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Performance issues when running unfurl_version_range_v2advisor (and others)

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评估

难度
5/5
预计耗时
一周以上
新手友好度
25/100
Issue 类型
缺陷
描述清晰度
需要澄清
活跃度
冷清
技术栈
postgresql, python
领域
backend, databases, performance

调研方向

报告指出了 unfurl_version_range_v2advisor improver 以及管理命令 import --all 和 improve --all；首先根据提供的 PostgreSQL 表大小和 pg_stat_user_tables 查询，对这些入口点进行性能分析。一项贡献需要明确且可复现的性能变更，以及可度量的完成标准，而当前 issue 尚未指定这两者中的任何一个。

由索引模型根据 Issue 内容生成。

描述

Hi Team,

first release: v34.0.0rc4 (or earlier)
current release: 38.1.0

Our database dates back 2+years and has grown a lot. I checked the sizes of some of the tables using this command:

SELECT
    schemaname AS schema,
    relname AS table_name,
    n_live_tup AS row_count
FROM
    pg_stat_user_tables
ORDER BY
    n_live_tup DESC;

Results were staggering:

"vulnerabilities_impactedpackageaffecting"	735403239 =735 mln (!!!)
"vulnerabilities_advisory"	54630236  = 54 mln
"vulnerabilities_advisoryrelatedalias"	54622255 = 54 mln
"vulnerabilities_packagechangelog"	34332358 =  34 mln
"vulnerabilities_vulnerability_severities"	32511124 =  32 mln
"vulnerabilities_vulnerabilityseverity"	32221132 =  32 mln
"vulnerabilities_impactedpackage"	        25634100 =  25 mln
"vulnerabilities_impactedpackagefixedby"	8558740 =  8mln

We are running VulnerableCode on OpenShift and decided that we wanted a regular updates of the importers and improvers, so we created CronJobs that would import/improve just one thing each (for example python manage.py import nvd_importer_v2). With the new version we have 137 pods running running at different times of day. While this was a lot but sort of manageable (at least we thought) we have starting noticing problems our database was under enormous load especially when improvers run. While I know that this is caused by parallel runs there is one specific improver that causes our nodes to crash and push the database beyond CPU limits - unfurl_version_range_v2advisor it was running usually for 4 days straight and using north of 44 GiB of RAM on OpenShift and 10-12 CPUs on PostgreSQL side.

With this we tried running importers and improvers as it says in documentation import --all and improve --all but those takes days or weeks to do.

Most recent importer --all run:

CommandError: 8 failed!: apache_kafka_importer_v2,mattermost_importer_v2,gitlab_importer,pypa_importer,npm_importer,nginx_importer,pysec_importer,vulnerabilities.importers.istio.IstioImporter

and it still took 26 hrs. and that is just importers on their own.

We haven't been able to do full run of the improve -all as of yet as I have seen some of those improvers can run for 3-4 days each.

So the question here arises, what to do with this as with this version it is unusable for our production instances.

what to do?

  1. Delete the whole thing and start over with new db
  2. Run importers once every 26 hrs, and then start improve --all which will take most likely weeks
  3. Skip most resource intensive improvers and just do the rest?

I would love to hear your suggestions on possible solutions, or how you run it?

主要语言
Python
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平均合并
12 小时 15 分钟
30 天内合并 PR
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环境准备

  • 提供 Dockerfile 或 Docker Compose 文件
  • 没有 Pull Request 模板
  • 没有贡献指南

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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