RaptorDB - The Document Store

The Document Store Database Engine

Preface

The code for RaptorDB is on github https://github.com/mgholam/RaptorDB-Document, I will be actively maintaining and adding features to this project as I believe very strongly in it. I will keep this article and the source code in sync.

RaptorDB is also on nuget : PM> Install-Package RaptorDB_doc

Introduction

• fastJSON : JSON object serializer and deserializer found here (http://www.codeproject.com/Articles/159450/fastJSON)
• fastBinaryJSON : a binary format JSON serializer found here (http://www.codeproject.com/Articles/345070/fastBinaryJSON)
• miniLog4net : a mini log4net replacement found here (http://www.codeproject.com/Articles/136118/Mini-Drop-in-Replacement-for-log4net)
• WAHBitArray : a Word Aligned Hybrid Compression technique for BitArrays found here (http://www.codeproject.com/Articles/214997/Word-Aligned-Hybrid-WAH-Compression-for-BitArrays)
• hOOt : full text search engine found here (http://www.codeproject.com/Articles/224722/hOOt-full-text-search-engine)
• RaptorDB the key value store : using the MGIndex for the indexing found here (http://www.codeproject.com/Articles/316816/RaptorDB-The-Key-Value-Store-V2)

Why?

• Development easier : writing less code means less bugs and less testing.
• Making changes faster : no need to edit database schema's with all the hassle of using other tools.
• Knowledge requirements lowest : you don't need to know the SQL language, indexing techniques, configuration parameters, just plain old c# (vb.net) will do.
• Maintenance simpler : changes are isolated so you are free to make them without worrying about breaking things elsewhere.
• Setup time & cost minimal : to get up and running you just need the .net framework and an IDE, no setting up database servers, running scripts, editing config files etc. (even on netbooks).
• Very fast execution : all this is done with speeds that put expensive servers to shame on mere laptops and netbooks.

Why another database?

• I believe that you can do a better job in pure .net like operating at 80% hard disk speed.
• Writing drivers and marshaling across process boundaries can have a performance hit.
• Implementing fundamental algorithms is an educational process.
• We have to push the boundaries of what is possible too see that the only limitation is our imagination and resolve.
• Someone will find it useful and who knows it might become one of the "big boys", they all started as "little boys" anyway.

Possible Uses

• The back-end store for your web based :
  • Forums
  • Blogs
  • Wikis
  • Content management systems
  • Web sites
• Easily build a SharePoint clone.
• Stand-alone applications that require storage ( no more installing SQL Server for a phone book app).
• Real world business applications (with caveats).

How we use data

• Viewing lists of things (list of customers, products, inventory transaction,...)
• Filtering lists of things (customer in country X)
• Aggregating lists of things (sum of qty in stock)
• Searching for things (much like filtering but may span multiple lists)
• Viewing a document (open the invoice number 123)
• Pivoting lists or building intelligence reports

1. Filtering lists
2. Aggregating lists

What is a Document Store database?

• Schema less design (just save it mentality) :
  • You don't need to define tables and columns before hand.
  • Your application can evolve and expand as needed without schema pains.
• Operational speed :
  • You read the data as it was saved the first time, so you can read in one disk operation and have the whole object hierarchy without multiple reads to tables and patching an object with data retrieved.
  • Does away with locks and deadlocks, so its much faster and scales better.
• Simplicity in application design :
  • The data access layer for the application is orders of magnitude simpler.
  • Changes to the application can be made anytime and on-site to the customers requirements.
• Lower development costs : Simpler and less code means development and testing is faster, easier with lower knowledge requirements for developers and maintainers.
• Historical data : Ability to have history of changes (essential for business applications).
• Easy and simple replication : Because the data is already encapsulated (the original document) replication is simple and painless as you just transfer the document and save it at the other end, without the pains of inconsistent tables like relational models.
• Operational cost savings : not requiring RDBM server licenses could offer considerable savings especially for web hosted and cloud based applications.

Foul ! the relational purists cry...

Is it for everybody?

With all the benefits stated, documents store databases are not for every body and every situation. The main point in divergence is if you require up to the millisecond data consistency across everything ( the cornerstone RDBM systems). If data validity is not an issue or your perfectly fine having the result valid at a certain point in time like the inventory count is valid for today at 9:00am then in my experience and you can get away with it for most web based application and probably 90% of business applications (this does not mean that these databases are consistent after hours but typically seconds) .

If your not willing to sacrifice this level of timed concurrency, then NoSql / document store databases are not for you. In my experience not being willing, is more a psychological barrier of the developers/designers than a technical requirement of the application and users.

Features

RaptorDB has been built with the following features in mind:

• Core Features :
  • Built on the algorithms of persisted dictionary version of RaptorDB (so you get all the benefits of that).
  • Built on pure .net so there is considerable performance benefits of not marshaling data across process boundaries via drivers.
  • Tiny size 194 KB (even smaller than the great Sqlite).
  • Strings are stored in UTF8 or Unicode format for views.
  • Documents can be stored as ASCII JSON, the JSON standard already encodes unicode in ASCII format or as Binary JSON for speed.
  • Embedded design.
  • Compressed data transfer over the network.
  • Transaction support added in v1.6.
• View Storage :
  • You don't need to specify column widths for string columns and RaptorDB can handle any size string (indexing is however limited to max 255 bytes for normal string columns).
  • Views (output from map functions) save data as binary not JSON ( much faster for indexing).
  • Primary list/View on object type definition for immediate access to the object saved (immediate call to a primary map function)
• Document Storage :
  • Ability to save byte[] data as well so you can save files etc. ( these are saved in a separate storage file).
• Indexing Engine :
  • Support for special hybrid bitmap index for views using Word Aligned Hybrid (WAH) compression.
  • Automatic indexing of views (self maintained no administration required).
  • All columns are indexed with the MGIndex.
  • Fast full text search support on string columns (you choose normal or full text search of strings in your view) .
• LINQ Query Processor :
  • Query Filter parser with nested parenthesis, AND and OR expressions.
• Map Function Engine :
  • Map functions are compiled .net code not JavaScript like competitors ( order of magnitude faster, see the performance tests).
  • Map functions have access to an API for doing queries and document fetches (you can do complex business logic in them).
  • Map functions type check the data saved, which makes reading the data easier and everything more consistent.

Limitations

The following limitations are in this release:

• Requires at least .net 4 (uses the Task library).
• Document and View Item count is limited to 4 billion items or Int32.
• This release is not code backward compatible with the key/value store version.
• Aggregation on views.
• Standalone service version is not available at the moment.
• Revision checking on documents is not supported at the moment.
• Sharding / Replication is not supported at the moment.
• Query filters only work with literal right hand sides (e.g. Name='bob' not StatusColumn > LastStatusColumn referencing another column)
• In transaction mode, currently the active thread does not see it's own data changes.

Rules

To help you work with RaptorDB better, below are some rules which will help you:

• You must have a Primary View for each type you are going to save.
• If a view has ConsistentView = True it will act like a Primary View.
• If a Primary View has TransactionMode = True then all operations on it and all views associated with it will be in a transaction and will Rollback if any view fails.
• BackgroudIndexing is turned off in transaction mode.
• Queries in a transaction will only see their own updates.

The Competition

There are a number competing storage systems, a few of which I have looked at, are below:

• MongoDB (c++): Great database which I love and is the main inspiration behind RaptorDB, although I have issues with its 32bit 4Gb database size limit and the memory map file design which could potentially corrupt easily. (Polymorphism has a workaround in mongodb if anyone wants to know).
• CouchDB / CouchBase (erlang): Another standard in document databases. The design is elegant.
• RavenDB (.net): Work done by Ayende which is a .net document database built on the Windows ESENT storage system.
• OrientDB (java) : The performance specs are impressive.

Performance Tests

Javascript vs .NET compiled map functions

Insert object performance

Query Performance

2012-04-29 12:38:40|DEBUG|1|RaptorDB.Views.ViewHandler|| query bitmap done (ms) : 40.0023
2012-04-29 12:38:40|DEBUG|1|RaptorDB.Views.ViewHandler|| query rows fetched (ms) : 33.0019
2012-04-29 12:38:40|DEBUG|1|RaptorDB.Views.ViewHandler|| query rows count : 300

2012-04-29 12:38:40|DEBUG|1|RaptorDB.Views.ViewHandler|| query bitmap done (ms) : 1.0001
2012-04-29 12:38:40|DEBUG|1|RaptorDB.Views.ViewHandler|| query rows fetched (ms) : 469.0268
2012-04-29 12:38:40|DEBUG|1|RaptorDB.Views.ViewHandler|| query rows count : 25875 

2012-04-29 12:38:45|DEBUG|1|RaptorDB.Views.ViewHandler|| query bitmap done (ms) : 4.0002
2012-04-29 12:38:45|DEBUG|1|RaptorDB.Views.ViewHandler|| query rows fetched (ms) : 6.0003
2012-04-29 12:38:45|DEBUG|1|RaptorDB.Views.ViewHandler|| query rows count : 500 

2012-04-29 12:38:45|DEBUG|1|RaptorDB.Views.ViewHandler|| query bitmap done (ms) : 0
2012-04-29 12:38:45|DEBUG|1|RaptorDB.Views.ViewHandler|| query rows fetched (ms) : 677.0387
2012-04-29 12:38:45|DEBUG|1|RaptorDB.Views.ViewHandler|| query rows count : 50000  

If you want to torture someone, make them write a LINQ Provider!

It took me around a month of intense research, debugging and tinkering to get my head around the LINQ provider interface and how it works, while the title of this section is a bit harsh but I hope it conveys the frustration I felt at the time.
To be fair what emerged is very clean, concise and elegant. Admittedly it is only the Expression evaluation part of LINQ and is a fraction of what you have to go through for a full LINQ provider. This was all I needed for RaptorDB so I will try to explain how it was done here for anybody wanting to continue as resources are very rare on this subject.

For RaptorDB we want a "where" clause parser in LINQ which will essentially filter the view data and give us the rows, this is done with the following command :

int j = 1000; 
var result = db.Query(typeof(SalesInvoice),
                 (SalesInvoice s) => (s.Serial > j &&  s.CustomerName == "aaa")
             ); 

The main part we are focusing on is the line :

(SalesInvoice s) => (s.Serial > j && s.CustomerName == "aaa") 

From this we want to parse the expression which reads : given the SalesInvoice type (used for denoting the property/column names, and serves no other purpose) filter where the [serial number is greater than j and the customer name is "aaa"] or the count is greater than zero. From this the query engine must determine the "column names" used and fetch them from the index file and get the associated values from that index and apply logical arithmetic on the results to get what we want.

The quirks of LINQ parsing

There are two quirks in parsing LINQ queries :

• Variables (the j in the above example) are replaced with a compiler placeholder and not the value.
• All the work is done in the VisitBinary method, for logical expression parsing and clause evaluation, so you have to be able to distinguish and handle them both.

How the LINQ parser works in RaptorDB

In RaptorDB we want to be able to extract and query the index for each clause in the filter expression based on the order and logic of the expression. Because the indexes are built on the WAHBitArray the result will be a WAHBitArray. All this is done in the following very small code (compared to writing a language parser) :

delegate WAHBitArray QueryExpression(string colname, RDBExpression exp, object from);

internal class QueryVisitor : ExpressionVisitor
{
    public QueryVisitor(QueryExpression express)
    {
        qexpression = express;
    }
    public Stack<object> _stack = new Stack<object>();
    public Stack<object> _bitmap = new Stack<object>();
    QueryExpression qexpression;

    protected override Expression VisitBinary(BinaryExpression b)
    {
        this.Visit(b.Left);
        ExpressionType t = b.NodeType;

        if (t == ExpressionType.Equal || t == ExpressionType.LessThan || t == ExpressionType.LessThanOrEqual ||
            t == ExpressionType.GreaterThan || t == ExpressionType.GreaterThanOrEqual)
            _stack.Push(b.NodeType);

        this.Visit(b.Right);
        t = b.NodeType;
        if (t == ExpressionType.Equal || t == ExpressionType.NotEqual ||
            t == ExpressionType.LessThanOrEqual || t == ExpressionType.LessThan ||
            t == ExpressionType.GreaterThanOrEqual || t == ExpressionType.GreaterThan)
        {
            // binary expression
            object lv = _stack.Pop();
            ExpressionType lo = (ExpressionType)_stack.Pop();
            object ln = _stack.Pop();
            RDBExpression exp = RDBExpression.Equal;

            if (lo == ExpressionType.LessThan)
                exp = RDBExpression.Less;
            else if (lo == ExpressionType.LessThanOrEqual)
                exp = RDBExpression.LessEqual;
            else if (lo == ExpressionType.GreaterThan)
                exp = RDBExpression.Greater;
            else if (lo == ExpressionType.GreaterThanOrEqual)
                exp = RDBExpression.GreaterEqual;

            _bitmap.Push(qexpression("" + ln, exp, lv));
        }

        if (t == ExpressionType.And || t == ExpressionType.AndAlso ||
            t == ExpressionType.Or || t == ExpressionType.OrElse)
        {
            // do bitmap operations
            WAHBitArray r = (WAHBitArray)_bitmap.Pop();
            WAHBitArray l = (WAHBitArray)_bitmap.Pop();

            if (t == ExpressionType.And || t == ExpressionType.AndAlso)
                _bitmap.Push(r.And(l));
            if (t == ExpressionType.Or || t == ExpressionType.OrElse)
                _bitmap.Push(r.Or(l));
        }
        return b;
    }

    protected override Expression VisitMethodCall(MethodCallExpression m)
    {
        string s = m.ToString();
        _stack.Push(s.Substring(s.IndexOf('.') + 1));
        return m;
    }

    protected override Expression VisitMember(MemberExpression m)
    {
        var e = base.VisitMember(m);
        var c = m.Expression as ConstantExpression;
        if (c != null)
        {
            Type t = c.Value.GetType();
            var x = t.InvokeMember(m.Member.Name, BindingFlags.GetField, null, c.Value, null);
            _stack.Push(x);
        }
        if (m.Expression != null && m.Expression.NodeType == ExpressionType.Parameter)
        {
            _stack.Push(m.Member.Name);
            return e;
        }
        return e;
    }

    protected override Expression VisitConstant(ConstantExpression c)
    {
        IQueryable q = c.Value as IQueryable;
        if (q != null)
            _stack.Push(q.ElementType.Name);
        else if (c.Value == null)
            _stack.Push(null);
        else
        {
            _stack.Push(c.Value);
            if (Type.GetTypeCode(c.Value.GetType()) == TypeCode.Object)
                _stack.Pop();
        }
        return c;
    }
}

Most of the work is done in the VisitBinary method (for evaluating logical [&& || ] operations and clauses [b>3] ) so to distinguish the two a stack is used store the clause values for further processing. VisitBinary will be called recursively for left and right sides of expressions so a stack of bitmap is also required for aggregating the results of the expression.

The constructor to the class takes two delegates which are supplied by the caller for handles to the underlying indexes which this class calls when a binary clause is completely parsed. The results are push onto the bitmap stack.

The VisitMember method is responsible for replacing the compiler generated code for constant values with the appropriate value ( the j in the above example).

The rest of the code is generally for extracting the "column names" without the prefixes (s.Serial -> Serial etc.).

A sample application

1. Define your Entities (plain c# objects) as you would doing domain driven development.
2. Create a Primary View for your base Entities.
3. Register your views with RaptorDB.
4. Save and query your data.
5. Add new views based on your requirements.

As you will see below this is so easy and simple that it just happens and you don't need to learn anything new or worry about configurations or breaking things at runtime as the compiler will catch your error at compile time.
A great feature is the total absence of anything SQL related, the associated schema pains and having to switch to a database management product to define and check tables and columns as everything is in your source file.

1. Creating Entities

The first thing you should do is define your entities or data classes (referred to as domain driven development), these are plain c# (vb.net) classes or POCO's like the following :

public class LineItem
{
    public decimal QTY { get; set; }
    public string Product { get; set; }
    public decimal Price { get; set; }
    public decimal Discount { get; set; }
}

public class SalesInvoice
{
    public SalesInvoice()
    {
        ID = Guid.NewGuid();
    }
    public Guid ID { get; set; }
    public string CustomerName { get; set; }
    public string Address { get; set; }
    public List<LineItem> Items { get; set; }
    public DateTime Date { get; set; }
    public int Serial { get; set; }
    public byte Status { get; set; }
}

There is nothing special about the above other than the lack of anything extra you need to do like adding attributes etc. (even Serializable) as they are not needed.

2. Creating Views

Next you create your primary view for your entities as follows :

public class SalesInvoiceView : View<SalesInvoice> // create a view for the SalesInvoice type
 {
     public class RowSchema  // define the schema for this view
     {
         public NormalString CustomerName; // CustomerName is a normal string index
         public DateTime InvoiceDate;
         public string Address;
         public int Serial;
         public byte Status;
     }

     public SalesInvoiceView()
     {
         this.Name = "SalesInvoice";
         this.Description = "A primary view for SalesInvoices";
         this.isPrimaryList = true;
         this.isActive = true;
         this.BackgroundIndexing = true;

         this.Schema = typeof(SalesInvoiceView.RowSchema);

         this.Mapper = (api, docid, doc) =>
         {
             api.Emit(docid, doc.CustomerName, doc.Date, doc.Address, doc.Serial, doc.Status);
         };
     }
 }

This is pretty straight forward also.

• This view is for a SalesInvoice object type.
• RowSchema is a class which defines the columns for this view
  • You can name this class anything as long as you register it with the Schema property
  • All value types are supported (int, string, decimal etc.)
  • NormalString is a special type which instructs the indexer to index this column as a string so you will have to specify all the string when querying.
  • If you specify a string property then RaptorDB will do full text indexing on that column, so you can search for words within that column when querying.
• BackgroundIndexing controls how the indexer does it's work on this view (i.e. block saves until each document is indexed when false).
• AddFireOnTypes controls when this view is called based on the input document type
• Mapper is the map function which will populate this view (i.e. extract information from the input document), you can add logic here if you need it. The order of the items must be the same as the schema you defined.
• With api you can Fetch a document, log debug information and Query another view.

3. Registering Views

Registering a view is as simple as :

rap.RegisterView(new SalesInvoiceView());

RaptorDB will do some checks on your view and if everything is fine it will return true, which means you are good to go.

4. Saving and querying data

Now you can use RaptorDB and save documents as follows :

var inv = new SalesInvoice()
{
    Date = FastDateTime.Now,
    Serial = i % 10000,
    CustomerName = "me " + i % 10,
    Status = (byte)(i % 4),
    Address = "df asd sdf asdf asdf"
};
inv.Items = new List<LineItem>();
for (int k = 0; k < 5; k++)
    inv.Items.Add(new LineItem()
         { Product = "prod " + k, Discount = 0, Price = 10 + k, QTY = 1 + k });

rap.Save(inv.ID, inv); // save to RaptorDB

Querying is as simple as writing LINQ predicates like the following:

var q = rap.Query(typeof(SalesInvoice), // call by the view type or the primary document type
                (SalesInvoice s) => (s.Serial < j) && (s.Status == 1 || s.Status == 3));

q = rap.Query("SalesItemRows",  // call by the view name
        (LineItem l) => (l.Product == "prod 1" || l.Product == "prod 3")); 

As you can see you can call the query in 2 ways by specifying the type of the view (or type of the document type for primary views) or by calling the string name of the view.

Screen Shots

From the below image you can see the test application doing its work. RaptorDB was configured to do background indexing, so 100,000 documents were inserted in 12 secs and the primary view was populated (the query results for 500 items) and the background indexer is working on populating the other view defined, which after a couple of queries shows the final results of 50,000 items.

How It Works

The diagram above shows a high level view of how RaptorDB works, a document is first inserted into the storage file then immediately a primary map function is called to generate a primary view for the document at which point control returns to the calling method. After a timer has elapsed other map functions are called to generate other views for that document.
There some term which you must be aware of when using RaptorDB document data store, these are the following:

• Document : is an object/entity which gets serialized as JSON
• DocID : is a Guid which uniquely identifies a document and must be given with the document
• View : is a like a standard database table
• Map Function : is a function that takes a document and emits values from that document into a view
• Index : is used to retrieve information when querying views.

What's a View?

{ invoice GUID, date, invoice number, status, salesman name }

{ invoice GUID, date, total sales amount, total sales discounts, salesman name, customer name }  

What's a map function?

What's a "Primary View" for?

The Main Cast of Characters in RaptorDB

• RaptorDB main interface : what your code sees and the main loader of other parts.
• Storage File : a fast and multi-threaded file storage mechanism used for both documents storage and view data.
• View : see below
• ViewManager : see below
• ViewHandler : see below

ViewHandler

1. Storing view data
2. Indexing columns based on query use

ViewManager

1. Loading and creating Views
2. Compiling view map functions.
3. Keeping track of mappings between the object types and views, or which view to send the object for inserting/updating.
4. Keeping track of last documents sent to views and the storage record numbers.
5. Periodic updating the views with new documents coming in

Query Executor

1. Parse the LINQ expression.
2. Check the column names exist in the view schema, validation error will be caught here.
3. Check to see if indexes exist for the columns used in the filter
4. Extract the bitmap indexes needed.
5. Extract the Deleted records bitmap.
6. Execute the filter and fetch the view rows from the view storage.

RaptorDB Setup

For RaptorDB to function you must follow these steps:

1. Define a primary view for objects which will allow immediate access to inserted objects via a list, inherited objects are supported for simplicity so you can define one for the base class.

The Save Process

The save process follows the following steps:

1. GetPrimaryListForType() : will try to get the primary map function for the object type and recurs the hierarchy until found.
2. SaveData() : saves the data to storage file, creates log entry in the log file and in memory log, the background indexer will process the log.
3. SavePrimaryView() : will call the primary map function for this type.
4. SaveInOtherViews() : will save to any other view that is defined.

Views and Map functions

1. Find the last DocID processed and increment.
2. FindMapFunctionsForType() : a list of map functions to execute for this object
   1. ExecuteMapFunction() : on the object and retrieve the new view rows with a reference to the DocID.
   2. DeleteFromView(DocID) : flag delete the old values for DocID.
   3. InsertView(newData) : add the new data from the map function to the view.
3. Find the next DocID and recurs the process.

The power of MGIndexs

* The above picture shows a b+tree/bitmap index structure not the MGIndex which uses a dictionary, but the principle is the same.

The real power is shown in the example below where you want to query for the following "Name='bob' and Code=between(1,3)", the query processor will take the filter, parse the values for that filter and generate an execution plan like below:

As you can see from the above diagram, the execution plan is a series of BitArrays and all you have to do to get the result is follow the bit arithmetic logic based on the parsed filter and {AND ,OR, NOT} the BitArrays together. These operations are typically in the sub millisecond range even for millions or rows in your database. The result is an position indexed row finder to the row contents which you read off disk, and send to the caller where 1 indicates read the row and 0 means skip the row contents (i.e. 100001... -> read record numbers :1,6,...)

Full Text Search

On the cutting room floor

• Paging of results.
• LINQ based aggregation (sum, count, ...).
• Compression support in storage.
• Usage statistics and monitoring information.
• Query caching.
• View schema changes.
• Revision checking for documents

Closing Remarks

This is a work in progress, I will be happy if anyone wants to join in.

Appendix v1.2

Some major features were added in this release so here they are:

• View Versioning
• Full text indexing attribute
• String queries
• Quering view types

View Versioning

In this version you can change the view schema or properties, and also add new views to existing documents and have the engine rebuild the view. This is controlled via a Version property in your view definition.

The responsiblity of incrementing this version number is up to you and you can decide when to do so and when it would make sense. RaptorDB will just check the verison numbers and act accordingly.

Full Text Indexing

A breaking change was the removal of the NormalString type in the schema of your view and replacing it with string and a [FullText] attribute, which is much more simpler and user friendly.

public class RowSchema  // define the schema for this view
{
    [FullText]
    public string CustomerName; // CustomerName is a hOOt index
    public DateTime InvoiceDate;
    public string Address;
    public int Serial;
    public byte Status;
}

String Queries

RaptorDB can now parse string LINQ queries and give you the results. This can be seen in the updated console application. You would probably want to stick to LINQ in your source code, but this might be useful if you need your users to generate filters in an UI for example.

This feature will be more prevalent in the server version as LINQ does not serialize accross boundries.

An interesting feature is that you can get near SQL syntax like :

var q = rap.Query(typeof(SalesItemRows), 
                "product = \"prod 1\" or product = \"prod 3\""));  

Where column can be non cased and you can use single '=' and have 'or' instead of c# style '||' etc.

Quering View Types

You can now give the view type to the Query function for querying.

var q = rap.Query(typeof(SalesItemRows), 
                ((LineItem l) => (l.Product == "prod 1" || l.Product == "prod 3"));  

Appendix v1.3

Some major features were added in this version:

• Result Schema Rows
• Windows Query App
• Client side LINQ aggregate
• api.EmitObject

Windows Query App

A windows application project was added to show case the data binding capabilities of RaptorDB. You can do the same functions of the console app but with visual feedback. To query the views just enter your view name and the query string in the text box and press enter.

In the menu a client side sum has been added which will give you the following results.

Client side LINQ aggregates

You can do client side aggregate queries like the following which is very powerful.

var q = rap.Query(typeof(SalesItemRowsView), (LineItem l) => (l.Product == "prod 1" || l.Product == "prod 3"));

// grouping
List<SalesItemRowsView.RowSchema> list = q.Rows.Cast<SalesItemRowsView.RowSchema>().ToList();
var e = from item in list group item by item.Product into grouped
        select new { Product = grouped.Key,
                     TotalPrice = grouped.Sum(product => product.Price),
                     TotalQTY = grouped.Sum(product => product.QTY)
                   };

The main point in the above is the Cast method which will give you the types so you can sum on.

api.EmitObject

To help you write less code you can use the api.EmitObject method in your mapper code which will match the object given to the view schema column names, you must make sure the names match.

this.Mapper = (api, docid, doc) =>
{
    if (doc.Status == 3 && doc.Items != null)
        foreach (var item in doc.Items)
            api.EmitObject(docid, item);
            // instead of writing the following
            //api.Emit(docid, item.Product, item.QTY, item.Price, item.Discount);
};

Appendix v1.4

A lot of major changes were made in this release, some of which are :

• Source code restructuring into separate projects.

• Server Mode

Parameters

Here are some of the parameters in the Globals.cs file which control the workings of RaptorDB.

Tweaking, Performance and Consistency

You can use RaptorDB from synchronous to eventually consistent manner by setting some parameters. You have the power to decide which parts of the data handling you need to be totally consistent to eventually consistent. This is done by some properties on your View definition.

• BackgroundIndexing
• ConsistentSaveToThisView

Obviously you will get less throughput when going synchronous.

Server Mode

In this release you can run RaptorDB in server mode which will take TCP connections from clients. Views in this release can be built into DLL files and deployed in the server's "Extensions" folder and the server will automatically read and use them on start-up. TCP Port numbers and data folder files are configurable.

RaptorDBServer.exe

RaptorDBServer allows you to install RaptorDB as a windows service or run on the command line, with the -i parameter you can install multiple service instances which can be differentiated by the -n name and the -p port parameters.

Client Usage

Using RaptorDB on a client is as simple as the following:

RaptorDBClient db = new RaptorDB.RaptorDBClient("localhost", 90 , "admin", "admin"); //  server, port, username, password

All the functionality of the embedded version is available via this interface.

Server Usage

You can start RaptorDB in server mode within your own code via the following:

var server = new RaptorDBServer(90, @"..\..\..\RaptorDBdata"); // port, data folder 

IRaptorDB interface

To make the experience of using the embedded version or the client version seamless, like yo can start embedded then progress to the client server version based on your usage, the IRaptorDB interface was created to isolate you from changes. You can see this in action in the sample query executor program supplied.

Dog fooding

I did a Scrum Backlog/Todo List type of application while I was testing MongoDB, so I decided to port it to RaptorDB. The conversion process was very simple and trivial, mostly because of the nature of document databases. The only change was that MongoDB returns a list of actual objects while RaptorDB doesn't so you need to do a Fetch for an object when you need one.
Unfortunately, I used commercial components so I can't post the source code here. Below is the source code differences:

public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }

        Mongo _mongo;
        IMongoDatabase _db;
        IMongoCollection<ToDo> _collection;

        private void eXITToolStripMenuItem_Click(object sender, EventArgs e)
        {
            // TODO : message box here
            this.Close();
        }


        private void exGrid1_RowDoubleClick(object sender, Janus.Windows.GridEX.RowActionEventArgs e)
        {
            openitem();
        }

        private void openitem()
        {
            var r = exGrid1.GetRow();
            if (r != null)
            {
                ShowItem(r.DataRow as ToDo);
            }
        }


        private void ShowItem(ToDo item)
        {
            frmTODO f = new frmTODO();
            f.SetData(item);
            var dr = f.ShowDialog();
            if (dr == DialogResult.OK)
            {
                var o = f.GetData() as ToDo;
                var doc = new Document();
                doc["GUID"] = o.GUID;
                _collection.Remove(doc);
                _collection.Save(o);
                FillCollection();
            }
        }

        private void frmMain_Load(object sender, EventArgs e)
        {
            _mongo = new Mongo();
            _mongo.Connect();
            _db = _mongo.GetDatabase("mytodo");
            _collection = _db.GetCollection<ToDo>();
            FillCollection();
            exGrid1.AddContextMenu("delete", "Delete", null);
            exGrid1.AddContextMenu("open", "Open", null);
            exGrid1.ContextMenuClicked += new JanusGrid.ContextMenuClicked(exGrid1_ContextMenuClicked);
        }

        void exGrid1_ContextMenuClicked(object sender, JanusGrid.ContextMenuClickedEventArg e)
        {
            if (e.MenuItem == "delete")
            {
                DialogResult dr = MessageBox.Show("delete?", "DELETE", 
MessageBoxButtons.YesNo, MessageBoxIcon.Stop, 
MessageBoxDefaultButton.Button2);
                if (dr == DialogResult.Yes)
                {
                    var o = exGrid1.GetRow().DataRow as ToDo;
                    var doc = new Document();
                    doc["GUID"] = o.GUID;
                    _collection.Remove(doc);
                    FillCollection();
                }
            }
            if (e.MenuItem == "open")
                openitem();
        }

        private void FillCollection()
        {
            var o = _collection.FindAll();
            BindingSource bs = new BindingSource();
            bs.AllowNew = true;
            bs.AddingNew += new AddingNewEventHandler(bs_AddingNew);
            bs.DataSource = o.Documents;
            exGrid1.DataSource = bs;
            exGrid1.AutoSizeColumns(true);
        }

        void bs_AddingNew(object sender, AddingNewEventArgs e)
        {
            e.NewObject = new ToDo();
        }

        private void addToolStripMenuItem_Click(object sender, EventArgs e)
        {
            ShowItem(new ToDo());
        }

        bool _editmode = false;
        private void editInPlaceToolStripMenuItem_Click(object sender, EventArgs e)
        {
            if (_editmode == false)
            {
                exGrid1.AllowEdit = Janus.Windows.GridEX.InheritableBoolean.True;
                exGrid1.AutoEdit = true;
                exGrid1.AllowAddNew = Janus.Windows.GridEX.InheritableBoolean.True;
                exGrid1.RecordUpdated += new EventHandler(exGrid1_RecordUpdated);
                exGrid1.RecordAdded += new EventHandler(exGrid1_RecordAdded);
                _editmode = true;
            }
        }

        void exGrid1_RecordAdded(object sender, EventArgs e)
        {
            var o = exGrid1.GetRow().DataRow as ToDo;
            var doc = new Document();
            doc["GUID"] = o.GUID;
            _collection.Remove(doc);
            _collection.Save(o);
        }

        void exGrid1_RecordUpdated(object sender, EventArgs e)
        {
            var o = exGrid1.GetRow().DataRow as ToDo;
            var doc = new Document();
            doc["GUID"] = o.GUID;
            _collection.Remove(doc);
            _collection.Save(o);
        }













        private void normalModeToolStripMenuItem_Click(object sender, EventArgs e)
        {
            if (_editmode == true)
            {
                exGrid1.AllowEdit = Janus.Windows.GridEX.InheritableBoolean.False;
                exGrid1.AllowAddNew = Janus.Windows.GridEX.InheritableBoolean.False;
                exGrid1.RecordUpdated -= new EventHandler(exGrid1_RecordUpdated);
                exGrid1.RecordAdded -= new EventHandler(exGrid1_RecordAdded);
                _editmode = false;
            }
        }

        void FontToolStripMenuItemClick(object sender, EventArgs e)
        {
            FontDialog ofd = new FontDialog();
            ofd.Font = this.Font;
            if (ofd.ShowDialog() == DialogResult.OK)
            {
                this.Font = ofd.Font;
                exGrid1.AutoSizeColumns(true);
            }
        }

        private void designerToolStripMenuItem_Click(object sender, EventArgs e)
        {
            //try
            {
                JanusGrid.ExGrid grid = new JanusGrid.ExGrid();
                grid.Name = "GridControl";
                MemoryStream ms = new MemoryStream();
                exGrid1.SaveLayoutFile(ms);
                ms.Seek(0l, SeekOrigin.Begin);
                grid.LoadLayoutFile(ms);
                Janus.Windows.GridEX.Design.GridEXDesigner gd = new Janus.Windows.GridEX.Design.GridEXDesigner();
                //try
                {
                    gd.Initialize(grid);
                    System.ComponentModel.Design.DesignerVerb dv = gd.Verbs[0];
                    dv.Invoke(grid);
                }
                //catch { }
                gd.Dispose();
                ms = new MemoryStream();
                grid.SaveLayoutFile(ms);
                ms.Seek(0l, SeekOrigin.Begin);
                exGrid1.LoadLayoutFile(ms);
                exGrid1.ResumeLayout();
            }
            //catch { }
        }
    }

public partial class frmMain : Form
   {
       public frmMain()
       {
           InitializeComponent();
       }

       RaptorDB.RaptorDB rap;



       private void eXITToolStripMenuItem_Click(object sender, EventArgs e)
       {
           // TODO : message box here
           rap.Shutdown();
           this.Close();
       }

       private void exGrid1_RowDoubleClick(object sender, Janus.Windows.GridEX.RowActionEventArgs e)
       {
           openitem();
       }

       private void openitem()
       {
           var r = exGrid1.GetRow();
           if (r != null)
           {
               ToDo t = (ToDo)rap.Fetch((r.DataRow as TodoView.RowSchema).docid);
               ShowItem(t);
           }
       }

       private void ShowItem(ToDo item)
       {
           frmTODO f = new frmTODO();
           f.SetData(item);
           var dr = f.ShowDialog();
           if (dr == DialogResult.OK)
           {
               var o = f.GetData() as ToDo;
               rap.Save(o.GUID, o);
               FillCollection();
           }
       }



       private void frmMain_Load(object sender, EventArgs e)
       {
           rap = RaptorDB.RaptorDB.Open("mytodo");
           rap.RegisterView(new TodoView());

           FillCollection();
           exGrid1.AddContextMenu("delete", "Delete", null);
           exGrid1.AddContextMenu("open", "Open", null);
           exGrid1.ContextMenuClicked += new JanusGrid.ContextMenuClicked(exGrid1_ContextMenuClicked);
       }



       void exGrid1_ContextMenuClicked(object sender, JanusGrid.ContextMenuClickedEventArg e)
       {
           if (e.MenuItem == "delete")
           {
               DialogResult dr = MessageBox.Show("delete?", "DELETE", MessageBoxButtons.YesNo, MessageBoxIcon.Stop, MessageBoxDefaultButton.Button2);
               if (dr == DialogResult.Yes)
               {
                   var o = exGrid1.GetRow().DataRow as ToDo;
                   //rap.Delete(o.GUID, o); // FIX : implement this
                   FillCollection();
               }
           }
           if (e.MenuItem == "open")
               openitem();
       }




       private void FillCollection()
       {
           var o = rap.Query(typeof(TodoView));
           BindingSource bs = new BindingSource();
           bs.AllowNew = true;
           bs.AddingNew += new AddingNewEventHandler(bs_AddingNew);
           bs.DataSource = o.Rows;
           exGrid1.DataSource = bs;
           exGrid1.AutoSizeColumns(true);
       }


       void bs_AddingNew(object sender, AddingNewEventArgs e)
       {
           e.NewObject = new TodoView.RowSchema();
       }

       private void addToolStripMenuItem_Click(object sender, EventArgs e)
       {
           ShowItem(new ToDo());
       }

       bool _editmode = false;
       private void editInPlaceToolStripMenuItem_Click(object sender, EventArgs e)
       {
           if (_editmode == false)
           {
               exGrid1.AllowEdit = Janus.Windows.GridEX.InheritableBoolean.True;
               exGrid1.AutoEdit = true;
               exGrid1.AllowAddNew = Janus.Windows.GridEX.InheritableBoolean.True;
               exGrid1.RecordUpdated += new EventHandler(exGrid1_RecordUpdated);
               exGrid1.RecordAdded += new EventHandler(exGrid1_RecordAdded);
               _editmode = true;
           }
       }

       void exGrid1_RecordAdded(object sender, EventArgs e)
       {
           var o = exGrid1.GetRow().DataRow as TodoView.RowSchema;
           ToDo t = new ToDo();
           FillProperties(t, o);
           rap.Save(t.GUID, t);
       }

       private void FillProperties(ToDo t, TodoView.RowSchema o)
       {
           // fill properties
           t.Category = o.Category;
           t.Date = o.Date;
           t.Done = o.Done;
           t.Priority = o.Priority;
           t.Project = o.Project;
           t.Status = o.Status;
           t.Subject = o.Subject;
           t.TimeEstimate = o.TimeEstimate;
       }

       void exGrid1_RecordUpdated(object sender, EventArgs e)
       {
           var o = exGrid1.GetRow().DataRow as TodoView.RowSchema;
           ToDo t = (ToDo)rap.Fetch(o.docid);
           if (t == null) t = new ToDo();
           FillProperties(t, o);
           rap.Save(t.GUID, t);
       }

       private void normalModeToolStripMenuItem_Click(object sender, EventArgs e)
       {
           if (_editmode == true)
           {
               exGrid1.AllowEdit = Janus.Windows.GridEX.InheritableBoolean.False;
               exGrid1.AllowAddNew = Janus.Windows.GridEX.InheritableBoolean.False;
               exGrid1.RecordUpdated -= new EventHandler(exGrid1_RecordUpdated);
               exGrid1.RecordAdded -= new EventHandler(exGrid1_RecordAdded);
               _editmode = false;
           }
       }

       void FontToolStripMenuItemClick(object sender, EventArgs e)
       {
           FontDialog ofd = new FontDialog();
           ofd.Font = this.Font;
           if (ofd.ShowDialog() == DialogResult.OK)
           {
               this.Font = ofd.Font;
               exGrid1.AutoSizeColumns(true);
           }
       }

       private void designerToolStripMenuItem_Click(object sender, EventArgs e)
       {
           //try
           {
               JanusGrid.ExGrid grid = new JanusGrid.ExGrid();
               grid.Name = "GridControl";
               MemoryStream ms = new MemoryStream();
               exGrid1.SaveLayoutFile(ms);
               ms.Seek(0L, SeekOrigin.Begin);
               grid.LoadLayoutFile(ms);
               Janus.Windows.GridEX.Design.GridEXDesigner gd = new Janus.Windows.GridEX.Design.GridEXDesigner();
               //try
               {
                   gd.Initialize(grid);
                   System.ComponentModel.Design.DesignerVerb dv = gd.Verbs[0];
                   dv.Invoke(grid);
               }
               //catch { }
               gd.Dispose();
               ms = new MemoryStream();
               grid.SaveLayoutFile(ms);
               ms.Seek(0l, SeekOrigin.Begin);
               exGrid1.LoadLayoutFile(ms);
               exGrid1.ResumeLayout();
           }
           //catch { }
       }

       private void refreshToolStripMenuItem_Click(object sender, EventArgs e)
       {
           FillCollection();
       }

       private void frmMain_FormClosing(object sender, FormClosingEventArgs e)
       {
           rap.Shutdown();
       }
   }

And here is the view definition:

public class TodoView : RaptorDB.View<ToDo>
    {
        public class RowSchema : RaptorDB.RDBSchema
        {
            public DateTime Date;
            public string Project;
            public string Subject;
            public string Status;
            public string Category;
            public bool Done;
            public int Priority;
            public int TimeEstimate;
        }

        public TodoView()
        {
            this.isPrimaryList = true;
            this.Name = "Todolist";
            this.isActive = true;
            this.BackgroundIndexing = false;
            this.Schema = typeof(RowSchema);

            this.Mapper = (api, docid, doc) =>
                {
                    api.EmitObject(docid, doc);
                };
        }
    }

Consistent View

A notion of a Consistent View has been added since v1.3 which allows you to have non primary views act like primary views which will complete data insert and update within the Save call in RaptorDB.
This allows you to override the backround filling of the view, and have the views updated in real time. Obvoiusly there will be a performance hit when doing so.

All you need to do is set the ConsistentSaveToThisView property of the view to True.

Project Guidelines

You should be aware of some of the following guidelines which will help you getting the most out of RaptorDB and possibly your application design.

• Create a Entity project which will hold you data objects : because "entity/data" objects will be used all over your UI / Business Logic / Data storage, it is better to create a project for them which you can include in your other project, this will force you to use data only mentality and isolate you from including "forms" code in your entities and messing your references. (including a UI component reference in your data access code etc.)
• Include your View Schema definition in your Entities project : because the view row schema is being used in your views and your UI (for binding to the Result of your queries), it would be better to include this in your "entity" projects. You can put it in the View projects but you will have to include this project in your UI/Logic projects references and deploy accordingly.

Appendix v1.5

Some more major features were added to this version :

• Backup and Active Restore
• Users
• Delete document and delete files
• Misc

Backup and Active Restore

Backup and Restore feature was added in this version. Backups can be done manually or automaically in server mode at mid night every day (currently hardcoded).

Backups will be done incrementally from the previous backup set meaning that for every Backup call a incremental backup file will be created. The first Backup call will do a full backup to the last document in RaptorDB.

Backup files are created in the "Data\Backup" folder, these files are compressed typically up to 95%. A counter is maintained in that folder which will indicate the last backup position in the documents storage file.

Restore is done by calling the Restore method and RaptorDB will start processing the files in the "Data\Restore" folder. The restore process is non blocking and you can use RaptorDB in the meantime. Once a backup file is processed and restored it will be moved to the "Data\Restore\Done" folder.

Restoring documents is an additive process meaning that if an existing same GUID document exists then it will be revisioned, and will exist as a duplicate.

Users

Authenticating users will now check a users.config file for user names and password hashes.

You can add users or change passwords for users with a call to the AddUser method.

Delete

The ability to delete documents and files was added in this version which will flag the Guid as deleted (they still exist in the storage file). Deleted documents are now handled when rebuilding views and restoring backups.

Misc

To improve performance in server mode network traffic is compressed over a confguration limit of Param.CompressDataOver in the NetworkClient.cs file, this is set to 1Mb as a default meaning that any data over 1Mb will be compressed with MiniLZO for the client.

When filtering with a string query, RaptorDB now supports Guid and DateTime values, i.e.:

CreateDate = "2012/5/30 11:00" and docid = "f144c10f-0c9e-4068-a99a-1416098b5170" 

Appendix v1.6 - Transactions

While normally transactions are not part of the Nosql movement, but by popular demand it is required for "business applications" where you need to control the flow of the data. In these circumstances, transactions make a lot of sense and add weight to the use of RaptorDB in such applications.

All you need to do is set the TransactionMode property of the primary view and all saves to that view and associated document type views will be handled consistently in a transaction. With-in your mapper method you can build complex business logic and Rollback if needed, if no rollback or exceptions occur then the transaction is automatically comitted.

On a rollback or exception no data is written to the docs file strorage and no updates are made to the indexes, so RaptorDB will be in a consistant state.

Example

Below is a sample view defintion with the properties set for transaction mode operation.

[RegisterView]
public class SalesInvoiceView : View<SalesInvoice>
{
    public class RowSchema : RDBSchema
    {
        [FullText]
        public string CustomerName;
        public DateTime Date;
        public string Address;
        public int Serial;
        public byte Status;
    }

    public SalesInvoiceView()
    {
        this.Name = "SalesInvoice";
        this.Description = "A primary view for SalesInvoices";
        this.isPrimaryList = true;
        this.isActive = true;
        this.BackgroundIndexing = true;
        this.TransactionMode = true; // <-- all you need to set

        this.Schema = typeof(SalesInvoiceView.RowSchema);

        this.Mapper = (api, docid, doc) =>
        {
            if (doc.Serial == 0) // <-- some complex business logic
                api.RollBack();  // <-- rollback this transaction
            api.EmitObject(docid, doc);
        };
    }
}

Because this view is a Primary view then all other view updates will happen in this transaction so if an inner view Rollbacks then the whole transaction and updates will be rolled back. All the updates will be done consistently and in one thread.

Appendix v1.7 - Server Side Queries

Prior to v1.7 you had to do aggregate queries on the client side which meant that the data must be transfered to the client for this to work. Obvoiusly when you have a large number of data rows in your views then this would be time consuming and bandwidth intensive (even with automatic compression). To overcome this in this version you can create "stored procedure" like functions which you can deploy to your server and execute the aggregate queries on the server and only transfer the results to the client.

This is done by the following source code sample:

public class ServerSide
{
// so the result can be serialized and is not an anonymous type
// since this uses fields, derive from the BindableFields for data binding to work
    public class sumtype : RaptorDB.BindableFields
    {
        public string Product;
        public decimal TotalPrice;
        public decimal TotalQTY;
    }

    public static List<object> Sum_Products_based_on_filter(IRaptorDB rap, string filter)
    {
        var q = rap.Query(typeof(SalesItemRowsView), filter);

        List<SalesItemRowsView.RowSchema> list = q.Rows.Cast<SalesItemRowsView.RowSchema>().ToList();
        var res = from item in list
                  group item by item.Product into grouped
                  select new sumtype // avoid anonymous types
                  {
                      Product = grouped.Key,
                      TotalPrice = grouped.Sum(product => product.Price),
                      TotalQTY = grouped.Sum(product => product.QTY)
                  };

        return res.ToList<object>();
    }
}

All you need to do is create a method with the parameter type structure which takes a IRaptorDB interface and a string filter property.
To call this method in your code all you need to do is the following :

var q = rap.ServerSide(Views.ServerSide.Sum_Products_based_on_filter,
                //"product = \"prod 1\"" // string type filter
                (LineItem l) => (l.Product == "prod 1" || l.Product == "prod 3")
                ).ToList();
            dataGridView1.DataSource = qq;

The ServerSide method allows you to supply filters in LINQ or string format like normal Query calls.

Quirks

Normally aggregate queries use anonymous classes where the compiler generates the return class type at compile time and you magically have things work. Anonymous types can not be serialized and don't have a set method on them, so you must define the return type yourself when doing sum queries (an inconvieniece as you have to type more).
From the code sample above you can see that I have defined the sumtype class which encapsulates the sum query's data structure. Since I have defined the class with fields and not properties (no getter/setter), you must also derive the class from the RaptorDB.BindableFields type so databinding will work and the base class library controls can bind to it and show the data.

Again since ServerSide returns object[] and the BCL DataGrid doesn't understand this you need to convert the results to a generic list with ToList before data binding.

Appendix v1.8.3 Count and Paging

In this release I have added the ability to Count data on the server in an extreamly fast way and return the results, so you can do the following :

int c = rap.Count("SalesItemRows", "product = \"prod 1\""); // string version
int cc = rap.Count(typeof(SalesItemRowsView), 
        (LineItem l) => (l.Product == "prod 1" || l.Product == "prod 3")); // linq version 

Also you can now page the results returned from your queries for network performance so all the Query overloads now take start and count parameters:

Result q = rap.Query("SalesItemRows", "product = \"prod 1\"", 100, 1000); // skip 100 and give me 1000 rows

Appendix logo designs

By popular demand I have added new logo proposals here which you can vote on:

New logo	New logo	Proposed by : Bill Woodruff

Appendix v1.9.0

A lot of changes were made in this version the most important of which are :

• Support for MonoDroid
• A new Query model and typed results
• Bug fixes

MonoDroid Support

After getting my self a Asus TF700 Android tablet device, I decided to see if I could get RaptorDB working on it. The bad news was that MonoDroid does not support the CodeDOM, so I had to rewrite a very small part of RaptorDB using Reflection.Emit which turned out quite well. After compiling the code succesfully I found that it did not work on Android and when debugging I found that the problem was with the path separator character differences between Windows '\' and Unix '/' so I changed all the code to use Path.DirectorySeparatorChar.

This is a really exciting development and opens the door to use RaptorDB on mobile devices. I will write more on this in another article sometime.

New Query Model

Now you can write the following code :

int c = rap.Count<SalesInvoiceView.RowSchema>(x => x.Serial < 100);

var q = rap.Query<SalesInvoiceView.RowSchema>(x => x.Serial < 100, 0, 10); // start at 0 take 10
dataGridView1.DataSource = q.Rows; // Rows is List<SalesInvoiceView.RowSchema>

q= rap.Query<SalesInvoiceView.RowSchema>("serial <100"); // string filter

string s = q.Rows[0].CustomerName; // results are now typed so you don't need casting

As you can see you define the schema you want and write really short linq statements and RaptorDB will determine the view your schema belongs to and give you typed results so there is no need for casting. This means less typing which is always great. The old query model will still work.

Notes :

• When using this style you are now free to define your schema's for the views your self and define them outside the view assemblies (most likely in your "entitiy" assemblies) and get typed results in your application without being dependant on RDBSchema. Now when doing so you must make sure you define a docid property in your schema and if you want databinding in .net to work with the BCL controls you must define properties (commerial components can bind to fields also but the BCL can't).

• When using your own schema's you can use the view.FullTextColumns property and supply the column names for full text indexing (any case is supported).

[RegisterView]
public class SalesInvoiceView : View<SalesInvoice>
{
    // define your own row schema below (you must define a 'docid' property)
    // this can be in any assembly that this view can see
    public class RowSchema // my own schema not derived from RDBSchema
    {
        public string CustomerName { get; set; }
        public DateTime Date { get; set; }
        public string Address { get; set; }
        public int Serial { get; set; }
        public byte Status { get; set; }
        public bool Approved { get; set; }

        public Guid docid { get; set; } // must exist
    }

    public SalesInvoiceView()
    {
        this.Name = "SalesInvoice";
        this.Description = "A primary view for SalesInvoices";
        this.isPrimaryList = true;
        this.isActive = true;
        this.BackgroundIndexing = true;
        this.Version = 3;
        //// uncomment the following for transaction mode
        //this.TransactionMode = true;

        this.Schema = typeof(SalesInvoiceView.RowSchema);

        // you can define full text column this way also
        this.FullTextColumns = new List<string> { "customername" };

        this.Mapper = (api, docid, doc) =>
        {
            //int c = api.Count("SalesItemRows", "product = \"prod 1\"");
            if (doc.Serial == 0)
                api.RollBack();
            api.EmitObject(docid, doc);
        };
    }
}

Bug Fixes

A lot of bug fixes and enhancments were made also, the most prominent being Not queries on bitmap indexes now expand to encompass all the view rows and deleted documents are handled correctly on a view rebuild. Unfortunately the storage file changed for the deleted file handling and is not backward compatible.

Appendix v1.9.2

Case insensitive column searching is now supported by setting the CaseInsensitive attribute on your schema column or by adding the column name to the CaseInsensitiveColumns property on the view (the latter is for the time when you don't want to propogate the dependancies to RaptorDB among your entity classes i.e. defining your entity classes and schema definitions as a standalone dll without depending on RaptorDB).

...
        public class RowSchema : RDBSchema
        {
            [FullText]
            public string CustomerName;
            [CaseInsensitive]
            public string NoCase; // case insensitive searching
            public DateTime Date;
            public string Address;
            public int Serial;
            public byte Status;
            public bool Approved;
        }

        public SalesInvoiceView()
        {
            ...

            this.Schema = typeof(SalesInvoiceView.RowSchema);

            this.CaseInsensitiveColumns.Add("nocase"); // this or the attribute

Also the ability to query != or NotEqual in LINQ is now supported :

int c = rap.Count<SalesInvoiceView.RowSchema>(x => x.Serial != 100);

Appendix v2.0.0

In this release the ability to fulltext search the original document irrespective of views has been added, to support this and the ability to fetch document changes the storage file has been changed so existing data in the storage files will not work ( the ability for storage file upgrade has been omitted for now, if you need this please contact me).

To work with the new features all you need to do is :

int[] results = rap.FullTextSearch("search"); // hoot/lucene style query string
object o = rap.FetchVersion(results[0]); // fetch the first in the list

To get a documents change history :

int[] history = rap.FetchHistory(guid);
object o = rap.FetchVersion(history[0]); 

The same interfaces exist for file storage (bytes) : FetchBytesHistory() and FetchBytesVersion()

Appendix v2.0.5

In this version RaptorDB now supports memory limiting via the following configuration points:

• Global.MemoryLimit : the default is 100 which means if the value of GC.GetTotalMemory() > 100mb then start freeing memory. You can increase this value to store more index data in memory for performance. GC.GetTotalMemory does not give the same values you get in the task manager (you will see higher values in the task manager).
• Global.FreeMemoryTimerSeconds : the default is 60 seconds and will fire to check memory usage and free up if needed.

Also to make things more robust the view index data will be saved to disk on a timer, which will speed up shutdowns.

Appendix v3.0.0 - Replication

A lot of changes went into this version, which can be considered a major upgrade, some of the changes are below:

• all data files are opened in shared read mode so you can copy backup a running server's data folder for online backup.
• cron scheduler has been added for timed jobs
• new storage format for data files
• operate in key/value mode with Global.RequirePrimaryView = false
• script views
• HQ-Branch replication

New Storage Format

To overcome a major shortcoming of flexibility and my bad initial decision of making the format too brittle I have changed the storage file format to the diagram below:

This new format will hopefully be the last breaking change since it is more flexible and robust than the previous
versions. The new format operates in the following 2 modes:

• meta data mode : each record has associated meta data
• raw mode : used for view data storage with no meta data

Meta data is stored in BinaryJSON format but you can choose between BinaryJSON or text JSON for object serialization, the deserializer will give you the correct format which ever you choose (within the same file). The meta data has the following properties at the moment (more may be added in the future without breaking):

• object type name (fully qualified)
• is deleted
• is replicated
• key value
• serialized object byte size
• date and time of save

Cron Schedules

A cron scheduling daemon has been added so you can control different aspects of the workings within RaptorDB. Cron can be daunting at first but once you get the hang of it it is really really powerful. To help you get started by example here is a quick start guide:

• cron has a resolution of a minute.
• cron string format is : Minutes Hours Day_Of_Month Month_Of_Year Day_Of_Week
• you enter numbers in the place holders
• you can enter an asterisk in the place holders which means "every"
• you can enter ranges with a dash (-) with no spaces : 0-10 means zero to ten
• you can enter discrete values with a comma (,) with no spaces : 2,4,6
• Day_Of_Week starts at 0=Sunday...
• you specify a divisor with a slash (/)
• you can omit tailing values and (*) is assumed : */5 equals */5 * * * *

Examples :

* * * * *            : every minute
*/10 * * * *         : every 10 minutes
0 * * * *            : every hour at the top of the hour
0 0 * * *            : every day at midnight
* 0 * * *            : every minute between midnight and 1am
15 * * * *           : every hour at 15 minutes past the hour
0 12 * * 1,2,3,4,5   : every week day at 12 noon
*/2 * * * *          : every even minute
0-30/3 9-17 * * *    : every third minute at the top of each hour from 9am to 5pm

C# Script Views

You can now define views in script form and place them in the Views folder on the server and RaptorDB will automatically build and use them. This is a powerful feature which allows you to tweak view definitions at run-time with notepad (or any text editor of your choice).

• if you require a reference in your script file you can add the following at the top (usually to access entity classes i.e. SalesInvoice in the sample below) : // ref : filename.dll
• you can define a row schema within you script file for dynamic views (obviously if you require access to the properties from within your client side code i.e. row.Name etc. then the row schema should be defined elsewhere and referenced on both client and server like a data entity class)
• if the row schema is defined in the script file then upon use at run-time the script DLL file will be transferred to the client and loaded so deserialization will work and you see your data. This is a really powerful feature.

// ref: views.dll
using System;
using System.Collections.Generic;
using RaptorDB;

namespace SampleViews
{
    [RegisterView]
    public class testing : View<SalesInvoice>
    {
        // row schema defined in the script file 
        // and will be transferred to the client when needed 
        public class RowSchema : RDBSchema
        {
            public string Product;
            public decimal QTY;
            public decimal Price;
            public decimal Discount;
        }
        
        public testing()
        {
            this.Name = "testing";
            this.Description = "";
            this.isPrimaryList = false;
            this.isActive = true;
            this.BackgroundIndexing = true;
            this.Version = 3;

            this.Schema = typeof(RowSchema);

            this.Mapper = (api, docid, doc) =>
            {
                foreach (var i in doc.Items)
                    api.EmitObject(docid, i);
            };
        }
    }
}

Replication

A major feature in this version is the ability for RaptorDB to replicate data across process boundaries and servers. This allows you to create and maintain "Enterprise" applications in different locations. RaptorDB implements replication in semi-online headquarters/branch style where data is transferred between servers periodically.

• currently there is no high level security for the TCP connection so if you really need that then you are advised to setup a VPN connection between your servers first.

Replication can be considered to be the following:

• What : what you want to replicate, meaning the document types that need to be transferred.
• Where : where the data should go or the destination points
• When : when the data should be transferred or the frequency of moving data
• How : how the data should be transferred or the protocol used.

How is currently a TCP based data serialization protocol on a port number of your choice with the default being 9999. When is a cron based schedule.

Setting up Replication

For replication to work you need 1 master or headquarter server and at least 1 branch server. To setup replication you need to edit the RaptorDB-Replication.config file on your HQ server and edit the RaptorDB-Branch.config file on your other servers.

• All the action will take place in the Replication folder in RaptorDB's data directory.
• You can do replication in embedded mode also with the above files being configured.
• You can configure how many items to transfer in each package the default is 10,000 documents, this is to limit the file size to manageable amount for transfer over the wire.
• You would probably configure the cron job to do replication over a period of time say every 5 minutes from midnight to 1am for example if there is a potential for more than 10,000 documents generated on a server.

Troubleshooting

Failures can occur in the event that the documents saved in the data transfer package cannot be deserialized on the target machine.

• all operations and exceptions are logged in the log file in the data folder.
• if the replication inbox contains a file like "0000000n.error.txt" then an error occurred and the contents will show what went wrong along with the offending json.
• you can skip the offending document if you increment the "0000000n.counter" file (when you can't overcome the exception)
• in the event of failures you can selectively delete files in the replication folders or even delete the entire folder and the system will restart, and given that documents are transferred and are inherently re-entrant then all should be good.

All operations are resume-able and will continue from the last point on restarts or errors.

Config files

All the configuration files in this version have a "RaptorDB-" prefix so the "users.config" file in the previous versions is now "RaptorDB-Users.config".

• the engine starts-up and see there is no configuration file then it will create a sample file with a "-" prefix so you can edit it and restart from a template.

RaptorDB.config

Below is a sample for the general configuration file (in JSON format):

{
    "BitmapOffsetSwitchOverCount" : 10,
    "BackgroundSaveToOtherViews" : true,
    "DefaultStringKeySize" : 60,
    "FreeBitmapMemoryOnSave" : false,
    "PageItemCount" : 10000,
    "SaveIndexToDiskTimerSeconds" : 60,
    "FlushStorageFileImmediatley" : false,
    "SaveAsBinaryJSON" : true,
    "TaskCleanupTimerSeconds" : 3,
    "BackgroundSaveViewTimer" : 1,
    "BackgroundViewSaveBatchSize" : 1000000,
    "RestoreTimerSeconds" : 10,
    "FullTextTimerSeconds" : 15,
    "BackgroundFullIndexSize" : 10000,
    "FreeMemoryTimerSeconds" : 60,
    "MemoryLimit" : 100,
    "BackupCronSchedule" : "0 1 * * *",
    "RequirePrimaryView" : true,
    "PackageSizeItemCountLimit" : 10000
}

This file is essentially the internal parameters for RaptorDB so you can fine tune the engine at runtime without recompiling the server.

• you are advised not to change these if you are not sure.

RaptorDB-Replication.config

Below is a sample for the server side replication configuration file (in JSON format) :

{
   "ReplicationPort" : 9999,
   "Where" : 
   [
        { 
            "BranchName" : "b1", 
            "Password" : "xxxxx", 
            "What" : "default", 
            "When" : "0 * * * *"
        },
        { 
            "BranchName" : "b2", 
            "Password" : "yyyyy", 
            "What" : "b2",      
            "When" : "*/5 * * * *" 
        },
   ],

   "What" : 
   [
       { 
           "Name" : "default", 
           "Version" : 1,
           "PropogateHQDeletes" : true,
           "PackageItemLimit" : 10000,
           "HQ2Btypes" : [""], 
           "B2HQtypes" : ["*"] 
       },
       { 
           "Name" : "b2",
           "Version" : 1,
           "PropogateHQDeletes" : false,
           "PackageItemLimit" : 0,
           "HQ2Btypes" : [""], 
           "B2HQtypes" : ["namespaceX.type1", "namespaceY.*"] 
       }
   ]
}

There are 2 main sections "where" and "what" :

• you first define the where section for your branch servers which includes the "when" cron schedule and the "what" reference name the section below.
• you can define a "default" what configuration if you want or a branch named "what".
• HQ2Btypes is the types you want to transfer from your HQ to branches.
• B2HQtypes is the types you want to transfer from you branches to HQ.
• The types are defined as a list of .net type names.
• "" in the type list means nothing
• * is a place holder for multiple types matching that definition so "*" means send everything and "namespaceX.*" means send everything matching the namespace "namespaceX" etc.
• you can limit the package document count to a number or set it to 0 which means the global limit defined in "RaptorDB.config"
• PropogateHQDeletes controls whether a document delete occurs on the branch servers if it was deleted on the HQ.
• the password is stored as is without any encryption etc.

RaptorDB-Branch.config

Below is a sample for the branch configuration file (in JSON format) :

{
    "ServerAddress" : "192.168.1.6",
    "ServerReplicationPort" : 9999,
    "Password" : "xxxxx",
    "BranchName" : "b1"
}

Just complete the data for your branch server and you are good to go.

• the password is stored as is without encryption etc.

Folder Structures

DATA Folder
     |
     |- Replication > (branch mode)
     |          |-: branch.dat
     |          |
     .          |- Inbox >
                |      |-:  0000000n.mgdat.gz,  
                |
                |- Outbox >  

• if inbox contains a file like "0000000n.counter" then an error occurred and the text is in "0000000n.error.txt"
• you can skip the offending document if you increment the "counter" file (when you can't overcome the exception)
• files will be downloaded to the inbox folder in branch mode
• "branch.dat" in the "Replication" folder stores counter information for replication

DATA Folder
     |
     |- Replication > (HQ mode)
     |          |-
     |          |
     |          |- Inbox >
     |          |      |
     .          |      |- BranchName1 >
                |      |         |-:   0000000n.mgdat.gz
                |      |         |
                |
                |- Outbox >
                |      | 
                |      |- BranchName1 > 
                |      |- BranchName2 >

• if inbox contains : "0000000n.error.txt" then error occurred
• you can skip the offending document if you increment the "counter" file (when you can't overcome the exception)
• *.last files store the branch last document transferred counters

Appendix v3.1.0 - Query Sorting

In this version the old style query model (specifing types etc.) has been removed in favour of the new model where you have generic interfaces and specify the row schema.

Also a major update is the ability to sort your data on view columns which is needed when you do paging. So the overloads now support a "orderby" string parameter in which you specify the column you want to sort on. You can sort in decrementing order by appending a "desc" to the string.

// page the results in 10's and sort in decrementing order on the serial column
var q = rap.Query<SalesInvoiceViewRowSchema>(x => x.Serial < 100, 0, 10, "serial desc");  

Appendix v3.1.3 - Dog fooding and real world usage

I recently had the opportunity to practice what I preach and use RaptorDB in a real world setting and build a business application solely on it. The details I will omit since it is not as important as the concepts and best practices.
Most of what has changed in version 3.1.1 to 3.1.3 are a direct result of the insight gained in creating that application.
It is still astounding that the heart of the application is 2 DLL's of 200kb size which is peanuts compared to the whizbang UI components used (over 30mb).

Serials and row numbers

Most applications require a sort of unique incrementing number (row number, ID etc) which is generated in the database layer, so how can this be done in RaptorDB?

Say we have :

class Invoice { InvoiceNumber : int }

in the mapping function for the invoice view the following api method was added :

Mapper = (api, guid, doc) => 
   {
     if (doc.InvoiceNumber == 0) // check if not set in the doc
           doc.InvoiceNumber = api.NextRowNumber(); // get a row number

     api.EmitObject(guid, doc);
   }; 

now there is a side note here on how RaptorDB saves data:

• Generally RaptorDB will save the document to doc storage first then save to views so in the case of the InvoiceNumber the doc will be saved with 0 and the row number will be lost when we fetch that invoice.

• Thankfully if you have enabled Transactions on your primary view for that document then the save order is reversed as follows :
  • call the relevant views first
  • if all is ok (i.e. no RollBack) then save the doc to disk.
  • So in the case of our InvoiceNumber any changes to it (or any other properties) from calling map functions will be preserved and saved.

Deleting from Views

Now generally inserting and updating a view is easy but how do you delete from a view? Well if you have enabled DeleteBeforeInsert = true on your view then RaptorDB will delete the document by the Guid before inserting the new data so all you need to do is for an invoice with a delete flag :

Mapper = (api, guid, doc) =>
  {
       if (doc.isDeleted == false) // if the delete flag is not set on the invoice
       {
            if (doc.InvoiceNumber == 0)
                  doc.InvoiceNumber = api.NextRowNumber();
            api.EmitObject(guid, doc);
       }
  }; 

or you could save a subclass like DeleteSupplierRequest and have this mapper :

Mapper = (api, guid, doc) =>
  {
       if (doc.GetType() != typeof(DeleteSupplierRequest)) // otherwise it is deleted
       {
            doc.orgItem.Compute();
            doc.Description = doc.orgItem.Description;
            api.EmitObject(guid, doc);
       }
  };

both of the above are not outputting any rows to a view so in conjunction with DeleteBeforeInsert you have deleted data from a view.

A very important note here:

• we are not loosing any data since RaptorDB is append only and stores all the documents you have saved regardless of deletes so if you need to you can change the mapper and rebuild the view for recovery etc and if you know the Guid for the document, get it's history with FetchHistory() or FetchHistoryInfo() which gives dates of change also.

Operations on other views

if (_raptorDB.Save(_invoice.GUID, _invoice))
{
    // reload to get changed values (invoice number)
    var inv = (Invoice)_raptorDB.Fetch(_invoice.GUID);
    if (inv == null)
       return false;
    _invoice = inv;

    // save all products
    _invoice.Items.ForEach(x => _raptorDB.Save(x.id, x));

    // save supplier request data here
    foreach (var i in _invoice.Items)
    {
        SupplierRequest s = new SupplierRequest(_invoice, i);
        _raptorDB.Save(s.GUID, s);
    }

    return true;
}

Now arguably the above code would run on your server business layer but it would be cleaner if it were in the map function of the Invoice save.

Development tips

Here are a few useful development tips:

• While you can always increment the view.Version number (and you must do so if you have deployed to a client) for view changes a quick workaround for debugging is just deleting the the views folder and RaptorDB will rebuild that view.
• For business apps you should probably set BackgroundIndexing = false and TransactionMode = true so you instantly see changes and document manipulation in view map functions are preserved.
• Create separate Entity and View projects and put your schema definitions in the entity project, that way you can manipulate and work with the Query() return values and have intellisense statement completion.

Appendix v3.1.4 - ViewDelete & ViewInsert

Continuing my dog fooding and real world usage, I was converting another app from MySql to RaptorDB and came across the need to be able to delete and insert directly into views since re-architecting the application to a document-orientated version was too much work, so now you can with the following :

// this will directly delete rows from the view with the schema of PermissionSchema
int rowsdeleted = raptorDB.ViewDelete<PermissionsSchema>(x => x.Path == "there"); // based on this filter

// create a row schema object to insert
var c = new PermissionsSchema();
c.docid = Guid.NewGuid();
c.Path = "here";

// insert the above row into a view with the schema of PermissionSchema
raptorDB.ViewInsert<PermissionsSchema>(c.docid, c);

The above direct manipulations on views are fully rebuildable and are internally backed by a "document" in the main storage file, so it will be as if the operation was done normally and you have a log for it.

Appendix v3.1.6 - Split storage files

Continuing the dog-fooding in production, in this version you can split the document storage files by setting the  Global.SplitStorageFilesMegaBytes configuration to a value (the default is 0 which is off).

This feature is for the devops/admins guys and enables them to take smaller incremental daily backups e.g. instead of taking a 100gb daily backup of the entire changed .mgdat file the backup will only be the daily changed amount which will allow to take more backups until the backup medium is full.

• You can set and unset this value any time and RaptorDB will handle it correctly.
• Once set the .mgdat files will split to a new file around the size limit (it may go over the size since it will keep the last document in the same file and not split it into two files).
• The last file will always be .mgdat and the first will be .mgdat00000
• View rebuilds are uneffected and will work correctly.

Appendix v3.2.0 - High Frequency Storage File

Some significant changes were made in this version some of which are:

•  You can now compress the documents with the Global.CompressDocumentOverKiloBytes configuration the default is anything over 100kb
•  Views will now check for integrity on start-up and rebuild if needed (i.e. a failed Shutdown)
•  A new Key/Value storage file has been added for high frequency update items that will recycle the disk space (i.e. not append-only)

Introduction

The new storage system is essentially defined as a Dictionary<string,object> key/value store with a string key which ensures the widest range of use-cases. You must note the following:

•  Keys are currently limited to 255 bytes as a UTF8 string.
•  Views are not effected by saving to this storage (no mapping will occur).
•  You will not have previous values and old values will be over written.
•  The storage file is implemented as a linked-list of disk blocks of size Global.HighFrequencyKVDiskBlockSize which is set to a default of 2048 bytes (do not use anything below 512 unless you know what you are doing) see the File Format section below.
• Deletes will immediately free all the blocks used by the key and set the start block of the old value to deleted.

The new storage file is good for rapid and periodic saving of the same key data like state information where you don't care about history of the data but the current value.

Data Integrity

Since the new storage file is recycling free space (as opposed to the append only model) the potential for data corruption is great. To ensure data integrity RaptorDB does the following :

•  New items use the free space or append to the end of the file, then if the old key exists all it's blocks are marked as free. This ensures that in the case of a failure, at least the old values are not over written and can be recovered.
•  In case of a non-clean Shutdown RaptorDB will do an automatic rebuild of the key indexes and free block list from the contents of the storage file by going through each block.  

File Format

A new file format has been created which is implemented as a linked list of disk blocks. Each block has a header section which contains information about how data is saved in the block as well as the next block, then the key bytes follow limited to 255 bytes after which the actual data is written up to the block size.

The header and key section is repeated for each block in the list to ensure data integrity.

The default block size is 2048 bytes and you should not go below 512 bytes unless you know exactly what kind of data values you are saving and the maximum key length you intend to use.

Some things to note:

• A block will be allocated regardless of the free bytes left in each block i.e. space within a block is lost if the data is smaller than the block size.
• Using a smaller block size may offer better/smaller file size at the expense of more seeks if the data is larger than the space for each block.
• Since the header is 15 bytes and the maximum key size is 255 then you cannot go below 270 bytes unless you are limiting the key length and your data is short.  
• You can call CompactStorageHF() anytime to reclaim storage space and reduce the storage file size. After compaction the original files are saved to the old folder.

Usage

You can get to the new features via the following interface:

// when rdb is a RaptorDB.RaptorDB class or the RaptorDB.IRaptorDB interface
var kv = rdb.GetKVHF(); // returns the IKeyStoreHF interface

The above interface has the following methods:

public interface IKeyStoreHF
{
    object GetObjectHF(string key);
    bool SetObjectHF(string key, object obj);
    bool DeleteKeyHF(string key);
    int CountHF();
    bool ContainsHF(string key);
    string[] GetKeysHF();
    void CompactStorageHF();
}

The above interface is pretty self explanatory.

Advanced topics

On all writes a special file is created by the name of temp.$ which signifies a data changed operation so in the event of a system failure RaptorDB will know to rebuild indexes.

You can trigger a rebuild process by adding the above file to the view folder or the DataHF folder for the new key/value store.

On a different note

Continuing the testing on other platforms, below is an animated GIF of RaptorDB working as is on an Ubuntu virtual machine which I previously installed mono-complete on:

Appendix v3.2.5 - New String Indexes

One long time nagging problem was the storage cost of string indexes, since strings are variable in size, when you come to index them you are left no choice but to take the largest length possible and allocate storage for it regardless of it's actual size since you had to contend with variable length of index pages, which can get very messy.

So in this version and on the back of the new recycle-able storage file format MGIndex<string> type indexes will store the actual strings in an external MGHF file and store the reference in the IDX file which means huge storage size reduction for these types of indexes.

You can control the creation of this new file with the Global.EnableOptimizedStringIndex flag which is true by default, note :

• Previous style indexes still work and are backward compatible.
• New indexes will be built on View rebuilds when required and the above flag allows it.

Appendix v3.2.14 - HFKV Increment() Decrement()

Continuing my real world usage of RaptorDB I have added atomic Increment/Decrement of int,decimal values to the high frequency key value store and RaptorDB ensures that updates will be atomic and consistent which can be used for :

• Counters
• Inventory values

Also fulltext search edge cases should be finally covered so you can do :

• address = "-oak -*l" = not oak and not ending  in l ( not hill, laurel etc.)

Appendix v3.2.15 - Fulltext Search Changes

From this version on the way full text search is handled is changed so using + now means OR (this is a breaking change and hopefully makes more sense).

So :

"15 franklin" = 15 and franklin
"15 franklins" = 15 and franklins -> which will show no results since fanklins does not exist
"15 +franklin" = 15 or franklin

Appendix v3.3.0 - Web Studio

A longtime coming, but finally RaptorDB has a web interface. The wait was mainly for me to "get" javascript and web development which is still ongoing. Parts of this update are from the REST api article (http://www.codeproject.com/Articles/678295/RaptorDB-REST) which has been integrated into the main code base and will live on here.

Currently this web interface is readonly and does non-destructive operations and defaults to local machine only access for security reasons, his might change in the future when I get the access control to work.

This UI uses plain javascript and was written from scratch with no dependancies, even the '$' operation was written by me as a standin.

The javascript requires modern browsers and will work with IE10+ (IE8, IE9 were to much trouble and not worth the effort given I wrote everything from scatch).

The javascript gurus amoung you will probably complain about the code.

How to enable Web Studio

By default the web Studio UI is disabled and you can control it with the following config parameters:

"EnableWebStudio" : true,
"WebStudioPort" : 91,

For security reasons the web UI is local host only by default, you can enbale connection from any address by setting :

"LocalOnlyWebStudio" : false

What you can do

Most of the main features of RaptorDB are available through the Web UI like :

• Queries
• View Schema
• Document view and history
• System Information and logs
• High frequency key/value browse

Queries

You can get at your data with the query tab which supports paging, filtering, sorting and exporting to excel.

To filter a query you can click on the column name which will add it to your filter text box and add the filter you want, like :

   Address = "hill" and serial < 100
or
   Address = "hill -oak" and serial < 100

You can click on the column name for sorting the columns data. If you click on the link under the `docid` column then you will see the underlying document which is associated with the row.

Document view and history

You can view the actual document json and see any revisions which were made under the "Documents" menu. Full text searching of the original json documents is also supported.

System Information

Under the system information menu you can see the last 100 log items along with debug information from the server and the current configuration parameters.

Under this tab you can also backup the current data and optimize the high frequency data store.

High frequency key store

Under this menu you can browse the high frequency key/value store and see the json representation of what you stored for each key.

Getting started

• Download sample.cs.zip

Concepts

Installing

• RaptorDB.dll : the main server code
• RaptorDB.Common.dll : common library for both embedded and client/server

Your first Project

class Program
{
    static RaptorDB.RaptorDB rdb; // 1 instance

    static void Main(string[] args)
    {
        rdb = RaptorDB.RaptorDB.Open("data"); // a "data" folder beside the executable
        RaptorDB.Global.RequirePrimaryView = false;

        DoWork();

        Console.WriteLine("press any key...");
        Console.ReadKey();
        Console.WriteLine("\r\nShutting down...");
        rdb.Shutdown(); // explicit shutdown
    }
...

static void DoWork()
{
    Console.Write("Inserting 100,000 documents...");
    int count = 100000;

    for (int i = 0; i < count; i++)
    {
        var inv = CreateInvoice(i);

        // save here
        rdb.Save(inv.ID, inv);
    }

    Console.WriteLine("done.");
}

static SalesInvoice CreateInvoice(int counter)
{
    // new invoice
    var inv = new SalesInvoice()
    {
        Date = Faker.DateTimeFaker.BirthDay(),
        Serial = counter % 10000,
        CustomerName = Faker.NameFaker.Name(),
        NoCase = "Me " + counter % 10,
        Status = (byte)(counter % 4),
        Address = Faker.LocationFaker.Street(),
        Approved = counter % 100 == 0 ? true : false
    };
    // new line items
    inv.Items = new List<LineItem>();
    for (int k = 0; k < 5; k++)
        inv.Items.Add(new LineItem() { Product = "prod " + k, Discount = 0, Price = 10 + k, QTY = 1 + k });

    return inv;
}

var obj = rdb.Fetch(known_guid); // obj will be a SalesInvoice from the above data

public class SalesInvoiceViewRowSchema : RDBSchema
{
    public string CustomerName;
    public string NoCase;
    public DateTime Date;
    public string Address;
    public int Serial;
}

[RegisterView]
public class SalesInvoiceView : View<SalesInvoice>
{
    public SalesInvoiceView()
    {
        this.Name = "SalesInvoice";
        this.Description = "A primary view for SalesInvoices";
        this.isPrimaryList = true;
        this.isActive = true;
        this.BackgroundIndexing = true;
        this.Version = 1;

        this.Schema = typeof(SalesInvoiceViewRowSchema);

        this.Mapper = (api, docid, doc) =>
        {
            api.EmitObject(docid, doc);
        };
    }
}

static void Main(string[] args)
{
    rdb = RaptorDB.RaptorDB.Open("data"); // a "data" folder beside the executable
    RaptorDB.Global.RequirePrimaryView = false;

    Console.WriteLine("Registering views..");
    rdb.RegisterView(new SalesInvoiceView());
...

static void DoWork()
{
    long c = rdb.DocumentCount();
    if (c > 0) // not the first time running
    {
        var result = rdb.Query<SalesInvoiceViewRowSchema>(x => x.Serial < 100);
        // show the rows
        Console.WriteLine(fastJSON.JSON.ToNiceJSON(result.Rows, new fastJSON.JSONParameters { UseExtensions = false, UseFastGuid = false }));
        // show the count
        Console.WriteLine("Query result count = " + result.Count);
        return;
    }

    Console.Write("Inserting 100,000 documents...");
    int count = 100000;

    for (int i = 0; i < count; i++)
    {
        var inv = CreateInvoice(i);

        // save here
        rdb.Save(inv.ID, inv);
    }

    Console.WriteLine("done.");
}

• we are using the beautified output of fastJSON to show the data return which is built-in to RaptorDB.
• on re-running our changed code, the view was automatically rebuilt with the data we already saved, and our query works (you will have to rerun a second time for the query to show results since the engine will be rebuilding the view in the background and will not be available at the time the query is executed).
• we did nothing to define indexes and all that was taken care of for us.
• the data was returned to us in a typed format much like a typed DataSet if you are familiar with that.
• data was returned from the view we defined and not the original documents
• the entire process is type safe and the compiler will help you if a mistake was made, rather than at runtime much like string SQL queries (if you need dynamic queries you can use string filters also, but obviously the compiler can't help you with mistakes).

// string based view names and object row returns
public Result<object> Query(string viewname);
public Result<object> Query(string viewname, string filter);
public Result<object> Query(string viewname, int start, int count);
public Result<object> Query(string viewname, string filter, int start, int count);
public Result<object> Query(string viewname, string filter, int start, int count, string orderby);

// string based filters and typed row returns (the view is determined by the schema you used)
public Result<TRowSchema> Query<TRowSchema>(string filter);
public Result<TRowSchema> Query<TRowSchema>(string filter, int start, int count);
public Result<TRowSchema> Query<TRowSchema>(string filter, int start, int count, string orderby);

// LINQ predicate filters and typed row returns (the view is determined by the schema you used)
public Result<TRowSchema> Query<TRowSchema>(Expression<Predicate<TRowSchema>> filter);
public Result<TRowSchema> Query<TRowSchema>(Expression<Predicate<TRowSchema>> filter, int start, int count);
public Result<TRowSchema> Query<TRowSchema>(Expression<Predicate<TRowSchema>> filter, int start, int count, string orderby);

Oooops! things change

public class SalesInvoiceViewRowSchema : RDBSchema
{
    public string CustomerName;
    public string NoCase;
    public DateTime Date;
    public string Address;
    public int Serial;
    public byte Status;    // added to the view
    public bool? Approved; // added to the view
}

[RegisterView]
public class SalesInvoiceView : View<SalesInvoice>
{
    public SalesInvoiceView()
    {
        this.Name = "SalesInvoice";
        this.Description = "A primary view for SalesInvoices";
        this.isPrimaryList = true;
        this.isActive = true;
        this.BackgroundIndexing = true;
        this.Version = 2; // <- increment when you make changes and want a rebuild

        this.Schema = typeof(SalesInvoiceViewRowSchema);

        this.Mapper = (api, docid, doc) =>
        {
            if (doc.Status == 0)  // status = 0 means a draft and should not be saved
                return;

            api.EmitObject(docid, doc);
        };
    }
}

Side Note

• The above view is primary list and we probably don't want to not save since there will be no reference to retrieve the SalesInvoice from, you would probably do this kind of filtering based on Status for example in other detail or summation views. The above is only for demonstration purposes.
• As you can see we can change the view schema and/or how documents are processed and all we need to do is change the Version number to let RaptorDB know when to rebuild the view's contents.

Appendix v3.3.8 - nscript.exe

In this release I have added nscript.exe tool which allows for easy running and testing of c# script files which I have had for a long time. This file will compile and run the script given. Also the sample.cs file is now part of the main zip and is now in the "test script" folder.

So now you can easily run any c# file like this:

# run any cs file

c:\rdb\test script> ..\tools\nscript.exe sample.cs

# or just run the batch file
 
c:\rdb\test script> run.cmd  

The sample.cs file now contains a comment section at the top for specifing references used which will tell nscript.exe where to find the dll files:

// ref : ..\output\raptordb.dll
// ref : ..\output\raptordb.common.dll
// ref : ..\faker.dll
using System;
using System.Collections.Generic;
...

Hopefully this will allow for easy testing and tinkering the sample given without going through the process of creating a project file which has been a stumbling block for some people.

Appendix v3.3.9 - Between() and Date Parts

In this release you can now do Between() on DateTime, int, long, decimal types like this:

// using strings for dates
var result = rdb.Query<SalesInvoiceViewRowSchema>(x => x.Date.Between("2001-1-1", "2010-1-1") && x.Status == 2);

// using normal dates
var d1 = DateTime.Parse("2001-1-1");
var d2 = DateTime.Parse("2010-1-1");
var result = rdb.Query<SalesInvoiceViewRowSchema>(x => x.Date.Between(d1, d2));

// string based
var result = rdb.Query("salesinvoice", "date.between(\"2001-1-1\",\"2010-1-1\"");

var result = rdb.Query("salesinvoice", "serial.between(20,30)");

The same works for the WebStudio interface.

Also you can query any date column parts like this :

var result = rdb.Query<SalesInvoiceViewRowSchema>(x => x.Date.Year == 2016 && x.Date.Month == 8);
var result = rdb.Query<SalesInvoiceViewRowSchema>(x => x.Date.Hour < 10);

Appendix v3.3.12 - In()

In this release you can now use In() on column and date parts so :

var result = rdb.Query<SalesInvoiceViewRowSchema>(x => x.Serial.In(1,3,5,7));

// or with array parameters 
var result = rdb.Query<SalesInvoiceViewRowSchema>(x => x.Serial.In(new int[] { 1,3,5,7 }));

// or with array variables
var arr = new int[] {1,3,5,7};
var result = rdb.Query<SalesInvoiceViewRowSchema>(x => x.Serial.In(arr));

// or on date parts 
var result = rdb.Query<SalesInvoiceViewRowSchema>(x => x.Date.Year.In(2000, 2010));

// or as string 
var result = rdb.Query("salesinvoice", "serial.in(1,3,5,7)");

Also Between() has been extended to work with date parts :

var result = rdb.Query<SalesInvoiceViewRowSchema>(x => x.Date.Hour.Between(12, 15);

Appendix v4.0.0 - MGRB

In this version I have replaced the WAHBitarray data structure with my rewritten MGRB bitarray structure which is based on Daniel Lemire's roaring bitmap (https://roaringbitmap.org/) for better memory usage. My version has the following added benifits (which I might write an article on) :

• ability to set bits outside of the consrtuctor
• sparse containers i.e. only encoding used blocks of bits
• inverted container for storing zeros in a block of ones
• optimized choosing of container blocks based on bits set

In the sample app the ininital memory usage for storing 100,000 docs from a blank database went down from 450Mb to 350Mb and the free memory works much better and optimizes in memory structures better.

The speed of saving and indexing is about the same.

Appendix v4.0.9 - Web Studio with Svelte

I recently came across Svelte which is similar to Vue I used and loved. I must say that I really like Svelte as it has the same concepts and capabilities of Vue but is much simpler and produces really tiny output without the "framework" library overhead.

The node_modules required and the rollup packager are also really tiny in comparison to others.

I decided to rewrite my old jQuery like Web Studio code that I wrote by hand (my first time writing javascript), in Svelte and the result was much smaller around 30% less output minified than my old code but infinitely more maintainable ( the actual code is about 50% less).

The thing which I really like and was a real pain and a source of many bugs, is that you don't need the this reference like in Vue.

To implement the tabs interface I needed some advanced features if Svelte which are :

• runtime creating components and setting target and props on them in code
• using $on and $destroy on runtime components
• using <svelte:options accessors={true} /> for access to component internals

To allow debugging the code in action I needed to change the server URL to the actual RaptorDB server address and not the dev server on port 5000. To do this I added the debug.js file :

import './main.js'
window.ServerURL = "http://localhost:91/";

The main.js file has :

...
window.ServerURL = document.location.protocol + "//" + 
                   document.location.hostname + ":" + 
                   document.location.port + "/";    
...

The default build commands built the code to the public folder which is not ideal. To remedy this I also changed the rollup.config.js file to differentiate debug and production builds and use the dist folder instead.

...
const production = !process.env.ROLLUP_WATCH;
const dir = production ? "dist" : "public";  
export default {
    input: production ? 'src/main.js' : 'src/debug.js',  
...

The thing I couldn't get to work was using JavaScript's fetch() command and it gave me CORS errors when debugging so I used my old XMLHttpRequest() code and added them to the window object to use throughout the app.

All in all, I enjoyed the process and again Visual Studio Code is a delight to use.

Previous Versions

You can download previous versions here:

• RaptorDB_doc_v1.0.zip
• Download RaptorDB_doc_v1.1.zip
• Download RaptorDB_doc_v1.2.zip
• Download RaptorDB_doc_v1.3.zip
• Download RaptorDB_doc_v1.4.zip
• Download RaptorDB_doc_v1.5.zip
• Download RaptorDB_doc_v1.6.zip
• Download RaptorDB_doc_v1.7.zip
• Download RaptorDB_doc_v1.8.1.zip
• Download RaptorDB_doc_v1.8.2.zip
• Download RaptorDB_doc_v1.8.3.zip
• Download RaptorDB_doc_v1.9.0.zip
• Download RaptorDB_doc_v1.9.1.zip
• Download RaptorDB_doc_v1.9.2.zip
• Download RaptorDB_doc_v2.0.0.zip
• Download RaptorDB_doc_v2.0.5.zip
• Download RaptorDB_doc_v2.0.6.zip
• Download RaptorDB_doc_v2.0.6.1.zip
• Download RaptorDB_doc_v3.0.0.zip
• Download RaptorDB_doc_v3.0.1.zip
• Download RaptorDB_doc_v3.0.5.zip
• Download RaptorDB_doc_v3.0.6.zip
• Download RaptorDB_doc_v3.1.0.zip
• Download RaptorDB_doc_v3.1.2.zip
• Download RaptorDB_doc_v3.1.3.zip
• Download RaptorDB_doc_v3.1.4.zip
• Download RaptorDB_doc_v3.1.5.zip
• Download RaptorDB_doc_v3.1.6.zip
• Download RaptorDB_doc_v3.2.0.zip
• Download RaptorDB_doc_v3.2.5.zip
• Download RaptorDB_doc_v3.2.6.zip
• Download RaptorDB_doc_v3.2.7.zip
• Download RaptorDB_doc_v3.2.8.zip
• Download RaptorDB_doc_v3.2.9.zip
• Download RaptorDB_doc_v3.2.10.zip
• Download RaptorDB_doc_v3.2.11.zip
• Download RaptorDB_doc_v3.2.12.zip
• Download RaptorDB_doc_v3.2.13.zip
• Download RaptorDB_doc_v3.2.14.zip
• Download RaptorDB_doc_v3.2.15.zip
• Download RaptorDB_doc_v3.2.16.zip
• Download RaptorDB_doc_v3.2.17.zip
• Download RaptorDB_doc_v3.2.18.zip
• Download RaptorDB_doc_v3.3.0.zip
• Download RaptorDB_doc_v3.3.1.zip
• Download RaptorDB_doc_v3.3.2.zip
• Download RaptorDB_doc_v3.3.3.zip
• Download RaptorDB_doc_v3.3.4.zip
• Download RaptorDB_doc_v3.3.5.zip
• Download RaptorDB_doc_v3.3.6.zip
• Download RaptorDB_doc_v3.3.8.zip
• Download RaptorDB_doc_v3.3.9.zip
• Download RaptorDB_doc_v3.3.10.zip
• Download RaptorDB_doc_v3.3.11.zip
• Download RaptorDB_v3.3.12.zip
• Download RaptorDB_v3.3.13.zip
• Download RaptorDB_doc_v3.3.14.zip
• Download RaptorDB_doc_v3.3.15.zip
• Download RaptorDB_doc_v3.3.16.zip
• Download RaptorDB_doc_v3.3.17.zip
• Download RaptorDB_doc_v3.3.19.zip
• Download RaptorDB_doc_v3.3.19.1.zip
• Download RaptorDB_doc_v4.0.0.zip
• Download RaptorDB_doc_v4.0.2.zip
• Download RaptorDB_doc_v4.0.5.zip
• Download RaptorDB_doc_v4.0.6.zip
• Download RaptorDB_doc_v4.0.7.zip
• Download RaptorDB_doc_v4.0.8.zip

History

• Initial release v1.0 : 29^th April 2012
• Update v1.1 : 4^th May 2012
  • fulltext indexing via attribute
  • string query parser
  • fix shutdown flusing indexes to disk
  • rudementary console application
  • lowercase viewnames for string queries
  • fulltext search defaults to AND if + - characters not present in query
  • query now works when suppling the view type
  • save pauses indexer for better insert performance ~30% faster
• Update v1.2 : 11^th May 2012
  • View versioning and rebuild
  • code cleanup
  • removed indent logic from fastJSON
  • added schema of the query to the Result
• Update v1.3 : 17^th May 2012
  • Results.Rows are now row schema objects and bindable (even when fields)
  • View.Schema must now derive from RaptorDB.RDBSchema
  • removed columns from Result (not needed anymore)
  • RegisterView throws exceptions instead of returning a Result
  • added a rudimentary query viewer project
  • null values are ignored when indexing
  • bool index filename will end in ".idx"
  • sample apps will create data files in the main soultion folder for easy sharing
  • you can now do aggregate queries on the results on the client side
  • added api.EmitObject for easier mapping (less code to write)
  • upgrade to fastJSON v1.9.8
  • bug fix datetime in fastBinaryJSON
• Update v1.4 : 31^st May 2012
  • break up the source into projects
  • created client, server dlls
  • upgrade to fastBinaryJSON v1.1
  • changed to SafeSortedList for thread safe indexes
  • add auto installer RaptorDBServer service
  • performance optimized tcp network layer
  • added dual mode usage to the windows application (embedded, server)
  • code cleanup
  • added IRaptorDB interface to allow you to switch between embedded and client seamlessly
  • load views from the Extensions folder in server mode
• Update v1.5 : 10^th June 2012
  • compressing network traffic over Param.CompressDataOver limit with MiniLZO
  • added Delete(docid) and DeleteBytes(fileid)
  • added ability to query Guid and DateTime in string form
  • bug fix reading boolean indexes
  • rebuild view and background indexer handles deleted docs
  • added authentication via users.config file in server mode
  • Backup & Restore data
  • AddUser() method for user
  • handle isDeleted when restoring data and rebuilding View
  • Auto backup in server mode @ 00:00 time
• Update v1.6 : 30^th June 2012
  • query lambda caching
  • transaction support
  • bug in datetime serialization
  • Rules section added to article
  • Transaction section added to article
• Update v1.7 : 14^th July 2012
  • server side aggregate queries
  • fixed the build script for views to copy the dll to the extensions folder
  • server side queries can have filters
  • login form default buttons fix
• Update v1.8.1 : 11^th August 2012
  • bug fix hoot index loadwords when file size is zero
  • bug fix linq binding ServerSide -> c.val == stringvariable
  • bug fix linq binding -> c.val == stringvariable
  • bug fix reflection code in serializers
  • speed increase WAH bitmap Set() code
  • bug fix concurrent save bitmap index to disk
  • upgrade to fastBinaryJSON v1.3
  • upgrade to fastJSON v2.0.1
• Update v1.8.2 : 16^th August 2012
  • bug fix linq binding -> c.val == obj.property (Thanks to Joe Dluzen)
  • added lock to the bitmap index for concurrency
  • optimized $types output in JSON and BJSON
  • bug fix null check for SafeSortedList.Remove
  • bug fix server mode data transfer
• Update v1.8.3 : 23^rd September 2012
  • upgrade to fastJSON v2.0.6
  • upgrade to fastBinaryJSON v1.3.4
  • bug fix linq2string with date,guid parameters
  • added double,float types to the indexer valid data types
  • added a lock to the IndexFile for concurrency issues (thanks to Antonello Oliveri)
  • fixed lock on _que in the logger for concurrency (thanks to Antonello Oliveri)
  • fixed the reflection binding to the insert method (thanks to Antonello Oliveri)
  • added Count() on views
  • added support for paging of results
  • the mapper can now see changes it has made in it's own thread in transaction mode while quering
  • added a new logo section
• Update v1.9.0 : 26^th November 2012
  • speed increase writing bitmap indexes to disk
  • bug fix hoot search with wildcards
  • bug fix datetime indexing with UTC time (all times are localtime)
  • upgrade to fastJSON v2.0.9
  • upgrade to fastBinaryJSON v1.3.5
  • changed CodeDOM to Reflection.Emit for MonoDroid compatibility
  • more optimized bitmap storage format (save offsets if smaller than WAH)
  • fixed path seperator character for monodroid and windows compatibility changed to Path.DirectorySeparatorChar
  • new generic Query interface with typed results (thanks to seer_tenedos2)
  • changed to Result<T>
  • WAH bitcount speed increase
  • bitmap index uses buffered stream for speed
  • added between query (work in progress)
  • bug fix storage file and deleted items
  • new query model for mapper api interface
  • you can now define your own schema for rows with caveats
  • bug fix NOT on bitmap indexes to resize to the total row count first
  • when defining your own schema you can define the fulltext columns in the view without attributes
• Update v1.9.1 : 30^th December 2012
  • bug fix edge case WAHBitarray
  • sync code with changes in hOOt
  • bug fix missing server mode SaveBytes()
  • bug fix server side queries in server mode
  • bug fix embedded guid in query : v => v.docid == new Guid("...")
• Update v1.9.2 : 6^th April 2013
  • SafeDictionary.Add() will update if item exists
  • BitmapIndex using new lock mechanism
  • CaseInsensitive attribute
  • bug fix lowercase hoot indexing
  • case insensitive string indexing and searching
  • nocase samples
  • fixed handling != (not equal) in linq query
• Update v2.0.0 : 28^th April 2013
  • added more method documentations
  • * breaking change in doc storage file from hashed guid to guid keys *
  • added FetchHistory() and FetchVersion() for docs and files to get revisions
  • upgrade to fastJSON v2.0.14
  • upgrade to fastBinaryJSON v1.3.7
  • full text indexing and search for the entire original document
  • bug fix linq query with boolean parameter
• Update v2.0.5 : 18^th May 2013
  • added FreeMemory to classes
  • memory limiting and free memory timer added
  • views background save indexes to disk on timer
  • fixed RaptorDBServer.csproj to AnyCPU build
• Update v2.0.6 : 15^th June 2013
  • bug fix WAHBitArray
  • upgrade to fastJSON v2.0.15
  • bug fix hoot fulltext index on last word
  • save deleted items bitmap on save timer
• Update v2.0.6.1 : 22^nd June 2013
  • bug fix WAHBitArray
• Update v3.0.0 : 23^rd August 2013
  • index files are opened in share mode for online copy
  • add cron daemon (thanks to Kevin Coylar)
  • backups are now on a cron schedule
  • restructured storage file for future proofing and replication support
  • storage files now store meta data about objects stored
  • * storage files are not backward compatible *
  • dirty index pages are sorted on save for read performance
  • restore is now resumable after a shutdown
  • you can disable the primary view to be defined on save with Global.RequirePrimaryView (K/V mode)
  • view rebuilds are now done in the background (non-blocking on restart)
  • you can define views in c# script format (*.view) to be compiled at runtime in 'datafolder\views'
  • row schema defined in script views will be transferred to the client if they don't exist
  • fastJSON now serializes static properties
  • upgrade to fastJSON v2.0.18
  • upgrade to fastBinaryJSON v1.3.8
  • added HQ-Branch replication feature
  • automatic generate config files if they don't exist with a '-' prefix
  • 'output' in the root of the solution folder is the new build destination of projects for easy access
• Update v3.0.1 : 6^th October 2013
  • upgrade to fastJSON v2.0.22
  • upgrade to fastBinaryJSON v1.3.10
  • detect process exit and shutdown cleanly so you can omit the explicit Shutdown()
  • bug fix WAH bitarray
• Update v3.0.5 : 11^th October 2013
  • bug fix saving page list to disk for counts > 50 million items
• Update v3.0.6 : 2^nd November 2013
  • Result.TotalCount reflects the original row count and differs from Result.Count when paging
  • internal changed FireOnType to handle Type instead of strings
  • Query() can now handle empty filter strings correctly
  • Upgrade to fastJSON v2.0.24
  • Upgrade to fastBinaryJSON v1.3.11
• Update v3.1.0 : 5^th December 2013
  • added sort for queries
  • removed extra query overloads in favour of the new model
• Update v3.1.2 : 17^th May 2014
  • added signed assemblies the assembly version will stay at 3.0.0.0 and the file version will increment
  • added nuget build
  • Upgrade to fastJSON v2.1.1
  • Upgrade to fastBinaryJSON v1.4.1
  • bug fixes in WAH and Query2 from Richard Hauer
  • changed all singleton implementations
  • bug fix indexing String.Empty
  • *breaking change* removed FireOnType from view definitions
  • Views can now correctly work with subclass of the T defined (i.e. SpecialInvoice : Invoice)
  • bug fix index bitmap.Not(size)
• Update v3.1.3 : 27^th May 2014
  • added FetchHistoryInfo() and FetchBytesHistoryInfo() with date change information
  • added api.NextRowNumber()
  • moved all config files to the data folder which you should have write access to (thanks to Detlef Kroll)
  • bug fixed delete before insert with no rows
• Update v3.1.4 : 21^st June 2014
  • added StringIndexLength attribute for view schema to control string index size for the index file
  • added ViewDelete() to delete directly from views
  • added ViewInsert() to insert directly into views
  • added Faker.dll (http://faker.codeplex.com) to generate nicer data
  • FreeMemory() will save indexes to disk also
  • moved server mode files to output\server so you don't get conflicts loading views.dll
  • page list is also saved to disk on SaveIndex()
  • bug fix view schema when not inheriting from RDBSchema
  • replaced T with more meaningful TRowSchema in code intellisense
• Update v3.1.5 : 3^rd January 2015
  • added View.NoIndexingColumns definition to override indexing of selected columns
  • Upgrade to fastJSON v2.1.7
  • Upgrade to fastBinaryJSON v1.4.5
  • added DocumentCount() to get how many items in the storage file
  • Shutdown() now waits for View rebuilds to finish
  • more intellisense help
• Update v3.1.6 : 9^th January 2015
  • document storage files can now be split with Global.SplitStorageFilesMegaBytes configuration
  • refactoring StorageFile.cs
  • Upgrade to fastJSON v2.1.8
  • Upgrade to fastBinaryJSON v1.4.6
  • bug fix .config files were not saved correctly
• Update v3.2.0 : 25^th January 2015
  • you can compress the documents in the storage file with Global.CompressDocumentOverKiloBytes configuration
  • Upgrade to fastJSON v2.1.9
  • added integrity check for views with auto rebuild if not shutdown cleanly
  • bug fix disable timers before Shutdown()
  • added high frequency update key/value storage file
• Update v3.2.5 : 6^th February 2015
  • new optimized for storage string MGIndex file
  • added Global.EnableOptimizedStringIndex flag to control the new index usage
• Update v3.2.6 : 27^th February 2015
  • optimizations done by Stainslav Lukeš
  • upgrade to fastJSON v2.1.10
  • upgrade to fastBinaryJSON v1.4.7
  • bug fix bitmap indexes
  • bug fix file name conflicts with deleted bitmap indexes
  • added version checking of views and RaptorDB engine with auto rebuild for engine upgrades
  • changed deleted bitmap indexes to .deleted extension
  • changed version number files to text mode with .version extension
• Update v3.2.7 : 27^th February 2015
  • bug fix wait on view rebuild while Shutdown() was being cut off in 2 secs mid process (ProcessExit)
• Update v3.2.8 : 3^rd March 2015
  • bug fix duplicates showing in queries related to the deleted bitmap index
• Update v3.2.9 : 8^th March 2015
  • upgrade to fastJSON v2.1.11
  • upgrade to fastBinaryJSON v1.4.8
  • added support for vb.net string linq queries
  • added vb test project
• Update v3.2.10 : 24^th April 2015
  • renamed Form1 to frmMain
  • added sortable fulltext indexes
  • fixed path names for linux systems
  • changed default save and free memory timers to 30 min instead of 60 sec
  • optimized query sorting with internal cache ~100x faster
• Update v3.2.11 : 25^th April 2015
  • bug fix sorting cache
• Update v3.2.12 : 17^th May 2015
  • code cleanup
  • bug fix full text searching with + - prefixes
  • upgrade to fastJSON v2.1.13
  • upgrade to fastBinaryJSON v1.4.10
• Update v3.2.13 : 31^st May 2015
  • code refactoring
  • bug fix full text index search with leading not "-oak hill"
  • fix time output in logs
  • upgrade to fastJSON v2.1.14
  • upgrade to fastBinaryJSON v1.4.11
• Update v3.2.14 : 6^th August 2015
  • server keeps track of connected clients count with auto clear every 30 secs
  • added atomic Increment() Decrement() to HFKV int,decimal values
  • bug fix wildcard search in full text indexes
  • added not wildcard search in full text -> "-oak -*l"
  • fulltext indexes break text on spaces and char.IsPunctuation()
  • api.NextRowNumber() blocks until rebuild finishes
  • view map functions has access to high frequency KV through the api interface
• Update v3.2.15 : 13^th September 2015
  • *breaking change* + in full text search means OR now
  • full text search bug fix edge case
• Update v3.2.16 : 26^th January 2016
  • added support for ushort in views (thanks to dragosc)
  • bug fix FetchHistory() return values in server mode (thanks to DrDeadCrash)
  • bug fix edge case bitmap index commits (thanks to dragosc)
  • bug fix hOOt fulltext index shutdown
  • bug fix FullTextSerach() to filter out json characters before indexing
• Update v3.2.17 : 19^th February 2016
  • bug fix empty rows in GenericResult() in client/server mode
  • bug fix if property/field defined in schema but not in entity hence null error when getting rows
• Update v3.2.18 : 15^th March 2016
  • usings cleanup
  • FreeMemory() in indexes, saves data first
  • added indexed word list to hOOt index
  • hOOt optimize index saves data first
  • hOOt optimize index will block until done
  • hOOt FindDocuments() is now generic
  • added server side function with object[] args
  • added auto build number incrementor
  • bug fix not equal != convert linq to string in queries
  • upgrade to fastJSON v2.1.15
  • upgrade to fastBJSON v1.4.12
• Update v3.3.0 : 7^th April 2016
  • added WEB Studio interface
  • logger keeps last 100 logs for recall
  • bug fix HF storage file seek position overflow (thanks to ozzel)
• Update v3.3.1 : 14^th April 2016
  • bug fix mixed property and fields in view schema definition (invalid cast exception on query)
• Update v3.3.2 : 25^th April 2016
  • bug fix edge case duplicates showing in queries
• Update v3.3.3 : 6^th May 2016
  • bug fix SafeSortedList.Add()
  • bug fix WAHBitArray.FreeMemory()
• Update v3.3.4 : 20^th May 2016
  • streamlined shutdown process (no extra index saves)
  • web ui content height set to browser height
  • memory usage reduction in BimapIndex (removed record cache)
  • memory usage reduction in WAHBitArray (offset Dictionary -> SortedList)
  • memory usage reduction in MGIndex (cache Dictionary -> SortedList)
  • memory usage reduction in Hoot (words Dictionary -> SortedList)
  • log error if web server started when not run as administrator
  • frmMain form close handles shutdown correctly
  • changed default Globals.FreeMemoryTimerSeconds to 5 mins from 30 mins
• Update v3.3.5 : 31^st May 2016
  • bug fix != in query return all rows if RH expression not found (thanks to Lutz Wellhausen)
  • linq (x => true) predicate returns all rows
  • linq (x => false) predicate returns no rows
• Update v3.3.6 : 25^th July 2016
  • upgrade to fastJSON v2.1.18
  • upgrade to fastBinaryJSON v1.4.15
  • changed default view.ConsistentSaveToThisView = true
  • changed default view.isPrimaryList = true
  • re-factored web server
  • restructured nav.js
• Update v3.3.8 : 12^th August 2016
  • synced with hOOt
  • added nscript.exe tool
  • added sample.cs test script with command line run
  • changed log message types
  • bug fix fulltext search  
  • fulltext tokenizer breaks a.b.c words and numbers
• Update v3.3.9 : 14^th August 2016
  • query date parts e.g. x => x.Date.Year == 2016 or "date.year = 2016"
  • Between() for dates e.g. x => x.Date.Between("2000-1-1","2002-6-16")
  • optimized internal query engine for ranges
  • internal query from to implementation
  • Between() for int, long, decimal
• Update v3.3.10 : 21^st August 2016
  • code cleanup
  • generic Between<T>()
  • bug fix BitmapIndex.FreeMemory()
  • bug fix MGIndex.FreeMemory()
  • added generic Fetch<T>() (thanks to Norbert Haberl)
  • added ZipStorer from [https://github.com/jaime-olivares/zipstorer] (thanks to Jaime Olivares)
  • added auto compress old log files
• Update v3.3.11 : 25^th August 2016
  • added query bitmap caching for faster queries
• Update v3.3.12 : 28^th August 2016
  • added In<T>() e.g. x => x.Serial.In(1,3,5,7) or "serial.in(1,3,5,7)"
  • query cache for Count() and all other overloads
  • code refactoring/cleanup in ViewHandler.cs
  • date parts work with In() e.g. x => x.Date.Year.In(2000,2001)
  • date parts work with Between() e.g. x => x.Date.Year.Between(2000,2010)
• Update v3.3.13 : 3^rd September 2016
  • bug fix x=>true predicate case
  • upgrade to fastJSON v2.1.19
  • upgrade to fastBinaryJSON v1.4.16
  • bug fix string keys indexes with Global.EnableOptimizedStringIndex = true
  • ** incremented RaptorDBVersion ** will rebuild old index data if exists
  • bug fix WAHBitArray.Fill()
• Update v3.3.14 : 12^th November 2016
  • upgrade to fastJSON v2.1.21
  • upgrade to fastBinaryJSON v1.4.18
  • bug fix doc full text search results < paging count
• Update v3.3.15 : 6^th June 2017
  • upgrade to fastBinaryJSON v1.4.19
  • bug fix logger zip file names
  • bug fix local/UTC time in FastDateTime
  • changed to public WAHBitArray
  • changed to public Hoot
  • changed to public CronDaemon
  • Zipstorer default to UTF8 filenames
  • bug fix ViewInsert() and rebuild index *old data is not recoverable, rebuild your database if you can*
  • remove query bitmap cache because of incorrect results
  • upgrade to fastJSON v2.1.25
  • bug fix HFKV store rebuild on failed shutdown
• Update v3.3.16 : 7^th June 2017
  • bug fix HFKV rebuild
• Update v3.3.17 : 9^th June 2017
  • added ITokenizer full text search overload to RaptorDB.Open()
  • tokenizer rewrite
  • bug fix HFKV rebuild
• Update v3.3.19 : 25^th January 2018
  • upgrade to fastJSON v2.1.28
  • upgrade to fastBinaryJSON v1.4.21
  • support for .netcore 2.0 and netstandard 2.0
• Update v3.3.19.1 : 26^th January 2018
  • bug fix shutting down in .net core on ubuntu
• Update v4.0.0 : 5^th October 2018
  • upgrade to fastJSON v2.2.0
  • upgrade to fastBinaryJSON v1.5.1
  • changed to faster unicode <-> bytes conversion
  • optimized view data bytes size without bjson typed arrays
  • new sparse bitmap index for less memory usage (MGRB based on roaring bitmap)
  • * possible breaking change if you are using High Frequency key store *
  • bug fix full text search in columns for case insensitive words
  • parallel shutdown indexes for better performance
  • fixed .strings files kept growing
• Update v4.0.2 : 8^th October 2018
  • bug fix missing raptordb.version file in data folder
  • bug fix MGRB.Fill()
• Update v4.0.5 : 11^th October 2018
  • bug fix MGRB.Fill(length) respect length value
  • bug fix MGRB.CountOnes() when length = 0
  • MGRB added locks for integrity
  • MGRB added InvertedContainer for better memory usage
  • MGRB respect lengths in computations
  • truncate StorageFileHF end free list data on read
• Update v4.0.6 : 17^th December 2018
  • upgrade to fastBinaryJSON v1.5.2
  • bug fix loading empty hOOt words file
  • bug fix query MGRB index with less than 64 rows
• Update v4.0.7 : 25^th February 2019
  • bug fix StorageFileHF reloading free list from previous failed shutdown
• Update v4.0.8 : 23^rd June 2019
  • bug fix .string index files growing over time
  • upgrade to fastJSON v2.2.5
  • upgrade to fastBinaryJSON v1.5.3
• Update v4.0.9 : 24^th July 2019
  • rewritten the web studio with Svelte