Synopse Open Source

From the beginning, server-side storage tables which were not store in a SQLite3 database were implemented via some classes inheriting from TSQLRestServerStatic.
This TSQLRestServerStatic was inheriting from TSQLRestServer, which did not make much sense (but was made for laziness years ago, if I remember well).

Now, a new TSQLRestStorage class, directly inheriting from TSQLRest, is used for per-table storage.
This huge code refactoring results in a much cleaner design, and will enhance code maintainability.
Documentation has been updated to reflect the changes.

Note that this won't change anything when using the framework (but the new class names): it is an implementation detail, which had to be fixed.

MongoDB (from "humongous") is a cross-platform document-oriented database system, and certainly the best known NoSQL database.
According to http://db-engines.com in April 2014, MongoDB is in 5th place of the most popular types of database management systems, and first place for NoSQL database management systems.
Our mORMot gives premium access to this database, featuring full NoSQL and Object-Document Mapping (ODM) abilities to the framework.

Integration is made at two levels:

• Direct low-level access to the MongoDB server, in the SynMongoDB.pas unit;
• Close integration with our ORM (which becomes defacto an ODM), in the mORMotMongoDB.pas unit.

MongoDB eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON), which matches perfectly mORMot's RESTful approach.

This second article will focus on integration of MongoDB with our ORM.

We have just released a set of slides introducing  ORM, SOA, REST, JSON, MVC, MVVM, SOLID, Mocks/Stubs, Domain-Driven Design concepts with Delphi,  and showing some sample code using our Open Source mORMot framework. You can follow the public link on Google Drive! This is a great opportunity to  […]

For several units of our framework, we allow late-binding of data values, using a variant and direct named access to properties:
- In SynCommons, we defined our TDocVariant custom variant type, able to store any JSON/BSON document-based content;
- In SynBigTable, we use the TSynTableVariantType custom variant type, as defined in SynCommons;
- In SynDB, we defined a TSQLDBRowVariantType, ready to access any column of a RDBMS data result set row;
- In mORMot, we allow access to TSQLTableRowVariantType column values.

It's a very convenient way of accessing result rows values. Code is still very readable, and safe at the same time.

For instance, we can write:

var V: variant;
 ...
  TDocVariant.New(V); // or slightly slower V := TDocVariant.New;
  V.name := 'John';
  V.year := 1972;
  // now V contains {"name":"john","year":1982}

This is just another implementation of KISS design in our framework.

Since Delphi XE2, some modifications were introduced to the official DispInvoke() RTL implementation:

1. A new varUStrArg kind of parameter has been defined, which will allow to transmit UnicodeString property values;
2. All text property values would be transmitted as BSTR / WideString / varOleStr variants to the invoked variant type;
3. All textual property names were normalized to be in UPPERCASE.

Those modifications are worth considering...
And we may have discovered two regressions: one about speed, and the other about an unexpected logic bug...

We just tested, benchmarked and validated Oracle MySQL, IBM DB2 and PostgreSQL support for our SynDB database classes and the mORMot's ORM core.
This article will also show all updated results, including our newly introduced multi-value INSERT statement generations, which speed up a lot BATCH insertion.

Stay tuned!

Purpose here is not to say that one library or database is better or faster than another, but publish a snapshot of mORMot persistence layer abilities, depending on each access library.

In this timing, we do not benchmark only the "pure" SQL/DB layer access (SynDB units), but the whole Client-Server ORM of our framework.

Process below includes all aspects of our ORM:

• Access via high level CRUD methods (Add/Update/Delete/Retrieve, either per-object or in BATCH mode);
• Read and write access of TSQLRecord instances, via optimized RTTI;
• JSON marshaling of all values (ready to be transmitted over a network);
• REST routing, with security, logging and statistic;
• Virtual cross-database layer using its SQLite3 kernel;
• SQL on-the-fly generation and translation (in virtual mode);
• Access to the database engines via several libraries or providers.

In those tests, we just bypassed the communication layer, since TSQLRestClient and TSQLRestServer are run in-process, in the same thread - as a TSQLRestServerDB instance. So you have here some raw performance testimony of our framework's ORM and RESTful core, and may expect good scaling abilities when running on high-end hardware, over a network.

On a recent notebook computer (Core i7 and SSD drive), depending on the back-end database interfaced, mORMot excels in speed, as will show the following benchmark:

• You can persist up to 570,000 objects per second, or retrieve 870,000 objects per second (for our pure Delphi in-memory engine);
• When data is retrieved from server or client 38, you can read more than 900,000 objects per second, whatever the database back-end is;
• With a high-performance database like Oracle, and our direct access classes, you can write 70,000 (via array binding) and read 160,000 objects per second, over a 100 MB network;
• When using alternate database access libraries (e.g. Zeos, or DB.pas based classes), speed is lower (even if comparable for DB2, MS SQL, PostgreSQL, MySQL) but still enough for most work, due to some optimizations in the mORMot code (e.g. caching of prepared statements, SQL multi-values insertion, direct export to/from JSON, SQlite3 virtual mode design, avoid most temporary memory allocation...).

Difficult to find a faster ORM, I suspect.

One of the main features you may miss when discovering NoSQL ("Not-Only SQL"?) databases, coming from a RDBMS background, is ACID.

ACID (Atomicity, Consistency, Isolation, Durability) is a set of properties that guarantee that database transactions are processed reliably. In the context of databases, a single logical operation on the data is called a transaction. For example, a transfer of funds from one bank account to another, even involving multiple changes such as debiting one account and crediting another, is a single transaction. (Wikipedia)

But are there any ACID NoSQL database?

Please ensure you read the Martin Fowler introduction about NoSQL databases.
And the corresponding video.

First of all, we can distinguish two types of NoSQL databases:

1. Aggregate-oriented databases;
2. Graph-oriented databases (e.g. Neo4J).

By design, most Graph-oriented databases are ACID!
This is a first good point.

Then, what about the other type?
In Aggregate-oriented databases, we can identify three sub-types:

• Document-based NoSQL databases (e.g. MongoDB, CouchDB);
• Key/Value NoSQL databases (e.g. Redis);
• Column family NoSQL databases (e.g. Cassandra).

One year ago, we already made a quick presentation of Domain-Driven Design, in the context of our mORMot framework.
After one year of real-world application of those patterns, it is now time to give more light to DDD.

Let's continue with part 4, which will define Domain-Driven Design as could be implemented with our Synopse mORMot framework

One year ago, we already made a quick presentation of Domain-Driven Design, in the context of our mORMot framework.
After one year of real-world application of those patterns, it is now time to give more light to DDD.

Let's continue with part 2, which will define Domain-Driven Design high-level model principles.

On an recent notebook computer (Core i7 and SSD drive), depending on the back-end database interfaced, mORMot excels in speed:

• You can persist up to 570,000 objects per second, or retrieve more than 900,000 objects per second (for our pure Delphi in-memory engine);
• When data is retrieved from server or client cache, you can read more than 900,000 objects per second, whatever the database back-end is;
• With a high-performance database like Oracle and our direct access classes, you can write 65,000 (via array binding) and read 160,000 objects per second, over a 100 MB network;
• When using alternate database access libraries (e.g. Zeos, or DB.pas based classes), speed is lower, but still enough for most work.

Difficult to find a faster ORM, I suspect.

The following tables try to sum up all available possibilities, and give some benchmark (average objects/second for writing or read).

In these tables:

• 'SQLite3 (file full/off/exc)' indicates use of the internal SQLite3 engine, with or without Synchronous := smOff and/or DB.LockingMode := lmExclusive;
• 'SQLite3 (mem)' stands for the internal SQLite3 engine running in memory;
• 'SQLite3 (ext ...)' is about access to a SQLite3 engine as external database - either as file or memory;
• 'TObjectList' indicates a TSQLRestServerStaticInMemory instance, either static (with no SQL support) or virtual (i.e. SQL featured via SQLite3 virtual table mechanism) which may persist the data on disk as JSON or compressed binary;
• 'Oracle' shows the results of our direct OCI access layer (SynDBOracle.pas);
• 'NexusDB' is the free embedded edition, available from official site;
• 'Zeos *' indicates that the database was accessed directly via the ZDBC layer;
• 'FireDAC *' stands for FireDAC library;
• 'UniDAC *' stands for UniDAC library;
• 'BDE *' when using a BDE connection;
• 'ODBC *' for a direct access to ODBC;
• 'Jet' stands for a MSAccess database engine, accessed via OleDB;
• 'MSSQL local' for a local connection to a MS SQL Express 2008 R2 running instance (this was the version installed with Visual Studio 2010), accessed via OleDB.

This list of database providers is to be extended in the future. Any feedback is welcome!

Numbers are expressed in rows/second (or objects/second). This benchmark was compiled with Delphi 7, so newer compilers may give even better results, with in-lining and advanced optimizations.

Note that these tests are not about the relative speed of each database engine, but reflect the current status of the integration of several DB libraries within the mORMot database access.

Purpose here is not to say that one library or database is better or faster than another, but publish a snapshot of current mORMot persistence layer abilities.

In this timing, we do not benchmark only the "pure" SQL/DB layer access (SynDB units), but the whole Client-Server ORM of our framework: process below includes read and write RTTI access of a TSQLRecord, JSON marshaling, CRUD/REST routing, virtual cross-database layer, SQL on-the-fly translation. We just bypass the communication layer, since TSQLRestClient and TSQLRestServer are run in-process, in the same thread - as a TSQLRestServerDB instance. So you have here some raw performance testimony of our framework's ORM and RESTful core.

You can compile the "15 - External DB performance" supplied sample code, and run the very same benchmark on your own configuration.

During this summer, warleyalex did meet some mORMots in the mountains of REST, Java, AJAX and JSON. (picture may differ from actual user:) ) He did some videos of his experiment with our little rodent. At this time, there are 11 videos available! Latest one is a Java client application,  […]