Kafka vs RedPanda Benchmark (also Tarantool and Clickhouse as queue)

Using default settings from their docker-compose example, today we’re gonna benchmark one of popular MQ/PubSub software. I never used MQ extensively before (only NATS, Google PubSub, ActiveMQ, and Amazon SQS), usually just using standard database that stores event is sufficient (the consumer using pull, tailing from last primary key counter, and if need to fan-out just use multiple goroutine and multiple channel), because my projects never been a latency sensitive applications.

1. the benchmark has locking (atomic counters, sync.Map, etc), so consumer might not utilize whole CPU cores.
2. confluent’s kafka docker always error when starting because /var/lib/kafka/data not writable, so I bind on /var/lib/kafka instead. Clickhouse also always failed to start when bind to /var/lib/clickhouse/data, so I don’t bind volume for Clickhouse.
3. RedPanda failed to start when fs.aio-max-nr even when it’s already ~1 million (originally only 64K), so I set it to 4194304

Benchmarking 1000 goroutines publishing 2000 messages each, with 100 goroutines consuming in parallel.

These benchmark using default settings that exists in the docker examples I found, except SMP (I set it to the same amount of cores in the server that used to benchmark to make it fair with Kafka that uses JVM that by default can utilize all cores — apparently this has insignificant impact). Current conclusion is, RedPanda way faster than Kafka, in terms of publishing speed (around ~1μs per message, 477K-837K msg/s) and consuming latency (432ms to 2.7s per message), while Kafka (around ~3μs per message, 301K-313K msg/s) and 8.5s to 12s per message. The RAM statistics tho, RedPanda uses 12GB for each node (10% of server’s RAM), while Kafka only uses 355MB, 375MB, 788MB for nodes, and 120MB for zookeeper. The repo to reproduce this benchmark is here on 2021mq directory.

Btw if you’re looking for Kafka/RedPanda GUI, try KOwl, this way more beautiful than ActiveMQ default Web UI.

Bonus rounds, using one of the fastest OLTP database: Tarantool and one of the fastest OLAP database: Clickhouse as Queue, by laveraging sequence (auto increment) or internal function to generate a sequence, the difference is there’s only one consumer group (have to manually fan out using goroutine), no json encode and decode since it’s structured database:

The result recap table (ms = millisecond, us = microsecond, ns = nanosecond):

Conclusion: Tarantool probably the only single node database that can compete with Kafka for queue use case (we can have multi-master replica but not recommended, it’s better to use master-slave config where slave used as failover), for other database especially RDBMS that persist to disk pretty sure can only do ~50K tps, Clickhouse can be multi-master, and last time i check, it can do ~600K inserts per seconds (while this time it’s around 1M inserts per seconds), I simulate the atomic counter on Clickhouse using TimeStamp64Milli, the query limited to 100 queries per second but it’s quite good enough for pub-sub use case. The benefit of using database as MQ/PubSub is that you can do a very flexible query (SQL support), mostly better tooling (especially Clickhouse), or update the record for new consumer, but the cons is that you must fan-out, track the ack/retries and the read offset of the workers yourself.

Originally published at http://kokizzu.blogspot.com.