Usage case of HBase for real-time application

•

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Edward Yoon

Technology

A usage case of HBase
for real-time application

Edward J.Yoon

udanax.org
• URL shortener service
• Provides HTTP, JSON and RESTful API
• Used for some twitter clients and a
number of web sites
• Rows are increasing as almost 30 per
second
• Runs on 5 nodes HBase cluster

Block Diagram
Clients ...
Apache Web Server

Tomcat 1 Tomcat 2 ...
HBase
Hadoop HDFS

$HBase Table Schema row longURL: creator: clickLog: longURL: creator: clickLog:{referrer url} 3XqT <LONG_URL> <IP address> <IP address> ... longURL: 3xqS ... ... <LONG_URL> ... ...$

MR and Hama BSP

• Information-Flow Analysis
• Web Structure Mining
• User propensity Analysis

Hadoop Scheduler

• FIFO: Queue-based scheduler
• FSS: Fair share scheduler

fair share scheduler

• Group jobs into “pools”
• Assign each pool a guaranteed minimum
share
• Divide excess capacity evenly between
pools

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Speaker: Vladimir Rodionov (bigbase.org) This talks introduces a totally new implementation of a multilayer caching in HBase called BigBase. BigBase has a big advantage over HBase 0.94/0.96 because of an ability to utilize all available server RAM in the most efficient way, and because of a novel implementation of a L3 level cache on fast SSDs. The talk will show that different type of caches in BigBase work best for different type of workloads, and that a combination of these caches (L1/L2/L3) increases the overall performance of HBase by a very wide margin.

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If you’re running an HBase cluster in production, you’ve probably noticed that HBase shares a number of useful metrics about everything from your block cache performance to your HDFS latencies over JMX (or Ganglia, or just a file). The problem is that it’s sometimes hard to know what these metrics mean to you and your users. Should you be worried if your memstore SizeMB is 1.5GB? What if your regionservers have a hundred stores each? This talk will explain how to understand and interpret the metrics HBase exports. Along the way we’ll cover some high-level background on HBase’s internals, and share some battle tested rules-of-thumb about how to interpret and react to metrics you might see.

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Speakers: Dheeraj Kapur, Rajiv Chittajallu & Anish Mathew (Yahoo!) In early 2013, Yahoo! introduced multi-tenancy to HBase to offer it as a platform service for all Hadoop users. A certain degree of customization per tenant (a user or a project) was achieved through RegionServer groups, namespaces, and customized configs for each tenant. This talk covers how to accommodate diverse needs to individual tenants on the cluster, as well as operational tips and techniques that allow Yahoo! to automate the management of multi-tenant clusters at petabyte scale without errors.

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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#

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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#

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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.

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Apache HDFS, the file system on which HBase is most commonly deployed, was originally designed for high-latency high-throughput batch analytic systems like MapReduce. Over the past two to three years, the rising popularity of HBase has driven many enhancements in HDFS to improve its suitability for real-time systems, including durability support for write-ahead logs, high availability, and improved low-latency performance. This talk will give a brief history of some of the enhancements from Hadoop 0.20.2 through 0.23.0, discuss some of the most exciting work currently under way, and explore some of the future enhancements we expect to develop in the coming years. We will include both high-level overviews of the new features as well as practical tips and benchmark results from real deployments.

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eBay marketplace has been working hard on the next generation search infrastructure and software system, code-named Cassini. The new search engine processes over 250 million search queries and serves more than 2 billion page views each day. Its indexing platform is based on Apache Hadoop and Apache HBase. Apache HBase is a distributed persistent layer built on Hadoop to support billions of updates per day. Its easy sharding character, fast writes, and table scans, super fast data bulk load, and natural integration to Hadoop provide the cornerstones for successful continuous index builds. We will share with the audience the technical details and share the difficulties and challenges that we’ve gone through and that we are still facing in the process.

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Nitin Verma, Pravin Mittal, and Maxim Lukiyanov (Microsoft) This session presents our success story of enabling a big internal customer on Microsoft Azure’s HBase service along with the methodology and tools used to meet high-throughput goals. We will also present how new features in HBase (like BucketCache and MultiWAL) are helping our customers in the medium-latency/high-bandwidth cloud-storage scenario.

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Usage case of HBase for real-time application

1. A usage case of HBase for real-time application Edward J.Yoon

2. udanax.org • URL shortener service • Provides HTTP, JSON and RESTful API • Used for some twitter clients and a number of web sites • Rows are increasing as almost 30 per second • Runs on 5 nodes HBase cluster

3. Block Diagram Clients ... Apache Web Server Tomcat 1 Tomcat 2 ... HBase Hadoop HDFS

4. HBase Table Schema row longURL: creator: clickLog: longURL: creator: clickLog:{referrer url} 3XqT <LONG_URL> <IP address> <IP address> ... longURL: 3xqS ... ... <LONG_URL> ... ...

5. MR and Hama BSP • Information-Flow Analysis • Web Structure Mining • User propensity Analysis

6. Hadoop Scheduler • FIFO: Queue-based scheduler • FSS: Fair share scheduler

7. fair share scheduler • Group jobs into “pools” • Assign each pool a guaranteed minimum share • Divide excess capacity evenly between pools

8. fair share scheduler

Usage case of HBase for real-time application

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