blob: 078f0c5bacb015f5a242775d5ac1a32a8627e86e [file] [log] [blame]
import datetime
import random
import logging
from dtest import Tester, create_ks
from tools.assertions import assert_length_equal
status_messages = (
"I''m going to the Cassandra Summit in June!",
"C* is awesome!",
"All your sstables are belong to us.",
"Just turned on another 50 C* nodes at <insert tech startup here>, scales beautifully.",
"Oh, look! Cats, on reddit!",
"Netflix recommendations are really good, wonder why?",
"Spotify playlists are always giving me good tunes, wonder why?"
clients = (
logger = logging.getLogger(__name__)
class TestWideRows(Tester):
def test_wide_rows(self):
def write_wide_rows(self):
cluster = self.cluster
node1 = cluster.nodelist()[0]
session = self.patient_cql_connection(node1)
start_time =
create_ks(session, 'wide_rows', 1)
# Simple timeline: user -> {date: value, ...}
logger.debug('Create Table....')
session.execute('CREATE TABLE user_events (userid text, event timestamp, value text, PRIMARY KEY (userid, event));')
date =
# Create a large timeline for each of a group of users:
for user in ('ryan', 'cathy', 'mallen', 'joaquin', 'erin', 'ham'):
logger.debug("Writing values for: %s" % user)
for day in range(5000):
date_str = (date + datetime.timedelta(day)).strftime("%Y-%m-%d")
client = random.choice(clients)
msg = random.choice(status_messages)
query = "UPDATE user_events SET value = '{msg:%s, client:%s}' WHERE userid='%s' and event='%s';" % (msg, client, user, date_str)
# logger.debug(query)
# logger.debug('Duration of test: %s' % ( - start_time))
# Pick out an update for a specific date:
query = "SELECT value FROM user_events WHERE userid='ryan' and event='%s'" % \
(date + datetime.timedelta(10)).strftime("%Y-%m-%d")
rows = session.execute(query)
for value in rows:
assert len(value[0]) > 0
def test_column_index_stress(self):
"""Write a large number of columns to a single row and set
'column_index_size_in_kb' to a sufficiently low value to force
the creation of a column index. The test will then randomly
read columns from that row and ensure that all data is
returned. See CASSANDRA-5225.
cluster = self.cluster
(node1,) = cluster.nodelist()
cluster.set_configuration_options(values={'column_index_size_in_kb': 1}) # reduce this value to force column index creation
session = self.patient_cql_connection(node1)
create_ks(session, 'wide_rows', 1)
create_table_query = 'CREATE TABLE test_table (row varchar, name varchar, value int, PRIMARY KEY (row, name));'
# Now insert 100,000 columns to row 'row0'
insert_column_query = "UPDATE test_table SET value = {value} WHERE row = '{row}' AND name = '{name}';"
for i in range(100000):
row = 'row0'
name = 'val' + str(i)
session.execute(insert_column_query.format(value=i, row=row, name=name))
# now randomly fetch columns: 1 to 3 at a time
for i in range(10000):
select_column_query = "SELECT value FROM test_table WHERE row='row0' AND name in ('{name1}', '{name2}', '{name3}');"
values2fetch = [str(random.randint(0, 99999)) for i in range(3)]
# values2fetch is a list of random values. Because they are random, they will not be unique necessarily.
# To simplify the template logic in the select_column_query I will not expect the query to
# necessarily return 3 values. Hence I am computing the number of unique values in values2fetch
# and using that in the assert at the end.
expected_rows = len(set(values2fetch))
rows = list(session.execute(select_column_query.format(name1="val" + values2fetch[0],
name2="val" + values2fetch[1],
name3="val" + values2fetch[2])))
assert_length_equal(rows, expected_rows)