Ruby,no sql and tokyocabinet

•Download as PPTX, PDF•

6 likes•964 views

This document discusses the transition from relational databases to NoSQL databases. It provides an overview of key-value databases like Tokyo Cabinet and how they provide high performance and scalability alternatives to SQL databases. It also describes how Tokyo Cabinet can be used with an ActiveRecord adapter to integrate NoSQL style databases into Rails applications, bridging the gap between SQL and NoSQL. Finally, it outlines a vision for further developing Tokyo Cabinet into a cloud database that can provide a unified interface to both SQL and NoSQL databases.

Technology Education

Ruby,NoSQL and TokyoCabinet 盛大创新院庄表伟 http://www.zhuangbiaowei.com/blog/

From RDBMS to NoSQL (1) Long long ago … Network Database (GE IDS 1961) Hierarchical Database (IBM IMS 1968) Relational database era Edgar Frank Codd -- Since 1969 Relational model SQL Management system Over $ 20 billion (2008)

From RDBMS to NoSQL (2) Now is Web2.0 era Have huge storage(TB/PB data size) Need high performance Need high scalability Need high availability NoSQL now coming New theory support New API style New buzzword

Projects or papers… Google BigTable (2004) Tokyo Cabinet (2006) Amazon Dynamo (2007) MongoDB,CouchDB,Cassandra,Voldemort (2008) Redis,Riak,HBase… (2009) More and more… From RDBMS to NoSQL (3)

CAP (Eric Brewer 2000) Consistency Availability Partition tolerance BASE Basically Available Soft-state Eventual Consistency MapReduce From functional programming Distributed computing framework Important NoSQL theory

Stored procedure convert to map/reduce script

System refactoring or rewriteChoose NoSQL means…

TokyoCabinet -- lightweight database library

TokyoTyrant -- lightweight database server

Insert: 0.4sec/1M records(2,500,000 qps)

What's hot

Netflix’s Big Data Platform team manages data warehouse in Amazon S3 with over 60 petabytes of data and writes hundreds of terabytes of data every day. With a data warehouse at this scale, it is a constant challenge to keep improving performance. This talk will focus on Iceberg, a new table metadata format that is designed for managing huge tables backed by S3 storage. Iceberg decreases job planning time from minutes to under a second, while also isolating reads from writes to guarantee jobs always use consistent table snapshots. In this session, you'll learn: • Some background about big data at Netflix • Why Iceberg is needed and the drawbacks of the current tables used by Spark and Hive • How Iceberg maintains table metadata to make queries fast and reliable • The benefits of Iceberg's design and how it is changing the way Netflix manages its data warehouse • How you can get started using Iceberg Speaker Ryan Blue, Software Engineer, Netflix

Iceberg: a fast table format for S3

DataWorks Summit

In Apache Cassandra Lunch #67, we discussed how to move data from Open Source Cassandra to Datastax Astra using dsbulk/scylla migratory. https://github.com/DataStax-Examples/dsbulk-to-astra/ Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-67-moving-data-from-cassandra-to-datastax-astra-with-dsbulk Accompanying Youtube: https://youtu.be/0k7RBf5vi5M Sign Up For Our Newsletter: http://eepurl.com/grdMkn Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/ Cassandra.Link: https://cassandra.link/ Follow Us and Reach Us At: Anant: https://www.anant.us/ Awesome Cassandra: https://github.com/Anant/awesome-cassandra Cassandra.Lunch: https://github.com/Anant/Cassandra.Lunch Email: solutions@anant.us LinkedIn: https://www.linkedin.com/company/anant/ Twitter: https://twitter.com/anantcorp Eventbrite: https://www.eventbrite.com/o/anant-1072927283 Facebook: https://www.facebook.com/AnantCorp/

Apache Cassandra Lunch #67: Moving Data from Cassandra to Datastax Astra

Anant Corporation

19compression

guestcddbe2

SANSA ISWC 2017 Talk

Jens Lehmann

To exchange data between processes without the overhead of locking/syscall, shared memory can be used. libshdata/telemetry offers a lock free circular buffer with timestamped data chunks to handle some typical uses cases found in drones: One process for flying stack, and one a more processes for video computing. Video processing can access drone attitude precisely with the frame Logging of all the telemetry exchanged in shared memory is easily written in a file or to a remote ground station Also work in AMP configurations

Embedded Recipes 2018 - Shared memory / telemetry - Yves-Marie Morgan

Anne Nicolas

MongoDB SF Ruby

Mike Dirolf

DARTS: Differentiable Architecture Search at 社内論文読み会

Masashi Shibata

Graph Data Processing With uRIKA Appliance

David Prat

Real time Analytics Using Storm and Kafka

Sidhartha Ray

Omid: A transactional Framework for HBase

Francisco Pérez-Sorrosal

Lodstats: The Data Web Census Dataset. Kobe, Japan, 2016

Ivan Ermilov

Gems in the python standard library

jasonscheirer

Graph basedrdf storeforapachecassandra

Ravindra Ranwala

Using Ceph for Large Hadron Collider Data

Rob Gardner

In the last few years, Netflix’s S3 data warehouse has grown to more than 100 PB. In that time, the company has shared several techniques and released open source tools for working around S3’s quirks, including s3mper to work around eventual consistency, S3 multipart committers to commit data without renames, and the batchid pattern for cross-partition atomic commits. Ryan Blue and Daniel Weeks share lessons learned, the tools Netflix currently uses and those it has retired, and the improvements it is rolling out, including Iceberg, a new table format for S3 that is replacing many of the company’s current tools. Iceberg enables a new generation of improvements, including: * Snapshot isolation with no directory listing or file renames * Distributed planning to relieve metastore bottlenecks * Improved data layout for S3 performance * Immediately available writes from streaming applications * Opportunistic compaction and data optimization

The evolution of Netflix's S3 data warehouse (Strata NY 2018)

Ryan Blue

TiDB Introduction - San Francisco MySQL Meetup

Morgan Tocker

Real-Time Data Processing at RTB House – Architecture & Lessons Learned

Bartosz Łoś

Entity Framework Core

Kiran Shahi

In this talk, we would like to share our experience connected with building and scaling our real-time data processing infrastructure at RTB House to buy advertising space effectively. We had a pleasure to present our solution during Big Data Tech Warsaw 2017. This time we would like to explain how we managed to scale it ten times in last two years, to provide full multi-dc architecture with data synchronization between DCs and merging streams of events but also to improve and rewrite all our processing components. Additionally we would like to present our Kafka Workers lib which we decided to open-source and which gives some features we really needed: higher level of distribution, tighter control of offsets commits, possibility to pause and resume processing for given partition, asynchronous processing and backpressure mechanism.

How we have grown 10x within 2 years

Bartosz Łoś

DGraph: Introduction To Basics & Quick Start W/Ratel

Knoldus Inc.

What's hot (20)

Iceberg: a fast table format for S3

Apache Cassandra Lunch #67: Moving Data from Cassandra to Datastax Astra

19compression

SANSA ISWC 2017 Talk

Embedded Recipes 2018 - Shared memory / telemetry - Yves-Marie Morgan

MongoDB SF Ruby

DARTS: Differentiable Architecture Search at 社内論文読み会

Graph Data Processing With uRIKA Appliance

Real time Analytics Using Storm and Kafka

Omid: A transactional Framework for HBase

Lodstats: The Data Web Census Dataset. Kobe, Japan, 2016

Gems in the python standard library

Graph basedrdf storeforapachecassandra

Using Ceph for Large Hadron Collider Data

The evolution of Netflix's S3 data warehouse (Strata NY 2018)

TiDB Introduction - San Francisco MySQL Meetup

Real-Time Data Processing at RTB House – Architecture & Lessons Learned

Entity Framework Core

How we have grown 10x within 2 years

DGraph: Introduction To Basics & Quick Start W/Ratel

Similar to Ruby,no sql and tokyocabinet

About "Apache Cassandra"

Jihyun Ahn

Oracle's history

Georgi Sotirov

With the increasing complexity of modern distributed systems, concerns around latency, availability, and consistency have become almost 'universal'. In response, a new generation of distributed databases is taking over: databases capable of harnessing the power and capabilities of the multi-cloud ecosystem. This new generation of distributed databases is challenging many of the traditional tradeoffs between relational and non-relational models. This webinar will explore the technologies and trends behind this new generation of distributed databases, then take a technical deep dive into one example: the open source non-relational database ScyllaDB. ScyllaDB was built specifically for extreme low latencies, but has recently increased consistency by implementing the Raft consensus protocol. Engineers will share how they are implementing a low-latency architecture, and how strongly consistent topology and schema changes enable highly reliable and safe systems, without sacrificing low-latency characteristics.

Renegotiating the boundary between database latency and consistency

ScyllaDB

Calton pu experimental methods on performance in cloud and accuracy in big da...

jins0618

MOUG17 Keynote: Oracle OpenWorld Major Announcements

Monica Li

ScyllaDB recently announced Project Alternator, a new open source project that will enable Amazon DynamoDB users to easily migrate to an open source database that runs anywhere — on most cloud platforms, on-premises, on bare-metal, virtual machines or via Kubernetes — all while preserving their investments in their existing application code. Project Alternator will help DynamoDB users achieve much better and more reliable performance, reduce database costs by 80% - 90%, support large items (10s of MBs) and large partitions (multiple GBs), control the number of replicas, balance cost vs. redundancy, and much more. Join ScyllaDB founders Avi Kivity and Dor Laor and lead engineer Nadav Har’El for a live webinar on September 25th, where they will share an overview of Project Alternator,

Alternator webinar september 2019

Nadav Har'El

RedisConf17 - Doing More With Redis - Ofer Bengal and Yiftach Shoolman

Redis Labs

ScyllaDB recently announced Project Alternator, a new open source project that will enable Amazon DynamoDB users to easily migrate to an open-source database that runs anywhere — on most cloud platforms, on-premises, on bare-metal, virtual machines or via Kubernetes — all while preserving their investments in their existing application code. Project Alternator will help DynamoDB users achieve much better and more reliable performance, reduce database costs by 80% - 90%, support large items (10s of MBs) and large partitions (multiple GBs), control the number of replicas, balance cost vs. redundancy, and much more. Join ScyllaDB founders Avi Kivity and Dor Laor and lead engineer Nadav Har’El for a live webinar on September 25th, where they will share an overview of Project Alternator, including: Alternator’s design implementation and goals How to configure Alternator (ok, add alternator_port: 8000 to your scylla.yaml) Demo how to easily run it from docker/rpm Run several examples: Tic-tac-toe based DynamoDB example with Alternator How to benchmark Scylla Alternator with YCSB and considerations around it How to run a serverless application along with Alternator How to migrate DynamoDB data to Alternator using the Spark migrator Discuss the current limitations of Alternator Plus we will discuss current limitations of Alternator, describe different consistencies and active-active vs leader model, share the project roadmap, and answer your questions at the end.

Introducing Project Alternator - Scylla’s Open-Source DynamoDB-compatible API

ScyllaDB

Apache Cassandra, part 1 – principles, data model

Andrey Lomakin

Introduction of MariaDB AX / TX

GOTO Satoru

SQLCAT: Tier-1 BI in the World of Big Data

Denny Lee

Scaling CouchDB with BigCouch

Cloudant

NoSql Databases

Nimat Khattak

Introducing TiDB [Delivered: 09/27/18 at NYC SQL Meetup]

Kevin Xu

AWS CLOUD 2018- Amazon DynamoDB기반 글로벌 서비스 개발 방법 (김준형 솔루션즈 아키텍트)

Amazon Web Services Korea

Machine Learning at the Limit John Canny, UC Berkeley How fast can machine learning and graph algorithms be? In "roofline" design, every kernel is driven toward the limits imposed by CPU, memory, network etc. This can lead to dramatic improvements: BIDMach is a toolkit for machine learning that uses rooflined design and GPUs to achieve two- to three-orders of magnitude improvements over other toolkits on single machines. These speedups are larger than have been reported for *cluster* systems (e.g. Spark/MLLib, Powergraph) running on hundreds of nodes, and BIDMach with a GPU outperforms these systems for most common machine learning tasks. For algorithms (e.g. graph algorithms) which do require cluster computing, we have developed a rooflined network primitive called "Kylix". We can show that Kylix approaches the rooline limits for sparse Allreduce, and empirically holds the record for distributed Pagerank. Beyond rooflining, we believe there are great opportunities from deep algorithm/hardware codesign. Gibbs Sampling (GS) is a very general tool for inference, but is typically much slower than alternatives. SAME (State Augmentation for Marginal Estimation) is a variation of GS which was developed for marginal parameter estimation. We show that it has high parallelism, and a fast GPU implementation. Using SAME, we developed a GS implementation of Latent Dirichlet Allocation whose running time is 100x faster than other samplers, and within 3x of the fastest symbolic methods. We are extending this approach to general graphical models, an area where there is currently a void of (practically) fast tools. It seems at least plausible that a general-purpose solution based on these techniques can closely approach the performance of custom algorithms. Bio John Canny is a professor in computer science at UC Berkeley. He is an ACM dissertation award winner and a Packard Fellow. He is currently a Data Science Senior Fellow in Berkeley's new Institute for Data Science and holds a INRIA (France) International Chair. Since 2002, he has been developing and deploying large-scale behavioral modeling systems. He designed and protyped production systems for Overstock.com, Yahoo, Ebay, Quantcast and Microsoft. He currently works on several applications of data mining for human learning (MOOCs and early language learning), health and well-being, and applications in the sciences.

SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...

Chester Chen

Apache IOTDB: a Time Series Database for Industrial IoT

jixuan1989

Bay area Cassandra Meetup 2011

mubarakss

NoSQL: what does it mean, how did we get here, and why should I care? - Hugo ...

South London Geek Nights

MariaDB TX 3.0 新機能 / ロードマップ

GOTO Satoru

Similar to Ruby,no sql and tokyocabinet (20)

About "Apache Cassandra"

Oracle's history

Renegotiating the boundary between database latency and consistency

Calton pu experimental methods on performance in cloud and accuracy in big da...

MOUG17 Keynote: Oracle OpenWorld Major Announcements

Alternator webinar september 2019

RedisConf17 - Doing More With Redis - Ofer Bengal and Yiftach Shoolman

Introducing Project Alternator - Scylla’s Open-Source DynamoDB-compatible API

Apache Cassandra, part 1 – principles, data model

Introduction of MariaDB AX / TX

SQLCAT: Tier-1 BI in the World of Big Data

Scaling CouchDB with BigCouch

NoSql Databases

Introducing TiDB [Delivered: 09/27/18 at NYC SQL Meetup]

AWS CLOUD 2018- Amazon DynamoDB기반 글로벌 서비스 개발 방법 (김준형 솔루션즈 아키텍트)

SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...

Apache IOTDB: a Time Series Database for Industrial IoT

Bay area Cassandra Meetup 2011

NoSQL: what does it mean, how did we get here, and why should I care? - Hugo ...

MariaDB TX 3.0 新機能 / ロードマップ

Recently uploaded

GenCyber Cyber Security Day Presentation

Michael W. Hawkins

Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Safe Software

Boost Fertility New Invention Ups Success Rates.pdf

sudhanshuwaghmare1

Data Cloud, More than a CDP by Matt Robison

Anna Loughnan Colquhoun

What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

HampshireHUG

Handwritten Text Recognition for manuscripts and early printed texts

Maria Levchenko

A Domino Admins Adventures (Engage 2024)

Gabriella Davis

The 7 Things I Know About Cyber Security After 25 Years | April 2024

Rafal Los

Strategies for Landing an Oracle DBA Job as a Fresher

Remote DBA Services

CNv6 Instructor Chapter 6 Quality of Service

giselly40

Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

Exploring the Future Potential of AI-Enabled Smartphone Processors

debabhi2

🐬 The future of MySQL is Postgres 🐘

RTylerCroy

MySQL Webinar, presented on the 25th of April, 2024. Summary: MySQL solutions enable the deployment of diverse Database Architectures tailored to specific needs, including High Availability, Disaster Recovery, and Read Scale-Out. With MySQL Shell's AdminAPI, administrators can seamlessly set up, manage, and monitor these solutions, ensuring efficiency and ease of use in their administration. MySQL Router, on the other hand, provides transparent routing from the application traffic to the backend servers in the architectures, requiring minimal configuration. Completely built in-house and supported by Oracle, these solutions have been adopted by enterprises of all sizes for their business-critical applications. In this presentation, we'll delve into various database architecture solutions to help you choose the right one based on your business requirements. Focusing on technical details and the latest features to maximize the potential of these solutions.

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Miguel Araújo

Automating Google Workspace (GWS) & more with Apps Script

wesley chun

How to Troubleshoot Apps for the Modern Connected Worker

ThousandEyes

BooK Now Call us at +918448380779 to hire a gorgeous and seductive call girl for sex. Take a Delhi Escort Service. The help of our escort agency is mostly meant for men who want sexual Indian Escorts In Delhi NCR. It should be noted that any impersonator will get 100 attention from our Young Girls Escorts in Delhi. They will assume the position of reliable allies. VIP Call Girl With Original Photos Book Tonight +918448380779 Our Cheap Price 1 Hour not available 2 Hours 5000 Full Night 8000 TAG: Call Girls in Delhi, Noida, Gurgaon, Ghaziabad, Connaught Place, Greater Kailash Delhi, Lajpat Nagar Delhi, Mayur Vihar Delhi, Chanakyapuri Delhi, New Friends Colony Delhi, Majnu Ka Tilla, Karol Bagh, Malviya Nagar, Saket, Khan Market, Noida Sector 18, Noida Sector 76, Noida Sector 51, Gurgaon Mg Road, Iffco Chowk Gurgaon, Rajiv Chowk Gurgaon All Delhi Ncr Free Home Deliver

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men

Delhi Call girls

08448380779 Call Girls In Greater Kailash - I Women Seeking Men

Delhi Call girls

The Raspberry Pi 5 was announced on October 2023. This new version of the popular embedded device comes with a new iteration of Broadcom’s VideoCore GPU platform, and was released with a fully open source driver stack, developed by Igalia. The presentation will discuss some of the major changes required to support this new Video Core iteration, the challenges we faced in the process and the solutions we provided in order to deliver conformant OpenGL ES and Vulkan drivers. The talk will also cover the next steps for the open source Raspberry Pi 5 graphics stack. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://eoss24.sched.com/event/1aBEx

Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...

Igalia

With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.

Boost PC performance: How more available memory can improve productivity

Principled Technologies

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Neo4j

Recently uploaded (20)

GenCyber Cyber Security Day Presentation

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Boost Fertility New Invention Ups Success Rates.pdf

Data Cloud, More than a CDP by Matt Robison

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

Handwritten Text Recognition for manuscripts and early printed texts

A Domino Admins Adventures (Engage 2024)

The 7 Things I Know About Cyber Security After 25 Years | April 2024

Strategies for Landing an Oracle DBA Job as a Fresher

CNv6 Instructor Chapter 6 Quality of Service

Exploring the Future Potential of AI-Enabled Smartphone Processors

🐬 The future of MySQL is Postgres 🐘

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Automating Google Workspace (GWS) & more with Apps Script

How to Troubleshoot Apps for the Modern Connected Worker

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men

08448380779 Call Girls In Greater Kailash - I Women Seeking Men

Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...

Boost PC performance: How more available memory can improve productivity

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Ruby,no sql and tokyocabinet

1. Ruby,NoSQL and TokyoCabinet 盛大创新院庄表伟 http://www.zhuangbiaowei.com/blog/

2. From RDBMS to NoSQL (1) Long long ago … Network Database (GE IDS 1961) Hierarchical Database (IBM IMS 1968) Relational database era Edgar Frank Codd -- Since 1969 Relational model SQL Management system Over $ 20 billion (2008)

3. From RDBMS to NoSQL (2) Now is Web2.0 era Have huge storage(TB/PB data size) Need high performance Need high scalability Need high availability NoSQL now coming New theory support New API style New buzzword

4. Projects or papers… Google BigTable (2004) Tokyo Cabinet (2006) Amazon Dynamo (2007) MongoDB,CouchDB,Cassandra,Voldemort (2008) Redis,Riak,HBase… (2009) More and more… From RDBMS to NoSQL (3)

5. CAP (Eric Brewer 2000) Consistency Availability Partition tolerance BASE Basically Available Soft-state Eventual Consistency MapReduce From functional programming Distributed computing framework Important NoSQL theory

7. Any data is allowed

8. Can not ensure data consistency

9. Eventual Consistency

10. No transaction

11. Stored procedure convert to map/reduce script

12. Lack support of management system

13. System refactoring or rewriteChoose NoSQL means…

14.

15. http://1978th.net/

16. TokyoCabinet -- lightweight database library

17. TokyoTyrant -- lightweight database server

18. Key/Value Database

19. Database types

20. Hash / B+ Tree / Fixed-length / Table

21. High performance

22. Insert: 0.4sec/1M records(2,500,000 qps)

23. Search: 0.33sec/1M records(3,000,000 qps)

24. High concurrency

25. High ScalabilityTokyoCabinet and TokyoTyrant

26.

27. Loss table struct and data type

28. Can not access by column

29. Reduction of performance

30. Read 400,000 record/sec(TCT)

31. Read 1,800,000 record/sec(TCH)

32. Read 1,000,000 record/sec(TCB)

33. When the data more than 100 million, will performance reduction

34. Network protocol is not perfectTC/TT table engine insufficiency

35.

36. Row access and column access

37. B+ tree data store

38. New binary api protocol

39. New data storage mode

40. Config TCH

41. Index  TCB

42. Data  TCBAbout TCDatabase

43. ActiveTokyoCabinet (1) Config

44. Model ActiveTokyoCabinet (2)

45. database.yml development: adapter: tcdb path: ./db database: blog model class Post < ActiveRecord::Base validates_presence_of :body, :title has_many :comments end About tcdb-adapter (1)

46. Migration class CreatePosts < ActiveRecord::Migration def self.up create_table :posts do |t| t.string :title t.text :body t.timestamps end end def self.down drop_table :posts end end About tcdb-adapter (2)

47. Demo

48. SQLite MySQL Oracle Money MySQL MySQL MySQL DBA RDBMS  NoSQL Risk RDBMS  tcdb-adapter  NoSQL SQL/NoSQL Bridge Storage architecture migration

49. Architecture diagram (today) Classic ActiveTokyoCabinet tcdb-adapter

50.

51. Architecture diagram (future - 1)

52. Architecture diagram (future - 2)

53. Architecture diagram (future - 3)

54. It will be open source!

55. Thanks