README

Laravel supports many different databases and therefore has to limit itself to the lowest common denominator of all databases. PostgreSQL, however, offers a ton more functionality which is being added to Laravel by this extension.

Installation

You can install the package via composer:

composer require tpetry/laravel-postgresql-enhanced

Versioning Policy

This package follows the semantic versioning policy. So you can always safely upgrade to a new minor and patch version without any issues.

As Laravel is a healthy framework that is still adding new database functionality, some things may break: For instance, in the past, Laravel introduced new builder methods that had already been part of this package for a long time - but with different semantics. In such cases, a new major version is promptly released with a breaking change to align with Laravel's implementation.

When upgrading Laravel, you may also have to upgrade this package to a new major version when something breaks! The minimal breaking changes of the past years are listed in the breaking changes section.

Features

IDE Autocomplete
PHPStan
Migration
Query
Eloquent
Expressions

IDE Autocomplete

Laravel provides many extension capabilities, making it hard for IDEs to do proper autocomplete. Currently, there is no generic way to tell IDEs about the added functionality of this enhanced driver. However, this package is integrated with specific IDE combinations:

PhpStorm with Laravel Idea: The IDE Helper for Laravel can detect all PostgreSQL features by running the Generate Helper Code (Eloquent, Maco, Facades, etc.) action from the Laravel -> Code Generation toolbar menu.

PHPStan

This extension is adding a lot of missing PostgreSQL functionality to Laravel. A custom set of PHPStan extensions have been developed to get full static analysis support!

All features provided this extension are automatically recognized by the phpstan/extension-installer. Otherwise, you have to manually add the following path to your includes list in phpstan.neon, your config should now look like this:

includes:
    - ./vendor/nunomaduro/larastan/extension.neon
    - ./vendor/tpetry/laravel-postgresql-enhanced/phpstan-extension.neon

Migration

Zero-Downtime Migration

For applications with 24/7 requirements, migrations must never impact availability. PostgreSQL provides many functionalities to execute changes on the schema without downtime. However, sometimes a change to the schema is not tested sufficiently and locks the tables for a longer period of time in order to make the desired change. To avoid this problem, a migration can be marked as zero-downtime migration. If the migration exceeds a specified time limit, it is cancelled and the schema is reset to its original state.

use Illuminate\Database\Migrations\Migration;
use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Schema\Concerns\ZeroDowntimeMigration;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

class Test123 extends Migration
{
    use ZeroDowntimeMigration;

    /**
     * Run the migrations.
     */
    public function up(): void
    {
        Schema::table('user', function (Blueprint $table) {
            $table->string('name', 128)->change();
        });
    }

    /**
     * Reverse the migrations.
     */
    public function down(): void
    {
        Schema::table('user', function (Blueprint $table) {
            $table->string('name', 32)->change();
        });
    }
}

The timeout for a maximum time limit of 1.0 second can be set separately for each migration. You can set private float $timeout = 5.0 on the migration for a up/down timeout. Or you can set the specific timeouts $timeoutUp and $timeoutDown to differentiate between the methods.

Extensions

Create Extensions

The Schema facade supports the creation of extensions with the createExtension and createExtensionIfNotExists methods:

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::createExtension('tablefunc');
Schema::createExtensionIfNotExists('tablefunc');

Dropping Extensions

To remove extensions, you may use the dropExtension and dropExtensionIfExists methods provided by the Schema facade:

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::dropExtension('tablefunc');
Schema::dropExtensionIfExists('tablefunc');

You may drop many extensions at once by passing multiple extension names:

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::dropExtension('tablefunc', 'fuzzystrmatch');
Schema::dropExtensionIfExists('tablefunc', 'fuzzystrmatch');

Functions

Create Functions

The Schema facade supports the creation of functions with the createFunction and createFunctionOrReplace methods. For the definition of your function you have to provide the name of the function, the parameters, the return type, the function's language and body:

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::createFunction(
  name: 'sales_tax',
  parameters: ['subtotal' => 'numeric'],
  return: 'numeric',
  language: 'plpgsql',
  body: '
    BEGIN
      RETURN subtotal * 0.06;
    END;
  '
);

A sixth parameter lets you define further options for the function. Please read the manual for the exact meaning, some of them set enable or disable ways for PostgreSQL to optimize the execution.

The former example can be optimized by using the special sql:expression language identifier created by this driver. The function body can only be one SQL expression, but it will be inlined in the query instead of executed with recent PostgreSQL versions for much better performance:

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::createFunction('sales_tax', ['subtotal' => 'numeric'], 'numeric', 'sql:expression', 'subtotal * 0.06', [
  'parallel' => 'safe',
  'volatility' => 'immutable',
]);

If you want your function to return a table, you have to provide the columns as return type:

Schema::createFunction('search_user', ['pattern' => 'text'], ['id' => 'int', 'email' => 'text'], 'plpgsql', "
  BEGIN
    RETURN QUERY select user_id, contactemail from users where name ilike '%' || pattern || '%';
  END;
");

Drop Functions

To remove functions, you may use the dropFunction and dropFunctionIfExists methods provided by the Schema facade:

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::dropFunction('sales_tax');
Schema::dropFunctionIfExists('sales_tax');

Triggers

Create Triggers

On your Blueprint you can add triggers to a table. You need to pass in a unique name, call of a function you've created before and the action that will fire the trigger:

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('projects', function (Blueprint $table): void {
    $table->trigger('rollup_quota', 'update_quota_by_projects()', 'AFTER INSERT OR DELETE');
});

The following table contains all of the available trigger modifiers:

Note

PostgreSQL always updates rows even if nothing changed, which may affect your performance. You can add the suppress_redundant_updates_trigger() trigger with a BEFORE UPDATE action to all tables.

Drop Triggers

To remove trigger, you may use the dropTrigger and dropTriggerIfExists methods provided by the table's Blueprint class:

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('projects', function (Blueprint $table): void {
    $table->dropTrigger('update_quota');
    $table->dropTriggerIfExists('update_quota');
});

Views

Create Views

The Schema facade supports the creation of views with the createView and createViewOrReplace methods. The definition of your view can be a sql query string or a query builder instance:

use Illuminate\Support\Facades\DB;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::createView('users_with_2fa', 'SELECT * FROM users WHERE two_factor_secret IS NOT NULL');
Schema::createViewOrReplace('users_without_2fa', DB::table('users')->whereNull('two_factor_secret'));

You can specify alternative names for the view's columns by passing an array as the last parameter:

use Illuminate\Support\Facades\DB;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::createView('users_with_2fa', DB::table('users')->select('id')->whereNull('two_factor_secret'), ['user_id']);

If you need to create recursive views the createRecursiveView and createRecursiveViewOrReplace methods can be used like in the former examples but you need to provide the available columns as last parameter:

use Illuminate\Support\Facades\DB;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

// TODO simple example explaining the concept
Schema::createRecursiveView('viewname', 'SELECT id, col1, col2 FROM ....', ['id', 'col1', 'col2']);
Schema::createRecursiveViewOrReplace('viewname', 'SELECT id, col1, col2 FROM ....', ['id', 'col1', 'col2']);

Dropping Views

To remove views, you may use the dropView and dropViewIfExists methods provided by the Schema facade. You don't have to distinguish normala and recursive views:

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::dropView('myview');
Schema::dropViewIfExists('myview');

You may drop many views at once by passing multiple view names:

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::dropView('myview1', 'myview2');
Schema::dropViewIfExists('myview1', 'myview2');

Materialized Views

With materialized views you can populate a view with the contents of a query's results at the time the query is executed. You can use them to cache expensive queries so they are not re-run all the time.

Materialized views are created (and dropped) the same as normal views. You can either pass in a query builder or raw sql query. A useful method to create materialized views for very slow queries is to create them without any data initially. By passing the withData: false parameter the materialized view is created instantly and no data is stored, you need to refresh it later to contain some data.

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::createMaterializedView('users_with_2fa', 'SELECT * FROM users WHERE two_factor_secret IS NOT NULL');
Schema::createMaterializedView('users_with_2fa', DB::table('users')->whereNull('two_factor_secret'));
Schema::createMaterializedView('users_with_2fa', DB::table('users')->select('id')->whereNull('two_factor_secret'), columns: ['user_id']);

Schema::createMaterializedView('very_slow_query_materialized', 'SELECT ...', withData: false);

Schema::dropMaterializedView('users_with_2fa');
Schema::dropMaterializedViewIfExists('users_with_2fa');

The stored values of a created materialized view can be refreshed whenever you want to. When passing the concurrently: true parameter the command will finish instantly and PostgreSQL will refresh the values in the background. You can also change the materialized views behaviour to (not) contain any data anymore with the withData: true and withData: false parameter.

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::refreshMaterializedView('users_with_2fa');
Schema::refreshMaterializedView('users_with_2fa', concurrently: true);
Schema::refreshMaterializedView('users_with_2fa', withData: false);
Schema::refreshMaterializedView('users_with_2fa', withData: true);

Indexes

Unique Indexes

Laravel provides uniqueness with the $table->unique() method but these are unique constraints instead of unique indexes. If you want to make values unique in the table they will behave identical. However, only for unique indexes advanced options like partial indexes, including further columns or column options are available.

To use these great features and not break compatibility with Laravel the method uniqueIndex has been added which can be used identical to unique:

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('users', function(Blueprint $table) {
    $table->uniqueIndex('email');
});

Drop If Exists

In addition to the Laravel methods to drop indexes, methods to drop indexes if they exist have been added. The methods dropFullTextIfExists, dropIndexIfExists, dropPrimaryIfExists, dropSpatialIndexIfExists and dropSpatialIndexIfExists match the semantics of their laravel originals.

Concurrently

With PostgreSQL, you can say goodbye to half-executed migrations on errors and the tedious effort to restore the database to a stable state. This is all thanks to its transactional approach: either all changes of a migration to your database will succeed or will be rolled back. Yay! Because of that, creating an index on a big table will take a long time and block all SQL queries during that time. You can now instruct PostgreSQL to create the index in the background without blocking any SQL query, but you must opt out of running those changes in a transaction.

<?php

use Illuminate\Database\Migrations\Migration;
use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

return new class extends Migration
{
    public $withinTransaction = false;

    public function up(): void
    {
        Schema::table('blog_visits', function (Blueprint $table) {
            $table->index(['url', 'ip_address'])->concurrently();
        });
    }
};

Nulls Not Distinct

NULL values in unique indexes are handled in a non-comprehensible way for most developers. When you e.g. save active subscriptions, you want to limit every user to have only one active subscription by e.g. creating a unique index on (user_id, cancelled_at). But as active subscriptions don't have a cancelled_at timestamp, multiple rows can be entered with the same user_id and a NULL value for cancelled_at because two NULL values are never the same. But with PostgreSQL 15 you can now instruct the database not to allow those duplicate rows by handling NULL values as not distinct:

use Illuminate\Database\Query\Builder;
use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::create('subscriptions', function(Blueprint $table) {
    $table->id('user_id');
    $table->timestampTz('cancelled_at');


    $table->uniqueIndex(['user_id', 'cancelled_at'])->nullsNotDistinct();
});

Note

For this example you could also use a unique partial index on user_id with limiting the rows to cancelled_at IS NOT NULL.

Partial Indexes

A partial index is an index built over a subset of a table; the subset is defined by a condition. The index contains entries only for those table rows that satisfy the condition. Partial indexes are a specialized feature, but there are several situations in which they are useful. Take for example you want to make the email address column of your users table unique and you are using soft-deletes. This is not possible because by deleting a user and creating it again the email address is used twice. With partial indexes this can be done by limiting the index to only untrashed rows:

use Illuminate\Database\Query\Builder;
use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('users', function(Blueprint $table) {
    $table->uniqueIndex('email')->where("deleted_at IS NULL");
    // or:
    $table->uniqueIndex('email')->where(fn (Builder $condition) => $condition->whereNull('deleted_at'));
});

Partial Indexes are created with the where method on an index created by fullText(), index(), spatialIndex() or uniqueIndex().

Include Columns

A really great feature of recent PostgreSQL versions is the ability to include columns in an index as non-key columns. A non-key column is not used for efficient lookups but PostgreSQL can use these columns to do index-only operations which won't need to load the specific columns from the table as they are already included in the index.

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('users', function(Blueprint $table) {
    // The query "SELECT firstname, lastname FROM users WHERE email = 'test@example.com'" can be executed as an index-only scan without loading the table data
    $table->index('email')->include(['firstname', 'lastname']);
});

Columns are included in an index with the include method on an index created by index(), spatialIndex or uniqueIndex.

If Not Exists

Sometimes, you fix performance issues by testing new indexes on the production database rather than pushing a new migration each time. But when you find the perfect one, you should also make a migration for it. Dropping the existing index to recreate it by the migration is silly. Right? You can now skip the index creation from the migration when the exact index already exists.

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('invoices', function(Blueprint $table) {
    $table->index(['target', 'division', 'date'])->ifNotExists();
});

Tip

Indexes are determined to be identical by their (automatically-generated) name. Either create the index statement to run in production by temporary migrations from your development machine or use specific index names.

Storage Parameters (Index)

In some cases you want to specify the storage parameters of an index. If you are using gin indexes you should read the article Debugging random slow writes in PostgreSQL why storage parameters for a gin index are important:

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('bookmarks', function(Blueprint $table) {
    $table->index('data')->algorithm('gin')->with(['fastupdate' => false]);
});

Storage parameters are defined with the with method on an index created by fullText(), index(), spatialIndex or uniqueIndex.

Functional Indexes / Column Options

Sometimes an index with only column specifications is not sufficient. For maximum performance, the extended index functionalities of PostgreSQL has to be used in some cases.

To create functional indexes the function must be bracketed and a separate index name must be specified, since an index name cannot be generated automatically from the expression.
Column specific properties like collation, opclass, sorting or positioning of NULL values can easily be specified like in a normal SQL query directly after the column name.

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('users', function(Blueprint $table) {
    $table->uniqueIndex('(LOWER(email))', 'users_email_unique');
    $table->index(['firstname ASC NULLS FIRST', 'lastname ASC NULLS FIRST']);
    $table->index('attributes jsonb_path_ops')->algorithm('gin');
});

Fulltext Indexes

Fulltext-search in PostgreSQL is language-dependent: For better results all words are stemmed to their root form. Laravel is using the english language by default, for your application you may have to use a different one. You can also use the generic simple language which is not doing any stemming, but your search term will then have to include the exact string to match the record.

Additionally, you can specify a relative weight for each column of the index to control the ranking. In this example the title column is a more relevant information than the description column, so it's relative weight has been set more important (A precedes B).

For more information on all the options for fulltext-search read this article: Fine Tuning Full Text Search with PostgreSQL 12.

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('book', function (Blueprint $table) {
    $table->fullText(['title', 'description'])
        ->language('spanish')
        ->weight(['A', 'B']);
});

Domain Types

A relational database like PostgreSQL provides a lot of data types you can choose from. But they are only generic types that should match thousands of applications. Your specific requirements are not covered. But with domain types, you can add application-specific types like a price column that has a specific amount of digits and is never negative: An existing base type (numeric(9,2)) is aliased to a new type with an optional condition that all values have to match. You can use that to create repeatable price columns in your tables or create completely new types like a license plate type that has to match a specific format.

Create A Domain Type

The Schema facade supports the creation of domain types with the createDomain method by passing the name, the base type and an optional SQL condition to validate any value.

use Tpetry\PostgresqlEnhanced\Query\Builder;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::createDomain('price', 'numeric(9,2)');
Schema::createDomain('price', 'numeric(9,2)', 'VALUE >= 0');
Schema::createDomain('price', 'numeric(9,2)', fn (Builder $query) => $query->where('VALUE', '>=', 0));

Use Domain Types

Your created domain types can be used in a migration like every other column by using the domain column type and using the column name and domain type name:

Schema::create('products', function (Blueprint $table): void {
  $table->id();
  $table->string('item_name');
  $table->domain('item_price', 'price');
  $table->timestampsTz();
});

Note

You can also utilize the domain type to use e.g. column types added by extensions or not yet supported by the package.

Altering Domain Types

The base type of a domain type can't be changed after it has been created. But you can change the condition to validate the values by removing it or replacing it with a new one:

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

// To drop the validation condition:
Schema::changeDomainConstraint('price', null);

// To change validation condition:
Schema::changeDomainConstraint('price', 'VALUE > 0');
Schema::changeDomainConstraint('price', fn (Builder $query) => $query->where('VALUE', '>', 0));

Dropping Domain Types

To remove domain types, you have first to drop all column using them (or change their type) and then use dropDomain or dropDomainIfExists provided by the Schema facade:

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::dropDomain('price');
Schema::dropDomainIfExists('price');

You may drop many domain types at once by passing multiple domain names:

use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::dropDomain('price', 'license_plate');
Schema::dropDomainIfExists('price', 'license_plate');

Table Options

Unlogged

You can mark high-write load tables as unlogged if losing that data is not an issue and you want a big speed boost for write operations. Unlogged tables are written to disk by PostgreSQL but some durability requirements to be crash-safe are skipped. They behave like every other table and keep their data on a clean shutdown while on a server crash all data is lost. This is a perfect option for temporary data which you are okay with to lose like e.g. sessions as every user can just login again. You can activate and deactivate the unlogged table mode with the unlogged method on the table blueprint.

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('sessions', function (Blueprint $table): void {
    // make the table unlogged
    $table->unlogged();
    
    // make the table crash-safe again
    $table->unlogged(false);
});

Storage Parameters (Table)

With storage parameters, you can fine-tune tables to your application requirements and it's specific workload. Storage parameters and options you may want to change:

fillfactor for faster UPDATE-s: HOT updates for better performance
autovacuum_analyze_scale_factor for tables with millions of rows: Explained configuration value, Table Maintenance after Bulk Modifications

You can find more suggestions for specific workloads in tuning autovacuum guide.

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('sessions', function (Blueprint $table): void {
    $table->with([
        // Tune statistics generation for tables with millions of records
        'autovacuum_analyze_scale_factor' => 0.02,
        // Tune table for frequent UPDATE statements
        'fillfactor' => 90,
    ]);
});

Column Options

Compression

PostgreSQL 14 introduced the possibility to specify the compression method for toast-able data types. You can choose between the default method pglz, the recently added lz4 algorithm and the value default to use the server default setting.

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('books', function (Blueprint $table): void {
    // @see https://www.postgresql.org/docs/current/storage-toast.html
    $table->string('summary')->compression('lz4');
});

Initial

Sometimes a new column needs to be added and all existing rows should get an initial value. With the initial modifier, you can assign a value to all present rows while all new ones will have no default value or a different one.

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::table('users', function (Blueprint $table): void {
    $table->boolean('acl_admin')->initial(false);
    $table->boolean('acl_read')->initial(false)->default(true);
});

Using

PostgreSQL forbids some data type changes in migrations when they violate the type system. You can't, e.g., change a varchar column storing one email address into a jsonb array storing multiple email addresses, as PostgreSQL doesn't know how to convert between these types automatically. You would get this error:

SQLSTATE[42804]: Datatype mismatch: 7 ERROR:  column "email" cannot be cast automatically to type jsonb

You can specify an expression how the current value has to be transformed to the new type with the using() modifier:

Schema::table('users', function (Blueprint $table): void {
    $table->jsonb('email')->using('jsonb_build_array(email)')->change();
});

Column Types

Arrays

The array data types store multiple values in one single column. They can be used e.g. to store multiple tag ids of categories a product belongs to.

// @see https://www.postgresql.org/docs/current/arrays.html
$table->integerArray(string $column);

Note

While PostgreSQL array types are powerful, only the integer array is supported. It is the sole array type with additional PostgreSQL enhancements for manipulation and querying compared to JSON columns. The intarray extensions provides extensive features that can be used to e.g. store and query tags with advanced boolean logic.

Ranges

The range data types store a range of values with optional start and end values. They can be used e.g. to describe the duration a meeting room is booked.

// @see https://www.postgresql.org/docs/current/rangetypes.html
$table->bigIntegerRange(string $column);
$table->bigIntegerMultiRange(string $column);
$table->dateRange(string $column);
$table->dateMultiRange(string $column);
$table->decimalRange(string $column);
$table->decimalMultiRange(string $column);
$table->integerRange(string $column);
$table->integerMultiRange(string $column);
$table->timestampRange(string $column);
$table->timestampMultiRange(string $column);
$table->timestampTzRange(string $column);
$table->timestampTzMultiRange(string $column);

Bit Strings

The bit string data types store strings of 0s and 1s. They can be used to e.g. store bitmaps.

// @see https://www.postgresql.org/docs/current/datatype-bit.html
$table->bit(string $column, int $length = 1);
$table->varbit(string $column, ?int $length = null);

Case Insensitive Text

The case insensitive text type is used to store a text that will be compared case insensitive. It can be used to e.g. store and compare e-mail addresses.

// @see https://www.postgresql.org/docs/current/citext.html
$table->caseInsensitiveText(string $column);

Note

You need to enable the citext extension with Schema::createExtension('citext') or Schema::createExtensionIfNotExists('citext') before.

Full Text Search

The tsvector type is used to store a processed dictionary for full text searching.

// @see https://www.postgresql.org/docs/10/datatype-textsearch.html
$table->tsvector(string $column);

IP Networks

The ip network datatype stores an ip network in cidr notation.

// @see https://www.postgresql.org/docs/current/datatype-net-types.html
$table->ipNetwork(string $column);

Hstore

The hstore data type is used store key/value pairs within a single PostgreSQL value. The new json data type is better in all aspects, so hstore should only be used for compatibility with old applications.

// @see https://www.postgresql.org/docs/current/hstore.html
$table->hstore(string $column);

Note

You need to enable the hstore extension with Schema::createExtensionIfNotExists('hstore') or Schema::createExtension('hstore') before.

Identity

The identity data type is the new PostgreSQL standard for automatic generated values. You can even specify whether the database should be the only one generating them (always = true) preventing accidental overwrites. They are used to define primary keys managed by the database or any other kind of automatically generated identification that needs to be unique.

$table->identity(always: true)->primary();
$table->identity('uniqid');

International Product Numbers

The international product number data types are used to store common product numbers types and validate them before saving.

// @see https://www.postgresql.org/docs/current/isn.html
$table->europeanArticleNumber13(string $column);
$table->internationalStandardBookNumber(string $column);
$table->internationalStandardBookNumber13(string $column);
$table->internationalStandardMusicNumber(string $column);
$table->internationalStandardMusicNumber13(string $column);
$table->internationalStandardSerialNumber(string $column);
$table->internationalStandardSerialNumber13(string $column);
$table->universalProductNumber(string $column);

Note

You need to enable the isn extension with Schema::createExtensionIfNotExists('isn') or Schema::createExtension('isn') before.

Label Tree

The ltree data type stores a label as its position in a tree. This provides an easy way to manage a tree without performance and complexity disadvantages compared to alternative solutions.

// @see https://www.postgresql.org/docs/current/ltree.html
$table->labelTree(string $column);

Note

You need to enable the ltree extension with Schema::createExtensionIfNotExists('ltree') or Schema::createExtension('ltree') before.

Vector

The vector type can be used to store and search for embeddings created by AI providers like OpenAI.

// @see https://github.com/pgvector/pgvector
$table->vector(string $column, int $dimensions = 1536);

Note

You need to enable the vector extension with Schema::createExtensionIfNotExists('vector') or Schema::createExtension('vector') before.

Note

The vector extension is not a standard PostgreSQL extension but available with most PostgreSQL cloud services. You can check for support with the following query: SELECT * FROM pg_available_extensions WHERE name = 'vector'

XML

The xml data type can be used to store an xml document.

// @see https://www.postgresql.org/docs/current/datatype-xml.html
$table->xml(string $column);

Query

Explain

Laravel has the ability to get the database query plan for any query you are building. Just calling explain() on the query will get a collection with the query plan.

This behaviour has been extended to be more PostgreSQL specific. There are multiple (optional) parameters for the explain statement, different for every version. The enhanced PostgreSQL driver will automatically activate all options available for your PostgreSQL version.

DB::table('migrations')->where('batch', 1)->explain()->dd();

// Output:
// array:1 [
//  0 => """
//    Seq Scan on public.migrations  (cost=0.00..11.75 rows=1 width=524)\n
//      Output: id, migration, batch\n
//      Filter: (migrations.batch = 1)\n
//    Settings: search_path = 'public'\n
//    Planning Time: 0.370 ms
//    """
//]

Additionally, you can also get the query plan with executing the query. The query plan will be extended by valuable runtime information like per-operation timing and buffer read/write statistics:

DB::table('migrations')->where('batch', 1)->explain(analyze:true)->dd();

// Output:
// array:1 [
//  0 => """
//    Seq Scan on public.migrations  (cost=0.00..11.75 rows=1 width=524) (actual time=0.014..0.031 rows=1 loops=1)\n
//      Output: id, migration, batch\n
//      Filter: (migrations.batch = 1)\n
//      Buffers: shared hit=1\n
//    Settings: search_path = 'public'\n
//    Planning:\n
//      Buffers: shared hit=61\n
//    Planning Time: 0.282 ms\n
//    Execution Time: 0.100 ms
//    """
//]

Fulltext Search

The PostgreSQL fulltext search implementation supports a lot of knobs to fine tune the search quality of your fulltext-search. In it's most basic form you are specifying the columns to search on and the search term to use:

Book::whereFullText(['title', 'description'], 'PostgreSQL')->get();

But the implementation does provide a lot more functionality hidden in the third optional parameter. For more information on all the options for fulltext-search read this article: Fine Tuning Full Text Search with PostgreSQL 12.

Language

By default the columns and the search term are stemmed to it's root form in the english language to also find results for e.g. singular or plural words. If your application is using a different language you can change it to e.g. spanish or use the simple language which is not doing any stemming.

Book::whereFullText(['title', 'description'], 'PostgreSQL', ['language' => 'spanish'])->get();

Search Mode

You can choose from three different search modes for the fulltext-search which is defaulting to the plainto mode. Depending on your requirements a search term can be handled completely different giving you a large amount of freedom to tune fulltext search for your needs.

plainto: All words in the search-term have to exist at least once in the columns.

Book::whereFullText(['title', 'description'], 'PostgreSQL', ['mode' => 'plain'])->get();

phrase: All words in the search-term have to appear in the exact same order in the columns.

Book::whereFullText(['title', 'description'], 'PostgreSQL database', ['mode' => 'phrase'])->get();

websearch: Complex search-term which supports quoting values, the or keyword and - to exclude a word but is missing parentheses.
```
Book::whereFullText(['title', 'description'], '"PostgreSQL database" -MySQL', ['mode' => 'websearch'])->get();
```

Weighting

When you want to rank your fulltext-search results you need a way declare some columns more relevant than others. With the weight option you set a relevance for every search column starting with an A and ending with a Z. If you want to you can use the same relative weight multiple times to make some columns equal important.

Book::whereFullText(['title', 'description'], '"PostgreSQL', ['weight' => ['A', 'B']])->get();

Lateral Subquery Joins

PostgreSQL does support the advanced lateral subquery joins. The simplest explanation is that you can access tables and their columns previously selected from, making it a dependent subquery. You will now be able to do joins equivalent to a foreach-loop in php which will offer a whole new set of possibilities.

This is a very advanced construct, you can read more about them in these articles:

They are used exactly like their Laravel counterparts but you are now using crossJoinSubLateral instead of crossJoinSub, joinSubLateral instead of joinSub and leftJoinSubLateral instead of leftJoinSubLateral.

A very common is case to use lateral subqueries in a for-each loop concept to e.g. get only the 3 orders with the highest price for every user:

User::select('users.email', 'orders.*')
    ->leftJoinSubLateral(
        Order::whereColumn('orders.user_id', 'users.id')
            ->orderBy('price', 'desc')
            ->limit(3),
        'orders',
    );

Returning Data From Modified Rows

Sometimes it is more useful to get the affected rows data of a INSERT, UPDATE, or DELETE query instead of just the number of affected rows. The PostgreSQL RETURNING feature changes the behaviour of data manipulation statements to SELECT the row's data after the manipulation.

You can use RETURNING when you e.g. want to get a list of users you need to update. Instead of selecting all the users into memory, iterating over them and manipulating each one you can run the manipulation statement directly and get all the affected rows data. A typical example is reporting which old users have been deleted:

use Illuminate\Support\Facades\DB;

$inactiveUsers = DB::table('users')
    ->where('lastlogin_at', '<', now()->subYear())
    ->get();
foreach ($inactiveUsers as $inactiveUser) {
  $inactiveUser->delete();
}
dump('deleted Users', $inactiveUsers);

// do this instead:

$inactiveUsers = DB::table('users')
    ->where('lastlogin_at', '<', now()->subYear())
    ->deleteReturning();
dump('deleted Users', $inactiveUsers);

The following modification queries have been added (analog to their Laravel implementation) which are returning the affected rows data instead of just the number of affected rows:

deleteReturning
insertOrIgnoreReturning
insertReturning
insertUsingReturning
updateFromReturning
updateOrInsertReturning
updateReturning
upsertReturning

Common Table Expressions (CTE)

You can use Common Table Expressions or CTEs for all select, insert, update and delete methods to write auxiliary statements for use in a larger query. The withExpression method needs to be passed an alias for the CTE, a query string or object and an optional array of options for more control on the CTE.

$query->withExpression($as, $query, $options = []);

$lastLoginQuery = Login::query()
    ->selectRaw('user_id, MAX(created_at) AS last_login_at')
    ->groupBy('user_id');
User::query()
    ->withExpression('users_lastlogin', $lastLoginQuery)
    ->join('users_lastlogin', 'users_lastlogin.user_id', 'users.id')
    ->where('users_lastlogin.created_at', '>=', now()->subHour());

In addition to the basic form of a Common Table Expression these optional settings are available to support all PostgreSQL options:

Note

When you are using recursive CTEs always use the cycle option to prevent infinite running queries because of loops in the data.

Lazy By Cursor

If you need to iterate over a large amount rows your memory may most probably not big enough. For these operations Laravel provides the lazy() method which is repeatedly using offset pagination which is getting slower and slower with increasing offsets. Or you can use the more efficient lazyById which is using the primary key to paginate the data which is much more efficient but still needs to execute the same query many times.

In PostgreSQL you can do all of this a lot more efficient by using cursors: The query is executed once and the application can request more rows whenever it wants to so it hasn't to copy everything into memory all at once.

use Illuminate\Support\Facades\DB;

DB::transaction(function() {
    User::lazyByCursor()->each(function (User $user) {
        dump($user);
    });

    // Maximum 500 rows should be loaded into memory for every chunk.
    User::lazyByCursor(500)->each(function (User $user) {
        dump($user);
    });

    // Lazy loading rows also works for the query builder.
    DB::table('users')->where('active', true)->lazyByCursor()->each(function (object $user) {
        dump($user);
    });
});

Where Clauses

Any/All

PostgreSQL provides very nifty filtering functions to check a column against multiple values without writing many AND or OR conditions. You can say that at least one of the values needs to match the operator with the' ANY' keyword. While for the ALL keyword, all values have to match.

// instead of:
$query->where('invoice', 'like', 'RV-%')->orWhere('invoice', 'like', 'RZ-%');
$query->where('json', '??', 'key1')->where('json', '??', 'key2');

// you can do:
$query->whereAnyValue('invoice', 'like', ['RV-%', 'RZ-%']);
$query->whereAllValues('json', '??', ['key1', 'key2']);

$query->whereAllValues($column, string $operator, iterable $values);
$query->whereNotAllValues($column, string $operator, iterable $values);
$query->orWhereAllValues($column, string $operator, iterable $values);
$query->orWhereNotAllValues($column, string $operator, iterable $values)
$query->whereAnyValue($column, string $operator, iterable $values);
$query->whereNotAnyValue($column, string $operator, iterable $values);
$query->orWhereAnyValue($column, string $operator, iterable $values);
$query->orWhereNotAnyValue($column, string $operator, iterable $values)

Caution

The suffixes Value and Values had to be added to the method names since release 0.36.0 because the Laravel query builder also started to use these method names.

Boolean

As Laravel always casts boolean values to integers you will get a PostgreSQL errors like operator does not exist: boolean = integer sometimes. In most cases PostgreSQL is intelligent enough to cast the value but when e.g. creating partial indexes you will get the error. To resolve that problem you can use the special whereBoolean functions that do not cast a boolean to 0 or 1.

$query->whereBoolean($column, bool $value);
$query->whereNotBoolean($column, bool $value);
$query->orWhereBoolean($column, bool $value);
$query->orWhereNotBoolean($column, bool $value);

Like

With the whereLike scope you can do case-(in)sensitive like comparisons between a column and a value.

$query->whereLike($column, $value, $caseSensitive = false);
$query->orWhereLike($column, $value, $caseSensitive = false);

Between Symmetric

Laravel already provides a whereBetween clause, but you have to provide the values in sorted order that the smaller value is the first and the bigger one the second array item ([4, 80]). With PostgreSQL's BETWEEN SYMMETRIC keyword you don't have to do this anymore, it will automatically reorder the values.

You can now use e.g. min/max values with the following code without having to reorder these values if the meaning has been swapped by the user when entering them:

$min = $request->integer('min');
$max = $request->integer('max');

// before:
$query->whereBetween('price', [min($min, $max), max($min, $max)]);

// now:
$query->whereBetweenSymmetric('price', [$min, $max]);

$query->whereBetweenSymmetric($column, iterable $values);
$query->whereNotBetweenSymmetric($column, iterable $values);
$query->orWhereBetweenSymmetric($column, iterable $values);
$query->orWhereNotBetweenSymmetric($column, iterable $values);

Integer Array Matches

With arrays, you can store a collection of integers like with JSON columns. But in contrast to JSON columns, those collections can be queried with the intarray extension by complex rules including the operators & (AND), | (OR), and ! (NOT). Parentheses can be used as needed. For example, the query 1&(2|3) matches integer arrays that contain 1 and also include either 2 or 3.

$query->whereIntegerArrayMatches($column, string $query);
$query->orWhereIntegerArrayMatches($column, string $query);

// The tags column should have values 3, 4, 5 or 6 and not 7.
$query->whereIntegerArrayMatches('tags', '3&4&(5|6)&!7');

Order By

NULLS FIRST/LAST

By default, NULL values are sorted before everything in descending order and after everything in ascending order. This may not be your preferred way of ordering when e.g. displaying in a table to users. With the nulls first/last option, you can specify the exact behaviour you want:

$query->orderBy($column, string $direction = 'asc'|'desc', string $nulls = 'default'|'first'|'last');
$query->orderByNullsFirst($column, string $direction = 'asc'|'desc', string $nulls = 'default'|'first'|'last');
$query->orderByNullsLast($column, string $direction = 'asc'|'desc', string $nulls = 'default'|'first'|'last');

// Sort the table by the age descending with all NULL values presented last.
$query->orderBy('age', 'desc', nulls: 'last');
$query->orderByNullsLast('age', 'desc');

Warning

You have to create a matching index when using a non-default sorting order - a standard one does not work! The exact index $table->index('age DESC NULLS LAST') matches the query or $table->index('age NULLS FIRT') because of the default ascending column order.

Vector Similarity

With the orderByVectorSimilarity method you can compare a column storing embeddings to other embeddings.

$query->orderByVectorSimilarity($column, $vector, string $distance = 'cosine'|'l2');

// The five rows with the highest similarity to the provided embeddings.
$query->orderByVectorSimilarity('embeddings', [0.9569, 0.1113, 0.0107])->limit(5);

Eloquent

Casts

Some of the PostgreSQL types are represented in a string format that a Laravel application can't use natively. To make those types usable, these casts can be used with your eloquent models:

Refresh Data on Save

When you are using Laravel's storedAs($expression) feature in migrations to have dynamically computed columns in your database or triggers to update these columns, eloquent's behaviour is not doing exactly what you are expecting. After you saved the model these computed properties are not available in your model, you need to refresh it because Laravel is only updating the primary key.

use Tpetry\PostgresqlEnhanced\Schema\Blueprint;
use Tpetry\PostgresqlEnhanced\Support\Facades\Schema;

Schema::create('example', function (Blueprint $table) {
    $table->id();
    $table->string('text');
    $table->string('text_uppercase')->storedAs('UPPER(text)');
});

$example = Example::create(['text' => 'test']);
dump($example); // ['id' => 1, 'text' => 'test']

$example->refresh();
dump($example); // ['id' => 1, 'text' => 'test', 'text_uppercase' => 'TEST']

$example->fill(['text' => 'test2'])->save();
dump($example); // ['id' => 1, 'text' => 'test2']

$example->refresh();
dump($example); // ['id' => 1, 'text' => 'test', 'text_uppercase' => 'TEST2']

By using the new RefreshDataOnSave trait the new RETURNING statements are used for saving models. Whenever Laravel saves a model any changes in the rows are automatically reflected in your model:

use Illuminate\Database\Eloquent\Model;
use Tpetry\PostgresqlEnhanced\Eloquent\Concerns\RefreshDataOnSave;

class Example extends Model
{
    use RefreshDataOnSave;

    // ...
}

$example = Example::create(['text' => 'test']);
dump($example); // ['id' => 1, 'text' => 'test', 'text_uppercase' => 'TEST']

$example->fill(['text' => 'test2'])->save();
dump($example); // ['id' => 1, 'text' => 'test2', 'text_uppercase' => 'TES2T']

Date Formats

Laravel migrations support more dates than the standard Y-m-d H:i:s format: You can use the improved timestampTz date format that correctly handles the time zone or opt-in to save milliseconds if you want to. However, standard eloquent models do not work flawless with those extended formats until you change the $dateFormat of your models. But when you mix different date types in a table, you can run into different problems. Two new traits have been added to solve this:

The new AutomaticDateFormat trait should be used when your table has ->timestampTz() columns:

use Illuminate\Database\Eloquent\Model;
use Tpetry\PostgresqlEnhanced\Eloquent\Concerns\AutomaticDateFormat;

class Example extends Model
{
    use AutomaticDateFormat;

    // ...
}

The new AutomaticDateFormatWithMilliseconds trait should be used when you also store milliseconds for some of the ->timestamp() or ->timestampTz() columns:

use Illuminate\Database\Eloquent\Model;
use Tpetry\PostgresqlEnhanced\Eloquent\Concerns\AutomaticDateFormatWithMilliseconds;

class Example extends Model
{
    use AutomaticDateFormatWithMilliseconds;

    // ...
}

Caution

When you mix columns with and without milliseconds in a table, the columns without milliseconds may behave unexpectedly to you: Instead of truncating the milliseconds, they are rounded by PostgreSQL. When the value is rounded up, your timestamp will be in the future.

Expressions

Laravel 10 added the functionality to use pre-made expressions with the query builder like this that generate vendor-specific SQL for complex operations:

BlogVisit::select([
    'url',
    new TimestampBin('created_at', DateInterval::createFromDateString('5 minutes')),
    new Count('*'),
])->groupBy(
    'url',
    new TimestampBin('created_at', DateInterval::createFromDateString('5 minutes'))
);

I've already released a lot of expressions with my package tpetry/laravel-query-expressions that are usable on all databases supported by Laravel. But some functionality just can't be built for all database. So here are some PostgreSQL specific ones:

Uuid7

You can now generate time-sorted UUIDv7 IDs directly in the database. The drawback of using Str::orderedUuid() is that inserting new rows can only be done from Laravel: You loose the ability to insert new rows manually with a GUI, simple INSERT queries or efficient approaches like INSERT INTO ... SELECT. But all of these are still possible with IDs generated at the database.

use Tpetry\PostgresqlEnhanced\Expressions\Uuid7;

Schema::create('comments', function (Blueprint $table) {
    $table->id();
    $table->uuid()->default(new Uuid7())->unique();
    $table->text('text');
});

Breaking Changes

2.0.0
- Laravel 11.25 released a new vector migration type so the behaviour had to be aligned with Laravel's implementation:
  - A missing $dimensions parameter now leads to a vector type with unspecified dimension size (formerly defaulted to $dimensions = 1536)
1.0.0
- Laravel 11.17 released a new whereLike and orWhereLike builder method so the behaviour had to be aligned with Laravel's implementation:
  - The value is now searched case-insensitive by default instead of case-sensitive
  - The $caseSensitive parameter had to be replaced by the $caseInsensitive parameter
0.39.0 -> 0.40.0
- The Enhanced PostgreSQL Driver PHPStan extension is now automatically registered with the PHPStan Extension Installer. The manual registration of the extension needs to be removed when PHPStan crashed because the extension is registered twice.
0.35.0 -> 0.36.0
- Some query builder methods had to be changed because they've now overlapped with new ones added by Laravel 10.47:
  - whereAll -> whereAllValues
  - whereNotAll -> whereNotAllValues
  - orWhereAll -> orWhereAllValues
  - orWhereNotAll -> orWhereNotAllValues
  - whereAny -> whereAnyValue
  - whereNotAny -> whereNotAnyValue
  - orWhereAny -> orWhereAnyValue
  - orWhereNotAny -> orWhereNotAnyValue
0.10.0 -> 0.11.0
- The ZeroDowntimeMigration concern namespace moved from Tpetry\PostgresqlEnhanced\Concerns to Tpetry\PostgresqlEnhanced\Schema\Concerns.
0.12.0 -> 0.12.1
- The return type of all returning statements was changed from array to Collection to replicate the Query\Builder::get() method signature.

Contribution

If you want to contribute code to this package, please open an issue first. To avoid unnecessary effort for you it is very beneficial to first discuss the idea, the functionality and its API.

Changelog

Please see CHANGELOG for more information on what has changed recently.

Security Vulnerabilities

If you discover any security related issues, please email github@tpetry.me instead of using the issue tracker.

License

The MIT License (MIT). Please see License File for more information.

tpetry / laravel-postgresql-enhanced

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Migration

Zero-Downtime Migration

Extensions

Create Extensions

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Functions

Create Functions

Drop Functions

Triggers

Create Triggers

Drop Triggers

Views

Create Views

Dropping Views

Materialized Views

Indexes

Unique Indexes

Drop If Exists

Concurrently

Nulls Not Distinct

Partial Indexes

Include Columns

If Not Exists

Storage Parameters (Index)

Functional Indexes / Column Options

Fulltext Indexes

Domain Types

Create A Domain Type

Use Domain Types

Altering Domain Types

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Table Options

Unlogged

Storage Parameters (Table)

Column Options

Compression

Initial

Using

Column Types

Arrays

Ranges

Bit Strings

Case Insensitive Text

Full Text Search

IP Networks

Hstore

Identity

International Product Numbers

Label Tree

Vector

XML

Query

Explain

Fulltext Search

Language

Search Mode

Weighting

Lateral Subquery Joins

Returning Data From Modified Rows

Common Table Expressions (CTE)

Lazy By Cursor

Where Clauses

Any/All

Boolean

Like

Between Symmetric

Integer Array Matches

Order By

NULLS FIRST/LAST

Vector Similarity

Eloquent