SlideShare uma empresa Scribd logo
1 de 11
REDIS DATA MODEL SAMPLE
Terry’s Redis
1.LogWriter
• Problem domain : Collect all logs from distributed server and merge it into single log file
Server
Server
Redis
Key:’WAS:log’
Value : Log (Single String)
Append log to String
Log File
Flush to log file
Problem
String append bring memory re-
allocation. So every log write makes a
memory relocation
Log String
Log String
Log String
Server
Server
Key:’WAS:log’
:
Log File
Redis
(List)
lpush
rpop
Solution
Use List data type and push the log
and pop & write the log into log file
2.Visitor count
• Problem domain
– Count total event page visit #
– Count visit # per each event page
event:click:total
event:click:{event page# id}
visit #
visit #
event:click:{event page# id} visit #
:
Key
Value
(String Type)
incr
• Enhancement Request
– Count total event page visit # per day
– Count visit # per each event page per day
visit #
visit #
event:click:daily:total:{date}
event:click:daily:{date}:{event page# id}
event:click:daily:{date}:{event page# id} visit #
Key
Value
(String Type)
incr
2.Visitor count
• Problem
– It cannot find event start & end date because of that it is hard to find “key name”
• Solution
– Use hash data type
– Sort by using java.util.SortedHashMap
event:click:total:hash date visit #
date visit #
date visit #
Key Value (Hash)
event:click:total:hash:{eventid} date visit #
date visit #
date visit #
Total event page visit per day
Daily visit # per day for each event
page
hincrBy
java.util.SortedHashMap Sorted by date
Redis
3. Shopping Basket
• Problem domain
– Make shopping basket which can support
• add product
• remove product
• empty shopping basket
• list products in the shopping basket
• remove product which expires 3 days
{userNo}:cart:product
{
‘productNo’:’{productNo}’,
‘prodctName’:{productName}’,
‘quantity’:’{quantity}’
}
{userNo}:cart:productid:{productNo}
(개별상품 주문정보)
Value (String)
{ ‘productNo’,’productNo’,….}
{
‘productNo’:’{productNo}’,
‘prodctName’:{productName}’,
‘quantity’:’{quantity}’
}
{userNo}:cart:productid:{productNo}
(개별상품 주문정보)
setex(key,{EXPIRE
TIME(3days)},JSON VALUE);
Key
SimpleJson is used
org.json.simple
3. Shopping Basket
• Problem
– getProductList()
for(productsNo){
json=jedis.get(product)
result+=json
}
{userNo}:cart:product { ‘productNo’,’productNo’,….}
{
‘productNo’:’{productNo}’,
‘prodctName’:{productName}’,
‘quantity’:’{quantity}’
}
{userNo}:cart:productid:{productNo}
(개별상품 주문정보)
It makes # of calls to redis
• Solution
– Use Redis pipeline call
– p = redis.pipelined()
getProductList()
for(productsNo){
p.get(product)
}
List<Object> redisResult = p.syncAndReturnAll();
for(item:redisResult){
json.add(item)
}
4. Like it
• Problem domain
– Add Like to posing : sadd
– Remove Like from posting : srem
– Validate specific user’s Like :sismember
– Total count of Like in specific posting : scard
– Total count of Like in postings :pipleline (for postings) + scard
posting:like:{posting no}
Value (Set)
{userNo}
Key
{userNo}
{userNo}
:
Each value is unique in a Set
※ scard  160K scard/sec with pipe line
20개의 게시물별로 좋아요합을 출력하려면 160K/20 = 8000 TPS
If it needs more TPS, use read replica
5. Count unique visitor per day (not page view)
• Problem domain
– Capacity : it has 10M users
– Count unique vistor # per day
1 2 3 4 10
M….Key = unique:vistors:{date}
Value(String/Bit)
Map eash 10M user into bit
10M bit required = 1.9M per day
Redis.setbit(Key,{userNo},true);
Jedis.bitOffSet
CountUnqueVisitor # per day = Jedis.bitCount(Key)
5. Count unique visitor per day (not page view)
• Problem domain
– Capacity : it has 10M users
– Count unique vistor # per day
1 2 3 4 10
M….Key = unique:vistors:{date}
Value(String/Bit)
Map eash 10M user into bit
10M bit required = 1.9M per day
Redis.setbit(Key,{userNo},true);
Jedis.bitOffSet
CountUnqueVisitor # per day = Jedis.bitCount(Key)
5. Count unique visitor per day (not page view)
• Enhancement request
– Count unique visitor who visits every day in a week
• Solution
– AND operation in 1Week data and count bit
1 2 3 4 10
M….Key = unique:vistors:{date}
Value(String/Bit)
1 2 3 4 10
MKey = unique:vistors:{date}
1 2 3 4 10
MKey = unique:vistors:{date}
1 2 3 4 10
M….Key = unique:vistors:{date}
1 2 3 4 10
MKey = unique:vistors:{date}
1 2 3 4 10
MKey = unique:vistors:{date}
1 2 3 4 10
MKey = unique:vistors:{date}
1W
AND
bitop(BitOP.AND,{key},[unique:vistors:day1,
unique:vistors:day2,…])
1 2 3 4 10
MKey = {key}
Result =bitcount({key})
bitop From Redis
5. Count unique visitor per day (not page view)
• Enhancement request
– Get list of visitor who visited site every day.
– 근데 예제가 좀 이상함. AND 연산으로 구해서 1은 사용자만 구하면 될텐데.
• Solution
– Register Lua script and run it
• Register String sha1 = jedis.script.Load( (String)”LUA Script”);
• Run jedis.evalsha(sha1)
※ BitSet Order
– Bitset order between Redis and Program language(LUA) can be different (opposite direction)

Mais conteúdo relacionado

Mais procurados

Apache Cassandra at the Geek2Geek Berlin
Apache Cassandra at the Geek2Geek BerlinApache Cassandra at the Geek2Geek Berlin
Apache Cassandra at the Geek2Geek BerlinChristian Johannsen
 
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013mumrah
 
Introduction to Apache Cassandra
Introduction to Apache CassandraIntroduction to Apache Cassandra
Introduction to Apache CassandraRobert Stupp
 
Redis - Usability and Use Cases
Redis - Usability and Use CasesRedis - Usability and Use Cases
Redis - Usability and Use CasesFabrizio Farinacci
 
MongoDB at Scale
MongoDB at ScaleMongoDB at Scale
MongoDB at ScaleMongoDB
 
Spark - Alexis Seigneurin (Français)
Spark - Alexis Seigneurin (Français)Spark - Alexis Seigneurin (Français)
Spark - Alexis Seigneurin (Français)Alexis Seigneurin
 
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016DataStax
 
Introduction to Redis
Introduction to RedisIntroduction to Redis
Introduction to RedisDvir Volk
 
Introduction to Storm
Introduction to Storm Introduction to Storm
Introduction to Storm Chandler Huang
 
Meet Spilo, Zalando’s HIGH-AVAILABLE POSTGRESQL CLUSTER - Feike Steenbergen
Meet Spilo, Zalando’s HIGH-AVAILABLE POSTGRESQL CLUSTER - Feike SteenbergenMeet Spilo, Zalando’s HIGH-AVAILABLE POSTGRESQL CLUSTER - Feike Steenbergen
Meet Spilo, Zalando’s HIGH-AVAILABLE POSTGRESQL CLUSTER - Feike Steenbergendistributed matters
 
Optimizing RocksDB for Open-Channel SSDs
Optimizing RocksDB for Open-Channel SSDsOptimizing RocksDB for Open-Channel SSDs
Optimizing RocksDB for Open-Channel SSDsJavier González
 
Node.Js: Basics Concepts and Introduction
Node.Js: Basics Concepts and Introduction Node.Js: Basics Concepts and Introduction
Node.Js: Basics Concepts and Introduction Kanika Gera
 
Optimizing Delta/Parquet Data Lakes for Apache Spark
Optimizing Delta/Parquet Data Lakes for Apache SparkOptimizing Delta/Parquet Data Lakes for Apache Spark
Optimizing Delta/Parquet Data Lakes for Apache SparkDatabricks
 
Cassandra & puppet, scaling data at $15 per month
Cassandra & puppet, scaling data at $15 per monthCassandra & puppet, scaling data at $15 per month
Cassandra & puppet, scaling data at $15 per monthdaveconnors
 
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법Ji-Woong Choi
 
Aggregated queries with Druid on terrabytes and petabytes of data
Aggregated queries with Druid on terrabytes and petabytes of dataAggregated queries with Druid on terrabytes and petabytes of data
Aggregated queries with Druid on terrabytes and petabytes of dataRostislav Pashuto
 
ClickHouse Monitoring 101: What to monitor and how
ClickHouse Monitoring 101: What to monitor and howClickHouse Monitoring 101: What to monitor and how
ClickHouse Monitoring 101: What to monitor and howAltinity Ltd
 

Mais procurados (20)

Cloud arch patterns
Cloud arch patternsCloud arch patterns
Cloud arch patterns
 
Apache Cassandra at the Geek2Geek Berlin
Apache Cassandra at the Geek2Geek BerlinApache Cassandra at the Geek2Geek Berlin
Apache Cassandra at the Geek2Geek Berlin
 
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
 
Introduction to Apache Cassandra
Introduction to Apache CassandraIntroduction to Apache Cassandra
Introduction to Apache Cassandra
 
Redis - Usability and Use Cases
Redis - Usability and Use CasesRedis - Usability and Use Cases
Redis - Usability and Use Cases
 
MongoDB at Scale
MongoDB at ScaleMongoDB at Scale
MongoDB at Scale
 
Intro to HBase
Intro to HBaseIntro to HBase
Intro to HBase
 
Spark - Alexis Seigneurin (Français)
Spark - Alexis Seigneurin (Français)Spark - Alexis Seigneurin (Français)
Spark - Alexis Seigneurin (Français)
 
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
Myths of Big Partitions (Robert Stupp, DataStax) | Cassandra Summit 2016
 
Introduction to Redis
Introduction to RedisIntroduction to Redis
Introduction to Redis
 
Introduction to Storm
Introduction to Storm Introduction to Storm
Introduction to Storm
 
Meet Spilo, Zalando’s HIGH-AVAILABLE POSTGRESQL CLUSTER - Feike Steenbergen
Meet Spilo, Zalando’s HIGH-AVAILABLE POSTGRESQL CLUSTER - Feike SteenbergenMeet Spilo, Zalando’s HIGH-AVAILABLE POSTGRESQL CLUSTER - Feike Steenbergen
Meet Spilo, Zalando’s HIGH-AVAILABLE POSTGRESQL CLUSTER - Feike Steenbergen
 
Cassandra Database
Cassandra DatabaseCassandra Database
Cassandra Database
 
Optimizing RocksDB for Open-Channel SSDs
Optimizing RocksDB for Open-Channel SSDsOptimizing RocksDB for Open-Channel SSDs
Optimizing RocksDB for Open-Channel SSDs
 
Node.Js: Basics Concepts and Introduction
Node.Js: Basics Concepts and Introduction Node.Js: Basics Concepts and Introduction
Node.Js: Basics Concepts and Introduction
 
Optimizing Delta/Parquet Data Lakes for Apache Spark
Optimizing Delta/Parquet Data Lakes for Apache SparkOptimizing Delta/Parquet Data Lakes for Apache Spark
Optimizing Delta/Parquet Data Lakes for Apache Spark
 
Cassandra & puppet, scaling data at $15 per month
Cassandra & puppet, scaling data at $15 per monthCassandra & puppet, scaling data at $15 per month
Cassandra & puppet, scaling data at $15 per month
 
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법
 
Aggregated queries with Druid on terrabytes and petabytes of data
Aggregated queries with Druid on terrabytes and petabytes of dataAggregated queries with Druid on terrabytes and petabytes of data
Aggregated queries with Druid on terrabytes and petabytes of data
 
ClickHouse Monitoring 101: What to monitor and how
ClickHouse Monitoring 101: What to monitor and howClickHouse Monitoring 101: What to monitor and how
ClickHouse Monitoring 101: What to monitor and how
 

Destaque

Scaling Crashlytics: Building Analytics on Redis 2.6
Scaling Crashlytics: Building Analytics on Redis 2.6Scaling Crashlytics: Building Analytics on Redis 2.6
Scaling Crashlytics: Building Analytics on Redis 2.6Crashlytics
 
Redis data design by usecase
Redis data design by usecaseRedis data design by usecase
Redis data design by usecaseKris Jeong
 
Redis Use Patterns (DevconTLV June 2014)
Redis Use Patterns (DevconTLV June 2014)Redis Use Patterns (DevconTLV June 2014)
Redis Use Patterns (DevconTLV June 2014)Itamar Haber
 
High-Volume Data Collection and Real Time Analytics Using Redis
High-Volume Data Collection and Real Time Analytics Using RedisHigh-Volume Data Collection and Real Time Analytics Using Redis
High-Volume Data Collection and Real Time Analytics Using Rediscacois
 
Redis in Practice
Redis in PracticeRedis in Practice
Redis in PracticeNoah Davis
 
Kicking ass with redis
Kicking ass with redisKicking ass with redis
Kicking ass with redisDvir Volk
 
Everything you always wanted to know about Redis but were afraid to ask
Everything you always wanted to know about Redis but were afraid to askEverything you always wanted to know about Redis but were afraid to ask
Everything you always wanted to know about Redis but were afraid to askCarlos Abalde
 

Destaque (7)

Scaling Crashlytics: Building Analytics on Redis 2.6
Scaling Crashlytics: Building Analytics on Redis 2.6Scaling Crashlytics: Building Analytics on Redis 2.6
Scaling Crashlytics: Building Analytics on Redis 2.6
 
Redis data design by usecase
Redis data design by usecaseRedis data design by usecase
Redis data design by usecase
 
Redis Use Patterns (DevconTLV June 2014)
Redis Use Patterns (DevconTLV June 2014)Redis Use Patterns (DevconTLV June 2014)
Redis Use Patterns (DevconTLV June 2014)
 
High-Volume Data Collection and Real Time Analytics Using Redis
High-Volume Data Collection and Real Time Analytics Using RedisHigh-Volume Data Collection and Real Time Analytics Using Redis
High-Volume Data Collection and Real Time Analytics Using Redis
 
Redis in Practice
Redis in PracticeRedis in Practice
Redis in Practice
 
Kicking ass with redis
Kicking ass with redisKicking ass with redis
Kicking ass with redis
 
Everything you always wanted to know about Redis but were afraid to ask
Everything you always wanted to know about Redis but were afraid to askEverything you always wanted to know about Redis but were afraid to ask
Everything you always wanted to know about Redis but were afraid to ask
 

Semelhante a Redis data modeling examples

User Data Management with MongoDB
User Data Management with MongoDB User Data Management with MongoDB
User Data Management with MongoDB MongoDB
 
Marc s01 e02-crud-database
Marc s01 e02-crud-databaseMarc s01 e02-crud-database
Marc s01 e02-crud-databaseMongoDB
 
Webinarserie: Einführung in MongoDB: “Back to Basics” - Teil 3 - Interaktion ...
Webinarserie: Einführung in MongoDB: “Back to Basics” - Teil 3 - Interaktion ...Webinarserie: Einführung in MongoDB: “Back to Basics” - Teil 3 - Interaktion ...
Webinarserie: Einführung in MongoDB: “Back to Basics” - Teil 3 - Interaktion ...MongoDB
 
S01 e01 schema-design
S01 e01 schema-designS01 e01 schema-design
S01 e01 schema-designMongoDB
 
Norikra: SQL Stream Processing In Ruby
Norikra: SQL Stream Processing In RubyNorikra: SQL Stream Processing In Ruby
Norikra: SQL Stream Processing In RubySATOSHI TAGOMORI
 
Mobile 1: Mobile Apps with MongoDB
Mobile 1: Mobile Apps with MongoDBMobile 1: Mobile Apps with MongoDB
Mobile 1: Mobile Apps with MongoDBMongoDB
 
How to leverage what's new in MongoDB 3.6
How to leverage what's new in MongoDB 3.6How to leverage what's new in MongoDB 3.6
How to leverage what's new in MongoDB 3.6Maxime Beugnet
 
1140 p2 p04_and_1350_p2p05_and_1440_p2p06
1140 p2 p04_and_1350_p2p05_and_1440_p2p061140 p2 p04_and_1350_p2p05_and_1440_p2p06
1140 p2 p04_and_1350_p2p05_and_1440_p2p06MongoDB
 
Fast querying indexing for performance (4)
Fast querying   indexing for performance (4)Fast querying   indexing for performance (4)
Fast querying indexing for performance (4)MongoDB
 
Data_Modeling_MongoDB.pdf
Data_Modeling_MongoDB.pdfData_Modeling_MongoDB.pdf
Data_Modeling_MongoDB.pdfjill734733
 
Digital analytics with R - Sydney Users of R Forum - May 2015
Digital analytics with R - Sydney Users of R Forum - May 2015Digital analytics with R - Sydney Users of R Forum - May 2015
Digital analytics with R - Sydney Users of R Forum - May 2015Johann de Boer
 
1403 app dev series - session 5 - analytics
1403   app dev series - session 5 - analytics1403   app dev series - session 5 - analytics
1403 app dev series - session 5 - analyticsMongoDB
 
Webinar: Applikationsentwicklung mit MongoDB : Teil 5: Reporting & Aggregation
Webinar: Applikationsentwicklung mit MongoDB: Teil 5: Reporting & AggregationWebinar: Applikationsentwicklung mit MongoDB: Teil 5: Reporting & Aggregation
Webinar: Applikationsentwicklung mit MongoDB : Teil 5: Reporting & AggregationMongoDB
 
Indexing Strategies to Help You Scale
Indexing Strategies to Help You ScaleIndexing Strategies to Help You Scale
Indexing Strategies to Help You ScaleMongoDB
 
2006 - Basta!: Advanced server controls
2006 - Basta!: Advanced server controls2006 - Basta!: Advanced server controls
2006 - Basta!: Advanced server controlsDaniel Fisher
 
Advanced Schema Design Patterns
Advanced Schema Design PatternsAdvanced Schema Design Patterns
Advanced Schema Design PatternsMongoDB
 

Semelhante a Redis data modeling examples (20)

User Data Management with MongoDB
User Data Management with MongoDB User Data Management with MongoDB
User Data Management with MongoDB
 
Marc s01 e02-crud-database
Marc s01 e02-crud-databaseMarc s01 e02-crud-database
Marc s01 e02-crud-database
 
Webinarserie: Einführung in MongoDB: “Back to Basics” - Teil 3 - Interaktion ...
Webinarserie: Einführung in MongoDB: “Back to Basics” - Teil 3 - Interaktion ...Webinarserie: Einführung in MongoDB: “Back to Basics” - Teil 3 - Interaktion ...
Webinarserie: Einführung in MongoDB: “Back to Basics” - Teil 3 - Interaktion ...
 
S01 e01 schema-design
S01 e01 schema-designS01 e01 schema-design
S01 e01 schema-design
 
Norikra: SQL Stream Processing In Ruby
Norikra: SQL Stream Processing In RubyNorikra: SQL Stream Processing In Ruby
Norikra: SQL Stream Processing In Ruby
 
Mobile 1: Mobile Apps with MongoDB
Mobile 1: Mobile Apps with MongoDBMobile 1: Mobile Apps with MongoDB
Mobile 1: Mobile Apps with MongoDB
 
How to leverage what's new in MongoDB 3.6
How to leverage what's new in MongoDB 3.6How to leverage what's new in MongoDB 3.6
How to leverage what's new in MongoDB 3.6
 
1140 p2 p04_and_1350_p2p05_and_1440_p2p06
1140 p2 p04_and_1350_p2p05_and_1440_p2p061140 p2 p04_and_1350_p2p05_and_1440_p2p06
1140 p2 p04_and_1350_p2p05_and_1440_p2p06
 
Fast querying indexing for performance (4)
Fast querying   indexing for performance (4)Fast querying   indexing for performance (4)
Fast querying indexing for performance (4)
 
Super spike
Super spikeSuper spike
Super spike
 
Data_Modeling_MongoDB.pdf
Data_Modeling_MongoDB.pdfData_Modeling_MongoDB.pdf
Data_Modeling_MongoDB.pdf
 
Learning with F#
Learning with F#Learning with F#
Learning with F#
 
Amazon DynamoDB Design Workshop
Amazon DynamoDB Design WorkshopAmazon DynamoDB Design Workshop
Amazon DynamoDB Design Workshop
 
Digital analytics with R - Sydney Users of R Forum - May 2015
Digital analytics with R - Sydney Users of R Forum - May 2015Digital analytics with R - Sydney Users of R Forum - May 2015
Digital analytics with R - Sydney Users of R Forum - May 2015
 
DynamoDB Design Workshop
DynamoDB Design WorkshopDynamoDB Design Workshop
DynamoDB Design Workshop
 
1403 app dev series - session 5 - analytics
1403   app dev series - session 5 - analytics1403   app dev series - session 5 - analytics
1403 app dev series - session 5 - analytics
 
Webinar: Applikationsentwicklung mit MongoDB : Teil 5: Reporting & Aggregation
Webinar: Applikationsentwicklung mit MongoDB: Teil 5: Reporting & AggregationWebinar: Applikationsentwicklung mit MongoDB: Teil 5: Reporting & Aggregation
Webinar: Applikationsentwicklung mit MongoDB : Teil 5: Reporting & Aggregation
 
Indexing Strategies to Help You Scale
Indexing Strategies to Help You ScaleIndexing Strategies to Help You Scale
Indexing Strategies to Help You Scale
 
2006 - Basta!: Advanced server controls
2006 - Basta!: Advanced server controls2006 - Basta!: Advanced server controls
2006 - Basta!: Advanced server controls
 
Advanced Schema Design Patterns
Advanced Schema Design PatternsAdvanced Schema Design Patterns
Advanced Schema Design Patterns
 

Mais de Terry Cho

Kubernetes #6 advanced scheduling
Kubernetes #6   advanced schedulingKubernetes #6   advanced scheduling
Kubernetes #6 advanced schedulingTerry Cho
 
Kubernetes #4 volume &amp; stateful set
Kubernetes #4   volume &amp; stateful setKubernetes #4   volume &amp; stateful set
Kubernetes #4 volume &amp; stateful setTerry Cho
 
Kubernetes #3 security
Kubernetes #3   securityKubernetes #3   security
Kubernetes #3 securityTerry Cho
 
Kubernetes #2 monitoring
Kubernetes #2   monitoring Kubernetes #2   monitoring
Kubernetes #2 monitoring Terry Cho
 
Kubernetes #1 intro
Kubernetes #1   introKubernetes #1   intro
Kubernetes #1 introTerry Cho
 
머신러닝으로 얼굴 인식 모델 개발 삽질기
머신러닝으로 얼굴 인식 모델 개발 삽질기머신러닝으로 얼굴 인식 모델 개발 삽질기
머신러닝으로 얼굴 인식 모델 개발 삽질기Terry Cho
 
5. 솔루션 카달로그
5. 솔루션 카달로그5. 솔루션 카달로그
5. 솔루션 카달로그Terry Cho
 
4. 대용량 아키텍쳐 설계 패턴
4. 대용량 아키텍쳐 설계 패턴4. 대용량 아키텍쳐 설계 패턴
4. 대용량 아키텍쳐 설계 패턴Terry Cho
 
3. 마이크로 서비스 아키텍쳐
3. 마이크로 서비스 아키텍쳐3. 마이크로 서비스 아키텍쳐
3. 마이크로 서비스 아키텍쳐Terry Cho
 
서비스 지향 아키텍쳐 (SOA)
서비스 지향 아키텍쳐 (SOA)서비스 지향 아키텍쳐 (SOA)
서비스 지향 아키텍쳐 (SOA)Terry Cho
 
1. 아키텍쳐 설계 프로세스
1. 아키텍쳐 설계 프로세스1. 아키텍쳐 설계 프로세스
1. 아키텍쳐 설계 프로세스Terry Cho
 
애자일 스크럼과 JIRA
애자일 스크럼과 JIRA 애자일 스크럼과 JIRA
애자일 스크럼과 JIRA Terry Cho
 
REST API 설계
REST API 설계REST API 설계
REST API 설계Terry Cho
 
모바일 개발 트랜드
모바일 개발 트랜드모바일 개발 트랜드
모바일 개발 트랜드Terry Cho
 
소프트웨어 개발 트랜드 및 MSA (마이크로 서비스 아키텍쳐)의 이해
소프트웨어 개발 트랜드 및 MSA (마이크로 서비스 아키텍쳐)의 이해소프트웨어 개발 트랜드 및 MSA (마이크로 서비스 아키텍쳐)의 이해
소프트웨어 개발 트랜드 및 MSA (마이크로 서비스 아키텍쳐)의 이해Terry Cho
 
Micro Service Architecture의 이해
Micro Service Architecture의 이해Micro Service Architecture의 이해
Micro Service Architecture의 이해Terry Cho
 
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개머신 러닝 입문 #1-머신러닝 소개와 kNN 소개
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개Terry Cho
 
R 프로그래밍-향상된 데이타 조작
R 프로그래밍-향상된 데이타 조작R 프로그래밍-향상된 데이타 조작
R 프로그래밍-향상된 데이타 조작Terry Cho
 
R 프로그래밍 기본 문법
R 프로그래밍 기본 문법R 프로그래밍 기본 문법
R 프로그래밍 기본 문법Terry Cho
 
R 기본-데이타형 소개
R 기본-데이타형 소개R 기본-데이타형 소개
R 기본-데이타형 소개Terry Cho
 

Mais de Terry Cho (20)

Kubernetes #6 advanced scheduling
Kubernetes #6   advanced schedulingKubernetes #6   advanced scheduling
Kubernetes #6 advanced scheduling
 
Kubernetes #4 volume &amp; stateful set
Kubernetes #4   volume &amp; stateful setKubernetes #4   volume &amp; stateful set
Kubernetes #4 volume &amp; stateful set
 
Kubernetes #3 security
Kubernetes #3   securityKubernetes #3   security
Kubernetes #3 security
 
Kubernetes #2 monitoring
Kubernetes #2   monitoring Kubernetes #2   monitoring
Kubernetes #2 monitoring
 
Kubernetes #1 intro
Kubernetes #1   introKubernetes #1   intro
Kubernetes #1 intro
 
머신러닝으로 얼굴 인식 모델 개발 삽질기
머신러닝으로 얼굴 인식 모델 개발 삽질기머신러닝으로 얼굴 인식 모델 개발 삽질기
머신러닝으로 얼굴 인식 모델 개발 삽질기
 
5. 솔루션 카달로그
5. 솔루션 카달로그5. 솔루션 카달로그
5. 솔루션 카달로그
 
4. 대용량 아키텍쳐 설계 패턴
4. 대용량 아키텍쳐 설계 패턴4. 대용량 아키텍쳐 설계 패턴
4. 대용량 아키텍쳐 설계 패턴
 
3. 마이크로 서비스 아키텍쳐
3. 마이크로 서비스 아키텍쳐3. 마이크로 서비스 아키텍쳐
3. 마이크로 서비스 아키텍쳐
 
서비스 지향 아키텍쳐 (SOA)
서비스 지향 아키텍쳐 (SOA)서비스 지향 아키텍쳐 (SOA)
서비스 지향 아키텍쳐 (SOA)
 
1. 아키텍쳐 설계 프로세스
1. 아키텍쳐 설계 프로세스1. 아키텍쳐 설계 프로세스
1. 아키텍쳐 설계 프로세스
 
애자일 스크럼과 JIRA
애자일 스크럼과 JIRA 애자일 스크럼과 JIRA
애자일 스크럼과 JIRA
 
REST API 설계
REST API 설계REST API 설계
REST API 설계
 
모바일 개발 트랜드
모바일 개발 트랜드모바일 개발 트랜드
모바일 개발 트랜드
 
소프트웨어 개발 트랜드 및 MSA (마이크로 서비스 아키텍쳐)의 이해
소프트웨어 개발 트랜드 및 MSA (마이크로 서비스 아키텍쳐)의 이해소프트웨어 개발 트랜드 및 MSA (마이크로 서비스 아키텍쳐)의 이해
소프트웨어 개발 트랜드 및 MSA (마이크로 서비스 아키텍쳐)의 이해
 
Micro Service Architecture의 이해
Micro Service Architecture의 이해Micro Service Architecture의 이해
Micro Service Architecture의 이해
 
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개머신 러닝 입문 #1-머신러닝 소개와 kNN 소개
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개
 
R 프로그래밍-향상된 데이타 조작
R 프로그래밍-향상된 데이타 조작R 프로그래밍-향상된 데이타 조작
R 프로그래밍-향상된 데이타 조작
 
R 프로그래밍 기본 문법
R 프로그래밍 기본 문법R 프로그래밍 기본 문법
R 프로그래밍 기본 문법
 
R 기본-데이타형 소개
R 기본-데이타형 소개R 기본-데이타형 소개
R 기본-데이타형 소개
 

Último

HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKAR
HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKARHAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKAR
HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKARKOUSTAV SARKAR
 
Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...
Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...
Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...Call Girls Mumbai
 
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptx
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptxA CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptx
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptxmaisarahman1
 
NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...
NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...
NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...Amil baba
 
kiln thermal load.pptx kiln tgermal load
kiln thermal load.pptx kiln tgermal loadkiln thermal load.pptx kiln tgermal load
kiln thermal load.pptx kiln tgermal loadhamedmustafa094
 
Thermal Engineering Unit - I & II . ppt
Thermal Engineering  Unit - I & II . pptThermal Engineering  Unit - I & II . ppt
Thermal Engineering Unit - I & II . pptDineshKumar4165
 
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXssuser89054b
 
GEAR TRAIN- BASIC CONCEPTS AND WORKING PRINCIPLE
GEAR TRAIN- BASIC CONCEPTS AND WORKING PRINCIPLEGEAR TRAIN- BASIC CONCEPTS AND WORKING PRINCIPLE
GEAR TRAIN- BASIC CONCEPTS AND WORKING PRINCIPLEselvakumar948
 
Employee leave management system project.
Employee leave management system project.Employee leave management system project.
Employee leave management system project.Kamal Acharya
 
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...drmkjayanthikannan
 
1_Introduction + EAM Vocabulary + how to navigate in EAM.pdf
1_Introduction + EAM Vocabulary + how to navigate in EAM.pdf1_Introduction + EAM Vocabulary + how to navigate in EAM.pdf
1_Introduction + EAM Vocabulary + how to navigate in EAM.pdfAldoGarca30
 
PE 459 LECTURE 2- natural gas basic concepts and properties
PE 459 LECTURE 2- natural gas basic concepts and propertiesPE 459 LECTURE 2- natural gas basic concepts and properties
PE 459 LECTURE 2- natural gas basic concepts and propertiessarkmank1
 
Kuwait City MTP kit ((+919101817206)) Buy Abortion Pills Kuwait
Kuwait City MTP kit ((+919101817206)) Buy Abortion Pills KuwaitKuwait City MTP kit ((+919101817206)) Buy Abortion Pills Kuwait
Kuwait City MTP kit ((+919101817206)) Buy Abortion Pills Kuwaitjaanualu31
 
S1S2 B.Arch MGU - HOA1&2 Module 3 -Temple Architecture of Kerala.pptx
S1S2 B.Arch MGU - HOA1&2 Module 3 -Temple Architecture of Kerala.pptxS1S2 B.Arch MGU - HOA1&2 Module 3 -Temple Architecture of Kerala.pptx
S1S2 B.Arch MGU - HOA1&2 Module 3 -Temple Architecture of Kerala.pptxSCMS School of Architecture
 
School management system project Report.pdf
School management system project Report.pdfSchool management system project Report.pdf
School management system project Report.pdfKamal Acharya
 
"Lesotho Leaps Forward: A Chronicle of Transformative Developments"
"Lesotho Leaps Forward: A Chronicle of Transformative Developments""Lesotho Leaps Forward: A Chronicle of Transformative Developments"
"Lesotho Leaps Forward: A Chronicle of Transformative Developments"mphochane1998
 
Hostel management system project report..pdf
Hostel management system project report..pdfHostel management system project report..pdf
Hostel management system project report..pdfKamal Acharya
 
Double Revolving field theory-how the rotor develops torque
Double Revolving field theory-how the rotor develops torqueDouble Revolving field theory-how the rotor develops torque
Double Revolving field theory-how the rotor develops torqueBhangaleSonal
 

Último (20)

HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKAR
HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKARHAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKAR
HAND TOOLS USED AT ELECTRONICS WORK PRESENTED BY KOUSTAV SARKAR
 
Cara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak Hamil
Cara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak HamilCara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak Hamil
Cara Menggugurkan Sperma Yang Masuk Rahim Biyar Tidak Hamil
 
Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...
Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...
Bhubaneswar🌹Call Girls Bhubaneswar ❤Komal 9777949614 💟 Full Trusted CALL GIRL...
 
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptx
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptxA CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptx
A CASE STUDY ON CERAMIC INDUSTRY OF BANGLADESH.pptx
 
NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...
NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...
NO1 Top No1 Amil Baba In Azad Kashmir, Kashmir Black Magic Specialist Expert ...
 
kiln thermal load.pptx kiln tgermal load
kiln thermal load.pptx kiln tgermal loadkiln thermal load.pptx kiln tgermal load
kiln thermal load.pptx kiln tgermal load
 
Thermal Engineering Unit - I & II . ppt
Thermal Engineering  Unit - I & II . pptThermal Engineering  Unit - I & II . ppt
Thermal Engineering Unit - I & II . ppt
 
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
 
GEAR TRAIN- BASIC CONCEPTS AND WORKING PRINCIPLE
GEAR TRAIN- BASIC CONCEPTS AND WORKING PRINCIPLEGEAR TRAIN- BASIC CONCEPTS AND WORKING PRINCIPLE
GEAR TRAIN- BASIC CONCEPTS AND WORKING PRINCIPLE
 
Employee leave management system project.
Employee leave management system project.Employee leave management system project.
Employee leave management system project.
 
Call Girls in South Ex (delhi) call me [🔝9953056974🔝] escort service 24X7
Call Girls in South Ex (delhi) call me [🔝9953056974🔝] escort service 24X7Call Girls in South Ex (delhi) call me [🔝9953056974🔝] escort service 24X7
Call Girls in South Ex (delhi) call me [🔝9953056974🔝] escort service 24X7
 
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...
Unit 4_Part 1 CSE2001 Exception Handling and Function Template and Class Temp...
 
1_Introduction + EAM Vocabulary + how to navigate in EAM.pdf
1_Introduction + EAM Vocabulary + how to navigate in EAM.pdf1_Introduction + EAM Vocabulary + how to navigate in EAM.pdf
1_Introduction + EAM Vocabulary + how to navigate in EAM.pdf
 
PE 459 LECTURE 2- natural gas basic concepts and properties
PE 459 LECTURE 2- natural gas basic concepts and propertiesPE 459 LECTURE 2- natural gas basic concepts and properties
PE 459 LECTURE 2- natural gas basic concepts and properties
 
Kuwait City MTP kit ((+919101817206)) Buy Abortion Pills Kuwait
Kuwait City MTP kit ((+919101817206)) Buy Abortion Pills KuwaitKuwait City MTP kit ((+919101817206)) Buy Abortion Pills Kuwait
Kuwait City MTP kit ((+919101817206)) Buy Abortion Pills Kuwait
 
S1S2 B.Arch MGU - HOA1&2 Module 3 -Temple Architecture of Kerala.pptx
S1S2 B.Arch MGU - HOA1&2 Module 3 -Temple Architecture of Kerala.pptxS1S2 B.Arch MGU - HOA1&2 Module 3 -Temple Architecture of Kerala.pptx
S1S2 B.Arch MGU - HOA1&2 Module 3 -Temple Architecture of Kerala.pptx
 
School management system project Report.pdf
School management system project Report.pdfSchool management system project Report.pdf
School management system project Report.pdf
 
"Lesotho Leaps Forward: A Chronicle of Transformative Developments"
"Lesotho Leaps Forward: A Chronicle of Transformative Developments""Lesotho Leaps Forward: A Chronicle of Transformative Developments"
"Lesotho Leaps Forward: A Chronicle of Transformative Developments"
 
Hostel management system project report..pdf
Hostel management system project report..pdfHostel management system project report..pdf
Hostel management system project report..pdf
 
Double Revolving field theory-how the rotor develops torque
Double Revolving field theory-how the rotor develops torqueDouble Revolving field theory-how the rotor develops torque
Double Revolving field theory-how the rotor develops torque
 

Redis data modeling examples

  • 1. REDIS DATA MODEL SAMPLE Terry’s Redis
  • 2. 1.LogWriter • Problem domain : Collect all logs from distributed server and merge it into single log file Server Server Redis Key:’WAS:log’ Value : Log (Single String) Append log to String Log File Flush to log file Problem String append bring memory re- allocation. So every log write makes a memory relocation Log String Log String Log String Server Server Key:’WAS:log’ : Log File Redis (List) lpush rpop Solution Use List data type and push the log and pop & write the log into log file
  • 3. 2.Visitor count • Problem domain – Count total event page visit # – Count visit # per each event page event:click:total event:click:{event page# id} visit # visit # event:click:{event page# id} visit # : Key Value (String Type) incr • Enhancement Request – Count total event page visit # per day – Count visit # per each event page per day visit # visit # event:click:daily:total:{date} event:click:daily:{date}:{event page# id} event:click:daily:{date}:{event page# id} visit # Key Value (String Type) incr
  • 4. 2.Visitor count • Problem – It cannot find event start & end date because of that it is hard to find “key name” • Solution – Use hash data type – Sort by using java.util.SortedHashMap event:click:total:hash date visit # date visit # date visit # Key Value (Hash) event:click:total:hash:{eventid} date visit # date visit # date visit # Total event page visit per day Daily visit # per day for each event page hincrBy java.util.SortedHashMap Sorted by date Redis
  • 5. 3. Shopping Basket • Problem domain – Make shopping basket which can support • add product • remove product • empty shopping basket • list products in the shopping basket • remove product which expires 3 days {userNo}:cart:product { ‘productNo’:’{productNo}’, ‘prodctName’:{productName}’, ‘quantity’:’{quantity}’ } {userNo}:cart:productid:{productNo} (개별상품 주문정보) Value (String) { ‘productNo’,’productNo’,….} { ‘productNo’:’{productNo}’, ‘prodctName’:{productName}’, ‘quantity’:’{quantity}’ } {userNo}:cart:productid:{productNo} (개별상품 주문정보) setex(key,{EXPIRE TIME(3days)},JSON VALUE); Key SimpleJson is used org.json.simple
  • 6. 3. Shopping Basket • Problem – getProductList() for(productsNo){ json=jedis.get(product) result+=json } {userNo}:cart:product { ‘productNo’,’productNo’,….} { ‘productNo’:’{productNo}’, ‘prodctName’:{productName}’, ‘quantity’:’{quantity}’ } {userNo}:cart:productid:{productNo} (개별상품 주문정보) It makes # of calls to redis • Solution – Use Redis pipeline call – p = redis.pipelined() getProductList() for(productsNo){ p.get(product) } List<Object> redisResult = p.syncAndReturnAll(); for(item:redisResult){ json.add(item) }
  • 7. 4. Like it • Problem domain – Add Like to posing : sadd – Remove Like from posting : srem – Validate specific user’s Like :sismember – Total count of Like in specific posting : scard – Total count of Like in postings :pipleline (for postings) + scard posting:like:{posting no} Value (Set) {userNo} Key {userNo} {userNo} : Each value is unique in a Set ※ scard  160K scard/sec with pipe line 20개의 게시물별로 좋아요합을 출력하려면 160K/20 = 8000 TPS If it needs more TPS, use read replica
  • 8. 5. Count unique visitor per day (not page view) • Problem domain – Capacity : it has 10M users – Count unique vistor # per day 1 2 3 4 10 M….Key = unique:vistors:{date} Value(String/Bit) Map eash 10M user into bit 10M bit required = 1.9M per day Redis.setbit(Key,{userNo},true); Jedis.bitOffSet CountUnqueVisitor # per day = Jedis.bitCount(Key)
  • 9. 5. Count unique visitor per day (not page view) • Problem domain – Capacity : it has 10M users – Count unique vistor # per day 1 2 3 4 10 M….Key = unique:vistors:{date} Value(String/Bit) Map eash 10M user into bit 10M bit required = 1.9M per day Redis.setbit(Key,{userNo},true); Jedis.bitOffSet CountUnqueVisitor # per day = Jedis.bitCount(Key)
  • 10. 5. Count unique visitor per day (not page view) • Enhancement request – Count unique visitor who visits every day in a week • Solution – AND operation in 1Week data and count bit 1 2 3 4 10 M….Key = unique:vistors:{date} Value(String/Bit) 1 2 3 4 10 MKey = unique:vistors:{date} 1 2 3 4 10 MKey = unique:vistors:{date} 1 2 3 4 10 M….Key = unique:vistors:{date} 1 2 3 4 10 MKey = unique:vistors:{date} 1 2 3 4 10 MKey = unique:vistors:{date} 1 2 3 4 10 MKey = unique:vistors:{date} 1W AND bitop(BitOP.AND,{key},[unique:vistors:day1, unique:vistors:day2,…]) 1 2 3 4 10 MKey = {key} Result =bitcount({key}) bitop From Redis
  • 11. 5. Count unique visitor per day (not page view) • Enhancement request – Get list of visitor who visited site every day. – 근데 예제가 좀 이상함. AND 연산으로 구해서 1은 사용자만 구하면 될텐데. • Solution – Register Lua script and run it • Register String sha1 = jedis.script.Load( (String)”LUA Script”); • Run jedis.evalsha(sha1) ※ BitSet Order – Bitset order between Redis and Program language(LUA) can be different (opposite direction)