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Lang updates (#235) 

* conventional progress

* Revert "conventional progress"

This reverts commit 9b404416b9.

* Spelling fixes, request for author inclusion

* Update schema & readme to match new naming convention

Co-authored-by: Alex Reich <Alex_Reich@mechanicsbank.com>
pull/240/head
Alex Reich 2021-09-14 21:17:46 -07:00 committed by GitHub
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8
README.md
View File

@ -18,7 +18,7 @@ To install this library, download the latest version of [NuGet Package](https://
There are several ways to populate workflows for the Rules Engine as listed below.
You need to store the rules based on the [schema definition](https://github.com/microsoft/RulesEngine/blob/main/schema/workflowRules-schema.json) given and they can be stored in any store as deemed appropriate like Azure Blob Storage, Cosmos DB, Azure App Configuration, [Entity Framework](https://github.com/microsoft/RulesEngine#entity-framework), SQL Servers, file systems etc. For RuleExpressionType `LamdaExpression`, the rule is written as a [lambda expressions](https://docs.microsoft.com/en-us/dotnet/csharp/programming-guide/statements-expressions-operators/lambda-expressions).
You need to store the rules based on the [schema definition](https://github.com/microsoft/RulesEngine/blob/main/schema/workflow-schema.json) given and they can be stored in any store as deemed appropriate like Azure Blob Storage, Cosmos DB, Azure App Configuration, [Entity Framework](https://github.com/microsoft/RulesEngine#entity-framework), SQL Servers, file systems etc. For RuleExpressionType `LamdaExpression`, the rule is written as a [lambda expressions](https://docs.microsoft.com/en-us/dotnet/csharp/programming-guide/statements-expressions-operators/lambda-expressions).
An example rule:
@ -33,7 +33,7 @@ An example rule:
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country == \"india\" AND input1.loyalityFactor <= 2 AND input1.totalPurchasesToDate >= 5000"
"Expression": "input1.country == \"india\" AND input1.loyaltyFactor <= 2 AND input1.totalPurchasesToDate >= 5000"
},
{
"RuleName": "GiveDiscount20",
@ -41,7 +41,7 @@ An example rule:
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country == \"india\" AND input1.loyalityFactor >= 3 AND input1.totalPurchasesToDate >= 10000"
"Expression": "input1.country == \"india\" AND input1.loyaltyFactor >= 3 AND input1.totalPurchasesToDate >= 10000"
}
]
}
@ -106,7 +106,7 @@ var bre = new RulesEngine.RulesEngine(wfr, null);
![](https://github.com/microsoft/RulesEngine/blob/main/assets/BlockDiagram.png)
The rules can be stored in any store and be fed to the system in a structure which adheres to the [schema](https://github.com/microsoft/RulesEngine/blob/main/schema/workflowRules-schema.json) of WorkFlow model.
The rules can be stored in any store and be fed to the system in a structure which adheres to the [schema](https://github.com/microsoft/RulesEngine/blob/main/schema/workflow-schema.json) of WorkFlow model.
A wrapper needs to be created over the Rules Engine package, which will get the rules and input message(s) from any store that your system dictates and put it into the Engine. The wrapper then handles the output using appropriate means.

2
RulesEngine.sln
View File

@ -18,7 +18,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
CHANGELOG.md = CHANGELOG.md
global.json = global.json
README.md = README.md
schema\workflowRules-schema.json = schema\workflowRules-schema.json
schema\workflow-schema.json = schema\workflow-schema.json
EndProjectSection
EndProject
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "RulesEngineBenchmark", "benchmark\RulesEngineBenchmark\RulesEngineBenchmark.csproj", "{C058809F-C720-4EFC-925D-A486627B238B}"

12
benchmark/RulesEngineBenchmark/Workflows/Discount.json
View File

@ -8,7 +8,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country == \"india\" AND input1.loyalityFactor <= 2 AND input1.totalPurchasesToDate >= 5000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
"Expression": "input1.country == \"india\" AND input1.loyaltyFactor <= 2 AND input1.totalPurchasesToDate >= 5000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
},
{
"RuleName": "GiveDiscount20",
@ -16,7 +16,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country == \"india\" AND input1.loyalityFactor == 3 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
"Expression": "input1.country == \"india\" AND input1.loyaltyFactor == 3 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
},
{
"RuleName": "GiveDiscount25",
@ -24,7 +24,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country != \"india\" AND input1.loyalityFactor >= 2 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 5"
"Expression": "input1.country != \"india\" AND input1.loyaltyFactor >= 2 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 5"
},
{
"RuleName": "GiveDiscount30",
@ -32,7 +32,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.loyalityFactor > 3 AND input1.totalPurchasesToDate >= 50000 AND input1.totalPurchasesToDate <= 100000 AND input2.totalOrders > 5 AND input3.noOfVisitsPerMonth > 15"
"Expression": "input1.loyaltyFactor > 3 AND input1.totalPurchasesToDate >= 50000 AND input1.totalPurchasesToDate <= 100000 AND input2.totalOrders > 5 AND input3.noOfVisitsPerMonth > 15"
},
{
"RuleName": "GiveDiscount30NestedOrExample",
@ -46,7 +46,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.loyalityFactor > 3 AND input1.totalPurchasesToDate >= 50000 AND input1.totalPurchasesToDate <= 100000"
"Expression": "input1.loyaltyFactor > 3 AND input1.totalPurchasesToDate >= 50000 AND input1.totalPurchasesToDate <= 100000"
},
{
"RuleName": "OrHasHighNumberOfTotalOrders",
@ -69,7 +69,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.loyalityFactor > 3"
"Expression": "input1.loyaltyFactor > 3"
},
{
"RuleName": "AndHasTotalPurchased100000",

2
demo/DemoApp/EFDemo.cs
View File

@ -20,7 +20,7 @@ namespace DemoApp
public void Run()
{
Console.WriteLine($"Running {nameof(EFDemo)}....");
var basicInfo = "{\"name\": \"hello\",\"email\": \"abcy@xyz.com\",\"creditHistory\": \"good\",\"country\": \"canada\",\"loyalityFactor\": 3,\"totalPurchasesToDate\": 10000}";
var basicInfo = "{\"name\": \"hello\",\"email\": \"abcy@xyz.com\",\"creditHistory\": \"good\",\"country\": \"canada\",\"loyaltyFactor\": 3,\"totalPurchasesToDate\": 10000}";
var orderInfo = "{\"totalOrders\": 5,\"recurringItems\": 2}";
var telemetryInfo = "{\"noOfVisitsPerMonth\": 10,\"percentageOfBuyingToVisit\": 15}";

2
demo/DemoApp/JSONDemo.cs
View File

@ -17,7 +17,7 @@ namespace DemoApp
public void Run()
{
Console.WriteLine($"Running {nameof(JSONDemo)}....");
var basicInfo = "{\"name\": \"hello\",\"email\": \"abcy@xyz.com\",\"creditHistory\": \"good\",\"country\": \"canada\",\"loyalityFactor\": 3,\"totalPurchasesToDate\": 10000}";
var basicInfo = "{\"name\": \"hello\",\"email\": \"abcy@xyz.com\",\"creditHistory\": \"good\",\"country\": \"canada\",\"loyaltyFactor\": 3,\"totalPurchasesToDate\": 10000}";
var orderInfo = "{\"totalOrders\": 5,\"recurringItems\": 2}";
var telemetryInfo = "{\"noOfVisitsPerMonth\": 10,\"percentageOfBuyingToVisit\": 15}";

12
demo/DemoApp/Workflows/Discount.json
View File

@ -8,7 +8,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country == \"india\" AND input1.loyalityFactor <= 2 AND input1.totalPurchasesToDate >= 5000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
"Expression": "input1.country == \"india\" AND input1.loyaltyFactor <= 2 AND input1.totalPurchasesToDate >= 5000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
},
{
"RuleName": "GiveDiscount20",
@ -16,7 +16,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country == \"india\" AND input1.loyalityFactor == 3 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
"Expression": "input1.country == \"india\" AND input1.loyaltyFactor == 3 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
},
{
"RuleName": "GiveDiscount25",
@ -24,7 +24,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country != \"india\" AND input1.loyalityFactor >= 2 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 5"
"Expression": "input1.country != \"india\" AND input1.loyaltyFactor >= 2 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 5"
},
{
"RuleName": "GiveDiscount30",
@ -32,7 +32,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.loyalityFactor > 3 AND input1.totalPurchasesToDate >= 50000 AND input1.totalPurchasesToDate <= 100000 AND input2.totalOrders > 5 AND input3.noOfVisitsPerMonth > 15"
"Expression": "input1.loyaltyFactor > 3 AND input1.totalPurchasesToDate >= 50000 AND input1.totalPurchasesToDate <= 100000 AND input2.totalOrders > 5 AND input3.noOfVisitsPerMonth > 15"
},
{
"RuleName": "GiveDiscount30NestedOrExample",
@ -46,7 +46,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.loyalityFactor > 3 AND input1.totalPurchasesToDate >= 50000 AND input1.totalPurchasesToDate <= 100000"
"Expression": "input1.loyaltyFactor > 3 AND input1.totalPurchasesToDate >= 50000 AND input1.totalPurchasesToDate <= 100000"
},
{
"RuleName": "OrHasHighNumberOfTotalOrders",
@ -69,7 +69,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.loyalityFactor > 3"
"Expression": "input1.loyaltyFactor > 3"
},
{
"RuleName": "AndHasTotalPurchased100000",

0
schema/workflowRules-schema.json → schema/workflow-schema.json
View File

2
src/RulesEngine/RulesEngine.csproj
View File

@ -6,7 +6,7 @@
<Copyright>Copyright (c) Microsoft Corporation.</Copyright>
<PackageLicenseFile>LICENSE</PackageLicenseFile>
<PackageProjectUrl>https://github.com/microsoft/RulesEngine</PackageProjectUrl>
<Authors>Abbas Cyclewala, Dishant Munjal, Yogesh Prajapati</Authors>
<Authors>Abbas Cyclewala, Dishant Munjal, Yogesh Prajapati, Alex Reich</Authors>
<Description>Rules Engine is a package for abstracting business logic/rules/policies out of the system. This works in a very simple way by giving you an ability to put your rules in a store outside the core logic of the system thus ensuring that any change in rules doesn't affect the core system.</Description>
<PackageReleaseNotes>https://github.com/microsoft/RulesEngine/blob/main/CHANGELOG.md</PackageReleaseNotes>
<PackageTags>BRE, Rules Engine, Abstraction</PackageTags>

6
test/RulesEngine.UnitTest/BusinessRuleEngineTest.cs
View File

@ -775,7 +775,7 @@ namespace RulesEngine.UnitTest
private dynamic GetInput1()
{
var converter = new ExpandoObjectConverter();
var basicInfo = "{\"name\": \"Dishant\",\"email\": \"abc@xyz.com\",\"creditHistory\": \"good\",\"country\": \"canada\",\"loyalityFactor\": 3,\"totalPurchasesToDate\": 10000}";
var basicInfo = "{\"name\": \"Dishant\",\"email\": \"abc@xyz.com\",\"creditHistory\": \"good\",\"country\": \"canada\",\"loyaltyFactor\": 3,\"totalPurchasesToDate\": 10000}";
return JsonConvert.DeserializeObject<ExpandoObject>(basicInfo, converter);
}
@ -801,10 +801,10 @@ namespace RulesEngine.UnitTest
/// </returns>
private static dynamic[] GetInputs4()
{
var basicInfo = "{\"name\": \"Dishant\",\"email\": \"abc@xyz.com\",\"creditHistory\": \"good\",\"country\": \"canada\",\"loyalityFactor\": 3,\"totalPurchasesToDate\": 70000}";
var basicInfo = "{\"name\": \"Dishant\",\"email\": \"abc@xyz.com\",\"creditHistory\": \"good\",\"country\": \"canada\",\"loyaltyFactor\": 3,\"totalPurchasesToDate\": 70000}";
var orderInfo = "{\"totalOrders\": 50,\"recurringItems\": 2}";
var telemetryInfo = "{\"noOfVisitsPerMonth\": 10,\"percentageOfBuyingToVisit\": 15}";
var laborCategoriesInput = "[{\"country\": \"india\", \"loyalityFactor\": 2, \"totalPurchasesToDate\": 20000}]";
var laborCategoriesInput = "[{\"country\": \"india\", \"loyaltyFactor\": 2, \"totalPurchasesToDate\": 20000}]";
var currentLaborCategoryInput = "{\"CurrentLaborCategoryProp\":\"TestVal2\"}";
dynamic input1 = JsonConvert.DeserializeObject<List<RuleTestClass>>(laborCategoriesInput);

4
test/RulesEngine.UnitTest/RuleTestClass.cs
View File

@ -12,8 +12,8 @@ namespace RulesEngine.UnitTest
[JsonProperty("country")]
public string Country { get; set; }
[JsonProperty("loyalityFactor")]
public int LoyalityFactor { get; set; }
[JsonProperty("loyaltyFactor")]
public int loyaltyFactor { get; set; }
public int TotalPurchasesToDate { get; set; }
}
}

2
test/RulesEngine.UnitTest/TestData/rules1.json
View File

@ -7,7 +7,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country == \"canada\" AND input1.loyalityFactor <= 4"
"Expression": "input1.country == \"canada\" AND input1.loyaltyFactor <= 4"
}
]
}

6
test/RulesEngine.UnitTest/TestData/rules2.json
View File

@ -13,7 +13,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country == \"india\" AND input1.loyalityFactor <= 2 AND input1.totalPurchasesToDate >= 5000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
"Expression": "input1.country == \"india\" AND input1.loyaltyFactor <= 2 AND input1.totalPurchasesToDate >= 5000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
},
{
"RuleName": "GiveDiscount20",
@ -21,7 +21,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country == \"india\" AND input1.loyalityFactor == 3 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
"Expression": "input1.country == \"india\" AND input1.loyaltyFactor == 3 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 2"
},
{
"RuleName": "GiveDiscount25",
@ -29,7 +29,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.country != \"india\" AND input1.loyalityFactor >= 2 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 5"
"Expression": "input1.country != \"india\" AND input1.loyaltyFactor >= 2 AND input1.totalPurchasesToDate >= 10000 AND input2.totalOrders > 2 AND input3.noOfVisitsPerMonth > 5"
}
]
}

2
test/RulesEngine.UnitTest/TestData/rules3.json
View File

@ -7,7 +7,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input1.couy == \"india\" AND input1.loyalityFactor <= 2 AND input1.totalPurchasesToDate >= 5000 AND input2.totalOrders > 2 AND input2.noOfVisitsPerMonth > 2"
"Expression": "input1.couy == \"india\" AND input1.loyaltyFactor <= 2 AND input1.totalPurchasesToDate >= 5000 AND input2.totalOrders > 2 AND input2.noOfVisitsPerMonth > 2"
}
]
}

14
test/RulesEngine.UnitTest/TestData/rules4.json
View File

@ -5,7 +5,7 @@
{
"RuleName": "GiveDiscount10",
"SuccessEvent": "10",
"ErrorMessage": "One or more adjust rules failed, with loyalityFactor : $(model1.loyalityFactor), country : $(model1.country), totalPurchasesToDate : $(model1.totalPurchasesToDate), model2 : $(model2)",
"ErrorMessage": "One or more adjust rules failed, with loyaltyFactor : $(model1.loyaltyFactor), country : $(model1.country), totalPurchasesToDate : $(model1.totalPurchasesToDate), model2 : $(model2)",
"ErrorType": "Error",
"localParams": [
{
@ -18,7 +18,7 @@
}
],
"RuleExpressionType": "LambdaExpression",
"Expression": "model1.country == \"india\" AND model1.loyalityFactor <= 2 AND model1.totalPurchasesToDate >= 5000 AND model2"
"Expression": "model1.country == \"india\" AND model1.loyaltyFactor <= 2 AND model1.totalPurchasesToDate >= 5000 AND model2"
},
{
"RuleName": "GiveDiscount100",
@ -35,12 +35,12 @@
}
],
"RuleExpressionType": "LambdaExpression",
"Expression": "model1.country == \"india\" AND model1.loyalityFactor < 0 AND model1.totalPurchasesToDate >= 5000 AND model2"
"Expression": "model1.country == \"india\" AND model1.loyaltyFactor < 0 AND model1.totalPurchasesToDate >= 5000 AND model2"
},
{
"RuleName": "GiveDiscount25",
"SuccessEvent": "25",
"ErrorMessage": "One or more adjust rules failed, country : $(input4.country), loyalityFactor : $(input4.loyalityFactor), totalPurchasesToDate : $(input4.totalPurchasesToDate), totalOrders : $(input5.totalOrders), noOfVisitsPerMonth : $(input30.noOfVisitsPerMonth)",
"ErrorMessage": "One or more adjust rules failed, country : $(input4.country), loyaltyFactor : $(input4.loyaltyFactor), totalPurchasesToDate : $(input4.totalPurchasesToDate), totalOrders : $(input5.totalOrders), noOfVisitsPerMonth : $(input30.noOfVisitsPerMonth)",
"ErrorType": "Error",
"localParams": [
{
@ -53,7 +53,7 @@
}
],
"RuleExpressionType": "LambdaExpression",
"Expression": "input4.country == \"india\" AND input4.loyalityFactor >= 2 AND input4.totalPurchasesToDate <= 10 AND input5.totalOrders > 2 AND input3.noOfVisitsPerMonth > 5"
"Expression": "input4.country == \"india\" AND input4.loyaltyFactor >= 2 AND input4.totalPurchasesToDate <= 10 AND input5.totalOrders > 2 AND input3.noOfVisitsPerMonth > 5"
},
{
"RuleName": "GiveDiscount30",
@ -61,7 +61,7 @@
"ErrorMessage": "One or more adjust rules failed.",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input4.loyalityFactor > 30 AND input4.totalPurchasesToDate >= 50000 AND input4.totalPurchasesToDate <= 100000 AND input5.totalOrders > 5 AND input3.noOfVisitsPerMonth > 15"
"Expression": "input4.loyaltyFactor > 30 AND input4.totalPurchasesToDate >= 50000 AND input4.totalPurchasesToDate <= 100000 AND input5.totalOrders > 5 AND input3.noOfVisitsPerMonth > 15"
},
{
"RuleName": "GiveDiscount35",
@ -69,7 +69,7 @@
"ErrorMessage": "One or more adjust rules failed, totalPurchasesToDate : $(input4.totalPurchasesToDate), totalOrders : $(input5.totalOrders)",
"ErrorType": "Error",
"RuleExpressionType": "LambdaExpression",
"Expression": "input4.loyalityFactor > 30 AND input4.totalPurchasesToDate >= 100000 AND input5.totalOrders > 15 AND input3.noOfVisitsPerMonth > 25"
"Expression": "input4.loyaltyFactor > 30 AND input4.totalPurchasesToDate >= 100000 AND input5.totalOrders > 15 AND input3.noOfVisitsPerMonth > 25"
}
]
}