Working with Transactions and the DBAPI

SQLAlchemy 1.4 / 2.0 Tutorial

This page is part of the SQLAlchemy 1.4 / 2.0 Tutorial.

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Working with Transactions and the DBAPI

With the Engine object ready to go, we may now proceed to dive into the basic operation of an Engine and its primary interactive endpoints, the Connection and Result. We will additionally introduce the ORM’s facade for these objects, known as the Session.

Note to ORM readers

When using the ORM, the Engine is managed by another object called the Session. The Session in modern SQLAlchemy emphasizes a transactional and SQL execution pattern that is largely identical to that of the Connection discussed below, so while this subsection is Core-centric, all of the concepts here are essentially relevant to ORM use as well and is recommended for all ORM learners. The execution pattern used by the Connection will be contrasted with that of the Session at the end of this section.

As we have yet to introduce the SQLAlchemy Expression Language that is the primary feature of SQLAlchemy, we will make use of one simple construct within this package called the text() construct, which allows us to write SQL statements as textual SQL. Rest assured that textual SQL in day-to-day SQLAlchemy use is by far the exception rather than the rule for most tasks, even though it always remains fully available.

Getting a Connection

The sole purpose of the Engine object from a user-facing perspective is to provide a unit of connectivity to the database called the Connection. When working with the Core directly, the Connection object is how all interaction with the database is done. As the Connection represents an open resource against the database, we want to always limit the scope of our use of this object to a specific context, and the best way to do that is by using Python context manager form, also known as the with statement. Below we illustrate “Hello World”, using a textual SQL statement. Textual SQL is emitted using a construct called text() that will be discussed in more detail later:

>>> from sqlalchemy import text
>>> with engine.connect() as conn:
...     result = conn.execute(text("select 'hello world'"))
...     print(result.all())
BEGIN (implicit)
select 'hello world'
[...] ()
[('hello world',)]
ROLLBACK

In the above example, the context manager provided for a database connection and also framed the operation inside of a transaction. The default behavior of the Python DBAPI includes that a transaction is always in progress; when the scope of the connection is released, a ROLLBACK is emitted to end the transaction. The transaction is not committed automatically; when we want to commit data we normally need to call Connection.commit() as we’ll see in the next section.

Tip

“autocommit” mode is available for special cases. The section Setting Transaction Isolation Levels including DBAPI Autocommit discusses this.

The result of our SELECT was also returned in an object called Result that will be discussed later, however for the moment we’ll add that it’s best to ensure this object is consumed within the “connect” block, and is not passed along outside of the scope of our connection.

Committing Changes

We just learned that the DBAPI connection is non-autocommitting. What if we want to commit some data? We can alter our above example to create a table and insert some data, and the transaction is then committed using the Connection.commit() method, invoked inside the block where we acquired the Connection object:

# "commit as you go"
>>> with engine.connect() as conn:
...     conn.execute(text("CREATE TABLE some_table (x int, y int)"))
...     conn.execute(
...         text("INSERT INTO some_table (x, y) VALUES (:x, :y)"),
...         [{"x": 1, "y": 1}, {"x": 2, "y": 4}]
...     )
...     conn.commit()
BEGIN (implicit)
CREATE TABLE some_table (x int, y int)
[...] ()
<sqlalchemy.engine.cursor.CursorResult object at 0x...>
INSERT INTO some_table (x, y) VALUES (?, ?)
[...] ((1, 1), (2, 4))
<sqlalchemy.engine.cursor.CursorResult object at 0x...>
COMMIT

Above, we emitted two SQL statements that are generally transactional, a “CREATE TABLE” statement 1 and an “INSERT” statement that’s parameterized (the parameterization syntax above is discussed a few sections below in Sending Multiple Parameters). As we want the work we’ve done to be committed within our block, we invoke the Connection.commit() method which commits the transaction. After we call this method inside the block, we can continue to run more SQL statements and if we choose we may call Connection.commit() again for subsequent statements. SQLAlchemy refers to this style as commit as you go.

There is also another style of committing data, which is that we can declare our “connect” block to be a transaction block up front. For this mode of operation, we use the Engine.begin() method to acquire the connection, rather than the Engine.connect() method. This method will both manage the scope of the Connection and also enclose everything inside of a transaction with COMMIT at the end, assuming a successful block, or ROLLBACK in case of exception raise. This style may be referred towards as begin once:

# "begin once"
>>> with engine.begin() as conn:
...     conn.execute(
...         text("INSERT INTO some_table (x, y) VALUES (:x, :y)"),
...         [{"x": 6, "y": 8}, {"x": 9, "y": 10}]
...     )
BEGIN (implicit)
INSERT INTO some_table (x, y) VALUES (?, ?)
[...] ((6, 8), (9, 10))
<sqlalchemy.engine.cursor.CursorResult object at 0x...>
COMMIT

“Begin once” style is often preferred as it is more succinct and indicates the intention of the entire block up front. However, within this tutorial we will normally use “commit as you go” style as it is more flexible for demonstration purposes.

What’s “BEGIN (implicit)”?

You might have noticed the log line “BEGIN (implicit)” at the start of a transaction block. “implicit” here means that SQLAlchemy did not actually send any command to the database; it just considers this to be the start of the DBAPI’s implicit transaction. You can register event hooks to intercept this event, for example.

DDL refers to the subset of SQL that instructs the database to create, modify, or remove schema-level constructs such as tables. DDL such as “CREATE TABLE” is recommended to be within a transaction block that ends with COMMIT, as many databases uses transactional DDL such that the schema changes don’t take place until the transaction is committed. However, as we’ll see later, we usually let SQLAlchemy run DDL sequences for us as part of a higher level operation where we don’t generally need to worry about the COMMIT.

Basics of Statement Execution

We have seen a few examples that run SQL statements against a database, making use of a method called Connection.execute(), in conjunction with an object called text(), and returning an object called Result. In this section we’ll illustrate more closely the mechanics and interactions of these components.

Most of the content in this section applies equally well to modern ORM use when using the Session.execute() method, which works very similarly to that of Connection.execute(), including that ORM result rows are delivered using the same Result interface used by Core.

Fetching Rows

We’ll first illustrate the Result object more closely by making use of the rows we’ve inserted previously, running a textual SELECT statement on the table we’ve created:

>>> with engine.connect() as conn:
...     result = conn.execute(text("SELECT x, y FROM some_table"))
...     for row in result:
...         print(f"x: {row.x}  y: {row.y}")
BEGIN (implicit)
SELECT x, y FROM some_table
[...] ()
x: 1  y: 1
x: 2  y: 4
x: 6  y: 8
x: 9  y: 10
ROLLBACK

Above, the “SELECT” string we executed selected all rows from our table. The object returned is called Result and represents an iterable object of result rows.

Result has lots of methods for fetching and transforming rows, such as the Result.all() method illustrated previously, which returns a list of all Row objects. It also implements the Python iterator interface so that we can iterate over the collection of Row objects directly.

The Row objects themselves are intended to act like Python named tuples. Below we illustrate a variety of ways to access rows.

Tuple Assignment - This is the most Python-idiomatic style, which is to assign variables to each row positionally as they are received:
```
result = conn.execute(text("select x, y from some_table"))
for x, y in result:
    # ...
```

Integer Index - Tuples are Python sequences, so regular integer access is available too:

result = conn.execute(text("select x, y from some_table"))
  for row in result:
      x = row[0]

Attribute Name - As these are Python named tuples, the tuples have dynamic attribute names matching the names of each column. These names are normally the names that the SQL statement assigns to the columns in each row. While they are usually fairly predictable and can also be controlled by labels, in less defined cases they may be subject to database-specific behaviors:
```
result = conn.execute(text("select x, y from some_table"))
for row in result:
    y = row.y
    # illustrate use with Python f-strings
    print(f"Row: {row.x} {row.y}")
```
Mapping Access - To receive rows as Python mapping objects, which is essentially a read-only version of Python’s interface to the common dict object, the Result may be transformed into a MappingResult object using the Result.mappings() modifier; this is a result object that yields dictionary-like RowMapping objects rather than Row objects:
```
result = conn.execute(text("select x, y from some_table"))
for dict_row in result.mappings():
    x = dict_row['x']
    y = dict_row['y']
```

Sending Parameters

SQL statements are usually accompanied by data that is to be passed with the statement itself, as we saw in the INSERT example previously. The Connection.execute() method therefore also accepts parameters, which are referred towards as bound parameters. A rudimentary example might be if we wanted to limit our SELECT statement only to rows that meet a certain criteria, such as rows where the “y” value were greater than a certain value that is passed in to a function.

In order to achieve this such that the SQL statement can remain fixed and that the driver can properly sanitize the value, we add a WHERE criteria to our statement that names a new parameter called “y”; the text() construct accepts these using a colon format “:y”. The actual value for “:y” is then passed as the second argument to Connection.execute() in the form of a dictionary:

>>> with engine.connect() as conn:
...     result = conn.execute(
...         text("SELECT x, y FROM some_table WHERE y > :y"),
...         {"y": 2}
...     )
...     for row in result:
...        print(f"x: {row.x}  y: {row.y}")
BEGIN (implicit)
SELECT x, y FROM some_table WHERE y > ?
[...] (2,)
x: 2  y: 4
x: 6  y: 8
x: 9  y: 10
ROLLBACK

In the logged SQL output, we can see that the bound parameter :y was converted into a question mark when it was sent to the SQLite database. This is because the SQLite database driver uses a format called “qmark parameter style”, which is one of six different formats allowed by the DBAPI specification. SQLAlchemy abstracts these formats into just one, which is the “named” format using a colon.

Always use bound parameters

As mentioned at the beginning of this section, textual SQL is not the usual way we work with SQLAlchemy. However, when using textual SQL, a Python literal value, even non-strings like integers or dates, should never be stringified into SQL string directly; a parameter should always be used. This is most famously known as how to avoid SQL injection attacks when the data is untrusted. However it also allows the SQLAlchemy dialects and/or DBAPI to correctly handle the incoming input for the backend. Outside of plain textual SQL use cases, SQLAlchemy’s Core Expression API otherwise ensures that Python literal values are passed as bound parameters where appropriate.

Sending Multiple Parameters

In the example at Committing Changes, we executed an INSERT statement where it appeared that we were able to INSERT multiple rows into the database at once. For statements that operate upon data, but do not return result sets, namely DML statements such as “INSERT” which don’t include a phrase like “RETURNING”, we can send multi params to the Connection.execute() method by passing a list of dictionaries instead of a single dictionary, thus allowing the single SQL statement to be invoked against each parameter set individually:

>>> with engine.connect() as conn:
...     conn.execute(
...         text("INSERT INTO some_table (x, y) VALUES (:x, :y)"),
...         [{"x": 11, "y": 12}, {"x": 13, "y": 14}]
...     )
...     conn.commit()
BEGIN (implicit)
INSERT INTO some_table (x, y) VALUES (?, ?)
[...] ((11, 12), (13, 14))
<sqlalchemy.engine.cursor.CursorResult object at 0x...>
COMMIT

Behind the scenes, the Connection objects uses a DBAPI feature known as cursor.executemany(). This method performs the equivalent operation of invoking the given SQL statement against each parameter set individually. The DBAPI may optimize this operation in a variety of ways, by using prepared statements, or by concatenating the parameter sets into a single SQL statement in some cases. Some SQLAlchemy dialects may also use alternate APIs for this case, such as the psycopg2 dialect for PostgreSQL which uses more performant APIs for this use case.

Tip

you may have noticed this section isn’t tagged as an ORM concept. That’s because the “multiple parameters” use case is usually used for INSERT statements, which when using the ORM are invoked in a different way. Multiple parameters also may be used with UPDATE and DELETE statements to emit distinct UPDATE/DELETE operations on a per-row basis, however again when using the ORM, there is a different technique generally used for updating or deleting many individual rows separately.

Bundling Parameters with a Statement

The two previous cases illustrate a series of parameters being passed to accompany a SQL statement. For single-parameter statement executions, SQLAlchemy’s use of parameters is in fact more often than not done by bundling the parameters with the statement itself, which is a primary feature of the SQL Expression Language and makes for queries that can be composed naturally while still making use of parameterization in all cases. This concept will be discussed in much more detail in the sections that follow; for a brief preview, the text() construct itself being part of the SQL Expression Language supports this feature by using the TextClause.bindparams() method; this is a generative method that returns a new copy of the SQL construct with additional state added, in this case the parameter values we want to pass along:

>>> stmt = text("SELECT x, y FROM some_table WHERE y > :y ORDER BY x, y").bindparams(y=6)
>>> with engine.connect() as conn:
...     result = conn.execute(stmt)
...     for row in result:
...        print(f"x: {row.x}  y: {row.y}")
BEGIN (implicit)
SELECT x, y FROM some_table WHERE y > ? ORDER BY x, y
[...] (6,)
x: 6  y: 8
x: 9  y: 10
x: 11  y: 12
x: 13  y: 14
ROLLBACK

The interesting thing to note above is that even though we passed only a single argument, stmt, to the Connection.execute() method, the execution of the statement illustrated both the SQL string as well as the separate parameter tuple.

Executing with an ORM Session

As mentioned previously, most of the patterns and examples above apply to use with the ORM as well, so here we will introduce this usage so that as the tutorial proceeds, we will be able to illustrate each pattern in terms of Core and ORM use together.

The fundamental transactional / database interactive object when using the ORM is called the Session. In modern SQLAlchemy, this object is used in a manner very similar to that of the Connection, and in fact as the Session is used, it refers to a Connection internally which it uses to emit SQL.

When the Session is used with non-ORM constructs, it passes through the SQL statements we give it and does not generally do things much differently from how the Connection does directly, so we can illustrate it here in terms of the simple textual SQL operations we’ve already learned.

The Session has a few different creational patterns, but here we will illustrate the most basic one that tracks exactly with how the Connection is used which is to construct it within a context manager:

>>> from sqlalchemy.orm import Session
>>> stmt = text("SELECT x, y FROM some_table WHERE y > :y ORDER BY x, y").bindparams(y=6)
>>> with Session(engine) as session:
...     result = session.execute(stmt)
...     for row in result:
...        print(f"x: {row.x}  y: {row.y}")
BEGIN (implicit)
SELECT x, y FROM some_table WHERE y > ? ORDER BY x, y
[...] (6,)
x: 6  y: 8
x: 9  y: 10
x: 11  y: 12
x: 13  y: 14
ROLLBACK

The example above can be compared to the example in the preceding section in Bundling Parameters with a Statement - we directly replace the call to with engine.connect() as conn with with Session(engine) as session, and then make use of the Session.execute() method just like we do with the Connection.execute() method.

Also, like the Connection, the Session features “commit as you go” behavior using the Session.commit() method, illustrated below using a textual UPDATE statement to alter some of our data:

>>> with Session(engine) as session:
...     result = session.execute(
...         text("UPDATE some_table SET y=:y WHERE x=:x"),
...         [{"x": 9, "y":11}, {"x": 13, "y": 15}]
...     )
...     session.commit()
BEGIN (implicit)
UPDATE some_table SET y=? WHERE x=?
[...] ((11, 9), (15, 13))
COMMIT

Above, we invoked an UPDATE statement using the bound-parameter, “executemany” style of execution introduced at Sending Multiple Parameters, ending the block with a “commit as you go” commit.

Tip

The Session doesn’t actually hold onto the Connection object after it ends the transaction. It gets a new Connection from the Engine when executing SQL against the database is next needed.

The Session obviously has a lot more tricks up its sleeve than that, however understanding that it has an Session.execute() method that’s used the same way as Connection.execute() will get us started with the examples that follow later.

SQLAlchemy 1.4 / 2.0 Tutorial

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