Benchmark of KVP vs. Hstore - Slides

Key/Value Pair versus hstore - Benchmarking Entity-Attribute-Value Structures in PostgreSQL.

Michel Ott

June 17, 2011 1University of Applied Science Rapperswil

What is KVP?

A key-value pair (KVP) is a set of two linked data items: a key, which is a unique identifier for some item of data, and the value, which is

either the data that is identified or a pointer to the location of that data.

(Source: Techtarget, http://searchenterprisedesktop.techtarget.com/definition/key-value-pair)

an open-ended data structure that allows for future extension without modifying existing code or data.

(Source: Wikipedia, http://en.wikipedia.org/wiki/Attribute-value_pair)

June 17, 2011 University of Applied Science Rapperswil 2

1. { data : [

2. { amenity : restaurant,3. name : Godman },4. { amenity : university,5. name : Harvard University }6. ] }

Agenda

Introduction

What is hstore?

KVP Schema in PostgreSQL

Setup / Environment

Performance Benchmark Design

Test Environment

Benchmark (May 2011)

Results

Findings

Conclusion


INTRODUCTION What is hstore?



What is hstore?

Hstore in PostgreSQL

Storage for semistructural data (a'la perl hash)

Stores associative arrays in a attribute of a table

Is an abstract data type in PostgreSQL

Provides a bunch of PostgreSQL functions for querying, transforming, manipulating,

Usage of hstore


1. CREATE TABLE bench_hstore (

id BIGINT PRIMARY KEY,

(),kvp_hstore HSTORE

);

What is hstore?

Usage of hstore


1. SELECT hstore(kvp_hstore)->name as nameFROM bench_hstore

WHERE hstore(kvp_hstore)->amenity = restaurant;

id : BIGINT kvp_hstore : HSTORE

1 amenity=>restaurant, name=>Goodman

1. INSERT INTO bench_hstore(kvp_hstore) VALUES (

hstore(amenity=>restaurantname=>Godman

));


Schema

Two tables needed (one for the unforeseen arbitrary data = KVP and one for the additional data)

Usage


1. CREATE TABLE bench_kvp_info(


());

2. CREATE TABLE bench_kvp(

id BIGINT REFERENCES bench_kvp_main(id),

key TEXT NOT NULL,

value TEXT

);


Usage


1. INSERT INTO bench_kvp_info(id)

VALUES(1)

2. INSERT INTO bench_kvp(id, key, value)

VALUES(1, amenity, restaurant);3. INSERT INTO bench_kvp(id, key, value)

VALUES(1, name, Godman);

1. SELECT * FROM bench_kvp WHERE id = (

SELECT bench_id FROM bench_kvp

WHERE key = amenity' AND value = restaurant);

id : BIGINT key : TEXT value : TEXT

1 amenity restaurant

1 name Godman

SETUP / ENVIRONMENT Performance Benchmark Design

Table Schemas

Test Data

Test Environment



Table Schema


1. CREATE TABLE bench_kvp_info(


());

2. CREATE TABLE bench_kvp(

id BIGINT REFERENCES bench_kvp_main(id),

key TEXT NOT NULL,

value TEXT

);

1. CREATE TABLE bench_hstore (


(),kvp_hstore HSTORE

);


Test Data

12 different data sets with the following amount of records

Each data set testes twice (once with index and once without)

GiST (Generalized Search Tree) is used for hstore (basis for B-Tree and R-Tree)

Hence


10 100 500

1000 2500 5000

10000 20000 35000

50000 100000 250000

[cicles] 144 start] [warm 3indices] of [# 2 types]of [# 2length] of [# 12

Test Data

Test Data Schema

Example


Column Description

id : integer, sequence Mandatory. A unique sequence identifier.

surname : Text Mandatory. A fancy name.

forename : Text Optional: A fancy name. Can be empty to have a

variable KVP length.

zip : Integer Optional: A number between 1000 and 9000.

comment : Text Optional: A dummy text.

1,cucyp,ecnalehad,6593,lorem ipsum dolor sit amet 2,kasarzyc,,6593,

Test Environment


Test Environment


Technical Specification

Intel(R) Xeon(R) CPU E5520 @ 2.27GHz 64-bit

3CPUs, 4 cores and 8 threads

24 GB RAM

Software

Ubuntu 10.04.2 LTS

PostgreSQL 9.0.4

Python 2.6.5, Numpy, Scipy, Matplotlib

No tuning of Software

BENCHMARK MAY 2011 Results

Findings

Analyze statements

Functionality of hstore

Conclusion


Results


Findings

Table size

hstore:

KVP , whereas

Explain Analyze for KVP


tuplesentriesarray

array in the valuetuples nullvalue

KVP hstore

Without

index

Index on

keyCombined

index

Without

index

GiST index

Cost 0..437.03 0..406.88 0..215.91 0..213.72 0..11.33

Runtime 3.607 ms 2.770 ms 2.028 ms 1.883 ms 0.721 ms

Scans Seq scan 1 heap & 1

index scan

1 index

scan

Seq scan 1 heap & 1

index scan


Buffers the whole hstore

Each hstore key value pair knows:

its position in the string

its length

value and its length

Hstore data type


1. CREATE TYPE hstore (

INTERNALLENGTH = -1,

INPUT = hstore_in,

OUTPUT = hstore_out,

RECEIVE = hstore_recv,

SEND = hstore_send,

STORAGE = extended

);


-> as an example for the available operator

Procedure is linked to a PostgreSQL function


1. CREATE OPERATOR -> (

LEFTARG = hstore,

RIGHTARG = text,

PROCEDURE = fetchval

);

1. CREATE OR REPLACE FUNCTION fetchval(hstore,text)

2. RETURNS text

3. AS 'MODULE_PATHNAME','hstore_fetchval4. LANGUAGE C STRICT IMMUTABLE;


PostgreSQL function is linked to a C method

hstore_fetchval method returns the value by

calling get_val method, which loops over the buffer and returns the position

Example


id : BIGINT kvp_hstore : HSTORE

1 zip=>8000, surname=>ebsaveq

2 zip=>6489, surname=>epofod

3 zip=>8000, surname=>kjuefs

1. SELECT hstore(bench_hstore)->surnameFROM bench_hstore

WHERE hstore(bench_hstore)->zip=8000;

Conclusion

For small data sets (< 500 records) KVP is preferable

However

500 records is easily exceeded

Changing schema involves huge effort

Transposing data

Changing database table schema

Possibly refactoring software to new schema


If unsure about size use hstore as data typeKVP is only 0.45 ms faster at 500 records


Thank You

MerciGrazie

Gracias

Obrigado

Danke

Japanese

English

French

Russian

German

Italian

Spanish

Brazilian PortugueseArabic

Traditional Chinese

Simplified Chinese

Hindi

Tamil

Thai

Korean

BACKUP


Findings

Table size

hstore:

KVP , whereas

Explain Analyze for KVP


tuplesentriesarray

array in the valuetuples nullvalue

1. Seq Scan on bench_kvp

(cost=229.38..437.03 rows=3 width=60)

(actual time=3.125..3.579 rows=2 loops=1)

2. Filter: (bench_id = $0)

3. InitPlan 1 (returns $0)

4. -> Seq Scan on bench_kvp

(cost=0.00..229.38 rows=1 width=8)


5. Filter: ((key = 'id'::text) AND

(value = '1735'::text))

6. Total runtime: 3.607 ms

Findings

Explain Analyze for KVP with index on attribute key



(cost=199.48..406.88 rows=3 width=60)




4. -> Bitmap Heap Scan on bench_kvp

(cost=62.99..199.48 rows=1 width=8)


5. Recheck Cond: (key = 'id'::text)

6. Filter: (value = '1735'::text)

7. -> Bitmap Index Scan on kvpidx

(cost=0.00..62.99 rows=2499 width=0)


8. Index Cond: (key = 'id'::text)


Findings

Explain Analyze for KVP with combined index



(cost=8.27..215.91 rows=3 width=60)




4. -> Index Scan using kvpidx2 on bench_kvp

(cost=0.00..8.27 rows=1 width=8)


5. Index Cond: ((key = 'id'::text) AND

(value = '1735'::text))


7. (6 rows)

Findings

Explain Analyze for hstore

Explain Analyze for hstore with index


1. Seq Scan on bench_hstore

(cost=0.00..213.72 rows=45 width=40)


2. Filter: ((bench_hstore -> 'id'::text) = '1735'::text)


1. Bitmap Heap Scan on bench_hstore

(cost=4.27..11.33 rows=2 width=218)


2. Recheck Cond: (bench_hstore @> '"id"=>"1735"'::hstore)

3. -> Bitmap Index Scan on hidx_2_5k

(cost=0.00..4.27 rows=2 width=0)


4. Index Cond: (bench_hstore @> '"id"=>"1735"'::hstore)


Benchmark of KVP vs. Hstore - Slides

Documents

Transcript of Benchmark of KVP vs. Hstore - Slides