SlideShare a Scribd company logo

hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes

HBaseCon
HBaseCon

Zhiyong Bai As a high performance and scalable key value database, Zhihu use HBase to provide online data store system along with Mysql and Redis. Zhihu’s platform team had accumulated some experience in technology of container, and this time, based on Kubernetes, we build flexible platform of online HBase system, create multiple logic isolated HBase clusters on the shared physical cluster with fast rapid,and provide customized service for different business needs. Combined with Consul and DNS server, we implement high available access of HBase using client mainly written with Python. This presentation is mainly shared the architecture of online HBase platform in Zhihu and some practical experience in production environment. hbaseconasia2017 hbasecon hbase

hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes

1 of 36
Download to read offline
Building Online HBase Cluster

of 

Zhihu Based on Kubernetes
HBase at Zhihu
Agenda
PPT模板:www.1ppt.com/moban/ PPT素材:www.1ppt.com/sucai/
PPT背景:www.1ppt.com/beijing/ PPT图表:www.1ppt.com/tubiao/
PPT下载:www.1ppt.com/xiazai/ PPT教程: www.1ppt.com/powerpoint/
资料下载:www.1ppt.com/ziliao/ 范⽂下载:www.1ppt.com/fanwen/
试卷下载:www.1ppt.com/shiti/ 教案下载:www.1ppt.com/jiaoan/
PPT论坛:www.1ppt.cn PPT课件:www.1ppt.com/kejian/
语⽂课件:www.1ppt.com/kejian/yuwen/ 数学课件:www.1ppt.com/kejian/shuxue/
英语课件:www.1ppt.com/kejian/yingyu/ 美术课件:www.1ppt.com/kejian/meishu/
科学课件:www.1ppt.com/kejian/kexue/ 物理课件:www.1ppt.com/kejian/wuli/
化学课件:www.1ppt.com/kejian/huaxue/ ⽣物课件:www.1ppt.com/kejian/shengwu/
地理课件:www.1ppt.com/kejian/dili/ 历史课件:www.1ppt.com/kejian/lishi/
Using Kubernetes HBase Online Platform
HBase Online Platform
Using Kubernetes
HBase at Zhihu
• Offline
• Physical machine, hundreds of nodes.
• Work with Spark/Hadoop.
• Online
• Based on Kubernetes, more than 300 containers.
HBase at Zhihu
01
02
Our online storage
01
02
03
MySQL
used in most business
some need scale, some need transform
all SSD,expensive
Redis
cache and partial storage
no shard
expensive
HBase / Cassandra / RocksDB etc. ?
Challenges at the beginning
• All business at one big cluster
• Also runs NodeManager and ImpalaServer
• Basically operation
• Physical node level monitor

Recommended

HBaseCon2017 gohbase: Pure Go HBase Client
HBaseCon2017 gohbase: Pure Go HBase ClientHBaseCon2017 gohbase: Pure Go HBase Client
HBaseCon2017 gohbase: Pure Go HBase ClientHBaseCon
 
hbaseconasia2017: HBase Practice At XiaoMi
hbaseconasia2017: HBase Practice At XiaoMihbaseconasia2017: HBase Practice At XiaoMi
hbaseconasia2017: HBase Practice At XiaoMiHBaseCon
 
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBase
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBaseHBaseCon2017 Quanta: Quora's hierarchical counting system on HBase
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBaseHBaseCon
 
HBaseCon2017 Highly-Available HBase
HBaseCon2017 Highly-Available HBaseHBaseCon2017 Highly-Available HBase
HBaseCon2017 Highly-Available HBaseHBaseCon
 
HBaseCon2017 Improving HBase availability in a multi tenant environment
HBaseCon2017 Improving HBase availability in a multi tenant environmentHBaseCon2017 Improving HBase availability in a multi tenant environment
HBaseCon2017 Improving HBase availability in a multi tenant environmentHBaseCon
 
Samza memory capacity_2015_ieee_big_data_data_quality_workshop
Samza memory capacity_2015_ieee_big_data_data_quality_workshopSamza memory capacity_2015_ieee_big_data_data_quality_workshop
Samza memory capacity_2015_ieee_big_data_data_quality_workshopTao Feng
 
Kafka Summit SF 2017 - Infrastructure for Streaming Applications
Kafka Summit SF 2017 - Infrastructure for Streaming Applications Kafka Summit SF 2017 - Infrastructure for Streaming Applications
Kafka Summit SF 2017 - Infrastructure for Streaming Applications confluent
 

More Related Content

What's hot

Tales from Taming the Long Tail
Tales from Taming the Long TailTales from Taming the Long Tail
Tales from Taming the Long TailHBaseCon
 
HBaseCon 2015: OpenTSDB and AsyncHBase Update
HBaseCon 2015: OpenTSDB and AsyncHBase UpdateHBaseCon 2015: OpenTSDB and AsyncHBase Update
HBaseCon 2015: OpenTSDB and AsyncHBase UpdateHBaseCon
 
Argus Production Monitoring at Salesforce
Argus Production Monitoring at SalesforceArgus Production Monitoring at Salesforce
Argus Production Monitoring at SalesforceHBaseCon
 
HBaseCon2017 HBase at Xiaomi
HBaseCon2017 HBase at XiaomiHBaseCon2017 HBase at Xiaomi
HBaseCon2017 HBase at XiaomiHBaseCon
 
Real Time Data Streaming using Kafka & Storm
Real Time Data Streaming using Kafka & StormReal Time Data Streaming using Kafka & Storm
Real Time Data Streaming using Kafka & StormRan Silberman
 
HBaseCon2017 Transactions in HBase
HBaseCon2017 Transactions in HBaseHBaseCon2017 Transactions in HBase
HBaseCon2017 Transactions in HBaseHBaseCon
 
HBaseCon 2015: Blackbird Collections - In-situ Stream Processing in HBase
HBaseCon 2015: Blackbird Collections - In-situ  Stream Processing in HBaseHBaseCon 2015: Blackbird Collections - In-situ  Stream Processing in HBase
HBaseCon 2015: Blackbird Collections - In-situ Stream Processing in HBaseHBaseCon
 
Kafka on ZFS: Better Living Through Filesystems
Kafka on ZFS: Better Living Through Filesystems Kafka on ZFS: Better Living Through Filesystems
Kafka on ZFS: Better Living Through Filesystems confluent
 
HBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest
HBaseCon2017 Removable singularity: a story of HBase upgrade in PinterestHBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest
HBaseCon2017 Removable singularity: a story of HBase upgrade in PinterestHBaseCon
 
HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQL
HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQLHBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQL
HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQLCloudera, Inc.
 
hbaseconasia2017: hbase-2.0.0
hbaseconasia2017: hbase-2.0.0hbaseconasia2017: hbase-2.0.0
hbaseconasia2017: hbase-2.0.0HBaseCon
 
Benchmarking Apache Samza: 1.2 million messages per sec per node
Benchmarking Apache Samza: 1.2 million messages per sec per nodeBenchmarking Apache Samza: 1.2 million messages per sec per node
Benchmarking Apache Samza: 1.2 million messages per sec per nodeTao Feng
 
Streaming and Messaging
Streaming and MessagingStreaming and Messaging
Streaming and MessagingXin Wang
 
HBaseCon 2012 | Solbase - Kyungseog Oh, Photobucket
HBaseCon 2012 | Solbase - Kyungseog Oh, PhotobucketHBaseCon 2012 | Solbase - Kyungseog Oh, Photobucket
HBaseCon 2012 | Solbase - Kyungseog Oh, PhotobucketCloudera, Inc.
 
stream-processing-at-linkedin-with-apache-samza
stream-processing-at-linkedin-with-apache-samzastream-processing-at-linkedin-with-apache-samza
stream-processing-at-linkedin-with-apache-samzaAbhishek Shivanna
 
Connecting kafka message systems with scylla
Connecting kafka message systems with scylla   Connecting kafka message systems with scylla
Connecting kafka message systems with scylla Maheedhar Gunturu
 
Uber Real Time Data Analytics
Uber Real Time Data AnalyticsUber Real Time Data Analytics
Uber Real Time Data AnalyticsAnkur Bansal
 
TenMax Data Pipeline Experience Sharing
TenMax Data Pipeline Experience SharingTenMax Data Pipeline Experience Sharing
TenMax Data Pipeline Experience SharingChen-en Lu
 

What's hot (20)

Tales from Taming the Long Tail
Tales from Taming the Long TailTales from Taming the Long Tail
Tales from Taming the Long Tail
 
HBaseCon 2015: OpenTSDB and AsyncHBase Update
HBaseCon 2015: OpenTSDB and AsyncHBase UpdateHBaseCon 2015: OpenTSDB and AsyncHBase Update
HBaseCon 2015: OpenTSDB and AsyncHBase Update
 
Argus Production Monitoring at Salesforce
Argus Production Monitoring at SalesforceArgus Production Monitoring at Salesforce
Argus Production Monitoring at Salesforce
 
HBaseCon2017 HBase at Xiaomi
HBaseCon2017 HBase at XiaomiHBaseCon2017 HBase at Xiaomi
HBaseCon2017 HBase at Xiaomi
 
Real Time Data Streaming using Kafka & Storm
Real Time Data Streaming using Kafka & StormReal Time Data Streaming using Kafka & Storm
Real Time Data Streaming using Kafka & Storm
 
HBaseCon2017 Transactions in HBase
HBaseCon2017 Transactions in HBaseHBaseCon2017 Transactions in HBase
HBaseCon2017 Transactions in HBase
 
HBaseCon 2015: Blackbird Collections - In-situ Stream Processing in HBase
HBaseCon 2015: Blackbird Collections - In-situ  Stream Processing in HBaseHBaseCon 2015: Blackbird Collections - In-situ  Stream Processing in HBase
HBaseCon 2015: Blackbird Collections - In-situ Stream Processing in HBase
 
Kafka on ZFS: Better Living Through Filesystems
Kafka on ZFS: Better Living Through Filesystems Kafka on ZFS: Better Living Through Filesystems
Kafka on ZFS: Better Living Through Filesystems
 
HBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest
HBaseCon2017 Removable singularity: a story of HBase upgrade in PinterestHBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest
HBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest
 
HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQL
HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQLHBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQL
HBaseCon 2013: How (and Why) Phoenix Puts the SQL Back into NoSQL
 
Apache Kafka at LinkedIn
Apache Kafka at LinkedInApache Kafka at LinkedIn
Apache Kafka at LinkedIn
 
hbaseconasia2017: hbase-2.0.0
hbaseconasia2017: hbase-2.0.0hbaseconasia2017: hbase-2.0.0
hbaseconasia2017: hbase-2.0.0
 
Stream Processing made simple with Kafka
Stream Processing made simple with KafkaStream Processing made simple with Kafka
Stream Processing made simple with Kafka
 
Benchmarking Apache Samza: 1.2 million messages per sec per node
Benchmarking Apache Samza: 1.2 million messages per sec per nodeBenchmarking Apache Samza: 1.2 million messages per sec per node
Benchmarking Apache Samza: 1.2 million messages per sec per node
 
Streaming and Messaging
Streaming and MessagingStreaming and Messaging
Streaming and Messaging
 
HBaseCon 2012 | Solbase - Kyungseog Oh, Photobucket
HBaseCon 2012 | Solbase - Kyungseog Oh, PhotobucketHBaseCon 2012 | Solbase - Kyungseog Oh, Photobucket
HBaseCon 2012 | Solbase - Kyungseog Oh, Photobucket
 
stream-processing-at-linkedin-with-apache-samza
stream-processing-at-linkedin-with-apache-samzastream-processing-at-linkedin-with-apache-samza
stream-processing-at-linkedin-with-apache-samza
 
Connecting kafka message systems with scylla
Connecting kafka message systems with scylla   Connecting kafka message systems with scylla
Connecting kafka message systems with scylla
 
Uber Real Time Data Analytics
Uber Real Time Data AnalyticsUber Real Time Data Analytics
Uber Real Time Data Analytics
 
TenMax Data Pipeline Experience Sharing
TenMax Data Pipeline Experience SharingTenMax Data Pipeline Experience Sharing
TenMax Data Pipeline Experience Sharing
 

Similar to hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes

Ceph Community Talk on High-Performance Solid Sate Ceph
Ceph Community Talk on High-Performance Solid Sate Ceph Ceph Community Talk on High-Performance Solid Sate Ceph
Ceph Community Talk on High-Performance Solid Sate Ceph Ceph Community
 
Webinar - DreamObjects/Ceph Case Study
Webinar - DreamObjects/Ceph Case StudyWebinar - DreamObjects/Ceph Case Study
Webinar - DreamObjects/Ceph Case StudyCeph Community
 
Enabling Presto Caching at Uber with Alluxio
Enabling Presto Caching at Uber with AlluxioEnabling Presto Caching at Uber with Alluxio
Enabling Presto Caching at Uber with AlluxioAlluxio, Inc.
 
(ATS6-PLAT06) Maximizing AEP Performance
(ATS6-PLAT06) Maximizing AEP Performance(ATS6-PLAT06) Maximizing AEP Performance
(ATS6-PLAT06) Maximizing AEP PerformanceBIOVIA
 
Hannes end-of-the-router-tnc17
Hannes end-of-the-router-tnc17Hannes end-of-the-router-tnc17
Hannes end-of-the-router-tnc17Hannes Gredler
 
Introduction to HPC & Supercomputing in AI
Introduction to HPC & Supercomputing in AIIntroduction to HPC & Supercomputing in AI
Introduction to HPC & Supercomputing in AITyrone Systems
 
Trend Micro Big Data Platform and Apache Bigtop
Trend Micro Big Data Platform and Apache BigtopTrend Micro Big Data Platform and Apache Bigtop
Trend Micro Big Data Platform and Apache BigtopEvans Ye
 
Implementing data and databases on K8s within the Dutch government
Implementing data and databases on K8s within the Dutch governmentImplementing data and databases on K8s within the Dutch government
Implementing data and databases on K8s within the Dutch governmentDoKC
 
Fundamentals of performance tuning PHP on IBM i
Fundamentals of performance tuning PHP on IBM i  Fundamentals of performance tuning PHP on IBM i
Fundamentals of performance tuning PHP on IBM i Zend by Rogue Wave Software
 
Apache Tez – Present and Future
Apache Tez – Present and FutureApache Tez – Present and Future
Apache Tez – Present and FutureJianfeng Zhang
 
Apache Tez – Present and Future
Apache Tez – Present and FutureApache Tez – Present and Future
Apache Tez – Present and FutureRajesh Balamohan
 
Introduction to memcached
Introduction to memcachedIntroduction to memcached
Introduction to memcachedJurriaan Persyn
 
Netflix Open Source Meetup Season 4 Episode 2
Netflix Open Source Meetup Season 4 Episode 2Netflix Open Source Meetup Season 4 Episode 2
Netflix Open Source Meetup Season 4 Episode 2aspyker
 
Basic Application Performance Optimization Techniques (Backend)
Basic Application Performance Optimization Techniques (Backend)Basic Application Performance Optimization Techniques (Backend)
Basic Application Performance Optimization Techniques (Backend)Klas Berlič Fras
 
PAC 2019 virtual Mark Tomlinson
PAC 2019 virtual Mark TomlinsonPAC 2019 virtual Mark Tomlinson
PAC 2019 virtual Mark TomlinsonNeotys
 
Accelerating Ceph Performance with High Speed Networks and Protocols - Qingch...
Accelerating Ceph Performance with High Speed Networks and Protocols - Qingch...Accelerating Ceph Performance with High Speed Networks and Protocols - Qingch...
Accelerating Ceph Performance with High Speed Networks and Protocols - Qingch...Ceph Community
 
How the Development Bank of Singapore solves on-prem compute capacity challen...
How the Development Bank of Singapore solves on-prem compute capacity challen...How the Development Bank of Singapore solves on-prem compute capacity challen...
How the Development Bank of Singapore solves on-prem compute capacity challen...Alluxio, Inc.
 
SharePoint Saturday San Antonio: SharePoint 2010 Performance
SharePoint Saturday San Antonio: SharePoint 2010 PerformanceSharePoint Saturday San Antonio: SharePoint 2010 Performance
SharePoint Saturday San Antonio: SharePoint 2010 PerformanceBrian Culver
 

Similar to hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes (20)

Ceph Community Talk on High-Performance Solid Sate Ceph
Ceph Community Talk on High-Performance Solid Sate Ceph Ceph Community Talk on High-Performance Solid Sate Ceph
Ceph Community Talk on High-Performance Solid Sate Ceph
 
Webinar - DreamObjects/Ceph Case Study
Webinar - DreamObjects/Ceph Case StudyWebinar - DreamObjects/Ceph Case Study
Webinar - DreamObjects/Ceph Case Study
 
Enabling Presto Caching at Uber with Alluxio
Enabling Presto Caching at Uber with AlluxioEnabling Presto Caching at Uber with Alluxio
Enabling Presto Caching at Uber with Alluxio
 
(ATS6-PLAT06) Maximizing AEP Performance
(ATS6-PLAT06) Maximizing AEP Performance(ATS6-PLAT06) Maximizing AEP Performance
(ATS6-PLAT06) Maximizing AEP Performance
 
Hannes end-of-the-router-tnc17
Hannes end-of-the-router-tnc17Hannes end-of-the-router-tnc17
Hannes end-of-the-router-tnc17
 
Introduction to HPC & Supercomputing in AI
Introduction to HPC & Supercomputing in AIIntroduction to HPC & Supercomputing in AI
Introduction to HPC & Supercomputing in AI
 
Trend Micro Big Data Platform and Apache Bigtop
Trend Micro Big Data Platform and Apache BigtopTrend Micro Big Data Platform and Apache Bigtop
Trend Micro Big Data Platform and Apache Bigtop
 
Implementing data and databases on K8s within the Dutch government
Implementing data and databases on K8s within the Dutch governmentImplementing data and databases on K8s within the Dutch government
Implementing data and databases on K8s within the Dutch government
 
Fundamentals of performance tuning PHP on IBM i
Fundamentals of performance tuning PHP on IBM i  Fundamentals of performance tuning PHP on IBM i
Fundamentals of performance tuning PHP on IBM i
 
Apache Tez – Present and Future
Apache Tez – Present and FutureApache Tez – Present and Future
Apache Tez – Present and Future
 
Apache Tez – Present and Future
Apache Tez – Present and FutureApache Tez – Present and Future
Apache Tez – Present and Future
 
Introduction to memcached
Introduction to memcachedIntroduction to memcached
Introduction to memcached
 
Netflix Open Source Meetup Season 4 Episode 2
Netflix Open Source Meetup Season 4 Episode 2Netflix Open Source Meetup Season 4 Episode 2
Netflix Open Source Meetup Season 4 Episode 2
 
Scaling PHP apps
Scaling PHP appsScaling PHP apps
Scaling PHP apps
 
HDFCloud Workshop: HDF5 in the Cloud
HDFCloud Workshop: HDF5 in the CloudHDFCloud Workshop: HDF5 in the Cloud
HDFCloud Workshop: HDF5 in the Cloud
 
Basic Application Performance Optimization Techniques (Backend)
Basic Application Performance Optimization Techniques (Backend)Basic Application Performance Optimization Techniques (Backend)
Basic Application Performance Optimization Techniques (Backend)
 
PAC 2019 virtual Mark Tomlinson
PAC 2019 virtual Mark TomlinsonPAC 2019 virtual Mark Tomlinson
PAC 2019 virtual Mark Tomlinson
 
Accelerating Ceph Performance with High Speed Networks and Protocols - Qingch...
Accelerating Ceph Performance with High Speed Networks and Protocols - Qingch...Accelerating Ceph Performance with High Speed Networks and Protocols - Qingch...
Accelerating Ceph Performance with High Speed Networks and Protocols - Qingch...
 
How the Development Bank of Singapore solves on-prem compute capacity challen...
How the Development Bank of Singapore solves on-prem compute capacity challen...How the Development Bank of Singapore solves on-prem compute capacity challen...
How the Development Bank of Singapore solves on-prem compute capacity challen...
 
SharePoint Saturday San Antonio: SharePoint 2010 Performance
SharePoint Saturday San Antonio: SharePoint 2010 PerformanceSharePoint Saturday San Antonio: SharePoint 2010 Performance
SharePoint Saturday San Antonio: SharePoint 2010 Performance
 

More from HBaseCon

hbaseconasia2017: HBase on Beam
hbaseconasia2017: HBase on Beamhbaseconasia2017: HBase on Beam
hbaseconasia2017: HBase on BeamHBaseCon
 
hbaseconasia2017: HBase Disaster Recovery Solution at Huawei
hbaseconasia2017: HBase Disaster Recovery Solution at Huaweihbaseconasia2017: HBase Disaster Recovery Solution at Huawei
hbaseconasia2017: HBase Disaster Recovery Solution at HuaweiHBaseCon
 
hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest
hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinteresthbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest
hbaseconasia2017: Removable singularity: a story of HBase upgrade in PinterestHBaseCon
 
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程HBaseCon
 
hbaseconasia2017: Apache HBase at Netease
hbaseconasia2017: Apache HBase at Neteasehbaseconasia2017: Apache HBase at Netease
hbaseconasia2017: Apache HBase at NeteaseHBaseCon
 
hbaseconasia2017: HBase在Hulu的使用和实践
hbaseconasia2017: HBase在Hulu的使用和实践hbaseconasia2017: HBase在Hulu的使用和实践
hbaseconasia2017: HBase在Hulu的使用和实践HBaseCon
 
hbaseconasia2017: 基于HBase的企业级大数据平台
hbaseconasia2017: 基于HBase的企业级大数据平台hbaseconasia2017: 基于HBase的企业级大数据平台
hbaseconasia2017: 基于HBase的企业级大数据平台HBaseCon
 
hbaseconasia2017: HBase at JD.com
hbaseconasia2017: HBase at JD.comhbaseconasia2017: HBase at JD.com
hbaseconasia2017: HBase at JD.comHBaseCon
 
hbaseconasia2017: Large scale data near-line loading method and architecture
hbaseconasia2017: Large scale data near-line loading method and architecturehbaseconasia2017: Large scale data near-line loading method and architecture
hbaseconasia2017: Large scale data near-line loading method and architectureHBaseCon
 
hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei
hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huaweihbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei
hbaseconasia2017: Ecosystems with HBase and CloudTable service at HuaweiHBaseCon
 
HBaseCon2017 Democratizing HBase
HBaseCon2017 Democratizing HBaseHBaseCon2017 Democratizing HBase
HBaseCon2017 Democratizing HBaseHBaseCon
 
HBaseCon2017 Apache HBase at Didi
HBaseCon2017 Apache HBase at DidiHBaseCon2017 Apache HBase at Didi
HBaseCon2017 Apache HBase at DidiHBaseCon
 
HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...
HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...
HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...HBaseCon
 
HBaseCon2017 Efficient and portable data processing with Apache Beam and HBase
HBaseCon2017 Efficient and portable data processing with Apache Beam and HBaseHBaseCon2017 Efficient and portable data processing with Apache Beam and HBase
HBaseCon2017 Efficient and portable data processing with Apache Beam and HBaseHBaseCon
 
HBaseCon2017 HBase/Phoenix @ Scale @ Salesforce
HBaseCon2017 HBase/Phoenix @ Scale @ SalesforceHBaseCon2017 HBase/Phoenix @ Scale @ Salesforce
HBaseCon2017 HBase/Phoenix @ Scale @ SalesforceHBaseCon
 
HBaseCon2017 Community-Driven Graphs with JanusGraph
HBaseCon2017 Community-Driven Graphs with JanusGraphHBaseCon2017 Community-Driven Graphs with JanusGraph
HBaseCon2017 Community-Driven Graphs with JanusGraphHBaseCon
 
HBaseCon2017 Warp 10, a novel approach to managing and analyzing time series ...
HBaseCon2017 Warp 10, a novel approach to managing and analyzing time series ...HBaseCon2017 Warp 10, a novel approach to managing and analyzing time series ...
HBaseCon2017 Warp 10, a novel approach to managing and analyzing time series ...HBaseCon
 
HBaseCon2017 Analyzing cryptocurrencies in real time with hBase, Kafka and St...
HBaseCon2017 Analyzing cryptocurrencies in real time with hBase, Kafka and St...HBaseCon2017 Analyzing cryptocurrencies in real time with hBase, Kafka and St...
HBaseCon2017 Analyzing cryptocurrencies in real time with hBase, Kafka and St...HBaseCon
 
HBaseCon2017 Achieving HBase Multi-Tenancy with RegionServer Groups and Favor...
HBaseCon2017 Achieving HBase Multi-Tenancy with RegionServer Groups and Favor...HBaseCon2017 Achieving HBase Multi-Tenancy with RegionServer Groups and Favor...
HBaseCon2017 Achieving HBase Multi-Tenancy with RegionServer Groups and Favor...HBaseCon
 
OpenTSDB: HBaseCon2017
OpenTSDB: HBaseCon2017OpenTSDB: HBaseCon2017
OpenTSDB: HBaseCon2017HBaseCon
 

More from HBaseCon (20)

hbaseconasia2017: HBase on Beam
hbaseconasia2017: HBase on Beamhbaseconasia2017: HBase on Beam
hbaseconasia2017: HBase on Beam
 
hbaseconasia2017: HBase Disaster Recovery Solution at Huawei
hbaseconasia2017: HBase Disaster Recovery Solution at Huaweihbaseconasia2017: HBase Disaster Recovery Solution at Huawei
hbaseconasia2017: HBase Disaster Recovery Solution at Huawei
 
hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest
hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinteresthbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest
hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest
 
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程
 
hbaseconasia2017: Apache HBase at Netease
hbaseconasia2017: Apache HBase at Neteasehbaseconasia2017: Apache HBase at Netease
hbaseconasia2017: Apache HBase at Netease
 
hbaseconasia2017: HBase在Hulu的使用和实践
hbaseconasia2017: HBase在Hulu的使用和实践hbaseconasia2017: HBase在Hulu的使用和实践
hbaseconasia2017: HBase在Hulu的使用和实践
 
hbaseconasia2017: 基于HBase的企业级大数据平台
hbaseconasia2017: 基于HBase的企业级大数据平台hbaseconasia2017: 基于HBase的企业级大数据平台
hbaseconasia2017: 基于HBase的企业级大数据平台
 
hbaseconasia2017: HBase at JD.com
hbaseconasia2017: HBase at JD.comhbaseconasia2017: HBase at JD.com
hbaseconasia2017: HBase at JD.com
 
hbaseconasia2017: Large scale data near-line loading method and architecture
hbaseconasia2017: Large scale data near-line loading method and architecturehbaseconasia2017: Large scale data near-line loading method and architecture
hbaseconasia2017: Large scale data near-line loading method and architecture
 
hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei
hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huaweihbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei
hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei
 
HBaseCon2017 Democratizing HBase
HBaseCon2017 Democratizing HBaseHBaseCon2017 Democratizing HBase
HBaseCon2017 Democratizing HBase
 
HBaseCon2017 Apache HBase at Didi
HBaseCon2017 Apache HBase at DidiHBaseCon2017 Apache HBase at Didi
HBaseCon2017 Apache HBase at Didi
 
HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...
HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...
HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...
 
HBaseCon2017 Efficient and portable data processing with Apache Beam and HBase
HBaseCon2017 Efficient and portable data processing with Apache Beam and HBaseHBaseCon2017 Efficient and portable data processing with Apache Beam and HBase
HBaseCon2017 Efficient and portable data processing with Apache Beam and HBase
 
HBaseCon2017 HBase/Phoenix @ Scale @ Salesforce
HBaseCon2017 HBase/Phoenix @ Scale @ SalesforceHBaseCon2017 HBase/Phoenix @ Scale @ Salesforce
HBaseCon2017 HBase/Phoenix @ Scale @ Salesforce
 
HBaseCon2017 Community-Driven Graphs with JanusGraph
HBaseCon2017 Community-Driven Graphs with JanusGraphHBaseCon2017 Community-Driven Graphs with JanusGraph
HBaseCon2017 Community-Driven Graphs with JanusGraph
 
HBaseCon2017 Warp 10, a novel approach to managing and analyzing time series ...
HBaseCon2017 Warp 10, a novel approach to managing and analyzing time series ...HBaseCon2017 Warp 10, a novel approach to managing and analyzing time series ...
HBaseCon2017 Warp 10, a novel approach to managing and analyzing time series ...
 
HBaseCon2017 Analyzing cryptocurrencies in real time with hBase, Kafka and St...
HBaseCon2017 Analyzing cryptocurrencies in real time with hBase, Kafka and St...HBaseCon2017 Analyzing cryptocurrencies in real time with hBase, Kafka and St...
HBaseCon2017 Analyzing cryptocurrencies in real time with hBase, Kafka and St...
 
HBaseCon2017 Achieving HBase Multi-Tenancy with RegionServer Groups and Favor...
HBaseCon2017 Achieving HBase Multi-Tenancy with RegionServer Groups and Favor...HBaseCon2017 Achieving HBase Multi-Tenancy with RegionServer Groups and Favor...
HBaseCon2017 Achieving HBase Multi-Tenancy with RegionServer Groups and Favor...
 
OpenTSDB: HBaseCon2017
OpenTSDB: HBaseCon2017OpenTSDB: HBaseCon2017
OpenTSDB: HBaseCon2017
 

Recently uploaded

Tete thermostatique Zigbee MOES BRT-100 V2.pdf
Tete thermostatique Zigbee MOES BRT-100 V2.pdfTete thermostatique Zigbee MOES BRT-100 V2.pdf
Tete thermostatique Zigbee MOES BRT-100 V2.pdfDomotica daVinci
 
AWS for the beginning is cloud computing
AWS for the beginning  is  cloud computingAWS for the beginning  is  cloud computing
AWS for the beginning is cloud computingkajalghule1
 
Put a flag on it. A busy developer's guide to feature toggles.
Put a flag on it. A busy developer's guide to feature toggles.Put a flag on it. A busy developer's guide to feature toggles.
Put a flag on it. A busy developer's guide to feature toggles.Mateusz Kwasniewski
 
Introduction to Serverless with AWS Lambda in C#.pptx
Introduction to Serverless with AWS Lambda in C#.pptxIntroduction to Serverless with AWS Lambda in C#.pptx
Introduction to Serverless with AWS Lambda in C#.pptxBrandon Minnick, MBA
 
Semiconductor Review Magazine Feature.pdf
Semiconductor Review Magazine Feature.pdfSemiconductor Review Magazine Feature.pdf
Semiconductor Review Magazine Feature.pdfkeyaramicrochipusa
 
CIRCLE geometry lesson 2nd quarter grade 10
CIRCLE geometry lesson 2nd quarter  grade 10CIRCLE geometry lesson 2nd quarter  grade 10
CIRCLE geometry lesson 2nd quarter grade 10RicFernandez4
 
OTel Orientation_ How to Train Teams (OTel in Practice).pdf
OTel Orientation_ How to Train Teams (OTel in Practice).pdfOTel Orientation_ How to Train Teams (OTel in Practice).pdf
OTel Orientation_ How to Train Teams (OTel in Practice).pdfPaige Cruz
 
Quinto Z-Wave Heltun_HE-RS01_User_Manual_B9AH.pdf
Quinto Z-Wave Heltun_HE-RS01_User_Manual_B9AH.pdfQuinto Z-Wave Heltun_HE-RS01_User_Manual_B9AH.pdf
Quinto Z-Wave Heltun_HE-RS01_User_Manual_B9AH.pdfDomotica daVinci
 
Microsoft Azure News - Feb 2024
Microsoft Azure News - Feb 2024Microsoft Azure News - Feb 2024
Microsoft Azure News - Feb 2024Daniel Toomey
 
Azure Migration Guide for IT Professionals
Azure Migration Guide for IT ProfessionalsAzure Migration Guide for IT Professionals
Azure Migration Guide for IT ProfessionalsChristine Shepherd
 
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?GleecusTechlabs1
 
Bluetooth Low Energy(BLE) and beacons working
Bluetooth Low Energy(BLE) and beacons workingBluetooth Low Energy(BLE) and beacons working
Bluetooth Low Energy(BLE) and beacons workingshrey Ansh
 
Artificial-Intelligence-in-Marketing-Data.pdf
Artificial-Intelligence-in-Marketing-Data.pdfArtificial-Intelligence-in-Marketing-Data.pdf
Artificial-Intelligence-in-Marketing-Data.pdfIsidro Navarro
 
zigbee motion sensor user manual NAS-PD07B2.pdf
zigbee motion sensor user manual NAS-PD07B2.pdfzigbee motion sensor user manual NAS-PD07B2.pdf
zigbee motion sensor user manual NAS-PD07B2.pdfDomotica daVinci
 
From eSIMs to iSIMs: It’s Inside the Manufacturing
From eSIMs to iSIMs: It’s Inside the ManufacturingFrom eSIMs to iSIMs: It’s Inside the Manufacturing
From eSIMs to iSIMs: It’s Inside the ManufacturingSoracom Global, Inc.
 
2024 February Patch Tuesday
2024 February Patch Tuesday2024 February Patch Tuesday
2024 February Patch TuesdayIvanti
 
Navigating the Never Normal Strategies for Portfolio Leaders
Navigating the Never Normal Strategies for Portfolio LeadersNavigating the Never Normal Strategies for Portfolio Leaders
Navigating the Never Normal Strategies for Portfolio LeadersOnePlan Solutions
 
Traffic Signboard Classification with Voice alert to the driver.pptx
Traffic Signboard Classification with Voice alert to the driver.pptxTraffic Signboard Classification with Voice alert to the driver.pptx
Traffic Signboard Classification with Voice alert to the driver.pptxharimaxwell0712
 
Zi-Stick UBS Dongle ZIgbee from Aeotec manual
Zi-Stick UBS Dongle ZIgbee from  Aeotec manualZi-Stick UBS Dongle ZIgbee from  Aeotec manual
Zi-Stick UBS Dongle ZIgbee from Aeotec manualDomotica daVinci
 
DNA LIGASE BIOTECHNOLOGY BIOLOGY STUDY OF LIFE
DNA LIGASE BIOTECHNOLOGY BIOLOGY STUDY OF LIFEDNA LIGASE BIOTECHNOLOGY BIOLOGY STUDY OF LIFE
DNA LIGASE BIOTECHNOLOGY BIOLOGY STUDY OF LIFEandreiandasan
 

Recently uploaded (20)

Tete thermostatique Zigbee MOES BRT-100 V2.pdf
Tete thermostatique Zigbee MOES BRT-100 V2.pdfTete thermostatique Zigbee MOES BRT-100 V2.pdf
Tete thermostatique Zigbee MOES BRT-100 V2.pdf
 
AWS for the beginning is cloud computing
AWS for the beginning  is  cloud computingAWS for the beginning  is  cloud computing
AWS for the beginning is cloud computing
 
Put a flag on it. A busy developer's guide to feature toggles.
Put a flag on it. A busy developer's guide to feature toggles.Put a flag on it. A busy developer's guide to feature toggles.
Put a flag on it. A busy developer's guide to feature toggles.
 
Introduction to Serverless with AWS Lambda in C#.pptx
Introduction to Serverless with AWS Lambda in C#.pptxIntroduction to Serverless with AWS Lambda in C#.pptx
Introduction to Serverless with AWS Lambda in C#.pptx
 
Semiconductor Review Magazine Feature.pdf
Semiconductor Review Magazine Feature.pdfSemiconductor Review Magazine Feature.pdf
Semiconductor Review Magazine Feature.pdf
 
CIRCLE geometry lesson 2nd quarter grade 10
CIRCLE geometry lesson 2nd quarter  grade 10CIRCLE geometry lesson 2nd quarter  grade 10
CIRCLE geometry lesson 2nd quarter grade 10
 
OTel Orientation_ How to Train Teams (OTel in Practice).pdf
OTel Orientation_ How to Train Teams (OTel in Practice).pdfOTel Orientation_ How to Train Teams (OTel in Practice).pdf
OTel Orientation_ How to Train Teams (OTel in Practice).pdf
 
Quinto Z-Wave Heltun_HE-RS01_User_Manual_B9AH.pdf
Quinto Z-Wave Heltun_HE-RS01_User_Manual_B9AH.pdfQuinto Z-Wave Heltun_HE-RS01_User_Manual_B9AH.pdf
Quinto Z-Wave Heltun_HE-RS01_User_Manual_B9AH.pdf
 
Microsoft Azure News - Feb 2024
Microsoft Azure News - Feb 2024Microsoft Azure News - Feb 2024
Microsoft Azure News - Feb 2024
 
Azure Migration Guide for IT Professionals
Azure Migration Guide for IT ProfessionalsAzure Migration Guide for IT Professionals
Azure Migration Guide for IT Professionals
 
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?
Unlocking the Cloud's True Potential: Why Multitenancy Is The Key?
 
Bluetooth Low Energy(BLE) and beacons working
Bluetooth Low Energy(BLE) and beacons workingBluetooth Low Energy(BLE) and beacons working
Bluetooth Low Energy(BLE) and beacons working
 
Artificial-Intelligence-in-Marketing-Data.pdf
Artificial-Intelligence-in-Marketing-Data.pdfArtificial-Intelligence-in-Marketing-Data.pdf
Artificial-Intelligence-in-Marketing-Data.pdf
 
zigbee motion sensor user manual NAS-PD07B2.pdf
zigbee motion sensor user manual NAS-PD07B2.pdfzigbee motion sensor user manual NAS-PD07B2.pdf
zigbee motion sensor user manual NAS-PD07B2.pdf
 
From eSIMs to iSIMs: It’s Inside the Manufacturing
From eSIMs to iSIMs: It’s Inside the ManufacturingFrom eSIMs to iSIMs: It’s Inside the Manufacturing
From eSIMs to iSIMs: It’s Inside the Manufacturing
 
2024 February Patch Tuesday
2024 February Patch Tuesday2024 February Patch Tuesday
2024 February Patch Tuesday
 
Navigating the Never Normal Strategies for Portfolio Leaders
Navigating the Never Normal Strategies for Portfolio LeadersNavigating the Never Normal Strategies for Portfolio Leaders
Navigating the Never Normal Strategies for Portfolio Leaders
 
Traffic Signboard Classification with Voice alert to the driver.pptx
Traffic Signboard Classification with Voice alert to the driver.pptxTraffic Signboard Classification with Voice alert to the driver.pptx
Traffic Signboard Classification with Voice alert to the driver.pptx
 
Zi-Stick UBS Dongle ZIgbee from Aeotec manual
Zi-Stick UBS Dongle ZIgbee from  Aeotec manualZi-Stick UBS Dongle ZIgbee from  Aeotec manual
Zi-Stick UBS Dongle ZIgbee from Aeotec manual
 
DNA LIGASE BIOTECHNOLOGY BIOLOGY STUDY OF LIFE
DNA LIGASE BIOTECHNOLOGY BIOLOGY STUDY OF LIFEDNA LIGASE BIOTECHNOLOGY BIOLOGY STUDY OF LIFE
DNA LIGASE BIOTECHNOLOGY BIOLOGY STUDY OF LIFE
 

hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes

  • 1. Building Online HBase Cluster
 of 
 Zhihu Based on Kubernetes
  • 2. HBase at Zhihu Agenda PPT模板:www.1ppt.com/moban/ PPT素材:www.1ppt.com/sucai/ PPT背景:www.1ppt.com/beijing/ PPT图表:www.1ppt.com/tubiao/ PPT下载:www.1ppt.com/xiazai/ PPT教程: www.1ppt.com/powerpoint/ 资料下载:www.1ppt.com/ziliao/ 范⽂下载:www.1ppt.com/fanwen/ 试卷下载:www.1ppt.com/shiti/ 教案下载:www.1ppt.com/jiaoan/ PPT论坛:www.1ppt.cn PPT课件:www.1ppt.com/kejian/ 语⽂课件:www.1ppt.com/kejian/yuwen/ 数学课件:www.1ppt.com/kejian/shuxue/ 英语课件:www.1ppt.com/kejian/yingyu/ 美术课件:www.1ppt.com/kejian/meishu/ 科学课件:www.1ppt.com/kejian/kexue/ 物理课件:www.1ppt.com/kejian/wuli/ 化学课件:www.1ppt.com/kejian/huaxue/ ⽣物课件:www.1ppt.com/kejian/shengwu/ 地理课件:www.1ppt.com/kejian/dili/ 历史课件:www.1ppt.com/kejian/lishi/ Using Kubernetes HBase Online Platform
  • 3. HBase Online Platform Using Kubernetes HBase at Zhihu
  • 4. • Offline • Physical machine, hundreds of nodes. • Work with Spark/Hadoop. • Online • Based on Kubernetes, more than 300 containers. HBase at Zhihu 01 02
  • 5. Our online storage 01 02 03 MySQL used in most business some need scale, some need transform all SSD,expensive Redis cache and partial storage no shard expensive HBase / Cassandra / RocksDB etc. ?
  • 6. Challenges at the beginning • All business at one big cluster • Also runs NodeManager and ImpalaServer • Basically operation • Physical node level monitor
  • 7. What we want • From Business Sight • environment isolation • SLA definition • business level monition • From Operation Sight • balance resource ( CPU, I/O, RAM ) • friendly api • controllable costs 01 02
  • 8. Make HBase as a Service. In short:
  • 9. HBase Online Platform Using Kubernetes HBase at Zhihu
  • 10. Zhihu’s Unified Cluster Manage Platfom
  • 11. HBase online cluster • Platform controls cluster • Kubernetes schedule resources • Shared HDFS and ZK • Expose ZK address or ThriftServer to user
  • 12. Kubernetes Cluster resource manager and scheduler Using container to isolate resource Application management Perfect API and active community 01 02 03 04
  • 13. Component Design • Pod • infrastructure component • one Pod per component • ReplicationController -> HA • Define A cluster • 1 HMaster RC ( replica = 2 ) • 1 RegionServer RC ( replica = n, n >=1 ) • 1 ThriftServer RC ( replica = m, m>=0 )
  • 15. • HMaster -> use ZooKeeper • RegionServer -> Stateless designed • ThriftServer -> use proxy • HFile -> ??? Component Level
  • 16. Component Level - HFile • Shared HDFS Cluster • Keep the whole cluster stateless
  • 17. Cluster Level • What if cluster Pod is down ? • Kubernetes ReplicationController • What if Kubernetes is down ? • Mixed deployment • Few physical nodes with high CPU && RAM
  • 18. Data Replication • Replication in cluster • HDFS built in ( 3 replicas) • period hdfs fsck • Replication between clusters • snapshot + bulk load • offline cluster doing MR / Spark 01 02
  • 19. HBase Online Platform Using Kubernetes HBase at Zhihu
  • 20. Physical Node Resource CPU: 2 * 12 core Disk: 4 T Memory: 128 G
  • 21. Resource Definition (1) • Minimize the resource • Business scaled by number of containers • Pros • maximum resource usage on nodes • simplified debug • ease scale • Cons • minimum resource not easy to define by business • hardly tune params for RAMs and GC
  • 22. Resource Definition (2) • Customize container resource by business • Business scaled by number of containers • Pros • flexible RAM config and tuning • used in production
  • 23. Container Configuration • JAVA_HOME HBASE_HOME • inject to container via ENV • hdfs-site.xml core-site.xml • add xml config to container • hbase-site.xml hbase-env.sh • use start-env.sh to init configuration • Modify params during cluster running is permitted
  • 24. RegionServer Configuration • Basie Config • hbase.hregion.majorcompaction = 0 • hbase.regionserver.handler.count = 50 • hbase.regionserver.codecs = snappy • hfile.block.cache.size = 0.4 • Using G1GC ( thanks to Xiaomi )
  • 25. Network • Dedicated ip per pod • DNS register/deregister automatically • Modified /etc/hosts for pod
  • 26. Client Design • For Java/Scala • native HBase client • only offer ZK address to business • For Python • happybase • client proxy • service discovery
  • 27. API Server • A Bridge between Kubernetes and user • Encapsulate component of a HBase cluster • Restful API • Friendly interface
  • 28. Painful Points • Cons: • fully scan still impact whole cluster • speed limited coprocessor • locality && short circuit • SSD Disk
  • 29. Monitor Cluster • Physical nodes Level • nodes cpu loads && usage ( via IT ) • Cluster Level • Pods cpu loads ( via cAdvisor) • read && write rate , P95, cacheHit ( via JMX) • Table Level • client write speed && read latency ( via tracing ) • thrift server ( via JMX )
  • 30. Current Situation • 10+ online business, 300+ Pods • P95 average 20-30 ms • 99.99% SLA in 9 months
  • 32. • Almost no code needed • HBase container publish independently • Deployment and orchestration straight forward • Decoupled from physical nodes Easy
  • 33. • Resource isolation • CPU • memory • Business isolation • data • proxy • monitor Isolate
  • 34. • Multi version • mostly cdh5.5.0-hbase1.0.0 • one upgrade to 1.2 (HBASE-14283) • customize version easily • Configuration motivated by business • low latency -> read replica • etc. Flexible
  • 35. • Enhance performance • Netty on ThriftServer • Python HBase Client • SSD for Datanode • Auto scale • by RegionServer number • by JVM heap • etc. Next