semgrep VS ClickHouse

Semgrep OSS Owner/Maintainer: Semgrep Age: First release on GitHub on February 6th, 2020 License: GNU Lesser General Public License v2.1

Semgrep - https://semgrep.dev

For the SAST stage, I used SonarQube tool. SonarQube is an open-source platform developed by SonarSource for continuous inspection of code quality to perform automatic reviews with static analysis of code to detect bugs and code smells on more than 30 programming languages. I preferred SonarQube instead of other SAST tools because it has a detailed documentation and plugins about integration with Jenkins and SonarQube works with Java projects pretty well. Of course you can similar multi-language-supported tools such as Semgrep or language-specific tools such as Bandit.

> Not sure I understand your point.

The problem is using Treesitter (for syntax highlighting and "semantic movements") and an LSP at the same time. So if your language has a LSP, using Treesitter additionally is redundant at best and introduces inconcistency at worst.

I'm not talking about using Treesitter as the parser for the LSP.

> Most popular languages have language-specific tools

I'd say even less popular langauges like Coq^H^H^HRocq, Lean 4, Koka, Idris, Unison, ... have their "own" tools, I do not know of a language that uses a Treesitter parser in its LSP, but I do know about tools like https://semgrep.dev/ (written in OCaml) and Github's code search which use Treesitter.

Well, when I seach for "semgrep", I get a very nice corporate landing page with a "Book Demo" button. Which is a level of hassle that just isn't worth it for smaller teams, because "Book Demo" usually means "We're going to try to do a dance to see how much money we can extract from you." Which smaller teams may only want to do for a handful of key tools.

(4 years ago, I was more willing to put up with enterprise licensing. But in the last two years, I've seen way too many enterprise vendors try to squeeze every penny they can get from existing clients. An enterprise sales process now often means "Expect 30% annual price hikes once you're in too deep to back out.")

There's also an open source "semgrep" project here: https://github.com/semgrep/semgrep. But this seems to be basically a vulernability scanner, going by the README.

Whereas AST-grep seems to focus heavily on things like:

1. One-off searching: "Search my tree for this pattern."

2. Refactoring: "Replace this pattern with this other pattern."

AST-grep also includes a vulnerability scanning mode like semgrep.

It's possible that semgrep also has nice support for (1) and (2), but it isn't clearly visible on their corporate landing page or the first open source README I found.

7. Semgrep

This project is a compilation of Semgrep rules derived from the OWASP Mobile Application Security Testing Guide (MASTG) specifically for Android applications. The aim is to enhance and support Mobile Application Penetration Testing (MAPT) activities conducted by the ethical hacker community. The primary objective of these rules is to address the static tests outlined in the OWASP MASTG.

Yes, we are working on it! :) Taking some of the learnings from current experimental JSON Object datatype, we are now working on what will become the production-ready implementation. Details here: https://github.com/ClickHouse/ClickHouse/issues/54864

Variant datatype is already available as experimental in 24.1, Dynamic datatype is WIP (PR almost ready), and JSON datatype is next up. Check out the latest comment on that issue with how the Dynamic datatype will work: https://github.com/ClickHouse/ClickHouse/issues/54864#issuec...

In our repository, I've set up a few hard limits: each translation unit cannot spend more than a certain amount of memory for compilation and a certain amount of CPU time, and the compiled binary has to be not larger than a certain size.

When these limits are reached, the CI stops working, and we have to remove the bloat: https://github.com/ClickHouse/ClickHouse/issues/61121

Although these limits are too generous as of today: for example, the maximum CPU time to compile a translation unit is set to 1000 seconds, and the memory limit is 5 GB, which is ridiculously high.

I have a project dedicated to this topic: https://github.com/ClickHouse/ClickBench

It is important to explain the limitations of a benchmark, provide a methodology, and make it reproducible. It also has to be simple enough, otherwise it will not be realistic to include a large number of participants.

I'm also collecting all database benchmarks I could find: https://github.com/ClickHouse/ClickHouse/issues/22398

ClickHouse: A fast open-source column-oriented database management system. ClickHouse is designed for real-time analytics on large datasets and excels in high-speed data insertion and querying, making it ideal for real-time monitoring and reporting.

Today we're going to create an UDF (User-defined Function) in Golang that can be run inside Clickhouse query, this function will parse uuid v1 and return timestamp of it since Clickhouse doesn't have this function for now. Inspired from the python version with TabSeparated delimiter (since it's easiest to parse), UDF in Clickhouse will read line by line (each row is each line, and each text separated with tab is each column/cell value):

For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules.

Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in maintaining the freshness of results. The query in the streaming database focuses on recent data, making it suitable for continuous monitoring. Using streaming databases, you can run queries like finding the top 10 sold products where the “top 10 product list” might change in real-time.

Proton is a lightweight streaming processing "add-on" for ClickHouse, and we are making these delta parts as standalone as possible. Meanwhile contributing back to the ClickHouse community can also help a lot.

Please check this PR from the proton team: https://github.com/ClickHouse/ClickHouse/pull/54870

curl https://clickhouse.com/ | sh

But I continue to find garbage in some of our CI scripts.

Here is an example: https://github.com/ClickHouse/ClickHouse/pull/58794/files

The right way is to:

- always pin versions of all packages;