Call for real world benchmarks / use cases
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Nadie ha tomado este issue todavía.
Evaluación
- Dificultad
- 2/5
- Tiempo estimado
- 1-3 horas
- Aptitud para principiantes
- 35/100
- Tipo de issue
- Documentación
- Claridad
- Bastante claro
- Estado de actividad
- Activo
- Stack tecnológico
- rust
- Área
- performance
Línea de trabajo
Start with the benchmark examples linked in the issue and the contributor guidance. The requested work is to submit a real-world workload with a runnable reproducer, performance details, and one query pattern; contributors should reproduce it and post an analysis before proposing optimizations. The issue provides an example query but no specific workload to implement, so a code change cannot be scoped from this issue alone.
Escrito por el modelo de indexación a partir del texto del issue.
Descripción
Background
A core value proposition for DataFusion is its performance.
We have lots of benchmarks, but there are many real world use cases that are not covered. For example, queries on STRUCTs / LISTSs and other nested types, but there are many more.
There are many people looking for ways to help, and I want optimization effort to go to workloads people actually run rather than over optimize benchmarks like ClickBench or random things.
Open Call / Request
To that end, I this is an open call for users of DataFusion to file issues describing queries they would like to go faster. Obviously we will not be able to help everyone, however, supplying the information below I think will maximize the chance of making it happen.
Why: Ensure DataFusion stays focused on real users and actual use cases.
Who: Real users of DataFusion -- you are using DataFusion and would like some particular workload to go faster.
How: Open a new issue with the following information
Format
Please provide the following information, concisely (please don't dump multiple pages of AI generated spew with every imaginable detail 🙏 ).
Use case: 1-2 sentences (max) describing how you are using DataFusion. Examples: "Log data analysis from Parquet files on S3", "Full text search engine on local Vortex files", etc.
Local reproducer:
-
Data files. In order of preference:
- a script that generates the data
- a synthetic or anonymized sample with the same shape as your real data
- a public URL to the files
Ideally the data is no more than 10s of GB and the query runs in under a minute on a laptop.
-
A simple benchmark command (ideally a
.sqlfile we can run indatafusion-cli) that runs against your data files. -
1-2 sentences explaining what the data and query mean in English (for example the query descriptions in our current benchmarks)
If you can not share data, the schema and EXPLAIN ANALYZE output may still be useful, but issues with a runnable reproducer are far more likely to get attention and be acted on.
Current performance:
- DataFusion version, and any non-default configuration settings
- How you run DataFusion (e.g. SQL or dataframe, do you have custom optimzer rules, etc)
- Hardware
- Why you think it could be faster (for example, the time another engine takes, or a profile / explain plan showing where the time goes)
Restrictions:
- Only include 1 query or query pattern in each issue (not your whole workload, please) to keep the conversation focused.
Then please post a link to the new ticket below.
For contributors
If you are looking for a way to help, check out any usses
The first thing is not to code (or have your agent code) Instead, you should reproduce reported performance and analyze where the time is going and post a writeup to the ticket. Please include flamegraphs, diagrams, etc , not walls of AI generated analysis.
Then, explore possible optimizations (AI tools are great at this). Review each idea carefully, and if you find one that looks general and improves performance write it up (1 paragraph) explaining the high level optimization, why it works, and what classes of queries are affected.
Example
Here is an example using ClickBench Q22.
Use case: Web analytics over a single Parquet table (ClickBench hits).
Data: 14 GB, 100M rows, 105 columns
wget https://datasets.clickhouse.com/hits_compatible/hits.parquet
Query (q22.sql, run with datafusion-cli -f q22.sql):
CREATE EXTERNAL TABLE hits STORED AS PARQUET LOCATION 'hits.parquet';
SELECT "SearchPhrase", MIN("URL"), MIN("Title"), COUNT(*) AS c, COUNT(DISTINCT "UserID")
FROM hits
WHERE "Title" LIKE '%Google%' AND "URL" NOT LIKE '%.google.%' AND "SearchPhrase" <> ''
GROUP BY "SearchPhrase" ORDER BY c DESC LIMIT 10;
How this matches my real use case: TODO 1-2 sentences
DataFusion Version: 56.0.0
- Lenguaje dominante
- Rust
- Estrellas
- 9.6k
- Forks
- 2.6k
- Merge medio
- 3 d 4 h
- PR fusionados (30 d)
- 345
Preparar el entorno
Inicia el contenedor de desarrollo del proyecto en tu navegador, con tu propia cuenta de GitHub.
- Sin Dockerfile ni archivo de Docker Compose
- Tiene una plantilla de pull request
- Leer la guía de contribución
Primeros pasos
- Lee el issue completo y luego la guía de contribución del proyecto.
- Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
- Haz un fork del repositorio y trabaja en una rama.
- Abre un pull request que haga referencia al número del issue.
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