New data types

optional

Nullable values are represented as std::optional if you are using C++17 or later, sqlpp::compat::optional otherwise.

string_view

Text results are represented as std::string_view if you are using C++17 or later, sqlpp::compat::string_view otherwise.

span<uint8_t>

Blob results are represented as std::span<uint8_t> if you are using C++20 or later, sqlpp::compat::span<uint8_t> otherwise.

Result values

Result rows are represented as structs with the respective columns represented as data members. In version 1.0, these data members used to have a type that wrapped the actual data and provided conversion operators and functions to check for NULL.

Now data members have the correct data type, e.g. int64_t or optional<string_view>.

No read-only columns

Version 1.0 had the concept of read-only columns, e.g. you could not modify a column with auto-increment values.

This concept has been removed. In most cases, you will still not want to modify columns with auto-increment values, but you can do it now, if you want to.

IS DISCTINCT FROM

Version 1.0 used to have is_equal_to_or_null which translated to either = or IS NULL. While useful, this did not work with parameters.

The library now offers is_distinct_from and is_not_distinct_from which safely compares with actual values and NULL.

Selecting aggregate functions

In version 1.0, many functions automatically came with a name. This was convenient for selecting, but also rather unspecific, e.g. select(max(tab.price)).from(tab).unconditionally() would yield rows with data member called count_.

Now, functions do not come with names. While this will lead to lightly more code, the assumption is that this will lead to slightly more readable code, e.g.

SQLPP_ALIAS_PROVIDER(max_price);
// ...
  for (const auto& row : db(select(max(tab.price)).from(tab).unconditionally()))
  {
    std::cout << row.max_price << '\n';
  }

Dynamic queries

We don't always have a completely fixed structure for our queries. For instance, there might columns that we only want to select under certain circumstances. In version 1.0, this was handled by dynamic queries. Now we introduce conditional query parts that may or may not be used at runtime:

Select optional columns

select(tab.id, dynamic(condition, tab.bigData)).from(tab).where(tab.id == 17);

If condition == true then bigData will be selected, otherwise NULL will be selected.

Join optional table

select(tabA.id).from(tabA.cross_join(dynamic(condition, tabB))).where(tab.id == 17);

If condition == true then the cross join will be part of the query, otherwise not. Obviously, that means that you need to make sure that query parts that rely on tabB in this example also depend on the same condition.

Optional AND operand

select(tab.id).from(tab).where(tab.id == 17 and dynamic(condition, tab.desert != "cheesecake"));

If condition == true, then the dynamic part will evaluate to tab.desert != "cheesecake"). Otherwise it will be treated as true (and the AND expression will be collapsed).

Optional OR operand

select(tab.id).from(tab).where(tab.id == 17 or dynamic(condition, tab.desert != "cheesecake"));

If condition == true, then the dynamic part will evaluate to tab.desert != "cheesecake"). Otherwise it will be treated as false (and the OR expression will be collapsed).

Dropped features Unary operator+()

3.4 KiB Raw Blame History