...
Operator | Operand types | Description | ||
---|---|---|---|---|
<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="83eff86e16ec2f02-3f79a217-4ba24a28-8d15866c-ce06ad36380b7c2221eb0f91"><ac:plain-text-body><![CDATA[ | A[n] | A is an Array and n is an int | returns the nth element in the array A. The first element has index 0 e.g. if A is an array comprising of ['foo', 'bar'] then A[0] returns 'foo' and A[1] returns 'bar' | ]]></ac:plain-text-body></ac:structured-macro> |
<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="bcfb7db5526a9d4a-08e30c86-4aeb445a-bc1fa468-a748d7d497e0cd25c8143fed"><ac:plain-text-body><![CDATA[ | M[key] | M is a Map<K, V> and key has type K | returns the value corresponding to the key in the map e.g. if M is a map comprising of {'f' -> 'foo', 'b' -> 'bar', 'all' -> 'foobar'} then M['all'] returns 'foobar' | ]]></ac:plain-text-body></ac:structured-macro> |
S.x | S is a struct | returns the x field of S e.g for struct foobar {int foo, int bar} foobar.foo returns the integer stored in the foo field of the struct. |
...
Return Type | Aggregation Function Name (Signature) | Description | ||
---|---|---|---|---|
<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="ed1e6ded3fb22934-91c6febf-48d8481d-a9829781-ac3b30ade2ce8276110862ae"><ac:plain-text-body><![CDATA[ | BIGINT | count(*), count(expr), count(DISTINCT expr[, expr_.]) | count(*) - Returns the total number of retrieved rows, including rows containing NULL values; count(expr) - Returns the number of rows for which the supplied expression is non-NULL; count(DISTINCT expr[, expr]) - Returns the number of rows for which the supplied expression(s) are unique and non-NULL. | ]]></ac:plain-text-body></ac:structured-macro> |
DOUBLE | sum(col), sum(DISTINCT col) | returns the sum of the elements in the group or the sum of the distinct values of the column in the group | ||
DOUBLE | avg(col), avg(DISTINCT col) | returns the average of the elements in the group or the average of the distinct values of the column in the group | ||
DOUBLE | min(col) | returns the minimum value of the column in the group | ||
DOUBLE | max(col) | returns the maximum value of the column in the group |
...
Table of Content Zone | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Simple QueryFor all the active users, one can use the query of the following form:
Note that unlike SQL, we always insert the results into a table. We will illustrate later how the user can inspect these results and even dump them to a local file. You can also run the following query on Hive CLI:
This will be internally rewritten to some temporary file and displayed to the Hive client side. Partition Based QueryWhat partitions to use in a query is determined automatically by the system on the basis of where clause conditions on partition columns. For example, in order to get all the page_views in the month of 03/2008 referred from domain xyz.com, one could write the following query:
Note that page_views.date is used here because the table (above) was defined with PARTITIONED BY(date DATETIME, country STRING) ; if you name your partition something different, don't expect .date to do what you think! JoinsIn order to get a demographic breakdown (by gender) of page_view of 2008-03-03 one would need to join the page_view table and the user table on the userid column. This can be accomplished with a join as shown in the following query:
In order to do outer joins the user can qualify the join with LEFT OUTER, RIGHT OUTER or FULL OUTER keywords in order to indicate the kind of outer join (left preserved, right preserved or both sides preserved). For example, in order to do a full outer join in the query above, the corresponding syntax would look like the following query:
In order check the existence of a key in another table, the user can use LEFT SEMI JOIN as illustrated by the following example.
In order to join more than one tables, the user can use the following syntax:
Note that Hive only supports equi-joins. Also it is best to put the largest table on the rightmost side of the join to get the best performance. AggregationsIn order to count the number of distinct users by gender one could write the following query:
Multiple aggregations can be done at the same time, however, no two aggregations can have different DISTINCT columns .e.g while the following is possible
however, the following query is not allowed
Multi Table/File InsertsThe output of the aggregations or simple selects can be further sent into multiple tables or even to hadoop dfs files (which can then be manipulated using hdfs utilities). e.g. if along with the gender breakdown, one needed to find the breakdown of unique page views by age, one could accomplish that with the following query:
The first insert clause sends the results of the first group by to a Hive table while the second one sends the results to a hadoop dfs files. Dynamic-Partition InsertIn the previous examples, the user has to know which partition to insert into and only one partition can be inserted in one insert statement. If you want to load into multiple partitions, you have to use multi-insert statement as illustrated below.
In order to load data into all country partitions in a particular day, you have to add an insert statement for each country in the input data. This is very inconvenient since you have to have the priori knowledge of the list of countries exist in the input data and create the partitions beforehand. If the list changed for another day, you have to modify your insert DML as well as the partition creation DDLs. It is also inefficient since each insert statement may be turned into a MapReduce Job. Dynamic-partition insert (or multi-partition insert) is designed to solve this problem by dynamically determining which partitions should be created and populated while scanning the input table. This is a newly added feature that is only available from version 0.6.0. In the dynamic partition insert, the input column values are evaluated to determine which partition this row should be inserted into. If that partition has not been created, it will create that partition automatically. Using this feature you need only one insert statement to create and populate all necessary partitions. In addition, since there is only one insert statement, there is only one corresponding MapReduce job. This significantly improves performance and reduce the Hadoop cluster workload comparing to the multiple insert case. Below is an example of loading data to all country partitions using one insert statement:
There are several syntactic differences from the multi-insert statement:
Semantics of the dynamic partition insert statement:
Troubleshooting and best practices:
Additional documentation:
Inserting into Local FilesIn certain situations you would want to write the output into a local file so that you could load it into an excel spreadsheet. This can be accomplished with the following command:
SamplingThe sampling clause allows the users to write queries for samples of the data instead of the whole table. Currently the sampling is done on the columns that are specified in the CLUSTERED BY clause of the CREATE TABLE statement. In the following example we choose 3rd bucket out of the 32 buckets of the pv_gender_sum table:
In general the TABLESAMPLE syntax looks like:
y has to be a multiple or divisor of the number of buckets in that table as specified at the table creation time. The buckets chosen are determined if bucket_number module y is equal to x. So in the above example the following tablesample clause
would pick out the 3rd and 19th buckets. The buckets are numbered starting from 0. On the other hand the tablesample clause
would pick out half of the 3rd bucket. Union AllThe language also supports union all, e.g. if we suppose there are two different tables that track which user has published a video and which user has published a comment, the following query joins the results of a union all with the user table to create a single annotated stream for all the video publishing and comment publishing events:
Array OperationsArray columns in tables can only be created programmatically currently. We will be extending this soon to be available as part of the create table statement. For the purpose of the current example assume that pv.friends is of the type array<INT> i.e. it is an array of integers. The user can get a specific element in the array by its index as shown in the following command:
The select expressions gets the third item in the pv.friends array. The user can also get the length of the array using the size function as shown below:
Map (Associative Arrays) OperationsMaps provide collections similar to associative arrays. Such structures can only be created programmatically currently. We will be extending this soon. For the purpose of the current example assume that pv.properties is of the type map<String, String> i.e. it is an associative array from strings to string. Accordingly, the following query:
can be used to select the 'page_type' property from the page_views table. Similar to arrays, the size function can also be used to get the number of elements in a map as shown in the following query:
Custom Map/Reduce ScriptsUsers can also plug in their own custom mappers and reducers in the data stream by using features natively supported in the Hive language. e.g. in order to run a custom mapper script - map_script - and a custom reducer script - reduce_script - the user can issue the following command which uses the TRANSFORM clause to embed the mapper and the reducer scripts. Note that columns will be transformed to string and delimited by TAB before feeding to the user script, and the standard output of the user script will be treated as TAB-separated string columns. User scripts can output debug information to standard error which will be shown on the task detail page on hadoop.
Sample map script (weekday_mapper.py )
Of course, both MAP and REDUCE are "syntactic sugar" for the more general select transform. The inner query could also have been written as such:
Schema-less map/reduce: If there is no "AS" clause after "USING map_script", Hive assumes the output of the script contains 2 parts: key which is before the first tab, and value which is the rest after the first tab. Note that this is different from specifying "AS key, value" because in that case value will only contains the portion between the first tab and the second tab if there are multiple tabs. In this way, we allow users to migrate old map/reduce scripts without knowing the schema of the map output. User still needs to know the reduce output schema because that has to match what is in the table that we are inserting to.
Distribute By and Sort By: Instead of specifying "cluster by", the user can specify "distribute by" and "sort by", so the partition columns and sort columns can be different. The usual case is that the partition columns are a prefix of sort columns, but that is not required.
Co-GroupsAmongst the user community using map/reduce, cogroup is a fairly common operation wherein the data from multiple tables are sent to a custom reducer such that the rows are grouped by the values of certain columns on the tables. With the UNION ALL operator and the CLUSTER BY specification, this can be achieved in the Hive query language in the following way. Suppose we wanted to cogroup the rows from the actions_video and action_comments table on the uid column and send them to the 'reduce_script' custom reducer, the following syntax can be used by the user:
|