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TypeScript,\nPostgreSQL\n\n![Example Animation](employees_demo.gif)\n\n## Similar projects\n\n- [postgresql-typed](http://hackage.haskell.org/package/postgresql-typed)\n- [FSharp.Data.SqlClient](https://github.com/fsprojects/FSharp.Data.SqlClient)\n\n## What is mfsqlchecker\n\nmfsqlchecker is a thin layer on top of\n[node-postgres](https://github.com/brianc/node-postgres). You continue to\nwrite queries using regular SQL to interact with your PostgreSQL database, but\nmfsqlchecker automatically verifies that all your queries are correct, and\nthat the result columns are of the expected type. This verification step is\nperformed at compile time, so that you can have a high level of confidence\nthat there will be no errors during production.\n\n## Motivation and Benefits\n\nFor small projects that only have a few dozen queries, using\n[node-postgres](https://github.com/brianc/node-postgres) directly works fine.\n\nBut for large projects containing hundreds of SQL queries, things start to\nbreak down to due the lack of **compile-time checking**, and un-ergonomic\n**composability**\n\nmfsqlchecker helps address these issues:\n\n### Compile-Time Static Checking of Queries\n\nThe first benefit that you get from the static checking provided by\nmfsqlchecker is rapid feedback during development. Your queries are checked\nwhile you are writing them in your editor. Syntax errors will be immediately\ndetected, as well as typos in table names or column names, invalid type\ncomparisons, and any other SQL error.\n\nAdditionally, mfsqlchecker will verify that the column types returned by your\nquery match the TypeScript types in your code. mfsqlchecker will actually\nautomatically infer the returned column types for each query, and can generate\na corresponding TypeScript shim directly inline with the query.\n\nFor example: if your SQL query returns a result from a `TEXT` column in your\ndatabase, but you try to assign the result to a TypeScript variable of type\n`number`, then you will get a compile-time error.\n\nThe TypeScript/PostgreSQL type-checking also works in the other direction:\nmfsqlchecker validates that the types of the TypeScript parameters are valid\nbased on their usage in the SQL query.\n\nThis rapid-feedback speeds up development, but more importantly, the\nstatic-checking system allows much more fluid refactoring of your code and of\nyour database schema.\n\nExample: You have a *person* table with a *first_name* column and a\n*last_name* column. You want to combine them into a single *name* column. With\nmfsqlchecker, simply write a migration to perform the change. Then, the tool\nwill notify you of all of the SQL queries in your project that now fail\nbecause they reference *first_name* or *last_name*. After you correct them and\nhave no more errors, you can be confident that your refactoring is succesfully\ncomplete. This technique scales nicely to more complex refactorings (such as\nextracting a one-to-one foreign key to a new many-to-many junction table,\netc...)\n\nThis ease-of-refactoring is familiar to proponents of statically-typed\nlanguages, and mfsqlchecker's static checker offers the same benefits.\n\n#### Example Query\n\n```TypeScript\ninterface Employee {\n    name: string;\n    departmentName: string;\n    phoneNumber: string | null;\n    salary: number;\n}\n\nexport async function getEmployeesWithMinSalary(conn: Connection, minSalary: number): Promise<Employee[]> {\n    const rows = await conn.query<{\n        name: Req<string>,             // /---------------------------\\\n        department_name: Req<string>,  // |  This code block is       |\n        phonenumber: Opt<string>,      // |  generated automatically  |\n        salary: Req<number>            // \\---------------------------/\n    }>(conn.sql\n        `\n        SELECT\n            employee.name,\n            department.name AS department_name,\n            phonenumber,\n            salary\n        FROM employee\n        JOIN department ON employee.department_id = department.id\n        WHERE salary >= ${minSalary}\n        `);\n\n    return rows.map<Employee>(row => ({\n        name: row.name.val(),\n        departmentName: row.department_name.val(),\n        phoneNumber: row.phonenumber.valOpt(),\n        salary: row.salary.val()\n    }));\n}\n```\n\nWhen you run the mfsqlchecker tool, all of the following will be validated:\n\n- The SQL query itself is valid: Contains no syntax errors, all referenced\n  tables and columns exist, there are no SQL type errors, etc...\n- The `minSalary` parameter is allowed to be compared with the `\"salary\"`\n  column (In this case everything is good: `minSalary` is a TypeScript\n  \"number\", the `\"salary\"` column is a PostgreSQL `INT`, and so the comparison\n  is valid).\n- The return columns of the query match the names, type, and \"nullability\"\n  listed. Note that mfsqlchecker can automatically generate this stub code.\n  You should never manually write it. After editing your query, use the\n  mfsqlchecker \"Quick Fix\" to automatically update the return columns type.\n  (See the above animation for an example)\n- Each returned column can either be `Req` (Required) or `Opt` (Optional)\n- `Req` means that the column always returns a non-null value. This is\n  determined automatically by examining the originating table where the column\n  comes from, and checking if it is declared `NOT NULL`.\n- `Opt` means that the column may contain a `null` value. This can happen when\n  the originating table column may be null, or if the column is a result of\n  some SQL expression. If you are sure that the column does not contain null\n  (for example, if it is the concatenation of two non-null text columns), then\n  you can access it using the `forceNotNull()` method (instead of `valOpt()`)\n\n### Composability\n\nComposability means that when we have 2 queries that are similar, we don't\nwant ...\n\n\n### Security\n\nEven with an experienced team and strict dicipline, it is still\n\nSince all queries are guaranteed to ..., therefore SQL injection\nvulnerabilities are impossible\n\n### Performance\n\nBecause each query is known to be statically formed, we are guaranteed to get\nmaximum effectiveness of the database's internal query cache, minimizing query\nparse time and query planning time.\n\n### Basics\n\nIf you don't want to use the built-in mfsqlchecker migration engine, then you\ncan dump your PostgreSQL database schema to an .sql file and point\n`mfsqlchecker` to it\n\n### Queries\n\nTo make a query call one of the following 3 methods on your connection object:\n\n- `query`: For queries that can contain any number of rows(zero, one, or\n  more). This is usually what you want. This will return an array of \"Row\"\n  objects.\n\n- `queryOne`: For queries that will always return exactly one row. This should\n  only be used when the structure of your query guarantees that it will always\n  return a single row. For example `SELECT COUNT(*) FROM [..]`, or a top-level\n  `SELECT EXISTS(..)`. This will return a single \"Row\" object.\n\n- `queryOneOrNone`: For queries that always return either a single row, or no\n  rows. This should be used for queries that select a single row using based\n  on a unique id. If a matching row is found then returns a \"Row\" object,\n  otherwise returns `null`.\n\n### Easy, Automatic SQL Views\n\n... defineSqlView ...\n\n### Reusable SQL fragments\n\n... sqlFrag ...\n\n### Dedicated INSERT functionality\n\nWhen using mfsqlchecker, you use regular SQL syntax for all queries. But there\nis an optional dedicated syntax for \"INSERT\". You can still write INSERT\nqueries as regular queries, but it is recommended to use the dedicated insert\nfunctionality because:\n\n1. It is easier to read/write when there are lots of columns\n2. Simple syntax for inserting multiple rows\n3. It catches errors where you forgot to specify a required column\n\nHere is an example \"raw\" INSERT query:\n\n```TypeScript\nexport async function insertEmployee(conn: Connection, employee: Employee): Promise<void> {\n    await conn.query(conn.sql\n        `\n        INSERT INTO employee\n            (salary, phonenumber, name, manager_id)\n        VALUES\n            (${employee.salary}, ${employee.phoneNumber}, ${employee.name}, NULL)\n        `);\n}\n```\n\nIt is difficult and error-prone to verify that the column names match up with\nthe values. It is very easy to make a mistake by, for example, mixing the\norder of the *phonenumber* and *name* columns. This becomes more likely when\nthere are lots of columns.\n\nHere it is rewritten using mfsqlchecker `insert` method:\n\n```TypeScript\nexport async function insertEmployee(conn: Connection, employee: Employee): Promise<void> {\n    await conn.insert(\"employee\", {\n        salary: employee.salary,\n        phonenumber: employee.phoneNumber,\n        name: employee.name,\n        manager_id: null\n    });\n}\n```\n\nNow we can more easily see each value together with its column. mfsqlchecker\nwill still validate that the type of each value matches that of its table\ncolumn, and additionally it will check that all required columns are listed.\n\nIf we need to add additional SQL to our INSERT statement, such as a\n`RETURNING` clause, or an `ON CONFLICT` clause, we can add it as the 3rd\nargument:\n\n```TypeScript\nexport async function insertEmployee(conn: Connection, employee: Employee): Promise<number> {\n    const row = await conn.insert<{\n        id: Req<EmployeeId>\n    }>(\"employee\", {\n        salary: employee.salary,\n        phonenumber: employee.phoneNumber,\n        name: employee.name,\n        manager_id: null\n    }, conn.sql\n        `\n        RETURNING id\n        `);\n\n    return row.id.val();\n}\n```\n\n**NOTE**: If your query contains an `ON CONFLICT DO NOTHING` clause, or an `ON\nCONFLICT DO UPDATE ... WHERE` clause, then you should use the `insertMaybe()`\nmethod instead of `insert()`, because it is possible for such a query to\nreturn 0 rows.\n\nTo insert multiple rows, use the `insertMany()` method (which also will\nperform all of the static checks discussed earlier):\n\n```TypeScript\nexport async function insertEmployees(conn: Connection, employees: Employee[]): Promise<void> {\n    const vals = [];\n    for (const employee of employees) {\n        vals.push({\n            salary: employee.salary,\n            phonenumber: employee.phoneNumber,\n            name: employee.name,\n            manager_id: null\n        });\n    }\n\n    await conn.insertMany(\"employee\", vals);\n}\n```\n\n`insertMany` also supports an optional 3rd argument, in case you need to add a\n`RETURNING` clause or `ON CONFLICT` clause.\n\n**NOTE**: `insert`, `insertMaybe`, and `insertMany` do not support more\ncomplicated inserts involving subqueries, or computed SQL expressions. For\nthose, just write a regular INSERT query (using the `query` method).\n\n### Enhanced checking of foreign keys on \"id\" style columns\n\n... TODO ...\n\n## Gotchas\n\nFor the most part, if a query passes validation by mfsqlchecker's static\nchecker, then the query is guaranteed to succeed also during runtime. But\nthere are a few known cases where things break down.\n\n### Outer Joins and incorrectly detected `Req` Columns\n\nmfsqlchecker is currently unable to detect the usage of outer joins. A query\ncontaining an outer \n\n### Type information lost during UPDATE\n\n\n... add a cast ...\n\n### UNNEST\n\n... here is a trick that works ...\n\n## How it works (Behind the Scenes)\n\nmfsqlchecker analyzes all of TypeScript source code of your project, searching\nfor all the SQL queries. It submits each one to a temporary PostgreSQL\ndatabase, as a prepared statement. This allows it to check that the query is\nvalid (correct syntax, valid table & column names, etc...), as well as query\nthe type of each of the returned columns.\n","readmeFilename":"README.md","gitHead":"3012a08a50c79b14683c9e653eddad5d5ea9df30","_id":"mfsqlchecker@0.1.14","_nodeVersion":"18.4.0","_npmVersion":"8.12.1","dist":{"integrity":"sha512-kb5YKBDNibPzhE5pz92bGrh7gzuamjQyUvGWHu6GVSlHCUT6pqkiwOzYainxJB5DNqIB6Ln0/jOeWjNuuV1V+A==","shasum":"e6dc054301c39ffe9fdf7754c0fe5bb0f1d384f1","tarball":"https://registry.npmjs.org/mfsqlchecker/-/mfsqlchecker-0.1.14.tgz","fileCount":40,"unpackedSize":1553365,"signatures":[{"keyid":"SHA256:jl3bwswu80PjjokCgh0o2w5c2U4LhQAE57gj9cz1kzA","sig":"MEUCIAxbEutRgBnUB9wu7NDfcRW68WA15FF9yuYKApw+k3VCAiEAlcIn8rfdbkryTeo2s6JwRpcrxwSF416bzhRzQfxZvSo="}],"npm-signature":"-----BEGIN PGP SIGNATURE-----\r\nVersion: OpenPGP.js v4.10.10\r\nComment: https://openpgpjs.org\r\n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\r\n56nrGG3WnL1/PXZgm1l0c1TH8J2nq4iJdz8=\r\n=23dj\r\n-----END PGP SIGNATURE-----\r\n"},"_npmUser":{"name":"newbie012","email":"co.eliya2@gmail.com"},"directories":{},"maintainers":[{"name":"benny-medflyt","email":"benny@medflyt.com"},{"name":"newbie012","email":"co.eliya2@gmail.com"},{"name":"nirber1","email":"nir@medflyt.com"},{"name":"nirds.medflyt","email":"nir.shabo@medflyt.com"}],"_npmOperationalInternal":{"host":"s3://npm-registry-packages","tmp":"tmp/mfsqlchecker_0.1.14_1665473836014_0.7268733718346245"},"_hasShrinkwrap":false},"0.1.15":{"name":"mfsqlchecker","version":"0.1.15","description":"Statically validate correctness of all your SQL queries. 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validate correctness of all your SQL queries. TypeScript, PostgreSQL","homepage":"https://github.com/MedFlyt/mfsqlchecker#readme","keywords":["TypeScript","SQL","PostgreSQL","ORM"],"repository":{"type":"git","url":"git+https://github.com/MedFlyt/mfsqlchecker.git"},"author":{"name":"MedFlyt LLC."},"bugs":{"url":"https://github.com/MedFlyt/mfsqlchecker/issues"},"license":"MIT","readme":"# mfsqlchecker\n\nStatically validate correctness of all your SQL queries. TypeScript,\nPostgreSQL\n\n![Example Animation](employees_demo.gif)\n\n## Similar projects\n\n- [postgresql-typed](http://hackage.haskell.org/package/postgresql-typed)\n- [FSharp.Data.SqlClient](https://github.com/fsprojects/FSharp.Data.SqlClient)\n\n## What is mfsqlchecker\n\nmfsqlchecker is a thin layer on top of\n[node-postgres](https://github.com/brianc/node-postgres). You continue to\nwrite queries using regular SQL to interact with your PostgreSQL database, but\nmfsqlchecker automatically verifies that all your queries are correct, and\nthat the result columns are of the expected type. This verification step is\nperformed at compile time, so that you can have a high level of confidence\nthat there will be no errors during production.\n\n## Motivation and Benefits\n\nFor small projects that only have a few dozen queries, using\n[node-postgres](https://github.com/brianc/node-postgres) directly works fine.\n\nBut for large projects containing hundreds of SQL queries, things start to\nbreak down to due the lack of **compile-time checking**, and un-ergonomic\n**composability**\n\nmfsqlchecker helps address these issues:\n\n### Compile-Time Static Checking of Queries\n\nThe first benefit that you get from the static checking provided by\nmfsqlchecker is rapid feedback during development. Your queries are checked\nwhile you are writing them in your editor. Syntax errors will be immediately\ndetected, as well as typos in table names or column names, invalid type\ncomparisons, and any other SQL error.\n\nAdditionally, mfsqlchecker will verify that the column types returned by your\nquery match the TypeScript types in your code. mfsqlchecker will actually\nautomatically infer the returned column types for each query, and can generate\na corresponding TypeScript shim directly inline with the query.\n\nFor example: if your SQL query returns a result from a `TEXT` column in your\ndatabase, but you try to assign the result to a TypeScript variable of type\n`number`, then you will get a compile-time error.\n\nThe TypeScript/PostgreSQL type-checking also works in the other direction:\nmfsqlchecker validates that the types of the TypeScript parameters are valid\nbased on their usage in the SQL query.\n\nThis rapid-feedback speeds up development, but more importantly, the\nstatic-checking system allows much more fluid refactoring of your code and of\nyour database schema.\n\nExample: You have a *person* table with a *first_name* column and a\n*last_name* column. You want to combine them into a single *name* column. With\nmfsqlchecker, simply write a migration to perform the change. Then, the tool\nwill notify you of all of the SQL queries in your project that now fail\nbecause they reference *first_name* or *last_name*. After you correct them and\nhave no more errors, you can be confident that your refactoring is succesfully\ncomplete. This technique scales nicely to more complex refactorings (such as\nextracting a one-to-one foreign key to a new many-to-many junction table,\netc...)\n\nThis ease-of-refactoring is familiar to proponents of statically-typed\nlanguages, and mfsqlchecker's static checker offers the same benefits.\n\n#### Example Query\n\n```TypeScript\ninterface Employee {\n    name: string;\n    departmentName: string;\n    phoneNumber: string | null;\n    salary: number;\n}\n\nexport async function getEmployeesWithMinSalary(conn: Connection, minSalary: number): Promise<Employee[]> {\n    const rows = await conn.query<{\n        name: Req<string>,             // /---------------------------\\\n        department_name: Req<string>,  // |  This code block is       |\n        phonenumber: Opt<string>,      // |  generated automatically  |\n        salary: Req<number>            // \\---------------------------/\n    }>(conn.sql\n        `\n        SELECT\n            employee.name,\n            department.name AS department_name,\n            phonenumber,\n            salary\n        FROM employee\n        JOIN department ON employee.department_id = department.id\n        WHERE salary >= ${minSalary}\n        `);\n\n    return rows.map<Employee>(row => ({\n        name: row.name.val(),\n        departmentName: row.department_name.val(),\n        phoneNumber: row.phonenumber.valOpt(),\n        salary: row.salary.val()\n    }));\n}\n```\n\nWhen you run the mfsqlchecker tool, all of the following will be validated:\n\n- The SQL query itself is valid: Contains no syntax errors, all referenced\n  tables and columns exist, there are no SQL type errors, etc...\n- The `minSalary` parameter is allowed to be compared with the `\"salary\"`\n  column (In this case everything is good: `minSalary` is a TypeScript\n  \"number\", the `\"salary\"` column is a PostgreSQL `INT`, and so the comparison\n  is valid).\n- The return columns of the query match the names, type, and \"nullability\"\n  listed. Note that mfsqlchecker can automatically generate this stub code.\n  You should never manually write it. After editing your query, use the\n  mfsqlchecker \"Quick Fix\" to automatically update the return columns type.\n  (See the above animation for an example)\n- Each returned column can either be `Req` (Required) or `Opt` (Optional)\n- `Req` means that the column always returns a non-null value. This is\n  determined automatically by examining the originating table where the column\n  comes from, and checking if it is declared `NOT NULL`.\n- `Opt` means that the column may contain a `null` value. This can happen when\n  the originating table column may be null, or if the column is a result of\n  some SQL expression. If you are sure that the column does not contain null\n  (for example, if it is the concatenation of two non-null text columns), then\n  you can access it using the `forceNotNull()` method (instead of `valOpt()`)\n\n### Composability\n\nComposability means that when we have 2 queries that are similar, we don't\nwant ...\n\n\n### Security\n\nEven with an experienced team and strict dicipline, it is still\n\nSince all queries are guaranteed to ..., therefore SQL injection\nvulnerabilities are impossible\n\n### Performance\n\nBecause each query is known to be statically formed, we are guaranteed to get\nmaximum effectiveness of the database's internal query cache, minimizing query\nparse time and query planning time.\n\n### Basics\n\nIf you don't want to use the built-in mfsqlchecker migration engine, then you\ncan dump your PostgreSQL database schema to an .sql file and point\n`mfsqlchecker` to it\n\n### Queries\n\nTo make a query call one of the following 3 methods on your connection object:\n\n- `query`: For queries that can contain any number of rows(zero, one, or\n  more). This is usually what you want. This will return an array of \"Row\"\n  objects.\n\n- `queryOne`: For queries that will always return exactly one row. This should\n  only be used when the structure of your query guarantees that it will always\n  return a single row. For example `SELECT COUNT(*) FROM [..]`, or a top-level\n  `SELECT EXISTS(..)`. This will return a single \"Row\" object.\n\n- `queryOneOrNone`: For queries that always return either a single row, or no\n  rows. This should be used for queries that select a single row using based\n  on a unique id. If a matching row is found then returns a \"Row\" object,\n  otherwise returns `null`.\n\n### Easy, Automatic SQL Views\n\n... defineSqlView ...\n\n### Reusable SQL fragments\n\n... sqlFrag ...\n\n### Dedicated INSERT functionality\n\nWhen using mfsqlchecker, you use regular SQL syntax for all queries. But there\nis an optional dedicated syntax for \"INSERT\". You can still write INSERT\nqueries as regular queries, but it is recommended to use the dedicated insert\nfunctionality because:\n\n1. It is easier to read/write when there are lots of columns\n2. Simple syntax for inserting multiple rows\n3. It catches errors where you forgot to specify a required column\n\nHere is an example \"raw\" INSERT query:\n\n```TypeScript\nexport async function insertEmployee(conn: Connection, employee: Employee): Promise<void> {\n    await conn.query(conn.sql\n        `\n        INSERT INTO employee\n            (salary, phonenumber, name, manager_id)\n        VALUES\n            (${employee.salary}, ${employee.phoneNumber}, ${employee.name}, NULL)\n        `);\n}\n```\n\nIt is difficult and error-prone to verify that the column names match up with\nthe values. It is very easy to make a mistake by, for example, mixing the\norder of the *phonenumber* and *name* columns. This becomes more likely when\nthere are lots of columns.\n\nHere it is rewritten using mfsqlchecker `insert` method:\n\n```TypeScript\nexport async function insertEmployee(conn: Connection, employee: Employee): Promise<void> {\n    await conn.insert(\"employee\", {\n        salary: employee.salary,\n        phonenumber: employee.phoneNumber,\n        name: employee.name,\n        manager_id: null\n    });\n}\n```\n\nNow we can more easily see each value together with its column. mfsqlchecker\nwill still validate that the type of each value matches that of its table\ncolumn, and additionally it will check that all required columns are listed.\n\nIf we need to add additional SQL to our INSERT statement, such as a\n`RETURNING` clause, or an `ON CONFLICT` clause, we can add it as the 3rd\nargument:\n\n```TypeScript\nexport async function insertEmployee(conn: Connection, employee: Employee): Promise<number> {\n    const row = await conn.insert<{\n        id: Req<EmployeeId>\n    }>(\"employee\", {\n        salary: employee.salary,\n        phonenumber: employee.phoneNumber,\n        name: employee.name,\n        manager_id: null\n    }, conn.sql\n        `\n        RETURNING id\n        `);\n\n    return row.id.val();\n}\n```\n\n**NOTE**: If your query contains an `ON CONFLICT DO NOTHING` clause, or an `ON\nCONFLICT DO UPDATE ... WHERE` clause, then you should use the `insertMaybe()`\nmethod instead of `insert()`, because it is possible for such a query to\nreturn 0 rows.\n\nTo insert multiple rows, use the `insertMany()` method (which also will\nperform all of the static checks discussed earlier):\n\n```TypeScript\nexport async function insertEmployees(conn: Connection, employees: Employee[]): Promise<void> {\n    const vals = [];\n    for (const employee of employees) {\n        vals.push({\n            salary: employee.salary,\n            phonenumber: employee.phoneNumber,\n            name: employee.name,\n            manager_id: null\n        });\n    }\n\n    await conn.insertMany(\"employee\", vals);\n}\n```\n\n`insertMany` also supports an optional 3rd argument, in case you need to add a\n`RETURNING` clause or `ON CONFLICT` clause.\n\n**NOTE**: `insert`, `insertMaybe`, and `insertMany` do not support more\ncomplicated inserts involving subqueries, or computed SQL expressions. For\nthose, just write a regular INSERT query (using the `query` method).\n\n### Enhanced checking of foreign keys on \"id\" style columns\n\n... TODO ...\n\n## Gotchas\n\nFor the most part, if a query passes validation by mfsqlchecker's static\nchecker, then the query is guaranteed to succeed also during runtime. But\nthere are a few known cases where things break down.\n\n### Outer Joins and incorrectly detected `Req` Columns\n\nmfsqlchecker is currently unable to detect the usage of outer joins. A query\ncontaining an outer \n\n### Type information lost during UPDATE\n\n\n... add a cast ...\n\n### UNNEST\n\n... here is a trick that works ...\n\n## How it works (Behind the Scenes)\n\nmfsqlchecker analyzes all of TypeScript source code of your project, searching\nfor all the SQL queries. It submits each one to a temporary PostgreSQL\ndatabase, as a prepared statement. This allows it to check that the query is\nvalid (correct syntax, valid table & column names, etc...), as well as query\nthe type of each of the returned columns.\n","readmeFilename":"README.md"}