HBaseCon 2015 General Session: The Evolution of HBase @ Bloomberg

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This document discusses Bloomberg's use of HBase for its "medium data" needs in serving financial customers. It outlines challenges including speed, availability, and supporting many users. Bloomberg has invested in HBase to address these, seeing it as applicable to its "medium data" volumes. The document calls for continued work to further bolster HBase's reliability, take advantage of modern hardware, improve multi-tenancy, integrate Spark, and increase analytics efficiency. It expresses optimism for HBase's role in data platforms going forward.

Software

HBASEATBLOOMBERG//
THE EVOLUTION
OF BLOOMBERG
DATA SYSTEMSMEDIUM DATA NEEDS FOR THE FINANCIAL INDUSTRY
MAY // 07 // 2015

HBASEATBLOOMBERG//
BLOOMBERG
3
Leading Data and Analytics provider to the financial industry

HBASEATBLOOMBERG//
DATA IS OUR BUSINESS
4

HBASEATBLOOMBERG//
September 28: Full Workshop at Bloomberg
September 30: Showcase at Strata Hadoop
Call for papers at:
bloomberglabs.com/data-science
DATA SCIENCE
FOR SOCIAL GOOD:
GOVERNMENT INNOVATION,
PUBLIC HEALTH, ENVIRONMENT,
EDUCATION

HBASEATBLOOMBERG//
6
• We have a “medium data” problem…
• Speed and availability are paramount
• Hundreds of thousands of users with
expensive requests
We’ve built many systems
to address
DATA MANAGEMENT TODAY

HBASEATBLOOMBERG//
DATA MANAGEMENT CHALLENGES
7
• Single security
analytics on Big Iron
• Replication of
Systems and Data
• Complexity kills
Top 500 Supercomputer list, 2013
>96% Linux. 100% of top 40.

HBASEATBLOOMBERG//
DATA MANAGEMENT TOMORROW
8
• Simplicity and
performance
• Benefit from external
developments
• Retain our
independence
• Details matter

HBASEATBLOOMBERG//
THE PREMISE
9
• Can apply big data techniques to our medium
data problem, by addressing gaps in existing
open systems
• HBase is a good bet
• Part of a broader whole
• The Biggest community wins

HBASEATBLOOMBERG//
CHALLENGES
Our requirements from HBase are:
• Read performance – fast with low variability
• High availability
• Operational simplicity
• Efficient use of good hardware
• Expressive power
Bloomberg has been investing in all these
aspects of HBase

HBASEATBLOOMBERG//
WE’VE MADE THAT BET
11

HBASEATBLOOMBERG//
WE’RE NOT THE ONLY ONES
12
Google Cloud Bigtable

HBASEATBLOOMBERG//
AIMING HIGHER
We can make things better
by working together
Let’s be the gold standard

HBASEATBLOOMBERG//
FURTHER BOLSTER RELIABILITY
16
Great strides such as HBASE-10070 but more to do
• Improved reconciliation of
state between Master,
META and ZK
• More determinism in
Admin/Master operations

HBASEATBLOOMBERG//
BENEFIT FROM MODERN HARDWARE
17
• 32 cores - 256GB RAM – SSD - untapped potential
• CPU load max 20% , inadequate throughput
• Multi-RS administratively painful
• Much better story with memory

HBASEATBLOOMBERG//
IMPROVE MULTI-TENANCY
18
• Mixed workloads challenging
• interactive vs batch
• read vs write
• different read access
patterns
• Many solutions in progress
• Administrative simplicity is key

HBASEATBLOOMBERG//
SPARK INTEGRATION
19
• Analytical frameworks need a distributed database
• Columnar file format != column database
• Integrate with HBase to move towards the
universal database

HBASEATBLOOMBERG//
ANALYTICS: EFFICIENCY
20
• Choice of row and columnar storage engines
• Expose primitives for efficiency:
• Column pruning
• Predicate pushdowns
• Data locality

HBASEATBLOOMBERG//
THE FUTURE IS BRIGHT
21
• The state of the “Hadoop Database” union is strong
– Increasing adoption
– Strong foundation
– Great community
• Prominent role in the data & analytics platform of
the future
• Let’s go create the future

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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

HBaseCon

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 on Beam

HBaseCon

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: HBase Disaster Recovery Solution at Huawei

HBaseCon

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#

hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest

HBaseCon

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: Apache HBase at Netease

HBaseCon

Qianxi Zhang 1. Hulu是美国最受欢迎的在线视频网站之一，Hulu Beijing是Hulu第二大研发中心。北京大数据基础架构团队负责整个公司的大数据基础架构的研发和运维。 2. HBase在Hulu的概况 3. HBase在Hulu的使用 4. 用户画像系统，存放所有用户的基本信息，用户行为，第三方DMP数据和机器学习结果标签(几十万个Qualifier)，Spark和Spark Streaming读写HBase数据，运行各种机器学习模型，为公司的视频推荐，精准广告和Marketing团队服务 5. HBase在Hulu的优化 hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: HBase在Hulu的使用和实践

HBaseCon

Xinyu Zhang, Xueliang Chen and Zheng Fan 基于HBase的大数据平台已经成为中国人寿新一代综合业务处理系统中非常重要的基础性数据平台。目前基于该平台已经整合了上百TB的数据，并将几亿客户的客户、业务、接触数据整合到一个统一的数据模型中，并基于此形成了上千个客户标签。同时，基于该平台为客户、营销员和内部管理人员提供了销售支持、客户服务、运营支持等多类应用。通过APP、网页等形式提供了多种信息的检索和查询，并通过深度学习模型提供了反欺诈等方面的数据应用。 hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: 基于HBase的企业级大数据平台

HBaseCon

Xingbo Peng, Nan Zhang and Bang Wen 1.规模现状 HBase在京东CTO体系中经历了数年的发展，集群规模已经达到3000+台，支持了京东600+业务系统，京东CTO体系的HBase集群，已经经历了多次618和双11的考验。京东CTO体系是HBase的重要用户。 2.应用的业务场景介绍HBase在京东的典型应用的业务，包括监控、风控、推荐、广告等 3.高可用改进介绍我们在HBase集群高可用方面做的一些工作，包括跨机房容灾、多租户-资源分组、集群安全等 4.运维实践主要介绍我们在HBase集群运维上的一些实践，包括：HBase集群监控系统Mummut、报警系统、HBase集群与大数据平台结合、业务运营及数据迁移等 5.未来展望介绍我们正在基于HBase做的及未来要做的一些工作，包括：kylin、phoenix和容器化部署等 hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: HBase at JD.com

HBaseCon

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: Large scale data near-line loading method and architecture

HBaseCon

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#

hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei

HBaseCon

hbaseconasia2017: HBase Practice At XiaoMi

HBaseCon

hbaseconasia2017: hbase-2.0.0

HBaseCon

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.

HBaseCon2017 Democratizing HBase

HBaseCon

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.

HBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest

HBaseCon

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.

HBaseCon2017 Quanta: Quora's hierarchical counting system on HBase

HBaseCon

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.

HBaseCon2017 Transactions in HBase

HBaseCon

HBaseCon2017 Highly-Available HBase

HBaseCon

HBaseCon2017 gohbase: Pure Go HBase Client

HBaseCon

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 Improving HBase availability in a multi tenant environment

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 Spark HBase Connector: Feature Rich and Efficient Access to HBas...

HBaseCon

Mais de HBaseCon (20)

hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes

hbaseconasia2017: HBase on Beam

hbaseconasia2017: HBase Disaster Recovery Solution at Huawei

hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest

hbaseconasia2017: Apache HBase at Netease

hbaseconasia2017: HBase在Hulu的使用和实践

hbaseconasia2017: 基于HBase的企业级大数据平台

hbaseconasia2017: HBase at JD.com

hbaseconasia2017: Large scale data near-line loading method and architecture

hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei

hbaseconasia2017: HBase Practice At XiaoMi

hbaseconasia2017: hbase-2.0.0

HBaseCon2017 Democratizing HBase

HBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest

HBaseCon2017 Quanta: Quora's hierarchical counting system on HBase

HBaseCon2017 Transactions in HBase

HBaseCon2017 Highly-Available HBase

HBaseCon2017 gohbase: Pure Go HBase Client

HBaseCon2017 Improving HBase availability in a multi tenant environment

HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...

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Software Quality Assurance Interview Questions

Arshad QA

Investing in AI transformation today The modern business advantage: Uncovering deep insights with AI Organizations around the world have come to recognize AI as the transformative technology that enables them to gain real business advantage. AI’s ability to organize vast quantities of data allows those who implement it to uncover deep business insights, augment human expertise, drive operational efficiency, transform their products, and better serve their customers

Microsoft AI Transformation Partner Playbook.pdf

Willy Marroquin (WillyDevNET)

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How To Troubleshoot Collaboration Apps for the Modern Connected Worker

ThousandEyes

In the realm of real-time applications, Large Language Models (LLMs) have long dominated language-centric tasks, while tools like OpenCV have excelled in the visual domain. However, the future (maybe) lies in the fusion of LLMs and deep learning, giving birth to the revolutionary concept of Large Action Models (LAMs). Imagine a world where AI not only comprehends language but mimics human actions on technology interfaces. For example, the Rabbit r1 device presented at CES 2024, driven by an AI operating system and LAM, brings this vision to life. It executes complex commands, leveraging GUIs with unprecedented ease. In this presentation, join me on a journey as a software engineer tinkering with WebRTC, Janus, and LLM/LAMs. Together, we’ll evaluate the current state of these AI technologies, unraveling the potential they hold for shaping the future of real-time applications.

Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications

Alberto González Trastoy

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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

Steffen Staab

Test automation is a cornerstone of software development and quality assurance in today's rapidly evolving digital landscape. Its significance cannot be overstated. Businesses can enhance efficiency, productivity, and accelerate software delivery to market through automation, streamlining testing processes effectively. This comprehensive guide addresses the best practices for test automation in 2024. It offers a detailed checklist to empower you to optimize your automation efforts and maintain a competitive edge.

The Ultimate Test Automation Guide_ Best Practices and Tips.pdf

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Optimizing AI for immediate response in Smart CCTV

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HR Software Buyers Guide in 2024 - HRSoftware.com

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Many specialized tools cater to distinct stages within the software development lifecycle (SDLC). These tools target various aspects of development, delivery, and operations, each with its unique strengths. Uniting these diverse testing needs into a single continuous testing platform presents several challenges. Such a platform must seamlessly integrate with various development tools and environments, accommodate different testing methodologies, and remain flexible to adapt to organizational processes and quality standards.

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HBaseCon 2015 General Session: The Evolution of HBase @ Bloomberg

1. 1 hbasecon.com

2. HBASEATBLOOMBERG// THE EVOLUTION OF BLOOMBERG DATA SYSTEMSMEDIUM DATA NEEDS FOR THE FINANCIAL INDUSTRY MAY // 07 // 2015

3. HBASEATBLOOMBERG// BLOOMBERG 3 Leading Data and Analytics provider to the financial industry

4. HBASEATBLOOMBERG// DATA IS OUR BUSINESS 4

5. HBASEATBLOOMBERG// September 28: Full Workshop at Bloomberg September 30: Showcase at Strata Hadoop Call for papers at: bloomberglabs.com/data-science DATA SCIENCE FOR SOCIAL GOOD: GOVERNMENT INNOVATION, PUBLIC HEALTH, ENVIRONMENT, EDUCATION

6. HBASEATBLOOMBERG// 6 • We have a “medium data” problem… • Speed and availability are paramount • Hundreds of thousands of users with expensive requests We’ve built many systems to address DATA MANAGEMENT TODAY

7. HBASEATBLOOMBERG// DATA MANAGEMENT CHALLENGES 7 • Single security analytics on Big Iron • Replication of Systems and Data • Complexity kills Top 500 Supercomputer list, 2013 >96% Linux. 100% of top 40.

8. HBASEATBLOOMBERG// DATA MANAGEMENT TOMORROW 8 • Simplicity and performance • Benefit from external developments • Retain our independence • Details matter

9. HBASEATBLOOMBERG// THE PREMISE 9 • Can apply big data techniques to our medium data problem, by addressing gaps in existing open systems • HBase is a good bet • Part of a broader whole • The Biggest community wins

10. HBASEATBLOOMBERG// CHALLENGES Our requirements from HBase are: • Read performance – fast with low variability • High availability • Operational simplicity • Efficient use of good hardware • Expressive power Bloomberg has been investing in all these aspects of HBase

11. HBASEATBLOOMBERG// WE’VE MADE THAT BET 11

12. HBASEATBLOOMBERG// WE’RE NOT THE ONLY ONES 12 Google Cloud Bigtable

13. HBASEATBLOOMBERG// AIMING HIGHER We can make things better by working together Let’s be the gold standard

14. HBASEATBLOOMBERG// 14

15. >>>>>>>>>>>>>> CALL TO ACTION

16. HBASEATBLOOMBERG// FURTHER BOLSTER RELIABILITY 16 Great strides such as HBASE-10070 but more to do • Improved reconciliation of state between Master, META and ZK • More determinism in Admin/Master operations

17. HBASEATBLOOMBERG// BENEFIT FROM MODERN HARDWARE 17 • 32 cores - 256GB RAM – SSD - untapped potential • CPU load max 20% , inadequate throughput • Multi-RS administratively painful • Much better story with memory

18. HBASEATBLOOMBERG// IMPROVE MULTI-TENANCY 18 • Mixed workloads challenging • interactive vs batch • read vs write • different read access patterns • Many solutions in progress • Administrative simplicity is key

19. HBASEATBLOOMBERG// SPARK INTEGRATION 19 • Analytical frameworks need a distributed database • Columnar file format != column database • Integrate with HBase to move towards the universal database

20. HBASEATBLOOMBERG// ANALYTICS: EFFICIENCY 20 • Choice of row and columnar storage engines • Expose primitives for efficiency: • Column pruning • Predicate pushdowns • Data locality

21. HBASEATBLOOMBERG// THE FUTURE IS BRIGHT 21 • The state of the “Hadoop Database” union is strong – Increasing adoption – Strong foundation – Great community • Prominent role in the data & analytics platform of the future • Let’s go create the future

22. >>>>>>>>>>>>>> THANK YOU

23. 23 hbasecon.com

Notas do Editor

Welcome everyone! Today is going to be fantastic and we have quite the agenda for you. In the interest of time, lets dive right in.
Welcome everyone! Today is going to be fantastic and we have quite the agenda for you. In the interest of time, lets dive right in.

HBaseCon 2015 General Session: The Evolution of HBase @ Bloomberg

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Semelhante a HBaseCon 2015 General Session: The Evolution of HBase @ Bloomberg

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HBaseCon 2015 General Session: The Evolution of HBase @ Bloomberg

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