Parquet performance tuning: the missing guide
Parquet performance tuning focuses on optimizing Parquet reads by leveraging columnar organization, encoding, and filtering techniques. Statistics and dictionary filtering can eliminate unnecessary data reads by filtering at the row group and page levels. However, these optimizations require columns to be sorted and fully dictionary encoded within files. Increasing dictionary size thresholds and d
File Format Benchmarks - Avro, JSON, ORC, & Parquet
File Format Benchmarks - Avro, JSON, ORC, & Parquet Hadoop Summit June 2016 The landscape for storing your big data is quite complex, with several competing formats and different implementations of each format. Understanding your use of the data is critical for picking the format. Depending on your use case, the different formats perform very differently. Although you can use a hammer to drive a s
Parquet vs ORC vs ORC with Snappy
I am running a few tests on the storage formats available with Hive and using Parquet and ORC as major options. I included ORC once with default compression and once with Snappy. I have read many a documents that state Parquet to be better in time/space complexity as compared to ORC but my tests are opposite to the documents I went through. Follows some details of my data. Table A- Text File Forma
Apache Parquet - Wikipedia
Apache Parquet is a free and open-source column-oriented data storage format in the Apache Hadoop ecosystem. It is similar to RCFile and ORC, the other columnar-storage file formats in Hadoop, and is compatible with most of the data processing frameworks around Hadoop. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. The open-sou
Parquet
Documentation Download Apache Parquet is an open source, column-oriented data file format designed for efficient data storage and retrieval. It provides high performance compression and encoding schemes to handle complex data in bulk and is supported in many programming language and analytics tools.
Amazon Athena: カラムナフォーマット『Parquetã€ã§ã‚¯ã‚¨ãƒªã‚’試ã—ã¦ã¿ãŸ #reinvent | DevelopersIO
先日『AWS re:Invent 2016ã€ã«ã¦ç™ºè¡¨ã•ã‚ŒãŸæ–°ã‚µãƒ¼ãƒ“ス『Amazon Athenaã€ã¯ã€ãƒžãƒ‹ãƒ¥ã‚¢ãƒ«ã«ã‚‚ã‚る通りAWSãŒæä¾›ã™ã‚‹ãƒ•ãƒ«ãƒžãƒãƒ¼ã‚¸ãƒ‰Hiveサービスã¨è¨€ãˆã‚‹ã§ã—ょã†ã€‚DWH用途ã§è€ƒãˆã‚‹ã¨ãƒ¬ã‚³ãƒ¼ãƒ‰ã‚’フルスã‚ャンã™ã‚‹ã‚ˆã‚Šã‚‚特定ã®ã‚«ãƒ©ãƒ を集計・フィルタã™ã‚‹ã¨ã„ã†ãƒ¦ãƒ¼ã‚¹ã‚±ãƒ¼ã‚¹ãŒå¤šããªã‚Šãã†ã§ã™ã®ã§ã€ã‚«ãƒ©ãƒ ナフォーマット『Parquetã€ã‚’試ã—ãŸã¿ãŸã„ã¨æ€ã„ã¾ã—ãŸã€‚Parquetファイルã®å¤‰æ›ã‚„ã€ä¸€èˆ¬çš„ãªCSVã¨ã®ç°¡å˜ãªæ¯”較をã—ã¦ã¿ã¾ã—ãŸã€‚（æ„外ãªçµæžœãŒ...） カラムナフォーマット『Parquetã€ã¨ã¯ データ分æžã§ã¯å¤§ç¦å¸³ãƒ•ã‚©ãƒ¼ãƒžãƒƒãƒˆã®ãƒ†ãƒ¼ãƒ–ルデータã«å¯¾ã—ã¦ã€ç‰¹å®šã®åˆ—ã®å€¤ã‚’集計ã—ãŸã‚Šã€ãƒ•ã‚£ãƒ«ã‚¿ãƒªãƒ³ã‚°ã™ã‚‹ã“ã¨ãŒå¤šã„ãŸã‚ã€ã‚«ãƒ©ãƒ 毎ã«ãƒ‡ãƒ¼ã‚¿ãŒé€£ç¶šã—ã¦æ ¼ç´ã•ã‚Œã¦ã„ã‚‹ã¨å¿…è¦ãªãƒ‡ãƒ¼ã‚¿ã®ã¿ã‚’ピンãƒã‚¤ãƒ³ãƒˆã§èªã¿è¾¼ã‚€ã“ã¨ãŒã§ãã‚‹ã‹ã‚‰ã§ã™ã€‚ã¾ãŸã€åˆ—æ–¹å‘ã«ã¯åŒã˜ç¨®é¡žã®ãƒ‡ãƒ¼ã‚¿ãŒä¸¦ã‚“ã§ã„ã‚‹ãŸã‚ã€åœ§ç¸®
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GitHub - rdblue/parquet-cli: Parquet Command-line Tools
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