Import CSV

docs/faq.rst

@@ -45,6 +45,13 @@ visualizations.
 https://github.com/airbnb/superset/issues?q=label%3Aexample+is%3Aclosed
 
 
+Can I upload and visualize csv data?
+-------------------------------------
+
+Yes, using the ``Upload a CSV`` button under the ``Sources``
+menu item. This brings up a form that allows you specify required information. After creating the table from CSV, it can then be loadede like any other on the ``Sources -> Tables``page.
+
+
 Why are my queries timing out?
 ------------------------------
 

superset/config.py

@@ -56,9 +56,9 @@
 SECRET_KEY = '\2\1thisismyscretkey\1\2\e\y\y\h'  # noqa
 
 # The SQLAlchemy connection string.
-SQLALCHEMY_DATABASE_URI = 'sqlite:///' + os.path.join(DATA_DIR, 'superset.db')
+# SQLALCHEMY_DATABASE_URI = 'sqlite:///' + os.path.join(DATA_DIR, 'superset.db')
 # SQLALCHEMY_DATABASE_URI = 'mysql://myapp@localhost/myapp'
-# SQLALCHEMY_DATABASE_URI = 'postgresql://root:password@localhost/myapp'
+SQLALCHEMY_DATABASE_URI = 'postgresql://root:password@localhost/myapp'
 
 # In order to hook up a custom password store for all SQLACHEMY connections
 # implement a function that takes a single argument of type 'sqla.engine.url',
@@ -186,6 +186,10 @@
 ENABLE_CORS = False
 CORS_OPTIONS = {}
 
+# Allowed format types for upload on Database view
+# TODO: Add processing of other spreadsheet formats (xls, xlsx etc)
+ALLOWED_EXTENSIONS = set(['csv'])
+
 # CSV Options: key/value pairs that will be passed as argument to DataFrame.to_csv method
 # note: index option should not be overridden
 CSV_EXPORT = {
@@ -296,6 +300,14 @@ class CeleryConfig(object):
 # in SQL Lab by using the "Run Async" button/feature
 RESULTS_BACKEND = None
 
+# The S3 bucket where you want to store your external hive tables created
+# from CSV files. For example, 'companyname-superset'
+CSV_TO_HIVE_UPLOAD_S3_BUCKET = None
+
+# The directory within the bucket specified above that will
+# contain all the external tables
+CSV_TO_HIVE_UPLOAD_DIRECTORY = 'EXTERNAL_HIVE_TABLES/'
+
 # A dictionary of items that gets merged into the Jinja context for
 # SQL Lab. The existing context gets updated with this dictionary,
 # meaning values for existing keys get overwritten by the content of this

superset/db_engine_specs.py

@@ -17,21 +17,30 @@
 from __future__ import unicode_literals
 
 from collections import defaultdict, namedtuple
+import csv
 import inspect
 import logging
+import os
 import re
 import textwrap
 import time
 
+import boto3
+from flask import g
 from flask_babel import lazy_gettext as _
+import pandas
 from sqlalchemy import select
+from sqlalchemy.engine import create_engine
 from sqlalchemy.engine.url import make_url
 from sqlalchemy.sql import text
 import sqlparse
+from werkzeug.utils import secure_filename
 
-from superset import cache_util, conf, utils
+from superset import app, cache_util, conf, db, utils
 from superset.utils import QueryStatus, SupersetTemplateException
 
+config = app.config
+
 tracking_url_trans = conf.get('TRACKING_URL_TRANSFORMER')
 
 Grain = namedtuple('Grain', 'name label function')
@@ -73,6 +82,65 @@ def extra_table_metadata(cls, database, table_name, schema_name):
         """Returns engine-specific table metadata"""
         return {}
 
+    @staticmethod
+    def csv_to_df(**kwargs):
+        kwargs['filepath_or_buffer'] = \
+            app.config['UPLOAD_FOLDER'] + kwargs['filepath_or_buffer']
+        kwargs['encoding'] = 'utf-8'
+        kwargs['iterator'] = True
+        chunks = pandas.read_csv(**kwargs)
+        df = pandas.DataFrame()
+        df = pandas.concat(chunk for chunk in chunks)
+        return df
+
+    @staticmethod
+    def df_to_db(df, table, **kwargs):
+        df.to_sql(**kwargs)
+        table.user_id = g.user.id
+        table.schema = kwargs['schema']
+        table.fetch_metadata()
+        db.session.add(table)
+        db.session.commit()
+
+    @staticmethod
+    def create_table_from_csv(form, table):
+        def _allowed_file(filename):
+            # Only allow specific file extensions as specified in the config
+            extension = os.path.splitext(filename)[1]
+            return extension and extension[1:] in app.config['ALLOWED_EXTENSIONS']
+
+        filename = secure_filename(form.csv_file.data.filename)
+        if not _allowed_file(filename):
+            raise Exception('Invalid file type selected')
+        kwargs = {
+            'filepath_or_buffer': filename,
+            'sep': form.sep.data,
+            'header': form.header.data if form.header.data else 0,
+            'index_col': form.index_col.data,
+            'mangle_dupe_cols': form.mangle_dupe_cols.data,
+            'skipinitialspace': form.skipinitialspace.data,
+            'skiprows': form.skiprows.data,
+            'nrows': form.nrows.data,
+            'skip_blank_lines': form.skip_blank_lines.data,
+            'parse_dates': form.parse_dates.data,
+            'infer_datetime_format': form.infer_datetime_format.data,
+            'chunksize': 10000,
+        }
+        df = BaseEngineSpec.csv_to_df(**kwargs)
+
+        df_to_db_kwargs = {
+            'table': table,
+            'df': df,
+            'name': form.name.data,
+            'con': create_engine(form.con.data, echo=False),
+            'schema': form.schema.data,
+            'if_exists': form.if_exists.data,
+            'index': form.index.data,
+            'index_label': form.index_label.data,
+            'chunksize': 10000,
+        }
+        BaseEngineSpec.df_to_db(**df_to_db_kwargs)
+
     @classmethod
     def escape_sql(cls, sql):
         """Escapes the raw SQL"""
@@ -721,6 +789,45 @@ def fetch_result_sets(cls, db, datasource_type, force=False):
         return BaseEngineSpec.fetch_result_sets(
             db, datasource_type, force=force)
 
+    @staticmethod
+    def create_table_from_csv(form, table):
+        """Uploads a csv file and creates a superset datasource in Hive."""
+        def get_column_names(filepath):
+            with open(filepath, 'rb') as f:
+                return csv.reader(f).next()
+
+        table_name = form.name.data
+        filename = form.csv_file.data.filename
+
+        bucket_path = app.config['CSV_TO_HIVE_UPLOAD_BUCKET']
+
+        if not bucket_path:
+            logging.info('No upload bucket specified')
+            raise Exception(
+                'No upload bucket specified. You can specify one in the config file.')
+
+        upload_prefix = app.config['CSV_TO_HIVE_UPLOAD_DIRECTORY']
+        dest_path = os.path.join(table_name, filename)
+
+        upload_path = app.config['UPLOAD_FOLDER'] + \
+            secure_filename(form.csv_file.data.filename)
+        column_names = get_column_names(upload_path)
+        schema_definition = ', '.join(
+            [s + ' STRING ' for s in column_names])
+
+        s3 = boto3.client('s3')
+        location = os.path.join('s3a://', bucket_path, upload_prefix, table_name)
+        s3.upload_file(
+            upload_path, 'airbnb-superset',
+            os.path.join(upload_prefix, table_name, filename))
+        sql = """CREATE EXTERNAL TABLE {table_name} ( {schema_definition} )
+            ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS
+            TEXTFILE LOCATION '{location}'""".format(**locals())
+
+        logging.info(form.con.data)
+        engine = create_engine(form.con.data)
+        engine.execute(sql)
+
     @classmethod
     def convert_dttm(cls, target_type, dttm):
         tt = target_type.upper()

superset/forms.py

@@ -0,0 +1,123 @@
+"""Contains the logic to create cohesive forms on the explore view"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+from flask_appbuilder.fieldwidgets import BS3TextFieldWidget
+from flask_appbuilder.forms import DynamicForm
+from flask_babel import lazy_gettext as _
+from flask_wtf.file import FileAllowed, FileField, FileRequired
+from wtforms import (
+    BooleanField, IntegerField, SelectField, StringField)
+from wtforms.validators import DataRequired, NumberRange, Optional
+
+from superset import app
+
+config = app.config
+
+
+class CsvToDatabaseForm(DynamicForm):
+    name = StringField(
+        _('Table Name'),
+        description=_('Name of table to be created from csv data.'),
+        validators=[DataRequired()],
+        widget=BS3TextFieldWidget())
+    csv_file = FileField(
+        _('CSV File'),
+        description=_('Select a CSV file to be uploaded to a database.'),
+        validators=[
+            FileRequired(), FileAllowed(['csv'], _('CSV Files Only!'))])
+
+    con = SelectField(
+        _('Database'),
+        description=_('database in which to add above table.'),
+        validators=[DataRequired()],
+        choices=[])
+    sep = StringField(
+        _('Delimiter'),
+        description=_('Delimiter used by CSV file (for whitespace use \s+).'),
+        validators=[DataRequired()],
+        widget=BS3TextFieldWidget())
+    if_exists = SelectField(
+        _('Table Exists'),
+        description=_(
+            'If table exists do one of the following: '
+            'Fail (do nothing), Replace (drop and recreate table) '
+            'or Append (insert data).'),
+        choices=[
+            ('fail', _('Fail')), ('replace', _('Replace')),
+            ('append', _('Append'))],
+        validators=[DataRequired()])
+
+    schema = StringField(
+        _('Schema'),
+        description=_('Specify a schema (if database flavour supports this).'),
+        validators=[Optional()],
+        widget=BS3TextFieldWidget(),
+        filters=[lambda x: x or None])
+    header = IntegerField(
+        _('Header Row'),
+        description=_(
+            'Row containing the headers to use as '
+            'column names (0 is first line of data). '
+            'Leave empty if there is no header row.'),
+        validators=[Optional()],
+        widget=BS3TextFieldWidget(),
+        filters=[lambda x: x or None])
+    index_col = IntegerField(
+        _('Index Column'),
+        description=_(
+            'Column to use as the row labels of the '
+            'dataframe. Leave empty if no index column.'),
+        validators=[Optional(), NumberRange(0, 1E+20)],
+        widget=BS3TextFieldWidget(),
+        filters=[lambda x: x or None])
+    mangle_dupe_cols = BooleanField(
+        _('Mangle Duplicate Columns'),
+        description=_('Specify duplicate columns as "X.0, X.1".'))
+    skipinitialspace = BooleanField(
+        _('Skip Initial Space'),
+        description=_('Skip spaces after delimiter.'))
+    skiprows = IntegerField(
+        _('Skip Rows'),
+        description=_('Number of rows to skip at start of file.'),
+        validators=[Optional(), NumberRange(0, 1E+20)],
+        widget=BS3TextFieldWidget(),
+        filters=[lambda x: x or None])
+    nrows = IntegerField(
+        _('Rows to Read'),
+        description=_('Number of rows of file to read.'),
+        validators=[Optional(), NumberRange(0, 1E+20)],
+        widget=BS3TextFieldWidget(),
+        filters=[lambda x: x or None])
+    skip_blank_lines = BooleanField(
+        _('Skip Blank Lines'),
+        description=_(
+            'Skip blank lines rather than interpreting them '
+            'as NaN values.'))
+    parse_dates = BooleanField(
+        _('Parse Dates'),
+        description=_('Parse date values.'))
+    infer_datetime_format = BooleanField(
+        _('Infer Datetime Format'),
+        description=_(
+            'Use Pandas to interpret the datetime format '
+            'automatically.'))
+    decimal = StringField(
+        _('Decimal Character'),
+        description=_('Character to interpret as decimal point.'),
+        validators=[Optional()],
+        widget=BS3TextFieldWidget(),
+        filters=[lambda x: x or '.'])
+    index = BooleanField(
+        _('Dataframe Index'),
+        description=_('Write dataframe index as a column.'))
+    index_label = StringField(
+        _('Column Label(s)'),
+        description=_(
+            'Column label for index column(s). If None is given '
+            'and Dataframe Index is True, Index Names are used.'),
+        validators=[Optional()],
+        widget=BS3TextFieldWidget(),
+        filters=[lambda x: x or None])