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
HBaseCon2017 gohbase: Pure Go HBase ClientHBaseCon
gohbase is an implementation of an HBase client in pure Go: https://github.com/tsuna/gohbase. In this presentation we'll talk about its architecture and compare its performance against the native Java HBase client as well as AsyncHBase (http://opentsdb.github.io/asynchbase/) and some nice characteristics of golang that resulted in a simpler implementation.
hbaseconasia2017: HBase Practice At XiaoMiHBaseCon
Zheng Hu
We'll share some HBase experience at XiaoMi:
1. How did we tuning G1GC for HBase Clusters.
2. Development and performance of Async HBase Client.
hbaseconasia2017 hbasecon hbase xiaomi https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBaseHBaseCon
Hundreds of millions of people use Quora to find accurate, informative, and trustworthy answers to their questions. As it so happens, counting things at scale is both an important and a difficult problem to solve.
In this talk, we will be talking about Quanta, Quora's counting system built on top of HBase that powers our high-volume near-realtime analytics that serves many applications like ads, content views, and many dashboards. In addition to regular counting, Quanta supports count propagation along the edges of an arbitrary DAG. HBase is the underlying data store for both the counting data and the graph data.
We will describe the high-level architecture of Quanta and share our design goals, constraints, and choices that enabled us to build Quanta very quickly on top of our existing infrastructure systems.
In order to effectively predict and prevent online fraud in real time, Sift Science stores hundreds of terabytes of data in HBase—and needs it to be always available. This talk will cover how we used circuit-breaking, cluster failover, monitoring, and automated recovery procedures to improve our HBase uptime from 99.7% to 99.99% on top of unreliable cloud hardware and networks.
HBaseCon2017 Improving HBase availability in a multi tenant environmentHBaseCon
Infrastructure failures are a given in the cloud, but in a multi-tenant environment separating those failures from usage can be a challenge. I'll be presenting data gathered from over a hundred region server failures at HubSpot along with what we've done to improve our MTTR and what we're contributing back to the community. Covered topics will include separating usage-related failures from infrastructure and hardware failures, as well as steps we've taken to improve MTTR in both scenarios.
HBaseCon2017 gohbase: Pure Go HBase ClientHBaseCon
gohbase is an implementation of an HBase client in pure Go: https://github.com/tsuna/gohbase. In this presentation we'll talk about its architecture and compare its performance against the native Java HBase client as well as AsyncHBase (http://opentsdb.github.io/asynchbase/) and some nice characteristics of golang that resulted in a simpler implementation.
hbaseconasia2017: HBase Practice At XiaoMiHBaseCon
Zheng Hu
We'll share some HBase experience at XiaoMi:
1. How did we tuning G1GC for HBase Clusters.
2. Development and performance of Async HBase Client.
hbaseconasia2017 hbasecon hbase xiaomi https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBaseHBaseCon
Hundreds of millions of people use Quora to find accurate, informative, and trustworthy answers to their questions. As it so happens, counting things at scale is both an important and a difficult problem to solve.
In this talk, we will be talking about Quanta, Quora's counting system built on top of HBase that powers our high-volume near-realtime analytics that serves many applications like ads, content views, and many dashboards. In addition to regular counting, Quanta supports count propagation along the edges of an arbitrary DAG. HBase is the underlying data store for both the counting data and the graph data.
We will describe the high-level architecture of Quanta and share our design goals, constraints, and choices that enabled us to build Quanta very quickly on top of our existing infrastructure systems.
In order to effectively predict and prevent online fraud in real time, Sift Science stores hundreds of terabytes of data in HBase—and needs it to be always available. This talk will cover how we used circuit-breaking, cluster failover, monitoring, and automated recovery procedures to improve our HBase uptime from 99.7% to 99.99% on top of unreliable cloud hardware and networks.
HBaseCon2017 Improving HBase availability in a multi tenant environmentHBaseCon
Infrastructure failures are a given in the cloud, but in a multi-tenant environment separating those failures from usage can be a challenge. I'll be presenting data gathered from over a hundred region server failures at HubSpot along with what we've done to improve our MTTR and what we're contributing back to the community. Covered topics will include separating usage-related failures from infrastructure and hardware failures, as well as steps we've taken to improve MTTR in both scenarios.
Deepankar Reddy and Ishan Chhabra (Rocket Fuel)
Rocket Fuel is a marketing technology company that participates in 120+ billion real-time bidding auctions daily to show the right ad to the right user at the right time for our clients. In this talk, we discuss our efforts to systematically identify causes of, and how to decrease, long-tail read latencies.
HBaseCon 2015: OpenTSDB and AsyncHBase UpdateHBaseCon
OpenTSDB continues to scale along with HBase. A number of updates have been implemented to push writes over 2 million data points a second. Here we will discuss about HBase schema improvements, including salting, random UI assignment, and using append operations instead of puts. You'll also get AsyncHBase development updates about rate limiting, statistics, and security.
We’ll present details about Argus, a time-series monitoring and alerting platform developed at Salesforce to provide insight into the health of infrastructure as an alternative to systems such as Graphite and Seyren.
Our team is responsible for storage at Xiaomi and we provide storage services for dozens of businesses, such as personal cloud storage for smart phones and user profile data. So we will share some practices and improvements of HBase at Xiaomi:
1: We upgraded most of our cluster from 0.94 to 0.98 in the last year and will share some experience about upgrading.
2: We encountered some problems and made some improvements on replication.
3: We fixed or still fixing some confusing behavior from client side.
4: We introduced some improvements on scan to make users easy to use and reduce the time of RPC requests.
5: We implement an asynchronous hbase client which is an important feature for HBase 2.0.
In the age of NoSQL, big data storage engines such as HBase have given up ACID semantics of traditional relational databases, in exchange for high scalability and availability. However, it turns out that in practice, many applications require consistency guarantees to protect data from concurrent modification in a massively parallel environment. In the past few years, several transaction engines have been proposed as add-ons to HBase; three different engines, namely Omid, Tephra, and Trafodion were open-sourced in Apache alone. In this talk, we will introduce and compare the different approaches from various perspectives including scalability, efficiency, operability and portability, and make recommendations pertaining to different use cases.
HBaseCon 2015: Blackbird Collections - In-situ Stream Processing in HBaseHBaseCon
Blackbird is a large-scale object store built at Rocket Fuel, which stores 100+ TB of data and provides real time access to 10 billion+ objects in a 2-3 milliseconds at a rate of 1 million+ times per second. In this talk (an update from HBaseCon 2014), we will describe Blackbird's comprehensive collections API and various examples of how it can be used to model collections like sets, maps, and aggregates on these collections like counters, etc. We will also illustrate the flexibility and power of the API by modeling custom collection types that are unique to the Rocket Fuel context.
Kafka on ZFS: Better Living Through Filesystems confluent
(Hugh O'Brien, Jet.com) Kafka Summit SF 2018
You’re doing disk IO wrong, let ZFS show you the way. ZFS on Linux is now stable. Say goodbye to JBOD, to directories in your reassignment plans, to unevenly used disks. Instead, have 8K Cloud IOPS for $25, SSD speed reads on spinning disks, in-kernel LZ4 compression and the smartest page cache on the planet. (Fear compactions no more!)
Learn how Jet’s Kafka clusters squeeze every drop of disk performance out of Azure, all completely transparent to Kafka.
-Striping cheap disks to maximize instance IOPS
-Block compression to reduce disk usage by ~80% (JSON data)
-Instance SSD as the secondary read cache (storing compressed data), eliminating >99% of disk reads and safe across host redeployments
-Upcoming features: Compressed blocks in memory, potentially quadrupling your page cache (RAM) for free
We’ll cover:
-Basic Principles
-Adapting ZFS for cloud instances (gotchas)
-Performance tuning for Kafka
-Benchmarks
HBaseCon2017 Removable singularity: a story of HBase upgrade in PinterestHBaseCon
HBase is used to serve online facing traffic in Pinterest. It means no downtime is allowed. However, we were on HBase 94. To upgrade to latest version, we need to figure out a way to live upgrade while keeping Pinterest site live. Recently, we successfully upgrade 94 HBase cluster to 1.2 with no downtime. We made change to both Asynchbase and HBase server side. We will talk about what we did and how we did it. We will also talk about the finding in config and performance tuning we did to achieve low latency.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
HBase-2.0.0 has been a couple of years in the making. It is chock-a-block full of a long list of new features and fixes. In this session, the 2.0.0 release manager will perform the impossible, describing the release content inside the session time bounds.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
HBaseCon 2012 | Solbase - Kyungseog Oh, PhotobucketCloudera, Inc.
Solbase is an exciting new open-source, real-time search engine being developed at Photobucket to service the over 30 million daily search requests Photobucket handles. Solbase replaces Lucene’s file system-based index with HBase. This allows the system to update in real-time and linearly scale to serve millions of daily search requests on a large dataset. This session will explore the architecture of Solbase as well as some of Lucene/Solr’s inherent issues we overcame. Finally, we’ll go over performance metrics of Solbase against production traffic.
Building data pipelines is pretty hard! Building a multi-datacenter active-active real time data pipeline for multiple classes of data with different durability, latency and availability guarantees is much harder.
Real time infrastructure powers critical pieces of Uber (think Surge) and in this talk we will discuss our architecture, technical challenges, learnings and how a blend of open source infrastructure (Apache Kafka and Samza) and in-house technologies have helped Uber scale.
In this talk, we share our experience when we build up our data pipeline. We went from mongodb, and migrated to cassandra, and now we use kafka and spark to handle our data. We also talk about what problem encounter, why we select these solutions, and where we will go next.
Deepankar Reddy and Ishan Chhabra (Rocket Fuel)
Rocket Fuel is a marketing technology company that participates in 120+ billion real-time bidding auctions daily to show the right ad to the right user at the right time for our clients. In this talk, we discuss our efforts to systematically identify causes of, and how to decrease, long-tail read latencies.
HBaseCon 2015: OpenTSDB and AsyncHBase UpdateHBaseCon
OpenTSDB continues to scale along with HBase. A number of updates have been implemented to push writes over 2 million data points a second. Here we will discuss about HBase schema improvements, including salting, random UI assignment, and using append operations instead of puts. You'll also get AsyncHBase development updates about rate limiting, statistics, and security.
We’ll present details about Argus, a time-series monitoring and alerting platform developed at Salesforce to provide insight into the health of infrastructure as an alternative to systems such as Graphite and Seyren.
Our team is responsible for storage at Xiaomi and we provide storage services for dozens of businesses, such as personal cloud storage for smart phones and user profile data. So we will share some practices and improvements of HBase at Xiaomi:
1: We upgraded most of our cluster from 0.94 to 0.98 in the last year and will share some experience about upgrading.
2: We encountered some problems and made some improvements on replication.
3: We fixed or still fixing some confusing behavior from client side.
4: We introduced some improvements on scan to make users easy to use and reduce the time of RPC requests.
5: We implement an asynchronous hbase client which is an important feature for HBase 2.0.
In the age of NoSQL, big data storage engines such as HBase have given up ACID semantics of traditional relational databases, in exchange for high scalability and availability. However, it turns out that in practice, many applications require consistency guarantees to protect data from concurrent modification in a massively parallel environment. In the past few years, several transaction engines have been proposed as add-ons to HBase; three different engines, namely Omid, Tephra, and Trafodion were open-sourced in Apache alone. In this talk, we will introduce and compare the different approaches from various perspectives including scalability, efficiency, operability and portability, and make recommendations pertaining to different use cases.
HBaseCon 2015: Blackbird Collections - In-situ Stream Processing in HBaseHBaseCon
Blackbird is a large-scale object store built at Rocket Fuel, which stores 100+ TB of data and provides real time access to 10 billion+ objects in a 2-3 milliseconds at a rate of 1 million+ times per second. In this talk (an update from HBaseCon 2014), we will describe Blackbird's comprehensive collections API and various examples of how it can be used to model collections like sets, maps, and aggregates on these collections like counters, etc. We will also illustrate the flexibility and power of the API by modeling custom collection types that are unique to the Rocket Fuel context.
Kafka on ZFS: Better Living Through Filesystems confluent
(Hugh O'Brien, Jet.com) Kafka Summit SF 2018
You’re doing disk IO wrong, let ZFS show you the way. ZFS on Linux is now stable. Say goodbye to JBOD, to directories in your reassignment plans, to unevenly used disks. Instead, have 8K Cloud IOPS for $25, SSD speed reads on spinning disks, in-kernel LZ4 compression and the smartest page cache on the planet. (Fear compactions no more!)
Learn how Jet’s Kafka clusters squeeze every drop of disk performance out of Azure, all completely transparent to Kafka.
-Striping cheap disks to maximize instance IOPS
-Block compression to reduce disk usage by ~80% (JSON data)
-Instance SSD as the secondary read cache (storing compressed data), eliminating >99% of disk reads and safe across host redeployments
-Upcoming features: Compressed blocks in memory, potentially quadrupling your page cache (RAM) for free
We’ll cover:
-Basic Principles
-Adapting ZFS for cloud instances (gotchas)
-Performance tuning for Kafka
-Benchmarks
HBaseCon2017 Removable singularity: a story of HBase upgrade in PinterestHBaseCon
HBase is used to serve online facing traffic in Pinterest. It means no downtime is allowed. However, we were on HBase 94. To upgrade to latest version, we need to figure out a way to live upgrade while keeping Pinterest site live. Recently, we successfully upgrade 94 HBase cluster to 1.2 with no downtime. We made change to both Asynchbase and HBase server side. We will talk about what we did and how we did it. We will also talk about the finding in config and performance tuning we did to achieve low latency.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
HBase-2.0.0 has been a couple of years in the making. It is chock-a-block full of a long list of new features and fixes. In this session, the 2.0.0 release manager will perform the impossible, describing the release content inside the session time bounds.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
HBaseCon 2012 | Solbase - Kyungseog Oh, PhotobucketCloudera, Inc.
Solbase is an exciting new open-source, real-time search engine being developed at Photobucket to service the over 30 million daily search requests Photobucket handles. Solbase replaces Lucene’s file system-based index with HBase. This allows the system to update in real-time and linearly scale to serve millions of daily search requests on a large dataset. This session will explore the architecture of Solbase as well as some of Lucene/Solr’s inherent issues we overcame. Finally, we’ll go over performance metrics of Solbase against production traffic.
Building data pipelines is pretty hard! Building a multi-datacenter active-active real time data pipeline for multiple classes of data with different durability, latency and availability guarantees is much harder.
Real time infrastructure powers critical pieces of Uber (think Surge) and in this talk we will discuss our architecture, technical challenges, learnings and how a blend of open source infrastructure (Apache Kafka and Samza) and in-house technologies have helped Uber scale.
In this talk, we share our experience when we build up our data pipeline. We went from mongodb, and migrated to cassandra, and now we use kafka and spark to handle our data. We also talk about what problem encounter, why we select these solutions, and where we will go next.
How to get the maximum performance from your AEP server. This will discuss ways to improve execution time of short running jobs and how to properly configure the server depending on the expected number of users as well as the average size and duration of individual jobs. Included will be examples of making use of job pooling, Database connection sharing, and parallel subprotocol tuning. Determining when to make use of cluster, grid, or load balanced configurations along with memory and CPU sizing guidelines will also be discussed.
Introduction to HPC & Supercomputing in AITyrone Systems
Catch up with our live webinar on Natural Language Processing! Learn about how it works and how it applies to you. We have provided all the information in our video recording you would not miss out on.
Watch the Natural Language Processing webinar here!
Implementing data and databases on K8s within the Dutch governmentDoKC
A small walkthrough of projects within the dutch government running Data(bases) on OpenShift. This talk shares success stories, provides a proven recipe to `get it done` and debunks some of the FUD.
About Sebastiaan:
I have always been a weird DBA, trying to combine Databases with out-of-the-box thinking and a DevOps mindset. Around 2016 I fell in love with both Postgres and Kubernetes, and I then committed my life to enabling Dutch organisations with running their Database workloads CloudNative.
Over the last few years I worked as a private contractor for 2 large government agencies doing exactly that, and I want to share my and others (success stories) hoping to enable and inspire Data on Kubernetes adoption.
This presentation reviews of the many aspects of PHP performance that can impact day-to-day living. It explores basic concepts for resolution when PHP performance has got you down. The focus is on Zend Server configuration options including, but not limited to: caching, Apache settings, PHP syntax fundamentals, diagnosing bottlenecks, and DB2/SQL optimization.
Introduction to memcached, a caching service designed for optimizing performance and scaling in the web stack, seen from perspective of MySQL/PHP users. Given for 2nd year students of professional bachelor in ICT at Kaho St. Lieven, Gent.
Netflix Open Source Meetup Season 4 Episode 2aspyker
In this episode, we will take a close look at 2 different approaches to high-throughput/low-latency data stores, developed by Netflix.
The first, EVCache, is a battle-tested distributed memcached-backed data store, optimized for the cloud. You will also hear about the road ahead for EVCache it evolves into an L1/L2 cache over RAM and SSDs.
The second, Dynomite, is a framework to make any non-distributed data-store, distributed. Netflix's first implementation of Dynomite is based on Redis.
Come learn about the products' features and hear from Thomson and Reuters, Diego Pacheco from Ilegra and other third party speakers, internal and external to Netflix, on how these products fit in their stack and roadmap.
Basic performance application optimization techniques that can be applied to any application, from web to desktop or mobile, but with focus on php/mysql stack. How to identify bottlenecks and resolve them and what strategies to choose to avoid them upfront.
Live presentation:
https://www.youtube.com/watch?v=aas8oM7CLjk
SharePoint Saturday San Antonio: SharePoint 2010 PerformanceBrian Culver
Is your farm struggling to server your organization? How long is it taking between page requests? Where is your bottleneck in your farm? Is your SQL Server tuned properly? Worried about upgrading due to poor performance? We will look at various tools for analyzing and measuring performance of your farm. We will look at simple SharePoint and IIS configuration options to instantly improve performance. I will discuss advanced approaches for analyzing, measuring and implementing optimizations in your farm.
Similar to hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes (20)
Jingcheng Du
Apache Beam is an open source and unified programming model for defining batch and streaming jobs that run on many execution engines, HBase on Beam is a connector that allows Beam to use HBase as a bounded data source and target data store for both batch and streaming data sets. With this connector HBase can work with many batch and streaming engines directly, for example Spark, Flink, Google Cloud Dataflow, etc. In this session, I will introduce Apache Beam, and the current implementation of HBase on Beam and the future plan on this.
hbaseconasia2017 hbasecon hbase
https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
hbaseconasia2017: HBase Disaster Recovery Solution at HuaweiHBaseCon
Ashish Singhi
HBase Disaster recovery solution aims to maintain high availability of HBase service in case of disaster of one HBase cluster with very minimal user intervention. This session will introduce the HBase disaster recovery use cases and the various solutions adopted at Huawei like.
a) Cluster Read-Write mode
b) DDL operations synchronization with standby cluster
c) Mutation and bulk loaded data replication
d) Further challenges and pending work
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
hbaseconasia2017: Removable singularity: a story of HBase upgrade in PinterestHBaseCon
Tianying Chang
HBase is used to serve online facing traffic in Pinterest. It means no downtime is allowed. However, we were on HBase 94. To upgrade to latest version, we need to figure out a way to live upgrade while keeping Pinterest site live. Recently, we successfully upgrade 94 HBase cluster to 1.2 with no downtime. We made change to both Asynchbase and HBase server side. We will talk about what we did and how we did it. We will also talk about the finding in config and performance tuning we did to achieve low latency.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
Xinxin Fan and Hongxiang Jiang
First, we will give a brief introduction about the HBase service at Netease,include the basic cluster info and the key HBase service. And then we will talk same tips about the tuning practices for HBase. Last, we will introduce some improvements at the internal HBase version.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
hbaseconasia2017: Large scale data near-line loading method and architectureHBaseCon
Shuaifeng Zhou
When we do real-time data loading to HBase, we use put/putlist interface. After receiving put request, regionserver will write WAL, write data into memory store, flush memory store to disk-store, then compact files again and again. That precedure occupies too much resource and causing read/write performance decrease. To solve the problem, we provide a kind of near-line loading method and architecture, greatly increase the loading bandwidth, and decrease the influence to read operations.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
hbaseconasia2017: Ecosystems with HBase and CloudTable service at HuaweiHBaseCon
Jieshan Bi and Yanhui Zhong
1. CTBase: A light-weight HBase client for structured data.
1). Schematized table, more friendly for structured data storage.
2). Global secondary index for HBase.
3). HBase Query DSL. JSON based light-weight API.
4) Cluster table. Pre-joining with keys, a better solution for cross-table join queries from HBase.
2. Tagram: Distributed bitmap index implementation with HBase.
1). Distributed bitmap index for accelerating AD-HOC queries with low cardinality columns.
2). Powerful and flexible query API.
3). Tagram offers millisecond-level query latency.
3. CloudTable Service Introduction: HBase on Huawei cloud.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
As HBase and Hadoop continue to become routine across enterprises, these enterprises inevitably shift priorities from effective deployments to cost-efficient operations. Consolidation of infrastructure, the sum of hardware, software, and system-administrator effort, is the most common strategy to reduce costs. As a company grows, the number of business organizations, development teams, and individuals accessing HBase grows commensurately, creating a not-so-simple requirement: HBase must effectively service many users, each with a variety of use-cases. This is problem is known as multi-tenancy. While multi-tenancy isn’t a new problem, it also isn’t a solved one, in HBase or otherwise. This talk will present a high-level view of the common issues organizations face when multiple users and teams share a single HBase instance and how certain HBase features were designed specifically to mitigate the issues created by the sharing of finite resources.
In DiDi Chuxing Company, which is China’s most popular ride-sharing company. we use HBase to serve when we have a bigdata problem.
We run three clusters which serve different business needs. We backported the Region Grouping feature back to our internal HBase version so we could isolate the different use cases.
We built the Didi HBase Service platform which is popular amongst engineers at our company. It includes a workflow and project management function as well as a user monitoring view.
Internally we recommend users use Phoenix to simplify access.even more,we used row timestamp;multidimensional table schema to slove muti dimension query problems
C++, Go, Python, and PHP clients get to HBase via thrift2 proxies and QueryServer.
We run many important buisness applications out of our HBase cluster such as ETA/GPS/History Order/API metrics monitoring/ and Traffic in the Cloud. If you are interested in any aspects listed above, please come to our talk. We would like to share our experiences with you.
HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...HBaseCon
Both Spark and HBase are widely used, but how to use them together with high performance and simplicity is a very hard topic. Spark HBase Connector(SHC) provides feature rich and efficient access to HBase through Spark SQL. It bridges the gap between the simple HBase key value store and complex relational SQL queries and enables users to perform complex data analytics on top of HBase using Spark.
SHC implements the standard Spark data source APIs, and leverages the Spark catalyst engine for query optimization. To achieve high performance, SHC constructs the RDD from scratch instead of using the standard HadoopRDD. With the customized RDD, all critical techniques can be applied and fully implemented, such as partition pruning, column pruning, predicate pushdown and data locality. The design makes the maintenance very easy, while achieving a good tradeoff between performance and simplicity.
Also, SHC has supported Phoenix data as input to HBase in addition to Avro data. Defaulting to a simple native binary encoding seems susceptible to future changes and is a risk for users who write data from SHC into HBase. For example, with SHC going forward, backwards compatibility needs to be properly handled. So the default, SHC needs to support a more standard and well tested format like Phoenix.
In this talk, we will demo how SHC works, how to use SHC in secure/non-secure clusters, how SHC works with multi-HBase clusters, etc. This talk will also benefit people who use Spark and other data sources (besides HBase) as it inspires them with ideas of how to support high performance data source access at the Spark DataFrame level.
HBaseCon2017 Efficient and portable data processing with Apache Beam and HBaseHBaseCon
In this talk we introduce Apache Beam, a unified model to create efficient and portable data processing pipelines. Beam uses a single set of abstractions to implement both batch and streaming computations that can be executed in different environments, e.g. Apache Spark, Apache Flink and Google Dataflow. Beam not only does data processing, but can be used as a tool to ingest/extract data to/from different data stores including HBase. We will present interaction scenarios between HBase and Beam and explore Beam's Input/Output (IO) model and how we leverage it to provide support for HBase.
HBaseCon2017 Community-Driven Graphs with JanusGraphHBaseCon
Graphs are well-suited for many use cases to express and process complex relationships among entities in enterprise and social contexts. Fueled by the growing interest in graphs, there are various graph databases and processing systems that dot the graph landscape. JanusGraph is a community-driven project that continues the legacy of Titan, a pioneer of open source graph databases. JanusGraph is a scalable graph database optimized for large scale transactional and analytical graph processing. In the session, we will introduce JanusGraph, which features full integration with the Apache TinkerPop graph stack. We will discuss JanusGraph's optimized storage model that relies on HBase for fast graph transversal and processing.
by Jason Plurad and Jing Chen He of IBM
HBaseCon2017 Warp 10, a novel approach to managing and analyzing time series ...HBaseCon
There are lots of time series solutions out there, but when we started looking for one which could solve the sensor data problem we had none would fit, so we decided to roll our own.
And here we are, four years later, presenting Warp 10, a complete Open Source platform and toolsuite for managing and analyzing time series data.
This talk will describe the architecture of Warp 10 and specifically how we used HBase to support hundreds of trillions of datapoints, ensure query performance even when accessing series with a high label cardinality, and provide deletion capabilities at a scale we did not anticipate.
We will also dive into the data model and analytics capabilities through the WarpScript language and will describe how Warp 10 manages multi-tier data management and interacts with third party systems.
by Mathias Herbert
HBaseCon2017 Analyzing cryptocurrencies in real time with hBase, Kafka and St...HBaseCon
Unlike other blockchain technologies that take hours to settle, Ripple can confirm and settle a transaction in seconds with a throughput of a 1000 transactions a second. Designing a real-time analytics solution needed a something that was scalable, reliable and most importantly guaranteed consistency at the speed of availability.
The combination of hBase as storage, and Kafka and Storm as the processing framework allows Ripple to publish transactional activity within milliseconds of committed transaction. Queries are funnelled through a diverse set of hand crafted API. RippleCharts is the public facing visual analytics on nodeJS(D3) that heavily leverages those API, but we also leverage the same resources to provide to our compliance analysts valuable information for investigations and research.
by Abraham Tom and Warren Anderson of Ripple
HBaseCon2017 Achieving HBase Multi-Tenancy with RegionServer Groups and Favor...HBaseCon
Achieving HBase Multi-Tenancy with RegionServer Groups and Favored Nodes
At Yahoo! HBase has been running as a hosted multi-tenant service since 2013. In a single HBase cluster we have around 30 tenants running various types of workloads (ie batch, near real-time, ad-hoc, etc). Typically such a deployment would cause tenant workloads to negatively affect each other because of resource contention (disk, cpu, network, cache thrashing, etc). Using RegionServer Groups we are able to designate a dedicated subset of RegionServers in a cluster to host only tables of a given tenant (HBASE-6721).
Most HBase deployments use HDFS as their distributed filesystem, which in turn does not guarantee that a region’s data is locally available to the hosting regionserver. This poses a problem when providing isolation since the hdfs data blocks may have to be read remotely from a different tenant’s host thus contending for disk or network resources. Favored nodes addresses this problem by providing hints to HDFS on which datanodes data should be stored and only assigns regions to these favored regionservers (HBASE-15531).
We will walk through these features explaining our motivation, how they work as well as our experiences running these multi-tenant clusters. These features will be available in Apache HBase 2.0.
by Francis Liu and Thiruvel Thirumoolan
https://youtu.be/xorEYNyLMbM
Another year, another talk about OpenTSDB running on HBase.
We'll discuss topics like:
Yahoo's append co-processor saving CPU resources by resolving atomic appends at compaction or query time.
The pros and cons of HBASE-15181, Date Tiered compaction for time series data.
Yahoo's experiments with unbounded secondary index on HBase.
OpenTSDB's 3.0 featuring a new query engine and API.
by Chris Larsen of Yahoo!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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
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 )
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
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
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