Concepts

What is Hive

Hive is the next generation infrastructure made with the goal of providing tools to enable easy data summarization, adhoc querying and analysis of detail data. In addition it also provides a simple query language called QL which is based on SQL and which enables users familiar with SQL to do ad-hoc querying, summarization and data analysis. At the same time, this language also allows traditional map/reduce programmers to be able to plug in their custom mappers and reducers to do more sophisticated analysis which may not be supported by the built in capabilities of the language.

What is NOT Hive

Hive is based on Hadoop, which is a batch processing system. Accordingly, this system does not and cannot promise low latencies on queries. The paradigm here is strictly of submitting jobs and being notified when the jobs are completed as opposed to real time queries. As a result it should not be compared with systems like Oracle where analysis is done on a significantly smaller amount of data but the analysis proceeds much more iteratively with the response times between iterations being less than a few minutes. For Hive queries response times for even the smallest jobs can be of the order of 5-10 minutes and for larger jobs this may even run into hours.

In the following sections we provide a tutorial on the capabilities of the system. We start by describing the concepts of data types, tables and partitions (which are very similar to what you would find in a traditional relational database) and then illustrate the capabilities of the language with the help of some examples

Data Units

In order of granularity - Hive data is organized into:

• Tables: Homogeneous units of data which have the same schema. An example of a table could be page_views table. Each row of this table could comprise of the following columns, which define the schema of the table:
  • timestamp - which is an int that corresponds to a unix timestamp of when the page was viewed.
  • userid - which is a bigint (see Primitive Types) identifying the user who viewed the page.
  • page_url - which is a string that captures the location of the page.
  • referer_url - which is a string that captures the location of the page from where we arrived at the current page.
  • IP - which is a string that captures the IP address from where the page request was made.
• Partitions: Each Table can have one or more partition Keys which determines how the data is stored. Partitions - apart from being storage units - also allow the user to efficiently identify the rows that satisfy a certain criteria. e.g a date_partition of type Datestamp and country_partition of type String. Each unique specification of the partition keys defines a partition of the Table e.g. all "US" data from "2008-02-02" is a partition of the page_views table. Therefore, if you have to run analysis on only the "US" data for 2008-02-02, you can run that analysis only on the relevant partition of the table thereby speeding up the analysis significantly. (Note, however, that just because a partition is named 2008-02-02 does not mean that it contains all or only data from that date; partitions are named after dates for convenience but it is the user's job to guarantee the relationship between partition name and data content!). Partition columns are virtual columns, they are not part of the data itself but are derived on load.
• Buckets (Cluster) : Data in each partition may in turn be divided into Buckets based on a hash of some column of the Table. For example the page_views table may be bucketed by userid (which is one of the columns of the page_view table, unlike partitions column). These can be used to efficiently sample the data.

Note that it is not necessary for tables to be partitioned or bucketed, but these abstractions allow the system to prune large quantities of data during query processing, resulting in faster query execution.

Type System

Primitive Types

• Types are associated with the columns in the tables. The following Primitive types are supported:
• Integers
  • TINYINT - 1 byte integer
  • SMALLINT - 2 byte integer
  • INT - 4 byte integer
  • BIGINT - 8 byte integer
• Floating point numbers
  • DOUBLE - Double precision
• String type
  • STRING - sequence of characters in a specified character set

Type Conversion

The Types are organized in the following hierarchy (where the parent is a super type of all the children instances):

• Type
  • |→Primitive Type
    • |→Numbers
      • |→Double
        • |→BIGINT
          • |→INT
            • |→TINYINT
      |→Strings
    |→Complex Type

This type hierarchy defines how the types are implicitly converted in the query language. Implicit conversion is allowed for types from child to an ancestor. So when a query expression expects type1 and the data is of type2 type2 is implicitly converted to type1 if type1 is an ancestor of type2 in the type hierarchy. Apart from this fundamental rule for implicit conversion, implicit conversion is also allowed for the following cases:

• String to Double

Explicit type conversion can be done using the cast operator as shown in the Table of Built in Functions section below.

Complex Types

Complex Types can be built up from primitive types and other composite types using:

• Structs: the elements within the type can be accessed using the . notation e.g. for a column c of type struct {a int; b int} the a field is accessed by the expression a.c

• Maps (key-value tuples): The elements are accessed using ['element name'] notation e.g. in a map M comprising of a mapping from 'group' -> gid the gid value can be accessed using M['group']

• Arrays (indexable lists): The elements are accessed using the [n] notation where n is an index into the array e.g. for an array A having the elements ['a', 'b', 'c'], A[1] retruns 'b'. The index starts from 0.

Using the primitive types and the constructs for creating complex types, types with arbitrary levels of nesting can be created.

e.g. a type User may comprise of the following fields:

• id - which is a 4 byte integer.
• name - which is a string.
• age - which is an integer.
• weight - which is a floating point number.
• friends - which is a array of ids(integers).
• gender - which is an integer.
• active - which is a boolean.

The tables with columns that are an instance of a complex type can only be created programmatically and NOT through hive command line at this time. We will be adding ability to add such tables through the hive command line in the future.

Built in operators and functions

Built in operators

• Relational Operators - The following operators compare the passed operands and generate a TRUE or FALSE value depending on whether the comparison between the operands holds or not.

• Relational Operators

• Arithmetic Operators - The following operators support various common arithmetic operations on the operands. All of them return number types.

Arithmetic Operators

• Logical Operators - The following operators provide support for creating logical expressions. All of them return boolean TRUE or FALSE depending upon the boolean values of the operands.

• Logical Operators

* Operators on Complex Types - The following operators provide mechanisms to access elements in Complex Types

Operators on Complex Types

Built in functions

*The following built in functions are supported in hive: List of functions in source code: FunctionRegistry.java

Built in Functions

*The following built in aggregate functions are supported in hive:

• Built in Aggregate Functions

Language capabilities

Hive query language provides the basic SQL like operations. These operations work on tables or partitions. These operations are:

• Ability to filter rows from a table using a where clause.
• Ability to select certain columns from the table using a select clause.
• Ability to do equi-joins between two tables.
• Ability to evaluate aggregations on multiple "group by" columns for the data stored in a table.
• Ability to store the results of a query into another table.
• Ability to download the contents of a table to a local (e.g., nfs) directory.
• Ability to store the results of a query in a hadoop dfs directory.
• Ability to manage tables and partitions (create, drop and alter).
• Ability to plug in custom scripts in the language of choice for custom map/reduce jobs.

Usage and Examples

The following examples highlight some salient features of the system. A detailed set of query test cases can be found at Hive Query Test Cases and the corresponding results can be found at Query Test Case Results

Creating Tables

An example statement that would create the page_view table mentioned above would be like:

    CREATE TABLE page_view(viewTime INT, userid BIGINT,
                    page_url STRING, referrer_url STRING, 
                    ip STRING COMMENT 'IP Address of the User') 
    COMMENT 'This is the page view table' 
    PARTITIONED BY(dt STRING, country STRING) 
    STORED AS SEQUENCEFILE; 

In this example the columns of the table are specified with the corresponding types. Comments can be attached both at the column level as well as at the table level. Additionally the partitioned by clause defines the partitioning columns which are different from the data columns and are actually not stored with the data. When specified in this way, the data in the files is assumed to be delimited with ascii 001(ctrl-A) used as the field delimiter and newline used as a row delimiter.

These delimiters can be parametrized if the data is not in the above format as illustrated in the following example:

    CREATE TABLE page_view(viewTime INT, userid BIGINT,
                    page_url STRING, referrer_url STRING, 
                    ip STRING COMMENT 'IP Address of the User') 
    COMMENT 'This is the page view table' 
    PARTITIONED BY(dt STRING, country STRING) 
    ROW FORMAT DELIMITED
            FIELDS TERMINATED BY '1' 
            LINES TERMINATED BY '12' 
    STORED AS SEQUENCEFILE; 

The ROW FORMAT clause allows the user to specify both the field delimiters as well as the line delimiters.

It is also a good idea to bucket the tables on certain columns so that efficient sampling queries can be executed against the data set (note: If bucketing is absent, random sampling can still be done on the table). The following example illustrates the case of the page_view table which is bucketed on userid column:

    CREATE TABLE page_view(viewTime INT, userid BIGINT,
                    page_url STRING, referrer_url STRING, 
                    ip STRING COMMENT 'IP Address of the User') 
    COMMENT 'This is the page view table' 
    PARTITIONED BY(dt STRING, country STRING) 
    CLUSTERED BY(userid) SORTED BY(viewTime) INTO 32 BUCKETS 
    ROW FORMAT DELIMITED
            FIELDS TERMINATED BY '1' 
            COLLECTION ITEMS TERMINATED BY '2' 
            MAP KEYS TERMINATED BY '3' 
            LINES TERMINATED BY '12' 
    STORED AS SEQUENCEFILE; 

In the example above, the table is bucketed (clustered by) userid and within each bucket the data is sorted in the increasing order of viewTime. Such an organization allows the user to do efficient sampling on the clustered column - in this case userid. The sorting property allows internal operators to take advantage of the better-known data structure while evaluating queries, also increasing efficiency.

    CREATE TABLE page_view(viewTime INT, userid BIGINT,
                    page_url STRING, referrer_url STRING, 
                    friends ARRAY<BIGINT>, properties MAP<STRING, STRING> 
                    ip STRING COMMENT 'IP Address of the User') 
    COMMENT 'This is the page view table' 
    PARTITIONED BY(dt STRING, country STRING) 
    CLUSTERED BY(userid) SORTED BY(viewTime) INTO 32 BUCKETS 
    ROW FORMAT DELIMITED
            FIELDS TERMINATED BY '1' 
            COLLECTION ITEMS TERMINATED BY '2' 
            MAP KEYS TERMINATED BY '3' 
            LINES TERMINATED BY '12' 
    STORED AS SEQUENCEFILE; 

In this example the columns that comprise of the table row are specified in a similar way as the definition of types. Comments can be attached both at the column level as well as at the table level. Additionally the partitioned by clause defines the partitioning columns which are different from the data columns and are actually not stored with the data. The bucketed on clause specifies which column to use for bucketing as well as how many buckets to create. The delimited row format specifies how the rows are stored in the hive table. In the case of the delimited format, this specifies how the fields are terminated, how the items within collections (arrays or maps) are terminated and how the map keys are terminated. STORED AS SEQUENCEFILE indicates that this data is stored in a binary format (using hadoop SequenceFiles) on hdfs. The values shown for the ROW FORMAT and STORED AS clauses in the above example represent the system defaults.

Table names and column names are case insensitive.

Browsing Tables and Partitions

    SHOW TABLES; 

To list existing tables in the warehouse; there are many of these, likely more than you want to browse.

    SHOW TABLES 'page.*'; 

To list tables with prefix 'page'. The pattern follows Java regular expression syntax (so the period is a wildcard).

    SHOW PARTITIONS page_view; 

To list partitions of a table. If the table is not a partitioned table then an error is thrown.

    DESCRIBE page_view; 

To list columns and column types of table.

    DESCRIBE EXTENDED page_view; 

To list columns and all other properties of table. This prints lot of information and that too not in a pretty format. Usually used for debugging.

   DESCRIBE EXTENDED page_view PARTITION (ds='2008-08-08'); 

To list columns and all other properties of a partition. This also prints lot of information which is usually used for debugging.

Loading Data

There are multiple mechanisms of loading data into Hive tables. The user can create an external table that points to a specified location within hdfs. In this particular usage, the user can copy a file into the specified location using the hdfs put or copy commands and create a table pointing to this location with all the relevant row format information. Once this is done, the user can transform this data and insert into any other Hive table. e.g. if the file /tmp/pv_2008-06-08.txt contains comma separated page views served on 2008-06-08, and this needs to be loaded into the page_view table in the appropriate partition, the following sequence of commands can achieve this:

    CREATE EXTERNAL TABLE page_view_stg(viewTime INT, userid BIGINT,
                    page_url STRING, referrer_url STRING, 
                    ip STRING COMMENT 'IP Address of the User', 
                    country STRING COMMENT 'country of origination') 
    COMMENT 'This is the staging page view table' 
    ROW FORMAT DELIMITED FIELDS TERMINATED BY '54' LINES TERMINATED BY '12' 
    STORED AS TEXTFILE 
    LOCATION '/user/data/stagging/page_view'; 

    hadoop dfs -put /tmp/pv_2008-06-08.txt /user/data/staging/page_view 

    FROM page_view_stg pvs 
    INSERT OVERWRITE TABLE page_view PARTITION(dt='2008-06-08', country='US') 
    SELECT pvs.viewTime, pvs.userid, pvs.page_url, pvs.referrer_url, null, null, pvs.ip 
    WHERE pvs.country = 'US'; 

In the example above nulls are inserted for the array and map types in the destination tables but potentially these can also come from the external table if the proper row formats are specified.

This method is useful if there is already legacy data in hdfs on which the user wants to put some metadata so that that the data can be queried and manipulated using hive.

Additionally, the system also supports syntax that can load the data from a file in the local files system directly into a hive table where the input data format is same as the table format. If /tmp/pv_2008-06-08_us.txt already contains the data for US, then we do not need any additional filtering as shown in the previous example. The load in this case can be done using the following syntax:

   LOAD DATA LOCAL INPATH `/tmp/pv_2008-06-08_us.txt` INTO TABLE page_view PARTITION(date='2008-06-08', country='US')  

The path argument can take a directory (in which case all the files in the directory are loaded), a single file name, or a wildcard (in which case all the matching files are uploaded). If the argument is a directory - it cannot contain subdirectories. Similarly - the wildcard must match file names only.

In the case that the input file /tmp/pv_2008-06-08_us.txt is very large, the user may decide to do a parallel load of the data (using tools that are external to Hive). Once the file is in HDFS - the following syntax can be used to load the data into a Hive table:

   LOAD DATA INPATH '/user/data/pv_2008-06-08_us.txt' INTO TABLE page_view PARTITION(date='2008-06-08', country='US') 

It is assumed that the array and map fields in the input.txt files are null fields for these examples.

Simple Query

For all the active users, one can use the query of the following form:

    INSERT OVERWRITE TABLE user_active 
    SELECT user.* 
    FROM user 
    WHERE user.active = 1;  

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.

Partition Based Query

What partitions to use in a query is determined automatically by the system on the basis of where clause conditions on partition columns. e.g. 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:

    INSERT OVERWRITE TABLE xyz_com_page_views 
    SELECT page_views.* 
    FROM page_views 
    WHERE page_views.date >= '2008-03-01' AND page_views.date <= '2008-03-31' AND
          page_views.referrer_url like '%xyz.com';  

(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!)

Joins

In 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:

    INSERT OVERWRITE TABLE pv_users 
    SELECT pv.*, u.gender, u.age 
    FROM user u JOIN page_view pv ON (pv.userid = u.id) 
    WHERE pv.date = '2008-03-03';  

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). e.g. in order to do a full outer join in the query above, the corresponding syntax would look like the following query:

    INSERT OVERWRITE TABLE pv_users 
    SELECT pv.*, u.gender, u.age 
    FROM user u FULL OUTER JOIN page_view pv ON (pv.userid = u.id) 
    WHERE pv.date = '2008-03-03';  

In order to join more than one tables, the user can use the following syntax:

    INSERT OVERWRITE TABLE pv_friends 
    SELECT pv.*, u.gender, u.age, f.friends 
    FROM page_view pv JOIN user u ON (pv.userid = u.id) JOIN friend_list f ON (u.id = f.uid) 
    WHERE pv.date = '2008-03-03'; 

Note that Hive only supports equi-joins. Note also that it is best to put the largest table on the rightmost side of the join in order to avoid memory errors.

Aggregations

In order to count the number of distinct users by gender one could write the following query:

    INSERT OVERWRITE TABLE pv_gender_sum 
    SELECT pv_users.gender, count (DISTINCT pv_users.userid) 
    FROM pv_users 
    GROUP BY pv_users.gender; 

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

    INSERT OVERWRITE TABLE pv_gender_agg 
    SELECT pv_users.gender, count(DISTINCT pv_users.userid), count(1), sum(DISTINCT pv_users.userid) 
    FROM pv_users 
    GROUP BY pv_users.gender;  

however, the following query is not allowed

    INSERT OVERWRITE TABLE pv_gender_agg 
    SELECT pv_users.gender, count(DISTINCT pv_users.userid), count(DISTINCT pv_users.ip) 
    FROM pv_users 
    GROUP BY pv_users.gender;  

Multi Table/File Inserts

The 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:

    FROM pv_users 
    INSERT OVERWRITE TABLE pv_gender_sum
        SELECT pv_users.gender, count_distinct(pv_users.userid) 
        GROUP BY pv_users.gender 

    INSERT OVERWRITE DIRECTORY '/user/data/tmp/pv_age_sum'
        SELECT pv_users.age, count_distinct(pv_users.userid) 
        GROUP BY pv_users.age;  

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.

Inserting into local files

In 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:

    INSERT OVERWRITE LOCAL DIRECTORY '/tmp/pv_gender_sum' 
    SELECT pv_gender_sum.* 
    FROM pv_gender_sum; 

Sampling

The 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 BUCKETED ON 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:

    INSERT OVERWRITE TABLE pv_gender_sum_sample 
    SELECT pv_gender_sum.* 
    FROM pv_gender_sum TABLESAMPLE(BUCKET 3 OUT OF 32); 

In general the TABLESAMPLE syntax looks like:

    TABLESAMPLE(BUCKET x OUT OF y) 

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

      TABLESAMPLE(BUCKET 3 OUT OF 16) 

would pick out the 3rd and 19th buckets. The buckets are numbered starting from 0.

On the other hand the tablesample clause

     TABLESAMPLE(BUCKET 3 OUT OF 64 ON userid) 

would pick out half of the 3rd bucket.

Union all

The 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:

    INSERT OVERWRITE TABLE actions_users 
    SELECT u.id, actions.date
    FROM (
        SELECT av.uid AS uid 
        FROM action_video av 
        WHERE av.date = '2008-06-03' 

        UNION ALL 

        SELECT ac.uid AS uid 
        FROM action_comment ac 
        WHERE ac.date = '2008-06-03' 
        ) actions JOIN users u ON(u.id = actions.uid);

Array Operations

Array 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:

    SELECT pv.friends[2] 
    FROM page_views pv; 

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:

   SELECT pv.userid, size(pv.friends) 
   FROM page_view pv; 

Map(Associative Arrays) Operations

Maps 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:

    INSERT OVERWRITE page_views_map 
    SELECT pv.userid, pv.properties['page type'] 
    FROM page_views pv; 

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:

   SELECT size(pv.properties) 
   FROM page_view pv; 

Custom map/reduce scripts

Users 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.

   FROM (
        FROM pv_users 
        MAP pv_users.userid, pv_users.date 
        USING 'map_script' 
        AS dt, uid 
        CLUSTER BY dt) map_output 

    INSERT OVERWRITE TABLE pv_users_reduced
        REDUCE map_output.dt, map_output.uid 
        USING 'reduce_script' 
        AS date, count; 

Sample map script (weekday_mapper.py )

import sys
import datetime

for line in sys.stdin:
  line = line.strip()
  userid, unixtime = line.split('\t')
  weekday = datetime.datetime.fromtimestamp(float(unixtime)).isoweekday()
  print ','.join([userid, str(weekday)])

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:

    SELECT TRANSFORM(pv_users.userid, pv_users.date) USING 'map_script' AS dt, uid CLUSTER BY dt FROM pv_users;

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.

    FROM (
        FROM pv_users 
        MAP pv_users.userid, pv_users.date 
        USING 'map_script' 
        CLUSTER BY key) map_output 

    INSERT OVERWRITE TABLE pv_users_reduced

        REDUCE map_output.dt, map_output.uid 
        USING 'reduce_script' 
        AS date, count; 

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.

    FROM (
        FROM pv_users 
        MAP pv_users.userid, pv_users.date 
        USING 'map_script' 
        AS c1, c2, c3 
        DISTRIBUTE BY c2 
        SORT BY c2, c1) map_output 

    INSERT OVERWRITE TABLE pv_users_reduced

        REDUCE map_output.c1, map_output.c2, map_output.c3 
        USING 'reduce_script' 
        AS date, count; 

Co groups

Amongst 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:

   FROM (
        FROM (
                FROM action_video av 
                SELECT av.uid AS uid, av.id AS id, av.date AS date 
 
               UNION ALL 

                FROM action_comment ac 
                SELECT ac.uid AS uid, ac.id AS id, ac.date AS date 
        ) union_actions 
        SELECT union_actions.uid, union_actions.id, union_actions.date 
        CLUSTER BY union_actions.uid) map 

    INSERT OVERWRITE TABLE actions_reduced
        SELECT TRANSFORM(map.uid, map.id, map.date) USING 'reduce_script' AS (uid, id, reduced_val); 

Altering Tables

To rename existing table to a new name. If a table with new name already exists then an error is returned:

    ALTER TABLE old_table_name RENAME TO new_table_name; 

To rename the columns of an existing table. Be sure to use the same column types, and to include an entry for each preexisting column:

    ALTER TABLE old_table_name REPLACE COLUMNS (col1 TYPE, ...); 

To add columns to an existing table:

    ALTER TABLE tab1 ADD COLUMNS (c1 INT COMMENT 'a new int column', c2 STRING DEFAULT 'def val'); 

Note that a change in the schema (such as the adding of the columns), preserves the schema for the old partitions of the table in case it is a partitioned table. All the queries that access these columns and run over the old partitions implicitly return a null value or the specified default values for these columns.

In the later versions we can make the behavior of assuming certain values as opposed to throwing an error in case the column is not found in a particular partition configurable.

Dropping Tables and Partitions

Dropping tables is fairly trivial. A drop on the table would implicitly drop any indexes(this is a future feature) that would have been built on the table. The associated command is

    DROP TABLE pv_users; 

To dropping a partition. Alter the table to drop the partition.

    ALTER TABLE pv_users DROP PARTITION (ds='2008-08-08') 

Note that any data for this table or partitions will be dropped and may not be recoverable.