python-q-text-as-data 1.4.0-1 / usr / bin / python2-q-text-as-data

#! /usr/bin/python

#   Copyright (C) 2012-2014 Harel Ben-Attia
#
#   This program is free software; you can redistribute it and/or modify
#   it under the terms of the GNU General Public License as published by
#   the Free Software Foundation; either version 3, or (at your option)
#   any later version.
#
#   This program is distributed in the hope that it will be useful,
#   but WITHOUT ANY WARRANTY; without even the implied warranty of
#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#   GNU General Public License for more details (doc/LICENSE contains
#   a copy of it)
#
#
# Name      : q (With respect to The Q Continuum)
# Author    : Harel Ben Attia - harelba@gmail.com, harelba @ github, @harelba on twitter
# Requires  : python with sqlite3 (standard in python>=2.6)
#
#
# q allows performing SQL-like statements on tabular text data.
#
# Its purpose is to bring SQL expressive power to manipulating text data using the Linux command line.
#
# Full Documentation and details in https://github.com/harelba/q
#
# Run with --help for command line details
#
q_version = "1.4.0"

import os
import sys
import sqlite3
import gzip
import glob
from optparse import OptionParser,OptionGroup
import traceback as tb
import codecs
import locale
import time
import re
from ConfigParser import ConfigParser
import traceback
import csv

DEBUG = False

def get_stdout_encoding(encoding_override=None):
    if encoding_override is not None and encoding_override != 'none':
       return encoding_override

    if sys.stdout.isatty():
        return sys.stdout.encoding
    else:
        return locale.getpreferredencoding()

SHOW_SQL = False

p = ConfigParser()
p.read([os.path.expanduser('~/.qrc'), '.qrc'])


def get_option_with_default(p, option_type, option, default):
    if not p.has_option('options', option):
        return default
    if option_type == 'boolean':
        return p.getboolean('options', option)
    elif option_type == 'int':
        return p.getint('options', option)
    elif option_type == 'string':
        return p.get('options', option)
    elif option_type == 'escaped_string':
        return p.get('options', option).decode('string-escape')
    else:
        raise Exception("Unknown option type")

default_beautify = get_option_with_default(p, 'boolean', 'beautify', False)
default_gzipped = get_option_with_default(p, 'boolean', 'gzipped', False)
default_delimiter = get_option_with_default(
    p, 'escaped_string', 'delimiter', None)
default_output_delimiter = get_option_with_default(
    p, 'escaped_string', 'output_delimiter', None)
default_skip_header = get_option_with_default(p, 'int', 'skip_header', 0)
default_formatting = get_option_with_default(p, 'string', 'formatting', None)
default_encoding = get_option_with_default(p, 'string', 'encoding', 'UTF-8')
default_output_encoding = get_option_with_default(p, 'string', 'encoding', None)
default_query_encoding = get_option_with_default(p, 'string', 'query_encoding', locale.getpreferredencoding())
default_output_header = get_option_with_default(p, 'string', 'output_header', False)

parser = OptionParser(usage="""
	q allows performing SQL-like statements on tabular text data.

	Its purpose is to bring SQL expressive power to manipulating text data using the Linux command line.

	Basic usage is q "<sql like query>" where table names are just regular file names (Use - to read from standard input)
        When the input contains a header row, use -H, and column names will be set according to the header row content. If there isn't a header row, then columns will automatically be named c1..cN.

	Column types are detected automatically. Use -A in order to see the column name/type analysis.

	Delimiter can be set using the -d (or -t) option. Output delimiter can be set using -D

	All sqlite3 SQL constructs are supported.

	Examples:

          Example 1: ls -ltrd * | q "select c1,count(1) from - group by c1"
	    This example would print a count of each unique permission string in the current folder.

	  Example 2: seq 1 1000 | q "select avg(c1),sum(c1) from -"
	    This example would provide the average and the sum of the numbers in the range 1 to 1000

	  Example 3: sudo find /tmp -ls | q "select c5,c6,sum(c7)/1024.0/1024 as total from - group by c5,c6 order by total desc"
	    This example will output the total size in MB per user+group in the /tmp subtree


        See the help or https://github.com/harelba/q for more details.
""")

#-----------------------------------------------
parser.add_option("-v", "--version", dest="version", default=False, action="store_true",
                  help="Print version")
#-----------------------------------------------
input_data_option_group = OptionGroup(parser,"Input Data Options")
input_data_option_group.add_option("-H", "--skip-header", dest="skip_header", default=default_skip_header, action="store_true",
                  help="Skip header row. This has been changed from earlier version - Only one header row is supported, and the header row is used for column naming")
input_data_option_group.add_option("-d", "--delimiter", dest="delimiter", default=default_delimiter,
                  help="Field delimiter. If none specified, then space is used as the delimiter.")
input_data_option_group.add_option("-t", "--tab-delimited", dest="tab_delimited", default=False, action="store_true",
                  help="Same as -d <tab>. Just a shorthand for handling standard tab delimited file You can use $'\\t' if you want (this is how Linux expects to provide tabs in the command line")
input_data_option_group.add_option("-e", "--encoding", dest="encoding", default=default_encoding,
                  help="Input file encoding. Defaults to UTF-8. set to none for not setting any encoding - faster, but at your own risk...")
input_data_option_group.add_option("-z", "--gzipped", dest="gzipped", default=default_gzipped, action="store_true",
                  help="Data is gzipped. Useful for reading from stdin. For files, .gz means automatic gunzipping")
input_data_option_group.add_option("-A", "--analyze-only", dest="analyze_only", action='store_true',
                  help="Analyze sample input and provide information about data types")
input_data_option_group.add_option("-m", "--mode", dest="mode", default="relaxed",
                  help="Data parsing mode. fluffy, relaxed and strict. In strict mode, the -c column-count parameter must be supplied as well")
input_data_option_group.add_option("-c", "--column-count", dest="column_count", default=None,
                  help="Specific column count when using relaxed or strict mode")
input_data_option_group.add_option("-k", "--keep-leading-whitespace", dest="keep_leading_whitespace_in_values", default=False, action="store_true",
                  help="Keep leading whitespace in values. Default behavior strips leading whitespace off values, in order to provide out-of-the-box usability for simple use cases. If you need to preserve whitespace, use this flag.")
parser.add_option_group(input_data_option_group)
#-----------------------------------------------
output_data_option_group = OptionGroup(parser,"Output Options") 
output_data_option_group.add_option("-D", "--output-delimiter", dest="output_delimiter", default=default_output_delimiter,
                  help="Field delimiter for output. If none specified, then the -d delimiter is used if present, or space if no delimiter is specified")
output_data_option_group.add_option("-T", "--tab-delimited-output", dest="tab_delimited_output", default=False, action="store_true",
                  help="Same as -D <tab>. Just a shorthand for outputing tab delimited output. You can use -D $'\\t' if you want.")
output_data_option_group.add_option("-O", "--output-header", dest="output_header", default=default_output_header, action="store_true",help="Output header line. Output column-names are determined from the query itself. Use column aliases in order to set your column names in the query. For example, 'select name FirstName,value1/value2 MyCalculation from ...'. This can be used even if there was no header in the input.")
output_data_option_group.add_option("-b", "--beautify", dest="beautify", default=default_beautify, action="store_true",
                  help="Beautify output according to actual values. Might be slow...")
output_data_option_group.add_option("-f", "--formatting", dest="formatting", default=default_formatting,
                  help="Output-level formatting, in the format X=fmt,Y=fmt etc, where X,Y are output column numbers (e.g. 1 for first SELECT column etc.")
output_data_option_group.add_option("-E", "--output-encoding", dest="output_encoding", default=default_output_encoding,
                  help="Output encoding. Defaults to 'none', leading to selecting the system/terminal encoding")
parser.add_option_group(output_data_option_group)
#-----------------------------------------------
query_option_group = OptionGroup(parser,"Query Related Options") 
query_option_group.add_option("-q", "--query-filename", dest="query_filename", default=None,
                  help="Read query from the provided filename instead of the command line, possibly using the provided query encoding (using -Q).")
query_option_group.add_option("-Q", "--query-encoding", dest="query_encoding", default=default_query_encoding,
                  help="query text encoding. Experimental. Please send your feedback on this")
parser.add_option_group(query_option_group)
#-----------------------------------------------

def regexp(regular_expression, data):
    if data is not None:
        if type(data) is not str:
            data = str(data)
        return re.search(regular_expression, data) is not None
    else:
        return False

class Sqlite3DBResults(object):
    def __init__(self,query_column_names,results):
        self.query_column_names = query_column_names
        self.results = results

class Sqlite3DB(object):

    def __init__(self, show_sql=SHOW_SQL):
        self.show_sql = show_sql
        self.conn = sqlite3.connect(':memory:')
        self.last_temp_table_id = 10000
        self.cursor = self.conn.cursor()
        self.type_names = {
            str: 'TEXT', int: 'INT', long : 'INT' , float: 'FLOAT', None: 'TEXT'}
        self.numeric_column_types = set([int, long, float])
        self.add_user_functions()

    def add_user_functions(self):
        self.conn.create_function("regexp", 2, regexp)

    def is_numeric_type(self, column_type):
        return column_type in self.numeric_column_types

    def update_many(self, sql, params):
        try:
            if self.show_sql:
                print sql, " params: " + str(params)
            self.cursor.executemany(sql, params)
        finally:
            pass  # cursor.close()

    def execute_and_fetch(self, q):
        try:
            if self.show_sql:
                print repr(q)
            self.cursor.execute(q)
            if self.cursor.description is not None:
                # we decode the column names, so they can be encoded to any output format later on
                query_column_names = [c[0].decode('utf-8') for c in self.cursor.description]
            else:
                query_column_names = None
            result = self.cursor.fetchall()
        finally:
            pass  # cursor.close()
        return Sqlite3DBResults(query_column_names,result)

    def _get_as_list_str(self, l):
        return ",".join(['"%s"' % x.replace('"', '""') for x in l])

    def _get_col_values_as_list_str(self, col_vals, col_types):
        result = []
        for col_val, col_type in zip(col_vals, col_types):
            if col_val == '' and col_type is not str:
                col_val = "null"
            else:
                if col_val is not None:
                    if "'" in col_val:
                        col_val = col_val.replace("'", "''")
                    col_val = "'" + col_val + "'"
                else:
                    col_val = "null"

            result.append(col_val)
        return ",".join(result)

    def generate_insert_row(self, table_name, column_names):
        col_names_str = self._get_as_list_str(column_names)
        question_marks = ", ".join(["?" for i in range(0, len(column_names))])
        return 'INSERT INTO %s (%s) VALUES (%s)' % (table_name, col_names_str, question_marks)

    def generate_begin_transaction(self):
        return "BEGIN TRANSACTION"

    def generate_end_transaction(self):
        return "COMMIT"

    # Get a list of column names so order will be preserved (Could have used OrderedDict, but
    # then we would need python 2.7)
    def generate_create_table(self, table_name, column_names, column_dict):
        # Convert dict from python types to db types
        column_name_to_db_type = dict(
            (n, self.type_names[t]) for n, t in column_dict.iteritems())
        column_defs = ','.join(['"%s" %s' % (
            n.replace('"', '""'), column_name_to_db_type[n]) for n in column_names])
        return 'CREATE TABLE %s (%s)' % (table_name, column_defs)

    def generate_temp_table_name(self):
        self.last_temp_table_id += 1
        return "temp_table_%s" % self.last_temp_table_id

    def generate_drop_table(self, table_name):
        return "DROP TABLE %s" % table_name

    def drop_table(self, table_name):
        return self.execute_and_fetch(self.generate_drop_table(table_name))


class BadHeaderException(Exception):

    def __init__(self, msg):
        self.msg = msg

    def __str(self):
        return repr(self.msg)


class EmptyDataException(Exception):

    def __init__(self):
        pass


class FileNotFoundException(Exception):

    def __init__(self, msg):
        self.msg = msg

    def __str(self):
        return repr(self.msg)


class ColumnCountMismatchException(Exception):

    def __init__(self, msg):
        self.msg = msg

    def __str(self):
        return repr(self.msg)

# Simplistic Sql "parsing" class... We'll eventually require a real SQL parser which will provide us with a parse tree
#
# A "qtable" is a filename which behaves like an SQL table...


class Sql(object):

    def __init__(self, sql):
        # Currently supports only standard SELECT statements

        # Holds original SQL
        self.sql = sql
        # Holds sql parts
        self.sql_parts = sql.split()

        # Set of qtable names
        self.qtable_names = set()
        # Dict from qtable names to their positions in sql_parts. Value here is a *list* of positions,
        # since it is possible that the same qtable_name (file) is referenced in multiple positions
        # and we don't want the database table to be recreated for each
        # reference
        self.qtable_name_positions = {}
        # Dict from qtable names to their effective (actual database) table
        # names
        self.qtable_name_effective_table_names = {}

        self.query_column_names = None

        # Go over all sql parts
        idx = 0
        while idx < len(self.sql_parts):
            # Get the part string
            part = self.sql_parts[idx]
            # If it's a FROM or a JOIN
            if part.upper() in ['FROM', 'JOIN']:
                # and there is nothing after it,
                if idx == len(self.sql_parts) - 1:
                    # Just fail
                    raise Exception(
                        'FROM/JOIN is missing a table name after it')

                qtable_name = self.sql_parts[idx + 1]
                # Otherwise, the next part contains the qtable name. In most cases the next part will be only the qtable name.
                # We handle one special case here, where this is a subquery as a column: "SELECT (SELECT ... FROM qtable),100 FROM ...".
                # In that case, there will be an ending paranthesis as part of the name, and we want to handle this case gracefully.
                # This is obviously a hack of a hack :) Just until we have
                # complete parsing capabilities
                if ')' in qtable_name:
                    leftover = qtable_name[qtable_name.index(')'):]
                    self.sql_parts.insert(idx + 2, leftover)
                    qtable_name = qtable_name[:qtable_name.index(')')]
                    self.sql_parts[idx + 1] = qtable_name

                self.qtable_names.add(qtable_name)

                if qtable_name not in self.qtable_name_positions.keys():
                    self.qtable_name_positions[qtable_name] = []

                self.qtable_name_positions[qtable_name].append(idx + 1)
                idx += 2
            else:
                idx += 1

    def set_effective_table_name(self, qtable_name, effective_table_name):
        if qtable_name not in self.qtable_names:
            raise Exception("Unknown qtable %s" % qtable_name)
        if qtable_name in self.qtable_name_effective_table_names.keys():
            raise Exception(
                "Already set effective table name for qtable %s" % qtable_name)

        self.qtable_name_effective_table_names[
            qtable_name] = effective_table_name

    def get_effective_sql(self):
        if len(filter(lambda x: x is None, self.qtable_name_effective_table_names)) != 0:
            raise Exception('There are qtables without effective tables')

        effective_sql = [x for x in self.sql_parts]

        for qtable_name, positions in self.qtable_name_positions.iteritems():
            for pos in positions:
                effective_sql[pos] = self.qtable_name_effective_table_names[
                    qtable_name]

        return " ".join(effective_sql)

    def execute_and_fetch(self, db):
        db_results_obj = db.execute_and_fetch(self.get_effective_sql())
        return db_results_obj


class LineSplitter(object):

    def __init__(self, delimiter, expected_column_count):
        self.delimiter = delimiter
        self.expected_column_count = expected_column_count
        if delimiter is not None:
            escaped_delimiter = re.escape(delimiter)
            self.split_regexp = re.compile('(?:%s)+' % escaped_delimiter)
        else:
            self.split_regexp = re.compile(r'\s+')

    def split(self, line):
        if line and line[-1] == '\n':
            line = line[:-1]
        return self.split_regexp.split(line, max_split=self.expected_column_count)


class TableColumnInferer(object):

    def __init__(self, mode, expected_column_count, input_delimiter, skip_header=False):
        self.inferred = False
        self.mode = mode
        self.rows = []
        self.skip_header = skip_header
        self.header_row = None
        self.expected_column_count = expected_column_count
        self.input_delimiter = input_delimiter

    def analyze(self, col_vals):
        if self.inferred:
            raise Exception("Already inferred columns")

        if self.skip_header and self.header_row is None:
            self.header_row = col_vals
        else:
            self.rows.append(col_vals)

        if len(self.rows) < 100:
            return False

        self.do_analysis()
        return True

    def force_analysis(self):
        # This method is called whenever there is no more data, and an analysis needs
        # to be performed immediately, regardless of the amount of sample data that has
        # been collected
        self.do_analysis()

    def determine_type_of_value(self, value):
        if value is not None:
            value = value.strip()
        if value == '' or value is None:
            return None

        try:
            i = int(value)
            if type(i) == long:
                return long
            else:
                return int
        except:
            pass

        try:
            f = float(value)
            return float
        except:
            pass

        return str

    def determine_type_of_value_list(self, value_list):
        type_list = [self.determine_type_of_value(v) for v in value_list]
        all_types = set(type_list)
        if len(set(type_list)) == 1:
            # all the sample lines are of the same type
            return type_list[0]
        else:
            # check for the number of types without nulls,
            type_list_without_nulls = filter(
                lambda x: x is not None, type_list)
            # If all the sample lines are of the same type,
            if len(set(type_list_without_nulls)) == 1:
                # return it
                return type_list_without_nulls[0]
            else:
                return str

    def do_analysis(self):
        if self.mode == 'strict':
            self._do_strict_analysis()
        elif self.mode in ['relaxed', 'fluffy']:
            self._do_relaxed_analysis()
        else:
            raise Exception('Unknown parsing mode %s' % self.mode)

        if self.column_count == 1:
            print >>sys.stderr, "Warning: column count is one - did you provide the correct delimiter?"
        if self.column_count == 0:
            raise Exception("Detected a column count of zero... Failing")

        self.infer_column_types()

        self.infer_column_names()

    def validate_column_names(self, value_list):
        column_name_errors = []
        for v in value_list:
            if v is None:
                # we allow column names to be None, in relaxed mode it'll be filled with default names.
                # RLRL
                continue
            if ',' in v:
                column_name_errors.append(
                    (v, "Column name cannot contain commas"))
                continue
            if self.input_delimiter in v:
                column_name_errors.append(
                    (v, "Column name cannot contain the input delimiter. Please make sure you've set the correct delimiter"))
                continue
            if '\n' in v:
                column_name_errors.append(
                    (v, "Column name cannot contain newline"))
                continue
            if v != v.strip():
                column_name_errors.append(
                    (v, "Column name contains leading/trailing spaces"))
                continue
            try:
                v.encode("utf-8", "strict").decode("utf-8")
            except:
                column_name_errors.append(
                    (v, "Column name must be UTF-8 Compatible"))
                continue
            nul_index = v.find("\x00")
            if nul_index >= 0:
                column_name_errors.append(
                    (v, "Column name cannot contain NUL"))
                continue
            t = self.determine_type_of_value(v)
            if t != str:
                column_name_errors.append((v, "Column name must be a string"))
        return column_name_errors

    def infer_column_names(self):
        if self.header_row is not None:
            column_name_errors = self.validate_column_names(self.header_row)
            if len(column_name_errors) > 0:
                raise BadHeaderException("Header must contain only strings and not numbers or empty strings: '%s'\n%s" % (
                    ",".join(self.header_row), "\n".join(["'%s': %s" % (x, y) for x, y in column_name_errors])))

            # use header row in order to name columns
            if len(self.header_row) < self.column_count:
                if self.mode == 'strict':
                    raise ColumnCountMismatchException("Strict mode. Header row contains less columns than expected column count(%s vs %s)" % (
                        len(self.header_row), self.column_count))
                elif self.mode in ['relaxed', 'fluffy']:
                    # in relaxed mode, add columns to fill the missing ones
                    self.header_row = self.header_row + \
                        ['c%s' % (x + len(self.header_row) + 1)
                         for x in xrange(self.column_count - len(self.header_row))]
            elif len(self.header_row) > self.column_count:
                if self.mode == 'strict':
                    raise ColumnCountMismatchException("Strict mode. Header row contains more columns than expected column count (%s vs %s)" % (
                        len(self.header_row), self.column_count))
                elif self.mode in ['relaxed', 'fluffy']:
                    # In relaxed mode, just cut the extra column names
                    self.header_row = self.header_row[:self.column_count]
            self.column_names = self.header_row
        else:
            # Column names are cX starting from 1
            self.column_names = ['c%s' % (i + 1)
                                 for i in range(self.column_count)]

    def _do_relaxed_analysis(self):
        column_count_list = [len(col_vals) for col_vals in self.rows]

        if self.expected_column_count is not None:
            self.column_count = self.expected_column_count
        else:
            # If not specified, we'll take the largest row in the sample rows
            self.column_count = max(column_count_list)

    def get_column_count_summary(self, column_count_list):
        counts = {}
        for column_count in column_count_list:
            counts[column_count] = counts.get(column_count, 0) + 1
        return ", ".join(["%s rows with %s columns" % (v, k) for k, v in counts.iteritems()])

    def _do_strict_analysis(self):
        column_count_list = [len(col_vals) for col_vals in self.rows]

        if len(set(column_count_list)) != 1:
            raise ColumnCountMismatchException('Strict mode. Column Count is expected to identical. Multiple column counts exist at the first part of the file. Try to check your delimiter, or change to relaxed mode. Details: %s' % (
                self.get_column_count_summary(column_count_list)))

        self.column_count = len(self.rows[0])

        if self.expected_column_count is not None and self.column_count != self.expected_column_count:
            raise ColumnCountMismatchException('Strict mode. Column count is expected to be %s but is %s' % (
                self.expected_column_count, self.column_count))

        self.infer_column_types()

    def infer_column_types(self):
        self.column_types = []
        self.column_types2 = []
        for column_number in xrange(self.column_count):
            column_value_list = [
                row[column_number] if column_number < len(row) else None for row in self.rows]
            column_type = self.determine_type_of_value_list(column_value_list)
            self.column_types.append(column_type)

            column_value_list2 = [row[column_number] if column_number < len(
                row) else None for row in self.rows[1:]]
            column_type2 = self.determine_type_of_value_list(
                column_value_list2)
            self.column_types2.append(column_type2)

        comparison = map(
            lambda x: x[0] == x[1], zip(self.column_types, self.column_types2))
        if False in comparison and not self.skip_header:
            number_of_column_types = len(set(self.column_types))
            if number_of_column_types == 1 and list(set(self.column_types))[0] == str:
                print >>sys.stderr, 'Warning - There seems to be header line in the file, but -H has not been specified. All fields will be detected as text fields, and the header line will appear as part of the data'

    def get_column_dict(self):
        return dict(zip(self.column_names, self.column_types))

    def get_column_count(self):
        return self.column_count

    def get_column_names(self):
        return self.column_names

    def get_column_types(self):
        return self.column_types


def encoded_csv_reader(encoding, f, dialect, **kwargs):
    csv_reader = csv.reader(f, dialect, **kwargs)
    if encoding is not None and encoding != 'none':
        for row in csv_reader:
            yield [unicode(x, encoding) for x in row]
    else:
        for row in csv_reader:
            yield row


def normalized_filename(filename):
    if filename == '-':
        return 'stdin'
    else:
        return filename


class TableCreator(object):

    def __init__(self, db, filenames_str, line_splitter, skip_header=False, gzipped=False, encoding='UTF-8', mode='fluffy', expected_column_count=None, input_delimiter=None):
        self.db = db
        self.filenames_str = filenames_str
        self.skip_header = skip_header
        self.gzipped = gzipped
        self.table_created = False
        self.line_splitter = line_splitter
        self.encoding = encoding
        self.mode = mode
        self.expected_column_count = expected_column_count
        self.input_delimiter = input_delimiter
        self.column_inferer = TableColumnInferer(
            mode, expected_column_count, input_delimiter, skip_header)

        # Filled only after table population since we're inferring the table
        # creation data
        self.table_name = None

        self.pre_creation_rows = []
        self.buffered_inserts = []

        # Column type indices for columns that contain numeric types. Lazily initialized
        # so column inferer can do its work before this information is needed
        self.numeric_column_indices = None

    def get_table_name(self):
        return self.table_name

    def populate(self, analyze_only=False):
        # Get the list of filenames
        filenames = self.filenames_str.split("+")
        # for each filename (or pattern)
        for fileglob in filenames:
            # Allow either stdin or a glob match
            if fileglob == '-':
                files_to_go_over = ['-']
            else:
                files_to_go_over = glob.glob(fileglob)

            # If there are no files to go over,
            if len(files_to_go_over) == 0:
                raise FileNotFoundException(
                    "File %s has not been found" % fileglob)

            # For each match
            for filename in files_to_go_over:
                self.current_filename = filename
                self.lines_read = 0

                # Check if it's standard input or a file
                if filename == '-':
                    f = sys.stdin
                else:
                    f = file(filename, 'rb')

                # Wrap it with gzip decompression if needed
                if self.gzipped or filename.endswith('.gz'):
                    f = gzip.GzipFile(fileobj=f)

                self.read_file_using_csv(f, analyze_only)
                if not self.table_created:
                    self.column_inferer.force_analysis()
                    self._do_create_table()

    def _flush_pre_creation_rows(self):
        for i, col_vals in enumerate(self.pre_creation_rows):
            if self.skip_header and i == 0:
                # skip header line
                continue
            self._insert_row(col_vals)
        self._flush_inserts()
        self.pre_creation_rows = []

    def read_file_using_csv(self, f, analyze_only):
        csv_reader = encoded_csv_reader(self.encoding, f, dialect='q')
        try:
            for col_vals in csv_reader:
                self.lines_read += 1
                self._insert_row(col_vals)
                if analyze_only and self.column_inferer.inferred:
                    return
            if self.lines_read == 0 or (self.lines_read == 1 and self.skip_header):
                raise EmptyDataException()
        finally:
            if f != sys.stdin:
                f.close()
            self._flush_inserts()

    def _insert_row(self, col_vals):
        # If table has not been created yet
        if not self.table_created:
            # Try to create it along with another "example" line of data
            self.try_to_create_table(col_vals)

        # If the table is still not created, then we don't have enough data, just
        # store the data and return
        if not self.table_created:
            self.pre_creation_rows.append(col_vals)
            return

        # The table already exists, so we can just add a new row
        self._insert_row_i(col_vals)

    def initialize_numeric_column_indices_if_needed(self):
        # Lazy initialization of numeric column indices
        if self.numeric_column_indices is None:
            column_types = self.column_inferer.get_column_types()
            self.numeric_column_indices = [idx for idx, column_type in enumerate(
                column_types) if self.db.is_numeric_type(column_type)]

    def nullify_values_if_needed(self, col_vals):
        new_vals = col_vals[:]
        for i in self.numeric_column_indices:
            v = col_vals[i]
            if v == '':
                new_vals[i] = None
        return new_vals

    def normalize_col_vals(self, col_vals):
        # Make sure that numeric column indices are initializd
        self.initialize_numeric_column_indices_if_needed()

        col_vals = self.nullify_values_if_needed(col_vals)

        expected_col_count = self.column_inferer.get_column_count()
        actual_col_count = len(col_vals)
        if self.mode == 'strict':
            if actual_col_count != expected_col_count:
                raise ColumnCountMismatchException('Strict mode - Expected %s columns instead of %s columns in file %s row %s. Either use relaxed/fluffy modes or check your delimiter' % (
                    expected_col_count, actual_col_count, normalized_filename(self.current_filename), self.lines_read))
            return col_vals

        # in all non strict mode, we add dummy data to missing columns

        if actual_col_count < expected_col_count:
            col_vals = col_vals + \
                [None for x in xrange(expected_col_count - actual_col_count)]

        # in relaxed mode, we merge all extra columns to the last column value
        if self.mode == 'relaxed':
            if actual_col_count > expected_col_count:
                xxx = col_vals[:expected_col_count - 1] + \
                    [self.input_delimiter.join(
                        col_vals[expected_col_count - 1:])]
                return xxx
            else:
                return col_vals

        if self.mode == 'fluffy':
            if actual_col_count > expected_col_count:
                raise ColumnCountMismatchException('Deprecated fluffy mode - Too many columns in file %s row %s (%s fields instead of %s fields). Consider moving to either relaxed or strict mode' % (
                    normalized_filename(self.current_filename), self.lines_read, actual_col_count, expected_col_count))
            return col_vals

        raise Exception("Unidentified parsing mode %s" % self.mode)

    def _insert_row_i(self, col_vals):
        col_vals = self.normalize_col_vals(col_vals)
        effective_column_names = self.column_inferer.column_names[
            :len(col_vals)]

        if len(effective_column_names) > 0:
            self.buffered_inserts.append((effective_column_names, col_vals))
        else:
            self.buffered_inserts.append((["c1"], [""]))

        if len(self.buffered_inserts) < 5000:
            return
        self._flush_inserts()

    def _flush_inserts(self):
        # print self.db.execute_and_fetch(self.db.generate_begin_transaction())

        # If the table is still not created, then we don't have enough data
        if not self.table_created:
            return

        insert_row_stmt = self.db.generate_insert_row(
            self.table_name, self.buffered_inserts[0][0])
        params = [col_vals for col_names, col_vals in self.buffered_inserts]

        self.db.update_many(insert_row_stmt, params)
        # print self.db.execute_and_fetch(self.db.generate_end_transaction())
        self.buffered_inserts = []

    def try_to_create_table(self, col_vals):
        if self.table_created:
            raise Exception('Table is already created')

        # Add that line to the column inferer
        result = self.column_inferer.analyze(col_vals)
        # If inferer succeeded,
        if result:
            self._do_create_table()
        else:
            pass  # We don't have enough information for creating the table yet

    def _do_create_table(self):
        # Then generate a temp table name
        self.table_name = self.db.generate_temp_table_name()
        # Get the column definition dict from the inferer
        column_dict = self.column_inferer.get_column_dict()
        # Create the CREATE TABLE statement
        create_table_stmt = self.db.generate_create_table(
            self.table_name, self.column_inferer.get_column_names(), column_dict)
        # And create the table itself
        self.db.execute_and_fetch(create_table_stmt)
        # Mark the table as created
        self.table_created = True
        self._flush_pre_creation_rows()

    def drop_table(self):
        if self.table_created:
            self.db.drop_table(self.table_name)


def determine_max_col_lengths(m):
    if len(m) == 0:
        return []
    max_lengths = [0 for x in xrange(0, len(m[0]))]
    for row_index in xrange(0, len(m)):
        for col_index in xrange(0, len(m[0])):
            new_len = len(unicode(m[row_index][col_index]))
            if new_len > max_lengths[col_index]:
                max_lengths[col_index] = new_len
    return max_lengths

(options, args) = parser.parse_args()

if options.version:
    print "q version %s" % q_version
    sys.exit(0)

if len(args) > 1:
    print >>sys.stderr,"Must provide query as one parameter, enclosed in quotes, or through a file with the -f parameter"
    sys.exit(1)

if len(args) == 0 and options.query_filename is None:
    print >>sys.stderr,"Must provide a query in the command line, or through the a file with the -f parameter"
    sys.exit(1)

if options.query_filename is not None:
    if len(args) != 0:
        print >>sys.stderr,"Can't provide both a query file and a query on the command line"
        sys.exit(1)
    try:
        f = file(options.query_filename)
        query_str = f.read()
        f.close()
    except:
        print >>sys.stderr,"Could not read query from file %s" % options.query_filename
        sys.exit(1)
else:
    query_str = args[0]

if options.query_encoding is not None and options.query_encoding != 'none':
    try:
        query_str = query_str.decode(options.query_encoding)
    except:
        print >>sys.stderr,"Could not decode query using the provided query encoding (%s)" % options.query_encoding
        sys.exit(3)

query_str = query_str.strip()

if len(query_str) == 0:
    print >>sys.stderr,"Query cannot be empty"   
    sys.exit(1)

if options.mode not in ['fluffy', 'relaxed', 'strict']:
    print >>sys.stderr, "Parsing mode can be one of fluffy, relaxed or strict"
    sys.exit(13)

output_encoding = get_stdout_encoding(options.output_encoding)
try:
    STDOUT = codecs.getwriter(output_encoding)(sys.stdout)
except:
    print >>sys.stderr,"Could not create output stream using output encoding %s" % (output_encoding)
    sys.exit(200)

# Create DB object
db = Sqlite3DB()

# Create SQL statment
sql_object = Sql('%s' % query_str)

# If the user flagged for a tab-delimited file then set the delimiter to tab
if options.tab_delimited:
    options.delimiter = '\t'

if options.tab_delimited_output:
    options.output_delimiter = '\t'

if options.delimiter is None:
    options.delimiter = ' '
elif len(options.delimiter) != 1:
    print >>sys.stderr, "Delimiter must be one character only"
    sys.exit(5)

if options.keep_leading_whitespace_in_values:
    skip_initial_space = False
else:
    skip_initial_space = True

q_dialect = {'skipinitialspace': skip_initial_space, 'quoting': 0,
             'delimiter': options.delimiter, 'quotechar': '"', 'doublequote': False}
csv.register_dialect('q', **q_dialect)
file_reading_method = 'csv'

if options.column_count is not None:
    expected_column_count = int(options.column_count)
else:
    # infer automatically
    expected_column_count = None

# Create a line splitter
line_splitter = LineSplitter(options.delimiter, expected_column_count)

if options.encoding != 'none':
    try:
        codecs.lookup(options.encoding)
    except LookupError:
        print >>sys.stderr, "Encoding %s could not be found" % options.encoding
        sys.exit(10)

try:
    table_creators = []
    # Get each "table name" which is actually the file name
    for filename in sql_object.qtable_names:
        # Create the matching database table and populate it
        table_creator = TableCreator(db, filename, line_splitter, options.skip_header, options.gzipped, options.encoding,
                                     mode=options.mode, expected_column_count=expected_column_count, input_delimiter=options.delimiter)
        start_time = time.time()
        table_creator.populate(options.analyze_only)
        table_creators.append(table_creator)
        if DEBUG:
            print >>sys.stderr, "TIMING - populate time is %4.3f" % (
                time.time() - start_time)

        # Replace the logical table name with the real table name
        sql_object.set_effective_table_name(filename, table_creator.table_name)

    if options.analyze_only:
        for table_creator in table_creators:
            column_names = table_creator.column_inferer.get_column_names()
            print "Table for file: %s" % normalized_filename(table_creator.filenames_str)
            for k in column_names:
                column_type = table_creator.column_inferer.get_column_dict()[k]
                print "  `%s` - %s" % (k, db.type_names[column_type].lower())
        sys.exit(0)

    # Execute the query and fetch the data
    db_results_obj = sql_object.execute_and_fetch(db)
    m = db_results_obj.results
    output_column_name_list = db_results_obj.query_column_names
except EmptyDataException:
    print >>sys.stderr, "Warning - data is empty"
    sys.exit(0)
except FileNotFoundException, e:
    print >>sys.stderr, e.msg
    sys.exit(30)
except sqlite3.OperationalError, e:
    msg = str(e)
    print >>sys.stderr, "query error: %s" % msg
    if "no such column" in msg and options.skip_header:
        print >>sys.stderr, 'Warning - There seems to be a "no such column" error, and -H (header line) exists. Please make sure that you are using the column names from the header line and not the default (cXX) column names'
    sys.exit(1)
except ColumnCountMismatchException, e:
    print >>sys.stderr, e.msg
    sys.exit(2)
except (UnicodeDecodeError, UnicodeError), e:
    print >>sys.stderr, "Cannot decode data. Try to change the encoding by setting it using the -e parameter. Error:%s" % e
    sys.exit(3)
except BadHeaderException, e:
    print >>sys.stderr, "Bad header row: %s" % e.msg
    sys.exit(35)
except KeyboardInterrupt:
    print >>sys.stderr, "Interrupted"
    sys.exit(0)


# If the user requested beautifying the output
if options.beautify:
    max_lengths = determine_max_col_lengths(m)

if options.output_delimiter:
    # If output delimiter is specified, then we use it
    output_delimiter = options.output_delimiter
else:
    # Otherwise,
    if options.delimiter:
        # if an input delimiter is specified, then we use it as the output as
        # well
        output_delimiter = options.delimiter
    else:
        # if no input delimiter is specified, then we use space as the default
        # (since no input delimiter means any whitespace)
        output_delimiter = " "

if options.formatting:
    formatting_dict = dict(
        [(x.split("=")[0], x.split("=")[1]) for x in options.formatting.split(",")])
else:
    formatting_dict = None

try:
    if options.output_header and output_column_name_list is not None:
        m.insert(0,output_column_name_list)
    for rownum, row in enumerate(m):
        row_str = []
        for i, col in enumerate(row):
            if formatting_dict is not None and str(i + 1) in formatting_dict.keys():
                fmt_str = formatting_dict[str(i + 1)]
            else:
                if options.beautify:
                    fmt_str = "%%-%ss" % max_lengths[i]
                else:
                    fmt_str = "%s"

            if col is not None:
                row_str.append(fmt_str % col)
            else:
                row_str.append(fmt_str % "")

        STDOUT.write(output_delimiter.join(row_str) + "\n")
except (UnicodeEncodeError, UnicodeError), e:
    print >>sys.stderr, "Cannot encode data. Error:%s" % e
    sys.exit(3)
except IOError, e:
    if e.errno == 32:
        # broken pipe, that's ok
        pass
    else:
        # dont miss other problem for now
        raise
except KeyboardInterrupt:
    pass

try:
    # Prevent python bug when order of pipe shutdowns is reversed
    sys.stdout.flush()
except IOError, e:
    pass


table_creator.drop_table()