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README.md

Slonik

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A battle-tested PostgreSQL client with strict types, detail logging and assertions.

Tailing Slonik logs

(The above GIF shows Slonik producing query logs. Slonik produces logs using Roarr. Logs include stack trace of the actual query invocation location and values used to execute the query.)

Principles

  • Promotes writing raw SQL.
  • Discourages ad-hoc dynamic generation of SQL.

Read: Stop using Knex.js

Note: Using this project does not require TypeScript or Flow. It is a regular ES6 module. Ignore the type definitions used in the documentation if you do not use a type system.

Features

Contents

About Slonik

Battle-Tested

Slonik began as a collection of utilities designed for working with node-postgres. We continue to use node-postgres as it provides a robust foundation for interacting with PostgreSQL. However, what once was a collection of utilities has since grown into a framework that abstracts repeating code patterns, protects against unsafe connection handling and value interpolation, and provides rich debugging experience.

Slonik has been battle-tested with large data volumes and queries ranging from simple CRUD operations to data-warehousing needs.

Origin of the name

Slonik

The name of the elephant depicted in the official PostgreSQL logo is Slonik. The name itself is derived from the Russian word for "little elephant".

Read: The History of Slonik, the PostgreSQL Elephant Logo

Repeating code patterns and type safety

Among the primary reasons for developing Slonik, was the motivation to reduce the repeating code patterns and add a level of type safety. This is primarily achieved through the methods such as one, many, etc. But what is the issue? It is best illustrated with an example.

Suppose the requirement is to write a method that retrieves a resource ID given values defining (what we assume to be) a unique constraint. If we did not have the aforementioned convenience methods available, then it would need to be written as:

// @flow
import {
  sql
} from 'slonik';
import type {
  DatabaseConnectionType
} from 'slonik';

opaque type DatabaseRecordIdType = number;

const getFooIdByBar = async (connection: DatabaseConnectionType, bar: string): Promise<DatabaseRecordIdType> => {
  const fooResult = await connection.query(sql`
    SELECT id
    FROM foo
    WHERE bar = ${bar}
  `);

  if (fooResult.rowCount === 0) {
    throw new Error('Resource not found.');
  }

  if (fooResult.rowCount > 1) {
    throw new Error('Data integrity constraint violation.');
  }

  return fooResult[0].id;
};

oneFirst method abstracts all of the above logic into:

const getFooIdByBar = (connection: DatabaseConnectionType, bar: string): Promise<DatabaseRecordIdType> => {
  return connection.oneFirst(sql`
    SELECT id
    FROM foo
    WHERE bar = ${bar}
  `);
};

oneFirst throws:

  • NotFoundError if query returns no rows
  • DataIntegrityError if query returns multiple rows
  • DataIntegrityError if query returns multiple columns

This becomes particularly important when writing routines where multiple queries depend on the previous result. Using methods with inbuilt assertions ensures that in case of an error, the error points to the original source of the problem. In contrast, unless assertions for all possible outcomes are typed out as in the previous example, the unexpected result of the query will be fed to the next operation. If you are lucky, the next operation will simply break; if you are unlucky, you are risking data corruption and hard to locate bugs.

Furthermore, using methods that guarantee the shape of the results, allows us to leverage static type checking and catch some of the errors even before they executing the code, e.g.

const fooId = await connection.many(sql`
  SELECT id
  FROM foo
  WHERE bar = ${bar}
`);

await connection.query(sql`
  DELETE FROM baz
  WHERE foo_id = ${fooId}
`);

Static type check of the above example will produce a warning as the fooId is guaranteed to be an array and binding of the last query is expecting a primitive value.

Protecting against unsafe connection handling

Slonik only allows to check out a connection for the duration of the promise routine supplied to the pool#connect() method.

The primary reason for implementing only this connection pooling method is because the alternative is inherently unsafe, e.g.

// Note: This example is using unsupported API.

const main = async () => {
  const connection = await pool.connect();

  await connection.query(sql`SELECT foo()`);

  await connection.release();
};

In this example, if SELECT foo() produces an error, then connection is never released, i.e. the connection remains to hang.

A fix to the above is to ensure that connection#release() is always called, i.e.

// Note: This example is using unsupported API.

const main = async () => {
  const connection = await pool.connect();

  let lastExecutionResult;

  try {
    lastExecutionResult = await connection.query(sql`SELECT foo()`);
  } finally {
    await connection.release();
  }

  return lastExecutionResult;
};

Slonik abstracts the latter pattern into pool#connect() method.

const main = () => {
  return pool.connect((connection) => {
    return connection.query(sql`SELECT foo()`);
  });
};

Connection is always released back to the pool after the promise produced by the function supplied to connect() method is either resolved or rejected.

Protecting against unsafe transaction handling

Just like in the unsafe connection handling described above, Slonik only allows to create a transaction for the duration of the promise routine supplied to the connection#transaction() method.

connection.transaction(async (transactionConnection) => {
  await transactionConnection.query(sql`INSERT INTO foo (bar) VALUES ('baz')`);
  await transactionConnection.query(sql`INSERT INTO qux (quux) VALUES ('quuz')`);
});

This pattern ensures that the transaction is either committed or aborted the moment the promise is either resolved or rejected.

Protecting against unsafe value interpolation

SQL injections are one of the most well known attack vectors. Some of the biggest data leaks were the consequence of improper user-input handling. In general, SQL injections are easily preventable by using parameterization and by restricting database permissions, e.g.

// Note: This example is using unsupported API.

connection.query('SELECT $1', [
  userInput
]);

In this example, the query text (SELECT $1) and parameters (value of the userInput) are passed to the PostgreSQL server where the parameters are safely substituted into the query. This is a safe way to execute a query using user-input.

The vulnerabilities appear when developers cut corners or when they do not know about parameterization, i.e. there is a risk that someone will instead write:

// Note: This example is using unsupported API.

connection.query('SELECT \'' + userInput + '\'');

As evident by the history of the data leaks, this happens more often than anyone would like to admit. This is especially a big risk in Node.js community, where predominant number of developers are coming from frontend and have not had training working with RDBMSes. Therefore, one of the key selling points of Slonik is that it adds multiple layers of protection to prevent unsafe handling of user-input.

To begin with, Slonik does not allow to run plain-text queries.

connection.query('SELECT 1');

The above invocation would produce an error:

TypeError: Query must be constructed using sql tagged template literal.

This means that the only way to run a query is by constructing it using sql tagged template literal, e.g.

connection.query(sql`SELECT 1`);

To add a parameter to the query, user must use template literal placeholders, e.g.

connection.query(sql`SELECT ${userInput}`);

Slonik takes over from here and constructs a query with value bindings, and sends the resulting query text and parameters to the PostgreSQL. As sql tagged template literal is the only way to execute the query, it adds a strong layer of protection against accidental unsafe user-input handling due to limited knowledge of the SQL client API.

As Slonik restricts user's ability to generate and execute dynamic SQL, it provides helper functions used to generate fragments of the query and the corresponding value bindings, e.g. sql.identifier, sql.tuple, sql.tupleList, sql.unnest and sql.valueList. These methods generate tokens that the query executor interprets to construct a safe query, e.g.

connection.query(sql`
  SELECT ${sql.identifier(['foo', 'a'])}
  FROM (
    VALUES ${sql.tupleList([['a1', 'b1', 'c1'], ['a2', 'b2', 'c2']])}
  ) foo(a, b, c)
  WHERE foo.b IN (${sql.valueList(['c1', 'a2'])})
`);

This (contrived) example generates a query equivalent to:

SELECT "foo"."a"
FROM (
  VALUES
    ($1, $2, $3),
    ($4, $5, $6)
) foo(a, b, c)
WHERE foo.b IN ($7, $8)

That is executed with the parameters provided by the user.

Finally, if there comes a day that you must generate the whole or a fragment of a query using string concatenation, then Slonik provides sql.raw method. However, even when using sql.raw, we derisk the dangers of generating SQL by allowing developer to bind values only to the scope of the fragment that is being generated, e.g.

sql`
  SELECT ${sql.raw('$1', ['foo'])}
`;

Allowing to bind values only to the scope of the SQL that is being generated reduces the amount of code that the developer needs to scan in order to be aware of the impact that the generated code can have. Continue reading Using sql.raw to generate dynamic queries to learn further about sql.raw.

To sum up, Slonik is designed to prevent accidental creation of queries vulnerable to SQL injections.

Documentation

Usage

Install

npm install slonik

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Create connection

Use createPool to create a connection pool, e.g.

import {
  createPool
} from 'slonik';

const pool = createPool('postgres://');

Instance of Slonik connection pool can be then used to create a new connection, e.g.

pool.connect(async (connection) => {
  await connection.query(sql`SELECT 1`);
});

The connection will be kept alive until the promise resolves (the result of the method supplied to connect()).

Refer to query method documentation to learn about the connection methods.

If you do not require having a persistent connection to the same backend, then you can directly use pool to run queries, e.g.

pool.query(sql`SELECT 1`);

Beware that in the latter example, the connection picked to execute the query is a random connection from the connection pool, i.e. using the latter method (without explicit connect()) does not guarantee that multiple queries will refer to the same backend.

API

/**
 * @param connectionUri PostgreSQL [Connection URI](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING).
 */
createPool(
  connectionUri: string,
  clientConfiguration: ClientConfigurationType
): DatabasePoolType;

/**
 * @property captureStackTrace Dictates whether to capture stack trace before executing query. Middlewares access stack trace through query execution context. (Default: true)
 * @property connectionTimeout: Timeout (in milliseconds) after which an error is raised if cannot cannot be established. (Default: 5000)
 * @property idleTimeout Timeout (in milliseconds) after which idle clients are closed. (Default: 5000)
 * @property interceptors An array of [Slonik interceptors](https://github.com/gajus/slonik#slonik-interceptors).
 * @property maximumPoolSize Do not allow more than this many connections. (Default: 10)
 * @property minimumPoolSize Add more server connections to pool if below this number. (Default: 0)
 * @property typeParsers An array of [Slonik type parsers](https://github.com/gajus/slonik#slonik-type-parsers).
 */
type ClientConfigurationType = {|
  +captureStackTrace?: boolean,
  +connectionTimeout?: number,
  +idleTimeout?: number,
  +interceptors?: $ReadOnlyArray<InterceptorType>,
  +maximumPoolSize?: number,
  +minimumPoolSize?: number,
  +typeParsers?: $ReadOnlyArray<TypeParserType>
|};

Example:

import {
  createPool
} from 'slonik';

const pool = createPool('postgres://');

await pool.query(sql`SELECT 1`);

Default configuration

Default interceptors

None.

Check out slonik-interceptor-preset for an opinionated collection of interceptors.

Default type parsers

These type parsers are enabled by default:

Type name Implemnetation
int8 Produces an integer.
interval Produces interval in seconds (integer).
timestamp Produces a unix timestamp (in milliseconds).
timestamptz Produces a unix timestamp (in milliseconds).

To disable the default type parsers, pass an empty array, e.g.

createPool('postgres://', {
  typeParsers: []
});

You can create default type parser collection using createTypeParserPreset, e.g.

import {
  createTypeParserPreset
} from 'slonik';

createPool('postgres://', {
  typeParsers: [
    ...createTypeParserPreset()
  ]
});

Checking out a client from the connection pool

Slonik only allows to check out a connection for the duration of the promise routine supplied to the pool#connect() method.

import {
  createPool
} from 'slonik';

const pool = createPool('postgres://localhost');

const result = await pool.connect(async (connection) => {
  await connection.query(sql`SELECT 1`);
  await connection.query(sql`SELECT 2`);

  return 'foo';
});

result;
// 'foo'

Connection is released back to the pool after the promise produced by the function supplied to connect() method is either resolved or rejected.

Read: Protecting against unsafe connection handling

How are they different?

pg vs slonik

pg is built intentionally to provide unopinionated, minimal abstraction and encourages use of other modules to implement convenience methods.

Slonik is built on top of pg and it provides convenience methods for building queries and querying data.

Work on pg began on Tue Sep 28 22:09:21 2010. It is authored by Brian Carlson.

pg-promise vs slonik

As the name suggests, pg-promise was originally built to enable use of pg module with promises (at the time, pg only supported Continuation Passing Style (CPS), i.e. callbacks). Since then pg-promise added features for connection/ transaction handling, a powerful query-formatting engine and a declarative approach to handling query results.

The primary difference between Slonik and pg-promise:

Other differences are primarily in how the equivalent features are imlemented, e.g.

pg-promise Slonik
Custom type formatting. Not available in Slonik. The current proposal is to create an interceptor that would have access to the query fragment constructor.
formatting filters Slonik tagged template value expressions to construct query fragments and bind parameter values.
Query files. Use sql.raw to load query files.
Tasks. Use pool.connect.
Configurable transactions. Not available in Slonik. Track this issue.
Events. Use interceptors.

When weighting which abstraction to use, it would be unfair not to consider that pg-promise is a mature project with dozens of contributors. Meanwhile, Slonik is a young project (started in March 2017) that until recently was developed without active community input. However, if you do support the unique features that Slonik adds, the opinionated API design, and are not afraid of adopting a technology in its young days, then I warmly invite you to addopt Slonik and become a contributor to what I intend to make the standard PostgreSQL client in the Node.js community.

Work on pg-promise began Wed Mar 4 02:00:34 2015. It is authored by Vitaly Tomilov.

Type parsers

Type parsers describe how to parse PostgreSQL types.

type TypeParserType = {|
  +name: string,
  +parse: (value: string) => *
|};

Example:

{
  name: 'int8',
  parse: (value) => {
    return parseInt(value, 10);
  }
}

Note: Unlike pg-types that uses OIDs to identify types, Slonik identifies types using their names.

Use this query to find type names:

SELECT
  typname
FROM pg_type
ORDER BY typname ASC

Type parsers are configured using typeParsers client configuration.

Read: Default type parsers.

Built-in type parsers

Type name Implemnetation Factory function name
int8 Produces an integer. createBigintTypeParser
interval Produces interval in seconds (integer). createIntervalTypeParser
timestamp Produces a unix timestamp (in milliseconds). createTimestampTypeParser
timestamptz Produces a unix timestamp (in milliseconds). createTimestampWithTimeZoneParser

Built-in type parsers can be created using the exported factory functions, e.g.

import {
  createTimestampTypeParser
} from 'slonik';

createTimestampTypeParser();

// {
//   name: 'timestamp',
//   parse: (value) => {
//     return value === null ? value : Date.parse(value);
//   }
// }

Interceptors

Functionality can be added to Slonik client by adding interceptors (middleware).

Interceptors are configured using client configuration, e.g.

import {
  createPool
} from 'slonik';

const interceptors = [];

const connection = createPool('postgres://', {
  interceptors
});

Interceptors are executed in the order they are added.

Read: Default interceptors.

Interceptor methods

Interceptor is an object that implements methods that can change the behaviour of the database client at different stages of the connection life-cycle

type InterceptorType = {|
  +afterPoolConnection?: (
    connectionContext: ConnectionContextType,
    connection: DatabasePoolConnectionType
  ) => MaybePromiseType<void>,
  +afterQueryExecution?: (
    queryContext: QueryContextType,
    query: QueryType,
    result: QueryResultType<QueryResultRowType>
  ) => MaybePromiseType<QueryResultType<QueryResultRowType>>,
  +beforePoolConnection?: (
    connectionContext: ConnectionContextType
  ) => MaybePromiseType<?DatabasePoolType>,
  +beforePoolConnectionRelease?: (
    connectionContext: ConnectionContextType,
    connection: DatabasePoolConnectionType
  ) => MaybePromiseType<void>,
  +beforeQueryExecution?: (
    queryContext: QueryContextType,
    query: QueryType
  ) => MaybePromiseType<QueryResultType<QueryResultRowType>> | MaybePromiseType<void>,
  +transformQuery?: (
    queryContext: QueryContextType,
    query: QueryType
  ) => QueryType,
  +transformRow?: (
    queryContext: QueryContextType,
    query: QueryType,
    row: QueryResultRowType,
    fields: $ReadOnlyArray<FieldType>
  ) => QueryResultRowType
|};

afterPoolConnection

Executed after a connection is acquired from the connection pool (or a new connection is created), e.g.

const pool = createPool('postgres://');

// Interceptor is executed here. ↓
pool.connect();

afterQueryExecution

afterQueryExecution must return the result of the query, which will be passed down to the client.

Use afterQuery to modify the query result.

Note: When query is executed using stream, then afterQuery is called with empty result set and does not affect the query result.

beforeQueryExecution

This function can optionally return a direct result of the query which will cause the actual query never to be executed.

beforePoolConnectionRelease

Executed before connection is released back to the connection pool, e.g.

const pool = await createPool('postgres://');

pool.connect(async () => {
  await 1;

  // Interceptor is executed here. ↓
});

transformQuery

Executed before beforeQueryExecution.

Transforms query.

transformRow

Executed for each row.

Transforms row.

Use transformRow to modify the query result.

Community interceptors

Name Description
slonik-interceptor-field-name-transformation Transforms Slonik query result field names.
slonik-interceptor-query-benchmarking Benchmarks Slonik queries.
slonik-interceptor-query-logging Logs Slonik queries.
slonik-interceptor-query-normalisation Normalises Slonik queries.

Check out slonik-interceptor-preset for an opinionated collection of interceptors.

Recipes

Inserting large number of rows

Slonik provides sql.tupleList helper function to generate a list of tuples that can be used in the INSERT values expression, e.g.

await connection.query(sql`
  INSERT INTO (foo, bar, baz)
  VALUES ${sql.tupleList([
    [1, 2, 3],
    [4, 5, 6]
  ])}
`);

Produces:

{
  sql: 'INSERT INTO (foo, bar, baz) VALUES ($1, $2, $3), ($4, $5, $6)',
  values: [
    1,
    2,
    3,
    4,
    5,
    6
  ]
}

There are 2 downsides to this approach:

  1. The generated SQL is dynamic and will vary depending on the input.
  • You will not be able to track query stats.
  • Query parsing time increases with the query size.
  1. There is a maximum number of parameters that can be bound to the statement (65535).

As an alternative, we can use sql.unnest to create a set of rows using unnest. Using the unnest approach requires only 1 variable per every column; values for each column are passed as an array, e.g.

await connection.query(sql`
  INSERT INTO (foo, bar, baz)
  SELECT *
  FROM ${sql.unnest(
    [
      [1, 2, 3],
      [4, 5, 6]
    ],
    [
      'int4',
      'int4',
      'int4'
    ]
  )}
`);

Produces:

{
  sql: 'INSERT INTO (foo, bar, baz) SELECT * FROM unnest($1::int4[], $2::int4[], $2::int4[])',
  values: [
    [
      1,
      4
    ],
    [
      2,
      5
    ],
    [
      3,
      6
    ]
  ]
}

Inserting data this way ensures that the query is stable and reduces the amount of time it takes to parse the query.

Using sql.raw to generate dynamic queries

sql.raw can be used to generate fragments of an arbitrary SQL that are interpolated into the main query, e.g.

const uniquePairs = [
  ['a', 1],
  ['b', 2]
];

let placeholderIndex = 1;

const whereConditionSql = uniquePairs
  .map(() => {
    return needleColumns
      .map((column) => {
        return column + ' = $' + placeholderIndex++;
      })
      .join(' AND ');
  })
  .join(' OR ');

const values = [];

for (const pairValues of uniquePairs) {
  values.push(...pairValues);
}

const query = sql`
  SELECT
    id
  FROM foo
  WHERE
    ${sql.raw(whereConditionSql, values)}
`;

await connection.any(query);

In the above example, query is:

{
  sql: 'SELECT id FROM foo WHERE (a = $1 AND b = $2) OR (a = $3 AND b = $4)',
  values: [
    'a',
    1,
    'b',
    2
  ]
}

Multiple sql.raw fragments can be used to create a query.

Routing queries to different connections

If connection is initiated by a query (as opposed to a obtained explicitly using pool#connect()), then beforePoolConnection interceptor can be used to change the pool that will be used to execute the query, e.g.

const slavePool = createPool('postgres://slave');
const masterPool = createPool('postgres://master', {
  interceptors: [
    {
      beforePoolConnection: (connectionContext, pool) => {
        if (connectionContext.query && connectionContext.query.sql.includes('SELECT')) {
          return slavePool;
        }

        return pool;
      }
    }
  ]
});

// This query will use `postgres://slave` connection.
masterPool.query(sql`SELECT 1`);

// This query will use `postgres://master` connection.
masterPool.query(sql`UPDATE 1`);

Value placeholders

Tagged template literals

Slonik query methods can only be executed using sql tagged template literal, e.g.

import {
  sql
} from 'slonik'

connection.query(sql`
  SELECT 1
  FROM foo
  WHERE bar = ${'baz'}
`);

The above is equivalent to evaluating:

SELECT 1
FROM foo
WHERE bar = $1

query with 'baz' value binding.

Manually constructing the query

Manually constructing queries is not allowed.

There is an internal mechanism that checks to see if query was created using sql tagged template literal, i.e.

const query = {
  sql: 'SELECT 1 FROM foo WHERE bar = $1',
  type: 'SQL',
  values: [
    'baz'
  ]
};

connection.query(query);

Will result in an error:

Query must be constructed using sql tagged template literal.

This is a security measure designed to prevent unsafe query execution.

Furthermore, a query object constructed using sql tagged template literal is frozen to prevent further manipulation.

Nesting sql

sql tagged template literals can be nested, e.g.

const query0 = sql`SELECT ${'foo'} FROM bar`;
const query1 = sql`SELECT ${'baz'} FROM (${query0})`;

Produces:

{
  sql: 'SELECT $1 FROM (SELECT $2 FROM bar)',
  values: [
    'baz',
    'foo'
  ]
}

Query building

sql.valueList

Note: Before using sql.valueList evaluate if sql.array is not a better option.

(
  values: $ReadOnlyArray<PrimitiveValueExpressionType>
) => ValueListSqlTokenType;

Creates a list of values, e.g.

await connection.query(sql`
  SELECT (${sql.valueList([1, 2, 3])})
`);

Produces:

{
  sql: 'SELECT ($1, $2, $3)',
  values: [
    1,
    2,
    3
  ]
}

Value list can describe other SQL tokens, e.g.

await connection.query(sql`
  SELECT (${sql.valueList([1, sql.raw('to_timestamp($1)', [2]), 3])})
`);

Produces:

{
  sql: 'SELECT ($1, to_timestamp($2), $3)',
  values: [
    1,
    2,
    3
  ]
}

sql.array

(
  values: $ReadOnlyArray<PrimitiveValueExpressionType>
) => ValueListSqlTokenType;

Creates an array value binding, e.g.

await connection.query(sql`
  SELECT (${sql.array([1, 2, 3], 'int4')})
`);

Produces:

{
  sql: 'SELECT $1::int4[]',
  values: [
    [
      1,
      2,
      3
    ]
  ]
}

Unlike sql.valueList, sql.array generates a stable query of a predictable length, i.e. regardless of the number of the values in the array, the generated query remains the same:

  • Having a stable query enables pg_stat_statements to aggregate all query execution statistics.
  • Keeping the query length short reduces query parsing time.

Furthermore, unlike sql.valueList, sql.array can be used with an empty array of values.

Example:

sql`SELECT id FROM foo WHERE id IN (${sql.valueList([1, 2, 3])})`;
sql`SELECT id FROM foo WHERE id NOT IN (${sql.valueList([1, 2, 3])})`;

Is equivalent to:

sql`SELECT id FROM foo WHERE id = ANY(${sql.array([1, 2, 3], 'int4')})`;
sql`SELECT id FROM foo WHERE id != ALL(${sql.array([1, 2, 3], 'int4')})`;

In short, when the value list length is dynamic then sql.array should be preferred over sql.valueList.

sql.tuple

(
  values: $ReadOnlyArray<PrimitiveValueExpressionType>
) => TupleSqlTokenType;

Creates a tuple (typed row construct), e.g.

await connection.query(sql`
  INSERT INTO (foo, bar, baz)
  VALUES ${sql.tuple([1, 2, 3])}
`);

Produces:

{
  sql: 'INSERT INTO (foo, bar, baz) VALUES ($1, $2, $3)',
  values: [
    1,
    2,
    3
  ]
}

Tuple can describe other SQL tokens, e.g.

await connection.query(sql`
  INSERT INTO (foo, bar, baz)
  VALUES ${sql.tuple([1, sql.raw('to_timestamp($1)', [2]), 3])}
`);

Produces:

{
  sql: 'INSERT INTO (foo, bar, baz) VALUES ($1, to_timestamp($2), $3)',
  values: [
    1,
    2,
    3
  ]
}

sql.tupleList

(
  tuples: $ReadOnlyArray<$ReadOnlyArray<PrimitiveValueExpressionType>>
) => TupleListSqlTokenType;

Creates a list of tuples (typed row constructs), e.g.

await connection.query(sql`
  INSERT INTO (foo, bar, baz)
  VALUES ${sql.tupleList([
    [1, 2, 3],
    [4, 5, 6]
  ])}
`);

Produces:

{
  sql: 'INSERT INTO (foo, bar, baz) VALUES ($1, $2, $3), ($4, $5, $6)',
  values: [
    1,
    2,
    3,
    4,
    5,
    6
  ]
}

Tuple list can describe other SQL tokens, e.g.

await connection.query(sql`
  INSERT INTO (foo, bar, baz)
  VALUES ${sql.tupleList([
    [1, sql.raw('to_timestamp($1)', [2]), 3],
    [4, sql.raw('to_timestamp($1)', [5]), 6]
  ])}
`);

Produces:

{
  sql: 'INSERT INTO (foo, bar, baz) VALUES ($1, to_timestamp($2), $3), ($4, to_timestamp($5), $6)',
  values: [
    1,
    2,
    3,
    4,
    5,
    6
  ]
}

sql.unnest

(
  tuples: $ReadOnlyArray<$ReadOnlyArray<PrimitiveValueExpressionType>>,
  columnTypes: $ReadOnlyArray<string>
): UnnestSqlTokenType;

Creates an unnest expressions, e.g.

await connection.query(sql`
  SELECT bar, baz
  FROM ${sql.unnest(
    [
      [1, 'foo'],
      [2, 'bar']
    ],
    [
      'int4',
      'text'
    ]
  )} AS foo(bar, baz)
`);

Produces:

{
  sql: 'SELECT bar, baz FROM unnest($1::int4[], $2::text[]) AS foo(bar, baz)',
  values: [
    [
      1,
      2
    ],
    [
      'foo',
      'bar'
    ]
  ]
}

sql.identifier

(
  names: $ReadOnlyArray<string>
) => IdentifierTokenType;

Delimited identifiers are created by enclosing an arbitrary sequence of characters in double-quotes ("). To create create a delimited identifier, create an sql tag function placeholder value using sql.identifier, e.g.

sql`
  SELECT 1
  FROM ${sql.identifier(['bar', 'baz'])}
`;

Produces:

{
  sql: 'SELECT 1 FROM "bar"."bar"',
  values: []
}

sql.identifierList

(
  identifiers: $ReadOnlyArray<$ReadOnlyArray<string>>
) => IdentifierListTokenType;

Creates a list of identifiers, e.g.

sql`
  SELECT 1
  FROM ${sql.identifierList([
    ['bar', 'baz'],
    ['qux', 'quux']
  ])}
`;

Produces:

{
  sql: 'SELECT 1 FROM "bar"."baz", "qux"."quux"',
  values: []
}

Identifier aliases

A member of the identifier list can be aliased:

sql`
  SELECT 1
  FROM ${sql.identifierList([
    {
      alias: 'qux',
      identifier: ['bar', 'baz']
    },
    {
      alias: 'corge',
      identifier: ['quux', 'quuz']
    }
  ])}
`;

Produces:

{
  sql: 'SELECT 1 FROM "bar"."baz" "qux", "quux"."quuz" "corge"',
  values: []
}

sql.raw

(
  rawSql: string,
  values?: $ReadOnlyArray<PrimitiveValueExpressionType>
) => RawSqlTokenType;

Raw/ dynamic SQL can be inlined using sql.raw, e.g.

sql`
  SELECT 1
  FROM ${sql.raw('"bar"')}
`;

Produces:

{
  sql: 'SELECT 1 FROM "bar"',
  values: []
}

The second parameter of the sql.raw can be used to bind positional parameter values, e.g.

sql`
  SELECT ${sql.raw('$1', [1])}
`;

Produces:

{
  sql: 'SELECT $1',
  values: [
    1
  ]
}

Building dynamic queries

If you require to build a query based on a dynamic condition, then consider using an SQL builder for that specific query, e.g. Sqorn.

const query = sq
  .return({
    authorId: 'a.id',
    name: 'a.last_name'
  })
  .distinct
  .from({
    b: 'book'
    })
  .leftJoin({
    a: 'author'
  })
  .on`b.author_id = a.id`
  .where({
    title: 'Oathbringer',
    genre: 'fantasy'
  })
  .query;

sql`${sql.raw(query.text, query.args)}`

Named parameters

sql.raw supports named parameters, e.g.

sql`
  SELECT ${sql.raw(':foo, :bar', {bar: 'BAR', foo: 'FOO'})}
`;

Produces:

{
  sql: 'SELECT $1, $2',
  values: [
    'FOO',
    'BAR'
  ]
}

Named parameters are matched using /[\s,(]:([a-z_]+)/g regex.

sql.booleanExpression

(
  members: $ReadOnlyArray<ValueExpressionType>,
  operator: LogicalBooleanOperatorType
) => BooleanExpressionTokenType;

Boolean expression.

sql`
  SELECT ${sql.booleanExpression([3, 4], 'AND')}
`;

Produces:

{
  sql: 'SELECT $1 AND $2',
  values: [
    3,
    4
  ]
}

Boolean expressions can describe SQL tokens (including other boolean expressions), e.g.

sql`
  SELECT ${sql.booleanExpression([
    sql.comparisonPredicate(sql.identifier(['foo']), '=', sql.raw('to_timestamp($1)', 2)),
    sql.booleanExpression([
      3,
      4
    ], 'OR')
  ], 'AND')}
`;

Produces:

{
  sql: 'SELECT ("foo" = to_timestamp($1) AND ($1 OR $2))',
  values: [
    2,
    3,
    4
  ]
}

Note: Do not use sql.booleanExpression when expression consists of a single predicate. Use sql.comparisonPredicate.

sql.comparisonPredicate

(
  leftOperand: ValueExpressionType,
  operator: ComparisonOperatorType,
  rightOperand: ValueExpressionType
) => ComparisonPredicateTokenType;

A comparison predicate compares two expressions using a comparison operator.

sql`
  SELECT ${sql.comparisonPredicate(3, '=', 4)}
`;

Produces:

{
  sql: 'SELECT $1 = $2',
  values: [
    3,
    4
  ]
}

Comparison predicate operands can describe SQL tokens, e.g.

sql`
  SELECT ${sql.comparisonPredicate(sql.identifier(['foo']), '=', sql.raw('to_timestamp($1)', 2))}
`;

Produces:

{
  sql: 'SELECT "foo" = to_timestamp($1)',
  values: [
    2
  ]
}

sql.assignmentList

(
  namedAssignmentValueBindings: NamedAssignmentType
) => AssignmentListTokenType

Creates an assignment list, e.g.

await connection.query(sql`
  UPDATE foo
  SET ${sql.assignmentList({
    bar: 'baz',
    qux: 'quux'
  })}
`);

Produces:

{
  sql: 'UPDATE foo SET bar = $1, qux = $2',
  values: [
    'baz',
    'quux'
  ]
}

Assignment list can describe other SQL tokens, e.g.

await connection.query(sql`
  UPDATE foo
  SET ${sql.assignmentList({
    bar: sql.raw('to_timestamp($1)', ['baz']),
    qux: sql.raw('to_timestamp($1)', ['quux'])
  })}
`);

Produces:

{
  sql: 'UPDATE foo SET bar = to_timestamp($1), qux = to_timestamp($2)',
  values: [
    'baz',
    'quux'
  ]
}

Snake-case normalization

sql.assignmentList converts object keys to snake-case, e.g.

await connection.query(sql`
  UPDATE foo
  SET ${sql.assignmentList({
    barBaz: sql.raw('to_timestamp($1)', ['qux']),
    quuxQuuz: sql.raw('to_timestamp($1)', ['corge'])
  })}
`);

Produces:

{
  sql: 'UPDATE foo SET bar_baz = to_timestamp($1), quux_quuz = to_timestamp($2)',
  values: [
    'qux',
    'corge'
  ]
}

This behaviour might be sometimes undesirable.

There is currently no way to override this behaviour.

Use this issue https://github.com/gajus/slonik/issues/53 to describe your use case and propose a solution.

Query methods

any

Returns result rows.

Example:

const rows = await connection.any(sql`SELECT foo`);

#any is similar to #query except that it returns rows without fields information.

anyFirst

Returns value of the first column of every row in the result set.

  • Throws DataIntegrityError if query returns multiple rows.

Example:

const fooValues = await connection.anyFirst(sql`SELECT foo`);

many

Returns result rows.

  • Throws NotFoundError if query returns no rows.

Example:

const rows = await connection.many(sql`SELECT foo`);

manyFirst

Returns value of the first column of every row in the result set.

  • Throws NotFoundError if query returns no rows.
  • Throws DataIntegrityError if query returns multiple rows.

Example:

const fooValues = await connection.many(sql`SELECT foo`);

maybeOne

Selects the first row from the result.

  • Returns null if row is not found.
  • Throws DataIntegrityError if query returns multiple rows.

Example:

const row = await connection.maybeOne(sql`SELECT foo`);

// row.foo is the result of the `foo` column value of the first row.

maybeOneFirst

Returns value of the first column from the first row.

  • Returns null if row is not found.
  • Throws DataIntegrityError if query returns multiple rows.
  • Throws DataIntegrityError if query returns multiple columns.

Example:

const foo = await connection.maybeOneFirst(sql`SELECT foo`);

// foo is the result of the `foo` column value of the first row.

one

Selects the first row from the result.

  • Throws NotFoundError if query returns no rows.
  • Throws DataIntegrityError if query returns multiple rows.

Example:

const row = await connection.one(sql`SELECT foo`);

// row.foo is the result of the `foo` column value of the first row.

Note:

I've been asked "What makes this different from knex.js knex('foo').limit(1)?". knex('foo').limit(1) simply generates "SELECT * FROM foo LIMIT 1" query. knex is a query builder; it does not assert the value of the result. Slonik #one adds assertions about the result of the query.

oneFirst

Returns value of the first column from the first row.

  • Throws NotFoundError if query returns no rows.
  • Throws DataIntegrityError if query returns multiple rows.
  • Throws DataIntegrityError if query returns multiple columns.

Example:

const foo = await connection.oneFirst(sql`SELECT foo`);

// foo is the result of the `foo` column value of the first row.

query

API and the result shape are equivalent to pg#query.

Example:

await connection.query(sql`SELECT foo`);

// {
//   command: 'SELECT',
//   fields: [],
//   notices: [],
//   oid: null,
//   rowAsArray: false,
//   rowCount: 1,
//   rows: [
//     {
//       foo: 'bar'
//     }
//   ]
// }

stream

Streams query results.

Example:

await connection.stream(sql`SELECT foo`, (stream) => {
  stream.on('data', (datum) => {
    datum;
    // {
    //   fields: [
    //     {
    //       name: 'foo',
    //       tableID: 0,
    //       columnID: 0,
    //       dataTypeID: 23,
    //       dataTypeSize: 4,
    //       dataTypeModifier: -1,
    //       format: 'text'
    //     }
    //   ],
    //   row: {
    //     foo: 'bar'
    //   }
    // }
  });
});

Note: Implemneted using pg-query-stream.

transaction

transaction method is used wrap execution of queries in START TRANSACTION and COMMIT or ROLLBACK. COMMIT is called if the transaction handler returns a promise that resolves; ROLLBACK is called otherwise.

transaction method can be used together with createPool method. When used to create a transaction from an instance of a pool, a new connection is allocated for the duration of the transaction.

const result = await connection.transaction(async (transactionConnection) => {
  await transactionConnection.query(sql`INSERT INTO foo (bar) VALUES ('baz')`);
  await transactionConnection.query(sql`INSERT INTO qux (quux) VALUES ('corge')`);

  return 'FOO';
});

result === 'FOO';

Transaction nesting

Slonik uses SAVEPOINT to automatically nest transactions, e.g.

await connection.transaction(async (t1) => {
  await t1.query(sql`INSERT INTO foo (bar) VALUES ('baz')`);

  return t1.transaction((t2) => {
    return t2.query(sql`INSERT INTO qux (quux) VALUES ('corge')`);
  });
});

is equivalent to:

START TRANSACTION;
INSERT INTO foo (bar) VALUES ('baz');
SAVEPOINT slonik_savepoint_1;
INSERT INTO qux (quux) VALUES ('corge');
COMMIT;

Slonik automatically rollsback to the last savepoint if a query belonging to a transaction results in an error, e.g.

await connection.transaction(async (t1) => {
  await t1.query(sql`INSERT INTO foo (bar) VALUES ('baz')`);

  try {
    await t1.transaction(async (t2) => {
      await t2.query(sql`INSERT INTO qux (quux) VALUES ('corge')`);

      return Promise.reject(new Error('foo'));
    });
  } catch (error) {

  }
});

is equivalent to:

START TRANSACTION;
INSERT INTO foo (bar) VALUES ('baz');
SAVEPOINT slonik_savepoint_1;
INSERT INTO qux (quux) VALUES ('corge');
ROLLBACK TO SAVEPOINT slonik_savepoint_1;
COMMIT;

If error is unhandled, then the entire transaction is rolledback, e.g.

await connection.transaction(async (t1) => {
  await t1.query(sql`INSERT INTO foo (bar) VALUES ('baz')`);

  await t1.transaction(async (t2) => {
    await t2.query(sql`INSERT INTO qux (quux) VALUES ('corge')`);

    await t1.transaction(async (t3) => {
      await t3.query(sql`INSERT INTO uier (grault) VALUES ('garply')`);

      return Promise.reject(new Error('foo'));
    });
  });
});

is equivalent to:

START TRANSACTION;
INSERT INTO foo (bar) VALUES ('baz');
SAVEPOINT slonik_savepoint_1;
INSERT INTO qux (quux) VALUES ('corge');
SAVEPOINT slonik_savepoint_2;
INSERT INTO uier (grault) VALUES ('garply');
ROLLBACK TO SAVEPOINT slonik_savepoint_2;
ROLLBACK TO SAVEPOINT slonik_savepoint_1;
ROLLBACK;

Error handling

All Slonik errors extend from SlonikError, i.e. You can catch Slonik specific errors using the following logic.

import {
  SlonikError
} from 'slonik';

try {
  await query();
} catch (error) {
  if (error instanceof SlonikError) {
    // This error is thrown by Slonik.
  }
}

Original node-postgres error

When error originates from node-postgres, the original error is available under originalError property.

This propery is exposed for debugging purposes only. Do not use it for conditional checks – it can change.

If you require to extract meta-data about a specific type of error (e.g. contraint violation name), raise a GitHub issue describing your use case.

Handling ConnectionError

ConnectionError is thrown when connection cannot be established to the PostgreSQL server.

Handling QueryCancelledError

QueryCancelledError is thrown when a query is cancelled by the user, i.e. pg_cancel_backend.

It should be safe to use the same connection if the QueryCancelledError is handled, e.g.

await pool.connect(async (connection0) => {
  await pool.connect(async (connection1) => {
    const backendProcessId = await connection1.oneFirst(sql`SELECT pg_backend_pid()`);

    setTimeout(() => {
      connection0.query(sql`SELECT pg_cancel_backend(${backendProcessId})`)
    }, 2000);

    try {
      await connection1.query(sql`SELECT pg_sleep(30)`);
    } catch (error) {
      if (error instanceof QueryCancelledError) {
        // Safe to continue using the same connection.
      } else {
        throw error;
      }
    }
  });
});

Handling BackendTerminatedError

BackendTerminatedError is thrown when the backend is terminated by the user, i.e. pg_terminate_backend.

BackendTerminatedError must be handled at the connection level, i.e.

await pool.connect(async (connection0) => {
  try {
    await pool.connect(async (connection1) => {
      const backendProcessId = await connection1.oneFirst(sql`SELECT pg_backend_pid()`);

      setTimeout(() => {
        connection0.query(sql`SELECT pg_cancel_backend(${backendProcessId})`)
      }, 2000);

      try {
        await connection1.query(sql`SELECT pg_sleep(30)`);
      } catch (error) {
        // This code will not be executed.
      }
    });
  } catch (error) {
    if (error instanceof BackendTerminatedError) {
      // Handle backend termination.
    } else {
      throw error;
    }
  }
});

Handling NotFoundError

To handle the case where query returns less than one row, catch NotFoundError error.

import {
  NotFoundError
} from 'slonik';

let row;

try {
  row = await connection.one(sql`SELECT foo`);
} catch (error) {
  if (!(error instanceof NotFoundError)) {
    throw error;
  }
}

if (row) {
  // row.foo is the result of the `foo` column value of the first row.
}

Handling DataIntegrityError

To handle the case where the data result does not match the expectations, catch DataIntegrityError error.

import {
  NotFoundError
} from 'slonik';

let row;

try {
  row = await connection.one(sql`SELECT foo`);
} catch (error) {
  if (error instanceof DataIntegrityError) {
    console.error('There is more than one row matching the select criteria.');
  } else {
    throw error;
  }
}

Handling NotNullIntegrityConstraintViolationError

NotNullIntegrityConstraintViolationError is thrown when PostgreSQL responds with not_null_violation (23502) error.

Handling ForeignKeyIntegrityConstraintViolationError

ForeignKeyIntegrityConstraintViolationError is thrown when PostgreSQL responds with foreign_key_violation (23503) error.

Handling UniqueIntegrityConstraintViolationError

UniqueIntegrityConstraintViolationError is thrown when PostgreSQL responds with unique_violation (23505) error.

Handling CheckIntegrityConstraintViolationError

CheckIntegrityConstraintViolationError is thrown when PostgreSQL responds with check_violation (23514) error.

Types

This package is using Flow types.

Refer to ./src/types.js.

The public interface exports the following types:

  • DatabaseConnectionType
  • DatabasePoolConnectionType
  • DatabaseSingleConnectionType

Use these types to annotate connection instance in your code base, e.g.

// @flow

import type {
  DatabaseConnectionType
} from 'slonik';

export default async (
  connection: DatabaseConnectionType,
  code: string
): Promise<number> => {
  const countryId = await connection.oneFirst(sql`
    SELECT id
    FROM country
    WHERE code = ${code}
  `);

  return countryId;
};

Debugging

Logging

Slonik uses roarr to log queries.

To enable logging, define ROARR_LOG=true environment variable.

By default, Slonik logs only connection events, e.g. when connection is created, connection is acquired and notices.

Query-level logging can be added using slonik-interceptor-query-logging interceptor.

Capture stack trace

Note: Requires slonik-interceptor-query-logging.

Enabling captureStackTrace configuration will create a stack trace before invoking the query and include the stack trace in the logs, e.g.

{"context":{"package":"slonik","namespace":"slonik","logLevel":20,"executionTime":"357 ms","queryId":"01CV2V5S4H57KCYFFBS0BJ8K7E","rowCount":1,"sql":"SELECT schedule_cinema_data_task();","stackTrace":["/Users/gajus/Documents/dev/applaudience/data-management-program/node_modules/slonik/dist:162:28","/Users/gajus/Documents/dev/applaudience/data-management-program/node_modules/slonik/dist:314:12","/Users/gajus/Documents/dev/applaudience/data-management-program/node_modules/slonik/dist:361:20","/Users/gajus/Documents/dev/applaudience/data-management-program/node_modules/slonik/dist/utilities:17:13","/Users/gajus/Documents/dev/applaudience/data-management-program/src/bin/commands/do-cinema-data-tasks.js:59:21","/Users/gajus/Documents/dev/applaudience/data-management-program/src/bin/commands/do-cinema-data-tasks.js:590:45","internal/process/next_tick.js:68:7"],"values":[]},"message":"query","sequence":4,"time":1540915127833,"version":"1.0.0"}
{"context":{"package":"slonik","namespace":"slonik","logLevel":20,"executionTime":"66 ms","queryId":"01CV2V5SGS0WHJX4GJN09Z3MTB","rowCount":1,"sql":"SELECT cinema_id \"cinemaId\", target_data \"targetData\" FROM cinema_data_task WHERE id = ?","stackTrace":["/Users/gajus/Documents/dev/applaudience/data-management-program/node_modules/slonik/dist:162:28","/Users/gajus/Documents/dev/applaudience/data-management-program/node_modules/slonik/dist:285:12","/Users/gajus/Documents/dev/applaudience/data-management-program/node_modules/slonik/dist/utilities:17:13","/Users/gajus/Documents/dev/applaudience/data-management-program/src/bin/commands/do-cinema-data-tasks.js:603:26","internal/process/next_tick.js:68:7"],"values":[17953947]},"message":"query","sequence":5,"time":1540915127902,"version":"1.0.0"}

Use @roarr/cli to pretty-print the output.

Log Roarr pretty-print output.

Syntax highlighting

Atom

Using Atom IDE you can leverage the language-babel package in combination with the language-sql to enable highlighting of the SQL strings in the codebase.

Syntax highlighting in Atom

To enable highlighting, you need to:

  1. Install language-babel and language-sql packages.
  2. Configure language-babel "JavaScript Tagged Template Literal Grammar Extensions" setting to use language-sql to highlight template literals with sql tag (configuration value: sql:source.sql).
  3. Use sql helper to construct the queries.

For more information, refer to the JavaScript Tagged Template Literal Grammar Extensions documentation of language-babel package.

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