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60003ad211b92aaec8d5eb03cbe70865cecfde6f
impala/testdata/bin/generate-schema-statements.py
Lenni Kuff 76fa3b2ded Update DDL to support 'STORED AS PARQUET' and 'STORED AS AVRO' syntax 
This change updates our DDL syntax support to allow for using 'STORED AS PARQUET'
as well as 'STORED AS PARQUETFILE'. Moving forward we should prefer the new syntax,
but continue to support the old.  I made the same change for 'AVROFILE', but since
we have not yet documented the 'AVROFILE' syntax I left out support for the old syntax.

Change-Id: I10c73a71a94ee488c9ae205485777b58ab8957c9
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1053
Reviewed-by: Marcel Kornacker <marcel@cloudera.com>
Tested-by: jenkins
2014-01-08 10:54:18 -08:00

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#!/usr/bin/env python
# Copyright (c) 2012 Cloudera, Inc. All rights reserved.
# This script generates the "CREATE TABLE", "INSERT", and "LOAD" statements for loading
# test data and writes them to create-*-generated.sql and
# load-*-generated.sql. These files are then executed by hive or impala, depending
# on their contents. Additionally, for hbase, the file is of the form
# create-*hbase*-generated.create.
#
# The statements that are generated are based on an input test vector
# (read from a file) that describes the coverage desired. For example, currently
# we want to run benchmarks with different data sets, across different file types, and
# with different compression algorithms set. To improve data loading performance this
# script will generate an INSERT INTO statement to generate the data if the file does
# not already exist in HDFS. If the file does already exist in HDFS then we simply issue a
# LOAD statement which is much faster.
#
# The input test vectors are generated via the generate_test_vectors.py so
# ensure that script has been run (or the test vector files already exist) before
# running this script.
#
# Note: This statement generation is assuming the following data loading workflow:
# 1) Load all the data in the specified source table
# 2) Create tables for the new file formats and compression types
# 3) Run INSERT OVERWRITE TABLE SELECT * from the source table into the new tables
# or LOAD directly if the file already exists in HDFS.
import collections
import csv
import glob
import math
import json
import os
import random
import shutil
import subprocess
import sys
import tempfile
from itertools import product
from optparse import OptionParser
from tests.util.test_file_parser import *
from tests.common.test_dimensions import *
parser = OptionParser()
parser.add_option("-e", "--exploration_strategy", dest="exploration_strategy",
default="core", help="The exploration strategy for schema gen: 'core',"\
" 'pairwise', or 'exhaustive'")
parser.add_option("--hive_warehouse_dir", dest="hive_warehouse_dir",
default="/test-warehouse",
help="The HDFS path to the base Hive test warehouse directory")
parser.add_option("-w", "--workload", dest="workload",
help="The workload to generate schema for: tpch, hive-benchmark, ...")
parser.add_option("-s", "--scale_factor", dest="scale_factor", default="",
help="An optional scale factor to generate the schema for")
parser.add_option("-f", "--force_reload", dest="force_reload", action="store_true",
default= False, help='Skips HDFS exists check and reloads all tables')
parser.add_option("-v", "--verbose", dest="verbose", action="store_true",
default = False, help="If set, outputs additional logging.")
parser.add_option("-b", "--backend", dest="backend", default="localhost:21000",
help="Backend connection to use, default: localhost:21000")
parser.add_option("--table_names", dest="table_names", default=None,
help="Only load the specified tables - specified as a comma-seperated "\
"list of base table names")
parser.add_option("--table_formats", dest="table_formats", default=None,
help="Override the test vectors and load using the specified table "\
"formats. Ex. --table_formats=seq/snap/block,text/none")
parser.add_option("--hdfs_namenode", dest="hdfs_namenode", default="localhost:20500",
help="HDFS name node for Avro schema URLs, default localhost:20500")
(options, args) = parser.parse_args()
if options.workload is None:
print "A workload name must be specified."
parser.print_help()
sys.exit(1)
DATA_LOAD_DIR = '/tmp/data-load-files'
WORKLOAD_DIR = os.path.join(os.environ['IMPALA_HOME'], 'testdata', 'workloads')
DATASET_DIR = os.path.join(os.environ['IMPALA_HOME'], 'testdata', 'datasets')
AVRO_SCHEMA_DIR = "avro_schemas"
IMPALA_SUPPORTED_INSERT_FORMATS = ['parquet', 'hbase', 'text']
COMPRESSION_TYPE = "SET mapred.output.compression.type=%s;"
COMPRESSION_ENABLED = "SET hive.exec.compress.output=%s;"
COMPRESSION_CODEC = "SET mapred.output.compression.codec=%s;"
AVRO_COMPRESSION_CODEC = "SET avro.output.codec=%s;"
SET_DYNAMIC_PARTITION_STATEMENT = "SET hive.exec.dynamic.partition=true;"
SET_PARTITION_MODE_NONSTRICT_STATEMENT = "SET hive.exec.dynamic.partition.mode=nonstrict;"
SET_HIVE_INPUT_FORMAT = "SET mapred.max.split.size=256000000;\n"\
"SET hive.input.format=org.apache.hadoop.hive.ql.io.%s;\n"
SET_HIVE_HBASE_BULK_LOAD = "SET hive.hbase.bulk = true"
FILE_FORMAT_IDX = 0
DATASET_IDX = 1
CODEC_IDX = 2
COMPRESSION_TYPE_IDX = 3
COMPRESSION_MAP = {'def': 'org.apache.hadoop.io.compress.DefaultCodec',
'gzip': 'org.apache.hadoop.io.compress.GzipCodec',
'bzip': 'org.apache.hadoop.io.compress.BZip2Codec',
'snap': 'org.apache.hadoop.io.compress.SnappyCodec',
'lzo': 'com.hadoop.compression.lzo.LzopCodec',
'none': ''
}
AVRO_COMPRESSION_MAP = {
'def': 'deflate',
'snap': 'snappy',
'none': '',
}
FILE_FORMAT_MAP = {
'text': 'TEXTFILE',
'seq': 'SEQUENCEFILE',
'rc': 'RCFILE',
'parquet': 'PARQUET',
'text_lzo':
"\nINPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'" +
"\nOUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'",
'avro': 'AVRO',
'hbase': "'org.apache.hadoop.hive.hbase.HBaseStorageHandler'"
}
HIVE_TO_AVRO_TYPE_MAP = {
'STRING': 'string',
'INT': 'int',
'TINYINT': 'int',
'SMALLINT': 'int',
'BIGINT': 'long',
'BOOLEAN': 'boolean',
'FLOAT': 'float',
'DOUBLE': 'double',
# Avro has no timestamp type, so convert to string
# TODO: this allows us to create our Avro test tables, but any tests that use
# a timestamp column will fail. We probably want to convert back to timestamps
# in our tests.
'TIMESTAMP': 'string',
}
PARQUET_ALTER_STATEMENT = "ALTER TABLE %(table_name)s SET\n\
SERDEPROPERTIES ('blocksize' = '1073741824', 'compression' = '%(compression)s');"
HBASE_CREATE_STATEMENT = """
CREATE EXTERNAL TABLE IF NOT EXISTS {{db_name}}{{db_suffix}}.{{table_name}} (
{columns})
STORED BY {{file_format}}
WITH SERDEPROPERTIES (
"hbase.columns.mapping" =
"{hbase_column_mapping}")
{tbl_properties}{{hdfs_location}}"""
KNOWN_EXPLORATION_STRATEGIES = ['core', 'pairwise', 'exhaustive', 'lzo']
def build_create_statement(table_template, table_name, db_name, db_suffix,
file_format, compression, hdfs_location):
create_stmt = 'CREATE DATABASE IF NOT EXISTS %s%s;\n' % (db_name, db_suffix)
if (options.force_reload):
create_stmt += 'DROP TABLE IF EXISTS %s%s.%s;\n' % (db_name, db_suffix, table_name)
if compression == 'lzo':
file_format = '%s_%s' % (file_format, compression)
# hbase tables are external, and not read from hdfs. We don't need an hdfs_location.
if file_format == 'hbase':
hdfs_location = str()
create_stmt += table_template.format(db_name=db_name,
db_suffix=db_suffix,
table_name=table_name,
file_format=FILE_FORMAT_MAP[file_format],
hdfs_location=hdfs_location)
return create_stmt
def build_table_template(file_format, columns, partition_columns, row_format,
avro_schema_dir, table_name):
if file_format == 'hbase':
return build_hbase_create_stmt_in_hive(columns, partition_columns, table_name)
partitioned_by = str()
if partition_columns:
partitioned_by = 'PARTITIONED BY (%s)' % ', '.join(partition_columns.split('\n'))
row_format_stmt = str()
if row_format:
row_format_stmt = 'ROW FORMAT ' + row_format
tblproperties = str()
if file_format == 'avro':
tblproperties = "TBLPROPERTIES ('avro.schema.url'=" \
"'hdfs://%s/%s/%s/{table_name}.json')" \
% (options.hdfs_namenode, options.hive_warehouse_dir, avro_schema_dir)
elif file_format == 'parquet':
row_format_stmt = str()
# Note: columns are ignored but allowed if a custom serde is specified
# (e.g. Avro)
stmt = """
CREATE EXTERNAL TABLE IF NOT EXISTS {{db_name}}{{db_suffix}}.{{table_name}} (
{columns})
{partitioned_by}
{row_format}
STORED AS {{file_format}}
LOCATION '{{hdfs_location}}'
{tblproperties}
""".format(
row_format=row_format_stmt,
columns=',\n'.join(columns.split('\n')),
partitioned_by=partitioned_by,
tblproperties=tblproperties
).strip()
# Remove empty lines from the stmt string. There is an empty line for
# each of the sections that didn't have anything (e.g. partitioned_by)
stmt = os.linesep.join([s for s in stmt.splitlines() if s])
stmt += ';'
return stmt
def build_hbase_create_stmt_in_hive(columns, partition_columns, table_name):
# The hbase create statement differs sufficiently from the generic create to justify a
# separate method. Specifically, STORED AS becomes STORED BY. There is section called
# serdeproperties, the partition colmns have to be appended to columns in the schema.
columns = columns.split('\n')
# partition columns have to be appended to the columns in the schema.
# PARTITIONED BY is not supported and does not make sense for HBase.
if partition_columns:
columns.extend(partition_columns.split('\n'))
# stringid is a special case. It still points to functional_hbase.alltypesagg
if 'stringid' not in table_name:
tbl_properties = ('TBLPROPERTIES("hbase.table.name" = '
'"{db_name}{db_suffix}.{table_name}")')
else:
tbl_properties = ('TBLPROPERTIES("hbase.table.name" = '
'"{db_name}{db_suffix}.alltypesagg")')
# build hbase column mapping, the first column is implicitly the primary key
# which has a diffrerent representation [:key]
hbase_column_mapping = ["d:%s" % c.split(' ')[0] for c in columns[1:]]
hbase_column_mapping = ":key," + ','.join(hbase_column_mapping)
stmt = HBASE_CREATE_STATEMENT.format(
columns=',\n'.join(columns),
hbase_column_mapping=hbase_column_mapping,
tbl_properties=tbl_properties,
).strip()
return stmt + ';'
def avro_schema(columns):
record = {
"name": "a", # doesn't matter
"type": "record",
"fields": list()
}
for column_spec in columns.strip().split('\n'):
# column_spec looks something like "col_name col_type COMMENT comment"
# (comment may be omitted, we don't use it)
name = column_spec.split()[0]
type = column_spec.split()[1]
assert type.upper() in HIVE_TO_AVRO_TYPE_MAP, "Cannot convert to Avro type: %s" % type
record["fields"].append(
{'name': name,
'type': [HIVE_TO_AVRO_TYPE_MAP[type.upper()], "null"]}) # all columns nullable
return json.dumps(record)
def build_compression_codec_statement(codec, compression_type, file_format):
codec = AVRO_COMPRESSION_MAP[codec] if file_format == 'avro' else COMPRESSION_MAP[codec]
if not codec:
return str()
return (AVRO_COMPRESSION_CODEC % codec) if file_format == 'avro' else (
COMPRESSION_TYPE % compression_type.upper() + '\n' + COMPRESSION_CODEC % codec)
def build_codec_enabled_statement(codec):
compression_enabled = 'false' if codec == 'none' else 'true'
return COMPRESSION_ENABLED % compression_enabled
def build_insert_into_statement(insert, db_name, db_suffix, table_name, file_format,
hdfs_path, for_impala=False):
insert_statement = insert.format(db_name=db_name,
db_suffix=db_suffix,
table_name=table_name,
hdfs_location=hdfs_path)
if for_impala:
return insert_statement
statement = SET_PARTITION_MODE_NONSTRICT_STATEMENT + "\n"
statement += SET_DYNAMIC_PARTITION_STATEMENT + "\n"
statement += "set hive.auto.convert.join=true;\n"
# For some reason (hive bug?) we need to have the CombineHiveInputFormat set
# for cases where we are compressing in bzip or lzo on certain tables that
# have multiple files.
if 'multi' in table_name and ('bzip' in db_suffix or 'lzo' in db_suffix):
statement += SET_HIVE_INPUT_FORMAT % "CombineHiveInputFormat"
else:
statement += SET_HIVE_INPUT_FORMAT % "HiveInputFormat"
return statement + insert_statement
def build_hbase_insert(db_name, db_suffix, table_name):
hbase_insert = SET_HIVE_HBASE_BULK_LOAD + ';\n'
hbase_insert += ("INSERT OVERWRITE TABLE {db_name}{db_suffix}.{table_name}"
" SELECT * FROM {db_name}.{table_name};").\
format(db_name=db_name, db_suffix=db_suffix,table_name=table_name)
return hbase_insert
def build_insert(insert, db_name, db_suffix, file_format,
codec, compression_type, table_name, hdfs_path, create_hive=False):
# HBASE inserts don't need the hive options to be set, and don't require and HDFS
# file location, so they're handled separately.
if file_format == 'hbase' and not create_hive:
return build_hbase_insert(db_name, db_suffix, table_name)
output = build_codec_enabled_statement(codec) + "\n"
output += build_compression_codec_statement(codec, compression_type, file_format) + "\n"
output += build_insert_into_statement(insert, db_name, db_suffix,
table_name, file_format, hdfs_path) + "\n"
return output
def build_load_statement(load_template, db_name, db_suffix, table_name):
# hbase does not need the hdfs path.
if table_name.startswith('hbase'):
load_template = load_template.format(table_name=table_name,
db_name=db_name,
db_suffix=db_suffix)
else:
load_template = load_template.format(table_name=table_name,
db_name=db_name,
db_suffix=db_suffix,
impala_home = os.environ['IMPALA_HOME'])
return load_template
def build_hbase_create_stmt(db_name, table_name):
hbase_table_name = "{db_name}_hbase.{table_name}".format(db_name=db_name,
table_name=table_name)
create_stmt = list()
create_stmt.append("disable '%s'" % hbase_table_name)
create_stmt.append("drop '%s'" % hbase_table_name)
create_stmt.append("create '%s', 'd'" % hbase_table_name)
return create_stmt
def build_db_suffix(file_format, codec, compression_type):
if file_format == 'text' and codec != 'none' and codec != 'lzo':
print 'Unsupported combination of file_format (text) and compression codec.'
sys.exit(1)
elif file_format == 'text' and codec == 'none':
return ''
elif codec == 'none':
return '_%s' % (file_format)
elif compression_type == 'record':
return '_%s_record_%s' % (file_format, codec)
else:
return '_%s_%s' % (file_format, codec)
# Does a hdfs directory listing and returns array with all the subdir names.
def get_hdfs_subdirs_with_data(path):
tmp_file = tempfile.TemporaryFile("w+")
cmd = "hadoop fs -du %s | grep -v '^0' | awk '{print $2}'" % path
subprocess.call([cmd], shell = True, stderr = open('/dev/null'), stdout = tmp_file)
tmp_file.seek(0)
# Results look like:
# <acls> - <user> <group> <date> /directory/subdirectory
# So to get subdirectory names just return everything after the last '/'
return [line[line.rfind('/') + 1:].strip() for line in tmp_file.readlines()]
class Statements(object):
"""Simple container object for storing SQL statements to be output to a
file. Useful for ordering the statements correctly."""
def __init__(self):
self.create = list()
self.load = list()
self.load_base = list()
def write_to_file(self, filename):
# If there is no content to write, skip
if self.__is_empty(): return
output = self.create + self.load_base + self.load
with open(filename, 'w') as f:
f.write('\n\n'.join(output))
def __is_empty(self):
return not (self.create or self.load or self.load_base)
def generate_statements(output_name, test_vectors, sections,
schema_include_constraints, schema_exclude_constraints):
# TODO: This method has become very unwieldy. It has to be re-factored sooner than
# later.
# Parquet statements to be executed separately by Impala
hive_output = Statements()
hbase_output = Statements()
table_names = None
if options.table_names:
table_names = [name.lower() for name in options.table_names.split(',')]
existing_tables = get_hdfs_subdirs_with_data(options.hive_warehouse_dir)
for row in test_vectors:
impala_output = Statements()
impala_load = Statements()
file_format, data_set, codec, compression_type =\
[row.file_format, row.dataset, row.compression_codec, row.compression_type]
table_format = '%s/%s/%s' % (file_format, codec, compression_type)
for section in sections:
alter = section.get('ALTER')
create = section['CREATE']
create_hive = section['CREATE_HIVE']
insert = section['DEPENDENT_LOAD']
load = section['LOAD']
# For some datasets we may want to use a different load strategy when running local
# tests versus tests against large scale factors. The most common reason is to
# reduce he number of partitions for the local test environment
if not options.scale_factor and section['LOAD_LOCAL']:
load = section['LOAD_LOCAL']
base_table_name = section['BASE_TABLE_NAME']
columns = section['COLUMNS']
partition_columns = section['PARTITION_COLUMNS']
row_format = section['ROW_FORMAT']
# Force reloading of the table if the user specified the --force option or
# if the table is partitioned and there was no ALTER section specified. This is to
# ensure the partition metadata is always properly created. The ALTER section is
# used to create partitions, so if that section exists there is no need to force
# reload.
# TODO: Rename the ALTER section to ALTER_TABLE_ADD_PARTITION
force_reload = options.force_reload or (partition_columns and not alter)
table_name = base_table_name
db_suffix = build_db_suffix(file_format, codec, compression_type)
db_name = '{0}{1}'.format(data_set, options.scale_factor)
hdfs_location = '{0}.{1}{2}'.format(db_name, table_name, db_suffix)
# hdfs file names for hive-benchmark and functional datasets are stored
# directly under /test-warehouse
# TODO: We should not need to specify the hdfs file path in the schema file.
# This needs to be done programmatically.
if data_set in ['hive-benchmark', 'functional']:
hdfs_location = hdfs_location.split('.')[-1]
# hive does not allow hyphenated table names.
if data_set == 'hive-benchmark':
db_name = '{0}{1}'.format('hivebenchmark', options.scale_factor)
db = '{0}{1}'.format(db_name, db_suffix)
data_path = os.path.join(options.hive_warehouse_dir, hdfs_location)
# Empty tables (tables with no "LOAD" sections) are assumed to be used for insert
# testing. Since Impala currently only supports inserting into TEXT, PARQUET and
# HBASE we need to create these tables with a supported insert format.
create_file_format = file_format
create_codec = codec
if not load and not insert:
create_codec = 'none'
create_file_format = file_format
if file_format not in IMPALA_SUPPORTED_INSERT_FORMATS:
create_file_format = 'text'
if table_names and (table_name.lower() not in table_names):
print 'Skipping table: %s.%s' % (db, table_name)
continue
if schema_include_constraints[table_name.lower()] and \
table_format not in schema_include_constraints[table_name.lower()]:
print 'Skipping \'%s.%s\' due to include constraint match' % (db, table_name)
continue
if schema_exclude_constraints[base_table_name.lower()] and\
table_format in schema_exclude_constraints[base_table_name.lower()]:
print 'Skipping \'%s.%s\' due to exclude constraint match' % (db, table_name)
continue
output = impala_output
if create_hive or file_format == 'hbase':
output = hive_output
elif codec == 'lzo':
output = hive_output
# If a CREATE section is provided, use that. Otherwise a COLUMNS section
# must be provided (and optionally PARTITION_COLUMNS and ROW_FORMAT
# sections), which is used to generate the create table statement.
if create_hive:
table_template = create_hive
if file_format == 'avro':
print 'CREATE section not supported'
continue
elif create:
table_template = create
if file_format in ['avro', 'hbase']:
# We don't know how to generalize CREATE sections to Avro and hbase.
print ("CREATE section not supported with %s, "
"skipping: '%s'" % (file_format, table_name))
continue
else:
assert columns, "No CREATE or COLUMNS section defined for table " + table_name
avro_schema_dir = "%s/%s" % (AVRO_SCHEMA_DIR, data_set)
temp_table_name = table_name
table_template = build_table_template(
create_file_format, columns, partition_columns,
row_format, avro_schema_dir, table_name)
# Write Avro schema to local file
if file_format == 'avro':
if not os.path.exists(avro_schema_dir):
os.makedirs(avro_schema_dir)
with open("%s/%s.json" % (avro_schema_dir, table_name),"w") as f:
f.write(avro_schema(columns))
output.create.append(build_create_statement(table_template, table_name, db_name,
db_suffix, create_file_format, create_codec, data_path))
# HBASE create table
if file_format == 'hbase':
hbase_output.create.extend(build_hbase_create_stmt(db_name, table_name))
# The ALTER statement in hive does not accept fully qualified table names so
# insert a use statement. The ALTER statement is skipped for HBASE as it's
# used for adding partitions.
# TODO: Consider splitting the ALTER subsection into specific components. At the
# moment, it assumes we're only using ALTER for partitioning the table.
if alter and file_format != "hbase":
use_table = 'USE {db_name};\n'.format(db_name=db)
output.create.append(use_table + alter.format(table_name=table_name))
# If the directory already exists in HDFS, assume that data files already exist
# and skip loading the data. Otherwise, the data is generated using either an
# INSERT INTO statement or a LOAD statement.
if not force_reload and hdfs_location in existing_tables:
print 'HDFS path:', data_path, 'contains data. Data loading can be skipped.'
else:
print 'HDFS path:', data_path, 'does not exists or is empty. Data will be loaded.'
if not db_suffix:
if load:
hive_output.load_base.append(build_load_statement(load, db_name,
db_suffix, table_name))
else:
print 'Empty base table load for %s. Skipping load generation' % table_name
elif file_format == 'parquet':
if insert:
impala_load.load.append(build_insert_into_statement(insert, db_name,
db_suffix, table_name, 'parquet', data_path, for_impala=True))
else:
print \
'Empty parquet load for table %s. Skipping insert generation' % table_name
else:
if insert:
hive_output.load.append(build_insert(insert, db_name, db_suffix, file_format,
codec, compression_type, table_name, data_path,
create_hive=create_hive))
else:
print 'Empty insert for table %s. Skipping insert generation' % table_name
impala_output.write_to_file("load-%s-impala-generated-%s-%s-%s.sql" %
(output_name, file_format, codec, compression_type))
impala_load.write_to_file("load-%s-impala-load-generated-%s-%s-%s.sql" %
(output_name, file_format, codec, compression_type))
hive_output.write_to_file('load-' + output_name + '-hive-generated.sql')
hbase_output.create.append("exit")
hbase_output.write_to_file('load-' + output_name + '-hbase-generated.create')
def parse_schema_template_file(file_name):
VALID_SECTION_NAMES = ['DATASET', 'BASE_TABLE_NAME', 'COLUMNS', 'PARTITION_COLUMNS',
'ROW_FORMAT', 'CREATE', 'CREATE_HIVE', 'DEPENDENT_LOAD', 'LOAD',
'LOAD_LOCAL', 'ALTER']
return parse_test_file(file_name, VALID_SECTION_NAMES, skip_unknown_sections=False)
if __name__ == "__main__":
if options.table_formats is None:
if options.exploration_strategy not in KNOWN_EXPLORATION_STRATEGIES:
print 'Invalid exploration strategy:', options.exploration_strategy
print 'Valid values:', ', '.join(KNOWN_EXPLORATION_STRATEGIES)
sys.exit(1)
test_vectors = [vector.value for vector in\
load_table_info_dimension(options.workload, options.exploration_strategy)]
else:
table_formats = options.table_formats.split(',')
dataset = get_dataset_from_workload(options.workload)
test_vectors =\
[TableFormatInfo.create_from_string(dataset, tf) for tf in table_formats]
target_dataset = test_vectors[0].dataset
print 'Target Dataset: ' + target_dataset
dataset_load_dir = os.path.join(DATA_LOAD_DIR, target_dataset)
# If the directory containing the sql files does not exist, create it. Else nuke all the
# files corresponding to the current workload.
try:
os.makedirs(dataset_load_dir)
except OSError:
# Directory already exists, remove it.
shutil.rmtree(dataset_load_dir)
# Recreate the workload dir
os.makedirs(dataset_load_dir)
finally:
# Make sure that the directory was created and is empty.
assert os.path.isdir(dataset_load_dir)
assert len(os.listdir(dataset_load_dir)) == 0
# Make the dataset dir the current working directory
os.chdir(dataset_load_dir)
schema_template_file = os.path.join(DATASET_DIR, target_dataset,
'%s_schema_template.sql' % target_dataset)
if not os.path.isfile(schema_template_file):
print 'Schema file not found: ' + schema_template_file
sys.exit(1)
constraints_file = os.path.join(DATASET_DIR, target_dataset, 'schema_constraints.csv')
include_constraints, exclude_constraints = parse_table_constraints(constraints_file)
sections = parse_schema_template_file(schema_template_file)
generate_statements('%s-%s' % (options.workload, options.exploration_strategy),
test_vectors, sections, include_constraints, exclude_constraints)
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