Draft:Predicate pushdown
 | Review waiting, please be patient.
This may take 3 months or more, since drafts are reviewed in no specific order. There are 4,415 pending submissions waiting for review.
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
Reviewer tools
|
Comment: The red-linked category is strange. Is this AI-generated? —pythoncoder (talk | contribs) 19:01, 8 May 2026 (UTC)
- Comment: Not AI, just early brainstorming that I'd put down initially and forgot to remove because it was at the very bottom. Sorry about that! Removed now.
Predicate pushdown is a database management and query optimization technique that improves performance by implementing filters (also called selections) while the data is being read from the storage layer. In contrast to traditional filtering, where a system processes an entire dataset and then removes irrelevant rows in memory, predicate pushdown applies the filter as close to the source as possible. This results in significantly less data requiring transfer across networks and allocation in system memory.[1][2][3]
Overview
In database management systems, predicates are expressions that evaluate to a Boolean value (e.g., WHERE age > 21).[4] Early optimization engineers recognized that the primary bottleneck in query execution was often the latency involved in moving data from the physical disk into RAM.
With the advent of distributed computing and cloud-native storage (such as Amazon S3), this bottleneck was magnified because the entire dataset now had to travel across a network interface before it could be processed. Predicate pushdown addresses this by shifting the computational burden of filtering to the storage engine or remote node, ensuring only the relevant subset of data consumes bandwidth and memory.
This technique is an application of predication and is closely associated with data skipping, where metadata is used to bypass irrelevant data blocks entirely.[5]
Implementation
The effectiveness of a pushdown depends on the ability of the underlying storage format to understand and execute the predicate.
Metadata and data skipping
Modern columnar formats like Apache Parquet and ORC implement pushdown by storing summary statistics at the file, footer, or "row group" level. These statistics, often called zone maps, store the minimum and maximum values for each column within a specific block.[6] If a user queries WHERE price > 100 and a block's zone map indicates a max_price of 80, the entire block is skipped without being read.
Join and Bloom filter pushdown
In complex queries involving joins, systems may use Bloom filters. A filter representing the keys from one side of a join is "pushed" down to the scan of the other table. This allows the system to discard rows that would not satisfy the join condition before they are sent over the network for shuffling (a common operation in Apache Spark).
Limitations
Pushing a predicate down is not always possible. Complex User-Defined Functions (UDFs) cannot always be interpreted by the query optimizer, preventing it from translating the filter into a format the storage layer can execute directly.[7][8]
See also
References
- ^ "Predicate Pushdown". QuestDB. Retrieved 8 May 2026.
- ^ "Demystifying Predicate Pushdown: A Guide to Optimized Database Queries | Airbyte". airbyte.com. Retrieved 8 May 2026.
- ^ "The power of predicate pushdown". www.pola.rs. Retrieved 8 May 2026.
- ^ "Predicates - SQL Server". learn.microsoft.com. Retrieved 8 May 2026.
- ^ Ta-Shma, Paula; Khazma, Guy; Lushi, Gal; Feder, Oshrit (10 December 2020). "Extensible Data Skipping". 2020 IEEE International Conference on Big Data (Big Data). pp. 372–382. arXiv:2009.08150. doi:10.1109/BigData50022.2020.9377740. ISBN 978-1-7281-6251-5.
- ^ Kuiper, Laurens (22 January 2025). "Query Engines: Gatekeepers of the Parquet File Format". DuckDB. Retrieved 8 May 2026.
- ^ Braams, Boudewijn (December 2018). Predicate Pushdown in Parquet and Apache Spark (PDF) (Masters thesis). Universiteit van Amsterdam. Retrieved 8 May 2026.
- ^ Yan, Cong; Lin, Yin; He, Yeye (20 June 2023). "Predicate Pushdown for Data Science Pipelines". Proc. ACM Manag. Data. 1 (2): 136:1–136:28. doi:10.1145/3589281. Retrieved 8 May 2026.
Content Disclaimer
Informasi ini disarikan dari Wikipedia dan disajikan kembali untuk tujuan edukasi. Konten tersedia di bawah lisensi CC BY-SA 3.0. Kami tidak bertanggung jawab atas ketidakakuratan data yang bersumber dari kontribusi publik tersebut.
- The information displayed on this website is sourced in part or in whole from Wikipedia and has been adapted for the purpose of restating it. We strive to provide accurate and relevant information, however:
- There is no guarantee of absolute accuracy. Wikipedia is an open, collaborative project that can be edited by anyone, so information is subject to change.
- It is not intended to constitute professional advice. The content displayed is for informational and educational purposes only. For important decisions (e.g., medical, legal, or financial), please consult a professional.
- Content copyright. Wikipedia is licensed under the Creative Commons Attribution-ShareAlike License (CC BY-SA). This means that content may be reused with appropriate attribution and shared under a similar license.
- Responsible use. Any risk arising from the use of information from this website is entirely the responsibility of the user.