The document provides an overview of Hive, a data warehousing infrastructure built on Hadoop that allows for querying and managing large datasets. It covers its architecture, data models, installation instructions, configuration, and command-line interface functionalities. Hive is open source, supports rapid ad-hoc queries, and is suitable for handling vast amounts of data while providing a high-level querying interface.

1. Hive Quick Start
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2. Background
• Started at Facebook
• Data was collected
by nightly cron
jobs into Oracle DB
• “ETL” via hand-
coded python
• Grew from 10s of
GBs (2006) to 1 TB/
day new data
(2007), now 10x
that.
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3. Hadoop as Enterprise Data
Warehouse
• Scribe and MySQL data loaded
into Hadoop HDFS
• Hadoop MapReduce jobs to
process data
• Missing components:
– Command-line interface for “end users”
– Ad-hoc query support
• … without writing full MapReduce jobs
– Schema information
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4. Hive Applications
• Log processing
• Text mining
• Document indexing
• Customer-facing business
intelligence (e.g., Google
Analytics)
• Predictive modeling, hypothesis
testing
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5. Hive Architecture
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6. Data Model
• Tables
– Typed columns (int, float, string, date,
boolean)
– Also, array/map/struct for JSON-like data
• Partitions
– e.g., to range-partition tables by date
• Buckets
– Hash partitions within ranges (useful for
sampling, join optimization)
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7. Column Data Types
CREATE TABLE t (
s STRING,
f FLOAT,
a ARRAY<MAP<STRING, STRUCT<p1:INT,
p2:INT>>);
SELECT s, f, a[0][‘foobar’].p2 FROM t;
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8. Metastore
• Database: namespace containing
a set of tables
• Holds Table/Partition
definitions (column types,
mappings to HDFS directories)
• Statistics
• Implemented with DataNucleus
ORM. Runs on Derby, MySQL, and
many other relational databases
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9. Physical Layout
• Warehouse directory in HDFS
– e.g., /user/hive/warehouse
• Table row data stored in
subdirectories of warehouse
• Partitions form subdirectories of
table directories
• Actual data stored in flat files
– Control char-delimited text, or
SequenceFiles
– With custom SerDe, can use arbitrary
format
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10. Installing Hive
From a Release Tarball:
$ wget http://archive.apache.org/dist/
hadoop/hive/hive-0.5.0/hive-0.5.0-
bin.tar.gz
$ tar xvzf hive-0.5.0-bin.tar.gz
$ cd hive-0.5.0-bin
$ export HIVE_HOME=$PWD
$ export PATH=$HIVE_HOME/bin:$PATH
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11. Installing Hive
Building from Source:
$ svn co http://svn.apache.org/repos/asf/
hadoop/hive/trunk hive
$ cd hive
$ ant package
$ cd build/dist
$ export HIVE_HOME=$PWD
$ export PATH=$HIVE_HOME/bin:$PATH
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12. Installing Hive
Other Options:
• Use a Git Mirror:
– git://github.com/apache/hive.git
• Cloudera Hive Packages
– Redhat and Debian
– Packages include backported patches
– See archive.cloudera.com
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13. Hive Dependencies
• Java 1.6
• Hadoop 0.17-0.20
• Hive *MUST* be able to find
Hadoop:
– $HADOOP_HOME=<hadoop-install-dir>
– Add $HADOOP_HOME/bin to $PATH
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14. Hive Dependencies
• Hive needs r/w access to /tmp
and /user/hive/warehouse on
HDFS:
$ hadoop fs –mkdir /tmp
$ hadoop fs –mkdir /user/hive/warehouse
$ hadoop fs –chmod g+w /tmp
$ hadoop fs –chmod g+w /user/hive/warehouse
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15. Hive Configuration
• Default configuration in
$HIVE_HOME/conf/hive-default.xml
– DO NOT TOUCH THIS FILE!
• Re(Define) properties in
$HIVE_HOME/conf/hive-site.xml
• Use $HIVE_CONF_DIR to specify
alternate conf dir location
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16. Hive Configuration
• You can override Hadoop
configuration properties in
Hive’s configuration, e.g:
– mapred.reduce.tasks=1
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17. Logging
• Hive uses log4j
• Log4j configuration located in
$HIVE_HOME/conf/hive-
log4j.properties
• Logs are stored in /tmp/$
{user.name}/hive.log
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18. Starting the Hive CLI
• Start a terminal and run
$ hive
• Should see a prompt like:
hive>
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19. Hive CLI Commands
• Set a Hive or Hadoop conf prop:
– hive> set propkey=value;
• List all properties and values:
– hive> set –v;
• Add a resource to the DCache:
– hive> add [ARCHIVE|FILE|JAR]
filename;
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20. Hive CLI Commands
• List tables:
– hive> show tables;
• Describe a table:
– hive> describe <tablename>;
• More information:
– hive> describe extended
<tablename>;
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21. Hive CLI Commands
• List Functions:
– hive> show functions;
• More information:
– hive> describe function
<functionname>;
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22. Selecting data
hive> SELECT * FROM <tablename> LIMIT 10;
hive> SELECT * FROM <tablename>
WHERE freq > 100 SORT BY freq ASC
LIMIT 10;
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23. Manipulating Tables
• DDL operations
– SHOW TABLES
– CREATE TABLE
– ALTER TABLE
– DROP TABLE
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24. Creating Tables in Hive
• Most straightforward:
CREATE TABLE foo(id INT, msg STRING);
• Assumes default table layout
– Text files; fields terminated with ^A, lines terminated with
n
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25. Changing Row Format
• Arbitrary field, record
separators are possible.
e.g., CSV format:
CREATE TABLE foo(id INT, msg STRING)
DELIMITED FIELDS TERMINATED BY ‘,’
LINES TERMINATED BY ‘n’;
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26. Partitioning Data
• One or more partition columns may be
specified:
CREATE TABLE foo (id INT, msg STRING)
PARTITIONED BY (dt STRING);
• Creates a subdirectory for each value of
the partition column, e.g.:
/user/hive/warehouse/foo/dt=2009-03-20/
• Queries with partition columns in WHERE
clause will scan through only a subset of
the data
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27. Sqoop = SQL-to-Hadoop
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28. Sqoop: Features
• JDBC-based interface (MySQL, Oracle, PostgreSQL,
etc…)
• Automatic datatype generation
– Reads column info from table and generates Java classes
– Can be used in further MapReduce processing passes
• Uses MapReduce to read tables from database
– Can select individual table (or subset of columns)
– Can read all tables in database
• Supports most JDBC standard types and null values
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29. Example input
mysql> use corp;
Database changed
mysql> describe employees;
+------------+-------------+------+-----+---------+----------------+
| Field | Type | Null | Key | Default | Extra |
+------------+-------------+------+-----+---------+----------------+
| id | int(11) | NO | PRI | NULL | auto_increment |
| firstname | varchar(32) | YES | | NULL | |
| lastname | varchar(32) | YES | | NULL | |
| jobtitle | varchar(64) | YES | | NULL | |
| start_date | date | YES | | NULL | |
| dept_id | int(11) | YES | | NULL | |
+------------+-------------+------+-----+---------+----------------+
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30. Loading into HDFS
$ sqoop --connect jdbc:mysql://db.foo.com/corp
--table employees
• Imports “employees” table into HDFS directory
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31. Hive Integration
$ sqoop --connect jdbc:mysql://db.foo.com/
corp --hive-import --table employees
• Auto-generates CREATE TABLE / LOAD DATA
INPATH statements for Hive
• After data is imported to HDFS, auto-executes
Hive script
• Follow-up step: Loading into partitions
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32. Hive Project Status
• Open source, Apache 2.0 license
• Official subproject of Apache
Hadoop
• Current version is 0.5.0
• Supports Hadoop 0.17-0.20
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33. Conclusions
• Supports rapid iteration of ad-
hoc queries
• High-level Interface (HiveQL)
to low-level infrastructure
(Hadoop).
• Scales to handle much more data
than many similar systems
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34. Hive Resources
Documentation
• wiki.apache.org/hadoop/Hive
Mailing Lists
• hive-user@hadoop.apache.org
IRC
• ##hive on Freenode
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35. Carl Steinbach
carl@cloudera.com