def api(*, debug: bool, log_level: Optional[str], processes: int, threads: int) -> None:
from snuba import settings
if debug:
if processes > 1 or threads > 1:
raise click.ClickException("processes/threads can only be 1 in debug")
from snuba.web.views import application
from werkzeug.serving import WSGIRequestHandler
setup_logging(log_level)
WSGIRequestHandler.protocol_version = "HTTP/1.1"
application.run(port=settings.PORT, threaded=True, debug=debug)
else:
import mywsgi
if log_level:
os.environ["LOG_LEVEL"] = log_level
mywsgi.run(
"snuba.web.wsgi:application",
f"0.0.0.0:{settings.PORT}",
processes=processes,
threads=threads,
)
def perf(
*,
events_file: Optional[str],
repeat: int,
profile_process: bool,
profile_write: bool,
dataset_name: str,
log_level: Optional[str] = None,
) -> None:
from snuba.perf import run, logger
setup_logging(log_level)
dataset = get_dataset(dataset_name)
if not local_dataset_mode():
logger.error(
"The perf tool is only intended for local dataset environment.")
sys.exit(1)
run(
events_file,
dataset,
repeat=repeat,
profile_process=profile_process,
profile_write=profile_write,
)
def optimize(
*,
clickhouse_host: str,
clickhouse_port: int,
database: str,
dataset_name: str,
timeout: int,
log_level: Optional[str] = None,
) -> None:
from datetime import datetime
from snuba.clickhouse.native import ClickhousePool
from snuba.optimize import run_optimize, logger
setup_logging(log_level)
dataset = get_dataset(dataset_name)
table = enforce_table_writer(dataset).get_schema().get_local_table_name()
today = datetime.utcnow().replace(hour=0,
minute=0,
second=0,
microsecond=0)
clickhouse = ClickhousePool(clickhouse_host,
clickhouse_port,
send_receive_timeout=timeout)
num_dropped = run_optimize(clickhouse, database, table, before=today)
logger.info("Optimized %s partitions on %s" %
(num_dropped, clickhouse_host))
def bulk_load(
*,
dataset_name: Optional[str],
dest_table: Optional[str],
source: Optional[str],
log_level: Optional[str] = None,
) -> None:
setup_logging(log_level)
setup_sentry()
logger = logging.getLogger("snuba.load-snapshot")
logger.info("Start bulk load process for dataset %s, from source %s",
dataset_name, source)
dataset = get_dataset(dataset_name)
# TODO: Have a more abstract way to load sources if/when we support more than one.
snapshot_source = PostgresSnapshot.load(
product=settings.SNAPSHOT_LOAD_PRODUCT,
path=source,
)
loader = enforce_table_writer(dataset).get_bulk_loader(
snapshot_source, dest_table)
# TODO: see whether we need to pass options to the writer
writer = BufferedWriterWrapper(
enforce_table_writer(dataset).get_bulk_writer(table_name=dest_table),
settings.BULK_CLICKHOUSE_BUFFER,
)
loader.load(writer)
def perf(
*,
events_file: Optional[str],
repeat: int,
profile_process: bool,
profile_write: bool,
dataset_name: str,
log_level: Optional[str] = None,
) -> None:
from snuba.perf import run, logger
setup_logging(log_level)
dataset = get_dataset(dataset_name)
if not all(storage.get_cluster().is_single_node()
for storage in dataset.get_all_storages()):
logger.error(
"The perf tool is only intended for single node environment.")
sys.exit(1)
run(
events_file,
dataset,
repeat=repeat,
profile_process=profile_process,
profile_write=profile_write,
)
def migrate(*, log_level: Optional[str] = None) -> None:
click.echo("Warning: The migrate command is deprecated and will be removed soon\n")
setup_logging(log_level)
check_clickhouse_connections()
run()
def optimize(
*,
clickhouse_host: Optional[str],
clickhouse_port: Optional[int],
storage_name: str,
parallel: int,
log_level: Optional[str] = None,
) -> None:
from datetime import datetime
from snuba.clickhouse.native import ClickhousePool
from snuba.optimize import logger, run_optimize
setup_logging(log_level)
setup_sentry()
storage: ReadableTableStorage
storage_key = StorageKey(storage_name)
storage = get_storage(storage_key)
(clickhouse_user, clickhouse_password) = storage.get_cluster().get_credentials()
today = datetime.utcnow().replace(hour=0, minute=0, second=0, microsecond=0)
database = storage.get_cluster().get_database()
# TODO: In distributed mode, optimize currently must be run once for each node
# with the host and port of that node provided via the CLI. In the future,
# passing this information won't be necessary, and running this command once
# will ensure that optimize is performed on all of the individual nodes for
# that cluster.
if clickhouse_host and clickhouse_port:
connection = ClickhousePool(
clickhouse_host,
clickhouse_port,
clickhouse_user,
clickhouse_password,
database,
send_receive_timeout=ClickhouseClientSettings.OPTIMIZE.value.timeout,
)
elif not storage.get_cluster().is_single_node():
raise click.ClickException("Provide Clickhouse host and port for optimize")
else:
connection = storage.get_cluster().get_query_connection(
ClickhouseClientSettings.OPTIMIZE
)
num_dropped = run_optimize(
connection,
storage,
database,
before=today,
parallel=parallel,
clickhouse_host=clickhouse_host,
)
logger.info("Optimized %s partitions on %s" % (num_dropped, clickhouse_host))
def api(*, debug: bool, log_level: Optional[str] = None) -> None:
from snuba import settings
from snuba.web.views import application
from werkzeug.serving import WSGIRequestHandler
setup_logging(log_level)
WSGIRequestHandler.protocol_version = "HTTP/1.1"
host = "0.0.0.0"
application.run(host=host, port=settings.PORT, threaded=True, debug=debug)
def migrate(force: bool, log_level: Optional[str] = None) -> None:
"""
Runs all migrations. Blocking migrations will not be run unless --force is passed.
"""
setup_logging(log_level)
check_clickhouse_connections()
runner = Runner()
try:
runner.run_all(force=force)
except MigrationError as e:
raise click.ClickException(str(e))
click.echo("Finished running migrations")
def cleanup(
*,
clickhouse_host: Optional[str],
clickhouse_port: Optional[int],
dry_run: bool,
database: str,
storage_name: str,
log_level: Optional[str] = None,
) -> None:
"""
Deletes stale partitions for ClickHouse tables
"""
setup_logging(log_level)
from snuba.cleanup import run_cleanup, logger
from snuba.clickhouse.native import ClickhousePool
writable_storage = get_writable_storage(StorageKey(storage_name))
(
clickhouse_user,
clickhouse_password,
) = writable_storage.get_cluster().get_credentials()
table = writable_storage.get_table_writer().get_schema(
).get_local_table_name()
if clickhouse_host and clickhouse_port and database:
connection = ClickhousePool(
clickhouse_host,
clickhouse_port,
clickhouse_user,
clickhouse_password,
database,
)
elif not writable_storage.get_cluster().is_single_node():
raise click.ClickException(
"Provide ClickHouse host and port for cleanup")
else:
connection = writable_storage.get_cluster().get_query_connection(
ClickhouseClientSettings.CLEANUP)
num_dropped = run_cleanup(connection, database, table, dry_run=dry_run)
logger.info("Dropped %s partitions on %s" % (num_dropped, clickhouse_host))
def api(
*,
bind: Optional[str],
debug: bool,
log_level: Optional[str],
processes: int,
threads: int,
) -> None:
from snuba import settings
port: Union[int, str]
if bind:
if ":" in bind:
host, port = bind.split(":", 1)
port = int(port)
else:
raise click.ClickException("bind can only be in the format <host>:<port>")
else:
host, port = settings.HOST, settings.PORT
if debug:
if processes > 1 or threads > 1:
raise click.ClickException("processes/threads can only be 1 in debug")
from werkzeug.serving import WSGIRequestHandler
from snuba.web.views import application
setup_logging(log_level)
WSGIRequestHandler.protocol_version = "HTTP/1.1"
application.run(host=host, port=port, threaded=True, debug=debug)
else:
import mywsgi
if log_level:
os.environ["LOG_LEVEL"] = log_level
mywsgi.run(
"snuba.web.wsgi:application",
f"{host}:{port}",
processes=processes,
threads=threads,
)
def bulk_load(
*,
storage_name: str,
dest_table: str,
source: str,
log_level: Optional[str] = None,
) -> None:
setup_logging(log_level)
setup_sentry()
logger = logging.getLogger("snuba.load-snapshot")
logger.info("Start bulk load process for storage %s, from source %s",
storage_name, source)
storage = get_cdc_storage(StorageKey(storage_name))
table_writer = storage.get_table_writer()
# TODO: Have a more abstract way to load sources if/when we support more than one.
snapshot_source = PostgresSnapshot.load(
product=settings.SNAPSHOT_LOAD_PRODUCT,
path=source,
)
loader = table_writer.get_bulk_loader(
snapshot_source,
storage.get_postgres_table(),
dest_table,
storage.get_row_processor(),
)
# TODO: see whether we need to pass options to the writer
writer = BufferedWriterWrapper(
table_writer.get_batch_writer(
environment.metrics,
table_name=dest_table,
chunk_size=settings.BULK_CLICKHOUSE_BUFFER,
),
settings.BULK_CLICKHOUSE_BUFFER,
JSONRowEncoder(),
)
loader.load(writer)
def migrate(*,
log_level: Optional[str] = None,
dataset_name: Optional[str] = None) -> None:
from snuba.migrate import logger, run
setup_logging(log_level)
if not local_dataset_mode():
logger.error("The migration tool can only work on local dataset mode.")
sys.exit(1)
dataset_names = [dataset_name] if dataset_name else DATASET_NAMES
for name in dataset_names:
dataset = get_dataset(name)
logger.info("Migrating dataset %s", name)
clickhouse = Client(
host=settings.CLICKHOUSE_HOST,
port=settings.CLICKHOUSE_PORT,
)
run(clickhouse, dataset)
def run(
group: str,
migration_id: str,
force: bool,
fake: bool,
dry_run: bool,
log_level: Optional[str] = None,
) -> None:
"""
Runs a single migration.
--force must be passed in order to run blocking migrations.
--fake marks a migration as completed without running anything.
Migrations that are already in an in-progress or completed status will not be run.
"""
setup_logging(log_level)
if not dry_run:
check_clickhouse_connections()
runner = Runner()
migration_group = MigrationGroup(group)
migration_key = MigrationKey(migration_group, migration_id)
if dry_run:
runner.run_migration(migration_key, dry_run=True)
return
try:
if fake:
click.confirm(
"This will mark the migration as completed without actually running it. Your database may be in an invalid state. Are you sure?",
abort=True,
)
runner.run_migration(migration_key, force=force, fake=fake)
except MigrationError as e:
raise click.ClickException(str(e))
click.echo(f"Finished running migration {migration_key}")
def admin(
*,
debug: bool,
log_level: Optional[str],
processes: int,
threads: int,
) -> None:
from snuba import settings
host, port = settings.ADMIN_HOST, settings.ADMIN_PORT
if debug:
if processes > 1 or threads > 1:
raise click.ClickException(
"processes/threads can only be 1 in debug")
from werkzeug.serving import WSGIRequestHandler
from snuba.admin.views import application
setup_logging(log_level)
WSGIRequestHandler.protocol_version = "HTTP/1.1"
application.run(host=host, port=port, threaded=True, debug=debug)
else:
import mywsgi
if log_level:
os.environ["LOG_LEVEL"] = log_level
mywsgi.run(
"snuba.admin.wsgi:application",
f"{host}:{port}",
processes=processes,
threads=threads,
)
def cleanup(
*,
clickhouse_host: str,
clickhouse_port: int,
dry_run: bool,
database: str,
dataset_name: str,
log_level: Optional[str] = None,
) -> None:
"""
Deletes stale partitions for ClickHouse tables
"""
setup_logging(log_level)
from snuba.cleanup import run_cleanup, logger
from snuba.clickhouse.native import ClickhousePool
dataset = get_dataset(dataset_name)
table = enforce_table_writer(dataset).get_schema().get_local_table_name()
clickhouse = ClickhousePool(clickhouse_host, clickhouse_port)
num_dropped = run_cleanup(clickhouse, database, table, dry_run=dry_run)
logger.info("Dropped %s partitions on %s" % (num_dropped, clickhouse_host))
def reverse(
group: str,
migration_id: str,
force: bool,
fake: bool,
dry_run: bool,
log_level: Optional[str] = None,
) -> None:
"""
Reverses a single migration.
--force is required to reverse an already completed migration.
--fake marks a migration as reversed without doing anything.
"""
setup_logging(log_level)
if not dry_run:
check_clickhouse_connections()
runner = Runner()
migration_group = MigrationGroup(group)
migration_key = MigrationKey(migration_group, migration_id)
if dry_run:
runner.reverse_migration(migration_key, dry_run=True)
return
try:
if fake:
click.confirm(
"This will mark the migration as not started without actually reversing it. Your database may be in an invalid state. Are you sure?",
abort=True,
)
runner.reverse_migration(migration_key, force=force, fake=fake)
except MigrationError as e:
raise click.ClickException(str(e))
click.echo(f"Finished reversing migration {migration_key}")
def multistorage_consumer(
storage_names: Sequence[str],
consumer_group: str,
commit_log_topic: str,
max_batch_size: int,
max_batch_time_ms: int,
auto_offset_reset: str,
no_strict_offset_reset: bool,
queued_max_messages_kbytes: int,
queued_min_messages: int,
parallel_collect: bool,
processes: Optional[int],
input_block_size: Optional[int],
output_block_size: Optional[int],
log_level: Optional[str] = None,
dead_letter_topic: Optional[str] = None,
cooperative_rebalancing: bool = False,
) -> None:
DEFAULT_BLOCK_SIZE = int(32 * 1e6)
if processes is not None:
if input_block_size is None:
input_block_size = DEFAULT_BLOCK_SIZE
if output_block_size is None:
output_block_size = DEFAULT_BLOCK_SIZE
setup_logging(log_level)
setup_sentry()
logger.info("Consumer Starting")
storages = {
key: get_writable_storage(key)
for key in (getattr(StorageKey, name.upper())
for name in storage_names)
}
topics = {
storage.get_table_writer().get_stream_loader().get_default_topic_spec(
).topic_name
for storage in storages.values()
}
# XXX: The ``StreamProcessor`` only supports a single topic at this time,
# but is easily modified. The topic routing in the processing strategy is a
# bit trickier (but also shouldn't be too bad.)
topic = Topic(topics.pop())
if topics:
raise ValueError("only one topic is supported")
commit_log: Optional[Topic]
if commit_log_topic:
commit_log = Topic(commit_log_topic)
else:
# XXX: The ``CommitLogConsumer`` also only supports a single topic at this
# time. (It is less easily modified.) This also assumes the commit log
# topic is on the same Kafka cluster as the input topic.
commit_log_topics = {
spec.topic_name
for spec in (storage.get_table_writer().get_stream_loader(
).get_commit_log_topic_spec() for storage in storages.values())
if spec is not None
}
if commit_log_topics:
commit_log = Topic(commit_log_topics.pop())
else:
commit_log = None
if commit_log_topics:
raise ValueError("only one commit log topic is supported")
# XXX: This requires that all storages are associated with the same Kafka
# cluster so that they can be consumed by the same consumer instance.
# Unfortunately, we don't have the concept of independently configurable
# Kafka clusters in settings, only consumer configurations that are
# associated with storages and/or global default configurations. To avoid
# implementing yet another method of configuring Kafka clusters, this just
# piggybacks on the existing configuration method(s), with the assumption
# that most deployments are going to be using the default configuration.
storage_keys = [*storages.keys()]
kafka_topic = (storages[storage_keys[0]].get_table_writer().
get_stream_loader().get_default_topic_spec().topic)
consumer_configuration = build_kafka_consumer_configuration(
kafka_topic,
consumer_group,
auto_offset_reset=auto_offset_reset,
strict_offset_reset=not no_strict_offset_reset,
queued_max_messages_kbytes=queued_max_messages_kbytes,
queued_min_messages=queued_min_messages,
)
if cooperative_rebalancing is True:
consumer_configuration[
"partition.assignment.strategy"] = "cooperative-sticky"
for storage_key in storage_keys[1:]:
if (build_kafka_consumer_configuration(
storages[storage_key].get_table_writer().get_stream_loader().
get_default_topic_spec().topic,
consumer_group,
)["bootstrap.servers"] != consumer_configuration["bootstrap.servers"]):
raise ValueError(
"storages cannot be located on different Kafka clusters")
metrics = MetricsWrapper(
environment.metrics,
"consumer",
tags={
"group": consumer_group,
"storage": "_".join([storage_keys[0].value, "m"]),
},
)
# Collect metrics from librdkafka if we have stats_collection_freq_ms set
# for the consumer group, or use the default.
stats_collection_frequency_ms = get_config(
f"stats_collection_freq_ms_{consumer_group}",
get_config("stats_collection_freq_ms", 0),
)
if stats_collection_frequency_ms and stats_collection_frequency_ms > 0:
def stats_callback(stats_json: str) -> None:
stats = rapidjson.loads(stats_json)
metrics.gauge("librdkafka.total_queue_size",
stats.get("replyq", 0))
consumer_configuration.update({
"statistics.interval.ms": stats_collection_frequency_ms,
"stats_cb": stats_callback,
})
if commit_log is None:
consumer = KafkaConsumer(consumer_configuration)
else:
# XXX: This relies on the assumptions that a.) all storages are
# located on the same Kafka cluster (validated above.)
commit_log_topic_spec = (storages[storage_keys[0]].get_table_writer(
).get_stream_loader().get_commit_log_topic_spec())
assert commit_log_topic_spec is not None
producer = ConfluentKafkaProducer(
build_kafka_producer_configuration(commit_log_topic_spec.topic))
consumer = KafkaConsumerWithCommitLog(
consumer_configuration,
producer=producer,
commit_log_topic=commit_log,
)
dead_letter_producer: Optional[KafkaProducer] = None
dead_letter_queue: Optional[Topic] = None
if dead_letter_topic:
dead_letter_queue = Topic(dead_letter_topic)
dead_letter_producer = KafkaProducer(
build_kafka_producer_configuration(
StreamsTopic(dead_letter_topic)))
configure_metrics(StreamMetricsAdapter(metrics))
processor = StreamProcessor(
consumer,
topic,
MultistorageConsumerProcessingStrategyFactory(
[*storages.values()],
max_batch_size,
max_batch_time_ms / 1000.0,
parallel_collect=parallel_collect,
processes=processes,
input_block_size=input_block_size,
output_block_size=output_block_size,
metrics=metrics,
producer=dead_letter_producer,
topic=dead_letter_queue,
),
)
def handler(signum: int, frame: Any) -> None:
processor.signal_shutdown()
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
if dead_letter_producer:
with closing(dead_letter_producer):
processor.run()
else:
processor.run()
def consumer(
*,
raw_events_topic: Optional[str],
replacements_topic: Optional[str],
commit_log_topic: Optional[str],
consumer_group: str,
bootstrap_server: Sequence[str],
storage_name: str,
max_batch_size: int,
max_batch_time_ms: int,
auto_offset_reset: str,
no_strict_offset_reset: bool,
queued_max_messages_kbytes: int,
queued_min_messages: int,
parallel_collect: bool,
processes: Optional[int],
input_block_size: Optional[int],
output_block_size: Optional[int],
log_level: Optional[str] = None,
profile_path: Optional[str] = None,
cooperative_rebalancing: bool = False,
) -> None:
setup_logging(log_level)
setup_sentry()
logger.info("Consumer Starting")
storage_key = StorageKey(storage_name)
metrics = MetricsWrapper(
environment.metrics,
"consumer",
tags={"group": consumer_group, "storage": storage_key.value},
)
configure_metrics(StreamMetricsAdapter(metrics))
def stats_callback(stats_json: str) -> None:
stats = rapidjson.loads(stats_json)
metrics.gauge("librdkafka.total_queue_size", stats.get("replyq", 0))
consumer_builder = ConsumerBuilder(
storage_key=storage_key,
kafka_params=KafkaParameters(
raw_topic=raw_events_topic,
replacements_topic=replacements_topic,
bootstrap_servers=bootstrap_server,
group_id=consumer_group,
commit_log_topic=commit_log_topic,
auto_offset_reset=auto_offset_reset,
strict_offset_reset=not no_strict_offset_reset,
queued_max_messages_kbytes=queued_max_messages_kbytes,
queued_min_messages=queued_min_messages,
),
processing_params=ProcessingParameters(
processes=processes,
input_block_size=input_block_size,
output_block_size=output_block_size,
),
max_batch_size=max_batch_size,
max_batch_time_ms=max_batch_time_ms,
metrics=metrics,
profile_path=profile_path,
stats_callback=stats_callback,
parallel_collect=parallel_collect,
cooperative_rebalancing=cooperative_rebalancing,
)
consumer = consumer_builder.build_base_consumer()
def handler(signum: int, frame: Any) -> None:
consumer.signal_shutdown()
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
consumer.run()
def subscriptions_scheduler_executor(
*,
dataset_name: str,
entity_names: Sequence[str],
consumer_group: str,
followed_consumer_group: str,
max_concurrent_queries: int,
total_concurrent_queries: int,
auto_offset_reset: str,
no_strict_offset_reset: bool,
schedule_ttl: int,
delay_seconds: Optional[int],
stale_threshold_seconds: Optional[int],
log_level: Optional[str],
# TODO: Temporarily overrides the scheduling mode.
# Required for single tenant since some partitions may be empty.
# To be removed once transactions is no longer semantically partitioned.
scheduling_mode: Optional[str],
) -> None:
"""
Combined subscriptions scheduler and executor. Alternative to the separate scheduler and executor processes.
"""
setup_logging(log_level)
setup_sentry()
metrics = MetricsWrapper(
environment.metrics,
"subscriptions.scheduler_executor",
tags={"dataset": dataset_name},
)
configure_metrics(StreamMetricsAdapter(metrics))
# Just get the result topic configuration from the first entity. Later we
# check they all have the same result topic anyway before building the consumer.
entity_key = EntityKey(entity_names[0])
storage = get_entity(entity_key).get_writable_storage()
assert storage is not None
stream_loader = storage.get_table_writer().get_stream_loader()
result_topic_spec = stream_loader.get_subscription_scheduled_topic_spec()
assert result_topic_spec is not None
producer = KafkaProducer(
build_kafka_producer_configuration(
result_topic_spec.topic,
override_params={"partitioner": "consistent"},
)
)
processor = build_scheduler_executor_consumer(
dataset_name,
entity_names,
consumer_group,
followed_consumer_group,
producer,
auto_offset_reset,
not no_strict_offset_reset,
schedule_ttl,
delay_seconds,
stale_threshold_seconds,
max_concurrent_queries,
total_concurrent_queries,
metrics,
SchedulingWatermarkMode(scheduling_mode)
if scheduling_mode is not None
else None,
)
def handler(signum: int, frame: Any) -> None:
processor.signal_shutdown()
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
with closing(producer), flush_querylog():
processor.run()
def subscriptions(
*,
dataset_name: str,
topic: Optional[str],
partitions: Optional[int],
commit_log_topic: Optional[str],
commit_log_groups: Sequence[str],
consumer_group: str,
auto_offset_reset: str,
bootstrap_servers: Sequence[str],
max_batch_size: int,
max_batch_time_ms: int,
max_query_workers: Optional[int],
schedule_ttl: int,
result_topic: Optional[str],
log_level: Optional[str],
delay_seconds: Optional[int],
) -> None:
"""Evaluates subscribed queries for a dataset."""
setup_logging(log_level)
setup_sentry()
dataset = get_dataset(dataset_name)
storage = dataset.get_default_entity().get_writable_storage()
assert (
storage is not None
), f"Dataset {dataset_name} does not have a writable storage by default."
loader = enforce_table_writer(dataset).get_stream_loader()
commit_log_topic_spec = loader.get_commit_log_topic_spec()
assert commit_log_topic_spec is not None
result_topic_spec = loader.get_subscription_result_topic_spec()
assert result_topic_spec is not None
metrics = MetricsWrapper(
environment.metrics,
"subscriptions",
tags={
"group": consumer_group,
"dataset": dataset_name
},
)
consumer = TickConsumer(
SynchronizedConsumer(
KafkaConsumer(
build_kafka_consumer_configuration(
loader.get_default_topic_spec().topic,
consumer_group,
auto_offset_reset=auto_offset_reset,
bootstrap_servers=bootstrap_servers,
), ),
KafkaConsumer(
build_kafka_consumer_configuration(
commit_log_topic_spec.topic,
f"subscriptions-commit-log-{uuid.uuid1().hex}",
auto_offset_reset="earliest",
bootstrap_servers=bootstrap_servers,
), ),
(Topic(commit_log_topic) if commit_log_topic is not None else
Topic(commit_log_topic_spec.topic_name)),
set(commit_log_groups),
),
time_shift=(timedelta(seconds=delay_seconds *
-1) if delay_seconds is not None else None),
)
producer = ProducerEncodingWrapper(
KafkaProducer(
build_kafka_producer_configuration(
loader.get_default_topic_spec().topic,
bootstrap_servers=bootstrap_servers,
override_params={
"partitioner": "consistent",
"message.max.bytes": 50000000, # 50MB, default is 1MB
},
)),
SubscriptionTaskResultEncoder(),
)
executor = ThreadPoolExecutor(max_workers=max_query_workers)
logger.debug("Starting %r with %s workers...", executor,
getattr(executor, "_max_workers", 0))
metrics.gauge("executor.workers", getattr(executor, "_max_workers", 0))
with closing(consumer), executor, closing(producer):
from arroyo import configure_metrics
configure_metrics(StreamMetricsAdapter(metrics))
batching_consumer = StreamProcessor(
consumer,
(Topic(topic) if topic is not None else Topic(
loader.get_default_topic_spec().topic_name)),
BatchProcessingStrategyFactory(
SubscriptionWorker(
dataset,
executor,
{
index: SubscriptionScheduler(
RedisSubscriptionDataStore(redis_client, dataset,
PartitionId(index)),
PartitionId(index),
cache_ttl=timedelta(seconds=schedule_ttl),
metrics=metrics,
)
for index in
range(partitions if partitions is not None else loader.
get_default_topic_spec().partitions_number)
},
producer,
Topic(result_topic) if result_topic is not None else Topic(
result_topic_spec.topic_name),
metrics,
),
max_batch_size,
max_batch_time_ms,
),
)
def handler(signum: int, frame: Optional[Any]) -> None:
batching_consumer.signal_shutdown()
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
batching_consumer.run()
def replacer(
*,
replacements_topic: Optional[str],
consumer_group: str,
bootstrap_server: Sequence[str],
storage_name: str,
max_batch_size: int,
max_batch_time_ms: int,
auto_offset_reset: str,
queued_max_messages_kbytes: int,
queued_min_messages: int,
log_level: Optional[str] = None,
) -> None:
from snuba.replacer import ReplacerWorker
from snuba.utils.streams import Topic
from snuba.utils.streams.backends.kafka import (
KafkaConsumer,
TransportError,
build_kafka_consumer_configuration,
)
from snuba.utils.streams.processing import StreamProcessor
from snuba.utils.streams.processing.strategies.batching import (
BatchProcessingStrategyFactory, )
setup_logging(log_level)
setup_sentry()
storage_key = StorageKey(storage_name)
storage = get_writable_storage(storage_key)
metrics_tags = {"group": consumer_group, "storage": storage_name}
stream_loader = storage.get_table_writer().get_stream_loader()
default_replacement_topic_spec = stream_loader.get_replacement_topic_spec()
assert (
default_replacement_topic_spec is not None
), f"Storage {storage.get_storage_key().value} does not have a replacement topic."
replacements_topic = replacements_topic or default_replacement_topic_spec.topic_name
metrics = MetricsWrapper(
environment.metrics,
"replacer",
tags=metrics_tags,
)
replacer = StreamProcessor(
KafkaConsumer(
build_kafka_consumer_configuration(
bootstrap_servers=bootstrap_server,
group_id=consumer_group,
auto_offset_reset=auto_offset_reset,
queued_max_messages_kbytes=queued_max_messages_kbytes,
queued_min_messages=queued_min_messages,
), ),
Topic(replacements_topic),
BatchProcessingStrategyFactory(
worker=ReplacerWorker(storage, metrics=metrics),
max_batch_size=max_batch_size,
max_batch_time=max_batch_time_ms,
metrics=metrics,
),
metrics=metrics,
recoverable_errors=[TransportError],
)
def handler(signum: int, frame: Any) -> None:
replacer.signal_shutdown()
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
replacer.run()
def confirm_load(
*,
control_topic: Optional[str],
bootstrap_server: Sequence[str],
storage_name: str,
source: str,
log_level: Optional[str] = None,
) -> None:
"""
Confirms the snapshot has been loaded by sending the
snapshot-loaded message on the control topic.
"""
setup_logging(log_level)
setup_sentry()
logger = logging.getLogger("snuba.loaded-snapshot")
logger.info(
"Sending load completion message for storage %s, from source %s",
storage_name,
source,
)
storage_key = StorageKey(storage_name)
storage = get_cdc_storage(storage_key)
stream_loader = storage.get_table_writer().get_stream_loader()
control_topic = control_topic or storage.get_default_control_topic()
snapshot_source = PostgresSnapshot.load(
product=settings.SNAPSHOT_LOAD_PRODUCT, path=source,
)
descriptor = snapshot_source.get_descriptor()
producer = Producer(
build_kafka_producer_configuration(
stream_loader.get_default_topic_spec().topic,
bootstrap_servers=bootstrap_server,
override_params={
"partitioner": "consistent",
"message.max.bytes": 50000000, # 50MB, default is 1MB
},
)
)
msg = SnapshotLoaded(
id=descriptor.id,
transaction_info=TransactionData(
xmin=descriptor.xmin, xmax=descriptor.xmax, xip_list=descriptor.xip_list,
),
)
json_string = json.dumps(msg.to_dict())
def delivery_callback(error: KafkaError, message: Message) -> None:
if error is not None:
raise error
else:
logger.info("Message sent %r", message.value())
producer.produce(
control_topic, value=json_string, on_delivery=delivery_callback,
)
producer.flush()
def bulk_load(
*,
storage_name: str,
dest_table: Optional[str],
source: str,
ignore_existing_data: bool,
pre_processed: bool,
show_progress: bool,
log_level: Optional[str] = None,
) -> None:
setup_logging(log_level)
setup_sentry()
logger = logging.getLogger("snuba.load-snapshot")
logger.info(
"Start bulk load process for storage %s, from source %s", storage_name, source
)
storage = get_cdc_storage(StorageKey(storage_name))
table_writer = storage.get_table_writer()
# TODO: Have a more abstract way to load sources if/when we support more than one.
snapshot_source = PostgresSnapshot.load(
product=settings.SNAPSHOT_LOAD_PRODUCT, path=source,
)
loader = table_writer.get_bulk_loader(
snapshot_source,
storage.get_postgres_table(),
storage.get_row_processor(),
dest_table,
)
# TODO: see whether we need to pass options to the writer
def progress_callback(bar: progressbar.ProgressBar, progress: int) -> None:
bar.update(progress)
if show_progress:
progress = progressbar.ProgressBar(
max_value=snapshot_source.get_table_file_size(storage.get_postgres_table())
)
progress_func: Optional[ProgressCallback] = partial(progress_callback, progress)
else:
progress_func = None
table_descriptor = snapshot_source.get_descriptor().get_table(
storage.get_postgres_table()
)
if pre_processed:
writer = table_writer.get_bulk_writer(
metrics=environment.metrics,
encoding="gzip" if table_descriptor.zip else None,
column_names=[c.name for c in table_descriptor.columns or []],
table_name=dest_table,
)
loader.load_preprocessed(
writer, ignore_existing_data, progress_callback=progress_func
)
else:
buffer_writer = BufferedWriterWrapper(
table_writer.get_batch_writer(
environment.metrics,
table_name=dest_table,
chunk_size=settings.BULK_CLICKHOUSE_BUFFER,
),
settings.BULK_CLICKHOUSE_BUFFER,
JSONRowEncoder(),
)
loader.load(
buffer_writer, ignore_existing_data, progress_callback=progress_func
)
def consumer(
*,
raw_events_topic: Optional[str],
replacements_topic: Optional[str],
commit_log_topic: Optional[str],
control_topic: Optional[str],
consumer_group: str,
bootstrap_server: Sequence[str],
dataset_name: Optional[str],
storage_name: str,
max_batch_size: int,
max_batch_time_ms: int,
auto_offset_reset: str,
queued_max_messages_kbytes: int,
queued_min_messages: int,
stateful_consumer: bool,
rapidjson_deserialize: bool,
rapidjson_serialize: bool,
log_level: Optional[str] = None,
) -> None:
if not bootstrap_server:
if dataset_name:
bootstrap_server = settings.DEFAULT_DATASET_BROKERS.get(
dataset_name,
settings.DEFAULT_BROKERS,
)
else:
bootstrap_server = settings.DEFAULT_STORAGE_BROKERS.get(
storage_name,
settings.DEFAULT_BROKERS,
)
setup_logging(log_level)
setup_sentry()
# TODO: Remove this once dataset_name is no longer being passed
if dataset_name:
dataset_writable_storage = get_dataset(
dataset_name).get_writable_storage()
if not dataset_writable_storage:
raise click.ClickException(
f"Dataset {dataset_name} has no writable storage")
storage_name = {v: k
for k, v in WRITABLE_STORAGES.items()
}[dataset_writable_storage]
consumer_builder = ConsumerBuilder(
storage_name=storage_name,
raw_topic=raw_events_topic,
replacements_topic=replacements_topic,
max_batch_size=max_batch_size,
max_batch_time_ms=max_batch_time_ms,
bootstrap_servers=bootstrap_server,
group_id=consumer_group,
commit_log_topic=commit_log_topic,
auto_offset_reset=auto_offset_reset,
queued_max_messages_kbytes=queued_max_messages_kbytes,
queued_min_messages=queued_min_messages,
rapidjson_deserialize=rapidjson_deserialize,
rapidjson_serialize=rapidjson_serialize,
)
if stateful_consumer:
storage = get_cdc_storage(storage_name)
assert storage is not None, "Only CDC storages have a control topic thus are supported."
context = ConsumerStateMachine(
consumer_builder=consumer_builder,
topic=control_topic or storage.get_default_control_topic(),
group_id=consumer_group,
storage=storage,
)
def handler(signum, frame) -> None:
context.signal_shutdown()
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
context.run()
else:
consumer = consumer_builder.build_base_consumer()
def handler(signum, frame) -> None:
consumer.signal_shutdown()
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
consumer.run()
def confirm_load(
*,
control_topic: Optional[str],
bootstrap_server: Sequence[str],
dataset_name: str,
source: Optional[str],
log_level: Optional[str] = None,
) -> None:
"""
Confirms the snapshot has been loaded by sending the
snapshot-loaded message on the control topic.
"""
setup_logging(log_level)
setup_sentry()
logger = logging.getLogger("snuba.loaded-snapshot")
logger.info(
"Sending load completion message for dataset %s, from source %s",
dataset_name,
source,
)
dataset = get_dataset(dataset_name)
storage = dataset.get_writable_storage()
assert isinstance(
storage, CdcStorage
), "Only CDC storages have a control topic thus are supported."
control_topic = control_topic or storage.get_default_control_topic()
snapshot_source = PostgresSnapshot.load(
product=settings.SNAPSHOT_LOAD_PRODUCT,
path=source,
)
descriptor = snapshot_source.get_descriptor()
if not bootstrap_server:
bootstrap_server = settings.DEFAULT_DATASET_BROKERS.get(
dataset,
settings.DEFAULT_BROKERS,
)
producer = Producer({
"bootstrap.servers": ",".join(bootstrap_server),
"partitioner": "consistent",
"message.max.bytes": 50000000, # 50MB, default is 1MB
})
msg = SnapshotLoaded(
id=descriptor.id,
transaction_info=TransactionData(
xmin=descriptor.xmin,
xmax=descriptor.xmax,
xip_list=descriptor.xip_list,
),
)
json_string = json.dumps(msg.to_dict())
def delivery_callback(error, message) -> None:
if error is not None:
raise error
else:
logger.info("Message sent %r", message.value())
producer.produce(
control_topic,
value=json_string,
on_delivery=delivery_callback,
)
producer.flush()
def replacer(
*,
replacements_topic: Optional[str],
consumer_group: str,
bootstrap_server: Sequence[str],
dataset_name: Optional[str],
storage_name: str,
max_batch_size: int,
max_batch_time_ms: int,
auto_offset_reset: str,
queued_max_messages_kbytes: int,
queued_min_messages: int,
log_level: Optional[str] = None,
) -> None:
from snuba.clickhouse.native import ClickhousePool
from snuba.replacer import ReplacerWorker
from snuba.utils.codecs import PassthroughCodec
from snuba.utils.streams.batching import BatchingConsumer
from snuba.utils.streams.kafka import (
KafkaConsumer,
KafkaPayload,
TransportError,
build_kafka_consumer_configuration,
)
from snuba.utils.streams.types import Topic
setup_logging(log_level)
setup_sentry()
storage = get_writable_storage(storage_name)
metrics_tags = {"group": consumer_group, "storage": storage_name}
# If dataset_name is provided, use the writable storage from that dataset.
# This can be removed once we are passing storage_name instead of
# dataset_name everywhere
if dataset_name:
dataset = get_dataset(dataset_name)
storage = dataset.get_writable_storage()
metrics_tags = {"group": consumer_group, "dataset": dataset_name}
stream_loader = storage.get_table_writer().get_stream_loader()
default_replacement_topic_spec = stream_loader.get_replacement_topic_spec()
assert (default_replacement_topic_spec is not None
), f"Storage {type(storage)} does not have a replacement topic."
replacements_topic = replacements_topic or default_replacement_topic_spec.topic_name
metrics = MetricsWrapper(
environment.metrics,
"replacer",
tags=metrics_tags,
)
client_settings = {
# Replacing existing rows requires reconstructing the entire tuple for each
# event (via a SELECT), which is a Hard Thing (TM) for columnstores to do. With
# the default settings it's common for ClickHouse to go over the default max_memory_usage
# of 10GB per query. Lowering the max_block_size reduces memory usage, and increasing the
# max_memory_usage gives the query more breathing room.
"max_block_size": settings.REPLACER_MAX_BLOCK_SIZE,
"max_memory_usage": settings.REPLACER_MAX_MEMORY_USAGE,
# Don't use up production cache for the count() queries.
"use_uncompressed_cache": 0,
}
clickhouse = ClickhousePool(
settings.CLICKHOUSE_HOST,
settings.CLICKHOUSE_PORT,
client_settings=client_settings,
)
codec: PassthroughCodec[KafkaPayload] = PassthroughCodec()
replacer = BatchingConsumer(
KafkaConsumer(
build_kafka_consumer_configuration(
bootstrap_servers=bootstrap_server,
group_id=consumer_group,
auto_offset_reset=auto_offset_reset,
queued_max_messages_kbytes=queued_max_messages_kbytes,
queued_min_messages=queued_min_messages,
),
codec=codec,
),
Topic(replacements_topic),
worker=ReplacerWorker(clickhouse, storage, metrics=metrics),
max_batch_size=max_batch_size,
max_batch_time=max_batch_time_ms,
metrics=metrics,
recoverable_errors=[TransportError],
)
def handler(signum, frame) -> None:
replacer.signal_shutdown()
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
replacer.run()
def bootstrap(
*,
bootstrap_server: Sequence[str],
kafka: bool,
migrate: bool,
force: bool,
log_level: Optional[str] = None,
) -> None:
"""
Warning: Not intended to be used in production yet.
"""
if not force:
raise click.ClickException("Must use --force to run")
setup_logging(log_level)
logger = logging.getLogger("snuba.bootstrap")
import time
if kafka:
logger.debug("Using Kafka with %r", bootstrap_server)
from confluent_kafka.admin import AdminClient, NewTopic
attempts = 0
while True:
try:
logger.debug("Attempting to connect to Kafka (attempt %d)",
attempts)
client = AdminClient({
"bootstrap.servers":
",".join(bootstrap_server),
"socket.timeout.ms":
1000,
})
client.list_topics(timeout=1)
break
except Exception as e:
logger.error("Connection to Kafka failed (attempt %d)",
attempts,
exc_info=e)
attempts += 1
if attempts == 60:
raise
time.sleep(1)
topics = {}
for name in ACTIVE_DATASET_NAMES:
dataset = get_dataset(name)
for entity in dataset.get_all_entities():
writable_storage = entity.get_writable_storage()
if writable_storage:
table_writer = writable_storage.get_table_writer()
stream_loader = table_writer.get_stream_loader()
for topic_spec in stream_loader.get_all_topic_specs():
if topic_spec.topic_name in topics:
continue
logger.debug("Adding topic %s to creation list",
topic_spec.topic_name)
topics[topic_spec.topic_name] = NewTopic(
topic_spec.topic_name,
num_partitions=topic_spec.partitions_number,
replication_factor=topic_spec.replication_factor,
)
logger.debug("Initiating topic creation")
for topic, future in client.create_topics(list(topics.values()),
operation_timeout=1).items():
try:
future.result()
logger.info("Topic %s created", topic)
except Exception as e:
logger.error("Failed to create topic %s", topic, exc_info=e)
if migrate:
check_clickhouse_connections()
Runner().run_all(force=True)
def multistorage_consumer(
storage_names: Sequence[str],
consumer_group: str,
max_batch_size: int,
max_batch_time_ms: int,
auto_offset_reset: str,
queued_max_messages_kbytes: int,
queued_min_messages: int,
processes: Optional[int],
input_block_size: Optional[int],
output_block_size: Optional[int],
log_level: Optional[str] = None,
) -> None:
DEFAULT_BLOCK_SIZE = int(32 * 1e6)
if processes is not None:
if input_block_size is None:
input_block_size = DEFAULT_BLOCK_SIZE
if output_block_size is None:
output_block_size = DEFAULT_BLOCK_SIZE
setup_logging(log_level)
setup_sentry()
storages = {
key: get_writable_storage(key)
for key in (getattr(StorageKey, name.upper())
for name in storage_names)
}
topics = {
storage.get_table_writer().get_stream_loader().get_default_topic_spec(
).topic_name
for storage in storages.values()
}
# XXX: The ``StreamProcessor`` only supports a single topic at this time,
# but is easily modified. The topic routing in the processing strategy is a
# bit trickier (but also shouldn't be too bad.)
topic = Topic(topics.pop())
if topics:
raise ValueError("only one topic is supported")
# XXX: The ``CommitLogConsumer`` also only supports a single topic at this
# time. (It is less easily modified.) This also assumes the commit log
# topic is on the same Kafka cluster as the input topic.
commit_log_topics = {
spec.topic_name
for spec in (storage.get_table_writer().get_stream_loader(
).get_commit_log_topic_spec() for storage in storages.values())
if spec is not None
}
commit_log_topic: Optional[Topic]
if commit_log_topics:
commit_log_topic = Topic(commit_log_topics.pop())
else:
commit_log_topic = None
if commit_log_topics:
raise ValueError("only one commit log topic is supported")
# XXX: This requires that all storages are associated with the same Kafka
# cluster so that they can be consumed by the same consumer instance.
# Unfortunately, we don't have the concept of independently configurable
# Kafka clusters in settings, only consumer configurations that are
# associated with storages and/or global default configurations. To avoid
# implementing yet another method of configuring Kafka clusters, this just
# piggybacks on the existing configuration method(s), with the assumption
# that most deployments are going to be using the default configuration.
storage_keys = [*storages.keys()]
kafka_topic = (storages[storage_keys[0]].get_table_writer().
get_stream_loader().get_default_topic_spec().topic)
consumer_configuration = build_kafka_consumer_configuration(
kafka_topic,
consumer_group,
auto_offset_reset=auto_offset_reset,
queued_max_messages_kbytes=queued_max_messages_kbytes,
queued_min_messages=queued_min_messages,
)
for storage_key in storage_keys[1:]:
if (build_kafka_consumer_configuration(
storages[storage_key].get_table_writer().get_stream_loader().
get_default_topic_spec().topic,
consumer_group,
)["bootstrap.servers"] != consumer_configuration["bootstrap.servers"]):
raise ValueError(
"storages cannot be located on different Kafka clusters")
if commit_log_topic is None:
consumer = KafkaConsumer(consumer_configuration)
else:
# XXX: This relies on the assumptions that a.) all storages are
# located on the same Kafka cluster (validated above.)
commit_log_topic_spec = (storages[storage_keys[0]].get_table_writer(
).get_stream_loader().get_commit_log_topic_spec())
assert commit_log_topic_spec is not None
producer = ConfluentKafkaProducer(
build_kafka_producer_configuration(commit_log_topic_spec.topic))
consumer = KafkaConsumerWithCommitLog(
consumer_configuration,
producer=producer,
commit_log_topic=commit_log_topic,
)
metrics = MetricsWrapper(environment.metrics, "consumer")
configure_metrics(StreamMetricsAdapter(metrics))
processor = StreamProcessor(
consumer,
topic,
MultistorageConsumerProcessingStrategyFactory(
[*storages.values()],
max_batch_size,
max_batch_time_ms / 1000.0,
processes=processes,
input_block_size=input_block_size,
output_block_size=output_block_size,
metrics=metrics,
),
)
def handler(signum: int, frame: Any) -> None:
processor.signal_shutdown()
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
processor.run()
def subscriptions(
*,
dataset_name: str,
topic: Optional[str],
partitions: Optional[int],
commit_log_topic: Optional[str],
commit_log_groups: Sequence[str],
consumer_group: str,
auto_offset_reset: str,
bootstrap_servers: Sequence[str],
max_batch_size: int,
max_batch_time_ms: int,
schedule_ttl: int,
result_topic: Optional[str],
log_level: Optional[str],
) -> None:
"""Evaluates subscribed queries for a dataset."""
assert result_topic is not None
setup_logging(log_level)
setup_sentry()
dataset = get_dataset(dataset_name)
if not bootstrap_servers:
bootstrap_servers = settings.DEFAULT_DATASET_BROKERS.get(
dataset_name, settings.DEFAULT_BROKERS
)
loader = enforce_table_writer(dataset).get_stream_loader()
consumer = TickConsumer(
SynchronizedConsumer(
KafkaConsumer(
build_kafka_consumer_configuration(
bootstrap_servers,
consumer_group,
auto_offset_reset=auto_offset_reset,
),
PassthroughCodec(),
),
KafkaConsumer(
build_kafka_consumer_configuration(
bootstrap_servers,
f"subscriptions-commit-log-{uuid.uuid1().hex}",
auto_offset_reset="earliest",
),
CommitCodec(),
),
(
Topic(commit_log_topic)
if commit_log_topic is not None
else Topic(loader.get_commit_log_topic_spec().topic_name)
),
set(commit_log_groups),
)
)
producer = KafkaProducer(
{
"bootstrap.servers": ",".join(bootstrap_servers),
"partitioner": "consistent",
"message.max.bytes": 50000000, # 50MB, default is 1MB
},
SubscriptionResultCodec(),
)
with closing(consumer), closing(producer):
batching_consumer = BatchingConsumer(
consumer,
(
Topic(topic)
if topic is not None
else Topic(loader.get_default_topic_spec().topic_name)
),
SubscriptionWorker(
SubscriptionExecutor(
dataset,
ThreadPoolExecutor(
max_workers=settings.SUBSCRIPTIONS_MAX_CONCURRENT_QUERIES
),
),
{
index: SubscriptionScheduler(
RedisSubscriptionDataStore(
redis_client, dataset, PartitionId(index)
),
PartitionId(index),
cache_ttl=timedelta(seconds=schedule_ttl),
)
for index in range(
partitions
if partitions is not None
else loader.get_default_topic_spec().partitions_number
)
},
producer,
Topic(result_topic),
),
max_batch_size,
max_batch_time_ms,
create_metrics(
"snuba.subscriptions",
tags={"group": consumer_group, "dataset": dataset_name},
),
)
def handler(signum, frame) -> None:
batching_consumer.signal_shutdown()
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
batching_consumer.run()
