hive intro

Apache HiveAn Introduction

2

Agenda

OverviewA quick look at Hive and its background.

StructureA peek at the structure of Hive.

LanguageHow to write DDL and DML statements in Hive.

Hive at YahooWorking with Hive on Yahoo’s grids.

Advanced FeaturesSome more things you can do with Hive.

More InformationWhere to look when you need more details or help.

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Overview

4

Motivation for Hive

Companies are no longer dealing with gigabytes, but

rather terabytes

Large amount of data to analyze

Researchers want to study and understand the data

Business fold wants to slice and dice the data & metrics

in various ways

Every one impatient – Give me answers now

Joining across large data sets is quite tricky

5

Motivation for Hive

Started in January 2007 at Facebook

Query data on Hadoop without having to write complex

MapReduce programs in Java each time

SQL chosen for familiarity and tools-support

An active open-source project since August 2008

Top-level Apache project (hive.apache.org)

Used in many companies; a diverse set of contributors

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What Hive Is

A Hadoop-based system for managing and querying

structured data

Hive provides an view of your data as tables with rows

and columns

Uses HDFS for storing data

Provides a SQL-like interface for querying data

Uses MapReduce for executing queries

Scales well to handle massive data-sets

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Example

SELECT COUNT(1) AS job_count, t.wait_time

FROM

(SELECT ROUND(wait_time/1000) AS wait_time, job_id

FROM starling_jobs

WHERE grid = ‘MB’

AND dt >= ‘2011_07_11’

AND dt <= ‘2011_07_13’) t

GROUP BY t.wait_time;

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8 Simple steps

Login to grid gateway machine. 

Create a hdfs file to store your hive metadata,   Ex:-    hadoop fs -mkdir /user/vmoorthy/warehouse 

Go to hive shell by running hive 

SET mapred.job.queue.name=unfunded;   -- to run your job in the unfunded queue

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8 Simple steps (…)

Create a database specifying the location for meta data store.

Ex:-   CREATE DATABASE autos LOCATION '/user/vmoorthy/warehouse';

USE autos;   -- to work with previously created database named 'autos’

CREATE TABLE used_car(chromeTrimId INT,trimId INT, usedCarCondition STRING, usedCarMileage INT, usedCarPrice INT, chromeModelId INT, modelId INT)  ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' LINES TERMINATED BY '\n' LOCATION '/user/vmoorthy/usedCarTrim';  

-- create a table for the tab separated hdfs file named usedCarTrim

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8 Simple steps (…)

Now, you are ready to run select queries on the above table.

Ex:-   SELECT * FROM used_car WHERE chrometrimid > 3030;

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Structure

12

Architecture

Hive

Driver(Compiler, Optimizer, Executor)

Command-line Interface

WebInterface

ThriftServer

JDBC ODBC

Meta-store

Hadoop Database

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Query Execution

Parser

Logical Plan Generator

Optimizer

Physical PlanGenerator

Executor

Query

MapReduce Job(s)

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Storage

<warehouse-directory>

<database-directory>

<table-directory>

<partition-directory>

<data-file1>

<data-file2>

[…]

<data-filen>

Table metadata is stored in meta-store

Directories for databases, tables and partitions

Files for table-data

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Language

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Data Model

Database – a namespace for tables and other units of

data (“default” if none specified)

Table – a row-based store for data in a database; each

row having one or more columns

Partition – a key-based separation of data in a table for

reducing the amount of data scanned (Optional)

Bucket – cluster of data in a partition based on a hashing

a column-value (Optional)

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Primitive Data-types

Integers – TINYINT (1 byte), SMALLINT (2 bytes), INT (4

bytes), BIGINT (8 bytes)

Boolean – BOOLEAN (TRUE / FALSE)

Floating-Point – FLOAT, DOUBLE

String – STRING

Implicit and explicit casting supported

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Complex Data-types

Arrays – a list of elements of the same data-type accessible

using an index. “A[n]” denotes the element at index

“n”(starts from zero) in array “A”

Structs – a record with named elements. “foo.bar”

denotes the field “bar” in the struct “foo”

Maps – maintains mappings from keys to respective

values. “M[‘foo’]” denotes the value for “foo” in the

map “M”

Collections can be nested arbitrarily

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Operators

Relational – “=”, “!=”, “<”, “<=”, etc. as well as “IS

NULL”, “IS NOT NULL”, “LIKE”, etc. Generate TRUE

or FALSE based on comparison

Arithmetic – “+”, “-”, “*”, “/”, etc. Generate number

based on the result of the arithmetic operation

Logical – “AND”, “OR”, “NOT”, etc. Generate TRUE or

FALSE based on combining Boolean expressions

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Built-in Fuctions

Mathematical – “round()”, “floor()”, “rand()”, etc.

String – “concat()”, “substr()”,

“regexp_replace()”, etc.

Time – “to_date()”, “from_unixtime()”, “year()”,

“month()”, etc.

Aggregates – “count()”, “sum()”, “min()”, “max()”,

“avg()”

…and quite a lot more

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Creating a Table

CREATE TABLE employees(name STRING, age INT);

or

CREATE TABLE IF NOT EXISTS employees(name STRING, age INT);

or

CREATE TABLE employees(name STRING, age INT)

PARTITIONED BY (join_dt STRING);

or

CREATE TABLE employees(name STRING, age INT)

STORED AS SequenceFile;

etc.

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Loading Data

LOAD DATA INPATH '/foo/bar/snafu.txt'

INTO TABLE employees;

or

LOAD DATA LOCAL INPATH '/homes/wombat/emp_2011-12-01.txt'

INTO TABLE employees

PARTITION (join_dt=‘2011_12_01’);

or

INSERT OVERWRITE TABLE employees

SELECT name, age FROM all_employees

WHERE location = 'Bangalore';

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Querying Data

SELECT * FROM employees;

or

SELECT * FROM employees LIMIT 10;

or

SELECT name, age FROM employees

WHERE age > 30;

or

SET hive.exec.compress.output=false;

SET hive.cli.print.header=true;

INSERT OVERWRITE LOCAL DIRECTORY ‘/homes/wombat/blr’

SELECT * FROM all_employees

WHERE location = ‘Bangalore’;

etc.

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External Tables

CREATE EXTERNAL TABLE foo(name string, age int)

LOCATION ‘/user/bar/wombat’;

Data not managed by Hive

Useful when data is already processed and in a usable state

Manually clean up after dropping tables/partitions

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Altering a Table

ALTER TABLE employees RENAME TO blr_employees;

ALTER TABLE employees

REPLACE COLUMNS (emp_name STRING, emp_age INT);

ALTER TABLE employees ADD COLUMNS (emp_id STRING);

ALTER TABLE all_employees

DROP PARTITION (location=‘Slackville’);

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Databases

CREATE DATABASE foo;

orCREATE DATABASE IF NOT EXISTS foo;

orCREATE DATABASE foo LOCATION ‘/snafu/wombat’;

USE foo;SELECT * FROM bar LIMIT 10;

orSELECT * FROM foo.bar LIMIT 10;

DROP DATABASE foo;

orDROP DATABASE IF EXISTS foo;

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Other Operations

SHOW TABLES;

SHOW PARTITIONS all_employees;

SHOW PARTITIONS all_employees

PARTITION (location=‘Bangalore’);

DESCRIBE employees;

DROP TABLE employees;

or

DROP TABLE IF EXISTS employees;

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Joins

SELECT e.name, d.dept_name

FROM departments d JOIN all_employees e

ON (e.dept_id = d.dept_id);

or

SELECT e.name, d.dept_name

FROM departments d

LEFT OUTER JOIN all_employees e

ON (e.dept_id = d.dept_id);

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Ordering of Data

ORDER BY – global ordering of results based on the

selected columns

SORT BY – local ordering of results on each reducer

based on the selected columns

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File-formats

TextFile – plain-text files; fields delimited with ^A by

default

SequenceFile – serialized objects, possibly-

compressed

RCFile – columnar storage of serialized objects,

possibly-compressed

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TextFile Delimiters

Default field-separator is ^A; row-separator is \n

John Doe^A36\n

Jane Doe^A33\n

Default list-separator is ^B; value-separator is ^C

John Doe^Adept^Cfinance^Bemp_id^C2357\n

CREATE TABLE employees(name STRING, age INT)

ROW FORMAT DELIMITED

FIELDS TERMINATED by '\t';

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Buckets

CREATE TABLE employees(name STRING, age INT)

CLUSTERED BY (name) INTO 31 BUCKETS;

Distribute partition-data into files based on columns

Improves performance for filters with these columns

Works best when data is uniformly distributed

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Compressed Storage

Saves space and generally improves performance

Direct support for reading compressed files LOAD DATA LOCAL INPATH ‘/foo/bar/emp_data.bz2’ INTO TABLE all_employees;

Compressed TextFile cannot usually be split

SequenceFile or RCFile recommended instead

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Tips

Judicious use of partitions and buckets can drastically

improve the performance of your queries

Put always-used Hive CLI commands in

$HOME/.hiverc (e.g. SET

mapred.job.queue.name=unfunded;)

Use EXPLAIN to analyze a query before executing it

Use RCFile with compression to save storage and to

improve performance

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Hive at Yahoo

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Specifics

Hive CLI available as /home/y/bin/hive on gateways

of supported grids

Mandatory LOCATION clause in CREATE TABLE

Must specify MapReduce queue for submitted Jobs

(e.g. SET mapred.job.queue.name=unfunded;)

No JDBC / ODBC support

Integrated with HCatalog

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Advanced Features

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User-defined Functions

Many very useful built-in functions SHOW FUNCTIONS;

DESCRIBE FUNCTION foo;

Extensible using user-defined functions

User-defined Function (UDF) for one-to-one mappingE.g. round(), concat(), unix_timestamp(), etc.

User-defined Aggregate Function (UDAF) for many-to-one

mappingE.g. sum(), avg(), stddev(), etc.

User-defined Table-generating Function (UDTF) for one-to-many

mappingE.g. explode(), etc.

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Custom UDF

package com.yahoo.hive.udf;

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

import org.apache.hadoop.hive.ql.exec.Description;

import org.apache.hadoop.io.Text;

@Description(

name = "toupper",

value = "_FUNC_(str) - Converts a string to uppercase",

extended = "Example:\n" +

" > SELECT toupper(author_name) FROM authors a;\n" +

" STEPHEN KING"

)

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Custom UDF (…)

public class ToUpper extends UDF {

public Text evaluate(Text s) {

Text to_value = new Text("");

if (s != null) {

try {

to_value.set(s.toString().toUpperCase());

} catch (Exception e) { // Should never happen

to_value = new Text(s);

}

}

return to_value;

}

}

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UDF Usage

add jar build/ql/test/test-udfs.jar;

CREATE TEMPORARY FUNCTION TO_UPPER AS ’com.yahoo.hive.udf.ToUpper';

SELECT TO_UPPER(src.value) FROM src;

DROP TEMPORARY FUNCTION TO_UPPER;

42

Overloaded UDF

public class UDFExampleAdd extends UDF {

public Integer evaluate(Integer a, Integer b) {

if (a == null || b == null) return null;

return a + b;

}

public Double evaluate(Double a, Double b) {

if (a == null || b == null) return null;

return a + b;

}

}

43

Overloaded UDF

add jar build/contrib/hive_contrib.jar;

CREATE TEMPORARY FUNCTION example_add AS 'org.apache.hadoop.hive.contrib.udf.UDFExampleAdd';

SELECT example_add(1, 2) FROM src;

SELECT example_add(1.1, 2.2) FROM src;

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UDAF Example

SELECT page_url, count(1), count(DISTINCT user_id)FROM mylog;

public class UDAFCount extends UDAF {

public static class Evaluator implements UDAFEvaluator {

private int mCount;

public void init() {mcount = 0;}

public boolean iterate(Object o) {

if (o!=null) mCount++; return true;}

public Integer terminatePartial() {return mCount;}

public boolean merge(Integer o) {mCount += o; return true;}

public Integer terminate() {return mCount;}

}

45

Overloaded UDAF

public class UDAFSum extends UDAF {

public static class IntEvaluator implements UDAFEvaluator {

private int mSum;

public void init() {mSum = 0;}

public boolean iterate(Integer o)

{mSum += o; return true;}

public Integer terminatePartial() {return mSum;}

public boolean merge(Integer o) {mSum += o; return true;}

public Integer terminate() {return mSum;}

}

46

Overloaded UDAF

public static class DblEvaluator implements UDAFEvaluator {

private double mSum;

public void init() {mSum = 0;}

public boolean iterate(Double o)

{mSum += o; return true;}

public Double terminatePartial() {return mSum;}

public boolean merge(Double o)

{mSum += o; return true;}

public Double terminate() {return mSum;}

}

}

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What Hive Is Not

Not suitable for small data-sets

Does not provide real-time results

Does not support row-level updates

Imposes a schema on the data

Does not support transactions

Does not need expensive server-class hardware,

RDBMS licenses or god-like DBAs to scale

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More Information

49

External References

Hive home-page: hive.apache.org

Hive wiki: cwiki.apache.org/confluence/display/Hive

Hive tutorial: cwiki.apache.org/confluence/display/Hive/Tutorial

Hive language manual:

cwiki.apache.org/confluence/display/Hive/LanguageManual

Mailing-list: user@hive.apache.org

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Internal References

Hive at Yahoo: wiki.corp.yahoo.com/view/Grid/Hive

Hive FAQ: wiki.corp.yahoo.com/view/Grid/HiveFAQ

Troubleshooting: wiki.corp.yahoo.com/view/Grid/HiveTroubleShooting

Internal mailing-list: hive-users@yahoo-inc.com

Hive CLI yinst package: hive_cli

Installation instructions:

wiki.corp.yahoo.com/view/Grid/HiveInstallation

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Questions?