |\n| ${car_fuel_type} | string | LPG |\n| ${car_maker} | string | Seat |\n| ${car_model} | string | Camry Solara Convertible |\n| ${car_transmission_type} | string | Manual |\n| ${car_type} | string | Passenger car mini |\n| ${chrome_user_agent} | string | Mozilla/5.0 (X11; Linux i686) AppleWebKit/5310 (KHTML, like Gecko) Chrome/37.0.882.0 Mobile Safari/5310 |\n| ${city} | string | Memphis |\n| ${color} | string | DarkBlue |\n| ${company_suffix} | string | LLC |\n| ${company} | string | PlanetEcosystems |\n| ${connective_casual} | string | an effect of |\n| ${connective_complaint} | string | i.e. |\n| ${connective_examplify} | string | for example |\n| ${connective_listing} | string | next |\n| ${connective_time} | string | soon |\n| ${connective} | string | for instance |\n| ${country_abr} | string | VU |\n| ${country} | string | Eswatini |\n| ${credit_card_cvv} | string | 315 |\n| ${credit_card_exp} | string | 06/28 |\n| ${credit_card_type} | string | Mastercard |\n| ${currency_long} | string | Mozambique Metical |\n| ${currency_short} | string | SCR |\n| ${date} | time.Time | 2005-01-25 22:17:55.371781952 +0000 UTC |\n| ${day} | int | 27 |\n| ${dessert} | string | Chocolate coconut dream bars |\n| ${dinner} | string | Creole potato salad |\n| ${domain_name} | string | centralb2c.net |\n| ${domain_suffix} | string | com |\n| ${email} | string | ethanlebsack@lynch.name |\n| ${emoji} | string | ♻️ |\n| ${file_extension} | string | csv |\n| ${file_mime_type} | string | image/vasa |\n| ${firefox_user_agent} | string | Mozilla/5.0 (X11; Linux x86_64; rv:6.0) Gecko/1951-07-21 Firefox/37.0 |\n| ${first_name} | string | Kailee |\n| ${flipacoin} | string | Tails |\n| ${float32} | float32 | 2.7906555e+38 |\n| ${float64} | float64 | 4.314310154193861e+307 |\n| ${fruit} | string | Eggplant |\n| ${gender} | string | female |\n| ${hexcolor} | string | #6daf06 |\n| ${hobby} | string | Bowling |\n| ${hour} | int | 18 |\n| ${http_method} | string | DELETE |\n| ${http_status_code_simple} | int | 404 |\n| ${http_status_code} | int | 503 |\n| ${http_version} | string | HTTP/1.1 |\n| ${int16} | int16 | 18940 |\n| ${int32} | int32 | 2129368442 |\n| ${int64} | int64 | 5051946056392951363 |\n| ${int8} | int8 | 110 |\n| ${ipv4_address} | string | 191.131.155.85 |\n| ${ipv6_address} | string | 1642:94b:52d8:3a4e:38bc:4d87:846e:9c83 |\n| ${job_descriptor} | string | Senior |\n| ${job_level} | string | Identity |\n| ${job_title} | string | Executive |\n| ${language_abbreviation} | string | kn |\n| ${language} | string | Bengali |\n| ${last_name} | string | Friesen |\n| ${latitude} | float64 | 45.919913 |\n| ${longitude} | float64 | -110.313125 |\n| ${lunch} | string | Sweet and sour pork balls |\n| ${mac_address} | string | bd:e8:ce:66:da:5b |\n| ${minute} | int | 23 |\n| ${month_string} | string | April |\n| ${month} | int | 10 |\n| ${name_prefix} | string | Ms. |\n| ${name_suffix} | string | I |\n| ${name} | string | Paxton Schumm |\n| ${nanosecond} | int | 349669923 |\n| ${nicecolors} | []string | [#490a3d #bd1550 #e97f02 #f8ca00 #8a9b0f] |\n| ${noun_abstract} | string | timing |\n| ${noun_collective_animal} | string | brace |\n| ${noun_collective_people} | string | mob |\n| ${noun_collective_thing} | string | orchard |\n| ${noun_common} | string | problem |\n| ${noun_concrete} | string | town |\n| ${noun_countable} | string | cat |\n| ${noun_uncountable} | string | wisdom |\n| ${noun} | string | case |\n| ${opera_user_agent} | string | Opera/10.10 (Windows NT 5.01; en-US) Presto/2.11.165 Version/13.00 |\n| ${password} | string | 1k0vWN 9Z | 4f={B YPRda4ys. |\n| ${pet_name} | string | Bernadette |\n| ${phone_formatted} | string | (476)455-2253 |\n| ${phone} | string | 2692528685 |\n| ${phrase} | string | I'm straight |\n| ${preposition_compound} | string | ahead of |\n| ${preposition_double} | string | next to |\n| ${preposition_simple} | string | at |\n| ${preposition} | string | outside of |\n| ${programming_language} | string | PL/SQL |\n| ${pronoun_demonstrative} | string | those |\n| ${pronoun_interrogative} | string | whom |\n| ${pronoun_object} | string | us |\n| ${pronoun_personal} | string | I |\n| ${pronoun_possessive} | string | mine |\n| ${pronoun_reflective} | string | yourself |\n| ${pronoun_relative} | string | whom |\n| ${pronoun} | string | those |\n| ${quote} | string | \"Raw denim tilde cronut mlkshk photo booth kickstarter.\" - Gunnar Rice |\n| ${rgbcolor} | []int | [152 74 172] |\n| ${safari_user_agent} | string | Mozilla/5.0 (Windows; U; Windows 95) AppleWebKit/536.41.5 (KHTML, like Gecko) Version/5.2 Safari/536.41.5 |\n| ${safecolor} | string | gray |\n| ${second} | int | 58 |\n| ${snack} | string | Crispy fried chicken spring rolls |\n| ${ssn} | string | 783135577 |\n| ${state_abr} | string | AL |\n| ${state} | string | Kentucky |\n| ${street_name} | string | Way |\n| ${street_number} | string | 6234 |\n| ${street_prefix} | string | Port |\n| ${street_suffix} | string | stad |\n| ${street} | string | 11083 Lake Fall mouth |\n| ${time_zone_abv} | string | ADT |\n| ${time_zone_full} | string | (UTC-02:00) Coordinated Universal Time-02 |\n| ${time_zone_offset} | float32 | 3 |\n| ${time_zone_region} | string | Asia/Aqtau |\n| ${time_zone} | string | Mountain Standard Time (Mexico) |\n| ${uint128_hex} | string | 0xcd50930d5bc0f2e8fa36205e3d7bd7b2 |\n| ${uint16_hex} | string | 0x7c80 |\n| ${uint16} | uint16 | 25076 |\n| ${uint256_hex} | string | 0x61334b8c51fa841bf9a3f1f0ac3750cd1b51ca2046b0fb75627ac73001f0c5aa |\n| ${uint32_hex} | string | 0xfe208664 |\n| ${uint32} | uint32 | 783098878 |\n| ${uint64_hex} | string | 0xc8b91dc44e631956 |\n| ${uint64} | uint64 | 5722659847801560283 |\n| ${uint8_hex} | string | 0x65 |\n| ${uint8} | uint8 | 192 |\n| ${url} | string | https://www.leadcutting-edge.net/productize |\n| ${user_agent} | string | Opera/10.64 (Windows NT 5.2; en-US) Presto/2.13.295 Version/10.00 |\n| ${username} | string | Gutmann2845 |\n| ${uuid} | string | e6e34ff4-1def-41e5-9afb-f697a51c0359 |\n| ${vegetable} | string | Tomato |\n| ${verb_action} | string | knit |\n| ${verb_helping} | string | did |\n| ${verb_linking} | string | has |\n| ${verb} | string | be |\n| ${weekday} | string | Tuesday |\n| ${word} | string | month |\n| ${year} | int | 1962 |\n| ${zip} | string | 45618 |\n\n### Building releases locally\n\n```\n$ VERSION=0.1.0 make release\n```\n\n### Thanks\n\nThanks to the maintainers of the following fantastic packages, whose code this tools makes use of:\n\n- [samber/lo](https://github.com/samber/lo)\n- [brianvoe/gofakeit](https://github.com/brianvoe/gofakeit)\n- [go-yaml/yaml](https://github.com/go-yaml/yaml)\n- [stretchr/testify](github.com/stretchr/testify/assert)\n\n### Todos\n\n- Improve code coverage\n- Write file after generating, then only keep columns that other tables need\n- Support for range without a table count (e.g. the following results in zero rows unless a count is provided)\n\n```yaml\n- name: bet_types\n count: 3\n columns:\n - name: id\n type: range\n processor:\n type: int\n from: 1\n step: 1\n - name: description\n type: const\n processor:\n values: [Win, Lose, Draw]\n```\n","funding_links":[],"categories":["数据库","Database","Data Integration Frameworks"],"sub_categories":["数据库工具","Database Tools"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodingconcepts%2Fdg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodingconcepts%2Fdg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodingconcepts%2Fdg/lists"}