def exec_instructions(self, instructions):
filenames = instructions.statement_files
filenames = (os.path.join(self.spec_dir, i) for i in filenames)
lines = (line for fn in filenames for line in get_lines(fn))
statements = itertools.chain(as_statements(lines),
instructions.statements)
for stmt in statements:
aio.run(self.client.execute, stmt)
for data_file in instructions.data_files:
inserts = as_bulk_queries(self._to_inserts(data_file),
data_file.get('bulk_size', 5000))
concurrency = data_file.get('concurrency', 25)
aio.run_many(self.client.execute_many,
inserts,
concurrency=concurrency)
if self.client.is_cratedb:
aio.run(self.client.execute,
f"refresh table {data_file['target']}")
for data_cmd in instructions.data_cmds:
process = subprocess.Popen(data_cmd['cmd'],
stdout=subprocess.PIPE,
universal_newlines=True)
target = data_cmd['target']
dicts = dicts_from_lines(process.stdout)
inserts = as_bulk_queries((to_insert(target, d) for d in dicts),
data_cmd.get('bulk_size', 5000))
concurrency = data_cmd.get('concurrency', 25)
aio.run_many(self.client.execute_many,
inserts,
concurrency=concurrency)
if self.client.is_cratedb:
aio.run(self.client.execute, f"refresh table {target}")
def save_result(result):
if not table_created:
aio.run(client.execute, BENCHMARK_TABLE)
table_created.append(None)
stmt, args = to_insert('benchmarks', result.as_dict())
aio.run(client.execute, stmt, args)
log.result(result)
def insert_data(conn, schema, table, num_rows):
cols = columns_for_table(conn, schema, table)
stmt, args = to_insert(f'"{schema}"."{table}"', cols)
gen_row = create_row_generator(cols)
c = conn.cursor()
c.executemany(stmt, [gen_row() for x in range(num_rows)])
c.execute(f'REFRESH TABLE "{schema}"."{table}"')
def _to_inserts(self, data_spec):
target = data_spec['target']
source = os.path.join(self.spec_dir, data_spec['source'])
dicts = dicts_from_lines(get_lines(source))
return (to_insert(target, d) for d in dicts)
def insert_fake_data(hosts=None,
table=None,
num_records=1e5,
bulk_size=1000,
concurrency=25,
mapping_file=None):
"""Generate random data and insert it into a table.
This will read the table schema and then find suitable random data providers.
Which provider is choosen depends on the column name and data type.
Example:
A column named `name` will map to the `name` provider.
A column named `x` of type int will map to `random_int` because there
is no `x` provider.
Available providers are listed here:
https://faker.readthedocs.io/en/latest/providers.html
Additional providers:
- auto_inc:
Returns unique incrementing numbers.
Automatically used for columns named "id" of type int or long
- geo_point
Returns [<lon>, <lat>]
Automatically used for columns of type geo_point
Args:
hosts: <host>:[<port>] of the Crate node
table: The table name into which the data should be inserted.
Either fully qualified: `<schema>.<table>` or just `<table>`
num_records: Number of records to insert.
Usually a number but expressions like `1e4` work as well.
bulk_size: The bulk size of the insert statements.
concurrency: How many operations to run concurrently.
mapping_file: A JSON file that defines a mapping from column name to
fake-factory provider.
The format is as follows:
{
"column_name": ["provider_with_args", ["arg1", "arg"]],
"x": ["provider_with_args", ["arg1"]],
"y": "provider_without_args"
}
"""
with clients.client(hosts, concurrency=1) as client:
schema, table_name = parse_table(table)
columns = retrieve_columns(client, schema, table_name)
if not columns:
sys.exit('Could not find columns for table "{}"'.format(table))
print('Found schema: ')
columns_dict = {r.name: r.type_name for r in columns}
print(json.dumps(columns_dict, sort_keys=True, indent=4))
mapping = None
if mapping_file:
mapping = json.load(mapping_file)
bulk_size = min(num_records, bulk_size)
num_inserts = int(math.ceil(num_records / bulk_size))
gen_row = create_row_generator(columns, mapping)
stmt = to_insert('"{schema}"."{table_name}"'.format(**locals()),
columns_dict)[0]
print('Using insert statement: ')
print(stmt)
print('Will make {} requests with a bulk size of {}'.format(
num_inserts, bulk_size))
print('Generating fake data and executing inserts')
q = asyncio.Queue(maxsize=concurrency)
with clients.client(hosts, concurrency=concurrency) as client:
active = [True]
def stop():
asyncio.ensure_future(q.put(None))
active.clear()
loop.remove_signal_handler(signal.SIGINT)
if sys.platform != 'win32':
loop.add_signal_handler(signal.SIGINT, stop)
bulk_seq = _bulk_size_generator(num_records, bulk_size, active)
with ThreadPoolExecutor() as e:
tasks = asyncio.gather(
_gen_data_and_insert(q, e, client, stmt, gen_row, bulk_seq),
consume(q, total=num_inserts))
loop.run_until_complete(tasks)
