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1
Kafka Performances
Brice Leporini brice@confluent.io
Florent Ramière florent@confluent.io
2
3
● Founded in 2014
● Raised $205M from Benchmark, Index, Sequoia
● +950 Employees
● Offices in 20 countries
● Hundreds of enterprise subscription customers
4
Silos explained by Data Gravity concept
As data accumulates (builds mass) there is a greater
likelihood that additional services and applications will
be attracted to this data.
This is the same effect gravity has on objects around a
planet. As the mass or density increases, so does the
strength of gravitational pull.
5
6
7
8
9
10
Store & ETL ProcessPublish & Subscribe
11
• Scalability
• Retention
• Durability
• Replication
• Security
• Resiliency
• Throughput
• Ordering
• Exactly Once Semantic
• Transaction
• Idempotency
• Immutability
• …
12
And many more...
13
What means Kafka
performance?
Depends on your context!
Do you need throughput?
Do you need low latency ?
Do you need to optimize storage
/ bandwidth?
14
What is your SLA ?
1515
No SLA ?
Stop ! And gather them right away!
1616
But first ...
1717
Kafka in bash !
https://github.com/framiere/kafka-in-bash
1818
On the producer side
19
P
Broker 1 Broker 2 Broker 3
Topic1
partition1
Leader Follower
Topic1
partition1
Topic1
partition1
Producer Properties
acks=0
Producer Guarantees
20
P
Broker 1 Broker 2 Broker 3
Topic1
partition1
Leader Follower
Topic1
partition1
Topic1
partition1
ack
Producer Properties
acks=1
Producer Guarantees
21
Producer Guarantees
P
Broker 1 Broker 2 Broker 3
Topic1
partition1
Leader Follower
Topic1
partition1
Topic1
partition1
Producer Properties
acks=all
min.insync.replica=2
ack
22
batch.size
Default: 16KB
The producer will attempt to
batch records together into
fewer requests whenever
multiple records are being
sent to the same partition.
2323
but... (yes there is a but)
24
linger.ms
Default: O ms
This parameter may overrule
the batch size, especially with
the default value...
25
max.in.flight.requests
.per.connection
Default: 5
The maximum number of
unacknowledged requests
the client will send on a
single connection before
blocking.
2626
but... (yes there is a but)
27
max.in.flight.requests
.per.connection
Default: 5
Sending parallel requests to
the broker avoids ordering
guarantee.
2828
but... (yes there is a but)
29
enable.idempotence
Default: false Enabling idempotence avoids
any duplicate message and
restore the ordering
guaranteeretries
Default: 2147483647
30
compression.type
Default value: none Supported compression
algorithms are gzip, snappy,
lz4, or zstd.
“Compression is of full batches of data,
so the efficacy of batching will also
impact the compression ratio“
3131
On the consumer side
32
33
commit
Manually committing
aggressively…
Add a huge workload on
Apache Kafka
34
Consuming From Kafka - Single Consumer
C
35
Consuming From Kafka - Grouped Consumers
C C
C C
3636
On the broker side
37
38
3939
Never forget that
“Premature optimization is the root of
all evil (or at least most of it) in
programming.”
Donald E. Knuth
4040
Monitoring producers
41
Detect application
code performance
smells
Monitor Producer time spent in user
processes
io-ratio: fraction of time the I/0
thread spent doing I/0
io-wait-ratio: fraction of time the
I/0 thread spent waiting
User processing time =
1 - io-ratio - io-wait-ratio
If user processing time is high, the single Producer I/0
thread may be busy.
Check Producer code, including the callback which is
invoked when messages have been acknowledged.
42
Check your tuning
efficiency
batch-size-avg
compression-rate-avg
Small batch-size-avg : linger.ms too low?
Poor compression rate: batch.size too small?
linger.ms too low?
43
Check broker
response time
request-latency-avg
A high request latency on the producer side may
be the first clue of a deeper investigation!
44
Check the client
efficiency
buffer-available-bytes
buffer-total-bytes
waiting-threads
A high buffer usage may be a clue that your code
is producing at a very high pace. Consider the
following options:
● Running more application instances,
beware of ordering concerns
● Digging deeper: network → broker → disks
● acks=1 if compliant with the business
4545
Monitoring consumers
46
Infer consumer
performance
records-lag-max
records-lag
The maximum lag in terms of number of records
for any partition in this window.
An increasing value over time is your best
indication that the consumer group is not
keeping up with the producers.
4747
Monitoring brokers
4848
Monitoring leaders
and partitions
LeaderCount
PartitionCount
These gauges are critical to monitor
performance.
Give clues about how balanced is the load across
brokers and may explain if one broker seems to
face more load than another.
4949
I/O and Network
RequestHandlerAvgIdlePercent
I/O metric
NetworkProcessorAvgIdlePercent
Network metric
Values are between 0 and 1:
● 0 indicates all resources are used
● 1 indicates all resources are available
Values below 0.4 should trig alerts and raise
questions:
● Over usage?
● Infrastructure issue?
● Capacity planning to update?
● Thread pool sizing?
50
Monitoring Requests
on the broker
RequestQueueTimeMs : Time the request waits in
the request queue
LocalTimeMs: Time the request is processed at the
leader
RemoteTimeMs: Time the request waits for the
follower. This is non-zero for produce requests when
acks=all
ResponseQueueTimeMs : Time the request waits in
the response queue
ResponseSendTimeMs : Time to send the response
51
Disk usage > 60%
CPU usage > 60%
Network IO usage > 60%
File handle usage > 60%
⇒ Warning!
Monitoring the
broker host OS
5252
Hosting
53
Zookeeper
Critical component but not facing a huge traffic nor holding a lot of data.
Sensitive to latency
⇒ Fast drive (SSD)
⇒ Single digit network latency between nodes
In VMs, provision dedicated resources.
54
Broker
Brokers are designed to take benefit of the Kernel Page Cache
⇒ Keep half of the RAM free to allow Kernel to leverage it
Avoid sharing disks with other applications or brokers.
User several disks to maximize throughput:
● Through a RAID controller, except RAID5/6
● With multiple mount points
Avoid hosting brokers and Zookeeper on the same host as their running profiles are
completely different.
5555
Under a profiler
https://github.com/framiere/a-kafka-performance-story
5656
Optimizing Your Kafka Deployment
Throughput, Latency, Durability, and Availability
https://www.confluent.io/white-paper/optimizing-your-apache-kafka-deployment/
57
THANK YOU
@framiere - florent@confluent.io
@blep - brice@confluent.io
cnfl.io/blog
58

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Perfug 20-11-2019 - Kafka Performances