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Status

Current state:  [One of "Under Discussion", "Accepted", "Rejected"] Draft

Discussion thread: here [Change the link from the KIP proposal email archive to your own email thread]

JIRA: here [Change the link from KAFKA-1 to your own ticket]7722

Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).

Motivation

Describe the problems you are trying to solve.

Public Interfaces

Briefly list any new interfaces that will be introduced as part of this proposal or any existing interfaces that will be removed or changed. The purpose of this section is to concisely call out the public contract that will come along with this feature.

A public interface is any change to the following:

• Binary log format

• The network protocol and api behavior

• Any class in the public packages under clientsConfiguration, especially client configuration

  • org/apache/kafka/common/serialization

  • org/apache/kafka/common

  • org/apache/kafka/common/errors

  • org/apache/kafka/clients/producer

  • org/apache/kafka/clients/consumer (eventually, once stable)

• Monitoring

• Command line tools and arguments

• Anything else that will likely break existing users in some way when they upgrade

Proposed Changes

Describe the new thing you want to do in appropriate detail. This may be fairly extensive and have large subsections of its own. Or it may be a few sentences. Use judgement based on the scope of the change.

Compatibility, Deprecation, and Migration Plan

• What impact (if any) will there be on existing users?
• If we are changing behavior how will we phase out the older behavior?
• If we need special migration tools, describe them here.
• When will we remove the existing behavior?

Rejected Alternatives

The ProducerPerformance tool is the standard for benchmarking Kafka producer send performance. The tool uses sampling and Java’s System.currentTimeMillis() to record latencies and provide percentiles at the end of each run. This method works well in most cases but results in a lack of precision.

This is a problem in low-latency environments in which sending a message takes at most a couple milliseconds. For example, if a user wants to run a benchmark test comparing two different Kafka cluster setups, it is hard to compare results if low latency values are observed.

Example Scenario:

Benchmark #1: average latency = 2 ms

Benchmark #2: average latency = 3 ms

Since System.currentTimeMillis() gives values with millisecond precision:

• A result of 2 ms can have a true value between [1.5, 2.5) before rounding
• A result of 3 ms can have a true value between [2.5, 3.5) before rounding

If we want to calculate the increase in latency observed, then we have a big spread of either:

• (1) Smallest spread: 2.5 - (2.5) or ~0, resulting in approximately ~0% increase in latency
• (2) Largest spread: (3.5) - 1.5 or ~2, resulting in approximately 2/2 or ~100% increase in latency

This means we cannot effectively compare these two benchmark results as the latency in #2 is anywhere from 0 to 100% higher than in #1, which is a big spread and results in very different implications.

To achieve a higher precision to handle low-latency environments better, we can use Java’s System.nanoTime().

Proposed Changes

This KIP proposes using nanoTime() to record latencies to provide higher precision.

Latency will be recorded in microseconds by truncating the result of calling nanoTime() by 3 digits.

long startTimeUs = System.nanoTime() / 1000;     // value in microseconds
// Send message…
// Message sent and callback called…
long latency = System.nanoTime() / 1000 - startTimeUs;     // value in microseconds

The code change required in this KIP is rather small, but this small change has a few considerations to discuss and address.

Considerations

Consideration #1: How Much Precision?

The first question to answer is how much precision do we need?

Since ProducerPerformance currently uses millisecond precision, we will determine the spread using values close to 1.0.

The examples below use two numbers that differ by only 1 in the least significant digit (last/right digit). Bracket denotes inclusive, and parenthesis denotes exclusive.

NOTE: When calculating the spread using numbers with a larger difference, you will observe a higher minimum spread (will not be zero).

Using scale = 1 (1 decimal place)

If we record a latency of 1.1 ms for the first run, and 1.2 ms in the second run, then the true values are:

• 1.1 = [1.05, 1.15)
• 1.2 = [1.15, 1.25)

Min spread: 1.15 - (1.15) = ~0

Max spread: (1.25) - 1.05 = ~0.2

Conclusion: Benchmark 2 is 0 to 20% slower than benchmark 1.

Using scale = 2 (2 decimal places)

If we record a latency of 1.01 ms for the first run, and 1.02 ms in the second run, then the true values are:

• 1.01 = [1.005, 1.015)
• 1.02 = [1.015, 1.025)

Min spread: 1.015 - (1.015) = ~0

Max spread: (1.025) - 1.005 = ~0.02

Conclusion: Benchmark 2 is 0 to 2% slower than benchmark 1.

Using scale = 3 (3 decimal places)

If we record a latency of 1.001 ms for the first run, and 1.002 ms in the second run, then the true values are:

• 1.001 = [1.0005, 1.0015)
• 1.002 = [1.0015, 1.0025)

Min spread: 1.0015 - (1.0015) = ~0

Max spread: (1.0025) - 1.0005 = ~0.0020

Conclusion: Benchmark 2 is 0 to 0.2% slower than benchmark 1.

By using 3 decimal places in millisecond latency measurements, we are able to observe a ~0.2% spread in a low-latency environment dealing with latencies around 1 ms. 3 decimal places for milliseconds is equivalent to microseconds. This 0.2% spread is quite small, especially since other factors are likely to generate a larger variance.

Consideration #2: Long Overflow

It is important to look at potential overflow as we are saving latency in microseconds now, which means each value is multiplied by 1000 (1 millisecond is 1000 microseconds).

To calculate overall average latency, ProducerPerformance will save all observed latencies in a single `totalLatency` long variable.

We can run some calculations to observe how many record latencies can be stored in a Long based on different average latencies.

Image Added

The chart above shows that if the average latency is 1 minute, then a Long can hold 153 billion records (latencies). This covers most use cases.

Public Interfaces

This proposed change only impacts the output of the ProducerPerformance.java class by adding more precision.

Compatibility, Deprecation, and Migration Plan

This proposed change only impacts the output of the ProducerPerformance.java class by adding more precision.

Rejected Alternatives

1. Adding a "low-latency" option to enable recording measurements using System.nanoTime()If there are alternative ways of accomplishing the same thing, what were they? The purpose of this section is to motivate why the design is the way it is and not some other way.