Kafka worst practices - don't try those at home

1. When Bad Things Happen to
Good Kafka Clusters
True stories that actually happened to production Kafka clusters
As told by
Gwen Shapira, System Architect
@gwenshap 1

2. Disclaimer
I am talking about other people’s systems
Not yours.
I am sure you had perfectly good reasons to configure your system the
way you did.
This is not personal criticism
Just some stories and few lessons we learned the hard way
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3. POCs are super easy
Its time to go production
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4. We keep our data in
/tmp/logs
What can possible go
wrong?
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5. Replication-factor of 3 is way too much
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6. __consumer_offsets topic?
Never heard of it, so its probably
ok to delete.
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7. 7

8. What’s wrong with running Kafka 0.7?
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9. Remember that time when…
We accidentally lost all our data?
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10. We added new partitions…
And immediately ran out of memory
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11. We wanted to lookup records by time
The smaller the segments, the more accurate
the lookups
So we created 10k segments.
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12. We need REALLY LARGE messages
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13. We just serialize JSON
and throw it into a topic.
It’s easy.
The consumers will figure something out.
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14. Log4J is a great way to
reliably send data to Kafka
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15. Keep your Kafka safe!
“When it absolutely, positively has to be there:
Reliability guarantees in Apache Kafka”
Wednesday, 11:20am, Room 3D
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16. Thank you
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Visit Confluent in booth #929
Books, Kafka t-shirts & stickers, and more…
Gwen Shapira | gwen@confluent.io | @gwenshap