Synopse Open Source

Last weeks, we have enhanced mORMot support to one of the more powerful AARM64 CPU available: the Ampere Altra CPU, as made available on the Oracle Cloud Infrastructure.

Long story short, this is an amazing hardware to run on server side, with performance close to what Intel/AMD offers, but with almost linear multi-core scalability. The FPC compiler is able to run good code on it, and our mORMot 2 library is able to use the hardware accelerated opcodes for AES, SHA2, and crc32/crc32c.

The more I think of it, the more I am convinced it is time to change how the framework is versioned.
We have version 1.18 since years... difficult to follow... time to upgrade!

I would like to upgrade mORMot to version 2 - with a major refactoring.

In the last weeks/months, we worked a lot with FPC.
Delphi is still our main IDE, due to its better debugging experience under Windows, but we target to have premium support of FPC, on all platforms, especially Linux.

The new Delphi Linux compiler is out of scope, since it is heavily priced, its performance is not so good, and ARC broke memory management so would need a deep review/rewrite of our source code, which we can't afford - since we have FPC which is, from our opinion,  a much better compiler for Linux.
Of course, you can create clients for Delphi Linux and FMX, as usual, using the cross-platform client parts of mORMot. But for server side, this compiler is not supported, and will probably never be.

You know certainly that our mORMot Open Source framework is an ORM, i.e. mapping objects to a relational / SQL database (Object Relational Mapping).
You may have followed also that it is able to connect to a NoSQL database, like MongoDB, and that the objects are then mapped via an ODM (Object Document Mapping) - the original SQL SELECT are even translated on the fly to MongoDB queries.

But thanks to mORMot, it is not "SQL vs NoSQL" - but "SQL and NoSQL".
You are not required to make an exclusive choice.
You can share best of both worlds, depending on your application needs.

In fact, the framework is able to add NoSQL features to a regular relational / SQL database, by storing JSON documents in TEXT columns.

In your end-user code, you just define a variant field in the ORM, and store a TDocVariant document within.
We also added some dedicated functions at SQL level, so that SQLite3 could be used as embedded fast engine, and provide advanced WHERE clauses on this JSON content.

How would be 2015 like for our little rodents?
Due to popular request of several users of mORMot, we identified and designed some feature requests dedicated to BigData process.

In fact, your data is the new value, especially if you propose SaaS (Software As A Service) hosting to your customers, with a farm of mORMot servers.
Recent Linux support for mORMot servers, together with the high performance and installation ease of our executable, open the gate to cheap cloud-based hosting.
As a consequence, a lot of information would certainly be gathered by your mORMot servers, and a single monolithic database is not an option any more.

For mORMot solutions hosted in cloud, a lot of data may be generated. The default SQLite3 storage engine may be less convenient, once it reaches some GB of file content. Backup becomes to be slow and inefficient, and hosting this oldest data in the main DB, probably stored on an expensive SSD, may be a lost of resource. Vertical scaling is limited by hardware and price factors.

This is were data sharding comes into scene.
Note that sharding is not replication/backup, nor clustering, nor just spreading. We are speaking about application-level data splitting, to ease maintenance and horizontal scalability of mORMot servers.

Data sharding could already be implemented with mORMot servers, thanks to TSQLRestStorage:

• Using TSQLRestStorageExternal: any table may have its own external SQL database engine, may be in its separated DB server;
• Using TSQLRestStorageMongoDB: any table may be stored on a MongoDB cluster, with its own sharding abilities;
• Using TSQLRestStorageRemote: each table may have its own remote ORM/REST server.

But when data stored in a single table tends to grow without limit, this feature is not enough.
Let's see how the close future of mORMot looks like.

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.

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.

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.

Our SynDB classes feature now FireDAC / AnyDAC access, with full speed!

Up to now, only UniDAC, BDE or ZEOS components were available as source, but we just added FireDAC / AnyDAC.

FireDAC is an unique set of Universal Data Access Components for developing cross platform database applications on Delphi. This was in fact a third-party component set, bought by Embarcadero to DA-SOFT Technologies (formerly known as AnyDAC), and included with several editions of Delphi XE3 and up. This is the new official platform for high-speed database development in Delphi, in favor to the now deprecated DBExpress.

Our integration within SynDB.pas units and the mORMot persistence layer has been tuned. For instance, you can have direct access to high-speed FireDAC Array DML feature, via the ORM batch process, via so-called array binding.

Update: We now build the amalgamation file with mingw and release the latest version of SQLite3, from this direct SQLite3-64.7z link, as soon as it is published on the SQLite3 site. Up to now, there is no official Win64 version of the SQlite3 library released in http://sqlite.org.. It is in fact  […]