# hive-third-functions **Repository Path**: dongmoo/hive-third-functions ## Basic Information - **Project Name**: hive-third-functions - **Description**: hive-third-functions是一个hive udf库，包含各类hive udf库，尤其是array，map的各类函数 - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 10 - **Created**: 2018-03-16 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # hive-third-functions [![Build Status](https://travis-ci.org/aaronshan/hive-third-functions.svg?branch=master)](https://travis-ci.org/aaronshan/hive-third-functions) [![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://github.com/aaronshan/hive-third-functions/tree/master/README.md) [![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](https://github.com/aaronshan/hive-third-functions/tree/master/README-zh.md) [![Release](https://img.shields.io/github/release/aaronshan/hive-third-functions.svg)](https://github.com/aaronshan/hive-third-functions/releases) ## 简介 hive-third-functions 包含了一些很有用的hive udf函数，特别是数组和json函数. > 注意: > hive-third-functions支持hive-0.11.0或更高版本. ## 编译 ### 1. 安装依赖 目前, jdo2-api-2.3-ec.jar 在maven中央仓库中已经不可用, 因此我们不得不自己下载并安装到本地的maven库中. 命令如下： ``` wget http://www.datanucleus.org/downloads/maven2/javax/jdo/jdo2-api/2.3-ec/jdo2-api-2.3-ec.jar -O ~/jdo2-api-2.3-ec.jar mvn install:install-file -DgroupId=javax.jdo -DartifactId=jdo2-api -Dversion=2.3-ec -Dpackaging=jar -Dfile=~/jdo2-api-2.3-ec.jar ``` ### 2. 用mvn打包 ``` cd ${project_home} mvn clean package ``` 如果你想跳过单元测试，可以这样运行： ``` cd ${project_home} mvn clean package -DskipTests ``` 命令执行完成后, 将会在target目录下生成hive-third-functions-${version}-shaded.jar文件. 你也可以直接在发布页下载打包好了最新版本 [发布页](https://github.com/aaronshan/hive-third-functions/releases). > 当前最新的版本是 `2.1.2` ## 函数 ### 1. 字符函数 | 函数| 描述 | |:--|:--| |pinyin(string) -> string | 将汉字转换为拼音| |md5(string) -> string | md5 哈希| |sha256(string) -> string |sha256 哈希| ### 2. 数组函数 | 函数| 描述 | |:--|:--| |array_contains(array<E>, E) -> boolean | 判断数组是否包含某个值.| |array_equals(array<E>, array<E>) -> boolean | 判断两个数组是否相等.| |array_intersect(array, array) -> array | 返回两个数组的交集.| |array_max(array<E>) -> E | 返回数组中的最大值.| |array_min(array<E>) -> E | 返回数组中的最小值.| |array_join(array, delimiter, null_replacement) -> string | 使用给定的连接符来连接数组中的元素, `null_replacement`是一个可选项, 用来替代空值.| |array_distinct(array) -> array | 移除数组中的重复元素.| |array_position(array<E>, E) -> long | 返回给定元素在数组中第一次出现的位置 (如果没找到, 返回0).| |array_remove(array<E>, E) -> array | 删除数组中的给定元素.| |array_reverse(array) -> array | 反转一个数组.| |array_sort(array) -> array | 对数组排序, 数组中的元素必需是可排序的.| |array_concat(array, array) -> array | 连接两个数组.| |array_value_count(array<E>, E) -> long | 统计数组中包含给定元素的个数.| |array_slice(array, start, length) -> array | 对数组进行分片操作，start为正数从前开始分片, start为负数从后开始分片, 长度为指定的长度.| |array_element_at(array<E>, index) -> E | 返回指定位置的数组元素. 如果索引位置 < 0, 则从尾部开始计数并返回.| ### 3. map函数 | 函数| 描述 | |:--|:--| |map_build(x<K>, y<V>) -> map<K, V>| 根据指定的键/值对数组创建map.| |map_concat(x<K, V>, y<K, V>) -> map<K,V> | 返回两个map的并集. 如果一个键在 `x` 和 `y`中同时出现, 那对应值来自`y`.| |map_element_at(map<K, V>, key) -> V | 如果指定的`key`存在，返回对应的值, 否则返回 `NULL` .| |map_equals(x<K, V>, y<K, V>) -> boolean | 判断map x 和 map y是否相等.| ### 4. 日期函数 | 函数| 描述 | |:--|:--| |day_of_week(date_string \| date) -> int | 一周的第几天,周一返回 1, 周日返回 7, 出错返回null.| |day_of_year(date_string \| date) -> int | 一年的第几天. 值的范围从 1 到 366.| |zodiac_en(date_string \| date) -> string | 将日期转换为星座英文| |zodiac_cn(date_string \| date) -> string | 将日期转换为星座中文 | |type_of_day(date_string \| date) -> string | 获取日期的类型(1: 法定节假日, 2: 正常周末, 3: 正常工作日 4:攒假的工作日),错误返回-1. | ### 5. json函数 | 函数| 描述 | |:--|:--| |json_array_get(json, jsonPath) -> array(varchar) |returns the element at the specified index into the `json_array`. The index is zero-based.| |json_array_length(json, jsonPath) -> array(varchar) |returns the array length of `json` (a string containing a JSON array).| |json_array_extract(json, jsonPath) -> array(varchar) |extract json array by given jsonPath.| |json_array_extract_scalar(json, jsonPath) -> array(varchar) |like `json_array_extract`, but returns the result value as a string (as opposed to being encoded as JSON).| |json_extract(json, jsonPath) -> array(varchar) |extract json by given jsonPath.| |json_extract_scalar(json, jsonPath) -> array(varchar) |like `json_extract`, but returns the result value as a string (as opposed to being encoded as JSON).| |json_size(json, jsonPath) -> array(varchar) |like `json_extract`, but returns the size of the value. For objects or arrays, the size is the number of members, and the size of a scalar value is zero.| ### 6. 位函数 | 函数| 描述 | |:--|:--| |bit_count(x, bits) -> bigint | count the number of bits set in `x` (treated as bits-bit signed integer) in 2’s complement representation | |bitwise_and(x, y) -> bigint | returns the bitwise AND of `x` and `y` in 2’s complement arithmetic.| |bitwise_not(x) -> bigint | returns the bitwise NOT of `x` in 2’s complement arithmetic. | |bitwise_or(x, y) -> bigint | returns the bitwise OR of `x` and `y` in 2’s complement arithmetic.| |bitwise_xor(x, y) -> bigint | returns the bitwise XOR of `x` and `y` in 2’s complement arithmetic. | ### 7. 中国身份证函数 | 函数| 描述 | |:--|:--| |id_card_province(string) -> string |从身份证号获取省份| |id_card_city(string) -> string |从身份证号获取城市| |id_card_area(string) -> string |从身份证号获取区/县| |id_card_birthday(string) -> string |从身份证号获取生日| |id_card_gender(string) -> string |从身份证号获取性别| |is_valid_id_card(string) -> boolean |鉴定身份证号是否有效.| |id_card_info(string) -> json |获取身份证号信息. 包活省份、城市、区县等.| ### 8. 坐标系函数 | 函数| 描述 | |:--|:--| |wgs_distance(double lat1, double lng1, double lat2, double lng2) -> double | 计算 WGS84坐标距离, 单位米. | |gcj_to_bd(double,double) -> json | GCJ-02(火星坐标系) 转为 BD-09(百度坐标系), 谷歌、高德——>百度| |bd_to_gcj(double,double) -> json | BD-09(百度坐标系) 转为 GCJ-02(火星坐标系), 百度——>谷歌、高德| |wgs_to_gcj(double,double) -> json | WGS84(地球坐标系) 转为 GCJ02(火星坐标系)| |gcj_to_wgs(double,double) -> json | GCJ02(火星坐标系) 转为 GPS84(地球坐标系), 输出的坐标精度在1到2米.| |gcj_extract_wgs(double,double) -> json | GCJ02(火星坐标系) 转为 GPS84, 输出的坐标精度在0.5米. 但是计算比`gcj_to_wgs`耗时长. | > 关于互联网地图坐标系的说明见: [当前互联网地图的坐标系现状](https://github.com/aaronshan/hive-third-functions/tree/master/README-geo.md) ### 9. url函数 | 函数| 描述 | |:--|:--| |url_encode(value) -> string | escapes value by encoding it so that it can be safely included in URL query parameter names and values| |url_decode(value) -> string | unescape the URL encoded value. This function is the inverse of `url_encode`. | ## 用法 将下面这些内容写入 `${HOME}/.hiverc` 文件, 或者也可以按需在hive命令行环境中执行. ``` add jar ${jar_location_dir}/hive-third-functions-${version}-shaded.jar create temporary function array_contains as 'cc.shanruifeng.functions.array.UDFArrayContains'; create temporary function array_equals as 'cc.shanruifeng.functions.array.UDFArrayEquals'; create temporary function array_intersect as 'cc.shanruifeng.functions.array.UDFArrayIntersect'; create temporary function array_max as 'cc.shanruifeng.functions.array.UDFArrayMax'; create temporary function array_min as 'cc.shanruifeng.functions.array.UDFArrayMin'; create temporary function array_join as 'cc.shanruifeng.functions.array.UDFArrayJoin'; create temporary function array_distinct as 'cc.shanruifeng.functions.array.UDFArrayDistinct'; create temporary function array_position as 'cc.shanruifeng.functions.array.UDFArrayPosition'; create temporary function array_remove as 'cc.shanruifeng.functions.array.UDFArrayRemove'; create temporary function array_reverse as 'cc.shanruifeng.functions.array.UDFArrayReverse'; create temporary function array_sort as 'cc.shanruifeng.functions.array.UDFArraySort'; create temporary function array_concat as 'cc.shanruifeng.functions.array.UDFArrayConcat'; create temporary function array_value_count as 'cc.shanruifeng.functions.array.UDFArrayValueCount'; create temporary function array_slice as 'cc.shanruifeng.functions.array.UDFArraySlice'; create temporary function array_element_at as 'cc.shanruifeng.functions.array.UDFArrayElementAt'; create temporary function bit_count as 'cc.shanruifeng.functions.bitwise.UDFBitCount'; create temporary function bitwise_and as 'cc.shanruifeng.functions.bitwise.UDFBitwiseAnd'; create temporary function bitwise_not as 'cc.shanruifeng.functions.bitwise.UDFBitwiseNot'; create temporary function bitwise_or as 'cc.shanruifeng.functions.bitwise.UDFBitwiseOr'; create temporary function bitwise_xor as 'cc.shanruifeng.functions.bitwise.UDFBitwiseXor'; create temporary function map_build as 'cc.shanruifeng.functions.map.UDFMapBuild'; create temporary function map_concat as 'cc.shanruifeng.functions.map.UDFMapConcat'; create temporary function map_element_at as 'cc.shanruifeng.functions.map.UDFMapElementAt'; create temporary function map_equals as 'cc.shanruifeng.functions.map.UDFMapEquals'; create temporary function day_of_week as 'cc.shanruifeng.functions.date.UDFDayOfWeek'; create temporary function day_of_year as 'cc.shanruifeng.functions.date.UDFDayOfYear'; create temporary function type_of_day as 'cc.shanruifeng.functions.date.UDFTypeOfDay'; create temporary function zodiac_cn as 'cc.shanruifeng.functions.date.UDFZodiacSignCn'; create temporary function zodiac_en as 'cc.shanruifeng.functions.date.UDFZodiacSignEn'; create temporary function pinyin as 'cc.shanruifeng.functions.string.UDFChineseToPinYin'; create temporary function md5 as 'cc.shanruifeng.functions.string.UDFMd5'; create temporary function sha256 as 'cc.shanruifeng.functions.string.UDFSha256'; create temporary function json_array_get as 'cc.shanruifeng.functions.json.UDFJsonArrayGet'; create temporary function json_array_length as 'cc.shanruifeng.functions.json.UDFJsonArrayLength'; create temporary function json_array_extract as 'cc.shanruifeng.functions.json.UDFJsonArrayExtract'; create temporary function json_array_extract_scalar as 'cc.shanruifeng.functions.json.UDFJsonArrayExtractScalar'; create temporary function json_extract as 'cc.shanruifeng.functions.json.UDFJsonExtract'; create temporary function json_extract_scalar as 'cc.shanruifeng.functions.json.UDFJsonExtractScalar'; create temporary function json_size as 'cc.shanruifeng.functions.json.UDFJsonSize'; create temporary function id_card_province as 'cc.shanruifeng.functions.card.UDFChinaIdCardProvince'; create temporary function id_card_city as 'cc.shanruifeng.functions.card.UDFChinaIdCardCity'; create temporary function id_card_area as 'cc.shanruifeng.functions.card.UDFChinaIdCardArea'; create temporary function id_card_birthday as 'cc.shanruifeng.functions.card.UDFChinaIdCardBirthday'; create temporary function id_card_gender as 'cc.shanruifeng.functions.card.UDFChinaIdCardGender'; create temporary function is_valid_id_card as 'cc.shanruifeng.functions.card.UDFChinaIdCardValid'; create temporary function id_card_info as 'cc.shanruifeng.functions.card.UDFChinaIdCardInfo'; create temporary function wgs_distance as 'cc.shanruifeng.functions.geo.UDFGeoWgsDistance'; create temporary function gcj_to_bd as 'cc.shanruifeng.functions.geo.UDFGeoGcjToBd'; create temporary function bd_to_gcj as 'cc.shanruifeng.functions.geo.UDFGeoBdToGcj'; create temporary function wgs_to_gcj as 'cc.shanruifeng.functions.geo.UDFGeoWgsToGcj'; create temporary function gcj_to_wgs as 'cc.shanruifeng.functions.geo.UDFGeoGcjToWgs'; create temporary function gcj_extract_wgs as 'cc.shanruifeng.functions.geo.UDFGeoGcjExtractWgs'; create temporary function url_encode as 'cc.shanruifeng.functions.url.UDFUrlEncode'; create temporary function url_decode as 'cc.shanruifeng.functions.url.UDFUrlDecode'; ``` 你可以在hive的命令杭中使用下面的语句来查看函数的细节. ``` hive> describe function zodiac_cn; zodiac_cn(date) - from the input date string or separate month and day arguments, returns the sing of the Zodiac. ``` 或者 ``` hive> describe function extended zodiac_cn; zodiac_cn(date) - from the input date string or separate month and day arguments, returns the sing of the Zodiac. Example: > select zodiac_cn(date_string) from src; > select zodiac_cn(month, day) from src; ``` ### 示例 ``` select pinyin('中国') => zhongguo select md5('aaronshan') => 95686bc0483262afe170b550dd4544d1 select sha256('aaronshan') => d16bb375433ad383169f911afdf45e209eabfcf047ba1faebdd8f6a0b39e0a32 ``` ``` select day_of_week('2016-07-12') => 2 select day_of_year('2016-01-01') => 1 select type_of_day('2016-10-01') => 1 select type_of_day('2016-07-16') => 2 select type_of_day('2016-07-15') => 3 select type_of_day('2016-09-18') => 4 select zodiac_cn('1989-01-08') => 魔羯座 select zodiac_en('1989-01-08') => Capricorn ``` ``` select array_contains(array(16,12,18,9), 12) => true select array_equals(array(16,12,18,9), array(16,12,18,9)) => true select array_intersect(array(16,12,18,9,null), array(14,9,6,18,null)) => [null,9,18] select array_max(array(16,13,12,13,18,16,9,18)) => 18 select array_min(array(16,12,18,9)) => 9 select array_join(array(16,12,18,9,null), '#','=') => 16#12#18#9#= select array_distinct(array(16,13,12,13,18,16,9,18)) => [9,12,13,16,18] select array_position(array(16,13,12,13,18,16,9,18), 13) => 2 select array_remove(array(16,13,12,13,18,16,9,18), 13) => [16,12,18,16,9,18] select array_reverse(array(16,12,18,9)) => [9,18,12,16] select array_sort(array(16,13,12,13,18,16,9,18)) => [9,12,13,13,16,16,18,18] select array_concat(array(16,12,18,9,null), array(14,9,6,18,null)) => [16,12,18,9,null,14,9,6,18,null] select array_value_count(array(16,13,12,13,18,16,9,18), 13) => 2 select array_slice(array(16,13,12,13,18,16,9,18), -2, 3) => [9,18] select array_element_at(array(16,13,12,13,18,16,9,18), -1) => 18 ``` ``` select map_build(array('key1','key2'), array(16,12)) => {"key1":16,"key2":12} select map_concat(map_build(array('key1','key2'), array(16,12)), map_build(array('key1','key3'), array(17,18))) => {"key1":17,"key2":12,"key3":18} select map_element_at(map_build(array('key1','key2'), array(16,12)), 'key1') => 16 select map_equals(map_build(array('key1','key2'), array(16,12)), map_build(array('key1','key2'), array(16,12))) => true ``` ``` select id_card_info('110101198901084517') => {"valid":true,"area":"东城区","province":"北京市","gender":"男","city":"北京市"} ``` ``` select json_array_get("[{\"a\":{\"b\":\"13\"}}, {\"a\":{\"b\":\"18\"}}, {\"a\":{\"b\":\"12\"}}]", 1); => {"a":{"b":"18"}} select json_array_get('["a", "b", "c"]', 0); => a select json_array_get('["a", "b", "c"]', 1); => b select json_array_get('["c", "b", "a"]', -1); => a select json_array_get('["c", "b", "a"]', -2); => b select json_array_get('[]', 0); => null select json_array_get('["a", "b", "c"]', 10); => null select json_array_get('["c", "b", "a"]', -10); => null select json_array_length("[{\"a\":{\"b\":\"13\"}}, {\"a\":{\"b\":\"18\"}}, {\"a\":{\"b\":\"12\"}}]"); => 3 select json_array_extract("[{\"a\":{\"b\":\"13\"}}, {\"a\":{\"b\":\"18\"}}, {\"a\":{\"b\":\"12\"}}]", "$.a.b"); => ["\"13\"","\"18\"","\"12\""] select json_array_extract_scalar("[{\"a\":{\"b\":\"13\"}}, {\"a\":{\"b\":\"18\"}}, {\"a\":{\"b\":\"12\"}}]", "$.a.b") => ["13","18","12"] select json_extract("{\"a\":{\"b\":\"12\"}}", "$.a.b"); => "12" select json_extract_scalar("{\"a\":{\"b\":\"12\"}}", "$.a.b") => 12 select json_extract_scalar('[1, 2, 3]', '$[2]'); select json_extract_scalar(json, '$.store.book[0].author'); select json_size('{"x": {"a": 1, "b": 2}}', '$.x'); => 2 select json_size('{"x": [1, 2, 3]}', '$.x'); => 3 select json_size('{"x": {"a": 1, "b": 2}}', '$.x.a'); => 0 ``` ``` select gcj_to_bd(39.915, 116.404) => {"lng":116.41036949371029,"lat":39.92133699351022} select bd_to_gcj(39.915, 116.404) => {"lng":116.39762729119315,"lat":39.90865673957631} select wgs_to_gcj(39.915, 116.404) => {"lng":116.41024449916938,"lat":39.91640428150164} select gcj_to_wgs(39.915, 116.404) => {"lng":116.39775550083061,"lat":39.91359571849836} select gcj_extract_wgs(39.915, 116.404) => {"lng":116.39775549316407,"lat":39.913596801757805} ``` ``` select url_encode('http://shanruifeng.cc/') => http%3A%2F%2Fshanruifeng.cc%2F ```