def test_consume_json(pipeline, kafka, clean_db):
"""
Interpret consumed messages as JSON
"""
pipeline.create_stream('json_stream', payload='json')
pipeline.create_cv(
'count_cv',
"SELECT (payload->>'x')::integer AS x, COUNT(*) FROM json_stream GROUP BY x"
)
pipeline.create_cv(
'unpack_cv',
"SELECT (payload#>>'{nested,k}')::text AS k, json_array_elements_text(payload->'array') FROM json_stream"
)
kafka.create_topic('test_consume_json')
pipeline.consume_begin('test_consume_json', 'json_stream', format='json')
producer = kafka.get_producer('test_consume_json')
for n in range(200):
x = n % 100
payload = {'x': x, 'nested': {'k': 'v%d' % x}, 'array': [x, x, x]}
producer.produce(json.dumps(payload))
def count_cv():
rows = pipeline.execute('SELECT sum(count) FROM count_cv')
assert rows[0][0] == 200
rows = pipeline.execute('SELECT count(*) FROM count_cv')
assert rows[0][0] == 100
assert eventually(count_cv)
def unpack_cv():
# The JSON array gets unpacked for this CV so there are 3 rows for every message
rows = pipeline.execute('SELECT count(*) FROM unpack_cv')
assert rows[0][0] == 600
rows = pipeline.execute('SELECT count(distinct k) FROM unpack_cv')
assert rows[0][0] == 100
rows = pipeline.execute('SELECT * FROM unpack_cv ORDER BY k')
for row in rows:
k, arr_el = row[0], row[1]
assert k.replace('v', '') == arr_el
assert eventually(unpack_cv)
def test_consume_json(pipeline, kafka, clean_db):
"""
Interpret consumed messages as JSON
"""
pipeline.create_stream('json_stream', payload='json')
pipeline.create_cv('count_cv', "SELECT (payload->>'x')::integer AS x, COUNT(*) FROM json_stream GROUP BY x")
pipeline.create_cv('unpack_cv', "SELECT (payload#>>'{nested,k}')::text AS k, json_array_elements_text(payload->'array') FROM json_stream")
kafka.create_topic('test_consume_json')
pipeline.consume_begin('test_consume_json', 'json_stream', format='json')
producer = kafka.get_producer('test_consume_json')
for n in range(200):
x = n % 100
payload = {'x': x, 'nested': {'k': 'v%d' % x}, 'array': [x, x, x]}
producer.produce(json.dumps(payload))
def count_cv():
rows = pipeline.execute('SELECT sum(count) FROM count_cv')
assert rows[0][0] == 200
rows = pipeline.execute('SELECT count(*) FROM count_cv')
assert rows[0][0] == 100
assert eventually(count_cv)
def unpack_cv():
# The JSON array gets unpacked for this CV so there are 3 rows for every message
rows = pipeline.execute('SELECT count(*) FROM unpack_cv')
assert rows[0][0] == 600
rows = pipeline.execute('SELECT count(distinct k) FROM unpack_cv')
assert rows[0][0] == 100
rows = pipeline.execute('SELECT * FROM unpack_cv ORDER BY k')
for row in rows:
k, arr_el = row[0], row[1]
assert k.replace('v', '') == arr_el
assert eventually(unpack_cv)
def test_consume_text(pipeline, kafka, clean_db):
"""
Interpret consumed messages as text
"""
pipeline.create_stream('comma_stream',
x='integer',
y='integer',
z='integer')
pipeline.create_cv('comma_cv', 'SELECT x, y, z FROM comma_stream')
kafka.create_topic('test_consume_text_comma')
pipeline.create_stream('tab_stream', x='integer', y='integer', z='integer')
pipeline.create_cv('tab_cv', 'SELECT x, y, z FROM tab_stream')
kafka.create_topic('test_consume_text_tab')
pipeline.consume_begin('test_consume_text_comma',
'comma_stream',
delimiter=',')
pipeline.consume_begin('test_consume_text_tab',
'tab_stream',
delimiter='\t')
producer = kafka.get_producer('test_consume_text_comma')
for n in range(100):
message = ','.join(map(str, [n, n, n]))
producer.produce(message)
producer = kafka.get_producer('test_consume_text_tab')
for n in range(100):
message = '\t'.join(map(str, [n, n, n]))
producer.produce(message)
def delimited_messages():
rows = pipeline.execute('SELECT * from comma_cv ORDER BY x')
assert len(rows) == 100
for i, row in enumerate(rows):
assert tuple([row[0], row[1], row[2]]) == (i, i, i)
rows = pipeline.execute('SELECT * from tab_cv ORDER BY x')
assert len(rows) == 100
for i, row in enumerate(rows):
assert tuple([row[0], row[1], row[2]]) == (i, i, i)
assert eventually(delimited_messages)
def test_produce(pipeline, kafka, clean_db):
"""
Tests pipeline_kafka.emit_tuple and pipeline_kafka.produce_message
"""
pipeline.create_stream('stream0', payload='json')
pipeline.create_cv('cv', 'SELECT payload FROM stream0')
pipeline.create_table('t', x='integer', y='integer')
pipeline.execute("""CREATE TRIGGER tg AFTER INSERT ON t
FOR EACH ROW EXECUTE PROCEDURE pipeline_kafka.emit_tuple('topic')
""")
kafka.create_topic('topic', partitions=4)
pipeline.consume_begin('topic', 'stream0')
for i in range(100):
pipeline.insert('t', ('x', 'y'), [(i, 2 * i)])
def messages():
rows = pipeline.execute('SELECT * FROM cv')
assert len(rows) == 100
assert eventually(messages)
def test_produce(pipeline, kafka, clean_db):
"""
Tests pipeline_kafka.emit_tuple and pipeline_kafka.produce_message
"""
pipeline.create_stream('stream0', payload='json')
pipeline.create_cv('cv', 'SELECT payload FROM stream0')
pipeline.create_table('t', x='integer', y='integer')
pipeline.execute("""CREATE TRIGGER tg AFTER INSERT ON t
FOR EACH ROW EXECUTE PROCEDURE pipeline_kafka.emit_tuple('topic')
""")
kafka.create_topic('topic', partitions=4)
pipeline.consume_begin('topic', 'stream0')
for i in range(100):
pipeline.insert('t', ('x', 'y'), [(i, 2 * i)])
def messages():
rows = pipeline.execute('SELECT * FROM cv')
assert len(rows) == 100
assert eventually(messages)
def test_consume_stream_partitioned(pipeline, kafka, clean_db):
"""
Verify that messages with a stream name as their partition key
are properly mapped to streams
"""
for n in range(4):
pipeline.create_stream('stream%d' % n, x='integer')
pipeline.create_cv('cv%d' % n,
'SELECT x, COUNT(*) FROM stream%d GROUP BY x' % n)
kafka.create_topic('stream_partitioned_topic')
pipeline.consume_begin_stream_partitioned('stream_partitioned_topic')
def produce(producer, stream):
for n in range(100):
producer.produce(str(n), partition_key=stream)
threads = []
for n in range(4):
stream = 'stream%d' % n
producer = kafka.get_producer('stream_partitioned_topic')
t = threading.Thread(target=produce, args=(producer, stream))
t.daemon = True
t.start()
threads.append(t)
map(lambda t: t.join(), threads)
def messages_partitioned():
for n in range(4):
rows = pipeline.execute('SELECT sum(count) FROM cv%d' % n)
assert rows and rows[0][0] == 100
rows = pipeline.execute('SELECT count(*) FROM cv%d' % n)
assert rows and rows[0][0] == 100
assert eventually(messages_partitioned)
pipeline.consume_end()
def test_basic(pipeline, kafka, clean_db):
"""
Produce and consume several topics into several streams and verify that
all resulting data is correct
"""
pipeline.create_stream('stream0', x='integer')
pipeline.create_cv('basic', 'SELECT x, COUNT(*) FROM stream0 GROUP BY x')
kafka.create_topic('test_basic')
pipeline.consume_begin('test_basic', 'stream0')
producer = kafka.get_producer('test_basic')
for n in range(1000):
producer.produce(str(n))
def messages_consumed():
rows = pipeline.execute('SELECT sum(count) FROM basic')
assert rows and rows[0][0] == 1000
rows = pipeline.execute('SELECT count(*) FROM basic')
assert rows and rows[0][0] == 1000
assert eventually(messages_consumed)
def test_consume_stream_partitioned_safety(pipeline, kafka, clean_db):
'''
Produce without a key or non-existent stream key and make sure
thing work properly
'''
pipeline.create_stream('stream0', x='integer')
pipeline.create_cv('cv', 'SELECT count(*) FROM stream0')
kafka.create_topic('stream_partitioned_topic_safe')
pipeline.consume_begin_stream_partitioned('stream_partitioned_topic_safe')
producer = kafka.get_producer('stream_partitioned_topic_safe')
for n in range(100):
producer.produce(str(n), partition_key='')
producer.produce(str(n), partition_key='invalid')
producer.produce(str(n), partition_key='stream0')
def messages_partitioned():
rows = pipeline.execute('SELECT count FROM cv')
assert rows and rows[0][0] == 100
assert eventually(messages_partitioned)
pipeline.consume_end()
def test_basic(pipeline, kafka, clean_db):
"""
Produce and consume several topics into several streams and verify that
all resulting data is correct
"""
pipeline.create_stream('stream0', x='integer')
pipeline.create_cv('basic', 'SELECT x, COUNT(*) FROM stream0 GROUP BY x')
kafka.create_topic('test_basic')
pipeline.consume_begin('test_basic', 'stream0')
producer = kafka.get_producer('test_basic')
for n in range(1000):
producer.produce(str(n))
def messages_consumed():
rows = pipeline.execute('SELECT sum(count) FROM basic')
assert rows and rows[0][0] == 1000
rows = pipeline.execute('SELECT count(*) FROM basic')
assert rows and rows[0][0] == 1000
assert eventually(messages_consumed)
def test_consume_stream_partitioned(pipeline, kafka, clean_db):
"""
Verify that messages with a stream name as their partition key
are properly mapped to streams
"""
for n in range(4):
pipeline.create_stream('stream%d' % n, x='integer')
pipeline.create_cv('cv%d' % n, 'SELECT x, COUNT(*) FROM stream%d GROUP BY x' % n)
kafka.create_topic('stream_partitioned_topic')
pipeline.consume_begin_stream_partitioned('stream_partitioned_topic')
def produce(producer, stream):
for n in range(100):
producer.produce(str(n), partition_key=stream)
threads = []
for n in range(4):
stream = 'stream%d' % n
producer = kafka.get_producer('stream_partitioned_topic')
t = threading.Thread(target=produce, args=(producer, stream))
t.daemon = True
t.start()
threads.append(t)
map(lambda t: t.join(), threads)
def messages_partitioned():
for n in range(4):
rows = pipeline.execute('SELECT sum(count) FROM cv%d' % n)
assert rows and rows[0][0] == 100
rows = pipeline.execute('SELECT count(*) FROM cv%d' % n)
assert rows and rows[0][0] == 100
assert eventually(messages_partitioned)
pipeline.consume_end()
def test_consume_text(pipeline, kafka, clean_db):
"""
Interpret consumed messages as text
"""
pipeline.create_stream('comma_stream', x='integer', y='integer', z='integer')
pipeline.create_cv('comma_cv', 'SELECT x, y, z FROM comma_stream')
kafka.create_topic('test_consume_text_comma')
pipeline.create_stream('tab_stream', x='integer', y='integer', z='integer')
pipeline.create_cv('tab_cv', 'SELECT x, y, z FROM tab_stream')
kafka.create_topic('test_consume_text_tab')
pipeline.consume_begin('test_consume_text_comma', 'comma_stream', delimiter=',')
pipeline.consume_begin('test_consume_text_tab', 'tab_stream', delimiter='\t')
producer = kafka.get_producer('test_consume_text_comma')
for n in range(100):
message = ','.join(map(str, [n, n, n]))
producer.produce(message)
producer = kafka.get_producer('test_consume_text_tab')
for n in range(100):
message = '\t'.join(map(str, [n, n, n]))
producer.produce(message)
def delimited_messages():
rows = pipeline.execute('SELECT * from comma_cv ORDER BY x')
assert len(rows) == 100
for i, row in enumerate(rows):
assert tuple([row[0], row[1], row[2]]) == (i, i, i)
rows = pipeline.execute('SELECT * from tab_cv ORDER BY x')
assert len(rows) == 100
for i, row in enumerate(rows):
assert tuple([row[0], row[1], row[2]]) == (i, i, i)
assert eventually(delimited_messages)
def test_consume_stream_partitioned_safety(pipeline, kafka, clean_db):
'''
Produce without a key or non-existent stream key and make sure
thing work properly
'''
pipeline.create_stream('stream0', x='integer')
pipeline.create_cv('cv', 'SELECT count(*) FROM stream0')
kafka.create_topic('stream_partitioned_topic_safe')
pipeline.consume_begin_stream_partitioned('stream_partitioned_topic_safe')
producer = kafka.get_producer('stream_partitioned_topic_safe')
for n in range(100):
producer.produce(str(n), partition_key='')
producer.produce(str(n), partition_key='invalid')
producer.produce(str(n), partition_key='stream0')
def messages_partitioned():
rows = pipeline.execute('SELECT count FROM cv')
assert rows and rows[0][0] == 100
assert eventually(messages_partitioned)
pipeline.consume_end()
def test_lag(pipeline, kafka, clean_db):
"""
Verify that consumer lag is properly tracked
"""
kafka.create_topic('lag_topic0', partitions=8)
kafka.create_topic('lag_topic1', partitions=4)
pipeline.create_stream('stream0', x='integer')
pipeline.create_cv('lag0', 'SELECT count(*) FROM stream0')
pipeline.create_stream('stream1', x='integer')
pipeline.create_cv('lag1', 'SELECT count(*) FROM stream1')
pipeline.consume_begin('lag_topic0', 'stream0')
pipeline.consume_begin('lag_topic1', 'stream1')
producer = kafka.get_producer('lag_topic0')
for n in range(100):
producer.produce(str(n), partition_key=str(n))
producer = kafka.get_producer('lag_topic1')
for n in range(100):
producer.produce(str(n), partition_key=str(n))
def counts0():
rows = pipeline.execute('SELECT count FROM lag0')
assert rows[0][0] == 100
rows = pipeline.execute('SELECT count FROM lag1')
assert rows[0][0] == 100
assert eventually(counts0)
pipeline.consume_end()
# Now verify there is no reported lag
rows = pipeline.execute('SELECT sum(lag) FROM pipeline_kafka.consumer_lag')
assert rows
assert rows[0][0] == 0
# Now only start one consumer back up
pipeline.consume_begin('lag_topic0', 'stream0')
producer = kafka.get_producer('lag_topic0')
for n in range(100):
producer.produce(str(n), partition_key=str(n))
producer = kafka.get_producer('lag_topic1')
for n in range(100):
producer.produce(str(n), partition_key=str(n))
def counts1():
rows = pipeline.execute('SELECT count FROM lag0')
assert rows[0][0] == 200
rows = pipeline.execute('SELECT count FROM lag1')
assert rows[0][0] == 100
assert eventually(counts1)
# lag_topic0 should have no lag
rows = pipeline.execute(
"SELECT sum(lag) FROM pipeline_kafka.consumer_lag WHERE topic = 'lag_topic0'"
)
assert rows
assert rows[0][0] == 0
# lag_topic1 should have lag now since we didn't start its consumer
rows = pipeline.execute(
"SELECT sum(lag) FROM pipeline_kafka.consumer_lag WHERE topic = 'lag_topic1'"
)
assert rows
assert rows[0][0] == 100
pipeline.consume_end()
def test_consumers(pipeline, kafka, clean_db):
"""
Verify that offsets are properly maintained when storing them locally across consumer restarts
"""
pipeline.create_stream('stream0', x='integer')
pipeline.create_cv('basic', 'SELECT x, COUNT(*) FROM stream0 GROUP BY x')
kafka.create_topic('test_consumers', partitions=4)
pipeline.consume_begin('test_consumers', 'stream0', parallelism=4)
producer = kafka.get_producer('test_consumers')
for n in range(1000):
producer.produce(str(n))
def messages_consumed0():
rows = pipeline.execute('SELECT sum(count) FROM basic')
assert rows and rows[0][0] == 1000
rows = pipeline.execute('SELECT count(*) FROM basic')
assert rows and rows[0][0] == 1000
assert eventually(messages_consumed0)
pipeline.consume_end()
# Now verify offsets
rows = pipeline.execute(
'SELECT * FROM pipeline_kafka.offsets ORDER BY partition')
assert len(rows) == 4
# Consumer IDs should be the same
assert all(r[0] == rows[0][0] for r in rows)
# Partitions
assert rows[0][1] == 0
assert rows[1][1] == 1
assert rows[2][1] == 2
assert rows[3][1] == 3
# Offsets should sum to the number of (messages_produced - num_partitions)
assert sum(r[2] for r in rows) == 996
# Now produce more message while we're not consuming
for n in range(1000):
producer.produce(str(n))
pipeline.consume_begin('test_consumers', 'stream0', parallelism=4)
time.sleep(2)
# Verify count
def messages_consumed1():
rows = pipeline.execute('SELECT sum(count) FROM basic')
assert rows and rows[0][0] == 2000
rows = pipeline.execute('SELECT count(*) FROM basic')
assert rows and rows[0][0] == 1000
assert eventually(messages_consumed1)
pipeline.consume_end()
rows = pipeline.execute(
'SELECT * FROM pipeline_kafka.offsets ORDER BY partition')
assert len(rows) == 4
# Consumer IDs should be the same
assert all(r[0] == rows[0][0] for r in rows)
# Partitions
assert rows[0][1] == 0
assert rows[1][1] == 1
assert rows[2][1] == 2
assert rows[3][1] == 3
# Offsets should sum to the number of (messages_produced - num_partitions)
assert sum(r[2] for r in rows) == 1996
def test_grouped_consumer(pipeline, kafka, clean_db):
"""
Verify that consumers with a group.id store offsets in Kafka and consume
partitions correctly.
"""
pipeline.create_stream('stream0', x='integer')
pipeline.create_stream('stream1', x='integer')
pipeline.create_cv('group0', "SELECT x, COUNT(*) FROM stream0 GROUP BY x")
pipeline.create_cv('group1', "SELECT x, COUNT(*) FROM stream1 GROUP BY x")
kafka.create_topic('topic0', partitions=4)
kafka.create_topic('topic1', partitions=4)
pipeline.consume_begin('topic0', 'stream0', group_id='group0')
pipeline.consume_begin('topic1', 'stream1', group_id='group1')
producer0 = kafka.get_producer('topic0')
producer1 = kafka.get_producer('topic1')
for n in range(1, 101):
producer0.produce(str(n))
producer1.produce(str(n))
def counts():
rows = pipeline.execute('SELECT sum(count) FROM group0')
assert rows[0][0] == 100
rows = pipeline.execute('SELECT sum(count) FROM group1')
assert rows[0][0] == 100
rows = pipeline.execute('SELECT count(*) FROM group0')
assert rows[0][0] == 100
rows = pipeline.execute('SELECT count(*) FROM group1')
assert rows[0][0] == 100
assert eventually(counts)
pipeline.consume_end()
# Verify offsets are also stored locally
rows = pipeline.execute('SELECT * FROM pipeline_kafka.offsets')
assert rows
# Now produce some more
for n in range(1, 101):
producer0.produce(str(-n))
producer1.produce(str(-n))
pipeline.consume_begin('topic0', 'stream0', group_id='group0')
pipeline.consume_begin('topic1', 'stream1', group_id='group1')
# And verify that only the second batch of messages was read
def counts_after_restart():
rows = pipeline.execute('SELECT sum(x) FROM group0')
assert rows[0][0] == 0
rows = pipeline.execute('SELECT sum(x) FROM group1')
assert rows[0][0] == 0
rows = pipeline.execute('SELECT sum(count) FROM group0')
assert rows[0][0] == 200
rows = pipeline.execute('SELECT sum(count) FROM group1')
assert rows[0][0] == 200
rows = pipeline.execute('SELECT count(*) FROM group0')
assert rows[0][0] == 200
rows = pipeline.execute('SELECT count(*) FROM group1')
assert rows[0][0] == 200
assert eventually(counts_after_restart)
# Verify that we can still begin consuming from a specific offset
pipeline.create_cv('group2', "SELECT x FROM stream0")
pipeline.consume_end()
# Skip one row per partition
pipeline.consume_begin('topic0',
'stream0',
group_id='group2',
start_offset=1)
def from_offset():
rows = pipeline.execute('SELECT count(*) FROM group2')
assert rows[0][0] == 196
assert eventually(from_offset)
def test_grouped_consumer(pipeline, kafka, clean_db):
"""
Verify that consumers with a group.id store offsets in Kafka and consume
partitions correctly.
"""
pipeline.create_stream('stream0', x='integer')
pipeline.create_stream('stream1', x='integer')
pipeline.create_cv('group0', "SELECT x, COUNT(*) FROM stream0 GROUP BY x")
pipeline.create_cv('group1', "SELECT x, COUNT(*) FROM stream1 GROUP BY x")
kafka.create_topic('topic0', partitions=4)
kafka.create_topic('topic1', partitions=4)
pipeline.consume_begin('topic0', 'stream0', group_id='group0')
pipeline.consume_begin('topic1', 'stream1', group_id='group1')
producer0 = kafka.get_producer('topic0')
producer1 = kafka.get_producer('topic1')
for n in range(1, 101):
producer0.produce(str(n))
producer1.produce(str(n))
def counts():
rows = pipeline.execute('SELECT sum(count) FROM group0')
assert rows[0][0] == 100
rows = pipeline.execute('SELECT sum(count) FROM group1')
assert rows[0][0] == 100
rows = pipeline.execute('SELECT count(*) FROM group0')
assert rows[0][0] == 100
rows = pipeline.execute('SELECT count(*) FROM group1')
assert rows[0][0] == 100
assert eventually(counts)
pipeline.consume_end()
# Verify offsets are also stored locally
rows = pipeline.execute('SELECT * FROM pipeline_kafka.offsets')
assert rows
# Now produce some more
for n in range(1, 101):
producer0.produce(str(-n))
producer1.produce(str(-n))
pipeline.consume_begin('topic0', 'stream0', group_id='group0')
pipeline.consume_begin('topic1', 'stream1', group_id='group1')
# And verify that only the second batch of messages was read
def counts_after_restart():
rows = pipeline.execute('SELECT sum(x) FROM group0')
assert rows[0][0] == 0
rows = pipeline.execute('SELECT sum(x) FROM group1')
assert rows[0][0] == 0
rows = pipeline.execute('SELECT sum(count) FROM group0')
assert rows[0][0] == 200
rows = pipeline.execute('SELECT sum(count) FROM group1')
assert rows[0][0] == 200
rows = pipeline.execute('SELECT count(*) FROM group0')
assert rows[0][0] == 200
rows = pipeline.execute('SELECT count(*) FROM group1')
assert rows[0][0] == 200
assert eventually(counts_after_restart)
# Verify that we can still begin consuming from a specific offset
pipeline.create_cv('group2', "SELECT x FROM stream0")
pipeline.consume_end()
# Skip one row per partition
pipeline.consume_begin('topic0', 'stream0', group_id='group2', start_offset=1)
def from_offset():
rows = pipeline.execute('SELECT count(*) FROM group2')
assert rows[0][0] == 196
assert eventually(from_offset)
def test_consumers(pipeline, kafka, clean_db):
"""
Verify that offsets are properly maintained when storing them locally across consumer restarts
"""
pipeline.create_stream('stream0', x='integer')
pipeline.create_cv('basic', 'SELECT x, COUNT(*) FROM stream0 GROUP BY x')
kafka.create_topic('test_consumers', partitions=4)
pipeline.consume_begin('test_consumers', 'stream0', parallelism=4)
producer = kafka.get_producer('test_consumers')
for n in range(1000):
producer.produce(str(n))
def messages_consumed0():
rows = pipeline.execute('SELECT sum(count) FROM basic')
assert rows and rows[0][0] == 1000
rows = pipeline.execute('SELECT count(*) FROM basic')
assert rows and rows[0][0] == 1000
assert eventually(messages_consumed0)
pipeline.consume_end()
# Now verify offsets
rows = pipeline.execute('SELECT * FROM pipeline_kafka.offsets ORDER BY partition')
assert len(rows) == 4
# Consumer IDs should be the same
assert all(r[0] == rows[0][0] for r in rows)
# Partitions
assert rows[0][1] == 0
assert rows[1][1] == 1
assert rows[2][1] == 2
assert rows[3][1] == 3
# Offsets should sum to the number of (messages_produced - num_partitions)
assert sum(r[2] for r in rows) == 996
# Now produce more message while we're not consuming
for n in range(1000):
producer.produce(str(n))
pipeline.consume_begin('test_consumers', 'stream0', parallelism=4)
time.sleep(2)
# Verify count
def messages_consumed1():
rows = pipeline.execute('SELECT sum(count) FROM basic')
assert rows and rows[0][0] == 2000
rows = pipeline.execute('SELECT count(*) FROM basic')
assert rows and rows[0][0] == 1000
assert eventually(messages_consumed1)
pipeline.consume_end()
rows = pipeline.execute('SELECT * FROM pipeline_kafka.offsets ORDER BY partition')
assert len(rows) == 4
# Consumer IDs should be the same
assert all(r[0] == rows[0][0] for r in rows)
# Partitions
assert rows[0][1] == 0
assert rows[1][1] == 1
assert rows[2][1] == 2
assert rows[3][1] == 3
# Offsets should sum to the number of (messages_produced - num_partitions)
assert sum(r[2] for r in rows) == 1996
def test_lag(pipeline, kafka, clean_db):
"""
Verify that consumer lag is properly tracked
"""
kafka.create_topic('lag_topic0', partitions=8)
kafka.create_topic('lag_topic1', partitions=4)
pipeline.create_stream('stream0', x='integer')
pipeline.create_cv('lag0', 'SELECT count(*) FROM stream0')
pipeline.create_stream('stream1', x='integer')
pipeline.create_cv('lag1', 'SELECT count(*) FROM stream1')
pipeline.consume_begin('lag_topic0', 'stream0')
pipeline.consume_begin('lag_topic1', 'stream1')
producer = kafka.get_producer('lag_topic0')
for n in range(100):
producer.produce(str(n), partition_key=str(n))
producer = kafka.get_producer('lag_topic1')
for n in range(100):
producer.produce(str(n), partition_key=str(n))
def counts0():
rows = pipeline.execute('SELECT count FROM lag0')
assert rows[0][0] == 100
rows = pipeline.execute('SELECT count FROM lag1')
assert rows[0][0] == 100
assert eventually(counts0)
pipeline.consume_end()
# Now verify there is no reported lag
rows = pipeline.execute('SELECT sum(lag) FROM pipeline_kafka.consumer_lag')
assert rows
assert rows[0][0] == 0
# Now only start one consumer back up
pipeline.consume_begin('lag_topic0', 'stream0')
producer = kafka.get_producer('lag_topic0')
for n in range(100):
producer.produce(str(n), partition_key=str(n))
producer = kafka.get_producer('lag_topic1')
for n in range(100):
producer.produce(str(n), partition_key=str(n))
def counts1():
rows = pipeline.execute('SELECT count FROM lag0')
assert rows[0][0] == 200
rows = pipeline.execute('SELECT count FROM lag1')
assert rows[0][0] == 100
assert eventually(counts1)
# lag_topic0 should have no lag
rows = pipeline.execute("SELECT sum(lag) FROM pipeline_kafka.consumer_lag WHERE topic = 'lag_topic0'")
assert rows
assert rows[0][0] == 0
# lag_topic1 should have lag now since we didn't start its consumer
rows = pipeline.execute("SELECT sum(lag) FROM pipeline_kafka.consumer_lag WHERE topic = 'lag_topic1'")
assert rows
assert rows[0][0] == 100
pipeline.consume_end()