Apache Arrow - A cross-language development platform for in-memory dataKouhei Sutou
Apache Arrow is the future for data processing systems. This talk describes how to solve data sharing overhead in data processing system such as Spark and PySpark. This talk also describes how to accelerate computation against your large data by Apache Arrow.
Apache Arrow 1.0 - A cross-language development platform for in-memory dataKouhei Sutou
Apache Arrow is a cross-language development platform for in-memory data. You can use Apache Arrow to process large data effectively in Python and other languages such as R. Apache Arrow is the future of data processing. Apache Arrow 1.0, the first major version, was released at 2020-07-24. It's a good time to know Apache Arrow and start using it.
Apache Arrow - A cross-language development platform for in-memory dataKouhei Sutou
Apache Arrow is the future for data processing systems. This talk describes how to solve data sharing overhead in data processing system such as Spark and PySpark. This talk also describes how to accelerate computation against your large data by Apache Arrow.
Apache Arrow 1.0 - A cross-language development platform for in-memory dataKouhei Sutou
Apache Arrow is a cross-language development platform for in-memory data. You can use Apache Arrow to process large data effectively in Python and other languages such as R. Apache Arrow is the future of data processing. Apache Arrow 1.0, the first major version, was released at 2020-07-24. It's a good time to know Apache Arrow and start using it.
RubyKaigi 2022 - Fast data processing with Ruby and Apache ArrowKouhei Sutou
I introduced Ruby and Apache Arrow integration including the "super fast large data interchange and processing" Apache Arrow feature at RubyKaigi Takeout 2021.
This talk introduces how we can use the "super fast large data interchange and processing" Apache Arrow feature in Ruby. Here are some use cases:
* Fast data retrieval (fast pluck) from DB such as MySQL and PostgreSQL for batch processes in a Ruby on Rails application
* Fast data interchange with JavaScript for dynamic visualization in a Ruby on Rails application
* Fast OLAP with in-process DB such as DuckDB and Apache Arrow DataFusion in a Ruby on Rails application or irb session
RubyKaigi Takeout 2021 - Red Arrow - Ruby and Apache ArrowKouhei Sutou
To use Ruby for data processing widely, Apache Arrow support is important. We can do the followings with Apache Arrow:
* Super fast large data interchange and processing
* Reading/writing data in several famous formats such as CSV and Apache Parquet
* Reading/writing partitioned large data on cloud storage such as Amazon S3
This talk describes the followings:
* What is Apache Arrow
* How to use Apache Arrow with Ruby
* How to integrate with Ruby 3.0 features such as MemoryView and Ractor
csv, one of the standard libraries, in Ruby 2.6 has many improvements:
* Default gemified
* Faster CSV parsing
* Faster CSV writing
* Clean new CSV parser implementation for further improvements
* Reconstructed test suites for further improvements
* Benchmark suites for further performance improvements
These improvements are done without breaking backward compatibility.
This talk describes details of these improvements by a new csv maintainer.
PGroonga 2 – Make PostgreSQL rich full text search system backend!Kouhei Sutou
PGroonga 2.0 has been released with 2 years development since PGroonga 1.0.0. PGroonga 1.0.0 just provides fast full text search with all languages support. It's important because it's a lacked feature in PostgreSQL. PGroonga 2.0 provides more useful features to implement rich full text search system with PostgreSQL. This session shows how to implement rich full text search system with PostgreSQL!
This talk describes about PGroonga that resolves these problems.
PostgreSQL Conference Japan 2017の資料です。
PostgreSQLが苦手な全文検索機能を強力に補完する拡張機能PGroongaの最新情報を紹介します。PGroongaは単なる全文検索モジュールではありません。PostgreSQLで簡単にリッチな全文検索システムを実現するための機能一式を提供します。PGroongaは最新PostgreSQLへの対応も積極的です。例としてロジカルレプリケーションと組み合わせた活用方法も紹介します。
PGroonga is fast and flexible full text search extension for PostgreSQL. Zulip is a chat tool that uses PostgreSQL and PGroonga. This talk describes why PGroonga is suitable for Zulip.
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
8. Apache Arrow Flight - ビッグデータ用高速データ転送フレームワーク Powered by Rabbit 3.0.2
大量データの交換コスト
Figure 2: Communication between a client and a
server
Don’t Hold My Data Hostage –
A Case For Client Protocol Redesign
Mark Raasveldt
Centrum Wiskunde & Informatica
Amsterdam, The Netherlands
m.raasveldt@cwi.nl
Hannes Mühleisen
Centrum Wiskunde & Informatica
Amsterdam, The Netherlands
hannes@cwi.nl
Netcat (10.25s)
170.9
170.9
170.9
189.6
189.6
189.6
629.9
629.9
629.9
221.2
221.2
221.2
686.5
686.5
686.5
101.3
101.3
101.3
391.3
391.3
391.3
202
202
202
MongoDB
Hive
MySQL+C
MonetDB
PostgreSQL
DBMS X
DB2
MySQL
0 200 400 600
Wall clock time (s)
Operation
Connection
Query Execution
RSS + Transfer
Figure 1: Wall clock time for retrieving the lineitem
table (SF10) over a loopback connection. The
dashed line is the wall clock time for netcat to trans-
fer a CSV of the data. https://hannes.muehleisen.org/p852-muehleisen.pdf
9. Apache Arrow Flight - ビッグデータ用高速データ転送フレームワーク Powered by Rabbit 3.0.2
大量データの交換コスト
ボトルネックになりやすい
シリアライズ・デシリアライズ
a.
ネットワーク帯域
b.
✓
目指すところ
メイン処理がボトルネック
(メイン処理以外が十分速い)
✓
✓