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commit de596c53ecfaec5480cab1db7370c9fb5b37bb7a Author: Piotr Nowojski <[email protected]> AuthorDate: Wed Jul 7 14:38:36 2021 +0200 Rebuild website --- content/2021/07/07/backpressure.html | 448 +++++++++++++++++++++ content/blog/index.html | 2 +- .../img/blog/2021-07-07-backpressure/animated.png | Bin 0 -> 847082 bytes .../2021-07-07-backpressure/bottleneck-zoom.png | Bin 0 -> 185048 bytes .../2021-07-07-backpressure/simple-example.png | Bin 0 -> 102308 bytes .../2021-07-07-backpressure/sliding-window.png | Bin 0 -> 22391 bytes .../2021-07-07-backpressure/source-task-busy.png | Bin 0 -> 25852 bytes .../img/blog/2021-07-07-backpressure/subtasks.png | Bin 0 -> 137756 bytes content/index.html | 2 +- content/zh/index.html | 2 +- 10 files changed, 451 insertions(+), 3 deletions(-) diff --git a/content/2021/07/07/backpressure.html b/content/2021/07/07/backpressure.html new file mode 100644 index 0000000..1f7f6b4 --- /dev/null +++ b/content/2021/07/07/backpressure.html @@ -0,0 +1,448 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> + <meta http-equiv="X-UA-Compatible" content="IE=edge"> + <meta name="viewport" content="width=device-width, initial-scale=1"> + <!-- The above 3 meta tags *must* come first in the head; 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However, when it comes +to identifying and analyzing sources of backpressure, things have changed quite a bit in the recent Flink releases +(especially with new additions to metrics and the web UI in Flink 1.13). This post will try to clarify some of +these changes and go into more detail about how to track down the source of backpressure, but first…</p> + +<h2 id="what-is-backpressure">What is backpressure?</h2> + +<p>This has been explained very well in an old, but still accurate, <a href="https://www.ververica.com/blog/how-flink-handles-backpressure";>post by Ufuk Celebi</a>. +I highly recommend reading it if you are not familiar with this concept. For a much deeper and low-level understanding of +the topic and how Flink’s network stack works, there is a more <a href="https://alibabacloud.com/blog/analysis-of-network-flow-control-and-back-pressure-flink-advanced-tutorials_596632";>advanced explanation available here</a>.</p> + +<p>At a high level, backpressure happens if some operator(s) in the Job Graph cannot process records at the +same rate as they are received. This fills up the input buffers of the subtask that is running this slow operator. +Once the input buffers are full, backpressure propagates to the output buffers of the upstream subtasks. +Once those are filled up, the upstream subtasks are also forced to slow down their records’ processing +rate to match the processing rate of the operator causing this bottleneck down the stream. Backpressure +further propagates up the stream until it reaches the source operators.</p> + +<p>As long as the load and available resources are static and none of the operators produce short bursts of +data (like windowing operators), those input/output buffers should only be in one of two states: almost empty +or almost full. If the downstream operator or subtask is able to keep up with the influx of data, the +buffers will be empty. If not, then the buffers will be full [<sup>1</sup>]. In fact, checking the buffers’ usage metrics +was the basis of the previously recommended way on how to detect and analyze backpressure described <a href="https://flink.apache.org/2019/07/23/flink-network-stack-2.html#backpressure";>a couple +of years back by Nico Kruber</a>. +As I mentioned in the beginning, Flink now offers much better tools to do the same job, but before we get to that, +there are two questions worth asking.</p> + +<h3 id="why-should-i-care-about-backpressure">Why should I care about backpressure?</h3> + +<p>Backpressure is an indicator that your machines or operators are overloaded. The buildup of backpressure +directly affects the end-to-end latency of the system, as records are waiting longer in the queues before +being processed. Secondly, aligned checkpointing takes longer with backpressure, while unaligned checkpoints +will be larger (you can read more about aligned and unaligned checkpoints <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/concepts/stateful-stream-processing/#checkpointing";>in the documentation</a>. +If you are struggling with checkpoint barriers propagation times, taking care of backpressure would most +likely help to solve the problem. Lastly, you might just want to optimize your job in order to reduce +the costs of running the job.</p> + +<p>In order to address the problem for all cases, one needs to be aware of it, then locate and analyze it.</p> + +<h3 id="why-shouldnt-i-care-about-backpressure">Why shouldn’t I care about backpressure?</h3> + +<p>Frankly, you do not always have to care about the presence of backpressure. Almost by definition, lack +of backpressure means that your cluster is at least ever so slightly underutilized and over-provisioned. +If you want to minimize idling resources, you probably can not avoid incurring some backpressure. This +is especially true for batch processing.</p> + +<h2 id="how-to-detect-and-track-down-the-source-of-backpressure">How to detect and track down the source of backpressure?</h2> + +<p>One way to detect backpressure is to use <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/ops/metrics/#system-metrics";>metrics</a>, +however, in Flink 1.13 it’s no longer necessary to dig so deep. In most cases, it should be enough to just +look at the job graph in the Web UI.</p> + +<div class="row front-graphic"> + <img src="/img/blog/2021-07-07-backpressure/simple-example.png" /> +</div> + +<p>The first thing to note in the example above is that different tasks have different colors. Those colors +represent a combination of two factors: under how much backpressure this task is and how busy it is. Idling +tasks will be blue, fully busy tasks will be red hot, and fully backpressured tasks will be black. Anything +in between will be a combination/shade of those three colors. With this knowledge, one can easily spot the +backpressured tasks (black). The busiest (red) task downstream of the backpressured tasks will most likely +be the source of the backpressure (the bottleneck).</p> + +<p>If you click on one particular task and go into the “BackPressure” tab you will be able to further dissect +the problem and check what is the busy/backpressured/idle status of every subtask in that task. For example, +this is especially handy if there is a data skew and not all subtasks are equally utilized.</p> + +<div class="row front-graphic"> + <img src="/img/blog/2021-07-07-backpressure/subtasks.png" alt="Backpressure among subtasks" /> + <p class="align-center">Backpressure among subtasks</p> +</div> + +<p>In the above example, we can clearly see which subtasks are idling, which are backpressured, and that +none of them are busy. And frankly, in a nutshell, that should be enough to quickly understand what is +happening with your Job :) However, there are a couple of more details worth explaining.</p> + +<h3 id="what-are-those-numbers">What are those numbers?</h3> + +<p>If you are curious how it works underneath, we can go a little deeper. At the base of this new mechanism +we have three <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/ops/metrics/#io";>new metrics</a> +that are exposed and calculated by each subtask: +- <code>idleTimeMsPerSecond</code> +- <code>busyTimeMsPerSecond</code> +- <code>backPressuredTimeMsPerSecond</code> +Each of them measures the average time in milliseconds per second that the subtask spent being idle, +busy, or backpressured respectively. Apart from some rounding errors they should complement each other and +add up to 1000ms/s. In essence, they are quite similar to, for example, CPU usage metrics.</p> + +<p>Another important detail is that they are being averaged over a short period of time (a couple of seconds) +and they take into account everything that is happening inside the subtask’s thread: operators, functions, +timers, checkpointing, records serialization/deserialization, network stack, and other Flink internal +overheads. A <code>WindowOperator</code> that is busy firing timers and producing results will be reported as busy or backpressured. +A function doing some expensive computation in <code>CheckpointedFunction#snapshotState</code> call, for instance flushing +internal buffers, will also be reported as busy.</p> + +<p>One limitation, however, is that <code>busyTimeMsPerSecond</code> and <code>idleTimeMsPerSecond</code> metrics are oblivious +to anything that is happening in separate threads, outside of the main subtask’s execution loop. +Fortunately, this is only relevant for two cases: +- Custom threads that you manually spawn in your operators (a discouraged practice). +- Old-style sources that implement the deprecated <code>SourceFunction</code> interface. Such sources will report <code>NaN</code>/<code>N/A</code> +as the value for busyTimeMsPerSecond. For more information on the topic of Data Sources please +<a href="https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/dev/datastream/sources/";>take a look here</a>.</p> + +<div class="row front-graphic"> + <img src="/img/blog/2021-07-07-backpressure/source-task-busy.png" alt="Old-style sources do not report busy time" /> + <p class="align-center">Old-style sources do not report busy time</p> +</div> + +<p>In order to present those raw numbers in the web UI, those metrics need to be aggregated from all subtasks +(on the job graph we are showing only tasks). This is why the web UI presents the maximal value from all +subtasks of a given task and why the aggregated maximal values of busy and backpressured may not add up to 100%. +One subtask can be backpressured at 60%, while another can be busy at 60%. This can result in a task that +is both backpressured and busy at 60%.</p> + +<h3 id="varying-load">Varying load</h3> + +<p>There is one more thing. Do you remember that those metrics are measured and averaged over a couple of seconds? +Keep this in mind when analyzing jobs or tasks with varying load, such as (sub)tasks containing a <code>WindowOperator</code> +that is firing periodically. Both the subtask with a constant load of 50% and the subtask that alternates every +second between being fully busy and fully idle will be reporting the same value of <code>busyTimeMsPerSecond</code> +of 500ms/s.</p> + +<p>Furthermore, varying load and especially firing windows can move the bottleneck to a different place in +the job graph:</p> + +<div class="row front-graphic"> + <img src="/img/blog/2021-07-07-backpressure/bottleneck-zoom.png" alt="Bottleneck alternating between two tasks" /> + <p class="align-center">Bottleneck alternating between two tasks</p> +</div> + +<div class="row front-graphic"> + <img src="/img/blog/2021-07-07-backpressure/sliding-window.png" alt="SlidingWindowOperator" /> + <p class="align-center">SlidingWindowOperator</p> +</div> + +<p>In this particular example, <code>SlidingWindowOperator</code> was the bottleneck as long as it was accumulating records. +However, as soon as it starts to fire its windows (once every 10 seconds), the downstream task +<code>SlidingWindowCheckMapper -> Sink: SlidingWindowCheckPrintSink</code> becomes the bottleneck and <code>SlidingWindowOperator</code> +gets backpressured. As those busy/backpressured/idle metrics are averaging time over a couple of seconds, +this subtlety is not immediately visible and has to be read between the lines. On top of that, the web UI +is updating its state only once every 10 seconds, which makes spotting more frequent changes a bit more difficult.</p> + +<h2 id="what-can-i-do-with-backpressure">What can I do with backpressure?</h2> + +<p>In general this is a complex topic that is worthy of a dedicated blog post. It was, to a certain extent, +addressed in <a href="https://flink.apache.org/2019/07/23/flink-network-stack-2.html#:~:text=this%20is%20unnecessary.-,What%20to%20do%20with%20Backpressure%3F,-Assuming%20that%20you";>previous blog posts</a>. +In short, there are two high-level ways of dealing with backpressure. Either add more resources (more machines, +faster CPU, more RAM, better network, using SSDs…) or optimize usage of the resources you already have +(optimize the code, tune the configuration, avoid data skew). In either case, you first need to analyze +what is causing backpressure by: +1. Identifying the presence of backpressure. +2. Locating which subtask(s) or machines are causing it. +3. Digging deeper into what part of the code is causing it and which resource is scarce.</p> + +<p>Backpressure monitoring improvements and metrics can help you with the first two points. To tackle the +last one, profiling the code can be the way to go. To help with profiling, also starting from Flink 1.13, +<a href="http://www.brendangregg.com/flamegraphs.html";>Flame Graphs</a> are <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/ops/debugging/flame_graphs/";>integrated into Flink’s web UI</a>. +Flame Graphs is a well known profiling tool and visualization technique and I encourage you to give it a try.</p> + +<p>But keep in mind that after locating where the bottleneck is, you can analyze it the same way you would +any other non-distributed application (by checking resource utilization, attaching a profiler, etc). +Usually there is no silver bullet for problems like this. You can try to scale up but sometimes it might +not be easy or practical to do.</p> + +<p>Anyway… The aforementioned improvements to backpressure monitoring allow us to easily detect the source of backpressure, +and Flame Graphs can help us to analyze why a particular subtask is causing problems. Together those two +features should make the previously quite tedious process of debugging and performance analysis of Flink +jobs that much easier! Please upgrade to Flink 1.13.x and try them out!</p> + +<p>[<sup>1</sup>] There is a third possibility. In a rare case when network exchange is actually the bottleneck in your job, +the downstream task will have empty input buffers, while upstream output buffers will be full. <a class="anchor" id="1"></a></p> + + </article> + </div> + + <div class="row"> + <div id="disqus_thread"></div> + <script type="text/javascript"> + /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */ + var disqus_shortname = 'stratosphere-eu'; // required: replace example with your forum shortname + + /* * * DON'T EDIT BELOW THIS LINE * * */ + (function() { + var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; + dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js'; + (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); + })(); + </script> + </div> + </div> +</div> + </div> + </div> + + <hr /> + + <div class="row"> + <div class="footer text-center col-sm-12"> + <p>Copyright © 2014-2021 <a href="http://apache.org";>The Apache Software Foundation</a>. 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This blog post aims to introduce those changes and explain how to use them.</p> + <p>Apache Flink 1.13 introduced a couple of important changes in the area of backpressure monitoring and performance analysis of Flink Jobs. This blog post aims to introduce those changes and explain how to use them.</p> <p><a href="/2021/07/07/backpressure.html">Continue reading »</a></p> </article> diff --git a/content/img/blog/2021-07-07-backpressure/animated.png b/content/img/blog/2021-07-07-backpressure/animated.png new file mode 100644 index 0000000..40ea809 Binary files /dev/null and b/content/img/blog/2021-07-07-backpressure/animated.png differ diff --git a/content/img/blog/2021-07-07-backpressure/bottleneck-zoom.png b/content/img/blog/2021-07-07-backpressure/bottleneck-zoom.png new file mode 100644 index 0000000..b4e5b80 Binary files /dev/null and b/content/img/blog/2021-07-07-backpressure/bottleneck-zoom.png differ diff --git a/content/img/blog/2021-07-07-backpressure/simple-example.png b/content/img/blog/2021-07-07-backpressure/simple-example.png new file mode 100644 index 0000000..fceccb3 Binary files /dev/null and b/content/img/blog/2021-07-07-backpressure/simple-example.png differ diff --git a/content/img/blog/2021-07-07-backpressure/sliding-window.png b/content/img/blog/2021-07-07-backpressure/sliding-window.png new file mode 100644 index 0000000..dcd3240 Binary files /dev/null and b/content/img/blog/2021-07-07-backpressure/sliding-window.png differ diff --git a/content/img/blog/2021-07-07-backpressure/source-task-busy.png b/content/img/blog/2021-07-07-backpressure/source-task-busy.png new file mode 100644 index 0000000..8f72b54 Binary files /dev/null and b/content/img/blog/2021-07-07-backpressure/source-task-busy.png differ diff --git a/content/img/blog/2021-07-07-backpressure/subtasks.png b/content/img/blog/2021-07-07-backpressure/subtasks.png new file mode 100644 index 0000000..bf6ede6 Binary files /dev/null and b/content/img/blog/2021-07-07-backpressure/subtasks.png differ diff --git a/content/index.html b/content/index.html index fbc42c6..1260e15 100644 --- a/content/index.html +++ b/content/index.html @@ -578,7 +578,7 @@ <dl> <dt> <a href="/2021/07/07/backpressure.html">How to identify the source of backpressure?</a></dt> - <dd>Apache Flink 1.13 introduced a couple of important changes in the area of backpressure monitoring and performance analysing of Flink Jobs. This blog post aims to introduce those changes and explain how to use them.</dd> + <dd>Apache Flink 1.13 introduced a couple of important changes in the area of backpressure monitoring and performance analysis of Flink Jobs. This blog post aims to introduce those changes and explain how to use them.</dd> <dt> <a href="/news/2021/05/28/release-1.13.1.html">Apache Flink 1.13.1 Released</a></dt> <dd><p>The Apache Flink community released the first bugfix version of the Apache Flink 1.13 series.</p> diff --git a/content/zh/index.html b/content/zh/index.html index 3296781..b109d1d 100644 --- a/content/zh/index.html +++ b/content/zh/index.html @@ -571,7 +571,7 @@ <dl> <dt> <a href="/2021/07/07/backpressure.html">How to identify the source of backpressure?</a></dt> - <dd>Apache Flink 1.13 introduced a couple of important changes in the area of backpressure monitoring and performance analysing of Flink Jobs. This blog post aims to introduce those changes and explain how to use them.</dd> + <dd>Apache Flink 1.13 introduced a couple of important changes in the area of backpressure monitoring and performance analysis of Flink Jobs. This blog post aims to introduce those changes and explain how to use them.</dd> <dt> <a href="/news/2021/05/28/release-1.13.1.html">Apache Flink 1.13.1 Released</a></dt> <dd><p>The Apache Flink community released the first bugfix version of the Apache Flink 1.13 series.</p>
