Entity Framework migrations with multiple database contexts

The .NET Entity Framework provides functionality for automatic database migrations. Every time your application code requires a change of the database schema you should create a migration, so that the existing database schema is updated when a new version of the application is deployed. Examples for such changes are new entity classes or the addition and removal of properties of existing entity classes. The Entity Framework functionality for database migrations is called Code First Migrations.

Code First Migrations are managed via the so-called Package Manager Console. That’s how it’s called in Visual Studio, because its usually used for package management, but it’s basically a general Power Shell command line interface. After you have created the database context class and the entity model classes for your application, you create an initial migration (usually called InitialCreate), which captures the original state of the database schema for your application:

Add-Migration InitialCreate

This will create a new migration class called InitialCreate in the Migrations folder. The filename is prefixed with a timestamp: 201810702207458_InitialCreate.cs. Each migration class has an Up() method, which applies the migration and a Down() method, which rolls the migration back.

Each subsequent migration only describes the difference to its predecessor migration. For example, you add a new string property Email to your User entity class and add a new migration:

Add-Migration AddUserEmail

The tool will scan your entity classes, compare the current state to the state of the previous migration, calculate the difference and create a new migration class, which adds a new column to the database schema.

When the migrations are run on the target system they are tracked in a special database table called __MigrationHistory.

Multiple database contexts

The above usage of Code First Migrations is well documented. Here I want to describe a feature, that is documented in less detail, because it’s less commonly used: migrations with multiple database contexts.

Let’s assume you have two database contexts: CoreDataContext and MeasurementDataContext. In this case you have to create two migration configuration classes, which inherit DbMigrationsConfiguration. You want to create two subdirectories under the Migrations directory, one for each database context:

In each subdirectory you create a migrations Configuration class, one for each database context:

namespace Migrations.CoreData
  internal sealed class Configuration : DbMigrationsConfiguration<CoreDataContext>
    public Configuration()
      MigrationsDirectory = "Migrations\CoreData";
      AutomaticMigrationsEnabled = true;


namespace Migrations.MeasurementData
  internal sealed class Configuration : DbMigrationsConfiguration<MeasruementDataContext>
    public Configuration()
      MigrationsDirectory = "Migrations\MeasurementData";
      AutomaticMigrationsEnabled = true;

For each configuration you have to set the MigrationsDirectory property accordingly. The AutomaticMigrationsEnabled property is optional. If it’s set the migrations will be applied automatically at the start of the application.

Now, if you run a migration command like Add-Migration, you have to add the -ConfigurationTypeName option, which specifies the Configuration class for desired the database context:

Add-Migration InitialCreate -ConfigurationTypeName Migrations.CoreData.Configuration

Add-Migration InitialCreate -ConfigurationTypeName Migrations.MeasurementData.Configuration

Add-Migration AddUserEmail -ConfigurationTypeName Migrations.CoreData.Configuration

Add-Migration AddMeasurementTimestamp -ConfigurationTypeName Migrations.MeasurementData.Configuration

The migration classes will now be created in the correct subdirectories.

Handling database warnings with JDBC

Database administrators have the possibility to set lifetimes for user passwords. This can be considered a security feature, so that passwords get updated regularly. But if one of your software services logs into the database with such an account, you want to know when the password expires in good time before this happens, so that you can update the password. Otherwise your service will stop working unexpectedly.

Of course, you can mark the date in your calendar in order to be reminded beforehand, and you probably should. But there is an additional measure you can take. The database administrator can not only set the lifetime of a password, but also a “grace period”. For example:


This SQL command sets the password life time to 180 days (roughly six months) and the grace period to 14 days (two weeks). If you log into the database with this user you will see a warning two weeks before the password will expire. For Oracle databases the warning looks like this:

ORA-28002: the password will expire within 14 days

But your service logs in automatically, without any user interaction. Is it possible to programmatically detect a warning like this? Yes, it is. For example, with JDBC the following code detects warnings after a connection was established:

// Error codes for ORA-nnnnn warnings
static final int passwordWillExpireSoon = 28002;
static final int accountWillExpireSoon = 28011;

void handleWarnings(Connection connection) throws SQLException {
    SQLWarning warning = connection.getWarnings();
    while (null != warning) {
        String message = warning.getMessage();

        int code = warning.getErrorCode();
        if (code == passwordWillExpireSoon) {
            System.out.println("ORA-28002 warning detected");
            // handle appropriately
        if (code == accountWillExpireSoon) {
            System.out.println("ORA-28011 warning detected");
            // handle appropriately
        warning = warning.getNextWarning();

Instead of just logging the warnings, you can use this code to send an email to your address, so that you will get notified about a soon-to-be-expired password in advance. The error code depends on your database system.

With this in place you should not be unpleasantly surprised by an expired password. Of course, this only works if the administrator sets a grace period, so you should agree on this approach with your administrator.

Using PostgreSQL with Entity Framework

The most widespread O/R (object-relational) mapper for the .NET platform is the Entity Framework. It is most often used in combination with Microsoft SQL Server as database. But the architecture of the Entity Framework allows to use it with other databases as well. A popular and reliable is open-source SQL database is PostgreSQL. This article shows how to use a PostgreSQL database with the Entity Framework.

Installing the Data Provider

First you need an Entity Framework data provider for PostgreSQL. It is called Npgsql. You can install it via NuGet. If you use Entity Framework 6 the package is called EntityFramework6.Npgsql:

> Install-Package EntityFramework6.Npgsql

If you use Entity Framework Core for the new .NET Core platform, you have to install a different package:

> Install-Package Npgsql.EntityFrameworkCore.PostgreSQL

Configuring the Data Provider

The next step is to configure the data provider and the database connection string in the App.config file of your project, for example:

  <!-- ... -->

      <provider invariantName="Npgsql"
         type="Npgsql.NpgsqlServices, EntityFramework6.Npgsql" />

      <add name="Npgsql Data Provider"
           description="Data Provider for PostgreSQL"
           type="Npgsql.NpgsqlFactory, Npgsql"
           support="FF" />

    <add name="AppDatabaseConnectionString"
         providerName="Npgsql" />


Possible parameters in the connection string are Server, Port, Database, User Id and Password. Here’s an example connection string using all parameters:

Server=;Port=5432;Database=mydatabase;User Id=postgres;Password=topsecret

The database context class

To use the configured database you create a database context class in the application code:

class AppDatabase : DbContext
  private readonly string schema;

  public AppDatabase(string schema)
    : base("AppDatabaseConnectionString")
    this.schema = schema;

  public DbSet<User> Users { get; set; }

  protected override void OnModelCreating(DbModelBuilder builder)

The parameter to the super constructor call is the name of the configured connection string in App.config. In this example the method OnModelCreating is overridden to set the name of the used schema. Here the schema name is injected via constructor. For PostgreSQL the default schema is called “public”:

using (var db = new AppDatabase("public"))
  var admin = db.Users.First(user => user.UserName == "admin")
  // ...

The Entity Framework mapping of entity names and properties are case sensitive. To make the mapping work you have to preserve the case when creating the tables by putting the table and column names in double quotes:

create table public."Users" ("Id" bigserial primary key, "UserName" text not null);

With these basics you’re now set up to use PostgreSQL in combination with the Entity Framework.


Monitoring long running operations in Oracle databases

We regularly work with database tables with hundreds of millions of entries. Some operations on these table can take a while. Not necessarily queries, but operations in preparation to make queries fast, for example the creation of materialized views or indexes.

The problem with most SQL tools is: once you run your SQL statement you have no indication of how long it will take to complete the operation. No progress bar and no display of the remaining time. Will it take minutes or hours?

Oracle databases have a nice feature I learned about recently that can answer these questions. Operations that take longer than 6 seconds to complete are considered “long operations” and get an entry in a special view called V$SESSION_LONGOPS.

This view does not only contain the currently running long operations but also the history of completed long operations. You can query the status of the current long operations like this:

  WHERE time_remaining > 0;

This view contains columns like

  • TARGET (table or view on which the operation is carried out)
  • SOFAR (units of work done so far)
  • TOTALWORK (total units of work)
  • ELAPSED_SECONDS (number of elapsed seconds from the start of the operation)

Based on these values the view offers another column, which contains the estimated remaining time in seconds: TIME_REMAINING.

This remaining time is really just an estimate, because it assumes long running operations to be linear, which is not necessarily true. Also some SQL statements can spawn multiple consecutive operations, e.g. first a “Table Scan” operation and then a “Sort Output” operation, which will only become visible after the first operation has finished. Nevertheless I found this feature quite helpful to get a rough idea of how long I will have to wait or to inform decisions such as whether I really want to perform an operation until completion or if I want to cancel it.

Modern developer Issue 4: My SQL toolbox

SQL is such a basic and useful language but the underlying thinking is non-intuitive when you come from imperative languages like Java, Ruby and similar.
SQL is centered around sets and operations on them. The straight forward solution might not be the best one.


Let’s say we need the maximum value in a certain set. Easy:

select max(value) from table

But what if we need the row with the maximum value? Just adding the other columns won’t work since aggregations only work with other aggregations and group bys. Joining with the same table may be straight forward but better is to not do any joins:

select * from (select * from table order by value desc) where rownum<=1

Group by and having

Even duplicate values can be found without joining:

select value from table group by value having count(*) > 1

Grouping is a powerful operation in SQL land:

select max(value), TO_CHAR(time, 'YYYY-MM') from table group by TO_CHAR(time, 'YYYY-MM')

Finding us the maximum value in each month.

Mapping with outer joins

SQL is also good for calculations. Say we have one table with values and one with a mapping like a precalculated log table. Joining both gets the log of each of your values:

select t.value, log.y from table t left outer join log_table log on t.value=log.x

Simple calculations

We can even use a linear interpolation between two values. Say we have only the function values stored for integers but we values between them and these values between them can be interpolated linearly.

select t.value, (t.value-floor(t.value))*f.y + (ceil(t.value)-t.value)*g.y from table t left outer join function_table f on floor(t.value)=f.x left outer join function_table g on ceil(t.value)=g.x

When you need to calculate for large sets of values and insert them into another table it might be better to calculate in SQL and insert in one step without all the conversion and wrapping stuff present in programming languages.


Another often overlooked feature is to use a condition:

select case when MOD(t.value, 2) = 0 then 'divisible by 2' else 'not divisible by 2' end from table t

These handful operations are my basic toolbox when working with SQL, almost all queries I need can be formulated with them.

Dates and timestamps

One last reminder: when you work with time always specify the wanted time zone in your query.

Monitoring data integrity with health checks

An important aspect for systems, which are backed by a database storage, is to maintain data integrity. Most relational databases offer the possibility to define constraints in order to maintain data integrity, usually referential integrity and entity integrity. Typical constraints are foreign key constraints, not-null constraints, unique constraints and primary key constraints.

SQL also provides the CHECK constraint, which allows you to specify a condition on each row in a table:

   constraint_name CHECK ( predicate )

For example:

CHECK (AGE >= 18)

However, these check constraints are limited. They can’t be defined on views, they can’t refer to columns in other tables and they can’t include subqueries.

Health checks

In order to monitor data integrity on a higher level that is closer to the business rules of the domain, we have deployed a technique that we call health checks in some of our applications.

These health checks are database queries, which check that certain constraints are met in accordance with the business rules. The queries are usually designed to return an empty result set on success and to return the faulty data records otherwise.

The health checks are run periodically. For example, we use a Jenkins job to trigger the health checks of one of our web applications every couple of hours. In this case we don’t directly query the database, but the application does and returns the success or failure states of the health checks in the response of a HTTP GET request.

This way we can detect problems in the stored data in a timely manner and take countermeasures. Of course, if the application is bug free these health checks should never fail, and in fact they rarely do. We mostly use the health checks as an addition to regression tests after a bug fix, to ensure and monitor that the unwanted state in the data will never happen again in the future.

IS NULL or IS NOT NULL, that is the question

Today I’ll demonstrate a curiosity of SQL regarding the NOT IN operator in combination with a subquery and NULL values.

Let’s assume we have two database tables, users and profiles:

 users              profiles
+--------------+  +-------------+
| id  username |  | id  user_id |
| 0   'joe'    |  | 0   2       |
| 1   'kate'   |  | 1   0       |
| 2   'john'   |  | 2   NULL    |
| 3   'maria'  |  +-------------+

We want to find all users, which have no associated profile. The intuitive solution would be a negated membership test (“NOT IN”) on the result set of a subquery:

SELECT * FROM users WHERE id NOT IN (SELECT user_id FROM profiles);

The anticipated result is:

| id  username	|
| 1   'kate'    |
| 3   'maria'   |

However, the actual result is an empty set:

| id  username |

This is irritating, especially since the non-negated form produces a sensible result:

SELECT * FROM users WHERE id IN (SELECT user_id FROM profiles);

| id  username	|
| 0   'joe'    |
| 2   'john'   |

So why does the NOT IN operator produce this strange result?

To understand what happens we replace the result of the subquery with a set literal:

SELECT * FROM users WHERE id NOT IN (2, 0, NULL);

This statement is internally translated to:

SELECT * FROM users WHERE id<>2 AND id<>0 AND id<>NULL;

And here comes the twist: a field<>NULL clause evaluates to UNKNOWN in SQL, which is treated like FALSE in a boolean expression. The desired clause would be id IS NOT NULL, but this is not what is used by SQL. As a consequence the result set is empty.

The result for the non-negated membership test (“IN”) can be explained as well. The IN clause is internally translated to:

SELECT * FROM users WHERE id=2 OR id=0 OR id=NULL;

A field=NULL clause evaluates to UNKNOWN as well. But in this case it is of no consequence, since the clause is joined via OR.

Now that we know what’s going on, how can we fix it? There are two possibilities:

One is to use an outer join:

SELECT u.id FROM users u LEFT OUTER JOIN profiles p ON u.id=p.user_id WHERE p.id IS NULL;

The other option is to filter out all NULL values in the subquery:

SELECT id FROM users WHERE id NOT IN (SELECT user_id FROM profiles WHERE user_id IS NOT NULL);


Both field=NULL and field<>NULL evaluate to UNKNOWN in SQL. Unfortunately, SQL uses these clauses for IN and NOT IN set operations. The solution is to work around it.