Apache Spark: SparkSQLリファレンス〜関数編・Window関数〜
SparkSQLã§ã¯ãƒ‡ãƒ¼ã‚¿ã®åˆ‡ã‚Šå‡ºã—集計を行ã†Window関数を使ã†ã“ã¨ãŒã§ãã¾ã™ã€‚ Window計算自体ã¯Window関数以外ã®é›†è¨ˆé–¢æ•°ã§è¡Œã†ã“ã¨ã‚‚ã§ãã¾ã™ã€‚ avg(e: Column) avg(columnName: String) グループ内ã§ã®å¹³å‡å€¤ã‚’計算ã—ã¾ã™ã€‚ sql: select avg( e ) from table group by a DataFrame: df.groupBy( $"a" ).agg( avg( $"e" ) ) 例) グループ内アイテム㌠( 1, 3, 3, 9 )ã®å ´åˆã€4ãŒè¿”ã‚Šã¾ã™ã€‚ 関数 内容 ver.
How can I change column types in Spark SQL's DataFrame?
Suppose I'm doing something like: val df = sqlContext.load("com.databricks.spark.csv", Map("path" -> "cars.csv", "header" -> "true")) df.printSchema() root |-- year: string (nullable = true) |-- make: string (nullable = true) |-- model: string (nullable = true) |-- comment: string (nullable = true) |-- blank: string (nullable = true) df.show() year make model comment blank 2012 Tesla S No comment
1114#SELECT
シンボル表ç¾ã¨$メソッドã¯import sqlContext.implicits._ã‚’è¡Œã„ã€implicit classをインãƒãƒ¼ãƒˆã™ã‚‹ã¨ä½¿ãˆã‚‹ã‚ˆã†ã«ãªã‚Šã¾ã™ã€‚ ・WHERE WHEREã¯ãƒ‡ãƒ¼ã‚¿ã®ãƒ•ã‚£ãƒ«ã‚¿ãƒªãƒ³ã‚°æ¡ä»¶ã‚’指定ã—ã¾ã™ã€‚ ※ã“ã“らã¸ã‚“ã¯SQL99ç‰ã®ä»•æ§˜ã¨åŒã˜ã§ã™ã€‚ SQL >>> SELECT * FROM dataframe WHERE col2 > 0 DataFrame >>> DataFrameã§ã¯filterを使ã†ã“ã¨ã‚‚ã§ãã¾ã™ã€‚ DataFrame .where( $"col2" > 0 ) DataFrame .filter( $"col2".isNull ) ・JOIN JOINã¯è¤‡æ•°ã®ãƒ‡ãƒ¼ã‚¿(テーブル)ã‚’æ¡ä»¶ã«å¾“ã£ã¦çµåˆã—ã¾ã™ã€‚ 使ãˆã‚‹JOINタイプã¯ä¸‹è¨˜ã®ã¨ãŠã‚Šã§ã™ã€‚ å˜ã«JOINã¨ã ã‘指定ã™ã‚‹ã¨INNER JOINã¨ãªã‚Šã¾ã™ã€‚ LEFT SEMI JOINã¯
How to export data from Spark SQL to CSV
This command works with HiveQL: insert overwrite directory '/data/home.csv' select * from testtable; But with Spark SQL I'm getting an error with an org.apache.spark.sql.hive.HiveQl stack trace: java.lang.RuntimeException: Unsupported language features in query: insert overwrite directory '/data/home.csv' select * from testtable Please guide me to write export to CSV feature in Spark SQL.
Apache Spark: SparkSQLリファレンス〜関数編・集計関数〜
SQL関数ã®èŠ±å½¢ã€é›†è¨ˆé–¢æ•°ã§ã™ã€‚ 基本的ã«ã¯GROUP BYã¨ä¸€ç·’ã«ä½¿ç”¨ã—ã¾ã™ã€‚ ※GROUP BYを使用ã—ãªã„ã¨ã‚°ãƒ«ãƒ¼ãƒ”ングを伴ã‚ãªã„集計ã«ãªã‚Šã€å…¨ä»¶ã‚«ã‚¦ãƒ³ãƒˆãªã©ã«ä½¿ã„ã¾ã™ã€‚ approxCountDistinct(e: Column) approxCountDistinct(columnName: String) approxCountDistinct(e: Column, rsd: Double) approxCountDistinct(columnName: String, rsd: Double) グルーピング内ã®ã‚¢ã‚¤ãƒ†ãƒ æ•°ã®è¿‘似値を返ã—ã¾ã™ã€‚ アイテム数ãŒå°‘ãªã„å ´åˆã¯æ£ç¢ºãªå€¤ã‚’è¿”ã—ã¾ã™ã€‚アイテム数ãŒå¤šã„å ´åˆã«è¿‘似値ã¨ãªã‚Šã¾ã™ã€‚ 第２引数ã«ç›¸å¯¾æ¨™æº–åå·®(RSD)を指定ã—ã€è¿‘ä¼¼ã®è¨±å®¹åº¦ã‚’è¨å®šã™ã‚‹ã“ã¨ã‚‚ã§ãã¾ã™ã€‚ sql: select approxCountDistinct( e
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Apache Sparkã®DataFrameã§row_numberを実行ã™ã‚‹æ–¹æ³• - Qiita
IF/Case statement when using SparkSQL with Cassandra
[Mysql] SQLã§if文を使ㆠ- Qiita![[Mysql] SQLã§if文を使ㆠ- Qiita](https://cdn-ak-scissors.b.st-hatena.com/image/square/dbf4f2eb91ecf2b55f8bc4c2e045b3cb105db419/backend=imagemagick;height=288;version=1;width=512/https%3A%2F%2Fqiita-user-contents.imgix.net%2Fhttps%253A%252F%252Fqiita-user-contents.imgix.net%252Fhttps%25253A%25252F%25252Fcdn.qiita.com%25252Fassets%25252Fpublic%25252Farticle-ogp-background-afbab5eb44e0b055cce1258705637a91.png%253Fixlib%253Drb-4.0.0%2526w%253D1200%2526blend64%253DaHR0cHM6Ly9xaWl0YS11c2VyLXByb2ZpbGUtaW1hZ2VzLmltZ2l4Lm5ldC9odHRwcyUzQSUyRiUyRnMzLWFwLW5vcnRoZWFzdC0xLmFtYXpvbmF3cy5jb20lMkZxaWl0YS1pbWFnZS1zdG9yZSUyRjAlMkY2NjAwMyUyRjE1MTZjMzdkZmEzMThhNmJjMzU3NmFlNDM0MWFjODI0MDg3ZDM5NmUlMkZ4X2xhcmdlLnBuZyUzRjE1NzkwNzIwNTQ_aXhsaWI9cmItNC4wLjAmYXI9MSUzQTEmZml0PWNyb3AmbWFzaz1lbGxpcHNlJmJnPUZGRkZGRiZmbT1wbmczMiZzPWNjYWEyMGVlY2JiNTI5MmRkY2VjYjQzZDVjMDQxMzk4%2526blend-x%253D120%2526blend-y%253D467%2526blend-w%253D82%2526blend-h%253D82%2526blend-mode%253Dnormal%2526s%253D68f0b2de3389b543b5335d8c9686ebd3%3Fixlib%3Drb-4.0.0%26w%3D1200%26fm%3Djpg%26mark64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZ3PTk2MCZoPTMyNCZ0eHQ9JTVCTXlzcWwlNUQlMjBTUUwlRTMlODElQTdpZiVFNiU5NiU4NyVFMyU4MiU5MiVFNCVCRCVCRiVFMyU4MSU4NiZ0eHQtYWxpZ249bGVmdCUyQ3RvcCZ0eHQtY29sb3I9JTIzMUUyMTIxJnR4dC1mb250PUhpcmFnaW5vJTIwU2FucyUyMFc2JnR4dC1zaXplPTU2JnR4dC1wYWQ9MCZzPTU2ODc1MTQwMmI2OWQ1MzBiMjc3NWU5ODQ2ZDJhNGUw%26mark-x%3D120%26mark-y%3D112%26blend64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZ3PTgzOCZoPTU4JnR4dD0lNDBzdWlzdWluYSZ0eHQtY29sb3I9JTIzMUUyMTIxJnR4dC1mb250PUhpcmFnaW5vJTIwU2FucyUyMFc2JnR4dC1zaXplPTM2JnR4dC1wYWQ9MCZzPWI4NjgyZjRjYWQ3ZDdlMWJjOTJiNGVlNTk3NGNhZmQx%26blend-x%3D242%26blend-y%3D480%26blend-w%3D838%26blend-h%3D46%26blend-fit%3Dcrop%26blend-crop%3Dleft%252Cbottom%26blend-mode%3Dnormal%26s%3Db347eace75047b5be156cf8e6794574e)
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