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#!/usr/bin/env bash | ||
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CWD=$(dirname $0) | ||
CWD="$(dirname "${0}")" | ||
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declare -A argcommands=([sqlite]=sqlite | ||
[parquet]="parquet -i" | ||
[postgres]=postgres | ||
[clickhouse]=clickhouse | ||
[mysql]=mysql | ||
[impala]=impala) | ||
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if [[ "$#" == 0 ]]; then | ||
ARGS=(${!argcommands[@]}) # keys of argcommands | ||
else | ||
ARGS=($*) | ||
fi | ||
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python $CWD/datamgr.py download | ||
python $CWD/datamgr.py mysql & | ||
python $CWD/datamgr.py sqlite & | ||
python $CWD/datamgr.py parquet -i & | ||
python $CWD/datamgr.py postgres & | ||
python $CWD/datamgr.py clickhouse & | ||
python $CWD/impalamgr.py load --data & | ||
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for arg in ${ARGS[@]}; do | ||
if [[ "${arg}" == "impala" ]]; then | ||
python "${CWD}"/impalamgr.py load --data & | ||
else | ||
python "${CWD}"/datamgr.py ${argcommands[${arg}]} & | ||
fi | ||
done | ||
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FAIL=0 | ||
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for job in `jobs -p` | ||
do | ||
wait $job || let FAIL+=1 | ||
wait "${job}" || let FAIL+=1 | ||
done | ||
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if [ $FAIL -eq 0 ]; then | ||
echo "Done loading to SQLite, Postgres, Clickhouse and Impala" | ||
if [[ "${FAIL}" == 0 ]]; then | ||
echo "Done loading ${ARGS[@]}" | ||
exit 0 | ||
else | ||
echo "Failed loading the datasets" >&2 | ||
echo "Failed loading ${ARGS[@]}" >&2 | ||
exit 1 | ||
fi |
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.. _internals: | ||
.. _design: | ||
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********************* | ||
Ibis design internals | ||
********************* | ||
Design | ||
====== | ||
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More to come here. | ||
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.. _primary_goals: | ||
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Primary Goals | ||
------------- | ||
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#. Type safety | ||
#. Expressiveness | ||
#. Composability | ||
#. Familiarity | ||
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.. _flow_of_execution: | ||
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Flow of Execution | ||
----------------- | ||
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#. 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 | ||
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.. _expressions: | ||
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Expressions | ||
----------- | ||
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The main user-facing component of ibis is expressions. The base class of all | ||
expressions in ibis is the :class:`~ibis.expr.types.Expr` class. | ||
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Expressions provide the user facing API, defined in ``ibis/expr/api.py`` | ||
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.. _type_system: | ||
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Type System | ||
~~~~~~~~~~~ | ||
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Ibis's type system consists of a set of rules for specifying the types of | ||
inputs to :class:`~ibis.expr.types.Node` subclasses. Upon construction of a | ||
:class:`~ibis.expr.types.Node` subclass, ibis performs validation of every | ||
input to the node based on the rule that was used to declare the input. | ||
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Rules are defined in ``ibis/expr/rules.py`` | ||
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.. _expr_class: | ||
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The :class:`~ibis.expr.types.Expr` class | ||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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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. | ||
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Examples of expressions include :class:`~ibis.expr.types.Int64Column`, | ||
:class:`~ibis.expr.types.StringScalar`, and | ||
:class:`~ibis.expr.types.TableExpr`. | ||
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Here's an example of each type of expression: | ||
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.. code-block:: ipython | ||
import ibis | ||
t = ibis.table([('a', 'int64')]) | ||
int64_column = t.a | ||
type(int64_column) | ||
string_scalar = ibis.literal('some_string_value') | ||
type(string_scalar) | ||
table_expr = t.mutate(b=t.a + 1) | ||
type(table_expr) | ||
.. _node_class: | ||
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The :class:`~ibis.expr.types.Node` Class | ||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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:class:`~ibis.expr.types.Node` subclasses make up the core set of operations of | ||
ibis. Each node corresponds to a particular operation. | ||
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Most nodes are defined in the :mod:`~ibis.expr.operations` module. | ||
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Examples of nodes include :class:`~ibis.expr.operations.Add` and | ||
:class:`~ibis.expr.operations.Sum`. | ||
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Nodes have two important members (and often these are the only members defined): | ||
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#. ``input_type``: a list of rules | ||
#. ``output_type``: a rule or method | ||
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The ``input_type`` member is a list of rules that defines the types of | ||
the inputs to the operation. This is sometimes called the signature. | ||
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The ``output_type`` member is a rule or a method that defines the output type | ||
of the operation. This is sometimes called the return type. | ||
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An example of ``input_type``/``output_type`` usage is the | ||
:class:`~ibis.expr.operations.Log` class: | ||
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.. code-block:: ipython | ||
class Log(Node): | ||
input_type = [ | ||
rules.double(), | ||
rules.double(name='base', optional=True) | ||
] | ||
output_type = rules.shape_like_arg(0, 'double') | ||
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 base | ||
argument is ``None`` by default so that the expression will behave as the | ||
underlying database does. | ||
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These objects are instantiated when you use ibis APIs: | ||
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.. code-block:: ipython | ||
import ibis | ||
t = ibis.table([('a', 'double')]) | ||
log_1p = (1 + t.a).log() # an Add and a Log are instantiated here | ||
.. _expr_vs_ops: | ||
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Expressions vs Operations: Why are they different? | ||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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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. | ||
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Separation of the :class:`~ibis.expr.types.Node` and | ||
:class:`~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. | ||
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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 ``THEN`` | ||
expressions determine the output type of the expression. | ||
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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. | ||
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.. _compilation: | ||
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Compilation | ||
----------- | ||
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The next major component of ibis is the compilers. | ||
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The first few versions of ibis directly generated strings, but the compiler | ||
infrastructure was generalized to support compilation of `SQLAlchemy | ||
<https://docs.sqlalchemy.org/en/latest/core/tutorial.html>`_ based expressions. | ||
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The compiler works by translating the different pieces of SQL expression into a | ||
string or SQLAlchemy expression. | ||
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The main pieces of a ``SELECT`` statement are: | ||
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#. The set of column expressions (``select_set``) | ||
#. ``WHERE`` clauses (``where``) | ||
#. ``GROUP BY`` clauses (``group_by``) | ||
#. ``HAVING`` clauses (``having``) | ||
#. ``LIMIT`` clauses (``limit``) | ||
#. ``ORDER BY`` clauses (``order_by``) | ||
#. ``DISTINCT`` clauses (``distinct``) | ||
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Each of these pieces is translated into a SQL string and finally assembled by | ||
the instance of the :class:`~ibis.sql.compiler.ExprTranslator` subclass | ||
specific to the backend being compiled. For example, the | ||
:class:`~ibis.impala.compiler.ImpalaExprTranslator` is one of the subclasses | ||
that will perform this translation. | ||
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.. note:: | ||
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While ibis was designed with an explicit goal of first-class SQL support, | ||
ibis can target other systems such as pandas. | ||
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.. _execution: | ||
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Execution | ||
--------- | ||
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We presumably want to *do* something with our compiled expressions. This is | ||
where execution comes in. | ||
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This is least complex part of ibis, mostly only requiring ibis to correctly | ||
handle whatever the database hands back. | ||
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By and large, the execution of compiled SQL is handled by the database to which | ||
SQL is sent from ibis. | ||
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However, once the data arrives from the database we need to convert that | ||
data to a pandas DataFrame. | ||
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The Query class, with its :meth:`~ibis.sql.client.Query._fetch` method, | ||
provides a way for ibis :class:`~ibis.sql.client.SQLClient` objects to do any | ||
additional processing necessary after the database returns results to the | ||
client. |
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.. _extending: | ||
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Extending Ibis | ||
============== | ||
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Users typically want to extend ibis in one of two ways: | ||
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#. Add a new expression | ||
#. Add a new backend | ||
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Below we provide notebooks showing how to extend ibis in each of these ways. | ||
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Adding a New Expression | ||
----------------------- | ||
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.. note:: | ||
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Make sure you've run the following commands before executing the notebook | ||
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.. code-block:: sh | ||
docker-compose up -d --no-build postgres dns | ||
docker-compose run waiter | ||
docker-compose run ibis ci/load-data.sh postgres | ||
Here we show how to add a ``sha1`` method to the PostgreSQL backend: | ||
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.. toctree:: | ||
:maxdepth: 1 | ||
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notebooks/tutorial/9-Adding-a-new-expression.ipynb | ||
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Adding a New Backend | ||
-------------------- | ||
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TBD |
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@@ -86,6 +86,7 @@ places, but this will improve as things progress. | |
sql | ||
developer | ||
design | ||
extending | ||
release | ||
legal | ||
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