Hacktoberfest 2026：维护者为十月标记出来的 issue，仍然开放、适合新手。 浏览 Hacktoberfest issue

Dagster comparison is not correct

未关闭
  • 哪里不对？
#1,289 3 条评论 0 个 reaction 已指派 0 人 在 GitHub 查看

维护者通常 2 天内回复

还没有人认领这个 Issue。

评估

难度
4/5
预计耗时
3-5 天
新手友好度
35/100
Issue 类型
文档
描述清晰度
基本清楚
活跃度
停滞
技术栈
pandas, python
领域
data-engineering, documentation

调研方向

从 issue 中链接的 Dagster 对比页面开始，重点查看 Dataflow Definition 和 Dataflow Execution 部分以及主要对比表。根据 Dagster 的 IOManager、Declarative Materialization、Pipes、data versioning 和 dagster-dbt 参考资料，检查示例和相关说法；当对比内容准确、使用了对应的类比功能，并涵盖列出的遗漏项时，即表示完成。

由索引模型根据 Issue 内容生成。

描述

triage

Hi!

I was curious about Hamilton because I was looking for a lightweight DAG library.

Coming from Dagster, I got naturally interested in the Hamilton vs Dagster comparison and found this page in the docs: https://hamilton.dagworks.io/en/latest/code-comparisons/dagster/

I noticed it does not provide accurate information about Dagster and the code examples are not using some of Dagster's main features. More concrete:

  • issues with the first example:
    • It does not utilize the IOManager to decouple I/O from computations
    • It incorrectly states that asset descriptions have to be defined via metadata, which is not correct (they can be defined in native function docstrings or via the @asset(description=... argument).
  • issues with the second example:
    • it incorrectly states that the Dagster job can't be executed in a local Python process
    • it incorrectly states that I/O and computations are coupled (duplicate)
    • the comparison between loading environment variables at runtime and providing configuration time references like dagster.EnvVar does not make much sense. Dagster's configuration purposely enables deferring the setting of the exact configuration parameters (since Dagster runs can be executed remotely, e.g. in a Kubernetes pod, and the env var might not be available outside of the remote system). But nothing is preventing the user from setting the value with os.getenv directly if needed.

Minor (in the main comparison table):

  • important and unique Dagster features such as Declarative Materialization and Pipes are not mentioned
  • data versioning comparison is a bit strange: it's not very clear how does Hamilton automatically identify code versions (e.g. how does it distinguish between refactoring-like changes and changes in the actual business logic). Dagster's data versioning system enforces explicit code version management to avoid unwanted expensive materializations of the entire asset graph (see: declarative automation).
  • important Dagster integrations such as dagster-dbt are not mentioned

Current behavior

The Dagster example is not using relevant Dagster features and provides inaccurate information.

Expected behavior

The comparison between Hamilton and Dagster should use analogous features in both frameworks to be fair. In particular, it should use the IOManager as it's one of the main selling points of Dagster:

import dagster as dg
import pandas as pd

@dg.asset
def topstory_ids() -> pd.DataFrame: ...

@dg.asset
def topstories(topstory_ids: pd.DataFrame): ...

Note that some of the popular IOManagers for Pandas and Polars also support loading a subset of the dataframe columns: @asset(metadata={"columns": ["title"]}).

It should also provide accurate information on other topics mentioned above.

Additional context

Technically, this is not a bug, but I couldn't find a better label for this issue.

I am willing to help with improving these docs if my help is considered welcome!

主要语言
Jupyter Notebook
星标
2.6k
派生
221
平均合并
2 天 1 分钟
30 天内合并 PR
17

环境准备

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。

apache/hamilton 的其他 Issue

查看 apache/hamilton 的全部 Issue

相似的 Issue

更多 Data Engineering Issue

把新 issue 发到你的邮箱

精选适合新手参与的 GitHub issue 摘要。