Some useful custom hive udf functions, especial array and json functions.
Note: hive-third-functions support hive-0.11.0 or higher.
Now, jdo2-api-2.3-ec.jar not available in the maven central repository, so we have to manually install it into our local maven repository.
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
cd ${project_home}
mvn clean package
If you want to skip unit tests, please run:
cd ${project_home}
mvn clean package -DskipTests
It will generate hive-third-functions-${version}-shaded.jar in target directory.
You can also directly download file from release page.
current latest version is
2.2.1
Now, I had already release hive-third-functions to maven repositories. To add a dependency on hive-third-functions using Maven, use the following:
<dependency>
<groupId>com.github.aaronshan</groupId>
<artifactId>hive-third-functions</artifactId>
<version>2.2.1</version>
</dependency>
| function | description |
|---|---|
| pinyin(string) -> string | convert chinese to pinyin |
| md5(string) -> string | md5 hash |
| sha256(string) -> string | sha256 hash |
| codepoint(string) -> integer | Returns the Unicode code point of the only character of string. |
| hamming_distance(string1, string2) -> bigint | Returns the Hamming distance of string1 and string2. |
| levenshtein_distance(string1, string2) -> bigint | Returns the Levenshtein edit distance of string1 and string2. |
| normalize(string, form) -> varchar | Transforms string with the specified normalization form. form must be be one of the following keywords: Normalize Form Description |
| strpos(string, substring) -> bigint | Returns the starting position of the first instance of substring in string. Positions start with 1. If not found, 0 is returned. |
| split_to_map(string, entryDelimiter, keyValueDelimiter) -> map<varchar, varchar> | Splits string by entryDelimiter and keyValueDelimiter and returns a map. entryDelimiter splits string into key-value pairs. keyValueDelimiter splits each pair into key and value. |
| split_to_multimap(string, entryDelimiter, keyValueDelimiter) -> map(varchar, array(varchar)) | Splits string by entryDelimiter and keyValueDelimiter and returns a map containing an array of values for each unique key. entryDelimiter splits string into key-value pairs. keyValueDelimiter splits each pair into key and value. The values for each key will be in the same order as they appeared in string. |
| Form | Description |
|---|---|
| NFD | Canonical Decomposition |
| NFC | Canonical Decomposition, followed by Canonical Composition |
| NFKD | Compatibility Decomposition |
| NFKC | Compatibility Decomposition, followed by Canonical Composition |
| function | description |
|---|---|
| array_contains(array<E>, E) -> boolean | whether array contains value or not. |
| array_equals(array<E>, array<E>) -> boolean | whether two array equals or not. |
| array_intersect(array, array) -> array | returns the two array's intersection, without duplicates. |
| array_max(array<E>) -> E | returns the maximum value of input array. |
| array_min(array<E>) -> E | returns the minimum value of input array. |
| array_join(array, delimiter, null_replacement) -> string | concatenates the elements of the given array using the delimiter and an optional null_replacement to replace nulls. |
| array_distinct(array) -> array | remove duplicate values from the array. |
| array_position(array<E>, E) -> long | returns the position of the first occurrence of the element in array (or 0 if not found). |
| array_remove(array<E>, E) -> array | remove all elements that equal element from array. |
| array_reverse(array) -> array | reverse the array element. |
| array_sort(array) -> array | sorts and returns the array. The elements of array must be orderable. |
| array_concat(array, array) -> array | concatenates two arrays. |
| array_value_count(array<E>, E) -> long | count array's element number that element value equals given value. |
| array_slice(array, start, length) -> array | subsets array starting from index start (or starting from the end if start is negative) with a length of length. |
| array_element_at(array<E>, index) -> E | returns element of array at given index. If index < 0, element_at accesses elements from the last to the first. |
| array_shuffle(array) -> array | Generate a random permutation of the given array x. |
| sequence(start, end) -> array | Generate a sequence of integers from start to stop. |
| sequence(start, end, step) -> array | Generate a sequence of integers from start to stop, incrementing by step. |
| sequence(start_date_string, end_data_string, step) -> array | Generate a sequence of date string from start to stop, incrementing by step. |
| array_value_count(array<E>, E) -> long | count array's element number that element value equals given value.. |
| function | description |
|---|---|
| map_build(x<K>, y<V>) -> map<K, V> | returns a map created using the given key/value arrays. |
| map_concat(x<K, V>, y<K, V>) -> map<K,V> | returns the union of two maps. If a key is found in both x and y, that key’s value in the resulting map comes from y. |
| map_element_at(map<K, V>, key) -> V | returns value for given key, or NULL if the key is not contained in the map. |
| map_equals(x<K, V>, y<K, V>) -> boolean | whether map x equals with map y or not. |
| function | description |
|---|---|
| day_of_week(date_string | date) -> int | day of week,if monday,return 1, sunday return 7, error return null. |
| day_of_year(date_string | date) -> int | day of year. The value ranges from 1 to 366. |
| zodiac_en(date_string | date) -> string | convert date to zodiac |
| zodiac_cn(date_string | date) -> string | convert date to zodiac chinese |
| type_of_day(date_string | date) -> string | for chinese. 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. |
| function | description |
|---|---|
| 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. |
| function | description |
|---|---|
| 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. |
| function | description |
|---|---|
| id_card_province(string) -> string | get user's province |
| id_card_city(string) -> string | get user's city |
| id_card_area(string) -> string | get user's area |
| id_card_birthday(string) -> string | get user's birthday |
| id_card_gender(string) -> string | get user's gender |
| is_valid_id_card(string) -> boolean | determine is valid china id card No. |
| id_card_info(string) -> json | get china id card info. include province, city, area etc. |
| function | description |
|---|---|
| wgs_distance(double lat1, double lng1, double lat2, double lng2) -> double | calculate WGS84 coordinate distance, in meters. |
| gcj_to_bd(double,double) -> json | GCJ-02(火星坐标系) convert to 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) convert to 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) convert to GCJ02(火星坐标系) |
| gcj_to_wgs(double,double) -> json | GCJ02(火星坐标系) convert to GPS84(地球坐标系), output coordinate WGS-84 accuracy within 1 to 2 meters. |
| gcj_extract_wgs(double,double) -> json | GCJ02(火星坐标系) convert to GPS84, output coordinate WGS-84 accuracy within 0.5 meters. but compute cost more time than gcj_to_wgs. |
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
| function | description |
|---|---|
| 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. |
| function | description |
|---|---|
| infinity() -> double | Returns the constant representing positive infinity. |
| is_finite(x) -> boolean | Determine if x is finite. |
| is_infinite(x) -> boolean | Determine if x is infinite. |
| is_nan(x) -> boolean | Determine if x is not-a-number. |
| nan() -> double | Returns the constant representing not-a-number. |
| from_base(string, radix) -> bigint | Returns the value of string interpreted as a base-radix number. |
| to_base(x, radix) -> varchar | Returns the base-radix representation of x. |
| cosine_similarity(x, y) -> double | Returns the cosine similarity between the sparse vectors x and y |
| inverse_normal_cdf(mean, sd, p) -> double | Compute the inverse of the Normal cdf with given mean and standard deviation (sd) for the cumulative probability (p): P(N < n). The mean must be a real value and the standard deviation must be a real and positive value. The probability p must lie on the interval (0, 1). |
| normal_cdf(mean, sd, v) -> double | Compute the Normal cdf with given mean and standard deviation (sd): P(N < v; mean, sd). The mean and value v must be real values and the standard deviation must be a real and positive value. |
| function | description |
|---|---|
| regexp_like(string, pattern) -> boolean | Evaluates the regular expression pattern and determines if it is contained within string. |
| regexp_extract_all(string, pattern) -> array(varchar) | Returns the substring(s) matched by the regular expression pattern in string. |
| regexp_extract(string, pattern) -> varchar | Returns the first substring matched by the regular expression pattern in string. |
| regexp_replace(string, pattern) -> varchar | Removes every instance of the substring matched by the regular expression pattern from string. |
| regexp_replace(string, pattern, replacement) -> varchar | Replaces every instance of the substring matched by the regular expression pattern in string with replacement. |
Put these statements into ${HOME}/.hiverc or exec its on hive cli env.
add jar ${jar_location_dir}/hive-third-functions-${version}-shaded.jar
create temporary function array_contains as 'com.github.aaronshan.functions.array.UDFArrayContains';
create temporary function array_equals as 'com.github.aaronshan.functions.array.UDFArrayEquals';
create temporary function array_intersect as 'com.github.aaronshan.functions.array.UDFArrayIntersect';
create temporary function array_max as 'com.github.aaronshan.functions.array.UDFArrayMax';
create temporary function array_min as 'com.github.aaronshan.functions.array.UDFArrayMin';
create temporary function array_join as 'com.github.aaronshan.functions.array.UDFArrayJoin';
create temporary function array_distinct as 'com.github.aaronshan.functions.array.UDFArrayDistinct';
create temporary function array_position as 'com.github.aaronshan.functions.array.UDFArrayPosition';
create temporary function array_remove as 'com.github.aaronshan.functions.array.UDFArrayRemove';
create temporary function array_reverse as 'com.github.aaronshan.functions.array.UDFArrayReverse';
create temporary function array_sort as 'com.github.aaronshan.functions.array.UDFArraySort';
create temporary function array_concat as 'com.github.aaronshan.functions.array.UDFArrayConcat';
create temporary function array_value_count as 'com.github.aaronshan.functions.array.UDFArrayValueCount';
create temporary function array_slice as 'com.github.aaronshan.functions.array.UDFArraySlice';
create temporary function array_element_at as 'com.github.aaronshan.functions.array.UDFArrayElementAt';
create temporary function array_shuffle as 'com.github.aaronshan.functions.array.UDFArrayShuffle';
create temporary function sequence as 'com.github.aaronshan.functions.array.UDFSequence';
create temporary function array_value_count as 'com.github.aaronshan.functions.array.UDFArrayValueCount';
create temporary function bit_count as 'com.github.aaronshan.functions.bitwise.UDFBitCount';
create temporary function bitwise_and as 'com.github.aaronshan.functions.bitwise.UDFBitwiseAnd';
create temporary function bitwise_not as 'com.github.aaronshan.functions.bitwise.UDFBitwiseNot';
create temporary function bitwise_or as 'com.github.aaronshan.functions.bitwise.UDFBitwiseOr';
create temporary function bitwise_xor as 'com.github.aaronshan.functions.bitwise.UDFBitwiseXor';
create temporary function map_build as 'com.github.aaronshan.functions.map.UDFMapBuild';
create temporary function map_concat as 'com.github.aaronshan.functions.map.UDFMapConcat';
create temporary function map_element_at as 'com.github.aaronshan.functions.map.UDFMapElementAt';
create temporary function map_equals as 'com.github.aaronshan.functions.map.UDFMapEquals';
create temporary function day_of_week as 'com.github.aaronshan.functions.date.UDFDayOfWeek';
create temporary function day_of_year as 'com.github.aaronshan.functions.date.UDFDayOfYear';
create temporary function type_of_day as 'com.github.aaronshan.functions.date.UDFTypeOfDay';
create temporary function zodiac_cn as 'com.github.aaronshan.functions.date.UDFZodiacSignCn';
create temporary function zodiac_en as 'com.github.aaronshan.functions.date.UDFZodiacSignEn';
create temporary function pinyin as 'com.github.aaronshan.functions.string.UDFChineseToPinYin';
create temporary function md5 as 'com.github.aaronshan.functions.string.UDFMd5';
create temporary function sha256 as 'com.github.aaronshan.functions.string.UDFSha256';
create temporary function codepoint as 'com.github.aaronshan.functions.string.UDFCodePoint';
create temporary function hamming_distance as 'com.github.aaronshan.functions.string.UDFStringHammingDistance';
create temporary function levenshtein_distance as 'com.github.aaronshan.functions.string.UDFStringLevenshteinDistance';
create temporary function normalize as 'com.github.aaronshan.functions.string.UDFStringNormalize';
create temporary function strpos as 'com.github.aaronshan.functions.string.UDFStringPosition';
create temporary function split_to_map as 'com.github.aaronshan.functions.string.UDFStringSplitToMap';
create temporary function split_to_multimap as 'com.github.aaronshan.functions.string.UDFStringSplitToMultimap';
create temporary function json_array_get as 'com.github.aaronshan.functions.json.UDFJsonArrayGet';
create temporary function json_array_length as 'com.github.aaronshan.functions.json.UDFJsonArrayLength';
create temporary function json_array_extract as 'com.github.aaronshan.functions.json.UDFJsonArrayExtract';
create temporary function json_array_extract_scalar as 'com.github.aaronshan.functions.json.UDFJsonArrayExtractScalar';
create temporary function json_extract as 'com.github.aaronshan.functions.json.UDFJsonExtract';
create temporary function json_extract_scalar as 'com.github.aaronshan.functions.json.UDFJsonExtractScalar';
create temporary function json_size as 'com.github.aaronshan.functions.json.UDFJsonSize';
create temporary function id_card_province as 'com.github.aaronshan.functions.card.UDFChinaIdCardProvince';
create temporary function id_card_city as 'com.github.aaronshan.functions.card.UDFChinaIdCardCity';
create temporary function id_card_area as 'com.github.aaronshan.functions.card.UDFChinaIdCardArea';
create temporary function id_card_birthday as 'com.github.aaronshan.functions.card.UDFChinaIdCardBirthday';
create temporary function id_card_gender as 'com.github.aaronshan.functions.card.UDFChinaIdCardGender';
create temporary function is_valid_id_card as 'com.github.aaronshan.functions.card.UDFChinaIdCardValid';
create temporary function id_card_info as 'com.github.aaronshan.functions.card.UDFChinaIdCardInfo';
create temporary function wgs_distance as 'com.github.aaronshan.functions.geo.UDFGeoWgsDistance';
create temporary function gcj_to_bd as 'com.github.aaronshan.functions.geo.UDFGeoGcjToBd';
create temporary function bd_to_gcj as 'com.github.aaronshan.functions.geo.UDFGeoBdToGcj';
create temporary function wgs_to_gcj as 'com.github.aaronshan.functions.geo.UDFGeoWgsToGcj';
create temporary function gcj_to_wgs as 'com.github.aaronshan.functions.geo.UDFGeoGcjToWgs';
create temporary function gcj_extract_wgs as 'com.github.aaronshan.functions.geo.UDFGeoGcjExtractWgs';
create temporary function url_encode as 'com.github.aaronshan.functions.url.UDFUrlEncode';
create temporary function url_decode as 'com.github.aaronshan.functions.url.UDFUrlDecode';
create temporary function infinity as 'com.github.aaronshan.functions.math.UDFMathInfinity';
create temporary function is_finite as 'com.github.aaronshan.functions.math.UDFMathIsFinite';
create temporary function is_infinite as 'com.github.aaronshan.functions.math.UDFMathIsInfinite';
create temporary function nan as 'com.github.aaronshan.functions.math.UDFMathNaN';
create temporary function is_nan as 'com.github.aaronshan.functions.math.UDFMathIsNaN';
create temporary function from_base as 'com.github.aaronshan.functions.math.UDFMathFromBase';
create temporary function to_base as 'com.github.aaronshan.functions.math.UDFMathToBase';
create temporary function cosine_similarity as 'com.github.aaronshan.functions.math.UDFMathCosineSimilarity';
create temporary function normal_cdf as 'com.github.aaronshan.functions.math.UDFMathNormalCdf';
create temporary function inverse_normal_cdf as 'com.github.aaronshan.functions.math.UDFMathInverseNormalCdf';
create temporary function regexp_extract as 'com.github.aaronshan.functions.regexp.UDFRe2JRegexpExtract';
create temporary function regexp_extract_all as 'com.github.aaronshan.functions.regexp.UDFRe2JRegexpExtractAll';
create temporary function regexp_like as 'com.github.aaronshan.functions.regexp.UDFRe2JRegexpLike';
create temporary function regexp_replace as 'com.github.aaronshan.functions.regexp.UDFRe2JRegexpReplace';
create temporary function regexp_split as 'com.github.aaronshan.functions.regexp.UDFRe2JRegexpSplit';
You can use these statements on hive cli env get detail of function.
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.
or
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;
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 array_shuffle(array(16,12,18,9))
select sequence(1, 5) => [1, 2, 3, 4, 5]
select sequence(5, 1) => [5, 4, 3, 2, 1]
select sequence(1, 9, 4) => [1, 5, 9]
select sequence('2016-04-12 00:00:00', '2016-04-14 00:00:00', 24*3600*1000) => ['2016-04-12 00:00:00', '2016-04-13 00:00:00', '2016-04-14 00:00:00']
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
select cosine_similarity(map_build(array['a'], array[1.0]), map_build(array['a'], array[2.0])); => 1.0