Apache Hive

Introduction On Hive

Ajit Koti

Analysis of Data made by both engineering and non-engineering people.The data are growing fast. In 2007, the volume was 15TB and it grew up to 200TB in 2010.Current RDBMS can NOT handle it.Current solution are not available, not scalable, Expensive and Proprietary.

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Motivation

MapReduce is a programing model and an associated implementation introduced by Goolge in 2004.Apache Hadoop is a software framework inspired by Google's MapReduce.

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Map/Reduce -Apache Hadoop

Hadoop supports data-intensive distributed applications.

However...– Map-reduce hard to program (users know

sql/bash/python).– No schema.

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Motivation (cont.)

A data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis.

–ETL.–Structure.–Access to different storage.–Query execution via MapReduce.

Key Building Principles:–SQL is a familiar language–Extensibility – Types, Functions, Formats, Scripts

–Performance 5

What is HIVE?

Log processing Text mining Document indexing Customer-facing business intelligence (e.g., Google Analytics)

Predictive modeling, hypothesis testing

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Hive Applications

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Hive Architecture

Databases.Tables.Partitions.Buckets (or Clusters).

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Data Units

Primitive types– Integers:TINYINT, SMALLINT, INT, BIGINT.– Boolean: BOOLEAN.– Floating point numbers: FLOAT, DOUBLE .– String: STRING.

Complex types– Structs: {a INT; b INT}.–Maps: M['group'].– Arrays: ['a', 'b', 'c'], A[1] returns 'b'.

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Type System

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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Physical Layout

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HiveQL

CREATE TABLE sample (foo INT, bar STRING) PARTITIONED BY (ds STRING);

SHOW TABLES '.*s';

DESCRIBE sample;

ALTER TABLE sample ADD COLUMNS (new_col INT);

DROP TABLE sample;

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Examples – DDL Operations

LOAD DATA LOCAL INPATH './sample.txt' OVERWRITE INTO TABLE sample PARTITION (ds='2012-02-24');

LOAD DATA INPATH '/user/falvariz/hive/sample.txt' OVERWRITE INTO TABLE sample PARTITION (ds='2012-02-24');

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Examples – DML Operations

LOAD DATA LOCAL INPATH './sample.txt' OVERWRITE INTO TABLE sample PARTITION (ds='2012-02-24');

LOAD DATA INPATH '/user/falvariz/hive/sample.txt' OVERWRITE INTO TABLE sample PARTITION (ds='2012-02-24');

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Examples – DML Operations

SELECT foo FROM sample WHERE ds='2012-02-24';

INSERT OVERWRITE DIRECTORY '/tmp/hdfs_out' SELECT * FROM sample WHERE ds='2012-02-24';

INSERT OVERWRITE LOCAL DIRECTORY '/tmp/hive-sample-out' SELECT * FROM sample;

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SELECTS and FILTERS

SELECT MAX(foo) FROM sample;

SELECT ds, COUNT(*), SUM(foo) FROM sample GROUP BY ds;

FROM sample s INSERT OVERWRITE TABLE bar SELECT s.bar, count(*) WHERE s.foo > 0 GROUP BY s.bar;

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Aggregations and Groups

SELECT * FROM customer c JOIN order_cust o ON c.id=o.cus_id);SELECT c.id,c.name,c.address,ce.exp FROM customer c JOIN (SELECT cus_id,sum(price) AS exp FROM order_cust GROUP BY cus_id) ce ON (c.id=ce.cus_id);

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Join

CREATE TABLE customer (id INT,name STRING,address STRING) ROW FORMAT DELIMITED FIELDS TERMINATED BY '#';

CREATE TABLE order_cust (id INT,cus_id INT,prod_id INT,price INT) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';

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Extension mechanisms

package com.example.hive.udf;

import org.apache.hadoop.hive.ql.exec.UDF;import org.apache.hadoop.io.Text;

public final class Lower extends UDF { public Text evaluate(final Text s) { if (s == null) { return null; } return new Text(s.toString().toLowerCase()); }}

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User-defined function

Registering the class

CREATE FUNCTION my_lower AS 'com.example.hive.udf.Lower';

Using the function

SELECT my_lower(title), sum(freq) FROM titles GROUP BY my_lower(title);

Java code

Mathematical: round, floor, ceil, rand, exp...

Collection: size, map_keys, map_values, array_contains.

Type Conversion: cast.

Date: from_unixtime, to_date, year, datediff...

Conditional: if, case, coalesce.

String: length, reverse, upper, trim...

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Built-in Functions

my_append.py

Using the function

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Map/Reduce Scripts

for line in sys.stdin: line = line.strip() key = line.split('\t')[0] value = line.split('\t')[1] print key+str(i)+'\t'+value+str(i) i=i+1

SELECT TRANSFORM (foo, bar) USING 'python ./my_append.py' FROM sample;

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Install and Config 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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Installing Hive

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 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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Hive Dependencies

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 You can override Hadoop configuration properties

in Hive’s configuration, e.g:– mapred.reduce.tasks=1

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Hive Configuration

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Hive CLI

Start a terminal and run $ hive Should see a prompt like: hive> 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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Hive CLI Commands

List tables: – hive> show tables; Describe a table: – hive> describe <tablename>; More information: – hive> describe extended <tablename>; List Functions: – hive> show functions; More information: – hive> describe function<functionname>;

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Hive CLI Commands

A easy way to process large scale data.Support SQL-based queries.Provide more user defined interfaces to extend

Programmability.Files in HDFS are immutable. Tipically:– Log processing: Daily Report, User Activity

Measurement–Data/Text mining: Machine learning (Training Data)– Business intelligence: Advertising Delivery,Spam

Detection

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Conclusion

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Thank You

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