TokuDB is an ACID/transactional storage engine that makes MySQL even better by increasing performance, adding high compression, and allowing for true schema agility. All of these features are made possible by Tokutek's Fractal Tree indexes.
Introduction to TokuDB v7.5 and Read Free ReplicationTim Callaghan
TokuDB v7.5 introduced Read Free Replication, allowing MySQL slaves to run with virtually no read IO. This presentation discusses how Fractal Tree indexes work, what they enable in TokuDB, and they allow TokuDB to uniquely offer this replication innovation.
This document discusses Percona Fractal Tree (TokuDB) and compares it to B-Trees and LSM trees. It begins by explaining the limitations of B-Trees for write-heavy workloads and large datasets. It then introduces LSM trees and Fractal Trees as alternatives designed for better write performance. The bulk of the document describes the internals of Fractal Trees, including their use of messages to delay and combine writes. It provides recommendations for configuring Fractal Tree settings and discusses when Fractal Trees are most useful compared to other structures. In the end, it briefly mentions the history and applications of LSM trees.
TokuDB is an alternative storage engine for MySQL that offers features like compression, hot schema changes, amortized writes, and multiple clustering indexes. It can help get past limitations of InnoDB. When evaluating TokuDB, you should test queries and overall performance and make sure it meets your requirements. The minimum installation involves configuring parameters like open file limits, cache size, and direct I/O in MySQL and disabling transparent huge pages in Linux. Loading and backing up data may require using ALTER TABLE, SELECT INTO OUTFILE, or the TokuDB Hot Backup plugin. Key things to monitor include open file descriptors, swapping, and checkpoint durations.
Fractal Tree Indexes : From Theory to PracticeTim Callaghan
Fractal Tree Indexes are compared to the indexing incumbent, B-trees. The capabilities are then shown what they bring to MySQL (in TokuDB) and MongoDB (in TokuMX).
Presented at Percona Live London 2013.
This document provides an overview of in-memory databases, summarizing different types including row stores, column stores, compressed column stores, and how specific databases like SQLite, Excel, Tableau, Qlik, MonetDB, SQL Server, Oracle, SAP Hana, MemSQL, and others approach in-memory storage. It also discusses hardware considerations like GPUs, FPGAs, and new memory technologies that could enhance in-memory database performance.
An introduction to SQL Server in-memory OLTP EngineKrishnakumar S
This is an introduction to Microsoft SQL Server In-memory Engine that was earlier code named Hekaton. It describes the basic concepts and technologies involved in the in-memory engine - This has presented in Kerala - Microsoft Users Group Meeting on May 31, 2014
- The document discusses MySQL 5.0 features like views, stored procedures, triggers, precision math, XA support, and improvements to MySQL Cluster. It also covers how MySQL fits with the Fedora community, differences in packaging, and how users can contribute to open source. Upcoming features in MySQL 5.1 are mentioned like partitioning, replication improvements, and a new performance testing utility.
- MongoDB 3.0 introduces pluggable storage engines, with WiredTiger as the first integrated engine, providing document-level locking, compression, and improved concurrency over MMAPv1.
- WiredTiger uses a B+tree structure on disk and stores each collection and index in its own file, with no padding or in-place updates. It includes a write ahead transaction log for durability.
- To use WiredTiger, launch mongod with the --storageEngine=wiredTiger option, and upgrade existing deployments through mongodump/mongorestore or initial sync of a replica member. Some MMAPv1 options do not apply to WiredTiger.
InnoDB Architecture and Performance Optimization, Peter ZaitsevFuenteovejuna
This document provides an overview of the Innodb architecture and performance optimization. It discusses the general architecture including row-based storage, tablespaces, logs, and the buffer pool. It covers topics like indexing, transactions, locking, and multi-versioning concurrency control. Optimization techniques are presented such as tuning memory configuration, disk I/O, and garbage collection parameters. Understanding the internal workings is key to advanced performance tuning of the Innodb storage engine in MySQL.
In this talk, we'll walk through RocksDB technology and look into areas where MyRocks is a good fit by comparison to other engines such as InnoDB. We will go over internals, benchmarks, and tuning of MyRocks engine. We also aim to explore the benefits of using MyRocks within the MySQL ecosystem. Attendees will be able to conclude with the latest development of tools and integration within MySQL.
This is from a 2 hour talk introducing in-memory databases. First a look at traditional RDBMS architecture and some of it's limitations, then a look at some in-memory products and finally a closer look at OrigoDB, the open source in-memory database toolkit for NET/Mono.
This document discusses in-memory database systems and high performance computing. It begins with an overview of domestic and international in-memory databases like Altibase, Oracle TimesTen, McObject ExtremeDB, and KX Systems KDB+. It then discusses the advantages of in-memory architectures for achieving ultra-low latency. The rest of the document covers technologies that enable high performance like NUMA, SSDs, InfiniBand, message queues, and complex event processing. It concludes by discussing in-memory computing and high performance computing technologies.
M|18 How to use MyRocks with MariaDB ServerMariaDB plc
MyRocks in MariaDB summarizes MyRocks, a storage engine for MariaDB that is based on RocksDB. It discusses how MyRocks addresses some of the limitations of InnoDB such as high write and space amplification. It provides details on installing and using MyRocks, including data loading techniques, tuning considerations, and replication support. Parallel replication is supported, but the highest isolation level is repeatable-read and row-based replication must be used.
Some key value stores using log-structureZhichao Liang
This slides presents three key-value stores using log-structure, includes Riak, RethinkDB, LevelDB. BTW, i state that RethinkDB employs append-only B-tree and that is an estimate made by combining guessing wih reasoning!
Beyond Postgres: Interesting Projects, Tools and forksSameer Kumar
This ppt was used at Aug Meetup of Postgres User Group Singapore. We talked about
• Cool tools and extensions like PostGIS
• Great projects like pgpool and pgbouncer
• Interesting forks of PostgreSQL like EnterpriseDB, GreenPlum etc
Interesting take-away from the session was -
• Ease Oracle Migration
• Load Balancing in PostgreSQL
• Spatial data in PostgreSQL
• Connection pooling and resource management
• Your next Data Warehouse project
Meetup page- http://www.meetup.com/PUGS-Postgres-Users-Group-Singapore/
This document discusses InnoDB compression at Facebook. It describes how compression saves disk space and reduces I/O, allowing fewer servers. Benchmarks show compressed InnoDB performs as well or better than uncompressed. Facebook improved compression by reducing failures, adding statistics, removing compressed pages from redo logs, and adaptive padding. Future work includes more efficient compression and testing larger pages/other algorithms.
This document compares the two major open source databases: MySQL and PostgreSQL. It provides a brief history of each database's development. MySQL prioritized ease-of-use and performance early on, while PostgreSQL focused on features, security, and standards compliance. More recently, both databases have expanded their feature sets. The document discusses the most common uses, features, and performance of each database. It concludes that for simple queries on 2-core machines, MySQL may perform better, while PostgreSQL tends to perform better for complex queries that can leverage multiple CPU cores.
The Exadata X3 introduces new hardware with dramatically more and faster flash memory, more DRAM memory, faster CPUs, and more connectivity while maintaining the same price as the previous Exadata X2 platform. Key software enhancements include Exadata Smart Flash Write Caching which provides up to 20 times more write I/O performance, and Hybrid Columnar Compression which now supports write-back caching and provides storage savings of up to 15 times. The Exadata X3 provides higher performance, more storage capacity, and lower power usage compared to previous Exadata platforms.
PostgreSQL 9.5 includes several new features including enhanced security, improved integration with foreign data, support for big data and analytics, noSQL enhancements, development features, and better performance and scalability. Some key additions are row level security for data access control, improved foreign data wrapper functionality, BRIN indexes for large datasets, JSON enhancements, UPSERT functionality, and parallel vacuum for faster maintenance. EnterpriseDB's PPAS platform adds additional security, Oracle compatibility, and replication features as well.
Performance Benchmarking: Tips, Tricks, and Lessons LearnedTim Callaghan
Presentation covering 25 years worth of lessons learned while performance benchmarking applications and databases. Presented at Percona Live London in November 2014.
1) MongoDB databases can grow very large due to flexible document schemas that allow large and denormalized data. This leads to increased storage requirements.
2) MongoDB replication can introduce lag on secondary nodes as they process write operations. This limits the ability to use secondary nodes for scaling reads.
3) MongoDB performance declines dramatically when indexes do not fit in memory, requiring more RAM, sharding, or reduced write performance.
4) MongoDB implements database-level locking, limiting write concurrency and the ability to run multiple shards on a single server.
5) MongoDB does not support ACID transactions, multi-version concurrency control, or consistent reads in the presence of concurrent writes. Workarounds
This document provides an overview of in-memory databases, summarizing different types including row stores, column stores, compressed column stores, and how specific databases like SQLite, Excel, Tableau, Qlik, MonetDB, SQL Server, Oracle, SAP Hana, MemSQL, and others approach in-memory storage. It also discusses hardware considerations like GPUs, FPGAs, and new memory technologies that could enhance in-memory database performance.
An introduction to SQL Server in-memory OLTP EngineKrishnakumar S
This is an introduction to Microsoft SQL Server In-memory Engine that was earlier code named Hekaton. It describes the basic concepts and technologies involved in the in-memory engine - This has presented in Kerala - Microsoft Users Group Meeting on May 31, 2014
- The document discusses MySQL 5.0 features like views, stored procedures, triggers, precision math, XA support, and improvements to MySQL Cluster. It also covers how MySQL fits with the Fedora community, differences in packaging, and how users can contribute to open source. Upcoming features in MySQL 5.1 are mentioned like partitioning, replication improvements, and a new performance testing utility.
- MongoDB 3.0 introduces pluggable storage engines, with WiredTiger as the first integrated engine, providing document-level locking, compression, and improved concurrency over MMAPv1.
- WiredTiger uses a B+tree structure on disk and stores each collection and index in its own file, with no padding or in-place updates. It includes a write ahead transaction log for durability.
- To use WiredTiger, launch mongod with the --storageEngine=wiredTiger option, and upgrade existing deployments through mongodump/mongorestore or initial sync of a replica member. Some MMAPv1 options do not apply to WiredTiger.
InnoDB Architecture and Performance Optimization, Peter ZaitsevFuenteovejuna
This document provides an overview of the Innodb architecture and performance optimization. It discusses the general architecture including row-based storage, tablespaces, logs, and the buffer pool. It covers topics like indexing, transactions, locking, and multi-versioning concurrency control. Optimization techniques are presented such as tuning memory configuration, disk I/O, and garbage collection parameters. Understanding the internal workings is key to advanced performance tuning of the Innodb storage engine in MySQL.
In this talk, we'll walk through RocksDB technology and look into areas where MyRocks is a good fit by comparison to other engines such as InnoDB. We will go over internals, benchmarks, and tuning of MyRocks engine. We also aim to explore the benefits of using MyRocks within the MySQL ecosystem. Attendees will be able to conclude with the latest development of tools and integration within MySQL.
This is from a 2 hour talk introducing in-memory databases. First a look at traditional RDBMS architecture and some of it's limitations, then a look at some in-memory products and finally a closer look at OrigoDB, the open source in-memory database toolkit for NET/Mono.
This document discusses in-memory database systems and high performance computing. It begins with an overview of domestic and international in-memory databases like Altibase, Oracle TimesTen, McObject ExtremeDB, and KX Systems KDB+. It then discusses the advantages of in-memory architectures for achieving ultra-low latency. The rest of the document covers technologies that enable high performance like NUMA, SSDs, InfiniBand, message queues, and complex event processing. It concludes by discussing in-memory computing and high performance computing technologies.
M|18 How to use MyRocks with MariaDB ServerMariaDB plc
MyRocks in MariaDB summarizes MyRocks, a storage engine for MariaDB that is based on RocksDB. It discusses how MyRocks addresses some of the limitations of InnoDB such as high write and space amplification. It provides details on installing and using MyRocks, including data loading techniques, tuning considerations, and replication support. Parallel replication is supported, but the highest isolation level is repeatable-read and row-based replication must be used.
Some key value stores using log-structureZhichao Liang
This slides presents three key-value stores using log-structure, includes Riak, RethinkDB, LevelDB. BTW, i state that RethinkDB employs append-only B-tree and that is an estimate made by combining guessing wih reasoning!
Beyond Postgres: Interesting Projects, Tools and forksSameer Kumar
This ppt was used at Aug Meetup of Postgres User Group Singapore. We talked about
• Cool tools and extensions like PostGIS
• Great projects like pgpool and pgbouncer
• Interesting forks of PostgreSQL like EnterpriseDB, GreenPlum etc
Interesting take-away from the session was -
• Ease Oracle Migration
• Load Balancing in PostgreSQL
• Spatial data in PostgreSQL
• Connection pooling and resource management
• Your next Data Warehouse project
Meetup page- http://www.meetup.com/PUGS-Postgres-Users-Group-Singapore/
This document discusses InnoDB compression at Facebook. It describes how compression saves disk space and reduces I/O, allowing fewer servers. Benchmarks show compressed InnoDB performs as well or better than uncompressed. Facebook improved compression by reducing failures, adding statistics, removing compressed pages from redo logs, and adaptive padding. Future work includes more efficient compression and testing larger pages/other algorithms.
This document compares the two major open source databases: MySQL and PostgreSQL. It provides a brief history of each database's development. MySQL prioritized ease-of-use and performance early on, while PostgreSQL focused on features, security, and standards compliance. More recently, both databases have expanded their feature sets. The document discusses the most common uses, features, and performance of each database. It concludes that for simple queries on 2-core machines, MySQL may perform better, while PostgreSQL tends to perform better for complex queries that can leverage multiple CPU cores.
The Exadata X3 introduces new hardware with dramatically more and faster flash memory, more DRAM memory, faster CPUs, and more connectivity while maintaining the same price as the previous Exadata X2 platform. Key software enhancements include Exadata Smart Flash Write Caching which provides up to 20 times more write I/O performance, and Hybrid Columnar Compression which now supports write-back caching and provides storage savings of up to 15 times. The Exadata X3 provides higher performance, more storage capacity, and lower power usage compared to previous Exadata platforms.
PostgreSQL 9.5 includes several new features including enhanced security, improved integration with foreign data, support for big data and analytics, noSQL enhancements, development features, and better performance and scalability. Some key additions are row level security for data access control, improved foreign data wrapper functionality, BRIN indexes for large datasets, JSON enhancements, UPSERT functionality, and parallel vacuum for faster maintenance. EnterpriseDB's PPAS platform adds additional security, Oracle compatibility, and replication features as well.
Performance Benchmarking: Tips, Tricks, and Lessons LearnedTim Callaghan
Presentation covering 25 years worth of lessons learned while performance benchmarking applications and databases. Presented at Percona Live London in November 2014.
1) MongoDB databases can grow very large due to flexible document schemas that allow large and denormalized data. This leads to increased storage requirements.
2) MongoDB replication can introduce lag on secondary nodes as they process write operations. This limits the ability to use secondary nodes for scaling reads.
3) MongoDB performance declines dramatically when indexes do not fit in memory, requiring more RAM, sharding, or reduced write performance.
4) MongoDB implements database-level locking, limiting write concurrency and the ability to run multiple shards on a single server.
5) MongoDB does not support ACID transactions, multi-version concurrency control, or consistent reads in the presence of concurrent writes. Workarounds
Problems with PostgreSQL on Multi-core Systems with MultiTerabyte DataJignesh Shah
This document discusses PostgreSQL performance on multi-core systems with multi-terabyte data. It covers current market trends towards more cores and larger data sizes. Benchmark results show that PostgreSQL scales well on inserts up to a certain number of clients/cores but struggles with OLTP and TPC-E workloads due to lock contention. Issues are identified with sequential scans, index scans, and maintenance tasks like VACUUM as data sizes increase. The document proposes making PostgreSQL utilities and tools able to leverage multiple cores/processes to improve performance on modern hardware.
30334823 my sql-cluster-performance-tuning-best-practicesDavid Dhavan
This document provides guidance on performance tuning MySQL Cluster. It outlines several techniques including:
- Optimizing the database schema through denormalization, proper primary key selection, and optimizing data types.
- Tuning queries through rewriting slow queries, adding appropriate indexes, and utilizing simple access patterns like primary key lookups.
- Configuring MySQL server parameters and hardware settings for optimal performance.
- Leveraging techniques like batching operations and parallel scanning to minimize network roundtrips and improve throughput.
The overall goal is to minimize network traffic for common queries through schema design, query optimization, configuration tuning, and hardware scaling. Performance tuning is an ongoing process of measuring, testing and optimizing based on application
Software Engineering Advice from Google's Jeff Dean for Big, Distributed Systemsadrianionel
This document provides software engineering advice from Jeff Dean based on his experience building large-scale distributed systems at Google. Some key points include:
- Design systems to be simple, scalable, performant, reliable, and general while balancing different goals. Get advice on designs before coding.
- Distributed systems require careful data partitioning and high-capacity even within datacenters. Products are deployed across multiple datacenters worldwide.
- Real hardware is unreliable so systems must be designed to handle many types of failures gracefully.
- Prioritize low latency, consider data access patterns and encoding, use caching and parallelism where possible.
- Favor eventual consistency over strong consistency for availability.
- Add monitoring, debugging hooks,
The document outlines an agenda for a performance summit on data warehousing. It includes sessions on data warehousing, data loading, and questions to ask experts. The summit will cover interpreting system monitoring, loading 1TB of data, and challenges of data loading such as CPU/memory constraints and throughput of data sources.
AWS Redshift Introduction - Big Data AnalyticsKeeyong Han
Redshift is a scalable SQL database in AWS that can store up to 1.6PB of data across multiple servers. It uses a columnar data storage model that makes adding or removing columns fast. Data is uploaded from S3 using SQL COPY commands and queried using standard SQL. The document provides recommendations for getting started with Redshift, such as performing daily full refreshes initially and then implementing incremental update mechanisms to enable more frequent updates.
Reducing Your E-Business Suite Storage Footprint Using Oracle Advanced Compre...Andrejs Karpovs
This document discusses implementing Oracle Advanced Compression in an Oracle E-Business Suite environment to reduce storage footprint. It describes compressing the largest tables, which reduced the database size by over 500 GB. Some performance impacts were observed, such as a 7% increase in CPU usage and changed execution plans slowing some queries. Thorough testing is recommended before production use to understand specific impacts and ensure critical functionality is not affected. Advanced Compression can provide significant storage savings but also requires careful planning and testing due to potential performance trade-offs.
This document discusses best practices for migrating to Oracle Exadata. It outlines several migration methods including physical, logical, and hybrid approaches. A key point is that Exadata migration is similar to migrating to Oracle 11g Release 2 on Linux, but opportunities exist to simplify and optimize during migration. Faster networks can aid migration, but other bottlenecks may exist. Testing and proper planning are emphasized to ensure a successful, predictable migration.
[db tech showcase Tokyo 2014] B15: Scalability with MariaDB and MaxScale by ...Insight Technology, Inc.
Scalability with MariaDB and MaxScale talks about MariaDB 10, and MaxScale, a pluggable router for your queries. These are technologies developed at MariaDB Corporation, made opensource, and will help scale your MariaDB and MySQL workloads
This is a summary of the sessions I attended at PASS Summit 2017. Out of the week-long conference, I put together these slides to summarize the conference and present at my company. The slides are about my favorite sessions that I found had the most value. The slides included screenshotted demos I personally developed and tested alike the speakers at the conference.
Beyond the DSL - Unlocking the power of Kafka Streams with the Processor APIconfluent
Technical breakout during Confluent’s streaming event in Munich, presented by Antony Stubbs, Solution Architect at Confluent. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
InnoDB architecture and performance optimization (Пётр Зайцев)Ontico
This document discusses the Innodb architecture and performance optimization. It covers the general architecture including row-based storage, tablespaces, logs, and the buffer pool. It describes the physical structure and layout of tablespaces and logs. It also discusses various storage tuning parameters, memory allocation, disk I/O handling, and thread architecture. The goal is to provide transparency into the Innodb system to help with advanced performance optimization.
- The document provides an overview of MySQL and how it works internally. It discusses the key components of MySQL including the MySQL daemon (mysqld), storage engines like InnoDB and MyISAM, and the buffer pool.
- Schema changes in earlier versions of MySQL were blocking and required table locks. More recent versions support online schema changes using triggers to copy data to a new table in the background.
- InnoDB performs queries by loading relevant pages from the tablespace into the buffer pool in memory for fast random access, then writing changes to the redo log and periodically to the tablespace on disk.
SQL Server 2014 Memory Optimised Tables - AdvancedTony Rogerson
Hekaton is large piece of kit, this session will focus on the internals of how in-memory tables and native stored procedures work and interact – Database structure: use of File Stream, backup/restore considerations in HA and DR as well as Database Durability, in-memory table make up: hash and range indexes, row chains, Multi-Version Concurrency Control (MVCC). Design considerations and gottcha’s to watch out for.
The session will be demo led.
Note: the session will assume the basics of Hekaton are known, so it is recommended you attend the Basics session.
DatEngConf SF16 - Apache Kudu: Fast Analytics on Fast DataHakka Labs
This document discusses Apache Kudu, an open source columnar storage system for analytics workloads on Hadoop. Kudu is designed to enable both fast analytics queries as well as real-time updates on fast changing data. It aims to fill gaps in the current Hadoop storage landscape by supporting simultaneous high throughput scans, low latency reads/writes, and ACID transactions. An example use case described is for real-time fraud detection on streaming financial data.
Benchmarking MongoDB for Fame and FortuneTim Callaghan
This document provides tips for benchmarking MongoDB databases. It recommends creating realistic benchmarks that mimic real workloads, executing multiple runs to eliminate outliers, and methodically making single changes between runs such as varying memory or compression settings. Key steps are to measure system metrics, publish detailed results and methods so others can reproduce them, and draw conclusions by comparing different database versions or configurations. Overall it presents benchmarking as a way to gain recognition in MongoDB "bug hunts" and provides resources for learning to benchmark databases professionally.
So you want to be a software developer? (version 2.0)Tim Callaghan
This document provides advice for someone interested in becoming a software developer. It discusses the author's background and career in programming. It describes what software engineers do, including designing, developing, reviewing, testing, deploying, and documenting code. The software development life cycle is explained. Challenges of the field like fixing bugs are mentioned. Advice is provided such as taking a public speaking course, creating projects to showcase skills, getting real work experience through internships or volunteering, maintaining an online presence, and exercising to balance the sedentary nature of the work.
Use Your MySQL Knowledge to Become an Instant Cassandra GuruTim Callaghan
This document discusses how to leverage MySQL knowledge when working with Cassandra. It explains that while CQL is similar to SQL, Cassandra does not support features like joins, foreign keys, and complex secondary indexes. It recommends focusing on schema design and modeling data around query patterns. The document provides examples of creating tables, handling relationships, and time series data. It also covers topics like transactions, replication, partitioning, and conflict resolution in Cassandra. The overall recommendation is to rethink data modeling for Cassandra and use this knowledge as a starting point rather than trying to port MySQL applications directly.
Use Your MySQL Knowledge to Become a MongoDB GuruTim Callaghan
Leverage all of your MySQL knowledge and experience to get up to speed quickly with MongoDB.
Presented at Percona Live London 2013 with Robert Hodges of Continuent.
VoltDB is an in-memory database designed for high throughput transactional workloads. It partitions data across multiple servers and executes transactions in single threads to avoid locking and improve performance. VoltDB uses stored procedures and an asynchronous client model. It is optimized for high throughput over latency and supports SQL, full ACID compliance, and automatic recovery through snapshotting.
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfAbi john
Analyze the growth of meme coins from mere online jokes to potential assets in the digital economy. Explore the community, culture, and utility as they elevate themselves to a new era in cryptocurrency.
UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager APIUiPathCommunity
Join this UiPath Community Berlin meetup to explore the Orchestrator API, Swagger interface, and the Test Manager API. Learn how to leverage these tools to streamline automation, enhance testing, and integrate more efficiently with UiPath. Perfect for developers, testers, and automation enthusiasts!
📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.
Mobile App Development Company in Saudi ArabiaSteve Jonas
EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, presentation slides, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025BookNet Canada
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, transcript, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...SOFTTECHHUB
I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.
Technology Trends in 2025: AI and Big Data AnalyticsInData Labs
At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including:
-Artificial Intelligence Market Overview
-Strategies for AI Adoption in 2025
-Anticipated drivers of AI adoption and transformative technologies
-Benefits of AI and Big data for your business
-Tips on how to prepare your business for innovation
-AI and data privacy: Strategies for securing data privacy in AI models, etc.
Download your free copy nowand implement the key findings to improve your business.
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxAnoop Ashok
In today's fast-paced retail environment, efficiency is key. Every minute counts, and every penny matters. One tool that can significantly boost your store's efficiency is a well-executed planogram. These visual merchandising blueprints not only enhance store layouts but also save time and money in the process.
Role of Data Annotation Services in AI-Powered ManufacturingAndrew Leo
From predictive maintenance to robotic automation, AI is driving the future of manufacturing. But without high-quality annotated data, even the smartest models fall short.
Discover how data annotation services are powering accuracy, safety, and efficiency in AI-driven manufacturing systems.
Precision in data labeling = Precision on the production floor.
Quantum Computing Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Generative Artificial Intelligence (GenAI) in BusinessDr. Tathagat Varma
My talk for the Indian School of Business (ISB) Emerging Leaders Program Cohort 9. In this talk, I discussed key issues around adoption of GenAI in business - benefits, opportunities and limitations. I also discussed how my research on Theory of Cognitive Chasms helps address some of these issues
2. Tokutek: Database Performance Engines
What is Tokutek?
Tokutek® offers high performance and scalability for MySQL,
MariaDB and MongoDB. Our easy-to-use open source solutions
are compatible with your existing code and application
infrastructure.
Tokutek Performance Engines Remove Limitations
• Improve insertion performance by 20X
• Reduce HDD and flash storage requirements up to 90%
• No need to rewrite code
Tokutek Mission:
Empower your database to handle the Big Data requirements of
today’s applications
4. Housekeeping
• This presentation will be available for replay
following the event
• We welcome your questions; please use the console
on the right of your screen and we will answer
following the presentation
• A copy of the presentation is available upon request
5. Agenda
Lets answer the following questions, “How can you…?”
• Easily install and configure TokuDB.
• Dramatically increase performance without rewriting
code.
• Reduce the total cost of your servers and storage.
• Simply perform online schema changes.
• Avoid becoming the support staff for your
application.
• And Q+A
6. How easy is it to install and configure
TokuDB for
MySQL or MariaDB?
7. What is TokuDB?
• TokuDB = MySQL* Storage Engine + Patches**
– * MySQL, MariaDB, Percona Server
– ** Patches are required for full functionality
– TokuDB is more than a plugin
• Transactional, ACID + MVCC
– Like InnoDB
• Drop-in replacement for MySQL
• Open Source
– http://github.com/Tokutek/ft-engine
8. Where can I get TokuDB?
• Tokutek offers MySQL 5.5 and MariaDB 5.5 builds
– www.tokutek.com
• MariaDB 5.5 and 10
– www.mariadb.org
– Also in MariaDB 5.5 from various package repositories
• Experimental Percona Server 5.6 builds
– www.percona.com
9. Is it truly a “drop in replacement”?
• No Foreign Key support
– you’ll need to drop them
• No Windows or OSX binaries
– Virtual machines are helpful in evaluations
• No 32-bit builds
• Otherwise, yes
10. How do I get started?
• Start Fresh
– create table <table> engine=tokudb;
– mysqldump / load data infile
• Use your existing MySQL data folder
– alter table <table-to-convert> engine=tokudb;
• Measure the differences
– compression : load/convert your tables
– performance : run your workload
– online schema changes : add a column
11. Before you dive in – check you’re my.cnf
• TokuDB uses sensible server parameter defaults, but
• Be mindful of your memory
– Reduce innodb_buffer_pool_size (InnoDB) and
key_cache_size (MyISAM)
– Especially if converting tables
– tokudb_cache_size=?G
– Defaults to 50% of RAM, I recommend 80%
– tokudb_directio=1
• Leave everything else alone
12. How can I dramatically increase
performance without having to rewrite
code?
13. Where does the performance come from?
• Tokutek’s Fractal Tree® indexes
– Much faster than B-trees in > RAM workloads
– InnoDB and MyISAM use B-trees
– Significant IO reduction
– Messages defer IO on add/update/delete
– All reads and writes are compressed
– Enables users to add more indexes
– Queries go faster
• Lots of good webinar content on our website
– www.tokutek.com/resources/webinars
14. How much can I reduce my IO?
Converted from
InnoDB to TokuDB
15. How fast can I insert data into TokuDB?
• InnoDB’s B-trees
– Fast until the index not longer fits in RAM
• TokuDB’s Fractal Tree indexes
– Start fast, stay fast!
• iiBench benchmark
– Insert 1 billion rows
– 1000 inserts per batch
– Auto-increment PK
– 3 secondary indexes
19. How do secondary indexes work?
• InnoDB and TokuDB “cluster” the primary key index
– The key (PK) and all other columns are co-located in
memory and on disk
• Secondary indexes co-locate the “index key” and PK
– When a candidate row is found a second lookup
occurs into the PK index
– This means an additional IO is required
– MySQL’s “hidden join”
20. What is a clustered secondary index?
• “Covering” indexes remove this second lookup, but
require putting the right columns into the index
– create index idx_1 on t1 (c1, c2, c3, c4, c5, c6);
– If c1/c2 are queried, only c3/c4/c5/c6 are covered
– No additional IO, but c7 isn’t covered
• TokuDB supports clustered secondary indexes
– create clustering index idx_1 on t1 (c1, c2);
– All columns in t1 are covered, forever
– Even if new columns are added to the table
21. What are clustered secondary indexes good at?
• Two words, “RANGE SCANS”
• Several rows (maybe thousands) are scanned without
requiring additional lookups on the PK index
• Also, TokuDB blocks are much larger than InnoDB
– TokuDB = 4MB blocks = sequential IO
– InnoDB = 16KB blocks = random IO
• Can be orders of magnitude faster for range queries
22. Can SQL be optimized?
• Fractal Tree indexes support message injection
– The actual work (and IO) can be deferred
• Example:
– update t1 set k = k + 1 where pk = 5;
– InnoDB follows read-modify-write pattern
– If field “k” is not indexed, TokuDB avoids IO entirely
– An “increment” message is injected
• Current optimizations
– “replace into”, “insert ignore”, “update”, “insert on
duplicate key update”
23. How can I reduce the total cost of my
servers and storage?
24. How can I use less storage?
• Compression, compression, compression!
• All IO in TokuDB is compressed
– Reads and writes
– Usually ~5x compression (but I’ve seen 25x or more)
• TokuDB [currently] supports 3 compression algorithms
– lzma = highest compression (and high CPU)
– zlib = high compression (and much less CPU)
– quicklz = medium compression (even less CPU)
– pluggable architecture, lz4 and snappy “in the lab”
25. But doesn’t InnoDB support compression?
• Yes, but the compression achieved is far lower
– InnoDB compresses 16K blocks, TokuDB is 64K or 128K
– InnoDB requires fixed on-disk size, TokuDB is flexible
*log style data
26. But doesn’t InnoDB support compression?
• And InnoDB performance is severely impacted by it
– Compression “misses” are costly
*iiBench workload
27. How do I compress my data in TokuDB?
create table t1 (c1 bigint not null primary key)
engine=tokudb
row_format=[tokudb_lzma | tokudb_zlib | tokudb_quicklz];
NOTE: Compression is not optional in TokuDB, we use
compression to provide performance advantages as well as save
space.
29. What is an “online” schema change?
My definition
“An online schema change is the ability to add or drop a column
on an existing table without blocking further changes to the
table or requiring substantial server resources (CPU, RAM, IO,
disk) to accomplish the operation.”
P.S., I’d like for it to be instantaneous!
31. How have online schema changes evolved?
• MySQL 5.5
– Table is read-only while entire table is re-created
• “Manual” process
– Take slave offline, apply to slave, catch up to master,
switch places, repeat
• MySQL 5.6 (and ~ Percona’s pt-online-schema-change-tool)
– Table is rebuilt “in the background”
– Changes are captured, and replayed on new table
– Uses significant RAM, CPU, IO, and disk space
• TokuDB
– alter table t1 add column new_column bigint;
– Done!
32. What online schema changes can TokuDB handle?
• Add column
• Drop column
• Expand column
– integer types
– varchar, char, varbinary
• Index creation
33. How can I avoid becoming the support
staff for my application?
34. 34
TokuDB is offered in 2 editions
• Community
– Community support (Google Groups “tokudb-user”)
• Enterprise subscription
– Commercial support
– Wouldn’t you rather be developing another application?
– Extra features
– Hot backup, more on the way
– Access to TokuDB experts
– Input to the product roadmap
Where can I get TokuDB support?
35. 35
Tokutek: Database Performance Engines
Any Questions?
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