SlideShare a Scribd company logo
Redis Cluster 
design tradeoffs 
@antirez - Pivotal
What is performance? 
• Low latency. 
• IOPS. 
• Operations quality and data model.
Go Cluster 
• Redis Cluster must have same Redis use case. 
• Tradeoffs are inherently needed in DS. 
• CAP? Merge values? Strong consistency and 
consensus? How to replicate values?
CP systems 
CAP: consistency price is added latency 
Client S1 
S2 
S3 
S4
CP systems 
Reply to client after majority ACKs 
Client S1 
S2 
S3 
S4
And… there is the disk 
S1 S2 S3 
Disk Disk Disk 
CP algorithms may require fsync-befor-ack. 
Durability / Consistency not always orthogonal.
AP systems 
Eventual consistency with merges? 
(note: merge is not strictly part of EC) 
Client 
S1 
S2 
A = {1,2,3,8,12,13,14} 
Client 
A = {2,3,8,11,12,1}
Many kinds of consistencies 
• “C” of CAP is strong consistency. 
• It is not the only available tradeoff of course. 
• Consistency is the set of liveness and safety 
properties a given system provides. 
• Eventual consistency: like to say nothing at all. 
What liveness/safety properties if not “C”?
Redis Cluster 
Sharding and replication (asynchronous). 
Client 
A,B,C 
A,B,C 
A,B,C 
D,E,F 
D,E,F 
D,E,F
Asynchronous replication 
Client A,B,C 
A,B,C 
A,B,C 
A,B,C 
A,B,C 
A,B,C 
async ACK
Full Mesh 
A,B,C A,B,C 
D,E,F D,E,F 
• Heartbeats. 
• Nodes gossip. 
• Failover auth. 
• Config update.
No proxy, but redirections 
Client Client 
A? D? 
A,B,C D,E,F G,H,I L,M,N O,P,Q R,S,T
Failure detection 
• Failure reports within window of time (via gossip). 
• Trigger for actual failover. 
• Two main states: PFAIL -> FAIL.
Failure detection 
S1 is not responding? 
S1 
S2 
S3 
S4 
S1 = PFAIL 
S1 = PFAIL 
S1 = PFAIL
Failure detection 
PFAIL state propagates 
S1 
S2 
S3 
S4 
S1 = PFAIL 
S1 = PFAIL 
Reported by: 
S2, S4 
S1 = PFAIL
Failure detection 
PFAIL state propagates 
S1 
S2 
S3 
S4 
S1 = PFAIL 
S1 = FAIL 
S1 = PFAIL
Failure detection 
Force FAIL state 
S1 
S2 
S3 
S4 
S1 = FAIL 
S1 = FAIL 
S1 = FAIL
Global slots config 
• A master FAIL state triggers a failover. 
• Cluster needs a coherent view of configuration. 
• Who is serving this slot currently? 
• Slots config must eventually converge.
Raft and failover 
• Config propagation is solved using ideas from the 
Raft algorithm (just a subset). 
• Raft is a consensus algorithm built on top of 
different “layers”. 
• Raft paper is already a classic (highly 
recommended). 
• Full Raft not needed for Redis Cluster slots config.
Failover and config 
Failed 
Slave 
Slave 
Slave 
Master 
Master 
Master 
Epoch = Epoch+1 
(logical clock) 
Vote for me!
Too easy? 
• Why we don’t need full Raft? 
• Because our config is idempotent: when the 
partition heals we can trow away slots config for 
new versions. 
• Same algorithm is used in Sentinel v2 and works 
well.
Config propagation 
• After a successful failover, new slot config is 
broadcasted. 
• If there are partitions, when they heal, config will 
get updated (broadcasted from time to time, plus 
stale config detection and UPADTE messages). 
• Config with greater Epoch always wins.
Redis Cluster consistency? 
• Eventual consistent: last failover wins. 
• In the “vanilla” losing writes is unbound. 
• Mechanisms to avoid unbound data loss.
Failure mode… #1 
Client A,B,C 
A,B,C 
A,B,C 
Failed 
A,B,C 
A,B,C 
lost write…
Failure mode #2 
Client 
A,B,C 
A,B,C 
D,E,F 
G,H,I 
Minority side Majority side
Boud divergences 
Client 
A,B,C 
D,E,F 
G,H,I 
After node-timeot 
Minority side Majority side
More data safety? 
• OP logging until async ACK received. 
• Re-played to master when node turns into slave. 
• “Safe” connections, on demand. 
• Example SADD (idempotent + commutative). 
• SET-LWW foo bar <wall-clock>.
Multi key ops 
• Hey hashtags! 
• {user:1000}.following {user:1000}.followers. 
• Unavailable for small windows, but no data 
exchange between nodes.
Multi key ops 
(availability) 
• Single key ops: always available during resharding. 
• Multi key ops, available if: 
• No manual resharding of this hash slot in progress. 
• Resharding in progress, but source or destination 
node have all keys. 
• Otherwise we get a -TRYAGAIN error.
{User:1}.key_A {User:2}.Key_B 
{User:1}.key_A 
{User:1}.Key_B 
{User:1}.key_A 
{User:1}.Key_B 
SUNION key_A key_B 
-TRYAGAIN 
SUNION key_A key_B 
… output … 
SUNION key_A key_B 
… output …
Redis Cluster ETA 
• Release Candidate available. 
• We’ll go stable in Q1 2015. 
• Ask me anything.

More Related Content

What's hot

Apache Spark Architecture
Apache Spark ArchitectureApache Spark Architecture
Apache Spark Architecture
Alexey Grishchenko
 
Evening out the uneven: dealing with skew in Flink
Evening out the uneven: dealing with skew in FlinkEvening out the uneven: dealing with skew in Flink
Evening out the uneven: dealing with skew in Flink
Flink Forward
 
ClickHouse Deep Dive, by Aleksei Milovidov
ClickHouse Deep Dive, by Aleksei MilovidovClickHouse Deep Dive, by Aleksei Milovidov
ClickHouse Deep Dive, by Aleksei Milovidov
Altinity Ltd
 
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Databricks
 
RedisConf17- Using Redis at scale @ Twitter
RedisConf17- Using Redis at scale @ TwitterRedisConf17- Using Redis at scale @ Twitter
RedisConf17- Using Redis at scale @ Twitter
Redis Labs
 
Introduction to Storm
Introduction to Storm Introduction to Storm
Introduction to Storm Chandler Huang
 
[Meetup] a successful migration from elastic search to clickhouse
[Meetup] a successful migration from elastic search to clickhouse[Meetup] a successful migration from elastic search to clickhouse
[Meetup] a successful migration from elastic search to clickhouse
Vianney FOUCAULT
 
Introduction to Apache ZooKeeper
Introduction to Apache ZooKeeperIntroduction to Apache ZooKeeper
Introduction to Apache ZooKeeper
Saurav Haloi
 
Introduction to Redis
Introduction to RedisIntroduction to Redis
Introduction to Redis
TO THE NEW | Technology
 
BlueStore, A New Storage Backend for Ceph, One Year In
BlueStore, A New Storage Backend for Ceph, One Year InBlueStore, A New Storage Backend for Ceph, One Year In
BlueStore, A New Storage Backend for Ceph, One Year In
Sage Weil
 
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudAmazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud
Noritaka Sekiyama
 
RocksDB detail
RocksDB detailRocksDB detail
RocksDB detail
MIJIN AN
 
Scalability, Availability & Stability Patterns
Scalability, Availability & Stability PatternsScalability, Availability & Stability Patterns
Scalability, Availability & Stability Patterns
Jonas Bonér
 
Kubernetes dealing with storage and persistence
Kubernetes  dealing with storage and persistenceKubernetes  dealing with storage and persistence
Kubernetes dealing with storage and persistence
Janakiram MSV
 
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
mumrah
 
How to build a streaming Lakehouse with Flink, Kafka, and Hudi
How to build a streaming Lakehouse with Flink, Kafka, and HudiHow to build a streaming Lakehouse with Flink, Kafka, and Hudi
How to build a streaming Lakehouse with Flink, Kafka, and Hudi
Flink Forward
 
Running MariaDB in multiple data centers
Running MariaDB in multiple data centersRunning MariaDB in multiple data centers
Running MariaDB in multiple data centers
MariaDB plc
 
Introduction and Deep Dive Into Containerd
Introduction and Deep Dive Into ContainerdIntroduction and Deep Dive Into Containerd
Introduction and Deep Dive Into Containerd
Kohei Tokunaga
 
Spark-on-YARN: Empower Spark Applications on Hadoop Cluster
Spark-on-YARN: Empower Spark Applications on Hadoop ClusterSpark-on-YARN: Empower Spark Applications on Hadoop Cluster
Spark-on-YARN: Empower Spark Applications on Hadoop ClusterDataWorks Summit
 
Webinar slides: An Introduction to Performance Monitoring for PostgreSQL
Webinar slides: An Introduction to Performance Monitoring for PostgreSQLWebinar slides: An Introduction to Performance Monitoring for PostgreSQL
Webinar slides: An Introduction to Performance Monitoring for PostgreSQL
Severalnines
 

What's hot (20)

Apache Spark Architecture
Apache Spark ArchitectureApache Spark Architecture
Apache Spark Architecture
 
Evening out the uneven: dealing with skew in Flink
Evening out the uneven: dealing with skew in FlinkEvening out the uneven: dealing with skew in Flink
Evening out the uneven: dealing with skew in Flink
 
ClickHouse Deep Dive, by Aleksei Milovidov
ClickHouse Deep Dive, by Aleksei MilovidovClickHouse Deep Dive, by Aleksei Milovidov
ClickHouse Deep Dive, by Aleksei Milovidov
 
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
 
RedisConf17- Using Redis at scale @ Twitter
RedisConf17- Using Redis at scale @ TwitterRedisConf17- Using Redis at scale @ Twitter
RedisConf17- Using Redis at scale @ Twitter
 
Introduction to Storm
Introduction to Storm Introduction to Storm
Introduction to Storm
 
[Meetup] a successful migration from elastic search to clickhouse
[Meetup] a successful migration from elastic search to clickhouse[Meetup] a successful migration from elastic search to clickhouse
[Meetup] a successful migration from elastic search to clickhouse
 
Introduction to Apache ZooKeeper
Introduction to Apache ZooKeeperIntroduction to Apache ZooKeeper
Introduction to Apache ZooKeeper
 
Introduction to Redis
Introduction to RedisIntroduction to Redis
Introduction to Redis
 
BlueStore, A New Storage Backend for Ceph, One Year In
BlueStore, A New Storage Backend for Ceph, One Year InBlueStore, A New Storage Backend for Ceph, One Year In
BlueStore, A New Storage Backend for Ceph, One Year In
 
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudAmazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud
 
RocksDB detail
RocksDB detailRocksDB detail
RocksDB detail
 
Scalability, Availability & Stability Patterns
Scalability, Availability & Stability PatternsScalability, Availability & Stability Patterns
Scalability, Availability & Stability Patterns
 
Kubernetes dealing with storage and persistence
Kubernetes  dealing with storage and persistenceKubernetes  dealing with storage and persistence
Kubernetes dealing with storage and persistence
 
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
 
How to build a streaming Lakehouse with Flink, Kafka, and Hudi
How to build a streaming Lakehouse with Flink, Kafka, and HudiHow to build a streaming Lakehouse with Flink, Kafka, and Hudi
How to build a streaming Lakehouse with Flink, Kafka, and Hudi
 
Running MariaDB in multiple data centers
Running MariaDB in multiple data centersRunning MariaDB in multiple data centers
Running MariaDB in multiple data centers
 
Introduction and Deep Dive Into Containerd
Introduction and Deep Dive Into ContainerdIntroduction and Deep Dive Into Containerd
Introduction and Deep Dive Into Containerd
 
Spark-on-YARN: Empower Spark Applications on Hadoop Cluster
Spark-on-YARN: Empower Spark Applications on Hadoop ClusterSpark-on-YARN: Empower Spark Applications on Hadoop Cluster
Spark-on-YARN: Empower Spark Applications on Hadoop Cluster
 
Webinar slides: An Introduction to Performance Monitoring for PostgreSQL
Webinar slides: An Introduction to Performance Monitoring for PostgreSQLWebinar slides: An Introduction to Performance Monitoring for PostgreSQL
Webinar slides: An Introduction to Performance Monitoring for PostgreSQL
 

Viewers also liked

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
Carlos Abalde
 
Redis in Practice
Redis in PracticeRedis in Practice
Redis in Practice
Noah Davis
 
Redis/Lessons learned
Redis/Lessons learnedRedis/Lessons learned
Redis/Lessons learned
Tit Petric
 
美团点评技术沙龙010-Redis Cluster运维实践
美团点评技术沙龙010-Redis Cluster运维实践美团点评技术沙龙010-Redis Cluster运维实践
美团点评技术沙龙010-Redis Cluster运维实践
美团点评技术团队
 
Managing Redis with Kubernetes - Kelsey Hightower, Google
Managing Redis with Kubernetes - Kelsey Hightower, GoogleManaging Redis with Kubernetes - Kelsey Hightower, Google
Managing Redis with Kubernetes - Kelsey Hightower, Google
Redis Labs
 
Cassandra vs. Redis
Cassandra vs. RedisCassandra vs. Redis
Cassandra vs. RedisTim Lossen
 
Redis replication dcshi
Redis replication dcshiRedis replication dcshi
Redis replication dcshidcshi
 
美团点评沙龙012-从零到千万量级的实时物流平台架构实践
美团点评沙龙012-从零到千万量级的实时物流平台架构实践美团点评沙龙012-从零到千万量级的实时物流平台架构实践
美团点评沙龙012-从零到千万量级的实时物流平台架构实践
美团点评技术团队
 
美团点评技术沙龙09 - 美团外卖中的单量预估及列表优化
美团点评技术沙龙09 - 美团外卖中的单量预估及列表优化美团点评技术沙龙09 - 美团外卖中的单量预估及列表优化
美团点评技术沙龙09 - 美团外卖中的单量预估及列表优化
美团点评技术团队
 
美团点评沙龙 飞行中换引擎--美团配送业务系统的架构演进之路
美团点评沙龙 飞行中换引擎--美团配送业务系统的架构演进之路美团点评沙龙 飞行中换引擎--美团配送业务系统的架构演进之路
美团点评沙龙 飞行中换引擎--美团配送业务系统的架构演进之路
美团点评技术团队
 
[2B5]nBase-ARC Redis Cluster
[2B5]nBase-ARC Redis Cluster[2B5]nBase-ARC Redis Cluster
[2B5]nBase-ARC Redis Cluster
NAVER D2
 
美团点评技术沙龙09 - 外卖O2O的用户画像实践
美团点评技术沙龙09 - 外卖O2O的用户画像实践美团点评技术沙龙09 - 外卖O2O的用户画像实践
美团点评技术沙龙09 - 外卖O2O的用户画像实践
美团点评技术团队
 
Redis High availability and fault tolerance in a multitenant environment
Redis High availability and fault tolerance in a multitenant environmentRedis High availability and fault tolerance in a multitenant environment
Redis High availability and fault tolerance in a multitenant environment
Iccha Sethi
 
Dynomite: A Highly Available, Distributed and Scalable Dynamo Layer--Ioannis ...
Dynomite: A Highly Available, Distributed and Scalable Dynamo Layer--Ioannis ...Dynomite: A Highly Available, Distributed and Scalable Dynamo Layer--Ioannis ...
Dynomite: A Highly Available, Distributed and Scalable Dynamo Layer--Ioannis ...
Redis Labs
 
Redis, another step on the road
Redis, another step on the roadRedis, another step on the road
Redis, another step on the road
Yi-Feng Tzeng
 
Redis tutoring
Redis tutoringRedis tutoring
Redis tutoring
Chen-Tien Tsai
 
Redis Cluster by S. Sanfilippo
Redis Cluster by S. SanfilippoRedis Cluster by S. Sanfilippo
Redis Cluster by S. Sanfilippo
Corley S.r.l.
 
Redis Developers Day 2014 - Redis Labs Talks
Redis Developers Day 2014 - Redis Labs TalksRedis Developers Day 2014 - Redis Labs Talks
Redis Developers Day 2014 - Redis Labs Talks
Redis Labs
 
Redis Day Keynote Salvatore Sanfillipo Redis Labs
Redis Day Keynote Salvatore Sanfillipo Redis LabsRedis Day Keynote Salvatore Sanfillipo Redis Labs
Redis Day Keynote Salvatore Sanfillipo Redis Labs
Redis Labs
 
This is redis - feature and usecase
This is redis - feature and usecaseThis is redis - feature and usecase
This is redis - feature and usecase
Kris Jeong
 

Viewers also liked (20)

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
 
Redis in Practice
Redis in PracticeRedis in Practice
Redis in Practice
 
Redis/Lessons learned
Redis/Lessons learnedRedis/Lessons learned
Redis/Lessons learned
 
美团点评技术沙龙010-Redis Cluster运维实践
美团点评技术沙龙010-Redis Cluster运维实践美团点评技术沙龙010-Redis Cluster运维实践
美团点评技术沙龙010-Redis Cluster运维实践
 
Managing Redis with Kubernetes - Kelsey Hightower, Google
Managing Redis with Kubernetes - Kelsey Hightower, GoogleManaging Redis with Kubernetes - Kelsey Hightower, Google
Managing Redis with Kubernetes - Kelsey Hightower, Google
 
Cassandra vs. Redis
Cassandra vs. RedisCassandra vs. Redis
Cassandra vs. Redis
 
Redis replication dcshi
Redis replication dcshiRedis replication dcshi
Redis replication dcshi
 
美团点评沙龙012-从零到千万量级的实时物流平台架构实践
美团点评沙龙012-从零到千万量级的实时物流平台架构实践美团点评沙龙012-从零到千万量级的实时物流平台架构实践
美团点评沙龙012-从零到千万量级的实时物流平台架构实践
 
美团点评技术沙龙09 - 美团外卖中的单量预估及列表优化
美团点评技术沙龙09 - 美团外卖中的单量预估及列表优化美团点评技术沙龙09 - 美团外卖中的单量预估及列表优化
美团点评技术沙龙09 - 美团外卖中的单量预估及列表优化
 
美团点评沙龙 飞行中换引擎--美团配送业务系统的架构演进之路
美团点评沙龙 飞行中换引擎--美团配送业务系统的架构演进之路美团点评沙龙 飞行中换引擎--美团配送业务系统的架构演进之路
美团点评沙龙 飞行中换引擎--美团配送业务系统的架构演进之路
 
[2B5]nBase-ARC Redis Cluster
[2B5]nBase-ARC Redis Cluster[2B5]nBase-ARC Redis Cluster
[2B5]nBase-ARC Redis Cluster
 
美团点评技术沙龙09 - 外卖O2O的用户画像实践
美团点评技术沙龙09 - 外卖O2O的用户画像实践美团点评技术沙龙09 - 外卖O2O的用户画像实践
美团点评技术沙龙09 - 外卖O2O的用户画像实践
 
Redis High availability and fault tolerance in a multitenant environment
Redis High availability and fault tolerance in a multitenant environmentRedis High availability and fault tolerance in a multitenant environment
Redis High availability and fault tolerance in a multitenant environment
 
Dynomite: A Highly Available, Distributed and Scalable Dynamo Layer--Ioannis ...
Dynomite: A Highly Available, Distributed and Scalable Dynamo Layer--Ioannis ...Dynomite: A Highly Available, Distributed and Scalable Dynamo Layer--Ioannis ...
Dynomite: A Highly Available, Distributed and Scalable Dynamo Layer--Ioannis ...
 
Redis, another step on the road
Redis, another step on the roadRedis, another step on the road
Redis, another step on the road
 
Redis tutoring
Redis tutoringRedis tutoring
Redis tutoring
 
Redis Cluster by S. Sanfilippo
Redis Cluster by S. SanfilippoRedis Cluster by S. Sanfilippo
Redis Cluster by S. Sanfilippo
 
Redis Developers Day 2014 - Redis Labs Talks
Redis Developers Day 2014 - Redis Labs TalksRedis Developers Day 2014 - Redis Labs Talks
Redis Developers Day 2014 - Redis Labs Talks
 
Redis Day Keynote Salvatore Sanfillipo Redis Labs
Redis Day Keynote Salvatore Sanfillipo Redis LabsRedis Day Keynote Salvatore Sanfillipo Redis Labs
Redis Day Keynote Salvatore Sanfillipo Redis Labs
 
This is redis - feature and usecase
This is redis - feature and usecaseThis is redis - feature and usecase
This is redis - feature and usecase
 

Similar to Salvatore Sanfilippo – How Redis Cluster works, and why - NoSQL matters Barcelona 2014

Integrating Cache Oblivious Approach with Modern Processor Architecture: The ...
Integrating Cache Oblivious Approach with Modern Processor Architecture: The ...Integrating Cache Oblivious Approach with Modern Processor Architecture: The ...
Integrating Cache Oblivious Approach with Modern Processor Architecture: The ...
Tokyo Institute of Technology
 
Thoughts on consistency models
Thoughts on consistency modelsThoughts on consistency models
Thoughts on consistency models
rogerbodamer
 
Graph processing
Graph processingGraph processing
Graph processing
yeahjs
 
Data center pov 2017 v3
Data center pov 2017 v3Data center pov 2017 v3
Data center pov 2017 v3
Jeff Green
 
OMI - The Missing Piece of a Modular, Flexible and Composable Computing World
OMI - The Missing Piece of a Modular, Flexible and Composable Computing WorldOMI - The Missing Piece of a Modular, Flexible and Composable Computing World
OMI - The Missing Piece of a Modular, Flexible and Composable Computing World
Allan Cantle
 
VMworld 2017 - Top 10 things to know about vSAN
VMworld 2017 - Top 10 things to know about vSANVMworld 2017 - Top 10 things to know about vSAN
VMworld 2017 - Top 10 things to know about vSAN
Duncan Epping
 
Open west 2015 talk ben coverston
Open west 2015 talk ben coverstonOpen west 2015 talk ben coverston
Open west 2015 talk ben coverston
bcoverston
 
Disaggregated Networking - The Drivers, the Software & The High Availability
Disaggregated Networking - The Drivers, the Software & The High AvailabilityDisaggregated Networking - The Drivers, the Software & The High Availability
Disaggregated Networking - The Drivers, the Software & The High Availability
Open Networking Summit
 
The Cortex-A15 Verification Story
The Cortex-A15 Verification StoryThe Cortex-A15 Verification Story
The Cortex-A15 Verification StoryDVClub
 
What’s New in ScyllaDB Open Source 5.0
What’s New in ScyllaDB Open Source 5.0What’s New in ScyllaDB Open Source 5.0
What’s New in ScyllaDB Open Source 5.0
ScyllaDB
 
Concurrency in Distributed Systems : Leslie Lamport papers
Concurrency in Distributed Systems : Leslie Lamport papersConcurrency in Distributed Systems : Leslie Lamport papers
Concurrency in Distributed Systems : Leslie Lamport papers
Subhajit Sahu
 
Scaling web applications with cassandra presentation
Scaling web applications with cassandra presentationScaling web applications with cassandra presentation
Scaling web applications with cassandra presentationMurat Çakal
 
Real-Time Analytics with Kafka, Cassandra and Storm
Real-Time Analytics with Kafka, Cassandra and StormReal-Time Analytics with Kafka, Cassandra and Storm
Real-Time Analytics with Kafka, Cassandra and Storm
John Georgiadis
 
No stress with state
No stress with stateNo stress with state
No stress with state
Uwe Friedrichsen
 
When it Absolutely, Positively, Has to be There: Reliability Guarantees in Ka...
When it Absolutely, Positively, Has to be There: Reliability Guarantees in Ka...When it Absolutely, Positively, Has to be There: Reliability Guarantees in Ka...
When it Absolutely, Positively, Has to be There: Reliability Guarantees in Ka...
confluent
 
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedIn
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedInJay Kreps on Project Voldemort Scaling Simple Storage At LinkedIn
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedIn
LinkedIn
 
Storage interface sata_pata
Storage interface sata_pataStorage interface sata_pata
Storage interface sata_pata
PREMAL GAJJAR
 
Call me maybe: Jepsen and flaky networks
Call me maybe: Jepsen and flaky networksCall me maybe: Jepsen and flaky networks
Call me maybe: Jepsen and flaky networks
Shalin Shekhar Mangar
 
Transactions and Concurrency Control Patterns
Transactions and Concurrency Control PatternsTransactions and Concurrency Control Patterns
Transactions and Concurrency Control Patterns
J On The Beach
 

Similar to Salvatore Sanfilippo – How Redis Cluster works, and why - NoSQL matters Barcelona 2014 (20)

Integrating Cache Oblivious Approach with Modern Processor Architecture: The ...
Integrating Cache Oblivious Approach with Modern Processor Architecture: The ...Integrating Cache Oblivious Approach with Modern Processor Architecture: The ...
Integrating Cache Oblivious Approach with Modern Processor Architecture: The ...
 
Thoughts on consistency models
Thoughts on consistency modelsThoughts on consistency models
Thoughts on consistency models
 
Graph processing
Graph processingGraph processing
Graph processing
 
Data center pov 2017 v3
Data center pov 2017 v3Data center pov 2017 v3
Data center pov 2017 v3
 
OMI - The Missing Piece of a Modular, Flexible and Composable Computing World
OMI - The Missing Piece of a Modular, Flexible and Composable Computing WorldOMI - The Missing Piece of a Modular, Flexible and Composable Computing World
OMI - The Missing Piece of a Modular, Flexible and Composable Computing World
 
VMworld 2017 - Top 10 things to know about vSAN
VMworld 2017 - Top 10 things to know about vSANVMworld 2017 - Top 10 things to know about vSAN
VMworld 2017 - Top 10 things to know about vSAN
 
Open west 2015 talk ben coverston
Open west 2015 talk ben coverstonOpen west 2015 talk ben coverston
Open west 2015 talk ben coverston
 
Disaggregated Networking - The Drivers, the Software & The High Availability
Disaggregated Networking - The Drivers, the Software & The High AvailabilityDisaggregated Networking - The Drivers, the Software & The High Availability
Disaggregated Networking - The Drivers, the Software & The High Availability
 
The Cortex-A15 Verification Story
The Cortex-A15 Verification StoryThe Cortex-A15 Verification Story
The Cortex-A15 Verification Story
 
What’s New in ScyllaDB Open Source 5.0
What’s New in ScyllaDB Open Source 5.0What’s New in ScyllaDB Open Source 5.0
What’s New in ScyllaDB Open Source 5.0
 
Concurrency in Distributed Systems : Leslie Lamport papers
Concurrency in Distributed Systems : Leslie Lamport papersConcurrency in Distributed Systems : Leslie Lamport papers
Concurrency in Distributed Systems : Leslie Lamport papers
 
Scaling web applications with cassandra presentation
Scaling web applications with cassandra presentationScaling web applications with cassandra presentation
Scaling web applications with cassandra presentation
 
Real-Time Analytics with Kafka, Cassandra and Storm
Real-Time Analytics with Kafka, Cassandra and StormReal-Time Analytics with Kafka, Cassandra and Storm
Real-Time Analytics with Kafka, Cassandra and Storm
 
No stress with state
No stress with stateNo stress with state
No stress with state
 
When it Absolutely, Positively, Has to be There: Reliability Guarantees in Ka...
When it Absolutely, Positively, Has to be There: Reliability Guarantees in Ka...When it Absolutely, Positively, Has to be There: Reliability Guarantees in Ka...
When it Absolutely, Positively, Has to be There: Reliability Guarantees in Ka...
 
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedIn
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedInJay Kreps on Project Voldemort Scaling Simple Storage At LinkedIn
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedIn
 
SATA Protocol
SATA ProtocolSATA Protocol
SATA Protocol
 
Storage interface sata_pata
Storage interface sata_pataStorage interface sata_pata
Storage interface sata_pata
 
Call me maybe: Jepsen and flaky networks
Call me maybe: Jepsen and flaky networksCall me maybe: Jepsen and flaky networks
Call me maybe: Jepsen and flaky networks
 
Transactions and Concurrency Control Patterns
Transactions and Concurrency Control PatternsTransactions and Concurrency Control Patterns
Transactions and Concurrency Control Patterns
 

More from NoSQLmatters

Nathan Ford- Divination of the Defects (Graph-Based Defect Prediction through...
Nathan Ford- Divination of the Defects (Graph-Based Defect Prediction through...Nathan Ford- Divination of the Defects (Graph-Based Defect Prediction through...
Nathan Ford- Divination of the Defects (Graph-Based Defect Prediction through...
NoSQLmatters
 
Stefan Hochdörfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matt...
Stefan Hochdörfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matt...Stefan Hochdörfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matt...
Stefan Hochdörfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matt...
NoSQLmatters
 
Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015
Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015
Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015
NoSQLmatters
 
Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spar...
Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spar...Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spar...
Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spar...
NoSQLmatters
 
Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...
Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...
Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...
NoSQLmatters
 
Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015
Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015
Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015
NoSQLmatters
 
Prassnitha Sampath - Real Time Big Data Analytics with Kafka, Storm & HBase -...
Prassnitha Sampath - Real Time Big Data Analytics with Kafka, Storm & HBase -...Prassnitha Sampath - Real Time Big Data Analytics with Kafka, Storm & HBase -...
Prassnitha Sampath - Real Time Big Data Analytics with Kafka, Storm & HBase -...
NoSQLmatters
 
Akmal Chaudhri - How to Build Streaming Data Applications: Evaluating the Top...
Akmal Chaudhri - How to Build Streaming Data Applications: Evaluating the Top...Akmal Chaudhri - How to Build Streaming Data Applications: Evaluating the Top...
Akmal Chaudhri - How to Build Streaming Data Applications: Evaluating the Top...
NoSQLmatters
 
Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015
Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015
Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015
NoSQLmatters
 
Chris Ward - Understanding databases for distributed docker applications - No...
Chris Ward - Understanding databases for distributed docker applications - No...Chris Ward - Understanding databases for distributed docker applications - No...
Chris Ward - Understanding databases for distributed docker applications - No...
NoSQLmatters
 
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...
NoSQLmatters
 
Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters...
Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters...Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters...
Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters...
NoSQLmatters
 
Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015
Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015
Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015
NoSQLmatters
 
DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Pa...
DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Pa...DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Pa...
DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Pa...
NoSQLmatters
 
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...
NoSQLmatters
 
David Pilato - Advance search for your legacy application - NoSQL matters Par...
David Pilato - Advance search for your legacy application - NoSQL matters Par...David Pilato - Advance search for your legacy application - NoSQL matters Par...
David Pilato - Advance search for your legacy application - NoSQL matters Par...
NoSQLmatters
 
Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015
Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015
Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015
NoSQLmatters
 
Gregorry Letribot - Druid at Criteo - NoSQL matters 2015
Gregorry Letribot - Druid at Criteo - NoSQL matters 2015Gregorry Letribot - Druid at Criteo - NoSQL matters 2015
Gregorry Letribot - Druid at Criteo - NoSQL matters 2015
NoSQLmatters
 
Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQ...
Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQ...Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQ...
Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQ...
NoSQLmatters
 
Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015
Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015
Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015
NoSQLmatters
 

More from NoSQLmatters (20)

Nathan Ford- Divination of the Defects (Graph-Based Defect Prediction through...
Nathan Ford- Divination of the Defects (Graph-Based Defect Prediction through...Nathan Ford- Divination of the Defects (Graph-Based Defect Prediction through...
Nathan Ford- Divination of the Defects (Graph-Based Defect Prediction through...
 
Stefan Hochdörfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matt...
Stefan Hochdörfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matt...Stefan Hochdörfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matt...
Stefan Hochdörfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matt...
 
Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015
Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015
Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015
 
Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spar...
Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spar...Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spar...
Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spar...
 
Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...
Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...
Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...
 
Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015
Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015
Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015
 
Prassnitha Sampath - Real Time Big Data Analytics with Kafka, Storm & HBase -...
Prassnitha Sampath - Real Time Big Data Analytics with Kafka, Storm & HBase -...Prassnitha Sampath - Real Time Big Data Analytics with Kafka, Storm & HBase -...
Prassnitha Sampath - Real Time Big Data Analytics with Kafka, Storm & HBase -...
 
Akmal Chaudhri - How to Build Streaming Data Applications: Evaluating the Top...
Akmal Chaudhri - How to Build Streaming Data Applications: Evaluating the Top...Akmal Chaudhri - How to Build Streaming Data Applications: Evaluating the Top...
Akmal Chaudhri - How to Build Streaming Data Applications: Evaluating the Top...
 
Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015
Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015
Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015
 
Chris Ward - Understanding databases for distributed docker applications - No...
Chris Ward - Understanding databases for distributed docker applications - No...Chris Ward - Understanding databases for distributed docker applications - No...
Chris Ward - Understanding databases for distributed docker applications - No...
 
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...
Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...
 
Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters...
Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters...Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters...
Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters...
 
Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015
Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015
Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015
 
DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Pa...
DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Pa...DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Pa...
DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Pa...
 
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...
 
David Pilato - Advance search for your legacy application - NoSQL matters Par...
David Pilato - Advance search for your legacy application - NoSQL matters Par...David Pilato - Advance search for your legacy application - NoSQL matters Par...
David Pilato - Advance search for your legacy application - NoSQL matters Par...
 
Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015
Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015
Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015
 
Gregorry Letribot - Druid at Criteo - NoSQL matters 2015
Gregorry Letribot - Druid at Criteo - NoSQL matters 2015Gregorry Letribot - Druid at Criteo - NoSQL matters 2015
Gregorry Letribot - Druid at Criteo - NoSQL matters 2015
 
Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQ...
Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQ...Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQ...
Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQ...
 
Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015
Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015
Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015
 

Recently uploaded

Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdfCh03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
haila53
 
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
nscud
 
Machine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptxMachine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptx
balafet
 
Q1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year ReboundQ1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year Rebound
Oppotus
 
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project PresentationPredicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Boston Institute of Analytics
 
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
NABLAS株式会社
 
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
mbawufebxi
 
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
oz8q3jxlp
 
Adjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTESAdjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTES
Subhajit Sahu
 
Empowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptxEmpowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptx
benishzehra469
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
nscud
 
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
ewymefz
 
一比一原版(BU毕业证)波士顿大学毕业证成绩单
一比一原版(BU毕业证)波士顿大学毕业证成绩单一比一原版(BU毕业证)波士顿大学毕业证成绩单
一比一原版(BU毕业证)波士顿大学毕业证成绩单
ewymefz
 
原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样
原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样
原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样
u86oixdj
 
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
ewymefz
 
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Subhajit Sahu
 
一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单
enxupq
 
Opendatabay - Open Data Marketplace.pptx
Opendatabay - Open Data Marketplace.pptxOpendatabay - Open Data Marketplace.pptx
Opendatabay - Open Data Marketplace.pptx
Opendatabay
 
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
pchutichetpong
 
一比一原版(UofS毕业证书)萨省大学毕业证如何办理
一比一原版(UofS毕业证书)萨省大学毕业证如何办理一比一原版(UofS毕业证书)萨省大学毕业证如何办理
一比一原版(UofS毕业证书)萨省大学毕业证如何办理
v3tuleee
 

Recently uploaded (20)

Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdfCh03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
 
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
 
Machine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptxMachine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptx
 
Q1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year ReboundQ1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year Rebound
 
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project PresentationPredicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
 
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
 
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
 
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
 
Adjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTESAdjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTES
 
Empowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptxEmpowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptx
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
 
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
 
一比一原版(BU毕业证)波士顿大学毕业证成绩单
一比一原版(BU毕业证)波士顿大学毕业证成绩单一比一原版(BU毕业证)波士顿大学毕业证成绩单
一比一原版(BU毕业证)波士顿大学毕业证成绩单
 
原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样
原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样
原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样
 
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
 
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
 
一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单
 
Opendatabay - Open Data Marketplace.pptx
Opendatabay - Open Data Marketplace.pptxOpendatabay - Open Data Marketplace.pptx
Opendatabay - Open Data Marketplace.pptx
 
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
 
一比一原版(UofS毕业证书)萨省大学毕业证如何办理
一比一原版(UofS毕业证书)萨省大学毕业证如何办理一比一原版(UofS毕业证书)萨省大学毕业证如何办理
一比一原版(UofS毕业证书)萨省大学毕业证如何办理
 

Salvatore Sanfilippo – How Redis Cluster works, and why - NoSQL matters Barcelona 2014

  • 1. Redis Cluster design tradeoffs @antirez - Pivotal
  • 2. What is performance? • Low latency. • IOPS. • Operations quality and data model.
  • 3. Go Cluster • Redis Cluster must have same Redis use case. • Tradeoffs are inherently needed in DS. • CAP? Merge values? Strong consistency and consensus? How to replicate values?
  • 4. CP systems CAP: consistency price is added latency Client S1 S2 S3 S4
  • 5. CP systems Reply to client after majority ACKs Client S1 S2 S3 S4
  • 6. And… there is the disk S1 S2 S3 Disk Disk Disk CP algorithms may require fsync-befor-ack. Durability / Consistency not always orthogonal.
  • 7. AP systems Eventual consistency with merges? (note: merge is not strictly part of EC) Client S1 S2 A = {1,2,3,8,12,13,14} Client A = {2,3,8,11,12,1}
  • 8. Many kinds of consistencies • “C” of CAP is strong consistency. • It is not the only available tradeoff of course. • Consistency is the set of liveness and safety properties a given system provides. • Eventual consistency: like to say nothing at all. What liveness/safety properties if not “C”?
  • 9. Redis Cluster Sharding and replication (asynchronous). Client A,B,C A,B,C A,B,C D,E,F D,E,F D,E,F
  • 10. Asynchronous replication Client A,B,C A,B,C A,B,C A,B,C A,B,C A,B,C async ACK
  • 11. Full Mesh A,B,C A,B,C D,E,F D,E,F • Heartbeats. • Nodes gossip. • Failover auth. • Config update.
  • 12. No proxy, but redirections Client Client A? D? A,B,C D,E,F G,H,I L,M,N O,P,Q R,S,T
  • 13. Failure detection • Failure reports within window of time (via gossip). • Trigger for actual failover. • Two main states: PFAIL -> FAIL.
  • 14. Failure detection S1 is not responding? S1 S2 S3 S4 S1 = PFAIL S1 = PFAIL S1 = PFAIL
  • 15. Failure detection PFAIL state propagates S1 S2 S3 S4 S1 = PFAIL S1 = PFAIL Reported by: S2, S4 S1 = PFAIL
  • 16. Failure detection PFAIL state propagates S1 S2 S3 S4 S1 = PFAIL S1 = FAIL S1 = PFAIL
  • 17. Failure detection Force FAIL state S1 S2 S3 S4 S1 = FAIL S1 = FAIL S1 = FAIL
  • 18. Global slots config • A master FAIL state triggers a failover. • Cluster needs a coherent view of configuration. • Who is serving this slot currently? • Slots config must eventually converge.
  • 19. Raft and failover • Config propagation is solved using ideas from the Raft algorithm (just a subset). • Raft is a consensus algorithm built on top of different “layers”. • Raft paper is already a classic (highly recommended). • Full Raft not needed for Redis Cluster slots config.
  • 20. Failover and config Failed Slave Slave Slave Master Master Master Epoch = Epoch+1 (logical clock) Vote for me!
  • 21. Too easy? • Why we don’t need full Raft? • Because our config is idempotent: when the partition heals we can trow away slots config for new versions. • Same algorithm is used in Sentinel v2 and works well.
  • 22. Config propagation • After a successful failover, new slot config is broadcasted. • If there are partitions, when they heal, config will get updated (broadcasted from time to time, plus stale config detection and UPADTE messages). • Config with greater Epoch always wins.
  • 23. Redis Cluster consistency? • Eventual consistent: last failover wins. • In the “vanilla” losing writes is unbound. • Mechanisms to avoid unbound data loss.
  • 24. Failure mode… #1 Client A,B,C A,B,C A,B,C Failed A,B,C A,B,C lost write…
  • 25. Failure mode #2 Client A,B,C A,B,C D,E,F G,H,I Minority side Majority side
  • 26. Boud divergences Client A,B,C D,E,F G,H,I After node-timeot Minority side Majority side
  • 27. More data safety? • OP logging until async ACK received. • Re-played to master when node turns into slave. • “Safe” connections, on demand. • Example SADD (idempotent + commutative). • SET-LWW foo bar <wall-clock>.
  • 28. Multi key ops • Hey hashtags! • {user:1000}.following {user:1000}.followers. • Unavailable for small windows, but no data exchange between nodes.
  • 29. Multi key ops (availability) • Single key ops: always available during resharding. • Multi key ops, available if: • No manual resharding of this hash slot in progress. • Resharding in progress, but source or destination node have all keys. • Otherwise we get a -TRYAGAIN error.
  • 30. {User:1}.key_A {User:2}.Key_B {User:1}.key_A {User:1}.Key_B {User:1}.key_A {User:1}.Key_B SUNION key_A key_B -TRYAGAIN SUNION key_A key_B … output … SUNION key_A key_B … output …
  • 31. Redis Cluster ETA • Release Candidate available. • We’ll go stable in Q1 2015. • Ask me anything.