- Type safety
Flow of Execution¶
- User writes expression
- Each method or function call builds a new expression
- Expressions are type checked as you create them
- Expressions have some optimizations that happen as the user builds them
- Backend specific rewrites
- Expressions are compiled
- The SQL string that generated by the compiler is sent to the database and executed (this step is skipped for the pandas backend)
- The database returns some data that is then turned into a pandas DataFrame by ibis
The main user-facing component of ibis is expressions. The base class of all expressions in ibis is the ibis.expr.types.Expr class.
Expressions provide the user facing API, most of which is defined in
Ibis's type system consists of a set of rules for specifying the types of
ibis.expr.types.Node subclasses. Upon construction of a
subclass, ibis performs validation of every input to the node based on the rule
that was used to declare the input.
Rules are defined in
Expressions are a thin but important abstraction over operations, containing only type information and shape information, i.e., whether they are tables, columns, or scalars.
Node subclasses make up the core set of operations of ibis. Each node
corresponds to a particular operation.
Most nodes are defined in the
Examples of nodes include
Nodes (transitively) inherit from a class that allows node authors to define their node's input arguments directly in the class body.
output_type member of the class is a rule or method that
defines the shape (scalar or column) and element type of the operation.
An example of usage is a node that representats a logarithm operation:
import ibis.expr.rules as rlz from ibis.expr.operations import ValueOp class Log(ValueOp): # A double scalar or column arg = rlz.double # Optional argument, defaults to None base = rlz.optional(rlz.double) # Output expression's datatype will correspond to arg's datatype output_dtype = rlz.dtype_like('arg') # Output expression will be scalar if arg is scalar, column otherwise output_shape = rlz.shape_like('arg')
This class describes an operation called
Log that takes one required
argument: a double scalar or column, and one optional argument: a double scalar
or column named
base that defaults to nothing if not provided. The
None by default so that the expression will behave as the
underlying database does.
Similar objects are instantiated when you use ibis APIs:
import ibis t = ibis.table([('a', 'float')], name='t') log_1p = (1 + t.a).log() # an Add and a Log are instantiated here
Expressions vs Operations: Why are they different?¶
Separating expressions from their underlying operations makes it easy to generically describe and validate the inputs to particular nodes. In the log example, it doesn't matter what operation (node) the double-valued arguments are coming from, they must only satisfy the requirement denoted by the rule.
Separation of the
ibis.expr.types.Expr classes also allows the API to be tied to the
physical type of the expression rather than the particular operation, making it
easy to define the API in terms of types rather than specific operations.
Furthermore, operations often have an output type that depends on the input
type. An example of this is the
greatest function, which takes the maximum
of all of its arguments. Another example is
CASE statements, whose
expressions determine the output type of the expression.
This allows ibis to provide only the APIs that make sense for a particular type, even when an operation yields a different output type depending on its input. Concretely, this means that you cannot perform operations that don't make sense, like computing the average of a string column.
The next major component of ibis is the compilers.
The first few versions of ibis directly generated strings, but the compiler infrastructure was generalized to support compilation of SQLAlchemy based expressions.
The compiler works by translating the different pieces of SQL expression into a string or SQLAlchemy expression.
The main pieces of a
SELECT statement are:
!. The set of column expressions (
WHERE clauses (
GROUP BY clauses (
HAVING clauses (
LIMIT clauses (
ORDER BY clauses (
DISTINCT clauses (
Each of these pieces is translated into a SQL string and finally assembled by
the instance of the
specific to the backend being compiled. For example, the
ibis.impala.compiler.ImpalaExprTranslator is one of the subclasses
that will perform this translation.
Ibis can target other systems besides SQL
While ibis was designed with an explicit goal of first-class SQL support, ibis can target other systems such as pandas.
Presumably we want to do something with our compiled expressions. This is where execution comes in.
This is least complex part of ibis, mostly only requiring ibis to correctly handle whatever the database hands back.
By and large, the execution of compiled SQL is handled by the database to which SQL is sent from ibis.
However, once the data arrives from the database we need to convert that data to a pandas DataFrame.
The Query class, with its
ibis.sql.client.Query._fetch method, provides a way
ibis.sql.client.SQLClient objects to do any additional processing
necessary after the database returns results to the client.