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<title>Fast dimensional analysis for root cause analysis at scale</title>
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POSTED ON <span class="posted-on entry-date"><time class="published updated" datetime="2019-11-08">NOVEMBER 8, 2019</time></span> TO <span class="cat-links"><a href="https://engineering.fb.com/category/developer-tools/" rel="category tag">DevInfra</a>, <a href="https://engineering.fb.com/category/ml-applications/" rel="category tag">ML Applications</a></span>
<h1 class="entry-title"><div>Fast dimensional analysis for root cause analysis at scale</div></h1> </div><!-- .entry-meta -->
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By <a href="https://engineering.fb.com/author/nikolay-pavlovich-laptev/" title="Posts by Nikolay Pavlovich Laptev" class="author url fn" rel="author">Nikolay Pavlovich Laptev</a>, <a href="https://engineering.fb.com/author/fred-lin/" title="Posts by Fred Lin" class="author url fn" rel="author">Fred Lin</a>, <a href="https://engineering.fb.com/author/keyur-muzumdar/" title="Posts by Keyur Muzumdar " class="author url fn" rel="author">Keyur Muzumdar </a>, <a href="https://engineering.fb.com/author/mihai-valentin-curelea/" title="Posts by Mihai-Valentin Curelea" class="author url fn" rel="author">Mihai-Valentin Curelea</a> </div>
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<div class="entry-authors"><b style="color: inherit; font-family: inherit; font-size: 2.5rem;">What the research is: </b></div>
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<p>A fast dimensional analysis (FDA) framework that automates root cause analysis on structured logs with improved scalability. When a failure event happens in a large-scale distributed production environment, performing root cause analysis can be challenging. Various hardware, software, and tooling logs are often maintained separately, making it difficult to detect issues across multiple logs. Additionally, at scale, there could easily be millions of entities, each with hundreds of features, making it difficult to debug issues.</p>
<p>Our proposed FDA framework combines structured logs from a number of sources and provides a meaningful combination of features. That information arms engineers with actionable insights and helps them determine where to begin their investigation. And improved Apriori/FP-Growth algorithms sustain analysis at Facebook scale.</p>
<figure id="attachment_13730" aria-describedby="caption-attachment-13730" style="width: 2000px" class="wp-caption alignnone"><img decoding="async" class="size-full wp-image-13730" src="https://engineering.fb.com/wp-content/uploads/2019/11/graph.jpg" alt="In the figure above, our system finds highly correlated features that explain the exception spike (red line)." width="2000" height="643" srcset="https://engineering.fb.com/wp-content/uploads/2019/11/graph.jpg 2000w, https://engineering.fb.com/wp-content/uploads/2019/11/graph.jpg?resize=916,294 916w, https://engineering.fb.com/wp-content/uploads/2019/11/graph.jpg?resize=768,247 768w, https://engineering.fb.com/wp-content/uploads/2019/11/graph.jpg?resize=1024,329 1024w, https://engineering.fb.com/wp-content/uploads/2019/11/graph.jpg?resize=1536,494 1536w, https://engineering.fb.com/wp-content/uploads/2019/11/graph.jpg?resize=96,31 96w, https://engineering.fb.com/wp-content/uploads/2019/11/graph.jpg?resize=192,62 192w" sizes="(max-width: 992px) 100vw, 62vw" /><figcaption id="caption-attachment-13730" class="wp-caption-text">In the figure above, our system finds highly correlated features that explain the exception spike (red line).</figcaption></figure>
<h1><b>How it works:</b></h1>
<p>The FDA framework first fetches structured logs from various sources. Log data is deduplicated at query time (deduping significantly improves algorithm performance). Each duplicated row will have a samples column that counts original row frequency. The resulting data is then one-hot encoded (a Boolean 0/1 value, depending on whether a given feature is present in a given row) to transform it into a schema that fits the frequent pattern mining formulation. Frequent pattern mining is applied to identify frequent item-sets.</p>
<p>Support and lift are metrics that are used for filtering. Support of feature X with respect to data set D refers to the portion of transactions that contain X within D. Support has a downward closure property, which is important because downward closure implies that all subsets of a frequent item-set are also frequent, and all supersets of an infrequent item-set can be safely pruned because they will never be frequent.</p>
<figure id="attachment_13733" aria-describedby="caption-attachment-13733" style="width: 2000px" class="wp-caption alignnone"><img decoding="async" class="wp-image-13733 size-full" src="https://engineering.fb.com/wp-content/uploads/2019/11/forumula-1.jpg" alt="Formal definition of support and lift used by the fast dimensional analysis framework during the pruning of rules." width="2000" height="135" srcset="https://engineering.fb.com/wp-content/uploads/2019/11/forumula-1.jpg 2000w, https://engineering.fb.com/wp-content/uploads/2019/11/forumula-1.jpg?resize=916,62 916w, https://engineering.fb.com/wp-content/uploads/2019/11/forumula-1.jpg?resize=768,52 768w, https://engineering.fb.com/wp-content/uploads/2019/11/forumula-1.jpg?resize=1024,69 1024w, https://engineering.fb.com/wp-content/uploads/2019/11/forumula-1.jpg?resize=1536,104 1536w, https://engineering.fb.com/wp-content/uploads/2019/11/forumula-1.jpg?resize=96,6 96w, https://engineering.fb.com/wp-content/uploads/2019/11/forumula-1.jpg?resize=192,13 192w" sizes="(max-width: 992px) 100vw, 62vw" /><figcaption id="caption-attachment-13733" class="wp-caption-text">Formal definition of support and lift used by the fast dimensional analysis framework during the pruning of rules.</figcaption></figure>
<p>To measure significance or importance, we used lift, which measures how much more likely X and Y are to occur together relative to if they were independent. A lift value of 1 means independence between X and Y, and a value greater than 1 signifies dependence. Due to the focus on scalability, we propose pre- and post-processing, parallelism, and filters to further speed up the analysis and improve interpretability.</p>
<h1><b>Why it matters: </b></h1>
<p>During system outages, it is important for engineers to accurately isolate the problem and quickly find a solution. As we’ve mentioned, the challenges of performing root cause analysis in a large-scale distributed production environment make outage detection and mitigation difficult. We have successfully implemented this approach for a large-scale infrastructure environment and hope this work motivates further research and automation for similarly large and complex environments.</p>
<figure id="attachment_13734" aria-describedby="caption-attachment-13734" style="width: 2000px" class="wp-caption alignnone"><img loading="lazy" decoding="async" class="size-full wp-image-13734" src="https://engineering.fb.com/wp-content/uploads/2019/11/flow.jpg" alt="The proposed framework for the fast dimensional analysis. " width="2000" height="1125" srcset="https://engineering.fb.com/wp-content/uploads/2019/11/flow.jpg 2000w, https://engineering.fb.com/wp-content/uploads/2019/11/flow.jpg?resize=580,326 580w, https://engineering.fb.com/wp-content/uploads/2019/11/flow.jpg?resize=916,515 916w, https://engineering.fb.com/wp-content/uploads/2019/11/flow.jpg?resize=768,432 768w, https://engineering.fb.com/wp-content/uploads/2019/11/flow.jpg?resize=1024,576 1024w, https://engineering.fb.com/wp-content/uploads/2019/11/flow.jpg?resize=1536,864 1536w, https://engineering.fb.com/wp-content/uploads/2019/11/flow.jpg?resize=96,54 96w, https://engineering.fb.com/wp-content/uploads/2019/11/flow.jpg?resize=192,108 192w" sizes="auto, (max-width: 992px) 100vw, 62vw" /><figcaption id="caption-attachment-13734" class="wp-caption-text">The proposed framework for the fast dimensional analysis.</figcaption></figure>
<h2><b>Read the full paper:</b></h2>
<p><a href="https://arxiv.org/abs/1911.01225"><span style="font-weight: 400;">Fast Dimensional Analysis for Root Cause Investigation in Large-Scale Service Environment</span></a></p>
<p><em>We&#8217;d like to thank Seunghak Lee and Sriram Sankar for their contributions to this work.</em></p>
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<span class="posted-on"><time class="entry-date published" datetime="Sep 13, 2018">Sep 13, 2018</time></span>
<h4 class="entry-title">Finding and fixing software bugs automatically with SapFix and Sapienz</h4>
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