ODB 1.8.0 was released today.

In case you are not familiar with ODB, it is an object-relational mapping (ORM) system for C++. It allows you to persist C++ objects to a relational database without having to deal with tables, columns, or SQL, and manually writing any of the mapping code.

For the complete list of changes, see the official ODB 1.8.0 announcement. The biggest feature, however, is no doubt support for the Microsoft SQL Server database. As usual, below I am going to examine this and other notable new features in more detail. There are also some performance numbers that show how SQL Server stacks up against other databases that we support.

SQL Server support

Support for SQL Server is provided by the new libodb-mssql runtime library. All the standard ODB functionality is available to you when using SQL Server, including support for containers, object relationships, queries, date-time types in the Boost and Qt profiles, etc. In other words, this is complete, first-class support, similar to that provided for all the other databases. There are a few limitations, however, most of which are imposed by the underlying ODBC API, Native Client ODBC driver, or SQL Server. Those are discussed in Chapter 17, “Microsoft SQL Server Database” in the ODB Manual.

ODB supports SQL Server 2005 or later, though there are some additional limitations when using SQL Server 2005, mostly to do with the date-time type availability and the long data streaming (again, see Chapter 17 for details). You may have heard that recently Microsoft released the Linux version of their ODBC driver. I am happy to report that this driver works really well. ODB with SQL Server has been tested and is fully supported on both Windows and GNU/Linux.

For connection management in SQL Server, ODB provides two standard connection factories (you can also provide your own if so desired): new_conection_factory and conection_pool_factory .

The new connection factory creates a new connection whenever one is requested. Once the connection is no longer needed, it is closed.

The connection pool factory maintains a pool of connections and you can specify the min and max connection counts for each pool created. This factory is the default choice when creating a database instance.

If you have any prior experience with ODB, you are probably aware that one of our primary goals is high performance and low overhead. For that we use native database APIs and all the available performance-enhancing features (e.g., prepared statements). We also cache connections, statements, and even memory buffers extensively. The SQL Server runtime is no exception in this regard. To improve things even further we use streaming to handle long data. The question you are probably asking now is how does it stack up, performance-wise, against other databases that we support.

Well, the first benchmark that we tried is the one from the Performance of ODB vs C# ORMs post. Essentially we are measuring how fast we can load an object with a couple of dozen members from the database. For reference, it takes ODB with PostgreSQL 9.0.4 27ms per 500 iterations (54μs per object), MySQL 5.1.49 — 24ms (48μs per object) and SQLite 3.7.5 — 7ms (14μs per object). Oracle numbers cannot be shown because of the license restrictions.

The first test that we ran was on GNU/Linux and it gave us 282ms per 500 iterations (564μs per object). Things improved a little once we ran it on Windows 7 connecting to a local SQL Server instance: 222ms or 444μs per object. Things improved a little further once we ran the same test on Windows Server 2008R2 again connecting to a local SQL Server 2008R2 instance: 152ms or 304μs per object.

Update: I have re-done all the tests to get more accurate benchmark results.

As you can see the SQL Server numbers on this benchmark are not that great when compared to other databases. I am not exactly sure what is causing this since there are many parts involved in the chain (ODB runtime, ODBC driver manager, ODBC driver, driver-to-server transport, SQL Server itself), most of which are “black boxes”. My guess is that here we are paying for the abstract, “common denominator” ODBC interface and its two-layer architecture (driver manager and driver). It is also interesting to note that in all the tests neither the benchmark nor the SQL Server process utilized all the available resources (CPU, memory, disk, or network). If you would like to run the benchmark on your setup, feel free to download the benchmark source code and give it a try. The accompanying README file has more information on how to build and run the test.

Now, let’s look at the concurrent access performance. To measure this we use an update-heavy, highly-contentious multi-threaded test in the ODB test suite, the kind you run to make sure things work properly in multi-threaded applications (see odb-tests/common/threads if you are interested in details). It normally pushes my 2-CPU, 8-core Xeon E5520 machine, which runs the database server, close to 100% CPU utilization. As you may remember, PostgreSQL 9.0.4 was the star of this benchmark, beating both MySQL 5.1.49 with the InnoDB backend and SQLite 3.7.5 by a significant margin (12s vs 186s and 48s, respectively). SQL Server 2008R2 on Windows Server 2008R2 with 12 logical CPUs manages to complete this test in 59s. This result is much better compared to the previous test. It also showed a much better CPU utilization of up to 90%. Update: see more accurate results for this test as well.

Let me also note that these numbers should be taken as indications only. It is futile to try to extrapolate some benchmark results to your specific application when it comes to databases. The only reliable approach is to create a custom test that mimics your application’s data, concurrency, and access patterns. Luckily, with ODB, creating such a test is a very easy job. You can use the above-mentioned benchmark source code as a starting point.

Composite values as template instantiations

ODB now supports defining composite value types as C++ class template instantiations. For example:

template <typename T> struct point { T x; T y; T z; }; typedef point<int> int_point; #pragma db value(int_point) #pragma db object class object { ... int_point center_; };

For more information on this feature, refer to Section 7.2, “Composite Value Types” in the ODB manual.

Database schemas (database namespaces)

Some database implementations support what would be more accurately called a database namespace but is commonly called a schema. In this sense, a schema is a separate namespace in which tables, indexes, sequences, etc., can be created. For example, two tables that have the same name can coexist in the same database if they belong to different schemas.

ODB now allows you to specify a schema for tables of persistent classes and this can be done at the class level, C++ namespace level, or the file level.

If you want to assign a schema to a specific persistent class, then the first method will do the trick:

#pragma db object schema("accounting") class employee { ... };

If you are also assigning a table name, then you can use a shorter notation by specifying both the schema and the table name in one go:

#pragma db object table("accounting.employee") class employee { ... };

If you want to assign a schema to all the persistent classes in a C++ namespace, then, instead of specifying the schema for each class, you can specify it once at the C++ namespace level:

#pragma db namespace schema("accounting") namespace accounting { #pragma db object class employee { ... }; #pragma db object class employer { ... }; }

Finally, if you want to assign a schema to all the persistent classes in a file, then you can use the --schema ODB compiler option:

odb ... --schema accounting ...

For more information on this feature see Section 12.1.8, “Schema” in the ODB manual.