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from brewery import data_object, FieldList, open_store
from brewery import kernel as k
import brewery.base.iterator
import brewery.backends.sql

FACT_DATA = [
            [0, 1, 100.0],
            [1, 1, 200.0],
            [2, 2, 150.0],
            [3, 2, 250.0],

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k = OperationKernel()

@k.operation("sql", "sql")
def append(l, r):
    if l.engine == r.engine:
        return "UNION ALL statement"
    else:
        raise RetryOperation(["sql", "rows"])

@k.operation("sql", "rows")

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Cubes Datamart Package

Purpose: sharing of smaller datasets - whole cubes or portions of larger cubes.

Datamart package

Package can be a directory or a ZIP file with following structure:

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source CUBE_NAME
{
    type            = xmlpipe2
    xmlpipe_command = slicer-indexer --engine sphinx slicer.ini CUBE_NAME
}

index CUBE_NAME
{
    source         = CUBE_NAME
    path           = index/CUBE_NAME

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from collections import Counter
from itertools import combinations

def distinct_items(transactions, support=None):
    """Returns counted set of distinct items in transactions"""
    counter = Counter()
    for trans in transactions:
        counter.update(trans)

    if support is not None:

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(
        {
        on = 1;
        replace = "(c)";
        with = "\U00a9";
    },
        {
        on = 0;
        replace = "(r)";
        with = "\U00ae";

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from sqlalchemy.sql.expression import Executable, ClauseElement
from sqlalchemy.ext.compiler import compiles

class CreateTableAsSelect(Executable, ClauseElement):
    def __init__(self, table, select):
        self.table = table
        self.select = select

@compiles(CreateTableAsSelect)
def visit_create_table_as_select(element, compiler, **kw):

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import brewery.stores as stores

filename = "data.csv"

source = stores.CSVDataSource(filename, encoding="latin1", infer_fields=True,
                                read_header=True)

target = stores.SQLDataTarget(url="sqlite:///data.sqlite",
                              table="data", create=True, fields=source.fields,
                              replace=True)

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{
    "cubes":[
        {
            "dimensions": [
                            "start_date",
                            "middle_date",
                            "end_date"
                           ],
            "joins": [
                {"master":"start_date_id", "detail":"dim_date.id", "alias": "dim_start_date"},

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# Drill down by two dimensions
result = browser.aggregate(cell, drilldown=["year", "item"])

# Get presenter
rickshaw = cubes.create_presenter("rickshaw_multi_series")

# Create Rickshaw series.
# You can use color_map to map series keys to colors or you can use color_palette to 
# cycle through colors