Michio JP

Michio JP

1562395458

Build an OAuth 2.0 Authorization Server With Spring Boot and Spring Security

Learn more about building an OAuth 2.0 authorization server with Spring Boot and Spring Security.

User management is required for most web applications, but building it isn’t always an easy task. Many developers work around the clock to ensure their app is secure by seeking out individual vulnerabilities to patch. Luckily, you can increase your own efficiency by implementing OAuth 2.0 to your web application with Spring Security and Spring Boot. The process gets even easier by integrating with Oktaon top of Spring Boot.

In this tutorial, you’ll first build an OAuth 2.0 web application and authentication server using Spring Boot and Spring Security. After that, you’ll use Okta to get rid of your self-hosted authentication server and simplify your Spring Boot application even more.

Let’s get started!

Create an OAuth 2.0 Server

Start by going to the Spring Initializr and creating a new project with the following settings:

  • Change project type from Maven to Gradle.
  • Change the Group to com.okta.spring.
  • Change the Artifact to AuthorizationServerApplication.
  • Add one dependency: Web.

Download the project and copy it somewhere that makes sense on your hard drive. In this tutorial, you’re going to create three different projects, so you might want to create a parent directory, something like SpringBootOAuthsomewhere.

You need to add one dependency to the build.gradle file:

implementation 'org.springframework.security.oauth:spring-security-oauth2:2.3.3.RELEASE'

This adds in Spring’s OAuth goodness.

Update the src/main/resources/application.properties to match:

server.port=8081
server.servlet.context-path=/auth
user.oauth.clientId=R2dpxQ3vPrtfgF72
user.oauth.clientSecret=fDw7Mpkk5czHNuSRtmhGmAGL42CaxQB9
user.oauth.redirectUris=http://localhost:8082/login/oauth2/code/
user.oauth.user.username=Andrew
user.oauth.user.password=abcd

This sets the server port, servlet context path, and some default values for the in-memory, ad hoc generated tokens the server is going to return to the client, as well as for our user’s username and password. In production, you would need to have a bit more of a sophisticated back-end for a real authentication server without the hard-coded redirect URIs and usernames and passwords.

Update the AuthorizationServerApplication class to add @EnableResourceServer:

package com.okta.spring.AuthorizationServerApplication;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.security.oauth2.config.annotation.web.configuration.EnableResourceServer;
@SpringBootApplication
@EnableResourceServer
public class AuthorizationServerApplication {
    public static void main(String[] args) {
        SpringApplication.run(AuthorizationServerApplication.class, args);
    }
}

Create a new class AuthServerConfig in the same package as your application class com.okta.spring.AuthorizationServerApplication under src/main/java (from now on please create Java classes in src/main/java/com/okta/spring/AuthorizationServerApplication). This Spring configuration class enables and configures an OAuth authorization server.

package com.okta.spring.AuthorizationServerApplication;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Configuration;
import org.springframework.security.crypto.bcrypt.BCryptPasswordEncoder;
import org.springframework.security.oauth2.config.annotation.configurers.ClientDetailsServiceConfigurer;
import org.springframework.security.oauth2.config.annotation.web.configuration.AuthorizationServerConfigurerAdapter;
import org.springframework.security.oauth2.config.annotation.web.configuration.EnableAuthorizationServer;
import org.springframework.security.oauth2.config.annotation.web.configurers.AuthorizationServerSecurityConfigurer;
@Configuration
@EnableAuthorizationServer
public class AuthServerConfig extends AuthorizationServerConfigurerAdapter {
    @Value("${user.oauth.clientId}")
    private String ClientID;
    @Value("${user.oauth.clientSecret}")
    private String ClientSecret;
    @Value("${user.oauth.redirectUris}")
    private String RedirectURLs;
   private final PasswordEncoder passwordEncoder;
    public AuthServerConfig(PasswordEncoder passwordEncoder) {
        this.passwordEncoder = passwordEncoder;
    }
    @Override
    public void configure(
        AuthorizationServerSecurityConfigurer oauthServer) throws Exception {
        oauthServer.tokenKeyAccess("permitAll()")
            .checkTokenAccess("isAuthenticated()");
    }
    @Override
    public void configure(ClientDetailsServiceConfigurer clients) throws Exception {
        clients.inMemory()
            .withClient(ClientID)
            .secret(passwordEncoder.encode(ClientSecret))
            .authorizedGrantTypes("authorization_code")
            .scopes("user_info")
            .autoApprove(true)
            .redirectUris(RedirectURLs);
    }
}

The AuthServerConfig class is the class that will create and return our JSON web tokens when the client properly authenticates.

Create a SecurityConfiguration class:

package com.okta.spring.AuthorizationServerApplication;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.core.annotation.Order;
import org.springframework.security.config.annotation.authentication.builders.AuthenticationManagerBuilder;
import org.springframework.security.config.annotation.web.builders.HttpSecurity;
import org.springframework.security.config.annotation.web.configuration.WebSecurityConfigurerAdapter;
import org.springframework.security.crypto.bcrypt.BCryptPasswordEncoder;
@Configuration
@Order(1)
public class SecurityConfiguration extends WebSecurityConfigurerAdapter {
    @Value("${user.oauth.user.username}")
    private String username;
    @Value("${user.oauth.user.password}")
    private String password;
    @Override
    protected void configure(HttpSecurity http) throws Exception {
        http.requestMatchers()
            .antMatchers("/login", "/oauth/authorize")
            .and()
            .authorizeRequests()
            .anyRequest().authenticated()
            .and()
            .formLogin().permitAll();
    }
    @Override
    protected void configure(AuthenticationManagerBuilder auth) throws Exception {
        auth.inMemoryAuthentication()
            .withUser(username)
            .password(passwordEncoder().encode(password))
            .roles("USER");
    }
    @Bean
    public BCryptPasswordEncoder passwordEncoder() {
        return new BCryptPasswordEncoder();
    }
}

The SecurityConfiguration class is the class that actually authenticates requests to your authorization server. Notice near the top where it’s pulling in the username and password from the application.properties file.

Lastly, create a Java class called UserController:

package com.okta.spring.AuthorizationServerApplication;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;
import java.security.Principal;
@RestController
public class UserController {
    @GetMapping("/user/me")
    public Principal user(Principal principal) {
        return principal;
    }
}

This file allows the client apps to find out more about the users that authenticate with the server.

That’s your authorization server! Not too bad. Spring Boot makes it pretty easy. Four files and a few properties. In a little bit, you’ll make it even simpler with Okta, but for the moment, move on to creating a client app you can use to test the auth server.

Start the authorization server:

./gradlew bootRun

Wait a bit for it to finish running. The terminal should end with something like this:

...
2019-02-23 19:06:49.122  INFO 54333 --- [           main] o.s.b.w.embedded.tomcat.TomcatWebServer  : Tomcat started on port(s): 8081 (http) with context path '/auth  '
2019-02-23 19:06:49.128  INFO 54333 --- [           main] c.o.s.A.AuthorizationServerApplication   : Started AuthorizationServerApplication in 3.502 seconds (JVM running for 3.945)



NOTE: If you get an error about JAXB (java.lang.ClassNotFoundException: javax.xml.bind.JAXBException), it’s because you’re using Java 11. To fix this, add JAXB to your build.gradle.

implementation 'org.glassfish.jaxb:jaxb-runtime'

Build Your Client App

Back to Spring Initializr. Create a new project with the following settings:

  • Project type should be Gradle (not Maven).
  • Group: com.okta.spring.
  • Artifact: SpringBootOAuthClient.
  • Add three dependencies: Web, Thymeleaf, OAuth2 Client.

Download the project, copy it to its final resting place, and unpack it.

This time you need to add the following dependency to your build.gradle file:

implementation 'org.thymeleaf.extras:thymeleaf-extras-springsecurity5:3.0.4.RELEASE'

Rename the src/main/resources/application.properties to application.yml and update it to match the YAML below:

server:
  port: 8082
  session:
    cookie:
      name: UISESSION
spring:
  thymeleaf:
    cache: false
  security:
    oauth2:
      client:
        registration:
          custom-client:
            client-id: R2dpxQ3vPrtfgF72
            client-secret: fDw7Mpkk5czHNuSRtmhGmAGL42CaxQB9
            client-name: Auth Server
            scope: user_info
            provider: custom-provider
            redirect-uri-template: http://localhost:8082/login/oauth2/code/
            client-authentication-method: basic
            authorization-grant-type: authorization_code
        provider:
          custom-provider:
            token-uri: http://localhost:8081/auth/oauth/token
            authorization-uri: http://localhost:8081/auth/oauth/authorize
            user-info-uri: http://localhost:8081/auth/user/me
            user-name-attribute: name

Notice that here, you’re configuring the clientId and clientSecret, as well as various URIs for your authentication server. These need to match the values in the other project.

Update the SpringBootOAuthClientApplication class to match:

package com.okta.spring.SpringBootOAuthClient;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class SpringBootOAuthClientApplication {
    public static void main(String[] args) {
        SpringApplication.run(SpringBootOAuthClientApplication.class, args);
    }
}

Create a new Java class called WebController:

package com.okta.spring.SpringBootOAuthClient;
import org.springframework.stereotype.Controller;
import org.springframework.ui.Model;
import org.springframework.web.bind.annotation.RequestMapping;
import java.security.Principal;
@Controller
public class WebController {
    @RequestMapping("/securedPage")
    public String securedPage(Model model, Principal principal) {
        return "securedPage";
    }
    @RequestMapping("/")
    public String index(Model model, Principal principal) {
        return "index";
    }
}

This is the controller that maps incoming requests to your Thymeleaf template files (which you’ll make in a sec).

Create another Java class named SecurityConfiguration:

package com.okta.spring.SpringBootOAuthClient;
import org.springframework.context.annotation.Configuration;
import org.springframework.security.config.annotation.web.builders.HttpSecurity;
import org.springframework.security.config.annotation.web.configuration.WebSecurityConfigurerAdapter;
@Configuration
public class SecurityConfiguration extends WebSecurityConfigurerAdapter {
    @Override
    public void configure(HttpSecurity http) throws Exception {
        http.antMatcher("/**").authorizeRequests()
            .antMatchers("/", "/login**").permitAll()
            .anyRequest().authenticated()
            .and()
            .oauth2Login();
    }
}

This class defines the Spring Security configuration for your application: allowing all requests on the home path and requiring authentication for all other routes. It also sets up the Spring Boot OAuth login flow.

The last files you need to add are the two Thymeleaf template files. A full look at Thymeleaf templating is well beyond the scope of this tutorial, but you can take a look at their website for more info.

The templates go in the src/main/resources/templates directory. You’ll notice in the controller above that they’re simply returning strings for the routes. When the Thymeleaf dependencies are included the build, Spring Boot automatically assumes you’re returning the name of the template file from the controllers, and so, the app will look in src/main/resources/templates for a file name with the returned string plus .html.

Create the home template: src/main/resources/templates/index.html:

<!DOCTYPE html>  
<html lang="en">  
<head>  
    <meta charset="UTF-8">  
    <title>Home</title>  
</head>  
<body>  
    <h1>Spring Security SSO</h1>  
    <a href="securedPage">Login</a>  
</body>  
</html>

And the secured template: src/main/resources/templates/securedPage.html:

<!DOCTYPE html>  
<html xmlns:th="http://www.thymeleaf.org">  
<head>  
    <meta charset="UTF-8">  
    <title>Secured Page</title>  
</head>  
<body>  
    <h1>Secured Page</h1>  
    <span th:text="${#authentication.name}"></span>  
</body>  
</html>

I’ll just point out this one line:

<span th:text="${#authentication.name}"></span>

This is the line that will insert the name of the authenticated user. This line is why you needed the org.thymeleaf.extras:thymeleaf-extras-springsecurity5 dependency in the build.gradle file.

Start the client application:

./gradlew bootRun

Wait a moment for it to finish. The terminal should end with something like this:

...
2019-02-23 19:29:04.448  INFO 54893 --- [           main] o.s.b.w.embedded.tomcat.TomcatWebServer  : Tomcat started on port(s): 8082 (http) with context path ''
2019-02-23 19:29:04.453  INFO 54893 --- [           main] c.o.s.S.SpringBootOAuthClientApplication : Started SpringBootOAuthClientApplication in 3.911 seconds (JVM running for 4.403)



Test the Resource Server

Navigate in your browser of choice to your client app at <a href="http://localhost:8082/" target="_blank">http://localhost:8082/</a>.

Click the Login link.

You’ll be directed to the login page:

Enter username Andrew and password abcd (from the application.properties file from the authentication server).

Click Sign In and you’ll be taken to the super fancy securedPage.html template that should say “Secured Page” and “Andrew”.

Great! It works. Now you’re gonna make it even simpler.

You can stop both server and client Spring Boot apps.

Create an OpenID Connect Application

Okta is a SaaS (software-as-service) authentication and authorization provider. We provide free accounts to developers so they can develop OIDC apps with no fuss. Head over to developer.okta.com and sign upfor an account. After you’ve verified your email, log in and perform the following steps:

  • Go to Application > Add Application.
  • Select application type Web and click Next.
  • Give the app a name. I named mine “Spring Boot OAuth”.
  • Under Login redirect URIs change the value to <a href="http://localhost:8080/login/oauth2/code/okta" target="_blank">http://localhost:8080/login/oauth2/code/okta</a>. The rest of the default values will work.
  • Click Done.

Leave the page open of take note of the Client ID and Client Secret. You’ll need them in a moment.

Create a New Spring Boot App

Back to the Spring Initializr one more time. Create a new project with the following settings:

  • Change project type from Maven to Gradle.
  • Change the Group to com.okta.spring.
  • Change the Artifact to OktaOAuthClient.
  • Add three dependencies: Web, Thymeleaf, Okta.
  • Click Generate Project.

Copy the project and unpack it somewhere.

In the build.gradle file, add the following dependency:

implementation 'org.thymeleaf.extras:thymeleaf-extras-springsecurity5:3.0.4.RELEASE'

Also while you’re there, notice the dependency com.okta.spring:okta-spring-boot-starter:1.1.0. This is the Okta Spring Boot Starter. It’s a handy project that makes integrating Okta with Spring Boot nice and easy. For more info, take a look at the project’s GitHub.

Change the src/main/resources/application.properties to application.yml and add the following:

server:
  port: 8080
okta:
  oauth2:
    issuer: https://okta.okta.com/oauth2/default
    client-id: {yourClientId}
    client-secret: {yourClientSecret}
spring:
  thymeleaf:
    cache: false

Remember when I said you’ll need your ClientID and Client Secret above. Well, the time has come. You need to fill them into the file, as well as your Okta issuer URL. It’s gonna look something like this: dev-123456.okta.com. You can find it under API > Authorization Servers.

You also need two similar template files in the src/main/resources/templates directory. The index.htmltemplate file is exactly the same, and can be copied over if you like. The securedPage.html template file is slightly different because of the way the authentication information is returned from Okta as compared to the simple authentication server you built earlier.

Create the home template: src/main/resources/templates/index.html:

<!DOCTYPE html>  
<html lang="en">  
<head>  
    <meta charset="UTF-8">  
    <title>Home</title>  
</head>  
<body>  
    <h1>Spring Security SSO</h1>  
    <a href="securedPage">Login</a>  
</body>  
</html>

And the secured template: src/main/resources/templates/securedPage.html:

<!DOCTYPE html>  
<html xmlns:th="http://www.thymeleaf.org">  
<head>  
    <meta charset="UTF-8">  
    <title>Secured Page</title>  
</head>  
<body>  
    <h1>Secured Page</h1>  
    <span th:text="${#authentication.principal.attributes.name}">Joe Coder</span>  
</body>  
</html>

Create a Java class named WebController in the com.okta.spring.SpringBootOAuth package:

package com.okta.spring.OktaOAuthClient;
import org.springframework.stereotype.Controller;
import org.springframework.ui.Model;
import org.springframework.web.bind.annotation.RequestMapping;
import java.security.Principal;
@Controller
public class WebController {
    @RequestMapping("/securedPage")
    public String securedPage(Model model, Principal principal) {
        return "securedPage";
    }
    @RequestMapping("/")
    public String index(Model model, Principal principal) {
        return "index";
    }
}

This class simply creates two routes, one for the home route and one for the secured route. Again, Spring Boot and Thymeleaf are auto-magicking this to the two template files in src/main/resources/templates.

Finally, create another Java class names SecurityConfiguration:

package com.okta.spring.OktaOAuthClient;
import org.springframework.context.annotation.Configuration;
import org.springframework.security.config.annotation.web.builders.HttpSecurity;
import org.springframework.security.config.annotation.web.configuration.WebSecurityConfigurerAdapter;
@Configuration
public class SecurityConfiguration extends WebSecurityConfigurerAdapter {
    @Override
    public void configure(HttpSecurity http) throws Exception {
        http.antMatcher("/**").authorizeRequests()
            .antMatchers("/").permitAll()
            .anyRequest().authenticated()
            .and()
            .oauth2Login();
    }
}

That’s it! Bam!

Run the Okta-OAuth-powered client:

./gradlew bootRun

You should see a bunch of output that ends with:

...
2019-02-23 20:09:03.465  INFO 55890 --- [           main] o.s.b.w.embedded.tomcat.TomcatWebServer  : Tomcat started on port(s): 8080 (http) with context path ''
2019-02-23 20:09:03.470  INFO 55890 --- [           main] c.o.s.O.OktaOAuthClientApplication       : Started OktaOAuthClientApplication in 3.285 seconds (JVM running for 3.744)



Navigate to http://localhost:8080.

Click the Login button.

This time, you’ll be directed to the Okta login page. You may need to use an incognito browser or log out of your developer.okta.com dashboard here so that you don’t skip the login page and get directed immediately to the secured endpoint.

Log in, and you’ll see the secured page with your name!

Learn More About Spring Boot, Spring Security, and OAuth 2.0

So, that’s that. Super easy. In the previous tutorial, you looked at how to use Spring Boot and Spring Security to implement a very basic authentication server and client app. Next, you used Okta to make an even simpler client app with fully functioning SSO and OAuth authentication.

You can see the completed code for this tutorial on GitHub at oktadeveloper/okta-spring-boot-authz-server-example.

#spring-boot #security

What is GEEK

Buddha Community

Build an OAuth 2.0 Authorization Server With Spring Boot and Spring Security
Veronica  Roob

Veronica Roob

1653475560

A Pure PHP Implementation Of The MessagePack Serialization Format

msgpack.php

A pure PHP implementation of the MessagePack serialization format.

Features

Installation

The recommended way to install the library is through Composer:

composer require rybakit/msgpack

Usage

Packing

To pack values you can either use an instance of a Packer:

$packer = new Packer();
$packed = $packer->pack($value);

or call a static method on the MessagePack class:

$packed = MessagePack::pack($value);

In the examples above, the method pack automatically packs a value depending on its type. However, not all PHP types can be uniquely translated to MessagePack types. For example, the MessagePack format defines map and array types, which are represented by a single array type in PHP. By default, the packer will pack a PHP array as a MessagePack array if it has sequential numeric keys, starting from 0 and as a MessagePack map otherwise:

$mpArr1 = $packer->pack([1, 2]);               // MP array [1, 2]
$mpArr2 = $packer->pack([0 => 1, 1 => 2]);     // MP array [1, 2]
$mpMap1 = $packer->pack([0 => 1, 2 => 3]);     // MP map {0: 1, 2: 3}
$mpMap2 = $packer->pack([1 => 2, 2 => 3]);     // MP map {1: 2, 2: 3}
$mpMap3 = $packer->pack(['a' => 1, 'b' => 2]); // MP map {a: 1, b: 2}

However, sometimes you need to pack a sequential array as a MessagePack map. To do this, use the packMap method:

$mpMap = $packer->packMap([1, 2]); // {0: 1, 1: 2}

Here is a list of type-specific packing methods:

$packer->packNil();           // MP nil
$packer->packBool(true);      // MP bool
$packer->packInt(42);         // MP int
$packer->packFloat(M_PI);     // MP float (32 or 64)
$packer->packFloat32(M_PI);   // MP float 32
$packer->packFloat64(M_PI);   // MP float 64
$packer->packStr('foo');      // MP str
$packer->packBin("\x80");     // MP bin
$packer->packArray([1, 2]);   // MP array
$packer->packMap(['a' => 1]); // MP map
$packer->packExt(1, "\xaa");  // MP ext

Check the "Custom types" section below on how to pack custom types.

Packing options

The Packer object supports a number of bitmask-based options for fine-tuning the packing process (defaults are in bold):

NameDescription
FORCE_STRForces PHP strings to be packed as MessagePack UTF-8 strings
FORCE_BINForces PHP strings to be packed as MessagePack binary data
DETECT_STR_BINDetects MessagePack str/bin type automatically
  
FORCE_ARRForces PHP arrays to be packed as MessagePack arrays
FORCE_MAPForces PHP arrays to be packed as MessagePack maps
DETECT_ARR_MAPDetects MessagePack array/map type automatically
  
FORCE_FLOAT32Forces PHP floats to be packed as 32-bits MessagePack floats
FORCE_FLOAT64Forces PHP floats to be packed as 64-bits MessagePack floats

The type detection mode (DETECT_STR_BIN/DETECT_ARR_MAP) adds some overhead which can be noticed when you pack large (16- and 32-bit) arrays or strings. However, if you know the value type in advance (for example, you only work with UTF-8 strings or/and associative arrays), you can eliminate this overhead by forcing the packer to use the appropriate type, which will save it from running the auto-detection routine. Another option is to explicitly specify the value type. The library provides 2 auxiliary classes for this, Map and Bin. Check the "Custom types" section below for details.

Examples:

// detect str/bin type and pack PHP 64-bit floats (doubles) to MP 32-bit floats
$packer = new Packer(PackOptions::DETECT_STR_BIN | PackOptions::FORCE_FLOAT32);

// these will throw MessagePack\Exception\InvalidOptionException
$packer = new Packer(PackOptions::FORCE_STR | PackOptions::FORCE_BIN);
$packer = new Packer(PackOptions::FORCE_FLOAT32 | PackOptions::FORCE_FLOAT64);

Unpacking

To unpack data you can either use an instance of a BufferUnpacker:

$unpacker = new BufferUnpacker();

$unpacker->reset($packed);
$value = $unpacker->unpack();

or call a static method on the MessagePack class:

$value = MessagePack::unpack($packed);

If the packed data is received in chunks (e.g. when reading from a stream), use the tryUnpack method, which attempts to unpack data and returns an array of unpacked messages (if any) instead of throwing an InsufficientDataException:

while ($chunk = ...) {
    $unpacker->append($chunk);
    if ($messages = $unpacker->tryUnpack()) {
        return $messages;
    }
}

If you want to unpack from a specific position in a buffer, use seek:

$unpacker->seek(42); // set position equal to 42 bytes
$unpacker->seek(-8); // set position to 8 bytes before the end of the buffer

To skip bytes from the current position, use skip:

$unpacker->skip(10); // set position to 10 bytes ahead of the current position

To get the number of remaining (unread) bytes in the buffer:

$unreadBytesCount = $unpacker->getRemainingCount();

To check whether the buffer has unread data:

$hasUnreadBytes = $unpacker->hasRemaining();

If needed, you can remove already read data from the buffer by calling:

$releasedBytesCount = $unpacker->release();

With the read method you can read raw (packed) data:

$packedData = $unpacker->read(2); // read 2 bytes

Besides the above methods BufferUnpacker provides type-specific unpacking methods, namely:

$unpacker->unpackNil();   // PHP null
$unpacker->unpackBool();  // PHP bool
$unpacker->unpackInt();   // PHP int
$unpacker->unpackFloat(); // PHP float
$unpacker->unpackStr();   // PHP UTF-8 string
$unpacker->unpackBin();   // PHP binary string
$unpacker->unpackArray(); // PHP sequential array
$unpacker->unpackMap();   // PHP associative array
$unpacker->unpackExt();   // PHP MessagePack\Type\Ext object

Unpacking options

The BufferUnpacker object supports a number of bitmask-based options for fine-tuning the unpacking process (defaults are in bold):

NameDescription
BIGINT_AS_STRConverts overflowed integers to strings [1]
BIGINT_AS_GMPConverts overflowed integers to GMP objects [2]
BIGINT_AS_DECConverts overflowed integers to Decimal\Decimal objects [3]

1. The binary MessagePack format has unsigned 64-bit as its largest integer data type, but PHP does not support such integers, which means that an overflow can occur during unpacking.

2. Make sure the GMP extension is enabled.

3. Make sure the Decimal extension is enabled.

Examples:

$packedUint64 = "\xcf"."\xff\xff\xff\xff"."\xff\xff\xff\xff";

$unpacker = new BufferUnpacker($packedUint64);
var_dump($unpacker->unpack()); // string(20) "18446744073709551615"

$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_GMP);
var_dump($unpacker->unpack()); // object(GMP) {...}

$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_DEC);
var_dump($unpacker->unpack()); // object(Decimal\Decimal) {...}

Custom types

In addition to the basic types, the library provides functionality to serialize and deserialize arbitrary types. This can be done in several ways, depending on your use case. Let's take a look at them.

Type objects

If you need to serialize an instance of one of your classes into one of the basic MessagePack types, the best way to do this is to implement the CanBePacked interface in the class. A good example of such a class is the Map type class that comes with the library. This type is useful when you want to explicitly specify that a given PHP array should be packed as a MessagePack map without triggering an automatic type detection routine:

$packer = new Packer();

$packedMap = $packer->pack(new Map([1, 2, 3]));
$packedArray = $packer->pack([1, 2, 3]);

More type examples can be found in the src/Type directory.

Type transformers

As with type objects, type transformers are only responsible for serializing values. They should be used when you need to serialize a value that does not implement the CanBePacked interface. Examples of such values could be instances of built-in or third-party classes that you don't own, or non-objects such as resources.

A transformer class must implement the CanPack interface. To use a transformer, it must first be registered in the packer. Here is an example of how to serialize PHP streams into the MessagePack bin format type using one of the supplied transformers, StreamTransformer:

$packer = new Packer(null, [new StreamTransformer()]);

$packedBin = $packer->pack(fopen('/path/to/file', 'r+'));

More type transformer examples can be found in the src/TypeTransformer directory.

Extensions

In contrast to the cases described above, extensions are intended to handle extension types and are responsible for both serialization and deserialization of values (types).

An extension class must implement the Extension interface. To use an extension, it must first be registered in the packer and the unpacker.

The MessagePack specification divides extension types into two groups: predefined and application-specific. Currently, there is only one predefined type in the specification, Timestamp.

Timestamp

The Timestamp extension type is a predefined type. Support for this type in the library is done through the TimestampExtension class. This class is responsible for handling Timestamp objects, which represent the number of seconds and optional adjustment in nanoseconds:

$timestampExtension = new TimestampExtension();

$packer = new Packer();
$packer = $packer->extendWith($timestampExtension);

$unpacker = new BufferUnpacker();
$unpacker = $unpacker->extendWith($timestampExtension);

$packedTimestamp = $packer->pack(Timestamp::now());
$timestamp = $unpacker->reset($packedTimestamp)->unpack();

$seconds = $timestamp->getSeconds();
$nanoseconds = $timestamp->getNanoseconds();

When using the MessagePack class, the Timestamp extension is already registered:

$packedTimestamp = MessagePack::pack(Timestamp::now());
$timestamp = MessagePack::unpack($packedTimestamp);

Application-specific extensions

In addition, the format can be extended with your own types. For example, to make the built-in PHP DateTime objects first-class citizens in your code, you can create a corresponding extension, as shown in the example. Please note, that custom extensions have to be registered with a unique extension ID (an integer from 0 to 127).

More extension examples can be found in the examples/MessagePack directory.

To learn more about how extension types can be useful, check out this article.

Exceptions

If an error occurs during packing/unpacking, a PackingFailedException or an UnpackingFailedException will be thrown, respectively. In addition, an InsufficientDataException can be thrown during unpacking.

An InvalidOptionException will be thrown in case an invalid option (or a combination of mutually exclusive options) is used.

Tests

Run tests as follows:

vendor/bin/phpunit

Also, if you already have Docker installed, you can run the tests in a docker container. First, create a container:

./dockerfile.sh | docker build -t msgpack -

The command above will create a container named msgpack with PHP 8.1 runtime. You may change the default runtime by defining the PHP_IMAGE environment variable:

PHP_IMAGE='php:8.0-cli' ./dockerfile.sh | docker build -t msgpack -

See a list of various images here.

Then run the unit tests:

docker run --rm -v $PWD:/msgpack -w /msgpack msgpack

Fuzzing

To ensure that the unpacking works correctly with malformed/semi-malformed data, you can use a testing technique called Fuzzing. The library ships with a help file (target) for PHP-Fuzzer and can be used as follows:

php-fuzzer fuzz tests/fuzz_buffer_unpacker.php

Performance

To check performance, run:

php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

=============================================
Test/Target            Packer  BufferUnpacker
---------------------------------------------
nil .................. 0.0030 ........ 0.0139
false ................ 0.0037 ........ 0.0144
true ................. 0.0040 ........ 0.0137
7-bit uint #1 ........ 0.0052 ........ 0.0120
7-bit uint #2 ........ 0.0059 ........ 0.0114
7-bit uint #3 ........ 0.0061 ........ 0.0119
5-bit sint #1 ........ 0.0067 ........ 0.0126
5-bit sint #2 ........ 0.0064 ........ 0.0132
5-bit sint #3 ........ 0.0066 ........ 0.0135
8-bit uint #1 ........ 0.0078 ........ 0.0200
8-bit uint #2 ........ 0.0077 ........ 0.0212
8-bit uint #3 ........ 0.0086 ........ 0.0203
16-bit uint #1 ....... 0.0111 ........ 0.0271
16-bit uint #2 ....... 0.0115 ........ 0.0260
16-bit uint #3 ....... 0.0103 ........ 0.0273
32-bit uint #1 ....... 0.0116 ........ 0.0326
32-bit uint #2 ....... 0.0118 ........ 0.0332
32-bit uint #3 ....... 0.0127 ........ 0.0325
64-bit uint #1 ....... 0.0140 ........ 0.0277
64-bit uint #2 ....... 0.0134 ........ 0.0294
64-bit uint #3 ....... 0.0134 ........ 0.0281
8-bit int #1 ......... 0.0086 ........ 0.0241
8-bit int #2 ......... 0.0089 ........ 0.0225
8-bit int #3 ......... 0.0085 ........ 0.0229
16-bit int #1 ........ 0.0118 ........ 0.0280
16-bit int #2 ........ 0.0121 ........ 0.0270
16-bit int #3 ........ 0.0109 ........ 0.0274
32-bit int #1 ........ 0.0128 ........ 0.0346
32-bit int #2 ........ 0.0118 ........ 0.0339
32-bit int #3 ........ 0.0135 ........ 0.0368
64-bit int #1 ........ 0.0138 ........ 0.0276
64-bit int #2 ........ 0.0132 ........ 0.0286
64-bit int #3 ........ 0.0137 ........ 0.0274
64-bit int #4 ........ 0.0180 ........ 0.0285
64-bit float #1 ...... 0.0134 ........ 0.0284
64-bit float #2 ...... 0.0125 ........ 0.0275
64-bit float #3 ...... 0.0126 ........ 0.0283
fix string #1 ........ 0.0035 ........ 0.0133
fix string #2 ........ 0.0094 ........ 0.0216
fix string #3 ........ 0.0094 ........ 0.0222
fix string #4 ........ 0.0091 ........ 0.0241
8-bit string #1 ...... 0.0122 ........ 0.0301
8-bit string #2 ...... 0.0118 ........ 0.0304
8-bit string #3 ...... 0.0119 ........ 0.0315
16-bit string #1 ..... 0.0150 ........ 0.0388
16-bit string #2 ..... 0.1545 ........ 0.1665
32-bit string ........ 0.1570 ........ 0.1756
wide char string #1 .. 0.0091 ........ 0.0236
wide char string #2 .. 0.0122 ........ 0.0313
8-bit binary #1 ...... 0.0100 ........ 0.0302
8-bit binary #2 ...... 0.0123 ........ 0.0324
8-bit binary #3 ...... 0.0126 ........ 0.0327
16-bit binary ........ 0.0168 ........ 0.0372
32-bit binary ........ 0.1588 ........ 0.1754
fix array #1 ......... 0.0042 ........ 0.0131
fix array #2 ......... 0.0294 ........ 0.0367
fix array #3 ......... 0.0412 ........ 0.0472
16-bit array #1 ...... 0.1378 ........ 0.1596
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.1865 ........ 0.2283
fix map #1 ........... 0.0725 ........ 0.1048
fix map #2 ........... 0.0319 ........ 0.0405
fix map #3 ........... 0.0356 ........ 0.0665
fix map #4 ........... 0.0465 ........ 0.0497
16-bit map #1 ........ 0.2540 ........ 0.3028
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.2372 ........ 0.2710
fixext 1 ............. 0.0283 ........ 0.0358
fixext 2 ............. 0.0291 ........ 0.0371
fixext 4 ............. 0.0302 ........ 0.0355
fixext 8 ............. 0.0288 ........ 0.0384
fixext 16 ............ 0.0293 ........ 0.0359
8-bit ext ............ 0.0302 ........ 0.0439
16-bit ext ........... 0.0334 ........ 0.0499
32-bit ext ........... 0.1845 ........ 0.1888
32-bit timestamp #1 .. 0.0337 ........ 0.0547
32-bit timestamp #2 .. 0.0335 ........ 0.0560
64-bit timestamp #1 .. 0.0371 ........ 0.0575
64-bit timestamp #2 .. 0.0374 ........ 0.0542
64-bit timestamp #3 .. 0.0356 ........ 0.0533
96-bit timestamp #1 .. 0.0362 ........ 0.0699
96-bit timestamp #2 .. 0.0381 ........ 0.0701
96-bit timestamp #3 .. 0.0367 ........ 0.0687
=============================================
Total                  2.7618          4.0820
Skipped                     4               4
Failed                      0               0
Ignored                     0               0

With JIT:

php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

=============================================
Test/Target            Packer  BufferUnpacker
---------------------------------------------
nil .................. 0.0005 ........ 0.0054
false ................ 0.0004 ........ 0.0059
true ................. 0.0004 ........ 0.0059
7-bit uint #1 ........ 0.0010 ........ 0.0047
7-bit uint #2 ........ 0.0010 ........ 0.0046
7-bit uint #3 ........ 0.0010 ........ 0.0046
5-bit sint #1 ........ 0.0025 ........ 0.0046
5-bit sint #2 ........ 0.0023 ........ 0.0046
5-bit sint #3 ........ 0.0024 ........ 0.0045
8-bit uint #1 ........ 0.0043 ........ 0.0081
8-bit uint #2 ........ 0.0043 ........ 0.0079
8-bit uint #3 ........ 0.0041 ........ 0.0080
16-bit uint #1 ....... 0.0064 ........ 0.0095
16-bit uint #2 ....... 0.0064 ........ 0.0091
16-bit uint #3 ....... 0.0064 ........ 0.0094
32-bit uint #1 ....... 0.0085 ........ 0.0114
32-bit uint #2 ....... 0.0077 ........ 0.0122
32-bit uint #3 ....... 0.0077 ........ 0.0120
64-bit uint #1 ....... 0.0085 ........ 0.0159
64-bit uint #2 ....... 0.0086 ........ 0.0157
64-bit uint #3 ....... 0.0086 ........ 0.0158
8-bit int #1 ......... 0.0042 ........ 0.0080
8-bit int #2 ......... 0.0042 ........ 0.0080
8-bit int #3 ......... 0.0042 ........ 0.0081
16-bit int #1 ........ 0.0065 ........ 0.0095
16-bit int #2 ........ 0.0065 ........ 0.0090
16-bit int #3 ........ 0.0056 ........ 0.0085
32-bit int #1 ........ 0.0067 ........ 0.0107
32-bit int #2 ........ 0.0066 ........ 0.0106
32-bit int #3 ........ 0.0063 ........ 0.0104
64-bit int #1 ........ 0.0072 ........ 0.0162
64-bit int #2 ........ 0.0073 ........ 0.0174
64-bit int #3 ........ 0.0072 ........ 0.0164
64-bit int #4 ........ 0.0077 ........ 0.0161
64-bit float #1 ...... 0.0053 ........ 0.0135
64-bit float #2 ...... 0.0053 ........ 0.0135
64-bit float #3 ...... 0.0052 ........ 0.0135
fix string #1 ....... -0.0002 ........ 0.0044
fix string #2 ........ 0.0035 ........ 0.0067
fix string #3 ........ 0.0035 ........ 0.0077
fix string #4 ........ 0.0033 ........ 0.0078
8-bit string #1 ...... 0.0059 ........ 0.0110
8-bit string #2 ...... 0.0063 ........ 0.0121
8-bit string #3 ...... 0.0064 ........ 0.0124
16-bit string #1 ..... 0.0099 ........ 0.0146
16-bit string #2 ..... 0.1522 ........ 0.1474
32-bit string ........ 0.1511 ........ 0.1483
wide char string #1 .. 0.0039 ........ 0.0084
wide char string #2 .. 0.0073 ........ 0.0123
8-bit binary #1 ...... 0.0040 ........ 0.0112
8-bit binary #2 ...... 0.0075 ........ 0.0123
8-bit binary #3 ...... 0.0077 ........ 0.0129
16-bit binary ........ 0.0096 ........ 0.0145
32-bit binary ........ 0.1535 ........ 0.1479
fix array #1 ......... 0.0008 ........ 0.0061
fix array #2 ......... 0.0121 ........ 0.0165
fix array #3 ......... 0.0193 ........ 0.0222
16-bit array #1 ...... 0.0607 ........ 0.0479
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.0749 ........ 0.0824
fix map #1 ........... 0.0329 ........ 0.0431
fix map #2 ........... 0.0161 ........ 0.0189
fix map #3 ........... 0.0205 ........ 0.0262
fix map #4 ........... 0.0252 ........ 0.0205
16-bit map #1 ........ 0.1016 ........ 0.0927
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.1096 ........ 0.1030
fixext 1 ............. 0.0157 ........ 0.0161
fixext 2 ............. 0.0175 ........ 0.0183
fixext 4 ............. 0.0156 ........ 0.0185
fixext 8 ............. 0.0163 ........ 0.0184
fixext 16 ............ 0.0164 ........ 0.0182
8-bit ext ............ 0.0158 ........ 0.0207
16-bit ext ........... 0.0203 ........ 0.0219
32-bit ext ........... 0.1614 ........ 0.1539
32-bit timestamp #1 .. 0.0195 ........ 0.0249
32-bit timestamp #2 .. 0.0188 ........ 0.0260
64-bit timestamp #1 .. 0.0207 ........ 0.0281
64-bit timestamp #2 .. 0.0212 ........ 0.0291
64-bit timestamp #3 .. 0.0207 ........ 0.0295
96-bit timestamp #1 .. 0.0222 ........ 0.0358
96-bit timestamp #2 .. 0.0228 ........ 0.0353
96-bit timestamp #3 .. 0.0210 ........ 0.0319
=============================================
Total                  1.6432          1.9674
Skipped                     4               4
Failed                      0               0
Ignored                     0               0

You may change default benchmark settings by defining the following environment variables:

NameDefault
MP_BENCH_TARGETSpure_p,pure_u, see a list of available targets
MP_BENCH_ITERATIONS100_000
MP_BENCH_DURATIONnot set
MP_BENCH_ROUNDS3
MP_BENCH_TESTS-@slow, see a list of available tests

For example:

export MP_BENCH_TARGETS=pure_p
export MP_BENCH_ITERATIONS=1000000
export MP_BENCH_ROUNDS=5
# a comma separated list of test names
export MP_BENCH_TESTS='complex array, complex map'
# or a group name
# export MP_BENCH_TESTS='-@slow' // @pecl_comp
# or a regexp
# export MP_BENCH_TESTS='/complex (array|map)/'

Another example, benchmarking both the library and the PECL extension:

MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

===========================================================================
Test/Target            Packer  BufferUnpacker  msgpack_pack  msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0031 ........ 0.0141 ...... 0.0055 ........ 0.0064
false ................ 0.0039 ........ 0.0154 ...... 0.0056 ........ 0.0053
true ................. 0.0038 ........ 0.0139 ...... 0.0056 ........ 0.0044
7-bit uint #1 ........ 0.0061 ........ 0.0110 ...... 0.0059 ........ 0.0046
7-bit uint #2 ........ 0.0065 ........ 0.0119 ...... 0.0042 ........ 0.0029
7-bit uint #3 ........ 0.0054 ........ 0.0117 ...... 0.0045 ........ 0.0025
5-bit sint #1 ........ 0.0047 ........ 0.0103 ...... 0.0038 ........ 0.0022
5-bit sint #2 ........ 0.0048 ........ 0.0117 ...... 0.0038 ........ 0.0022
5-bit sint #3 ........ 0.0046 ........ 0.0102 ...... 0.0038 ........ 0.0023
8-bit uint #1 ........ 0.0063 ........ 0.0174 ...... 0.0039 ........ 0.0031
8-bit uint #2 ........ 0.0063 ........ 0.0167 ...... 0.0040 ........ 0.0029
8-bit uint #3 ........ 0.0063 ........ 0.0168 ...... 0.0039 ........ 0.0030
16-bit uint #1 ....... 0.0092 ........ 0.0222 ...... 0.0049 ........ 0.0030
16-bit uint #2 ....... 0.0096 ........ 0.0227 ...... 0.0042 ........ 0.0046
16-bit uint #3 ....... 0.0123 ........ 0.0274 ...... 0.0059 ........ 0.0051
32-bit uint #1 ....... 0.0136 ........ 0.0331 ...... 0.0060 ........ 0.0048
32-bit uint #2 ....... 0.0130 ........ 0.0336 ...... 0.0070 ........ 0.0048
32-bit uint #3 ....... 0.0127 ........ 0.0329 ...... 0.0051 ........ 0.0048
64-bit uint #1 ....... 0.0126 ........ 0.0268 ...... 0.0055 ........ 0.0049
64-bit uint #2 ....... 0.0135 ........ 0.0281 ...... 0.0052 ........ 0.0046
64-bit uint #3 ....... 0.0131 ........ 0.0274 ...... 0.0069 ........ 0.0044
8-bit int #1 ......... 0.0077 ........ 0.0236 ...... 0.0058 ........ 0.0044
8-bit int #2 ......... 0.0087 ........ 0.0244 ...... 0.0058 ........ 0.0048
8-bit int #3 ......... 0.0084 ........ 0.0241 ...... 0.0055 ........ 0.0049
16-bit int #1 ........ 0.0112 ........ 0.0271 ...... 0.0048 ........ 0.0045
16-bit int #2 ........ 0.0124 ........ 0.0292 ...... 0.0057 ........ 0.0049
16-bit int #3 ........ 0.0118 ........ 0.0270 ...... 0.0058 ........ 0.0050
32-bit int #1 ........ 0.0137 ........ 0.0366 ...... 0.0058 ........ 0.0051
32-bit int #2 ........ 0.0133 ........ 0.0366 ...... 0.0056 ........ 0.0049
32-bit int #3 ........ 0.0129 ........ 0.0350 ...... 0.0052 ........ 0.0048
64-bit int #1 ........ 0.0145 ........ 0.0254 ...... 0.0034 ........ 0.0025
64-bit int #2 ........ 0.0097 ........ 0.0214 ...... 0.0034 ........ 0.0025
64-bit int #3 ........ 0.0096 ........ 0.0287 ...... 0.0059 ........ 0.0050
64-bit int #4 ........ 0.0143 ........ 0.0277 ...... 0.0059 ........ 0.0046
64-bit float #1 ...... 0.0134 ........ 0.0281 ...... 0.0057 ........ 0.0052
64-bit float #2 ...... 0.0141 ........ 0.0281 ...... 0.0057 ........ 0.0050
64-bit float #3 ...... 0.0144 ........ 0.0282 ...... 0.0057 ........ 0.0050
fix string #1 ........ 0.0036 ........ 0.0143 ...... 0.0066 ........ 0.0053
fix string #2 ........ 0.0107 ........ 0.0222 ...... 0.0065 ........ 0.0068
fix string #3 ........ 0.0116 ........ 0.0245 ...... 0.0063 ........ 0.0069
fix string #4 ........ 0.0105 ........ 0.0253 ...... 0.0083 ........ 0.0077
8-bit string #1 ...... 0.0126 ........ 0.0318 ...... 0.0075 ........ 0.0088
8-bit string #2 ...... 0.0121 ........ 0.0295 ...... 0.0076 ........ 0.0086
8-bit string #3 ...... 0.0125 ........ 0.0293 ...... 0.0130 ........ 0.0093
16-bit string #1 ..... 0.0159 ........ 0.0368 ...... 0.0117 ........ 0.0086
16-bit string #2 ..... 0.1547 ........ 0.1686 ...... 0.1516 ........ 0.1373
32-bit string ........ 0.1558 ........ 0.1729 ...... 0.1511 ........ 0.1396
wide char string #1 .. 0.0098 ........ 0.0237 ...... 0.0066 ........ 0.0065
wide char string #2 .. 0.0128 ........ 0.0291 ...... 0.0061 ........ 0.0082
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0040 ........ 0.0129 ...... 0.0120 ........ 0.0058
fix array #2 ......... 0.0279 ........ 0.0390 ...... 0.0143 ........ 0.0165
fix array #3 ......... 0.0415 ........ 0.0463 ...... 0.0162 ........ 0.0187
16-bit array #1 ...... 0.1349 ........ 0.1628 ...... 0.0334 ........ 0.0341
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0345 ........ 0.0391 ...... 0.0143 ........ 0.0168
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0459 ........ 0.0473 ...... 0.0151 ........ 0.0163
16-bit map #1 ........ 0.2518 ........ 0.2962 ...... 0.0400 ........ 0.0490
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.2380 ........ 0.2682 ...... 0.0545 ........ 0.0579
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total                  1.5625          2.3866        0.7735          0.7243
Skipped                     4               4             4               4
Failed                      0               0            24              17
Ignored                    24              24             0               7

With JIT:

MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

===========================================================================
Test/Target            Packer  BufferUnpacker  msgpack_pack  msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0001 ........ 0.0052 ...... 0.0053 ........ 0.0042
false ................ 0.0007 ........ 0.0060 ...... 0.0057 ........ 0.0043
true ................. 0.0008 ........ 0.0060 ...... 0.0056 ........ 0.0041
7-bit uint #1 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0041
7-bit uint #2 ........ 0.0021 ........ 0.0043 ...... 0.0062 ........ 0.0041
7-bit uint #3 ........ 0.0022 ........ 0.0044 ...... 0.0061 ........ 0.0040
5-bit sint #1 ........ 0.0030 ........ 0.0048 ...... 0.0062 ........ 0.0040
5-bit sint #2 ........ 0.0032 ........ 0.0046 ...... 0.0062 ........ 0.0040
5-bit sint #3 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0040
8-bit uint #1 ........ 0.0054 ........ 0.0079 ...... 0.0062 ........ 0.0050
8-bit uint #2 ........ 0.0051 ........ 0.0079 ...... 0.0064 ........ 0.0044
8-bit uint #3 ........ 0.0051 ........ 0.0082 ...... 0.0062 ........ 0.0044
16-bit uint #1 ....... 0.0077 ........ 0.0094 ...... 0.0065 ........ 0.0045
16-bit uint #2 ....... 0.0077 ........ 0.0094 ...... 0.0063 ........ 0.0045
16-bit uint #3 ....... 0.0077 ........ 0.0095 ...... 0.0064 ........ 0.0047
32-bit uint #1 ....... 0.0088 ........ 0.0119 ...... 0.0063 ........ 0.0043
32-bit uint #2 ....... 0.0089 ........ 0.0117 ...... 0.0062 ........ 0.0039
32-bit uint #3 ....... 0.0089 ........ 0.0118 ...... 0.0063 ........ 0.0044
64-bit uint #1 ....... 0.0097 ........ 0.0155 ...... 0.0063 ........ 0.0045
64-bit uint #2 ....... 0.0095 ........ 0.0153 ...... 0.0061 ........ 0.0045
64-bit uint #3 ....... 0.0096 ........ 0.0156 ...... 0.0063 ........ 0.0047
8-bit int #1 ......... 0.0053 ........ 0.0083 ...... 0.0062 ........ 0.0044
8-bit int #2 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0044
8-bit int #3 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0043
16-bit int #1 ........ 0.0089 ........ 0.0097 ...... 0.0069 ........ 0.0046
16-bit int #2 ........ 0.0075 ........ 0.0093 ...... 0.0063 ........ 0.0043
16-bit int #3 ........ 0.0075 ........ 0.0094 ...... 0.0062 ........ 0.0046
32-bit int #1 ........ 0.0086 ........ 0.0122 ...... 0.0063 ........ 0.0044
32-bit int #2 ........ 0.0087 ........ 0.0120 ...... 0.0066 ........ 0.0046
32-bit int #3 ........ 0.0086 ........ 0.0121 ...... 0.0060 ........ 0.0044
64-bit int #1 ........ 0.0096 ........ 0.0149 ...... 0.0060 ........ 0.0045
64-bit int #2 ........ 0.0096 ........ 0.0157 ...... 0.0062 ........ 0.0044
64-bit int #3 ........ 0.0096 ........ 0.0160 ...... 0.0063 ........ 0.0046
64-bit int #4 ........ 0.0097 ........ 0.0157 ...... 0.0061 ........ 0.0044
64-bit float #1 ...... 0.0079 ........ 0.0153 ...... 0.0056 ........ 0.0044
64-bit float #2 ...... 0.0079 ........ 0.0152 ...... 0.0057 ........ 0.0045
64-bit float #3 ...... 0.0079 ........ 0.0155 ...... 0.0057 ........ 0.0044
fix string #1 ........ 0.0010 ........ 0.0045 ...... 0.0071 ........ 0.0044
fix string #2 ........ 0.0048 ........ 0.0075 ...... 0.0070 ........ 0.0060
fix string #3 ........ 0.0048 ........ 0.0086 ...... 0.0068 ........ 0.0060
fix string #4 ........ 0.0050 ........ 0.0088 ...... 0.0070 ........ 0.0059
8-bit string #1 ...... 0.0081 ........ 0.0129 ...... 0.0069 ........ 0.0062
8-bit string #2 ...... 0.0086 ........ 0.0128 ...... 0.0069 ........ 0.0065
8-bit string #3 ...... 0.0086 ........ 0.0126 ...... 0.0115 ........ 0.0065
16-bit string #1 ..... 0.0105 ........ 0.0137 ...... 0.0128 ........ 0.0068
16-bit string #2 ..... 0.1510 ........ 0.1486 ...... 0.1526 ........ 0.1391
32-bit string ........ 0.1517 ........ 0.1475 ...... 0.1504 ........ 0.1370
wide char string #1 .. 0.0044 ........ 0.0085 ...... 0.0067 ........ 0.0057
wide char string #2 .. 0.0081 ........ 0.0125 ...... 0.0069 ........ 0.0063
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0014 ........ 0.0059 ...... 0.0132 ........ 0.0055
fix array #2 ......... 0.0146 ........ 0.0156 ...... 0.0155 ........ 0.0148
fix array #3 ......... 0.0211 ........ 0.0229 ...... 0.0179 ........ 0.0180
16-bit array #1 ...... 0.0673 ........ 0.0498 ...... 0.0343 ........ 0.0388
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0148 ........ 0.0180 ...... 0.0156 ........ 0.0179
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0252 ........ 0.0201 ...... 0.0214 ........ 0.0167
16-bit map #1 ........ 0.1027 ........ 0.0836 ...... 0.0388 ........ 0.0510
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.1104 ........ 0.1010 ...... 0.0556 ........ 0.0602
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total                  0.9642          1.0909        0.8224          0.7213
Skipped                     4               4             4               4
Failed                      0               0            24              17
Ignored                    24              24             0               7

Note that the msgpack extension (v2.1.2) doesn't support ext, bin and UTF-8 str types.

License

The library is released under the MIT License. See the bundled LICENSE file for details.

Author: rybakit
Source Code: https://github.com/rybakit/msgpack.php
License: MIT License

#php 

Franz  Becker

Franz Becker

1648803600

Plpgsql Check: Extension That Allows to Check Plpgsql Source Code.

plpgsql_check

I founded this project, because I wanted to publish the code I wrote in the last two years, when I tried to write enhanced checking for PostgreSQL upstream. It was not fully successful - integration into upstream requires some larger plpgsql refactoring - probably it will not be done in next years (now is Dec 2013). But written code is fully functional and can be used in production (and it is used in production). So, I created this extension to be available for all plpgsql developers.

If you like it and if you would to join to development of this extension, register yourself to postgresql extension hacking google group.

Features

  • check fields of referenced database objects and types inside embedded SQL
  • using correct types of function parameters
  • unused variables and function argumens, unmodified OUT argumens
  • partially detection of dead code (due RETURN command)
  • detection of missing RETURN command in function
  • try to identify unwanted hidden casts, that can be performance issue like unused indexes
  • possibility to collect relations and functions used by function
  • possibility to check EXECUTE stmt agaist SQL injection vulnerability

I invite any ideas, patches, bugreports.

plpgsql_check is next generation of plpgsql_lint. It allows to check source code by explicit call plpgsql_check_function.

PostgreSQL PostgreSQL 10, 11, 12, 13 and 14 are supported.

The SQL statements inside PL/pgSQL functions are checked by validator for semantic errors. These errors can be found by plpgsql_check_function:

Active mode

postgres=# CREATE EXTENSION plpgsql_check;
LOAD
postgres=# CREATE TABLE t1(a int, b int);
CREATE TABLE

postgres=#
CREATE OR REPLACE FUNCTION public.f1()
RETURNS void
LANGUAGE plpgsql
AS $function$
DECLARE r record;
BEGIN
  FOR r IN SELECT * FROM t1
  LOOP
    RAISE NOTICE '%', r.c; -- there is bug - table t1 missing "c" column
  END LOOP;
END;
$function$;

CREATE FUNCTION

postgres=# select f1(); -- execution doesn't find a bug due to empty table t1
  f1 
 ────
   
 (1 row)

postgres=# \x
Expanded display is on.
postgres=# select * from plpgsql_check_function_tb('f1()');
─[ RECORD 1 ]───────────────────────────
functionid │ f1
lineno     │ 6
statement  │ RAISE
sqlstate   │ 42703
message    │ record "r" has no field "c"
detail     │ [null]
hint       │ [null]
level      │ error
position   │ 0
query      │ [null]

postgres=# \sf+ f1
    CREATE OR REPLACE FUNCTION public.f1()
     RETURNS void
     LANGUAGE plpgsql
1       AS $function$
2       DECLARE r record;
3       BEGIN
4         FOR r IN SELECT * FROM t1
5         LOOP
6           RAISE NOTICE '%', r.c; -- there is bug - table t1 missing "c" column
7         END LOOP;
8       END;
9       $function$

Function plpgsql_check_function() has three possible formats: text, json or xml

select * from plpgsql_check_function('f1()', fatal_errors := false);
                         plpgsql_check_function                         
------------------------------------------------------------------------
 error:42703:4:SQL statement:column "c" of relation "t1" does not exist
 Query: update t1 set c = 30
 --                   ^
 error:42P01:7:RAISE:missing FROM-clause entry for table "r"
 Query: SELECT r.c
 --            ^
 error:42601:7:RAISE:too few parameters specified for RAISE
(7 rows)

postgres=# select * from plpgsql_check_function('fx()', format:='xml');
                 plpgsql_check_function                     
────────────────────────────────────────────────────────────────
 <Function oid="16400">                                        ↵
   <Issue>                                                     ↵
     <Level>error</level>                                      ↵
     <Sqlstate>42P01</Sqlstate>                                ↵
     <Message>relation "foo111" does not exist</Message>       ↵
     <Stmt lineno="3">RETURN</Stmt>                            ↵
     <Query position="23">SELECT (select a from foo111)</Query>↵
   </Issue>                                                    ↵
  </Function>
 (1 row)

Arguments

You can set level of warnings via function's parameters:

Mandatory arguments

  • function name or function signature - these functions requires function specification. Any function in PostgreSQL can be specified by Oid or by name or by signature. When you know oid or complete function's signature, you can use a regprocedure type parameter like 'fx()'::regprocedure or 16799::regprocedure. Possible alternative is using a name only, when function's name is unique - like 'fx'. When the name is not unique or the function doesn't exists it raises a error.

Optional arguments

relid DEFAULT 0 - oid of relation assigned with trigger function. It is necessary for check of any trigger function.

fatal_errors boolean DEFAULT true - stop on first error

other_warnings boolean DEFAULT true - show warnings like different attributes number in assignmenet on left and right side, variable overlaps function's parameter, unused variables, unwanted casting, ..

extra_warnings boolean DEFAULT true - show warnings like missing RETURN, shadowed variables, dead code, never read (unused) function's parameter, unmodified variables, modified auto variables, ..

performance_warnings boolean DEFAULT false - performance related warnings like declared type with type modificator, casting, implicit casts in where clause (can be reason why index is not used), ..

security_warnings boolean DEFAULT false - security related checks like SQL injection vulnerability detection

anyelementtype regtype DEFAULT 'int' - a real type used instead anyelement type

anyenumtype regtype DEFAULT '-' - a real type used instead anyenum type

anyrangetype regtype DEFAULT 'int4range' - a real type used instead anyrange type

anycompatibletype DEFAULT 'int' - a real type used instead anycompatible type

anycompatiblerangetype DEFAULT 'int4range' - a real type used instead anycompatible range type

without_warnings DEFAULT false - disable all warnings

all_warnings DEFAULT false - enable all warnings

newtable DEFAULT NULL, oldtable DEFAULT NULL - the names of NEW or OLD transitive tables. These parameters are required when transitive tables are used.

Triggers

When you want to check any trigger, you have to enter a relation that will be used together with trigger function

CREATE TABLE bar(a int, b int);

postgres=# \sf+ foo_trg
    CREATE OR REPLACE FUNCTION public.foo_trg()
         RETURNS trigger
         LANGUAGE plpgsql
1       AS $function$
2       BEGIN
3         NEW.c := NEW.a + NEW.b;
4         RETURN NEW;
5       END;
6       $function$

Missing relation specification

postgres=# select * from plpgsql_check_function('foo_trg()');
ERROR:  missing trigger relation
HINT:  Trigger relation oid must be valid

Correct trigger checking (with specified relation)

postgres=# select * from plpgsql_check_function('foo_trg()', 'bar');
                 plpgsql_check_function                 
--------------------------------------------------------
 error:42703:3:assignment:record "new" has no field "c"
(1 row)

For triggers with transitive tables you can set a oldtable or newtable parameters:

create or replace function footab_trig_func()
returns trigger as $$
declare x int;
begin
  if false then
    -- should be ok;
    select count(*) from newtab into x; 

    -- should fail;
    select count(*) from newtab where d = 10 into x;
  end if;
  return null;
end;
$$ language plpgsql;

select * from plpgsql_check_function('footab_trig_func','footab', newtable := 'newtab');

Mass check

You can use the plpgsql_check_function for mass check functions and mass check triggers. Please, test following queries:

-- check all nontrigger plpgsql functions
SELECT p.oid, p.proname, plpgsql_check_function(p.oid)
   FROM pg_catalog.pg_namespace n
   JOIN pg_catalog.pg_proc p ON pronamespace = n.oid
   JOIN pg_catalog.pg_language l ON p.prolang = l.oid
  WHERE l.lanname = 'plpgsql' AND p.prorettype <> 2279;

or

SELECT p.proname, tgrelid::regclass, cf.*
   FROM pg_proc p
        JOIN pg_trigger t ON t.tgfoid = p.oid 
        JOIN pg_language l ON p.prolang = l.oid
        JOIN pg_namespace n ON p.pronamespace = n.oid,
        LATERAL plpgsql_check_function(p.oid, t.tgrelid) cf
  WHERE n.nspname = 'public' and l.lanname = 'plpgsql'

or

-- check all plpgsql functions (functions or trigger functions with defined triggers)
SELECT
    (pcf).functionid::regprocedure, (pcf).lineno, (pcf).statement,
    (pcf).sqlstate, (pcf).message, (pcf).detail, (pcf).hint, (pcf).level,
    (pcf)."position", (pcf).query, (pcf).context
FROM
(
    SELECT
        plpgsql_check_function_tb(pg_proc.oid, COALESCE(pg_trigger.tgrelid, 0)) AS pcf
    FROM pg_proc
    LEFT JOIN pg_trigger
        ON (pg_trigger.tgfoid = pg_proc.oid)
    WHERE
        prolang = (SELECT lang.oid FROM pg_language lang WHERE lang.lanname = 'plpgsql') AND
        pronamespace <> (SELECT nsp.oid FROM pg_namespace nsp WHERE nsp.nspname = 'pg_catalog') AND
        -- ignore unused triggers
        (pg_proc.prorettype <> (SELECT typ.oid FROM pg_type typ WHERE typ.typname = 'trigger') OR
         pg_trigger.tgfoid IS NOT NULL)
    OFFSET 0
) ss
ORDER BY (pcf).functionid::regprocedure::text, (pcf).lineno

Passive mode

Functions should be checked on start - plpgsql_check module must be loaded.

Configuration

plpgsql_check.mode = [ disabled | by_function | fresh_start | every_start ]
plpgsql_check.fatal_errors = [ yes | no ]

plpgsql_check.show_nonperformance_warnings = false
plpgsql_check.show_performance_warnings = false

Default mode is by_function, that means that the enhanced check is done only in active mode - by plpgsql_check_function. fresh_start means cold start.

You can enable passive mode by

load 'plpgsql'; -- 1.1 and higher doesn't need it
load 'plpgsql_check';
set plpgsql_check.mode = 'every_start';

SELECT fx(10); -- run functions - function is checked before runtime starts it

Limits

plpgsql_check should find almost all errors on really static code. When developer use some PLpgSQL's dynamic features like dynamic SQL or record data type, then false positives are possible. These should be rare - in well written code - and then the affected function should be redesigned or plpgsql_check should be disabled for this function.

CREATE OR REPLACE FUNCTION f1()
RETURNS void AS $$
DECLARE r record;
BEGIN
  FOR r IN EXECUTE 'SELECT * FROM t1'
  LOOP
    RAISE NOTICE '%', r.c;
  END LOOP;
END;
$$ LANGUAGE plpgsql SET plpgsql.enable_check TO false;

A usage of plpgsql_check adds a small overhead (in enabled passive mode) and you should use it only in develop or preprod environments.

Dynamic SQL

This module doesn't check queries that are assembled in runtime. It is not possible to identify results of dynamic queries - so plpgsql_check cannot to set correct type to record variables and cannot to check a dependent SQLs and expressions.

When type of record's variable is not know, you can assign it explicitly with pragma type:

DECLARE r record;
BEGIN
  EXECUTE format('SELECT * FROM %I', _tablename) INTO r;
  PERFORM plpgsql_check_pragma('type: r (id int, processed bool)');
  IF NOT r.processed THEN
    ...

Attention: The SQL injection check can detect only some SQL injection vulnerabilities. This tool cannot be used for security audit! Some issues should not be detected. This check can raise false alarms too - probably when variable is sanitized by other command or when value is of some compose type. 

Refcursors

plpgsql_check should not to detect structure of referenced cursors. A reference on cursor in PLpgSQL is implemented as name of global cursor. In check time, the name is not known (not in all possibilities), and global cursor doesn't exist. It is significant break for any static analyse. PLpgSQL cannot to set correct type for record variables and cannot to check a dependent SQLs and expressions. A solution is same like dynamic SQL. Don't use record variable as target when you use refcursor type or disable plpgsql_check for these functions.

CREATE OR REPLACE FUNCTION foo(refcur_var refcursor)
RETURNS void AS $$
DECLARE
  rec_var record;
BEGIN
  FETCH refcur_var INTO rec_var; -- this is STOP for plpgsql_check
  RAISE NOTICE '%', rec_var;     -- record rec_var is not assigned yet error

In this case a record type should not be used (use known rowtype instead):

CREATE OR REPLACE FUNCTION foo(refcur_var refcursor)
RETURNS void AS $$
DECLARE
  rec_var some_rowtype;
BEGIN
  FETCH refcur_var INTO rec_var;
  RAISE NOTICE '%', rec_var;

Temporary tables

plpgsql_check cannot verify queries over temporary tables that are created in plpgsql's function runtime. For this use case it is necessary to create a fake temp table or disable plpgsql_check for this function.

In reality temp tables are stored in own (per user) schema with higher priority than persistent tables. So you can do (with following trick safetly):

CREATE OR REPLACE FUNCTION public.disable_dml()
RETURNS trigger
LANGUAGE plpgsql AS $function$
BEGIN
  RAISE EXCEPTION SQLSTATE '42P01'
     USING message = format('this instance of %I table doesn''t allow any DML operation', TG_TABLE_NAME),
           hint = format('you should to run "CREATE TEMP TABLE %1$I(LIKE %1$I INCLUDING ALL);" statement',
                         TG_TABLE_NAME);
  RETURN NULL;
END;
$function$;

CREATE TABLE foo(a int, b int); -- doesn't hold data ever
CREATE TRIGGER foo_disable_dml
   BEFORE INSERT OR UPDATE OR DELETE ON foo
   EXECUTE PROCEDURE disable_dml();

postgres=# INSERT INTO  foo VALUES(10,20);
ERROR:  this instance of foo table doesn't allow any DML operation
HINT:  you should to run "CREATE TEMP TABLE foo(LIKE foo INCLUDING ALL);" statement
postgres=# 

CREATE TABLE
postgres=# INSERT INTO  foo VALUES(10,20);
INSERT 0 1

This trick emulates GLOBAL TEMP tables partially and it allows a statical validation. Other possibility is using a [template foreign data wrapper] (https://github.com/okbob/template_fdw)

You can use pragma table and create ephemeral table:

BEGIN
   CREATE TEMP TABLE xxx(a int);
   PERFORM plpgsql_check_pragma('table: xxx(a int)');
   INSERT INTO xxx VALUES(10);

Dependency list

A function plpgsql_show_dependency_tb can show all functions, operators and relations used inside processed function:

postgres=# select * from plpgsql_show_dependency_tb('testfunc(int,float)');
┌──────────┬───────┬────────┬─────────┬────────────────────────────┐
│   type   │  oid  │ schema │  name   │           params           │
╞══════════╪═══════╪════════╪═════════╪════════════════════════════╡
│ FUNCTION │ 36008 │ public │ myfunc1 │ (integer,double precision) │
│ FUNCTION │ 35999 │ public │ myfunc2 │ (integer,double precision) │
│ OPERATOR │ 36007 │ public │ **      │ (integer,integer)          │
│ RELATION │ 36005 │ public │ myview  │                            │
│ RELATION │ 36002 │ public │ mytable │                            │
└──────────┴───────┴────────┴─────────┴────────────────────────────┘
(4 rows)

Profiler

The plpgsql_check contains simple profiler of plpgsql functions and procedures. It can work with/without a access to shared memory. It depends on shared_preload_libraries config. When plpgsql_check was initialized by shared_preload_libraries, then it can allocate shared memory, and function's profiles are stored there. When plpgsql_check cannot to allocate shared momory, the profile is stored in session memory.

Due dependencies, shared_preload_libraries should to contains plpgsql first

postgres=# show shared_preload_libraries ;
┌──────────────────────────┐
│ shared_preload_libraries │
╞══════════════════════════╡
│ plpgsql,plpgsql_check    │
└──────────────────────────┘
(1 row)

The profiler is active when GUC plpgsql_check.profiler is on. The profiler doesn't require shared memory, but if there are not shared memory, then the profile is limmitted just to active session.

When plpgsql_check is initialized by shared_preload_libraries, another GUC is available to configure the amount of shared memory used by the profiler: plpgsql_check.profiler_max_shared_chunks. This defines the maximum number of statements chunk that can be stored in shared memory. For each plpgsql function (or procedure), the whole content is split into chunks of 30 statements. If needed, multiple chunks can be used to store the whole content of a single function. A single chunk is 1704 bytes. The default value for this GUC is 15000, which should be enough for big projects containing hundred of thousands of statements in plpgsql, and will consume about 24MB of memory. If your project doesn't require that much number of chunks, you can set this parameter to a smaller number in order to decrease the memory usage. The minimum value is 50 (which should consume about 83kB of memory), and the maximum value is 100000 (which should consume about 163MB of memory). Changing this parameter requires a PostgreSQL restart.

The profiler will also retrieve the query identifier for each instruction that contains an expression or optimizable statement. Note that this requires pg_stat_statements, or another similar third-party extension), to be installed. There are some limitations to the query identifier retrieval:

  • if a plpgsql expression contains underlying statements, only the top level query identifier will be retrieved
  • the profiler doesn't compute query identifier by itself but relies on external extension, such as pg_stat_statements, for that. It means that depending on the external extension behavior, you may not be able to see a query identifier for some statements. That's for instance the case with DDL statements, as pg_stat_statements doesn't expose the query identifier for such queries.
  • a query identifier is retrieved only for instructions containing expressions. This means that plpgsql_profiler_function_tb() function can report less query identifier than instructions on a single line.

Attention: A update of shared profiles can decrease performance on servers under higher load.

The profile can be displayed by function plpgsql_profiler_function_tb:

postgres=# select lineno, avg_time, source from plpgsql_profiler_function_tb('fx(int)');
┌────────┬──────────┬───────────────────────────────────────────────────────────────────┐
│ lineno │ avg_time │                              source                               │
╞════════╪══════════╪═══════════════════════════════════════════════════════════════════╡
│      1 │          │                                                                   │
│      2 │          │ declare result int = 0;                                           │
│      3 │    0.075 │ begin                                                             │
│      4 │    0.202 │   for i in 1..$1 loop                                             │
│      5 │    0.005 │     select result + i into result; select result + i into result; │
│      6 │          │   end loop;                                                       │
│      7 │        0 │   return result;                                                  │
│      8 │          │ end;                                                              │
└────────┴──────────┴───────────────────────────────────────────────────────────────────┘
(9 rows)

The profile per statements (not per line) can be displayed by function plpgsql_profiler_function_statements_tb:

        CREATE OR REPLACE FUNCTION public.fx1(a integer)
         RETURNS integer
         LANGUAGE plpgsql
1       AS $function$
2       begin
3         if a > 10 then
4           raise notice 'ahoj';
5           return -1;
6         else
7           raise notice 'nazdar';
8           return 1;
9         end if;
10      end;
11      $function$

postgres=# select stmtid, parent_stmtid, parent_note, lineno, exec_stmts, stmtname
             from plpgsql_profiler_function_statements_tb('fx1');
┌────────┬───────────────┬─────────────┬────────┬────────────┬─────────────────┐
│ stmtid │ parent_stmtid │ parent_note │ lineno │ exec_stmts │    stmtname     │
╞════════╪═══════════════╪═════════════╪════════╪════════════╪═════════════════╡
│      0 │             ∅ │ ∅           │      2 │          0 │ statement block │
│      1 │             0 │ body        │      3 │          0 │ IF              │
│      2 │             1 │ then body   │      4 │          0 │ RAISE           │
│      3 │             1 │ then body   │      5 │          0 │ RETURN          │
│      4 │             1 │ else body   │      7 │          0 │ RAISE           │
│      5 │             1 │ else body   │      8 │          0 │ RETURN          │
└────────┴───────────────┴─────────────┴────────┴────────────┴─────────────────┘
(6 rows)

All stored profiles can be displayed by calling function plpgsql_profiler_functions_all:

postgres=# select * from plpgsql_profiler_functions_all();
┌───────────────────────┬────────────┬────────────┬──────────┬─────────────┬──────────┬──────────┐
│        funcoid        │ exec_count │ total_time │ avg_time │ stddev_time │ min_time │ max_time │
╞═══════════════════════╪════════════╪════════════╪══════════╪═════════════╪══════════╪══════════╡
│ fxx(double precision) │          1 │       0.01 │     0.01 │        0.00 │     0.01 │     0.01 │
└───────────────────────┴────────────┴────────────┴──────────┴─────────────┴──────────┴──────────┘
(1 row)

There are two functions for cleaning stored profiles: plpgsql_profiler_reset_all() and plpgsql_profiler_reset(regprocedure).

Coverage metrics

plpgsql_check provides two functions:

  • plpgsql_coverage_statements(name)
  • plpgsql_coverage_branches(name)

Note

There is another very good PLpgSQL profiler - https://bitbucket.org/openscg/plprofiler

My extension is designed to be simple for use and practical. Nothing more or less.

plprofiler is more complex. It build call graphs and from this graph it can creates flame graph of execution times.

Both extensions can be used together with buildin PostgreSQL's feature - tracking functions.

set track_functions to 'pl';
...
select * from pg_stat_user_functions;

Tracer

plpgsql_check provides a tracing possibility - in this mode you can see notices on start or end functions (terse and default verbosity) and start or end statements (verbose verbosity). For default and verbose verbosity the content of function arguments is displayed. The content of related variables are displayed when verbosity is verbose.

postgres=# do $$ begin perform fx(10,null, 'now', e'stěhule'); end; $$;
NOTICE:  #0 ->> start of inline_code_block (Oid=0)
NOTICE:  #2   ->> start of function fx(integer,integer,date,text) (Oid=16405)
NOTICE:  #2        call by inline_code_block line 1 at PERFORM
NOTICE:  #2       "a" => '10', "b" => null, "c" => '2020-08-03', "d" => 'stěhule'
NOTICE:  #4     ->> start of function fx(integer) (Oid=16404)
NOTICE:  #4          call by fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:  #4         "a" => '10'
NOTICE:  #4     <<- end of function fx (elapsed time=0.098 ms)
NOTICE:  #2   <<- end of function fx (elapsed time=0.399 ms)
NOTICE:  #0 <<- end of block (elapsed time=0.754 ms)

The number after # is a execution frame counter (this number is related to deep of error context stack). It allows to pair start end and of function.

Tracing is enabled by setting plpgsql_check.tracer to on. Attention - enabling this behaviour has significant negative impact on performance (unlike the profiler). You can set a level for output used by tracer plpgsql_check.tracer_errlevel (default is notice). The output content is limited by length specified by plpgsql_check.tracer_variable_max_length configuration variable.

In terse verbose mode the output is reduced:

postgres=# set plpgsql_check.tracer_verbosity TO terse;
SET
postgres=# do $$ begin perform fx(10,null, 'now', e'stěhule'); end; $$;
NOTICE:  #0 start of inline code block (oid=0)
NOTICE:  #2 start of fx (oid=16405)
NOTICE:  #4 start of fx (oid=16404)
NOTICE:  #4 end of fx
NOTICE:  #2 end of fx
NOTICE:  #0 end of inline code block

In verbose mode the output is extended about statement details:

postgres=# do $$ begin perform fx(10,null, 'now', e'stěhule'); end; $$;
NOTICE:  #0            ->> start of block inline_code_block (oid=0)
NOTICE:  #0.1       1  --> start of PERFORM
NOTICE:  #2              ->> start of function fx(integer,integer,date,text) (oid=16405)
NOTICE:  #2                   call by inline_code_block line 1 at PERFORM
NOTICE:  #2                  "a" => '10', "b" => null, "c" => '2020-08-04', "d" => 'stěhule'
NOTICE:  #2.1       1    --> start of PERFORM
NOTICE:  #2.1                "a" => '10'
NOTICE:  #4                ->> start of function fx(integer) (oid=16404)
NOTICE:  #4                     call by fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:  #4                    "a" => '10'
NOTICE:  #4.1       6      --> start of assignment
NOTICE:  #4.1                  "a" => '10', "b" => '20'
NOTICE:  #4.1              <-- end of assignment (elapsed time=0.076 ms)
NOTICE:  #4.1                  "res" => '130'
NOTICE:  #4.2       7      --> start of RETURN
NOTICE:  #4.2                  "res" => '130'
NOTICE:  #4.2              <-- end of RETURN (elapsed time=0.054 ms)
NOTICE:  #4                <<- end of function fx (elapsed time=0.373 ms)
NOTICE:  #2.1            <-- end of PERFORM (elapsed time=0.589 ms)
NOTICE:  #2              <<- end of function fx (elapsed time=0.727 ms)
NOTICE:  #0.1          <-- end of PERFORM (elapsed time=1.147 ms)
NOTICE:  #0            <<- end of block (elapsed time=1.286 ms)

Special feature of tracer is tracing of ASSERT statement when plpgsql_check.trace_assert is on. When plpgsql_check.trace_assert_verbosity is DEFAULT, then all function's or procedure's variables are displayed when assert expression is false. When this configuration is VERBOSE then all variables from all plpgsql frames are displayed. This behaviour is independent on plpgsql.check_asserts value. It can be used, although the assertions are disabled in plpgsql runtime.

postgres=# set plpgsql_check.tracer to off;
postgres=# set plpgsql_check.trace_assert_verbosity TO verbose;

postgres=# do $$ begin perform fx(10,null, 'now', e'stěhule'); end; $$;
NOTICE:  #4 PLpgSQL assert expression (false) on line 12 of fx(integer) is false
NOTICE:   "a" => '10', "res" => null, "b" => '20'
NOTICE:  #2 PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:   "a" => '10', "b" => null, "c" => '2020-08-05', "d" => 'stěhule'
NOTICE:  #0 PL/pgSQL function inline_code_block line 1 at PERFORM
ERROR:  assertion failed
CONTEXT:  PL/pgSQL function fx(integer) line 12 at ASSERT
SQL statement "SELECT fx(a)"
PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
SQL statement "SELECT fx(10,null, 'now', e'stěhule')"
PL/pgSQL function inline_code_block line 1 at PERFORM

postgres=# set plpgsql.check_asserts to off;
SET
postgres=# do $$ begin perform fx(10,null, 'now', e'stěhule'); end; $$;
NOTICE:  #4 PLpgSQL assert expression (false) on line 12 of fx(integer) is false
NOTICE:   "a" => '10', "res" => null, "b" => '20'
NOTICE:  #2 PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:   "a" => '10', "b" => null, "c" => '2020-08-05', "d" => 'stěhule'
NOTICE:  #0 PL/pgSQL function inline_code_block line 1 at PERFORM
DO

Attention - SECURITY

Tracer prints content of variables or function arguments. For security definer function, this content can hold security sensitive data. This is reason why tracer is disabled by default and should be enabled only with super user rights plpgsql_check.enable_tracer.

Pragma

You can configure plpgsql_check behave inside checked function with "pragma" function. This is a analogy of PL/SQL or ADA language of PRAGMA feature. PLpgSQL doesn't support PRAGMA, but plpgsql_check detects function named plpgsql_check_pragma and get options from parameters of this function. These plpgsql_check options are valid to end of group of statements.

CREATE OR REPLACE FUNCTION test()
RETURNS void AS $$
BEGIN
  ...
  -- for following statements disable check
  PERFORM plpgsql_check_pragma('disable:check');
  ...
  -- enable check again
  PERFORM plpgsql_check_pragma('enable:check');
  ...
END;
$$ LANGUAGE plpgsql;

The function plpgsql_check_pragma is immutable function that returns one. It is defined by plpgsql_check extension. You can declare alternative plpgsql_check_pragma function like:

CREATE OR REPLACE FUNCTION plpgsql_check_pragma(VARIADIC args[])
RETURNS int AS $$
SELECT 1
$$ LANGUAGE sql IMMUTABLE;

Using pragma function in declaration part of top block sets options on function level too.

CREATE OR REPLACE FUNCTION test()
RETURNS void AS $$
DECLARE
  aux int := plpgsql_check_pragma('disable:extra_warnings');
  ...

Shorter syntax for pragma is supported too:

CREATE OR REPLACE FUNCTION test()
RETURNS void AS $$
DECLARE r record;
BEGIN
  PERFORM 'PRAGMA:TYPE:r (a int, b int)';
  PERFORM 'PRAGMA:TABLE: x (like pg_class)';
  ...

Supported pragmas

echo:str - print string (for testing)

status:check,status:tracer, status:other_warnings, status:performance_warnings, status:extra_warnings,status:security_warnings

enable:check,enable:tracer, enable:other_warnings, enable:performance_warnings, enable:extra_warnings,enable:security_warnings

disable:check,disable:tracer, disable:other_warnings, disable:performance_warnings, disable:extra_warnings,disable:security_warnings

type:varname typename or type:varname (fieldname type, ...) - set type to variable of record type

table: name (column_name type, ...) or table: name (like tablename) - create ephereal table

Pragmas enable:tracer and disable:tracerare active for Postgres 12 and higher

Compilation

You need a development environment for PostgreSQL extensions:

make clean
make install

result:

[pavel@localhost plpgsql_check]$ make USE_PGXS=1 clean
rm -f plpgsql_check.so   libplpgsql_check.a  libplpgsql_check.pc
rm -f plpgsql_check.o
rm -rf results/ regression.diffs regression.out tmp_check/ log/
[pavel@localhost plpgsql_check]$ make USE_PGXS=1 all
clang -O2 -Wall -Wmissing-prototypes -Wpointer-arith -Wdeclaration-after-statement -Wendif-labels -Wmissing-format-attribute -Wformat-security -fno-strict-aliasing -fwrapv -fpic -I/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/pl/plpgsql/src -I. -I./ -I/usr/local/pgsql/include/server -I/usr/local/pgsql/include/internal -D_GNU_SOURCE   -c -o plpgsql_check.o plpgsql_check.c
clang -O2 -Wall -Wmissing-prototypes -Wpointer-arith -Wdeclaration-after-statement -Wendif-labels -Wmissing-format-attribute -Wformat-security -fno-strict-aliasing -fwrapv -fpic -I/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/pl/plpgsql/src -shared -o plpgsql_check.so plpgsql_check.o -L/usr/local/pgsql/lib -Wl,--as-needed -Wl,-rpath,'/usr/local/pgsql/lib',--enable-new-dtags  
[pavel@localhost plpgsql_check]$ su root
Password: *******
[root@localhost plpgsql_check]# make USE_PGXS=1 install
/usr/bin/mkdir -p '/usr/local/pgsql/lib'
/usr/bin/mkdir -p '/usr/local/pgsql/share/extension'
/usr/bin/mkdir -p '/usr/local/pgsql/share/extension'
/usr/bin/install -c -m 755  plpgsql_check.so '/usr/local/pgsql/lib/plpgsql_check.so'
/usr/bin/install -c -m 644 plpgsql_check.control '/usr/local/pgsql/share/extension/'
/usr/bin/install -c -m 644 plpgsql_check--0.9.sql '/usr/local/pgsql/share/extension/'
[root@localhost plpgsql_check]# exit
[pavel@localhost plpgsql_check]$ make USE_PGXS=1 installcheck
/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/test/regress/pg_regress --inputdir=./ --psqldir='/usr/local/pgsql/bin'    --dbname=pl_regression --load-language=plpgsql --dbname=contrib_regression plpgsql_check_passive plpgsql_check_active plpgsql_check_active-9.5
(using postmaster on Unix socket, default port)
============== dropping database "contrib_regression" ==============
DROP DATABASE
============== creating database "contrib_regression" ==============
CREATE DATABASE
ALTER DATABASE
============== installing plpgsql                     ==============
CREATE LANGUAGE
============== running regression test queries        ==============
test plpgsql_check_passive    ... ok
test plpgsql_check_active     ... ok
test plpgsql_check_active-9.5 ... ok

=====================
 All 3 tests passed. 
=====================

Compilation on Ubuntu

Sometimes successful compilation can require libicu-dev package (PostgreSQL 10 and higher - when pg was compiled with ICU support)

sudo apt install libicu-dev

Compilation plpgsql_check on Windows

You can check precompiled dll libraries http://okbob.blogspot.cz/2015/02/plpgsqlcheck-is-available-for-microsoft.html

or compile by self:

  1. Download and install PostgreSQL for Win32 from http://www.enterprisedb.com
  2. Download and install Microsoft Visual C++ Express
  3. Lern tutorial http://blog.2ndquadrant.com/compiling-postgresql-extensions-visual-studio-windows
  4. Build plpgsql_check.dll
  5. Install plugin
  6. copy plpgsql_check.dll to PostgreSQL\14\lib
  7. copy plpgsql_check.control and plpgsql_check--2.1.sql to PostgreSQL\14\share\extension

Checked on

  • gcc on Linux (against all supported PostgreSQL)
  • clang 3.4 on Linux (against PostgreSQL 10)
  • for success regress tests the PostgreSQL 10 or higher is required

Compilation against PostgreSQL 10 requires libICU!

Licence

Copyright (c) Pavel Stehule (pavel.stehule@gmail.com)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Note

If you like it, send a postcard to address

Pavel Stehule
Skalice 12
256 01 Benesov u Prahy
Czech Republic

I invite any questions, comments, bug reports, patches on mail address pavel.stehule@gmail.com


Author: okbob
Source Code: https://github.com/okbob/plpgsql_check
License: View license

#postgresql 

Plpgsql Check: Extension That Allows to Check Plpgsql Source Code.

plpgsql_check

I founded this project, because I wanted to publish the code I wrote in the last two years, when I tried to write enhanced checking for PostgreSQL upstream. It was not fully successful - integration into upstream requires some larger plpgsql refactoring - probably it will not be done in next years (now is Dec 2013). But written code is fully functional and can be used in production (and it is used in production). So, I created this extension to be available for all plpgsql developers.

If you like it and if you would to join to development of this extension, register yourself to postgresql extension hacking google group.

Features

  • check fields of referenced database objects and types inside embedded SQL
  • using correct types of function parameters
  • unused variables and function argumens, unmodified OUT argumens
  • partially detection of dead code (due RETURN command)
  • detection of missing RETURN command in function
  • try to identify unwanted hidden casts, that can be performance issue like unused indexes
  • possibility to collect relations and functions used by function
  • possibility to check EXECUTE stmt agaist SQL injection vulnerability

I invite any ideas, patches, bugreports.

plpgsql_check is next generation of plpgsql_lint. It allows to check source code by explicit call plpgsql_check_function.

PostgreSQL PostgreSQL 10, 11, 12, 13 and 14 are supported.

The SQL statements inside PL/pgSQL functions are checked by validator for semantic errors. These errors can be found by plpgsql_check_function:

Active mode

postgres=# CREATE EXTENSION plpgsql_check;
LOAD
postgres=# CREATE TABLE t1(a int, b int);
CREATE TABLE

postgres=#
CREATE OR REPLACE FUNCTION public.f1()
RETURNS void
LANGUAGE plpgsql
AS $function$
DECLARE r record;
BEGIN
  FOR r IN SELECT * FROM t1
  LOOP
    RAISE NOTICE '%', r.c; -- there is bug - table t1 missing "c" column
  END LOOP;
END;
$function$;

CREATE FUNCTION

postgres=# select f1(); -- execution doesn't find a bug due to empty table t1
  f1 
 ────
   
 (1 row)

postgres=# \x
Expanded display is on.
postgres=# select * from plpgsql_check_function_tb('f1()');
─[ RECORD 1 ]───────────────────────────
functionid │ f1
lineno     │ 6
statement  │ RAISE
sqlstate   │ 42703
message    │ record "r" has no field "c"
detail     │ [null]
hint       │ [null]
level      │ error
position   │ 0
query      │ [null]

postgres=# \sf+ f1
    CREATE OR REPLACE FUNCTION public.f1()
     RETURNS void
     LANGUAGE plpgsql
1       AS $function$
2       DECLARE r record;
3       BEGIN
4         FOR r IN SELECT * FROM t1
5         LOOP
6           RAISE NOTICE '%', r.c; -- there is bug - table t1 missing "c" column
7         END LOOP;
8       END;
9       $function$

Function plpgsql_check_function() has three possible formats: text, json or xml

select * from plpgsql_check_function('f1()', fatal_errors := false);
                         plpgsql_check_function                         
------------------------------------------------------------------------
 error:42703:4:SQL statement:column "c" of relation "t1" does not exist
 Query: update t1 set c = 30
 --                   ^
 error:42P01:7:RAISE:missing FROM-clause entry for table "r"
 Query: SELECT r.c
 --            ^
 error:42601:7:RAISE:too few parameters specified for RAISE
(7 rows)

postgres=# select * from plpgsql_check_function('fx()', format:='xml');
                 plpgsql_check_function                     
────────────────────────────────────────────────────────────────
 <Function oid="16400">                                        ↵
   <Issue>                                                     ↵
     <Level>error</level>                                      ↵
     <Sqlstate>42P01</Sqlstate>                                ↵
     <Message>relation "foo111" does not exist</Message>       ↵
     <Stmt lineno="3">RETURN</Stmt>                            ↵
     <Query position="23">SELECT (select a from foo111)</Query>↵
   </Issue>                                                    ↵
  </Function>
 (1 row)

Arguments

You can set level of warnings via function's parameters:

Mandatory arguments

  • function name or function signature - these functions requires function specification. Any function in PostgreSQL can be specified by Oid or by name or by signature. When you know oid or complete function's signature, you can use a regprocedure type parameter like 'fx()'::regprocedure or 16799::regprocedure. Possible alternative is using a name only, when function's name is unique - like 'fx'. When the name is not unique or the function doesn't exists it raises a error.

Optional arguments

relid DEFAULT 0 - oid of relation assigned with trigger function. It is necessary for check of any trigger function.

fatal_errors boolean DEFAULT true - stop on first error

other_warnings boolean DEFAULT true - show warnings like different attributes number in assignmenet on left and right side, variable overlaps function's parameter, unused variables, unwanted casting, ..

extra_warnings boolean DEFAULT true - show warnings like missing RETURN, shadowed variables, dead code, never read (unused) function's parameter, unmodified variables, modified auto variables, ..

performance_warnings boolean DEFAULT false - performance related warnings like declared type with type modificator, casting, implicit casts in where clause (can be reason why index is not used), ..

security_warnings boolean DEFAULT false - security related checks like SQL injection vulnerability detection

anyelementtype regtype DEFAULT 'int' - a real type used instead anyelement type

anyenumtype regtype DEFAULT '-' - a real type used instead anyenum type

anyrangetype regtype DEFAULT 'int4range' - a real type used instead anyrange type

anycompatibletype DEFAULT 'int' - a real type used instead anycompatible type

anycompatiblerangetype DEFAULT 'int4range' - a real type used instead anycompatible range type

without_warnings DEFAULT false - disable all warnings

all_warnings DEFAULT false - enable all warnings

newtable DEFAULT NULL, oldtable DEFAULT NULL - the names of NEW or OLD transitive tables. These parameters are required when transitive tables are used.

Triggers

When you want to check any trigger, you have to enter a relation that will be used together with trigger function

CREATE TABLE bar(a int, b int);

postgres=# \sf+ foo_trg
    CREATE OR REPLACE FUNCTION public.foo_trg()
         RETURNS trigger
         LANGUAGE plpgsql
1       AS $function$
2       BEGIN
3         NEW.c := NEW.a + NEW.b;
4         RETURN NEW;
5       END;
6       $function$

Missing relation specification

postgres=# select * from plpgsql_check_function('foo_trg()');
ERROR:  missing trigger relation
HINT:  Trigger relation oid must be valid

Correct trigger checking (with specified relation)

postgres=# select * from plpgsql_check_function('foo_trg()', 'bar');
                 plpgsql_check_function                 
--------------------------------------------------------
 error:42703:3:assignment:record "new" has no field "c"
(1 row)

For triggers with transitive tables you can set a oldtable or newtable parameters:

create or replace function footab_trig_func()
returns trigger as $$
declare x int;
begin
  if false then
    -- should be ok;
    select count(*) from newtab into x; 

    -- should fail;
    select count(*) from newtab where d = 10 into x;
  end if;
  return null;
end;
$$ language plpgsql;

select * from plpgsql_check_function('footab_trig_func','footab', newtable := 'newtab');

Mass check

You can use the plpgsql_check_function for mass check functions and mass check triggers. Please, test following queries:

-- check all nontrigger plpgsql functions
SELECT p.oid, p.proname, plpgsql_check_function(p.oid)
   FROM pg_catalog.pg_namespace n
   JOIN pg_catalog.pg_proc p ON pronamespace = n.oid
   JOIN pg_catalog.pg_language l ON p.prolang = l.oid
  WHERE l.lanname = 'plpgsql' AND p.prorettype <> 2279;

or

SELECT p.proname, tgrelid::regclass, cf.*
   FROM pg_proc p
        JOIN pg_trigger t ON t.tgfoid = p.oid 
        JOIN pg_language l ON p.prolang = l.oid
        JOIN pg_namespace n ON p.pronamespace = n.oid,
        LATERAL plpgsql_check_function(p.oid, t.tgrelid) cf
  WHERE n.nspname = 'public' and l.lanname = 'plpgsql'

or

-- check all plpgsql functions (functions or trigger functions with defined triggers)
SELECT
    (pcf).functionid::regprocedure, (pcf).lineno, (pcf).statement,
    (pcf).sqlstate, (pcf).message, (pcf).detail, (pcf).hint, (pcf).level,
    (pcf)."position", (pcf).query, (pcf).context
FROM
(
    SELECT
        plpgsql_check_function_tb(pg_proc.oid, COALESCE(pg_trigger.tgrelid, 0)) AS pcf
    FROM pg_proc
    LEFT JOIN pg_trigger
        ON (pg_trigger.tgfoid = pg_proc.oid)
    WHERE
        prolang = (SELECT lang.oid FROM pg_language lang WHERE lang.lanname = 'plpgsql') AND
        pronamespace <> (SELECT nsp.oid FROM pg_namespace nsp WHERE nsp.nspname = 'pg_catalog') AND
        -- ignore unused triggers
        (pg_proc.prorettype <> (SELECT typ.oid FROM pg_type typ WHERE typ.typname = 'trigger') OR
         pg_trigger.tgfoid IS NOT NULL)
    OFFSET 0
) ss
ORDER BY (pcf).functionid::regprocedure::text, (pcf).lineno

Passive mode

Functions should be checked on start - plpgsql_check module must be loaded.

Configuration

plpgsql_check.mode = [ disabled | by_function | fresh_start | every_start ]
plpgsql_check.fatal_errors = [ yes | no ]

plpgsql_check.show_nonperformance_warnings = false
plpgsql_check.show_performance_warnings = false

Default mode is by_function, that means that the enhanced check is done only in active mode - by plpgsql_check_function. fresh_start means cold start.

You can enable passive mode by

load 'plpgsql'; -- 1.1 and higher doesn't need it
load 'plpgsql_check';
set plpgsql_check.mode = 'every_start';

SELECT fx(10); -- run functions - function is checked before runtime starts it

Limits

plpgsql_check should find almost all errors on really static code. When developer use some PLpgSQL's dynamic features like dynamic SQL or record data type, then false positives are possible. These should be rare - in well written code - and then the affected function should be redesigned or plpgsql_check should be disabled for this function.

CREATE OR REPLACE FUNCTION f1()
RETURNS void AS $$
DECLARE r record;
BEGIN
  FOR r IN EXECUTE 'SELECT * FROM t1'
  LOOP
    RAISE NOTICE '%', r.c;
  END LOOP;
END;
$$ LANGUAGE plpgsql SET plpgsql.enable_check TO false;

A usage of plpgsql_check adds a small overhead (in enabled passive mode) and you should use it only in develop or preprod environments.

Dynamic SQL

This module doesn't check queries that are assembled in runtime. It is not possible to identify results of dynamic queries - so plpgsql_check cannot to set correct type to record variables and cannot to check a dependent SQLs and expressions.

When type of record's variable is not know, you can assign it explicitly with pragma type:

DECLARE r record;
BEGIN
  EXECUTE format('SELECT * FROM %I', _tablename) INTO r;
  PERFORM plpgsql_check_pragma('type: r (id int, processed bool)');
  IF NOT r.processed THEN
    ...

Attention: The SQL injection check can detect only some SQL injection vulnerabilities. This tool cannot be used for security audit! Some issues should not be detected. This check can raise false alarms too - probably when variable is sanitized by other command or when value is of some compose type. 

Refcursors

plpgsql_check should not to detect structure of referenced cursors. A reference on cursor in PLpgSQL is implemented as name of global cursor. In check time, the name is not known (not in all possibilities), and global cursor doesn't exist. It is significant break for any static analyse. PLpgSQL cannot to set correct type for record variables and cannot to check a dependent SQLs and expressions. A solution is same like dynamic SQL. Don't use record variable as target when you use refcursor type or disable plpgsql_check for these functions.

CREATE OR REPLACE FUNCTION foo(refcur_var refcursor)
RETURNS void AS $$
DECLARE
  rec_var record;
BEGIN
  FETCH refcur_var INTO rec_var; -- this is STOP for plpgsql_check
  RAISE NOTICE '%', rec_var;     -- record rec_var is not assigned yet error

In this case a record type should not be used (use known rowtype instead):

CREATE OR REPLACE FUNCTION foo(refcur_var refcursor)
RETURNS void AS $$
DECLARE
  rec_var some_rowtype;
BEGIN
  FETCH refcur_var INTO rec_var;
  RAISE NOTICE '%', rec_var;

Temporary tables

plpgsql_check cannot verify queries over temporary tables that are created in plpgsql's function runtime. For this use case it is necessary to create a fake temp table or disable plpgsql_check for this function.

In reality temp tables are stored in own (per user) schema with higher priority than persistent tables. So you can do (with following trick safetly):

CREATE OR REPLACE FUNCTION public.disable_dml()
RETURNS trigger
LANGUAGE plpgsql AS $function$
BEGIN
  RAISE EXCEPTION SQLSTATE '42P01'
     USING message = format('this instance of %I table doesn''t allow any DML operation', TG_TABLE_NAME),
           hint = format('you should to run "CREATE TEMP TABLE %1$I(LIKE %1$I INCLUDING ALL);" statement',
                         TG_TABLE_NAME);
  RETURN NULL;
END;
$function$;

CREATE TABLE foo(a int, b int); -- doesn't hold data ever
CREATE TRIGGER foo_disable_dml
   BEFORE INSERT OR UPDATE OR DELETE ON foo
   EXECUTE PROCEDURE disable_dml();

postgres=# INSERT INTO  foo VALUES(10,20);
ERROR:  this instance of foo table doesn't allow any DML operation
HINT:  you should to run "CREATE TEMP TABLE foo(LIKE foo INCLUDING ALL);" statement
postgres=# 

CREATE TABLE
postgres=# INSERT INTO  foo VALUES(10,20);
INSERT 0 1

This trick emulates GLOBAL TEMP tables partially and it allows a statical validation. Other possibility is using a [template foreign data wrapper] (https://github.com/okbob/template_fdw)

You can use pragma table and create ephemeral table:

BEGIN
   CREATE TEMP TABLE xxx(a int);
   PERFORM plpgsql_check_pragma('table: xxx(a int)');
   INSERT INTO xxx VALUES(10);

Dependency list

A function plpgsql_show_dependency_tb can show all functions, operators and relations used inside processed function:

postgres=# select * from plpgsql_show_dependency_tb('testfunc(int,float)');
┌──────────┬───────┬────────┬─────────┬────────────────────────────┐
│   type   │  oid  │ schema │  name   │           params           │
╞══════════╪═══════╪════════╪═════════╪════════════════════════════╡
│ FUNCTION │ 36008 │ public │ myfunc1 │ (integer,double precision) │
│ FUNCTION │ 35999 │ public │ myfunc2 │ (integer,double precision) │
│ OPERATOR │ 36007 │ public │ **      │ (integer,integer)          │
│ RELATION │ 36005 │ public │ myview  │                            │
│ RELATION │ 36002 │ public │ mytable │                            │
└──────────┴───────┴────────┴─────────┴────────────────────────────┘
(4 rows)

Profiler

The plpgsql_check contains simple profiler of plpgsql functions and procedures. It can work with/without a access to shared memory. It depends on shared_preload_libraries config. When plpgsql_check was initialized by shared_preload_libraries, then it can allocate shared memory, and function's profiles are stored there. When plpgsql_check cannot to allocate shared momory, the profile is stored in session memory.

Due dependencies, shared_preload_libraries should to contains plpgsql first

postgres=# show shared_preload_libraries ;
┌──────────────────────────┐
│ shared_preload_libraries │
╞══════════════════════════╡
│ plpgsql,plpgsql_check    │
└──────────────────────────┘
(1 row)

The profiler is active when GUC plpgsql_check.profiler is on. The profiler doesn't require shared memory, but if there are not shared memory, then the profile is limmitted just to active session.

When plpgsql_check is initialized by shared_preload_libraries, another GUC is available to configure the amount of shared memory used by the profiler: plpgsql_check.profiler_max_shared_chunks. This defines the maximum number of statements chunk that can be stored in shared memory. For each plpgsql function (or procedure), the whole content is split into chunks of 30 statements. If needed, multiple chunks can be used to store the whole content of a single function. A single chunk is 1704 bytes. The default value for this GUC is 15000, which should be enough for big projects containing hundred of thousands of statements in plpgsql, and will consume about 24MB of memory. If your project doesn't require that much number of chunks, you can set this parameter to a smaller number in order to decrease the memory usage. The minimum value is 50 (which should consume about 83kB of memory), and the maximum value is 100000 (which should consume about 163MB of memory). Changing this parameter requires a PostgreSQL restart.

The profiler will also retrieve the query identifier for each instruction that contains an expression or optimizable statement. Note that this requires pg_stat_statements, or another similar third-party extension), to be installed. There are some limitations to the query identifier retrieval:

  • if a plpgsql expression contains underlying statements, only the top level query identifier will be retrieved
  • the profiler doesn't compute query identifier by itself but relies on external extension, such as pg_stat_statements, for that. It means that depending on the external extension behavior, you may not be able to see a query identifier for some statements. That's for instance the case with DDL statements, as pg_stat_statements doesn't expose the query identifier for such queries.
  • a query identifier is retrieved only for instructions containing expressions. This means that plpgsql_profiler_function_tb() function can report less query identifier than instructions on a single line.

Attention: A update of shared profiles can decrease performance on servers under higher load.

The profile can be displayed by function plpgsql_profiler_function_tb:

postgres=# select lineno, avg_time, source from plpgsql_profiler_function_tb('fx(int)');
┌────────┬──────────┬───────────────────────────────────────────────────────────────────┐
│ lineno │ avg_time │                              source                               │
╞════════╪══════════╪═══════════════════════════════════════════════════════════════════╡
│      1 │          │                                                                   │
│      2 │          │ declare result int = 0;                                           │
│      3 │    0.075 │ begin                                                             │
│      4 │    0.202 │   for i in 1..$1 loop                                             │
│      5 │    0.005 │     select result + i into result; select result + i into result; │
│      6 │          │   end loop;                                                       │
│      7 │        0 │   return result;                                                  │
│      8 │          │ end;                                                              │
└────────┴──────────┴───────────────────────────────────────────────────────────────────┘
(9 rows)

The profile per statements (not per line) can be displayed by function plpgsql_profiler_function_statements_tb:

        CREATE OR REPLACE FUNCTION public.fx1(a integer)
         RETURNS integer
         LANGUAGE plpgsql
1       AS $function$
2       begin
3         if a > 10 then
4           raise notice 'ahoj';
5           return -1;
6         else
7           raise notice 'nazdar';
8           return 1;
9         end if;
10      end;
11      $function$

postgres=# select stmtid, parent_stmtid, parent_note, lineno, exec_stmts, stmtname
             from plpgsql_profiler_function_statements_tb('fx1');
┌────────┬───────────────┬─────────────┬────────┬────────────┬─────────────────┐
│ stmtid │ parent_stmtid │ parent_note │ lineno │ exec_stmts │    stmtname     │
╞════════╪═══════════════╪═════════════╪════════╪════════════╪═════════════════╡
│      0 │             ∅ │ ∅           │      2 │          0 │ statement block │
│      1 │             0 │ body        │      3 │          0 │ IF              │
│      2 │             1 │ then body   │      4 │          0 │ RAISE           │
│      3 │             1 │ then body   │      5 │          0 │ RETURN          │
│      4 │             1 │ else body   │      7 │          0 │ RAISE           │
│      5 │             1 │ else body   │      8 │          0 │ RETURN          │
└────────┴───────────────┴─────────────┴────────┴────────────┴─────────────────┘
(6 rows)

All stored profiles can be displayed by calling function plpgsql_profiler_functions_all:

postgres=# select * from plpgsql_profiler_functions_all();
┌───────────────────────┬────────────┬────────────┬──────────┬─────────────┬──────────┬──────────┐
│        funcoid        │ exec_count │ total_time │ avg_time │ stddev_time │ min_time │ max_time │
╞═══════════════════════╪════════════╪════════════╪══════════╪═════════════╪══════════╪══════════╡
│ fxx(double precision) │          1 │       0.01 │     0.01 │        0.00 │     0.01 │     0.01 │
└───────────────────────┴────────────┴────────────┴──────────┴─────────────┴──────────┴──────────┘
(1 row)

There are two functions for cleaning stored profiles: plpgsql_profiler_reset_all() and plpgsql_profiler_reset(regprocedure).

Coverage metrics

plpgsql_check provides two functions:

  • plpgsql_coverage_statements(name)
  • plpgsql_coverage_branches(name)

Note

There is another very good PLpgSQL profiler - https://bitbucket.org/openscg/plprofiler

My extension is designed to be simple for use and practical. Nothing more or less.

plprofiler is more complex. It build call graphs and from this graph it can creates flame graph of execution times.

Both extensions can be used together with buildin PostgreSQL's feature - tracking functions.

set track_functions to 'pl';
...
select * from pg_stat_user_functions;

Tracer

plpgsql_check provides a tracing possibility - in this mode you can see notices on start or end functions (terse and default verbosity) and start or end statements (verbose verbosity). For default and verbose verbosity the content of function arguments is displayed. The content of related variables are displayed when verbosity is verbose.

postgres=# do $$ begin perform fx(10,null, 'now', e'stěhule'); end; $$;
NOTICE:  #0 ->> start of inline_code_block (Oid=0)
NOTICE:  #2   ->> start of function fx(integer,integer,date,text) (Oid=16405)
NOTICE:  #2        call by inline_code_block line 1 at PERFORM
NOTICE:  #2       "a" => '10', "b" => null, "c" => '2020-08-03', "d" => 'stěhule'
NOTICE:  #4     ->> start of function fx(integer) (Oid=16404)
NOTICE:  #4          call by fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:  #4         "a" => '10'
NOTICE:  #4     <<- end of function fx (elapsed time=0.098 ms)
NOTICE:  #2   <<- end of function fx (elapsed time=0.399 ms)
NOTICE:  #0 <<- end of block (elapsed time=0.754 ms)

The number after # is a execution frame counter (this number is related to deep of error context stack). It allows to pair start end and of function.

Tracing is enabled by setting plpgsql_check.tracer to on. Attention - enabling this behaviour has significant negative impact on performance (unlike the profiler). You can set a level for output used by tracer plpgsql_check.tracer_errlevel (default is notice). The output content is limited by length specified by plpgsql_check.tracer_variable_max_length configuration variable.

In terse verbose mode the output is reduced:

postgres=# set plpgsql_check.tracer_verbosity TO terse;
SET
postgres=# do $$ begin perform fx(10,null, 'now', e'stěhule'); end; $$;
NOTICE:  #0 start of inline code block (oid=0)
NOTICE:  #2 start of fx (oid=16405)
NOTICE:  #4 start of fx (oid=16404)
NOTICE:  #4 end of fx
NOTICE:  #2 end of fx
NOTICE:  #0 end of inline code block

In verbose mode the output is extended about statement details:

postgres=# do $$ begin perform fx(10,null, 'now', e'stěhule'); end; $$;
NOTICE:  #0            ->> start of block inline_code_block (oid=0)
NOTICE:  #0.1       1  --> start of PERFORM
NOTICE:  #2              ->> start of function fx(integer,integer,date,text) (oid=16405)
NOTICE:  #2                   call by inline_code_block line 1 at PERFORM
NOTICE:  #2                  "a" => '10', "b" => null, "c" => '2020-08-04', "d" => 'stěhule'
NOTICE:  #2.1       1    --> start of PERFORM
NOTICE:  #2.1                "a" => '10'
NOTICE:  #4                ->> start of function fx(integer) (oid=16404)
NOTICE:  #4                     call by fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:  #4                    "a" => '10'
NOTICE:  #4.1       6      --> start of assignment
NOTICE:  #4.1                  "a" => '10', "b" => '20'
NOTICE:  #4.1              <-- end of assignment (elapsed time=0.076 ms)
NOTICE:  #4.1                  "res" => '130'
NOTICE:  #4.2       7      --> start of RETURN
NOTICE:  #4.2                  "res" => '130'
NOTICE:  #4.2              <-- end of RETURN (elapsed time=0.054 ms)
NOTICE:  #4                <<- end of function fx (elapsed time=0.373 ms)
NOTICE:  #2.1            <-- end of PERFORM (elapsed time=0.589 ms)
NOTICE:  #2              <<- end of function fx (elapsed time=0.727 ms)
NOTICE:  #0.1          <-- end of PERFORM (elapsed time=1.147 ms)
NOTICE:  #0            <<- end of block (elapsed time=1.286 ms)

Special feature of tracer is tracing of ASSERT statement when plpgsql_check.trace_assert is on. When plpgsql_check.trace_assert_verbosity is DEFAULT, then all function's or procedure's variables are displayed when assert expression is false. When this configuration is VERBOSE then all variables from all plpgsql frames are displayed. This behaviour is independent on plpgsql.check_asserts value. It can be used, although the assertions are disabled in plpgsql runtime.

postgres=# set plpgsql_check.tracer to off;
postgres=# set plpgsql_check.trace_assert_verbosity TO verbose;

postgres=# do $$ begin perform fx(10,null, 'now', e'stěhule'); end; $$;
NOTICE:  #4 PLpgSQL assert expression (false) on line 12 of fx(integer) is false
NOTICE:   "a" => '10', "res" => null, "b" => '20'
NOTICE:  #2 PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:   "a" => '10', "b" => null, "c" => '2020-08-05', "d" => 'stěhule'
NOTICE:  #0 PL/pgSQL function inline_code_block line 1 at PERFORM
ERROR:  assertion failed
CONTEXT:  PL/pgSQL function fx(integer) line 12 at ASSERT
SQL statement "SELECT fx(a)"
PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
SQL statement "SELECT fx(10,null, 'now', e'stěhule')"
PL/pgSQL function inline_code_block line 1 at PERFORM

postgres=# set plpgsql.check_asserts to off;
SET
postgres=# do $$ begin perform fx(10,null, 'now', e'stěhule'); end; $$;
NOTICE:  #4 PLpgSQL assert expression (false) on line 12 of fx(integer) is false
NOTICE:   "a" => '10', "res" => null, "b" => '20'
NOTICE:  #2 PL/pgSQL function fx(integer,integer,date,text) line 1 at PERFORM
NOTICE:   "a" => '10', "b" => null, "c" => '2020-08-05', "d" => 'stěhule'
NOTICE:  #0 PL/pgSQL function inline_code_block line 1 at PERFORM
DO

Attention - SECURITY

Tracer prints content of variables or function arguments. For security definer function, this content can hold security sensitive data. This is reason why tracer is disabled by default and should be enabled only with super user rights plpgsql_check.enable_tracer.

Pragma

You can configure plpgsql_check behave inside checked function with "pragma" function. This is a analogy of PL/SQL or ADA language of PRAGMA feature. PLpgSQL doesn't support PRAGMA, but plpgsql_check detects function named plpgsql_check_pragma and get options from parameters of this function. These plpgsql_check options are valid to end of group of statements.

CREATE OR REPLACE FUNCTION test()
RETURNS void AS $$
BEGIN
  ...
  -- for following statements disable check
  PERFORM plpgsql_check_pragma('disable:check');
  ...
  -- enable check again
  PERFORM plpgsql_check_pragma('enable:check');
  ...
END;
$$ LANGUAGE plpgsql;

The function plpgsql_check_pragma is immutable function that returns one. It is defined by plpgsql_check extension. You can declare alternative plpgsql_check_pragma function like:

CREATE OR REPLACE FUNCTION plpgsql_check_pragma(VARIADIC args[])
RETURNS int AS $$
SELECT 1
$$ LANGUAGE sql IMMUTABLE;

Using pragma function in declaration part of top block sets options on function level too.

CREATE OR REPLACE FUNCTION test()
RETURNS void AS $$
DECLARE
  aux int := plpgsql_check_pragma('disable:extra_warnings');
  ...

Shorter syntax for pragma is supported too:

CREATE OR REPLACE FUNCTION test()
RETURNS void AS $$
DECLARE r record;
BEGIN
  PERFORM 'PRAGMA:TYPE:r (a int, b int)';
  PERFORM 'PRAGMA:TABLE: x (like pg_class)';
  ...

Supported pragmas

echo:str - print string (for testing)

status:check,status:tracer, status:other_warnings, status:performance_warnings, status:extra_warnings,status:security_warnings

enable:check,enable:tracer, enable:other_warnings, enable:performance_warnings, enable:extra_warnings,enable:security_warnings

disable:check,disable:tracer, disable:other_warnings, disable:performance_warnings, disable:extra_warnings,disable:security_warnings

type:varname typename or type:varname (fieldname type, ...) - set type to variable of record type

table: name (column_name type, ...) or table: name (like tablename) - create ephereal table

Pragmas enable:tracer and disable:tracerare active for Postgres 12 and higher

Compilation

You need a development environment for PostgreSQL extensions:

make clean
make install

result:

[pavel@localhost plpgsql_check]$ make USE_PGXS=1 clean
rm -f plpgsql_check.so   libplpgsql_check.a  libplpgsql_check.pc
rm -f plpgsql_check.o
rm -rf results/ regression.diffs regression.out tmp_check/ log/
[pavel@localhost plpgsql_check]$ make USE_PGXS=1 all
clang -O2 -Wall -Wmissing-prototypes -Wpointer-arith -Wdeclaration-after-statement -Wendif-labels -Wmissing-format-attribute -Wformat-security -fno-strict-aliasing -fwrapv -fpic -I/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/pl/plpgsql/src -I. -I./ -I/usr/local/pgsql/include/server -I/usr/local/pgsql/include/internal -D_GNU_SOURCE   -c -o plpgsql_check.o plpgsql_check.c
clang -O2 -Wall -Wmissing-prototypes -Wpointer-arith -Wdeclaration-after-statement -Wendif-labels -Wmissing-format-attribute -Wformat-security -fno-strict-aliasing -fwrapv -fpic -I/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/pl/plpgsql/src -shared -o plpgsql_check.so plpgsql_check.o -L/usr/local/pgsql/lib -Wl,--as-needed -Wl,-rpath,'/usr/local/pgsql/lib',--enable-new-dtags  
[pavel@localhost plpgsql_check]$ su root
Password: *******
[root@localhost plpgsql_check]# make USE_PGXS=1 install
/usr/bin/mkdir -p '/usr/local/pgsql/lib'
/usr/bin/mkdir -p '/usr/local/pgsql/share/extension'
/usr/bin/mkdir -p '/usr/local/pgsql/share/extension'
/usr/bin/install -c -m 755  plpgsql_check.so '/usr/local/pgsql/lib/plpgsql_check.so'
/usr/bin/install -c -m 644 plpgsql_check.control '/usr/local/pgsql/share/extension/'
/usr/bin/install -c -m 644 plpgsql_check--0.9.sql '/usr/local/pgsql/share/extension/'
[root@localhost plpgsql_check]# exit
[pavel@localhost plpgsql_check]$ make USE_PGXS=1 installcheck
/usr/local/pgsql/lib/pgxs/src/makefiles/../../src/test/regress/pg_regress --inputdir=./ --psqldir='/usr/local/pgsql/bin'    --dbname=pl_regression --load-language=plpgsql --dbname=contrib_regression plpgsql_check_passive plpgsql_check_active plpgsql_check_active-9.5
(using postmaster on Unix socket, default port)
============== dropping database "contrib_regression" ==============
DROP DATABASE
============== creating database "contrib_regression" ==============
CREATE DATABASE
ALTER DATABASE
============== installing plpgsql                     ==============
CREATE LANGUAGE
============== running regression test queries        ==============
test plpgsql_check_passive    ... ok
test plpgsql_check_active     ... ok
test plpgsql_check_active-9.5 ... ok

=====================
 All 3 tests passed. 
=====================

Compilation on Ubuntu

Sometimes successful compilation can require libicu-dev package (PostgreSQL 10 and higher - when pg was compiled with ICU support)

sudo apt install libicu-dev

Compilation plpgsql_check on Windows

You can check precompiled dll libraries http://okbob.blogspot.cz/2015/02/plpgsqlcheck-is-available-for-microsoft.html

or compile by self:

  1. Download and install PostgreSQL for Win32 from http://www.enterprisedb.com
  2. Download and install Microsoft Visual C++ Express
  3. Lern tutorial http://blog.2ndquadrant.com/compiling-postgresql-extensions-visual-studio-windows
  4. Build plpgsql_check.dll
  5. Install plugin
  6. copy plpgsql_check.dll to PostgreSQL\14\lib
  7. copy plpgsql_check.control and plpgsql_check--2.1.sql to PostgreSQL\14\share\extension

Checked on

  • gcc on Linux (against all supported PostgreSQL)
  • clang 3.4 on Linux (against PostgreSQL 10)
  • for success regress tests the PostgreSQL 10 or higher is required

Compilation against PostgreSQL 10 requires libICU!

Licence

Copyright (c) Pavel Stehule (pavel.stehule@gmail.com)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Note

If you like it, send a postcard to address

Pavel Stehule
Skalice 12
256 01 Benesov u Prahy
Czech Republic

I invite any questions, comments, bug reports, patches on mail address pavel.stehule@gmail.com


Author: okbob
Source Code: https://github.com/okbob/plpgsql_check
License: View license

#postgresql 

Enhance Amazon Aurora Read/Write Capability with ShardingSphere-JDBC

1. Introduction

Amazon Aurora is a relational database management system (RDBMS) developed by AWS(Amazon Web Services). Aurora gives you the performance and availability of commercial-grade databases with full MySQL and PostgreSQL compatibility. In terms of high performance, Aurora MySQL and Aurora PostgreSQL have shown an increase in throughput of up to 5X over stock MySQL and 3X over stock PostgreSQL respectively on similar hardware. In terms of scalability, Aurora achieves enhancements and innovations in storage and computing, horizontal and vertical functions.

Aurora supports up to 128TB of storage capacity and supports dynamic scaling of storage layer in units of 10GB. In terms of computing, Aurora supports scalable configurations for multiple read replicas. Each region can have an additional 15 Aurora replicas. In addition, Aurora provides multi-primary architecture to support four read/write nodes. Its Serverless architecture allows vertical scaling and reduces typical latency to under a second, while the Global Database enables a single database cluster to span multiple AWS Regions in low latency.

Aurora already provides great scalability with the growth of user data volume. Can it handle more data and support more concurrent access? You may consider using sharding to support the configuration of multiple underlying Aurora clusters. To this end, a series of blogs, including this one, provides you with a reference in choosing between Proxy and JDBC for sharding.

1.1 Why sharding is needed

AWS Aurora offers a single relational database. Primary-secondary, multi-primary, and global database, and other forms of hosting architecture can satisfy various architectural scenarios above. However, Aurora doesn’t provide direct support for sharding scenarios, and sharding has a variety of forms, such as vertical and horizontal forms. If we want to further increase data capacity, some problems have to be solved, such as cross-node database Join, associated query, distributed transactions, SQL sorting, page turning, function calculation, database global primary key, capacity planning, and secondary capacity expansion after sharding.

1.2 Sharding methods

It is generally accepted that when the capacity of a MySQL table is less than 10 million, the time spent on queries is optimal because at this time the height of its BTREE index is between 3 and 5. Data sharding can reduce the amount of data in a single table and distribute the read and write loads to different data nodes at the same time. Data sharding can be divided into vertical sharding and horizontal sharding.

1. Advantages of vertical sharding

  • Address the coupling of business system and make clearer.
  • Implement hierarchical management, maintenance, monitoring, and expansion to data of different businesses, like micro-service governance.
  • In high concurrency scenarios, vertical sharding removes the bottleneck of IO, database connections, and hardware resources on a single machine to some extent.

2. Disadvantages of vertical sharding

  • After splitting the library, Join can only be implemented by interface aggregation, which will increase the complexity of development.
  • After splitting the library, it is complex to process distributed transactions.
  • There is a large amount of data on a single table and horizontal sharding is required.

3. Advantages of horizontal sharding

  • There is no such performance bottleneck as a large amount of data on a single database and high concurrency, and it increases system stability and load capacity.
  • The business modules do not need to be split due to minor modification on the application client.

4. Disadvantages of horizontal sharding

  • Transaction consistency across shards is hard to be guaranteed;
  • The performance of associated query in cross-library Join is poor.
  • It’s difficult to scale the data many times and maintenance is a big workload.

Based on the analysis above, and the available studis on popular sharding middleware, we selected ShardingSphere, an open source product, combined with Amazon Aurora to introduce how the combination of these two products meets various forms of sharding and how to solve the problems brought by sharding.

ShardingSphere is an open source ecosystem including a set of distributed database middleware solutions, including 3 independent products, Sharding-JDBC, Sharding-Proxy & Sharding-Sidecar.

2. ShardingSphere introduction:

The characteristics of Sharding-JDBC are:

  1. With the client end connecting directly to the database, it provides service in the form of jar and requires no extra deployment and dependence.
  2. It can be considered as an enhanced JDBC driver, which is fully compatible with JDBC and all kinds of ORM frameworks.
  3. Applicable in any ORM framework based on JDBC, such as JPA, Hibernate, Mybatis, Spring JDBC Template or direct use of JDBC.
  4. Support any third-party database connection pool, such as DBCP, C3P0, BoneCP, Druid, HikariCP;
  5. Support any kind of JDBC standard database: MySQL, Oracle, SQLServer, PostgreSQL and any databases accessible to JDBC.
  6. Sharding-JDBC adopts decentralized architecture, applicable to high-performance light-weight OLTP application developed with Java

Hybrid Structure Integrating Sharding-JDBC and Applications

Sharding-JDBC’s core concepts

Data node: The smallest unit of a data slice, consisting of a data source name and a data table, such as ds_0.product_order_0.

Actual table: The physical table that really exists in the horizontal sharding database, such as product order tables: product_order_0, product_order_1, and product_order_2.

Logic table: The logical name of the horizontal sharding databases (tables) with the same schema. For instance, the logic table of the order product_order_0, product_order_1, and product_order_2 is product_order.

Binding table: It refers to the primary table and the joiner table with the same sharding rules. For example, product_order table and product_order_item are sharded by order_id, so they are binding tables with each other. Cartesian product correlation will not appear in the multi-tables correlating query, so the query efficiency will increase greatly.

Broadcast table: It refers to tables that exist in all sharding database sources. The schema and data must consist in each database. It can be applied to the small data volume that needs to correlate with big data tables to query, dictionary table and configuration table for example.

3. Testing ShardingSphere-JDBC

3.1 Example project

Download the example project code locally. In order to ensure the stability of the test code, we choose shardingsphere-example-4.0.0 version.

git clone https://github.com/apache/shardingsphere-example.git

Project description:

shardingsphere-example
  ├── example-core
  │   ├── config-utility
  │   ├── example-api
  │   ├── example-raw-jdbc
  │   ├── example-spring-jpa #spring+jpa integration-based entity,repository
  │   └── example-spring-mybatis
  ├── sharding-jdbc-example
  │   ├── sharding-example
  │   │   ├── sharding-raw-jdbc-example
  │   │   ├── sharding-spring-boot-jpa-example #integration-based sharding-jdbc functions
  │   │   ├── sharding-spring-boot-mybatis-example
  │   │   ├── sharding-spring-namespace-jpa-example
  │   │   └── sharding-spring-namespace-mybatis-example
  │   ├── orchestration-example
  │   │   ├── orchestration-raw-jdbc-example
  │   │   ├── orchestration-spring-boot-example #integration-based sharding-jdbc governance function
  │   │   └── orchestration-spring-namespace-example
  │   ├── transaction-example
  │   │   ├── transaction-2pc-xa-example #sharding-jdbc sample of two-phase commit for a distributed transaction
  │   │   └──transaction-base-seata-example #sharding-jdbc distributed transaction seata sample
  │   ├── other-feature-example
  │   │   ├── hint-example
  │   │   └── encrypt-example
  ├── sharding-proxy-example
  │   └── sharding-proxy-boot-mybatis-example
  └── src/resources
        └── manual_schema.sql  

Configuration file description:

application-master-slave.properties #read/write splitting profile
application-sharding-databases-tables.properties #sharding profile
application-sharding-databases.properties       #library split profile only
application-sharding-master-slave.properties    #sharding and read/write splitting profile
application-sharding-tables.properties          #table split profile
application.properties                         #spring boot profile

Code logic description:

The following is the entry class of the Spring Boot application below. Execute it to run the project.

The execution logic of demo is as follows:

3.2 Verifying read/write splitting

As business grows, the write and read requests can be split to different database nodes to effectively promote the processing capability of the entire database cluster. Aurora uses a reader/writer endpoint to meet users' requirements to write and read with strong consistency, and a read-only endpoint to meet the requirements to read without strong consistency. Aurora's read and write latency is within single-digit milliseconds, much lower than MySQL's binlog-based logical replication, so there's a lot of loads that can be directed to a read-only endpoint.

Through the one primary and multiple secondary configuration, query requests can be evenly distributed to multiple data replicas, which further improves the processing capability of the system. Read/write splitting can improve the throughput and availability of system, but it can also lead to data inconsistency. Aurora provides a primary/secondary architecture in a fully managed form, but applications on the upper-layer still need to manage multiple data sources when interacting with Aurora, routing SQL requests to different nodes based on the read/write type of SQL statements and certain routing policies.

ShardingSphere-JDBC provides read/write splitting features and it is integrated with application programs so that the complex configuration between application programs and database clusters can be separated from application programs. Developers can manage the Shard through configuration files and combine it with ORM frameworks such as Spring JPA and Mybatis to completely separate the duplicated logic from the code, which greatly improves the ability to maintain code and reduces the coupling between code and database.

3.2.1 Setting up the database environment

Create a set of Aurora MySQL read/write splitting clusters. The model is db.r5.2xlarge. Each set of clusters has one write node and two read nodes.

3.2.2 Configuring Sharding-JDBC

application.properties spring boot Master profile description:

You need to replace the green ones with your own environment configuration.

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create-drop
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true

#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#Activate master-slave configuration item so that sharding-jdbc can use master-slave profile
spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-master-slave.properties sharding-jdbc profile description:

spring.shardingsphere.datasource.names=ds_master,ds_slave_0,ds_slave_1
# data souce-master
spring.shardingsphere.datasource.ds_master.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master.password=Your master DB password
spring.shardingsphere.datasource.ds_master.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master.jdbc-url=Your primary DB data sourceurl spring.shardingsphere.datasource.ds_master.username=Your primary DB username
# data source-slave
spring.shardingsphere.datasource.ds_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_slave_0.password= Your slave DB password
spring.shardingsphere.datasource.ds_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_slave_0.jdbc-url=Your slave DB data source url
spring.shardingsphere.datasource.ds_slave_0.username= Your slave DB username
# data source-slave
spring.shardingsphere.datasource.ds_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_slave_1.password= Your slave DB password
spring.shardingsphere.datasource.ds_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_slave_1.jdbc-url= Your slave DB data source url
spring.shardingsphere.datasource.ds_slave_1.username= Your slave DB username
# Routing Policy Configuration
spring.shardingsphere.masterslave.load-balance-algorithm-type=round_robin
spring.shardingsphere.masterslave.name=ds_ms
spring.shardingsphere.masterslave.master-data-source-name=ds_master
spring.shardingsphere.masterslave.slave-data-source-names=ds_slave_0,ds_slave_1
# sharding-jdbc configures the information storage mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log,and you can see the conversion from logical SQL to actual SQL from the print
spring.shardingsphere.props.sql.show=true

 

3.2.3 Test and verification process description

  • Test environment data initialization: Spring JPA initialization automatically creates tables for testing.

  • Write data to the master instance

As shown in the ShardingSphere-SQL log figure below, the write SQL is executed on the ds_master data source.

  • Data query operations are performed on the slave library.

As shown in the ShardingSphere-SQL log figure below, the read SQL is executed on the ds_slave data source in the form of polling.

[INFO ] 2022-04-02 19:43:39,376 --main-- [ShardingSphere-SQL] Rule Type: master-slave 
[INFO ] 2022-04-02 19:43:39,376 --main-- [ShardingSphere-SQL] SQL: select orderentit0_.order_id as order_id1_1_, orderentit0_.address_id as address_2_1_, 
orderentit0_.status as status3_1_, orderentit0_.user_id as user_id4_1_ from t_order orderentit0_ ::: DataSources: ds_slave_0 
---------------------------- Print OrderItem Data -------------------
Hibernate: select orderiteme1_.order_item_id as order_it1_2_, orderiteme1_.order_id as order_id2_2_, orderiteme1_.status as status3_2_, orderiteme1_.user_id 
as user_id4_2_ from t_order orderentit0_ cross join t_order_item orderiteme1_ where orderentit0_.order_id=orderiteme1_.order_id
[INFO ] 2022-04-02 19:43:40,898 --main-- [ShardingSphere-SQL] Rule Type: master-slave 
[INFO ] 2022-04-02 19:43:40,898 --main-- [ShardingSphere-SQL] SQL: select orderiteme1_.order_item_id as order_it1_2_, orderiteme1_.order_id as order_id2_2_, orderiteme1_.status as status3_2_, 
orderiteme1_.user_id as user_id4_2_ from t_order orderentit0_ cross join t_order_item orderiteme1_ where orderentit0_.order_id=orderiteme1_.order_id ::: DataSources: ds_slave_1 

Note: As shown in the figure below, if there are both reads and writes in a transaction, Sharding-JDBC routes both read and write operations to the master library. If the read/write requests are not in the same transaction, the corresponding read requests are distributed to different read nodes according to the routing policy.

@Override
@Transactional // When a transaction is started, both read and write in the transaction go through the master library. When closed, read goes through the slave library and write goes through the master library
public void processSuccess() throws SQLException {
    System.out.println("-------------- Process Success Begin ---------------");
    List<Long> orderIds = insertData();
    printData();
    deleteData(orderIds);
    printData();
    System.out.println("-------------- Process Success Finish --------------");
}

3.2.4 Verifying Aurora failover scenario

The Aurora database environment adopts the configuration described in Section 2.2.1.

3.2.4.1 Verification process description

  1. Start the Spring-Boot project

2. Perform a failover on Aurora’s console

3. Execute the Rest API request

4. Repeatedly execute POST (http://localhost:8088/save-user) until the call to the API failed to write to Aurora and eventually recovered successfully.

5. The following figure shows the process of executing code failover. It takes about 37 seconds from the time when the latest SQL write is successfully performed to the time when the next SQL write is successfully performed. That is, the application can be automatically recovered from Aurora failover, and the recovery time is about 37 seconds.

3.3 Testing table sharding-only function

3.3.1 Configuring Sharding-JDBC

application.properties spring boot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create-drop
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true
#spring.profiles.active=sharding-databases
#Activate sharding-tables configuration items
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
# spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-sharding-tables.properties sharding-jdbc profile description

## configure primary-key policy
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# configure the binding relation of t_order and t_order_item
spring.shardingsphere.sharding.binding-tables[0]=t_order,t_order_item
# configure broadcast tables
spring.shardingsphere.sharding.broadcast-tables=t_address
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

 

3.3.2 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC routing rules are configured, the client executes DDL, and Sharding-JDBC automatically creates corresponding tables according to the table splitting rules. If t_address is a broadcast table, create a t_address because there is only one master instance. Two physical tables t_order_0 and t_order_1 will be created when creating t_order.

2. Write operation

As shown in the figure below, Logic SQL inserts a record into t_order. When Sharding-JDBC is executed, data will be distributed to t_order_0 and t_order_1 according to the table splitting rules.

When t_order and t_order_item are bound, the records associated with order_item and order are placed on the same physical table.

3. Read operation

As shown in the figure below, perform the join query operations to order and order_item under the binding table, and the physical shard is precisely located based on the binding relationship.

The join query operations on order and order_item under the unbound table will traverse all shards.

3.4 Testing database sharding-only function

3.4.1 Setting up the database environment

Create two instances on Aurora: ds_0 and ds_1

When the sharding-spring-boot-jpa-example project is started, tables t_order, t_order_itemt_address will be created on two Aurora instances.

3.4.2 Configuring Sharding-JDBC

application.properties springboot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true

# Activate sharding-databases configuration items
spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-sharding-databases.properties sharding-jdbc profile description

spring.shardingsphere.datasource.names=ds_0,ds_1
# ds_0
spring.shardingsphere.datasource.ds_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_0.jdbc-url= spring.shardingsphere.datasource.ds_0.username= 
spring.shardingsphere.datasource.ds_0.password=
# ds_1
spring.shardingsphere.datasource.ds_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_1.jdbc-url= 
spring.shardingsphere.datasource.ds_1.username= 
spring.shardingsphere.datasource.ds_1.password=
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
spring.shardingsphere.sharding.default-data-source-name=ds_0

spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

 

3.4.3 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC’s library splitting and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, physical tables will be created on ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.

2. Write operation

For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.

The tables t_order and t_order_item of the slave library are written on the table in the corresponding instance according to the slave library field and routing policy.

3. Read operation

Query order is routed to the corresponding Aurora instance according to the routing rules of the slave library .

Query Address. Since address is a broadcast table, an instance of address will be randomly selected and queried from the nodes used.

As shown in the figure below, perform the join query operations to order and order_item under the binding table, and the physical shard is precisely located based on the binding relationship.

3.5 Verifying sharding function

3.5.1 Setting up the database environment

As shown in the figure below, create two instances on Aurora: ds_0 and ds_1

When the sharding-spring-boot-jpa-example project is started, physical tables t_order_01, t_order_02, t_order_item_01,and t_order_item_02 and global table t_address will be created on two Aurora instances.

3.5.2 Configuring Sharding-JDBC

application.properties springboot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true
# Activate sharding-databases-tables configuration items
#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave

application-sharding-databases.properties sharding-jdbc profile description

spring.shardingsphere.datasource.names=ds_0,ds_1
# ds_0
spring.shardingsphere.datasource.ds_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_0.jdbc-url= 306/dev?useSSL=false&characterEncoding=utf-8
spring.shardingsphere.datasource.ds_0.username= 
spring.shardingsphere.datasource.ds_0.password=
spring.shardingsphere.datasource.ds_0.max-active=16
# ds_1
spring.shardingsphere.datasource.ds_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_1.jdbc-url= 
spring.shardingsphere.datasource.ds_1.username= 
spring.shardingsphere.datasource.ds_1.password=
spring.shardingsphere.datasource.ds_1.max-active=16
# default library splitting policy
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
# Tables that do not meet the library splitting policy are placed on ds_0
spring.shardingsphere.sharding.default-data-source-name=ds_0
# t_order table splitting policy
spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order_$->{0..1}
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.algorithm-expression=t_order_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
# t_order_item table splitting policy
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# sharding-jdbc mdoe
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

 

3.5.3 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC’s sharding and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, t_address will be created on both ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.

2. Write operation

For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.

The tables t_order and t_order_item of the sub-library are written to the table on the corresponding instance according to the slave library field and routing policy.

3. Read operation

The read operation is similar to the library split function verification described in section2.4.3.

3.6 Testing database sharding, table sharding and read/write splitting function

3.6.1 Setting up the database environment

The following figure shows the physical table of the created database instance.

3.6.2 Configuring Sharding-JDBC

application.properties spring boot master profile description

# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true

# activate sharding-databases-tables configuration items
#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
spring.profiles.active=sharding-master-slave

application-sharding-master-slave.properties sharding-jdbc profile description

The url, name and password of the database need to be changed to your own database parameters.

spring.shardingsphere.datasource.names=ds_master_0,ds_master_1,ds_master_0_slave_0,ds_master_0_slave_1,ds_master_1_slave_0,ds_master_1_slave_1
spring.shardingsphere.datasource.ds_master_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0.jdbc-url= spring.shardingsphere.datasource.ds_master_0.username= 
spring.shardingsphere.datasource.ds_master_0.password=
spring.shardingsphere.datasource.ds_master_0.max-active=16
spring.shardingsphere.datasource.ds_master_0_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0_slave_0.jdbc-url= spring.shardingsphere.datasource.ds_master_0_slave_0.username= 
spring.shardingsphere.datasource.ds_master_0_slave_0.password=
spring.shardingsphere.datasource.ds_master_0_slave_0.max-active=16
spring.shardingsphere.datasource.ds_master_0_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0_slave_1.jdbc-url= spring.shardingsphere.datasource.ds_master_0_slave_1.username= 
spring.shardingsphere.datasource.ds_master_0_slave_1.password=
spring.shardingsphere.datasource.ds_master_0_slave_1.max-active=16
spring.shardingsphere.datasource.ds_master_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1.jdbc-url= 
spring.shardingsphere.datasource.ds_master_1.username= 
spring.shardingsphere.datasource.ds_master_1.password=
spring.shardingsphere.datasource.ds_master_1.max-active=16
spring.shardingsphere.datasource.ds_master_1_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1_slave_0.jdbc-url=
spring.shardingsphere.datasource.ds_master_1_slave_0.username=
spring.shardingsphere.datasource.ds_master_1_slave_0.password=
spring.shardingsphere.datasource.ds_master_1_slave_0.max-active=16
spring.shardingsphere.datasource.ds_master_1_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1_slave_1.jdbc-url= spring.shardingsphere.datasource.ds_master_1_slave_1.username=admin
spring.shardingsphere.datasource.ds_master_1_slave_1.password=
spring.shardingsphere.datasource.ds_master_1_slave_1.max-active=16
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
spring.shardingsphere.sharding.default-data-source-name=ds_master_0
spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order_$->{0..1}
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.algorithm-expression=t_order_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# master/slave data source and slave data source configuration
spring.shardingsphere.sharding.master-slave-rules.ds_0.master-data-source-name=ds_master_0
spring.shardingsphere.sharding.master-slave-rules.ds_0.slave-data-source-names=ds_master_0_slave_0, ds_master_0_slave_1
spring.shardingsphere.sharding.master-slave-rules.ds_1.master-data-source-name=ds_master_1
spring.shardingsphere.sharding.master-slave-rules.ds_1.slave-data-source-names=ds_master_1_slave_0, ds_master_1_slave_1
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true

 

3.6.3 Test and verification process description

1. DDL operation

JPA automatically creates tables for testing. When Sharding-JDBC’s library splitting and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, t_address will be created on both ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.

2. Write operation

For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.

The tables t_order and t_order_item of the slave library are written to the table on the corresponding instance according to the slave library field and routing policy.

3. Read operation

The join query operations on order and order_item under the binding table are shown below.

3. Conclusion

As an open source product focusing on database enhancement, ShardingSphere is pretty good in terms of its community activitiy, product maturity and documentation richness.

Among its products, ShardingSphere-JDBC is a sharding solution based on the client-side, which supports all sharding scenarios. And there’s no need to introduce an intermediate layer like Proxy, so the complexity of operation and maintenance is reduced. Its latency is theoretically lower than Proxy due to the lack of intermediate layer. In addition, ShardingSphere-JDBC can support a variety of relational databases based on SQL standards such as MySQL/PostgreSQL/Oracle/SQL Server, etc.

However, due to the integration of Sharding-JDBC with the application program, it only supports Java language for now, and is strongly dependent on the application programs. Nevertheless, Sharding-JDBC separates all sharding configuration from the application program, which brings relatively small changes when switching to other middleware.

In conclusion, Sharding-JDBC is a good choice if you use a Java-based system and have to to interconnect with different relational databases — and don’t want to bother with introducing an intermediate layer.

Author

Sun Jinhua

A senior solution architect at AWS, Sun is responsible for the design and consult on cloud architecture. for providing customers with cloud-related design and consulting services. Before joining AWS, he ran his own business, specializing in building e-commerce platforms and designing the overall architecture for e-commerce platforms of automotive companies. He worked in a global leading communication equipment company as a senior engineer, responsible for the development and architecture design of multiple subsystems of LTE equipment system. He has rich experience in architecture design with high concurrency and high availability system, microservice architecture design, database, middleware, IOT etc.

Substrate Parachain Template: A New Cumulus-based Substrate Node

Substrate Cumulus Parachain Template

A new Cumulus-based Substrate node, ready for hacking :cloud:

This project is a fork of the Substrate Node Template modified to include dependencies required for registering this node as a parathread or parachain to an established relay chain.

👉 Learn more about parachains here, and parathreads here.

Build & Run

Follow these steps to prepare a local Substrate development environment :hammer_and_wrench:

Setup of Machine

If necessary, refer to the setup instructions at the Substrate Developer Hub.

Build

Once the development environment is set up, build the Cumulus Parachain Template. This command will build the Wasm Runtime and native code:

cargo build --release

Relay Chain

NOTE: In the following two sections, we document how to manually start a few relay chain nodes, start a parachain node (collator), and register the parachain with the relay chain.

We also have the polkadot-launch CLI tool that automate the following steps and help you easily launch relay chains and parachains. However it is still good to go through the following procedures once to understand the mechanism for running and registering a parachain.

To operate a parathread or parachain, you must connect to a relay chain. Typically you would test on a local Rococo development network, then move to the testnet, and finally launch on the mainnet. Keep in mind you need to configure the specific relay chain you will connect to in your collator chain_spec.rs. In the following examples, we will use rococo-local as the relay network.

Build Relay Chain

Clone and build Polkadot (beware of the version tag we used):

# Get a fresh clone, or `cd` to where you have polkadot already:
git clone -b v0.9.7 --depth 1 https://github.com/paritytech/polkadot.git
cd polkadot
cargo build --release

Generate the Relay Chain Chainspec

First, we create the chain specification file (chainspec). Note the chainspec file must be generated on a single node and then shared among all nodes!

👉 Learn more about chain specification here.

./target/release/polkadot build-spec \
--chain rococo-local \
--raw \
--disable-default-bootnode \
> rococo_local.json

Start Relay Chain

We need n + 1 full validator nodes running on a relay chain to accept n parachain / parathread connections. Here we will start two relay chain nodes so we can have one parachain node connecting in later.

From the Polkadot working directory:

# Start Relay `Alice` node
./target/release/polkadot \
--chain ./rococo_local.json \
-d /tmp/relay/alice \
--validator \
--alice \
--port 50555

Open a new terminal, same directory:

# Start Relay `Bob` node
./target/release/polkadot \
--chain ./rococo_local.json \
-d /tmp/relay/bob \
--validator \
--bob \
--port 50556

Add more nodes as needed, with non-conflicting ports, DB directories, and validator keys (--charlie, --dave, etc.).

Reserve a ParaID

To connect to a relay chain, you must first _reserve a ParaId for your parathread that will become a parachain. To do this, you will need sufficient amount of currency on the network account to reserve the ID.

In this example, we will use Charlie development account where we have funds available. Once you submit this extrinsic successfully, you can start your collators.

The easiest way to reserve your ParaId is via Polkadot Apps UI under the Parachains -> Parathreads tab and use the + ParaID button.

Parachain

Select the Correct Relay Chain

To operate your parachain, you need to specify the correct relay chain you will connect to in your collator chain_spec.rs. Specifically you pass the command for the network you need in the Extensions of your ChainSpec::from_genesis() in the code.

Extensions {
    relay_chain: "rococo-local".into(), // You MUST set this to the correct network!
    para_id: id.into(),
},

You can choose from any pre-set runtime chainspec in the Polkadot repo, by referring to the cli/src/command.rs and node/service/src/chain_spec.rs files or generate your own and use that. See the Cumulus Workshop for how.

In the following examples, we will use the rococo-local relay network we setup in the last section.

Export the Parachain Genesis and Runtime

We first generate the genesis state and genesis wasm needed for the parachain registration.

# Build the parachain node (from it's top level dir)
cd substrate-parachain-template
cargo build --release

# Folder to store resource files needed for parachain registration
mkdir -p resources

# Build the chainspec
./target/release/parachain-collator build-spec \
--disable-default-bootnode > ./resources/template-local-plain.json

# Build the raw chainspec file
./target/release/parachain-collator build-spec \
--chain=./resources/template-local-plain.json \
--raw --disable-default-bootnode > ./resources/template-local-raw.json

# Export genesis state to `./resources`, using 2000 as the ParaId
./target/release/parachain-collator export-genesis-state --parachain-id 2000 > ./resources/para-2000-genesis

# Export the genesis wasm
./target/release/parachain-collator export-genesis-wasm > ./resources/para-2000-wasm

NOTE: we have set the para_ID to be 2000 here. This must be unique for all parathreads/chains on the relay chain you register with. You must reserve this first on the relay chain for the testnet or mainnet.

Start a Parachain Node (Collator)

From the parachain template working directory:

# NOTE: this command assumes the chain spec is in a directory named `polkadot`
# that is at the same level of the template working directory. Change as needed.
#
# It also assumes a ParaId of 2000. Change as needed.
./target/release/parachain-collator \
-d /tmp/parachain/alice \
--collator \
--alice \
--force-authoring \
--ws-port 9945 \
--parachain-id 2000 \
-- \
--execution wasm \
--chain ../polkadot/rococo_local.json

Output:

2021-05-30 16:57:39 Parachain Collator Template
2021-05-30 16:57:39 ✌️  version 3.0.0-acce183-x86_64-linux-gnu
2021-05-30 16:57:39 ❤️  by Anonymous, 2017-2021
2021-05-30 16:57:39 📋 Chain specification: Local Testnet
2021-05-30 16:57:39 🏷 Node name: Alice
2021-05-30 16:57:39 👤 Role: AUTHORITY
2021-05-30 16:57:39 💾 Database: RocksDb at /tmp/parachain/alice/chains/local_testnet/db
2021-05-30 16:57:39 ⛓  Native runtime: template-parachain-1 (template-parachain-0.tx1.au1)
2021-05-30 16:57:41 Parachain id: Id(2000)
2021-05-30 16:57:41 Parachain Account: 5Ec4AhPUwPeyTFyuhGuBbD224mY85LKLMSqSSo33JYWCazU4
2021-05-30 16:57:41 Parachain genesis state: 0x0000000000000000000000000000000000000000000000000000000000000000000a96f42b5cb798190e5f679bb16970905087a9a9fc612fb5ca6b982b85783c0d03170a2e7597b7b7e3d84c05391d139a62b157e78786d8c082f29dcf4c11131400
2021-05-30 16:57:41 Is collating: yes
2021-05-30 16:57:41 [Parachain] 🔨 Initializing Genesis block/state (state: 0x0a96…3c0d, header-hash: 0xd42b…f271)
2021-05-30 16:57:41 [Parachain] ⏱  Loaded block-time = 12s from block 0xd42bb78354bc21770e3f0930ed45c7377558d2d8e81ca4d457e573128aabf271
2021-05-30 16:57:43 [Relaychain] 🔨 Initializing Genesis block/state (state: 0xace1…1b62, header-hash: 0xfa68…cf58)
2021-05-30 16:57:43 [Relaychain] 👴 Loading GRANDPA authority set from genesis on what appears to be first startup.
2021-05-30 16:57:44 [Relaychain] ⏱  Loaded block-time = 6s from block 0xfa68f5abd2a80394b87c9bd07e0f4eee781b8c696d0a22c8e5ba38ae10e1cf58
2021-05-30 16:57:44 [Relaychain] 👶 Creating empty BABE epoch changes on what appears to be first startup.
2021-05-30 16:57:44 [Relaychain] 🏷 Local node identity is: 12D3KooWBjYK2W4dsBfsrFA9tZCStb5ogPb6STQqi2AK9awXfXyG
2021-05-30 16:57:44 [Relaychain] 📦 Highest known block at #0
2021-05-30 16:57:44 [Relaychain] 〽️ Prometheus server started at 127.0.0.1:9616
2021-05-30 16:57:44 [Relaychain] Listening for new connections on 127.0.0.1:9945.
2021-05-30 16:57:44 [Parachain] Using default protocol ID "sup" because none is configured in the chain specs
2021-05-30 16:57:44 [Parachain] 🏷 Local node identity is: 12D3KooWADBSC58of6ng2M29YTDkmWCGehHoUZhsy9LGkHgYscBw
2021-05-30 16:57:44 [Parachain] 📦 Highest known block at #0
2021-05-30 16:57:44 [Parachain] Unable to listen on 127.0.0.1:9945
2021-05-30 16:57:44 [Parachain] Unable to bind RPC server to 127.0.0.1:9945. Trying random port.
2021-05-30 16:57:44 [Parachain] Listening for new connections on 127.0.0.1:45141.
2021-05-30 16:57:45 [Relaychain] 🔍 Discovered new external address for our node: /ip4/192.168.42.204/tcp/30334/ws/p2p/12D3KooWBjYK2W4dsBfsrFA9tZCStb5ogPb6STQqi2AK9awXfXyG
2021-05-30 16:57:45 [Parachain] 🔍 Discovered new external address for our node: /ip4/192.168.42.204/tcp/30333/p2p/12D3KooWADBSC58of6ng2M29YTDkmWCGehHoUZhsy9LGkHgYscBw
2021-05-30 16:57:48 [Relaychain] ✨ Imported #8 (0xe60b…9b0a)
2021-05-30 16:57:49 [Relaychain] 💤 Idle (2 peers), best: #8 (0xe60b…9b0a), finalized #5 (0x1e6f…567c), ⬇ 4.5kiB/s ⬆ 2.2kiB/s
2021-05-30 16:57:49 [Parachain] 💤 Idle (0 peers), best: #0 (0xd42b…f271), finalized #0 (0xd42b…f271), ⬇ 2.0kiB/s ⬆ 1.7kiB/s
2021-05-30 16:57:54 [Relaychain] ✨ Imported #9 (0x1af9…c9be)
2021-05-30 16:57:54 [Relaychain] ✨ Imported #9 (0x6ed8…fdf6)
2021-05-30 16:57:54 [Relaychain] 💤 Idle (2 peers), best: #9 (0x1af9…c9be), finalized #6 (0x3319…69a2), ⬇ 1.8kiB/s ⬆ 0.5kiB/s
2021-05-30 16:57:54 [Parachain] 💤 Idle (0 peers), best: #0 (0xd42b…f271), finalized #0 (0xd42b…f271), ⬇ 0.2kiB/s ⬆ 0.2kiB/s
2021-05-30 16:57:59 [Relaychain] 💤 Idle (2 peers), best: #9 (0x1af9…c9be), finalized #7 (0x5b50…1e5b), ⬇ 0.6kiB/s ⬆ 0.4kiB/s
2021-05-30 16:57:59 [Parachain] 💤 Idle (0 peers), best: #0 (0xd42b…f271), finalized #0 (0xd42b…f271), ⬇ 0 ⬆ 0
2021-05-30 16:58:00 [Relaychain] ✨ Imported #10 (0xc9c9…1ca3)

You see messages are from both a relaychain node and a parachain node. This is because a relay chain light client is also run next to the parachain collator.

Parachain Registration

Now that you have two relay chain nodes, and a parachain node accompanied with a relay chain light client running, the next step is to register the parachain in the relay chain with the following steps (for detail, refer to the Substrate Cumulus Worship):

  • Goto Polkadot Apps UI, connecting to your relay chain.
  • Execute a sudo extrinsic on the relay chain by going to Developer -> sudo page.
  • Pick paraSudoWrapper -> sudoScheduleParaInitialize(id, genesis) as the extrinsic type, shown below.

Polkadot Apps UI

  • Set the id: ParaId to 2,000 (or whatever ParaId you used above), and set the parachain: Bool option to Yes.
  • For the genesisHead, drag the genesis state file exported above, para-2000-genesis, in.
  • For the validationCode, drag the genesis wasm file exported above, para-2000-wasm, in.

Note: When registering to the public Rococo testnet, ensure you set a unique paraId larger than 1,000. Values below 1,000 are reserved exclusively for system parachains.

Restart the Parachain (Collator)

The collator node may need to be restarted to get it functioning as expected. After a new epoch starts on the relay chain, your parachain will come online. Once this happens, you should see the collator start reporting parachain blocks:

# Notice the relay epoch change! Only then do we start parachain collating!
#
2021-05-30 17:00:04 [Relaychain] 💤 Idle (2 peers), best: #30 (0xfc02…2a2a), finalized #28 (0x10ff…6539), ⬇ 1.0kiB/s ⬆ 0.3kiB/s
2021-05-30 17:00:04 [Parachain] 💤 Idle (0 peers), best: #0 (0xd42b…f271), finalized #0 (0xd42b…f271), ⬇ 0 ⬆ 0
2021-05-30 17:00:06 [Relaychain] 👶 New epoch 3 launching at block 0x68bc…0605 (block slot 270402601 >= start slot 270402601).
2021-05-30 17:00:06 [Relaychain] 👶 Next epoch starts at slot 270402611
2021-05-30 17:00:06 [Relaychain] ✨ Imported #31 (0x68bc…0605)
2021-05-30 17:00:06 [Parachain] Starting collation. relay_parent=0x68bcc93d24a31a2c89800a56c7a2b275fe9ca7bd63f829b64588ae0d99280605 at=0xd42bb78354bc21770e3f0930ed45c7377558d2d8e81ca4d457e573128aabf271
2021-05-30 17:00:06 [Parachain] 🙌 Starting consensus session on top of parent 0xd42bb78354bc21770e3f0930ed45c7377558d2d8e81ca4d457e573128aabf271
2021-05-30 17:00:06 [Parachain] 🎁 Prepared block for proposing at 1 [hash: 0xf6507812bf60bf53af1311f775aac03869be870df6b0406b2969784d0935cb92; parent_hash: 0xd42b…f271; extrinsics (2): [0x1bf5…1d76, 0x7c9b…4e23]]
2021-05-30 17:00:06 [Parachain] 🔖 Pre-sealed block for proposal at 1. Hash now 0x80fc151d7ccf228b802525022b6de257e42388ec7dc3c1dd7de491313650ccae, previously 0xf6507812bf60bf53af1311f775aac03869be870df6b0406b2969784d0935cb92.
2021-05-30 17:00:06 [Parachain] ✨ Imported #1 (0x80fc…ccae)
2021-05-30 17:00:06 [Parachain] Produced proof-of-validity candidate. block_hash=0x80fc151d7ccf228b802525022b6de257e42388ec7dc3c1dd7de491313650ccae
2021-05-30 17:00:09 [Relaychain] 💤 Idle (2 peers), best: #31 (0x68bc…0605), finalized #29 (0xa6fa…9e16), ⬇ 1.2kiB/s ⬆ 129.9kiB/s
2021-05-30 17:00:09 [Parachain] 💤 Idle (0 peers), best: #0 (0xd42b…f271), finalized #0 (0xd42b…f271), ⬇ 0 ⬆ 0
2021-05-30 17:00:12 [Relaychain] ✨ Imported #32 (0x5e92…ba30)
2021-05-30 17:00:12 [Relaychain] Moving approval window from session 0..=2 to 0..=3
2021-05-30 17:00:12 [Relaychain] ✨ Imported #32 (0x8144…74eb)
2021-05-30 17:00:14 [Relaychain] 💤 Idle (2 peers), best: #32 (0x5e92…ba30), finalized #29 (0xa6fa…9e16), ⬇ 1.4kiB/s ⬆ 0.2kiB/s
2021-05-30 17:00:14 [Parachain] 💤 Idle (0 peers), best: #0 (0xd42b…f271), finalized #0 (0xd42b…f271), ⬇ 0 ⬆ 0
2021-05-30 17:00:18 [Relaychain] ✨ Imported #33 (0x8c30…9ccd)
2021-05-30 17:00:18 [Parachain] Starting collation. relay_parent=0x8c30ce9e6e9867824eb2aff40148ac1ed64cf464f51c5f2574013b44b20f9ccd at=0x80fc151d7ccf228b802525022b6de257e42388ec7dc3c1dd7de491313650ccae
2021-05-30 17:00:19 [Relaychain] 💤 Idle (2 peers), best: #33 (0x8c30…9ccd), finalized #30 (0xfc02…2a2a), ⬇ 0.7kiB/s ⬆ 0.4kiB/s
2021-05-30 17:00:19 [Parachain] 💤 Idle (0 peers), best: #1 (0x80fc…ccae), finalized #0 (0xd42b…f271), ⬇ 0 ⬆ 0
2021-05-30 17:00:22 [Relaychain] 👴 Applying authority set change scheduled at block #31
2021-05-30 17:00:22 [Relaychain] 👴 Applying GRANDPA set change to new set [(Public(88dc3417d5058ec4b4503e0c12ea1a0a89be200fe98922423d4334014fa6b0ee (5FA9nQDV...)), 1), (Public(d17c2d7823ebf260fd138f2d7e27d114c0145d968b5ff5006125f2414fadae69 (5GoNkf6W...)), 1)]
2021-05-30 17:00:22 [Relaychain] 👴 Imported justification for block #31 that triggers command Changing authorities, signaling voter.
2021-05-30 17:00:24 [Relaychain] ✨ Imported #34 (0x211b…febf)
2021-05-30 17:00:24 [Parachain] Starting collation. relay_parent=0x211b3c53bebeff8af05e8f283d59fe171b7f91a5bf9c4669d88943f5a42bfebf at=0x80fc151d7ccf228b802525022b6de257e42388ec7dc3c1dd7de491313650ccae
2021-05-30 17:00:24 [Parachain] 🙌 Starting consensus session on top of parent 0x80fc151d7ccf228b802525022b6de257e42388ec7dc3c1dd7de491313650ccae
2021-05-30 17:00:24 [Parachain] 🎁 Prepared block for proposing at 2 [hash: 0x10fcb3180e966729c842d1b0c4d8d2c4028cfa8bef02b909af5ef787e6a6a694; parent_hash: 0x80fc…ccae; extrinsics (2): [0x4a6c…1fc6, 0x6b84…7cea]]
2021-05-30 17:00:24 [Parachain] 🔖 Pre-sealed block for proposal at 2. Hash now 0x5087fd06b1b73d90cfc3ad175df8495b378fffbb02fea212cc9e49a00fd8b5a0, previously 0x10fcb3180e966729c842d1b0c4d8d2c4028cfa8bef02b909af5ef787e6a6a694.
2021-05-30 17:00:24 [Parachain] ✨ Imported #2 (0x5087…b5a0)
2021-05-30 17:00:24 [Parachain] Produced proof-of-validity candidate. block_hash=0x5087fd06b1b73d90cfc3ad175df8495b378fffbb02fea212cc9e49a00fd8b5a0
2021-05-30 17:00:24 [Relaychain] 💤 Idle (2 peers), best: #34 (0x211b…febf), finalized #31 (0x68bc…0605), ⬇ 1.0kiB/s ⬆ 130.1kiB/s
2021-05-30 17:00:24 [Parachain] 💤 Idle (0 peers), best: #1 (0x80fc…ccae), finalized #0 (0xd42b…f271), ⬇ 0 ⬆ 0
2021-05-30 17:00:29 [Relaychain] 💤 Idle (2 peers), best: #34 (0x211b…febf), finalized #32 (0x5e92…ba30), ⬇ 0.2kiB/s ⬆ 0.1kiB/s
2021-05-30 17:00:29 [Parachain] 💤 Idle (0 peers), best: #1 (0x80fc…ccae), finalized #0 (0xd42b…f271), ⬇ 0 ⬆ 0
2021-05-30 17:00:30 [Relaychain] ✨ Imported #35 (0xee07…38a0)
2021-05-30 17:00:34 [Relaychain] 💤 Idle (2 peers), best: #35 (0xee07…38a0), finalized #33 (0x8c30…9ccd), ⬇ 0.9kiB/s ⬆ 0.3kiB/s
2021-05-30 17:00:34 [Parachain] 💤 Idle (0 peers), best: #1 (0x80fc…ccae), finalized #1 (0x80fc…ccae), ⬇ 0 ⬆ 0
2021-05-30 17:00:36 [Relaychain] ✨ Imported #36 (0xe8ce…4af6)
2021-05-30 17:00:36 [Parachain] Starting collation. relay_parent=0xe8cec8015c0c7bf508bf3f2f82b1696e9cca078e814b0f6671f0b0d5dfe84af6 at=0x5087fd06b1b73d90cfc3ad175df8495b378fffbb02fea212cc9e49a00fd8b5a0
2021-05-30 17:00:39 [Relaychain] 💤 Idle (2 peers), best: #36 (0xe8ce…4af6), finalized #33 (0x8c30…9ccd), ⬇ 0.6kiB/s ⬆ 0.1kiB/s
2021-05-30 17:00:39 [Parachain] 💤 Idle (0 peers), best: #2 (0x5087…b5a0), finalized #1 (0x80fc…ccae), ⬇ 0 ⬆ 0

Note the delay here! It may take some time for your relay chain to enter a new epoch.

Rococo & Westend Relay Chain Testnets

Is this Cumulus Parachain Template Rococo & Westend testnets compatible? Yes!

  • Rococo is the testnet of Kusama (join the Rococo Faucet to get testing funds).
  • Westend is the testnet of Polkadot (join the Westend Faucet to get testing funds).

See the Cumulus Workshop for the latest instructions to register a parathread/parachain on a relay chain.

NOTE: When running the relay chain and parachain, you must use the same tagged version of Polkadot and Cumulus so the collator would register successfully to the relay chain. You should test locally registering your parachain successfully before attempting to connect to any running relay chain network!

Find chainspec files to connect to live networks here. You want to be sure to use the correct git release tag in these files, as they change from time to time and must match the live network!

These networks are under constant development - so please follow the progress and update of your parachains in lock step with the testnet changes if you wish to connect to the network. Do join the Parachain Technical matrix chat room to ask questions and connect with the parachain building teams.

Learn More

  • More detailed instructions to use Cumulus parachains are found in the Cumulus Workshop.
  • Refer to the upstream Substrate Node Template to learn more about the structure of this project, the capabilities it encapsulates and the way in which those capabilities are implemented.
  • Learn more about how a parachain block is added to a finalized chain here.

Download Details:
Author: aresprotocols
Source Code: https://github.com/aresprotocols/substrate-parachain-template
License: Unlicense License

#rust  #blockchain #substrate #parachain #polkadot