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2015, Tuesday March 31

ORM Master/Slave Replication

As stated during TSQLRecord fields definition, the ORM is able to maintain a revision number for any TSQLRecord table, so that it the table may be easily synchronized remotely by another TSQLRestServer instance.
If you define a TRecordVersion published property, the ORM core will fill this field just before any write with a monotonically increasing revision number, and will take care of any deletion, so that those modifications may be replayed later on any other database.

This synchronization will work as a strict master/slave replication scheme, as a one-way on demand refresh of a replicated table.
Each write operation on the master database on a given table may be easily reflected on one or several slave databases, with almost no speed nor storage size penalty.

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2014, Wednesday May 7

MongoDB + mORMot benchmark

Here are some benchmark charts about MongoDB integration in mORMot's ORM.

MongoDB appears as a serious competitor to SQL databases, with the potential benefit of horizontal scaling and installation/administration ease - performance is very high, and its document-based storage fits perfectly with mORMot's advanced ORM features like Shared nothing architecture (or sharding).

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MongoDB + mORMot ORM = ODM

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.

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Direct MongoDB database access

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 framework 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.

In this first article, we will detail direct low-level access to the MongoDB server, via the SynMongoDB.pas unit.

Continue reading...

2013, Monday January 28

External database speed improvements

Some major speed improvements have been made to our SynDB* units, and how they are used within the mORMot persistence layer.
It results in an amazing speed increase, in some cases.

Here are some of the optimizations how took place in the source code trunk:

Overall, I observed from x2 to x10 performance boost with simple Add() operations, using ODBC, OleDB and direct Oracle access, when compare to previous benchmarks (which were already impressive).
BATCH mode performance is less impacted, since it by-passed some of those limitations, but even in this operation mode, there is some benefits (especially with ODBC and OleDB).

Here are some results, directly generated by the supplied "15 - External DB performance" sample.

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2012, Sunday October 28

SynDBOracle: Open Source native Oracle access

(this is an update of the article published in 2011/07)

For our mORMot framework, and in completion to our SynOleDB unit, we added a new Open Source unit, named SynDBOracle. It allows direct access to any remote Oracle server, using the Oracle Call Interface.

Oracle Call Interface (OCI) is the most comprehensive, high performance, native unmanaged interface to the Oracle Database that exposes the full power of the Oracle Database. We wrote a direct call of the oci.dll library, using our DB abstraction classes introduced for SynOleDB.

We tried to implement all best-practice patterns detailed in the official Building High Performance Drivers for Oracle document

Resulting speed is quite impressive: for all requests, SynDBOracle is 3 to 5 times faster than a SynOleDB connection using the native OleDB Provider supplied by Oracle. We noted also that our implementation is 10 times faster than the one provided with ZEOS/ZDBC, which is far from optimized.

You can use the latest version of the Oracle Instant Client provided by Oracle - see this link - which allows you to run your applications without installing the standard (huge) Oracle client or having an ORACLE_HOME. Just deliver the dll files in the same directory than your application, and it will work.

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2012, Friday September 14

Updated mORMot benchmarks on another HW configuration

With a refreshed hardware, and the latest code modifications of the framework code, I run again the benchmark sample.

The new PC has an Intel Core i7-2600 CPU, running on Windows Seven 64-bit, with anti-virus (Norton) fully enabled.
Oracle 11g database is remotely accessed over a corporate network, so latency and bandwidth is not optimal.
Still no SSD, but a standard 7200 rpm hard drive of 500 GB.

The results are impressive, when compared to the previous run using a Core 2 Duo CPU - mORMot's optimized code achieves amazing speed on this platform.
And I guess the 8MB of L3 cache of the Core i7 does wonders with our code.

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2012, Wednesday August 1

Managing unique properties

For real applications, retrieving objects per ID is not enough.
Your project may have the need to retrieve objects by a textual field, e.g. a name or identifier.
In this case, you can specify a published property of the TSQLRecord as stored false, and it will be defined as an unique column in the underlying database.

For instance, in the latest version of our performance benchmark sample code, you can define the UNIK conditional to define both LastName and FirstName properties as unique:

type
  TSQLRecordSample = class(TSQLRecord)
  private
    fFirstName: RawUTF8;
    fLastName: RawUTF8;
    fAmount: currency;
    fBirthDate: TDateTime;
    fLastChange: TModTime;
    fCreatedAt: TCreateTime;
  published
    property FirstName: RawUTF8 index 40 read fFirstName write fFirstName
      {$ifdef UNIK}stored false{$endif};
    property LastName: RawUTF8 index 40 read fLastName write fLastName
      {$ifdef UNIK}stored false{$endif};
    property Amount: currency read fAmount write fAmount;
    property BirthDate: TDateTime read fBirthDate write fBirthDate;
    property LastChange: TModTime read fLastChange;
    property CreatedAt: TCreateTime read fCreatedAt write fCreatedAt;
  end;

During insertion or update of records, the database will have to check for uniqueness of those column values. It will have an additional performance cost, since a search of the new value is to be performed among existing values.
In order to speed-up the process, a so-called index is created at the database level.
As a consequence, further lookup using this property will benefit for this index, and will be much faster than a classic loop throughout all data.

In the mORMot core, we just made some modifications related to this feature:

Let's see how it works on the benchmark side.

Continue reading...

2012, Wednesday July 25

Synopse mORMot benchmark

After having tested and enhanced the external database speed (including BATCH mode), we are now able to benchmark all database engines available in mORMot.

In fact, the ORM part of our framework has several potential database backends, in addition to the default SQLite3 file-based engine.
Each engine may have its own purpose, according to the application expectation.

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

In these tables:

  • 'internal' means use of the internal SQLite3 engine;
  • 'external' stands for an external access via SynDB;
  • '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;
  • 'trans' stands for Transaction, i.e. when the write process is nested within BeginTransaction / Commit calls;
  • 'batch' mode will be described in this article;
  • 'read one' states that one object is read per call (ORM generates a SELECT * FROM table WHERE ID=?);
  • 'read all' is when all 5000 objects are read in a single call (i.e. running SELECT * FROM table);
  • ACID is an acronym for "Atomicity Consistency Isolation Durability" properties, which guarantee that database transactions are processed reliably: for instance, in case of a power loss or hardware failure, the data will be saved on disk in a consistent way, with no potential loss of data.
In short: depending on the database you can persist up to 150,000 objects per second, or retrieve  240,000 objects per second.
With a high-performance database like Oracle and our direct access classes, you write 53,000 and read 72,000 objects per second.
Difficult to find a faster ORM, I suspect. :)

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2011, Sunday June 5

Synopse SQLite3 Framework 1.13

This is a major step for the framework.

Among a lot of new features and bug fixes:

Open Source project, for Delphi 6 up to XE, licensed under a MPL/LGPL/GPL tri-license.

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2011, Thursday June 2

BATCH sequences for adding/updating/deleting records

When use the so-called BATCH sequences?

In a standard Client-Server architecture, especially with the common understanding (and most implementations) of a RESTful service, any Add / Update / Delete method call requires a back and forth flow to then from the remote server.

In case of a remote connection via the Internet (or a slow network), you could have some 100 ms of latency: it's just the "ping" timing, i.e. the time spent for your IP packet to go to the server, then back to you.

If you are making a number of such calls (e.g. add 1000 records), you'll have 100*1000 ms = 100 s = 1:40 min just because of this network latency!

The BATCH sequence allows you to regroup those statements into just ONE remote call. Internally, it builds a JSON stream, then post this stream at once to the server. Then the server answers at once, after having performed all the modifications.

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