AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Datagrip execution plan12/27/2023 ^ Tabular layout and heatmap visualization of Postgres plans by. Here are couple of current Postgres plan visualizer examples that provide useful details and emphasize where in the plan to focus: Lots of scrolling back’n’forth is needed with large plans, trying to remember whatever was shown in some other section of the plan. However, this layout has a shortcoming, your plan may be way too large to fit on one screen. For example, the last, Activity% column tells you exactly on which plan line most of the time was spent. The tabular view below has lots of additional performance metrics (and that’s where I spend most of my time when doing Oracle SQL tuning). The tree gives a high level overview of the execution plan hierarchy, but doesn’t have a good way for visualizing which plan nodes or branches took the most time to run. Here’s an example plan of Query 72 of the TPC-DS benchmark (11-table join) visualized as a tree using the Oracle’s Real-Time SQL Monitoring tool: It’s worth mentioning that the Oracle Database already has great instrumentation and pretty good visualization of its execution plans. Current State of SQL Plan Visualization (Oracle & Postgres examples) I’m using Oracle’s DBMS_XPLAN with the statistics_level=all setting for the examples in this post. This visualization is not limited to Oracle only, it can be used on any RDBMS engine, as long as the engine reports actual time taken at execution plan operator (plan line) level.Įven though FlameCharts could be used for visualizing any cumulative metric (like amount of I/O generated in different stages of the plan), in this post I will measure what matters the most - the response time used by individual execution plan operators. In this blog post I won’t be doing traditional stack profiling, but will apply FlameGraphs in a new way for visualizing Oracle Database SQL execution plan metrics. If you don’t know what FlameGraphs are, I suggest you read Brendan’s explanation first. This technique is a great way for visualizing metrics in nested hierarchies, what stack-based program execution uses under the hood for invoking and tracking function calls. If you don't like how it's being used, then look to the person using it.Visualizing SQL Plan Execution Time With FlameGraphs Tanel Poder Introductionīrendan Gregg invented and popularized a way to profile & visualize program response time by sampling stack traces and using his FlameGraph concept & tools. ".when prescribing physicians disagree with UnitedHealth's determination of how much post-acute care their patients need, their judgments are overridden."ĭon't get distracted by the zeitgeist boogeyman. Here is the most damning quote in the entire hit piece: Isn't the root problem that insurance companies have the power to deny care? Or that their power to deny care isn't regulated, constrained, or transparent? And there are written policies that are equally as cruel and baseless. Honestly, who cares if they used "a bad AI model?" There are also some people who manually review prior auth requests and suck at it. The problem isn't even that the model they used was terrible. "UnitedHealth uses AI model with 90% error rate to deny care." We have to move past that industry, and start to use these tools more broadly. I still feel like the #Data industry is missing massive opportunities, with its fixation on marketing and sales. To improve the system, not to squeeze more money out of it. My goal is almost always to actually effect change on the system I'm interacting with. Human-in-the-loop is nearly always mandatory. Most of the time, interpretability is substantially more important than being right. I can't afford to make a distinction between Analyst, Engineer, and Scientist. I feel like I'm solving fairly different problems than other Data folks. Identify cases of fraud, waste, and abuse in Medicare Advantage populations. Find patients with unrecorded problems, to build out a more robust and complete longitudinal medical history. Suggest changes in the supplies used in countless Operating Rooms, optimizing for long-term patient outcomes, hospital metrics, and costs. I would have a hard time motivating myself to solve those problems. Serving ads with the best capture? Actually seems lightly evil. Finding the next trinket a consumer may buy? Gag me. I couldn't care less about jobs where analytics is a peripheral service. In short: I will only work at companies where analytics IS the company, or at least represents a sellable product in-and-of itself. I think I understand why my views on data are so different from many of my peers.
0 Comments
Read More
Leave a Reply. |