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Using SingleStoreDB & ChatGPT for Custom Data Sets
Since its launch in November 2022, Open AI’s ChatGPT has taken the world by storm. It’s a powerful language tool that taps into the unique capabilities of Artificial Intelligence (AI), helping users with tasks from writing emails to generating catchy names for a new podcast.ChatGPT’s ability to predict and generate text comes from its ability to learn from high volumes of data. It continually iterates based on what users input, allowing it to deliver more accurate outputs with smaller margins of error.While ChatGPT has an impressive array of capabilities, it also has its limitations. Mainly, it can only generate text that is similar to the text it was trained on. For everyday users tapping into publicly available data and information, this isn’t an issue — but what if you want to use ChatGPT to generate responses dependent on your own data sets? Imagine you want to sort customer reviews based on sentiment, or you’d like to make it easy for employees to search internal documentation to find answers to their questions. Using your own data, you can create a repository of information to sort through, empowering the tool to generate responses based entirely on your proprietary information.We’ll walk you through what you need from a database — like SingleStoreDB — to store your relevant company data, creating a centralized source of truth for ChatGPT to reference when generating responses to your questions.Why SingleStoreDB for ChatGPTThe next iteration of ChatGPT for businesses includes using it against custom company data — and that starts with the right database. To be efficient with search results and query speed, you want a database that:Stores and queries large amounts of data in various formatsFeatures real-time functionalities like low latency and high concurrencyStores data as vectors and includes semantic search capabilities to find relevant data in milliseconds
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Webinar Recap: Real-Time Data and the State of Translytical Platforms
In partnership with Forrester, our recent webinar takes a closer look at translytical platforms, what they mean for organizations and the impact they have on real-time data.What does real time mean to you? Is it the speed at which you’re able to access information through your organization’s BI tools? Or how quickly your dashboards reflect up-to-the-minute insights? The notions and expectations around real-time experiences — even in the last year — have drastically shifted, with more users expecting analytical applications to function like never before.But ‘real time’ doesn’t happen on its own. It’s the outcome of a foundation designed to power these experiences and use cases — one that relies on a unified, all-in-one data architecture: a translytical platform, the combination of transactional (OLTP) and analytical (OLAP) processing.Unifying OLTP and OLAP workloads is increasingly gaining traction in the market, powering use cases never before possible when workloads were separated and handled by specialty databases. Fraudulent payments are caught within milliseconds. Streaming services deliver “watch next” recommendations instantly. And ride share drivers quickly connect with riders. As more companies take on translytical platforms and databases, what does the future hold for this technology? And, how does widespread adoption of translytical databases impact expectations around real-time data? We sat down with Noel Yuhanna, Vice President and Principal Analyst at Forrester and Franck Leveneur, CEO at Data-Sleek to find out. Here’s what we discussed in our webinar, “Translytical Platforms, Use Cases & Trends Featuring Forrester and Data-Sleek.”What Is a Translytical Platform?According to the Forrester report, The Forrester Wave™: Translytical Data Platforms, Q4 2022, “Translytical platforms are next-generation data platforms that are built on a single database engine to support multiple data types and data models. They are designed to support transactional, operational, and analytical workloads without sacrificing data integrity, performance, and analytics scale.”As Yuhanna goes on to explain, translytical platforms also feature:Memory-enabled capabilitiesMulti-model structuresScale-out architectureAI/ML built-in componentsZero administrationTiered data storage“At least 25% of your applications and insights need translytical today,” says Yuhanna. “And that number is going to double, we believe, in the next four to five years. If you’re not on this translytical journey today…well, you may be left behind.”The Benefits of Translytical PlatformsBy combining previously siloed workloads into one, translytical platforms deliver a variety of long-term benefits to users — those listed by Yuhanna include:Support for real-time analytics, no ETL or additional data movement requiredOne platform to support multiple workloadsAccelerate development for new analytical applicationsLower costs by eliminating redundant, overly complex data platformsFewer administrative requirementsGreater business agility, and a competitive advantageCommon Use Cases for Translytical Platforms“The number of use cases for translytical have been growing,” says Yuhanna. Across industries like financial services, IoT analytics, insurance, manufacturing and retail, translytical platforms are key in helping organizations move toward unified, real-time analytics across their entire technology stack. Some ways organizations are harnessing translytical capabilities include:Real-time applications: Fraud detection; patient-health monitoring; airline and hotel reservations; eCommerce; gamingCustomer-focused outcomes: Consumer personalization; recommendation engines; minimizing churnIoT-focused applications: Machine analysis; predictive assessments; procurementOther common uses for translytical platforms include operational analytics, risk management, online trading, media and advertising, gaming and more.Finding a Translytical Platform to Transform Your Real-Time DataFor Franck Leveneur, CEO at Data-Sleek, real-time data bottlenecks arose when data sets were well handled by only one type of engine:“The engine built in the database is meant for [OLTP],” says Leveneur. “...it’s really an engine to track the transaction to make sure it’s captured properly, because the source and quality of the data is important.”But what about when he needed to conduct analytical queries?“This creates some issues when you start to have a large amount of data, and you want to perform some analytical queries…and I’m talking about 100 billion rows, for example. Then you start seeing issues. It takes too long, because the [analytical] engine is not really built for that.”As Leveneur goes on to explain, there are ways to scale reads using replicas — however even these options hit a wall. What he needed was an easier way to handle both workloads, without needing to move data, sacrifice query performance or rebuild their technology stack from the ground up.Leveneur’s need represents the crossroads commonly reached by businesses: As their organizations and data sets grow in complexity, so do their use cases — and single-engine databases won’t cut it.It was at a tech conference in San Francisco that Leveneur came across a translytical database that would allow him to handle both OLTP and OLAP workloads, without having to completely rewrite data: SingleStoreDB.Named a “Strong Performer” in the The Forrester Wave™: Translytical Data Platforms, Q4 2022, there were a few key criteria that made SingleStoreDB stand out:Unified data architecture, designed to handle both OLTP and OLAP workloadsMySQL wire protocol compatibility — or as Leveneur calls it, the “MySQL big brother”Rowstore and columnstore capabilitiesLow-latency analytical queriesFast ingest for large data sets, even those up to 100 billion rowsCurious to Hear the Rest of the Story? Watch the Webinar TodayTo learn more about the future of translytical platforms, what makes SingleStoreDB a stronger performer and see a live use case in action, watch our webinar “Translytical Platforms, Use Cases & Trends Featuring Forrester and Data-Sleek” on demand today.You can also get your copy of The Forrester Wave™: Translytical Data Platforms, Q4 2022.
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Why You Need a Real-Time Analytics Database
A real-time analytics database enables you to collect and access data instantly, empowering your organization to make better, more informed business decisions.Table of ContentsWhy You Need a Real-Time Analytics DatabaseThe Benefits of a Real-Time Analytics DatabaseReal-Time Analytics Database ArchitectureReal-Time Analytics Database Use CasesCybersecurityEnergy & UtilitiesIoT & TelematicsGaming & MediaFintechFinservMarketing & AdtechRetail & eCommerceSupply Chain AnalyticsSingleStoreDB: The Real-Time Analytics Database for Modern ApplicationsWhy You Need a Real-Time Analytics DatabaseBy most estimates, more than 2.5 quintillion bytes of data are generated daily — offering businesses the opportunity to leverage insights through advanced technology, like a real-time analytics database that allows you to deliver a more personalized customer experience, enhance operational efficiency and streamline processes.The benefits of real-time data are experienced in numerous industries, including manufacturing, healthcare, retail, education and agriculture. And in today’s increasingly competitive market and customer-first environment, tools like a real-time analytics platform that can deliver actionable insights have become critical to success.A real-time analytics database enables you to collect and access data instantly, empowering your organization to make better, more informed business decisions. With data streams increasingly growing larger and more complex, companies find that they must implement real-time data analytics strategies to stay competitive.With a traditional database, information is stored and retrieved by a data analyst (or a team of data analysts) in batches before being distributed in reports. This manual process can take hours, days or weeks — causing delays in your team’s ability to make decisions, or avert costly risks.Customer Success: How Kellogg Reduced 24-Hour ETL to Minutes and Boosted BI Speed by 20xThe Benefits of a Real-Time Analytics DatabaseWith a real-time analytics database, your team can access visualizations, summaries and reports in real time, allowing you to more readily complete various tasks and make vital decisions like minimizing risks in production, and monitoring and adjusting inventory.For example, a company can proactively adjust its marketing campaigns and customer experience with the latest data insights to ensure they are attracting new clients, while engaging and retaining existing clients. With real-time data on target behavior, buying habits and preferences, you can create a comprehensive and highly targeted campaign that resonates with your primary audiences.And, studies show the investment in real-time technology —like a real-time analytics platform — pays off. A survey from Harvard Business Review Analytic Services reveals that 58% of business leaders say real-time analytics technology leads to higher rates of customer retention.Another benefit of real-time data analytics platforms is they eliminate data silos often found in tech stacks. Through a customized dashboard that pulls data insights from various departments — including sales, marketing, production and engineering — executives and other team members gain access to important information to better guide decision making.Unlike traditional databases, a real-time analytics database paves the way for you to have access to up-to-the-minute insights, empowering your teams to make in-the-moment decisions that impact everything from customer experiences to your bottom line. Real-Time Analytics Database ArchitectureModern data analytics use cases, for the most part, require real-time analytics architecture that performs at high levels — particularly in ingesting, processing, analyzing and reporting on data. Due to the nature of data availability and access, a real-time analytics database requires a unique architecture that differs from traditional databases. For example, SingleStoreDB is built with a unique, three-tiered storage architecture that’s designed specifically for millisecond response times. Unlike traditional or specialized data engines that are designed for one type of workload, SingleStoreDB is a unified database — combining transactional and analytical processing within a multi-model structure. Due to its processing and data streaming capabilities, SingleStoreDB is the ideal database for real-time analytics and applications. See more: Why SingleStoreDB for Real-Time Analytics & ApplicationsReal-time query workloads are also more precise than classic data workloads, like those in data warehouses. Not to mention, these workloads have technical requirements that set them apart as real-time analytics use cases: Low latency streaming data ingestion. Data should be continuously ingested as its generated, and available immediately for indexing and querying. Batch data loading, or ETL, simply won’t cut it.Flexible indexing. This enables low latency data access in several scenarios, including selective queries, full-text search, geospatial queries and more.Reliable support for complex queries. This is especially true for ANSI SQL that matches top-tier data warehouses with data sizes in the 100s of GB to 10s of TB.Separation of storage and compute. Applications shouldn’t have to give up elasticity to get low latency ingest and query capabilities. And overall, this requirement helps to reduce long-term TCO.Strong high availability. The support to keep applications up and running — even while facing hardware failures — is critical for real-time analytics workloads.With the right real-time analytics database architecture, you gain the insights you need to take immediate action to cover the specific requirements of your industry. See more: The Technical Capabilities Your Database Needs for Real-Time AnalyticsReal-Time Analytics Database Use CasesVarious industries — from financial technology to IoT and cybersecurity — are increasingly showing the benefits of a real-time analytics database: CybersecuritySecurity threat detection and analysis over device telemetry dataRead the case study: Nucleus Security Replaces MariaDB With SingleStore and Improves Query Speed Up to 20xRead the case study: Armis Saves 70% on Data Pipeline Cost With SingleStore and Accelerates its Valuation to $3.4BEnergy & UtilitiesAnalysis of sensor data from oil wells is used to detect issues early, guide the drilling process and conduct profitability analyses. And, similar use cases apply to powering smart telemetry for electrical companies.Read the case study: SingleStore Improves Financial Operations for a Major Oil and Gas CompanyIoT & TelematicsAnalysis of cell tower telemetry for a large cell phone carrier to detect phone call quality issues quickly; Ingesting and analyzing IoT event streams (or video streams) for anomaly detection. Read the case study: Arcules Scales Video Surveillance and Analytics Platform with SingleStore to Support Thousands of Global UsersGaming & MediaBehavioral analysis on the click traffic from web games or streaming video services to optimize end-user experiences — like providing personalized recommendations, or monitoring stream quality. FintechLow latency stock portfolio analysis based on fresh market data for high-networth customers. Read the case study: Ant Money Migrates from PostgreSQL to SingleStoreDB Cloud, Boosting Performance 20-100x and Reducing TCO 10xFinServCredit card fraud detection over a stream of purchase data and other telemetry. Read the case study: Bitwyre Trades Redis for SingleStore — and Powers an Ultra-fast, Scalable, Resilient, Secure Cryptocurrency ExchangeMarketing & AdtechFaster time-to-insights for publishers and managers to oversee performance marketing channels for revenue generation; market segmentation and ad targeting based on application telemetry, geospatial data and clickstream data from various sources.Read the case study: Heylink Boosts Performance 200x and Reduces Cost 30% With SingleStore — Tackling Black Friday Traffic With EaseRetail & eCommerceLow latency dashboards — or “fastboards”— to provide a live, 360-degree view of company metricsRead the case study: How Kellogg’s Reduced 24-Hour ETL to Minutes and Boosted BI Speed by 20xSupply Chain AnalyticsUsing real-time sensor data with predictive analytics to power the future of connected supply chains.Read the case study: Dell Transforms its PRISM Inventory System with SingleStore to Run at the Speed of BusinessSingleStoreDB: The Real-Time Analytics Database for Modern ApplicationsSingleStoreDB meets — and exceeds — the previously mentioned requirements for a real-time analytics database. From streaming data ingestion to a unified architecture and reliable support for complex queries, the world’s leading brands choose SingleStoreDB every day for its proven ability to power real-time analytics and applications. Want to see it in action? Get your free SingleStoreDB demo today. Related Resources: Explore Additional Content for Real-Time Analytics DatabasesInfographic: Unlock the Power of Real-Time Data & InsightsWhite Paper: Real-Time Answers in a Fast-Data WorldWhite Paper: Building Scalable Real-Time AnalyticseBook: 3 Key Attributes of a Modern Application That’s Ready for Real Time
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What Are Analytical Applications?
What are analytical applications, and how do they impact your business? We’ve got everything you need to know about this essential, real-time tool.What Are Analytical Applications? As an increasing number of businesses seek ways to leverage data to enhance insights and gain efficiencies, analytical applications have emerged as essential tools for improving performance.Analytical applications deliver comprehensive and concise insights that business leaders can use to take action — empowering you to innovate, streamline operations and accelerate company growth. By processing vast amounts of raw data, analytical tools can produce reports and data visualization that provide intelligence, guiding executives and other company leaders in making strategic decisions.Businesses use these analytical applications, often referred to as business intelligence tools or BI analytical applications, to make decisions in real time about how to prioritize areas of your operations that can lead to the most impact, minimize abandonment of invaluable customers, ensure you achieve your goals and meet service-level agreements for your customers.Data analytics can improve business operations for companies in varying industries. Every organization can collect, maintain and analyze data to gain insights on how to improve performance and evaluate growth opportunities. Through analytical applications (or analytical apps, for short), you can leverage data in the following industries to achieve your objectives:Energy & UtilityEnergy companies rely on the ability to continuously monitor and analyze data on various complex systems to ensure they're operating at a high rate of efficiency — as well as maximizing profits and performance. Analytical applications give you the ability to make real-time decisions in areas like energy distribution, energy optimization and smart grid management.See how a major oil and gas company improves financial reporting in a volatile marketFinancial ServicesAnalytics applications also can give financial businesses, including portfolio managers, investment banks and endowments the tools needed to quickly gain real-time data insights on investment alternatives, make more informed investment decisions and streamline their analytical processes. This allows CFOs and other financial experts to get a more comprehensive view into budgeting, forecasting, financial planning and portfolio management.Here’s how Bitwyre powers an ultra-fast, scalable, resilient and secure cryptocurrency exchangeTransportationUsing analytical applications, companies specializing in transportation services can analyze data to help them meet customer expectations for delivery. Analytics can be used to anticipate traffic delays, plan alternate routes and make other predictive decisions to enhance and streamline operations.Digital AdvertisementCompanies specializing in advertising can use analytical applications to leverage data insights about the most effective advertising mediums for specific audiences. These can include advertising on websites, digital billboards and other advertising mediums.Find out how Tapjoy reaches more than 800 million active users a month with advertising an app monetizationMedia & EntertainmentUsing analytical applications, organizations can gain insights into customer and behavioral data to deliver the best entertainment experience for their customers based on their preferences.What Is An Analytics Application Tool?What is an analytics application tool? Since the late 1980s, when the amount of data being generated began to accelerate, innovations in analysis became increasingly important. The analytics application tool emerged as software that businesses use to measure and analyze data — and gain insights to improve the performance of their operations.Various types of mobile analytics and web app analytics tools are now leveraged as business intelligence (BI) to help executives make sense of vast amounts of data. These analytics application tools provide users with the insights they need to streamline operations, increase efficiencies and make other improvements in their business functions.An analytics application tool may include various features to enhance analysis, including dashboards, interactive data visualizations and reporting that provide you with real-time insights into your company’s data. These tools are used in various industries and across different departments including marketing, sales and finance.Advantages of Analytical AppsAnalytical apps, which can be leveraged by users at various levels, provide a logical and contextual view of the data your business generates. Depending on your needs, the analytical application can provide an easy-to-digest visualization or report of what you need to address specific questions.When using analytical applications, organizations gain the following advantages:Actionable insights. With analytical applications, you can identify areas for improvement, gain a better understanding of target audiences, increase profitability, reduce costs and maximize opportunities for improvement.Competitive advantage. By gaining business intelligence through an analytical application tool, you can make decisions based on data insights in real time — a significant advantage if competitors are not leveraging the same tools.Data-in-context. Without a team of experts on board, you may not be able to make sense of all the data a business generates. Manual analysis of data is also time consuming. Analytical applications provide easy-to-understand visualizations and reports from a user-friendly dashboard, minimizing the time it takes to generate an actionable, contextualized analysis.Application Of Analytics In BusinessWhen considering an application of analytics in business, it’s important to first consider the unique needs of your company, as well as the industry. For example, the application of business analytics in agriculture may be significantly different from the application of business analytics in finance.Currently, many agribusinesses leverage business analytics to address the need for more agricultural output to meet the needs of the growing world population. Analytical applications are used on initiatives to increase productivity through insights gathered from advanced technology like soil sensors, weather tracking and GPS-equipped tractors.By gaining access to real-time data on weather patterns and insect behaviors, farmers can make important decisions about crops. As a result, they can more effectively reduce waste, increase yield and boost profits. Analytics can also be used to better meet customer demands and improve supply chain management.Other considerations should be weighed, including the importance of business analytics in the processing and analysis of data for various departments and individual users. Organizations also should determine if a prospective vendor has provided applications of data analytics in business for companies of similar sizes and industries.For many organizations, the business analytics process typically involves participation among various departments to enhance collaboration for optimal outcomes. By sharing marketing and sales data in real time with operations or manufacturing, you and your team can better anticipate customer demands for products. As a result, you should consider extending the use of the application to various team members, not just data analysts.You also need to determine if the analytics tool is robust enough to gather, analyze and process the amount of data your organization generates from various sources. The tool also must be capable of providing visualizations and reports to deliver a comprehensive analysis in one location.Another consideration is the ability of the analytics tool to be personalized to meet the company's specific requirements. The tool should accommodate your company's goals — as well as ensure that all designated team members can use it.Application Of Data AnalyticsAs demonstrated among the organizations that use it, the application of data analytics can produce different outcomes based on the industries in which it is used. For that reason, many organizations seek data analytics providers that have worked in their respective industries. Besides finance and agriculture, some of the leading analytical applications have been used in education, healthcare and business (among others).Application of Data Analytics in EducationThrough data analysis in the education field, educators can identify academic areas where students are performing well, or struggling. They also can be used to determine a student’s learning style. As a result, schools can leverage data to give students a more personalized approach, promoting more comprehensive, successful education plans. The application of data analytics in education can also assess a student’s academic strengths, guiding their future education and career.From an administrative standpoint, analytical tools can be used to streamline tasks like enrollment, recruitment and processing student information. Application of Data Analytics in HealthcareWith medical facilities gathering various types of patient data from electronic health records, patient portals, smartphone apps and other devices, health professionals can get a more holistic view of each patient to determine how to best address their needs.Data analytics in healthcare can also use this information to capture overall trends in a specific location or among a certain demographic to determine if actions for a community are necessary. For instance, data analysis during the COVID-19 pandemic played a critical role in helping health experts and public officials determine high rates of infection in a certain area — and respond accordingly.Application of Data Analytics in BusinessMany businesses have used analytical apps to gain invaluable data insights to help them become more profitable. The benefits of data analytics in the business realm can be far-reaching — from helping companies personalize the customer experience to attract and retain more buyers, to streamlining operations and mitigating risks like theft.For instance, businesses in the retail industry can use data analytics to determine the optimal price for products based on current sales and predictive analysis of customer demand. They also can make long-term projections, like employee hires and increased production based on seasons.After making decisions based on business intelligence in a limited market, company leaders can also assess data to inform business decisions on a larger scale. As a result, a company can minimize losses and maximize profits.SingleStoreDB for Analytical ApplicationsTo truly be effective, analytical applications require an approach that taps into real-time capabilities — reducing time-to-insights for fast analytics on even the most dynamic data.To truly be effective, analytical applications require an approach that taps into real-time capabilities — reducing time-to-insights for fast analytics on even the most dynamic data. SingleStoreDB is design for real-time analytical applications. With a data architecture that unifies transactional and analytical workloads, SingleStoreDB mitigates the performance bottlenecks and unnecessary data movement that often contributes to lackluster application performance.Interested in learning more? Get started with a free trial of SingleStoreDB today. You can also check out more resources for analytical applications here:Blog: Why SingleStoreDB for Real-Time Analytics & ApplicationsSolution Brief: Supercharge Your SaaS ApplicationsCustomer Story: SingleStoreDB Powers IEX Cloud With Ultimate Real-Time Application Analytics
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World Economic Forum Reflections: Raj Verma
Our CEO Raj Verma was honored to represent SingleStore as part of its first delegation to the World Economic Forum in Davos, Switzerland.
During and leading up to the Annual Meeting, Raj shared his insights with the broader World Economic Forum community: What’s “modern data” and how can it be used to help vulnerable people? and 3 ways to keep pace with the real-time data revolution.
Below are excerpts from a conversation with Raj about his time in Davos:
Raj, what was your take on the issues addressed in Davos?
Raj: As we all recognize, there are a lot of important and urgent issues that the world is facing today. Inflation and macroeconomic uncertainty are just a couple of the near-term challenges we face. Recognizing the uncertainty in the world, I thought I might come away with a greater sense of alarm, but instead the discussions I had over the course of the week with a wide range of people, from industry, government and from all parts of the world, left me energized and optimistic for the future. In large part because when there are problems in the world, new businesses are created to solve them. The problems we face are vast and compounding, but they are indeed solvable.
You participated in a panel, “Start-ups in Austerity,” which was live streamed to the world. Was there one key point you wanted to come across?
Raj: More than one, but I wanted to make it very clear that SingleStore and the database industry is well positioned overall. The amount of modern data in our world today is unprecedented and there is a true need for real-time solutions. For that reason, even in this time of uncertainty, particularly for many tech start-ups, I am optimistic about our future as we have always prioritized business fundamentals.
Forecasting and long term planning have always been important to us. And I’d encourage other founders in our industry and across the start-up ecosystem to focus on fundamentals as well, which will help them endure and prosper in the good times and the bad.
Something else you discussed on your panel was this dichotomy between incumbents and innovation — specifically the question of will industry incumbents find innovation first or will innovators find distribution quicker. What did you mean by that?
Raj: This is a seminal question for entrepreneurs and investors. And what I mean by this question is how can innovators grow without getting ahead of themselves and how can established players innovate without being too risky, as they must satisfy shareholders and a wider range of stakeholders than an earlier stage company.
There is a way to find a balance between the strengths of incumbents and the strengths of innovators. While SingleStore is certainly an innovator in our industry, costs and revenue have never not mattered. However, caring about those basics doesn’t mean you should neglect innovation. By being innovative and constantly asking what would we do differently if we were to start SingleStore today instead of in the past, we stay ahead of the curve and preserve the ability to grapple with the new problems that appear in front of us.
As you come back to San Francisco, did you leave Davos with any ideas for SingleStore specifically?
Raj: Of course, the job of a CEO is to think ahead and consider how to make things better. Our goal at SingleStore has always been to build the fastest and most powerful database on the market. Whether for large scale problems like halting financial crime and cyberattacks, or small scale daily uses, like ensuring your Uber drops you off at the office in time for your first meeting of the day, massive amounts of data need to be processed and analyzed in seconds to power society.
One thing I am thinking about is how we can leverage SingleStore for optimizing health: with the launch of wearables and new technologies for monitoring sleep, heart rate and other indicators — how can SingleStore be part of the solution in not just storing people’s health data, but creating actionable insights for people to leverage.
If you had to write a headline for your week in Davos, what would it be?
The headline would be "Humans in Davos." The human connections I made surprised me in such a good way. I grabbed a hot tea on the street alongside a Pakistani man also enjoying a tea. India and Pakistan aren't always the best of friends, yet we had the most delightful conversation.
It was both warm and meaningful. I had so many interactions like this; with people who were interested in learning and want to leave a mark on the world — not by exploiting it, but by making it better. The human spirit that I felt in Davos, one that is kind, gentle and ready to give back, added to my spirit of optimism.
Sum up your experience in three words.
Raj: Energizing, hopeful… and freezing.
Thanks, Raj!
See more from Raj Verma:
Blog: Stay Hungry, Stay Foolish on Highway 101: How SingleStore “Sleighs” the Streets With Real-Time Creativity & InnovationKeynote Session: The Real-Time RevolutionCNBC Asia Street Signs: SingleStore CEO Raj Verma on Series F-2 Financing
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How to Build a Database Architecture for Modern, Real-Time Applications
From finding a new show based on a recommendation from your streaming service, to getting fraud alerts from your bank, you benefit from both real-time applications and analytics every day.
Behind the scenes, these daily touchpoints online and in the marketplace require a powerful database architecture to work as they should — one that is designed to keep up with fluctuations in users, scalability and demand.
Let’s be honest: most consumers aren’t concerned with how their experience is powered, so long as apps are available and functioning in real time. If an application takes too long to load, or provides recommendations that don’t fit with your taste, you’re likely to turn elsewhere.
But what about when you’re the one providing the application or service? How can you meet real-time experiences today’s users don’t just want, but expect? It starts with your database architecture.
Our eBook, “3 Key Attributes of a Modern Application That’s Ready for Real Time” dives into the key features your database architecture needs to make the most of live data — ensuring your application stays competitive.
Feature #1: Ultra-Fast Ingest
As your business — and user base — grows, so does your data. It’s critical your database architecture keeps up, continuously ingesting data from diverse sources as it’s generated while also making sure that data is available for indexing and querying.
The answer? SingleStoreDB Pipelines. Pipelines allow users to continuously extract, optionally transform and load data in parallel at ultra-fast speeds — as in, 100-billion-rows kind of fast.
Think your application could use that kind of power?
See more: SingleStore Pipelines: Real-Time Data Ingest With Exactly Once Semantics
Feature #2: High Concurrency With Low Latency
At normal times, your application might handle thousands of users. At peak times, this number can drastically increase into the hundreds of thousands. And if your database architecture can’t handle this high concurrency, your application won’t be available — and you’ve lost customers.
What you need is a database that delivers low-latency performance when high volumes of users are accessing your application at the same time. SingleStoreDB supports customer-facing applications with 40,000+ users through seekable columnstores. Simply put, SingleStoreDB incorporates hash indexes on columnstores, resulting in less information to sift through so the database can answer queries quickly — and has more capacity to handle multiple users.
See more: How GE Solved 100+ Use Cases and Reduced Auditing Costs by 40x With Self-Serve, Real-Time Data From SingleStoreDB
Feature #3: Artificial Intelligence (AI) and Machine Learning (ML) Capabilities
If you haven’t started incorporating AI and ML into your data architecture, you might be behind the curve. AI and ML can lower costs, increase scale, speed up the delivery of results and achieve critical business goals. Together, these capabilities deliver real-time analytics, predictive analytics and more.
But AI and ML can be difficult to incorporate without the right data infrastructure in place — which requires a high-performance platform to complete calculations within the model (usually across a mix of streaming and historical data). SingleStoreDB delivers maximum performance for both transactional (OLTP) and analytical (OLAP) workloads, using familiar relational data structures.
Additionally, the SQL compatibility of SingleStoreDB makes it an ideal choice as a platform for developers operationalizing ML models as part of user-facing, real-time applications.
See more: Epigen Powers Facial Recognition in the Cloud With SingleStoreDB
Ready for Real-Time Applications?
To read more about what your database architecture needs to support truly real-time applications, download your copy of the eBook,“3 Key Attributes of a Modern Application That’s Ready for Real Time”today.
Check out these additional resources for real-time applications:
What Is a Real-Time Analytics Database?Why SingleStoreDB for Real-Time Applications & AnalyticsWebinar: Accelerating Real-Time IoT Analytics With IBM Cognos & SingleStore
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4 Ways to Interact with SingleStore at AWS re: Invent 2022
Heading to AWS re: Invent? We’ll see you there. Here are a few ways to interact with SingleStore in Las Vegas.
Taking place Nov. 28 - Dec 2. across six Las Vegas hotels, AWS re:Invent is a great place to learn about next-generation tech firsthand, and connect with other peers and database industry thought leaders. SingleStore is excited to have an entire lineup of our own activities this year including product demos, customer parties and in classic Las Vegas fashion, a magician at our booth!
Here are four ways you can interact with us at the event and learn more about the world’s fastest real-time, distributed SQL database — and see the magic for yourself.
1. Magic and demos at booth #3630
See what makes SingleStoreDB magic
Catch Magician Dennis Kyriakos performing at our booth (#3630) during the conference Nov. 29 - Dec. 2. You’ll enjoy high-caliber, close-up magic that’s guaranteed to kick things off in an unforgettable way. After that, we’ll move on to the database magic, with an in-depth, hands-on demo of how SingleStoreDB works on AWS.
If you’re impressed with the demo and want to try it for yourself, snag a SingleStore branded poker chip — worth $500 in credits to be used for SingleStoreDB Cloud.
And of course, don’t forget to grab some SingleStore swag while you’re there.
2. Watch us on TheCUBE
Uncover what makes a modern database capable of powering real-time applications.
SingleStore’s SVP of Engineering Shireesh Thota will be front and center with our partner IBM for an exclusive interview with TheCube. We’ll be discussing all things real-time, distributed SQL, and the key features you need in a modern database that’s designed to power real-time applications and analytics. Take in the conversation live at booth 4050 on Monday, November 28 at 6:30 p.m. PST — or catch it on demand by following along on our social channels @SingleStoreDB.
3. Fast analytics and AI demo with Intel
Discover how the real-time performance of SingleStoreDB helps the world reduce human trafficking powered with chooch.ai.
Not only will we have our own demo at the SingleStore booth, but we’ll also be at the Intel booth (#1617) to demonstrate how SingleStoreDB gets the most out of the latest Intel processors, performing incredibly fast analytics and AI in SQL. We’ll start by demonstrating the rapid processing of a realistic summary aggregate query based on our groundbreaking use of operations directly on encoded (compressed) data with Intel SIMD instructions. This example only requires 42 clock cycles per row to process a query with over 30 field references and operators for each row.
Then, we’ll demonstrate how we evaluate deep-learning-based face recognition models within SQL using cosine similarity metrics based on a vector_dot product function, implemented with Intel SIMD. This approach blends advanced AI matching with SQL filters and joins for easy query expressions that combine symbolic and AI matching — all within the database.
You can check it out for yourself Monday afternoon from 4-7 p.m. PST, or all day Tuesday at booth #1617.
4. Engineering Under the Hood
Catch us on the road, starting at AWS re: Invent
Follow our Engineering Under the Hood series — live this month from AWS re: Invent. Join us Tuesday, Nov. 29 at 11:00 PST to hear SVP of Engineering Shireesh Thota’s perspective on high availability and scalability. And, we’ll be joined by a surprise special guest.
You can also check out previous episodes of our Under the Hood Series.
We can’t wait to see you at AWS re: Invent. In the meantime, explore some additional content ahead of Nov. 28 — and we’ll have plenty more for you on the show floor.
Resources
Blog: Why SingleStoreDB for Real-Time Analytics & ApplicationsWhite Paper: Real-Time Answers in a Fast Data WorldWebinar: Real-Time Retail With IBM and SingleStore
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Webinar Recap: Real-Time Retail With IBM and SingleStoreDB
The retail industry has faced unprecedented change in the last few years. Companies have shifted their focus from brick-and-mortar stores to online sales, aiming to adjust their strategies to accommodate changing customer behaviors, hyper-personalization and growing supply chain complexity. At the center of all this change? Data.
Data is crucial for retailers looking to create rich, customer experiences and generate powerful business insights — especially as leading retail companies use in-the-moment analytics to interact more effectively with their customers.
Yet traditional data architectures, especially those that rely on single-node, legacy databases, create hurdles for businesses. Front-end applications are too slow and refresh and don’t reflect the most real-time data, leading to customer churn and lost revenue. And at the back end, data analytics can be delayed up to hours — or even days — creating blindspots for decision makers that need to move quickly on insights.
How do you architect a real-time data infrastructure for interactive applications and analytics that creates successful customer journeys? Our webinar, “Real-Time Retail With IBM and SingleStoreDB” examines just that.
Here’s a look at what we covered with Mahesh Dodani, Industry Chief Engineer for Retail, CPG, Travel & Transportation Industries at IBM.
The Integral Role of Real-Time Analytics in Retail
“As we’re coming to the start of the peak season for retail, covering Thanksgiving, Black Friday, Cyber Monday, Christmas, etc., retailers are focused on delivering a differentiated, seamless shopping experience,” says Dodani.
And post-pandemic, customer expectations revolve around a hybrid shopping experience — think buy online, pick up in store; and guided shopping with help from store associates.
“Delivering this differentiated experience will require retailers to bring together capabilities and orchestrate them to deliver a personalized customer journey,” adds Dodani. This can look like:
Promotions and recommendations based on preferences and intentFast web and mobile application experiencesDynamic pricing
Retailers can use analytics and AI to provide these experiences, something that happens when they have the right, real-time data available instantly. However, retailers may experience constraints in getting there.
“When you talk about dynamic pricing, personalization and inventory management, these constraints are truly from technologies feeding applications,” says Sugandan Barathy, Senior Partner Solutions Architect at SingleStore. “For example, event-driven data is being generated at the source in a POS system, in environments that are in the store — but it’s not available for use since there are processes that need to be done and in place before the data is usable.”
That means when businesses are able to access data, it’s not in real time — not to mention, the speed at which data is made available is far behind what’s required to be sufficient for real-time applications.
The main culprit behind slow, outdated insights? Legacy data architectures that are complex, costly and hard to scale.
Infographic: Drive Next Generation Growth in Retail
Building a Data Infrastructure for Real-Time Retail With IBM & SingleStoreDB
The complexity of current approaches
In retail environments, businesses are generating data in large volumes — up to 30-50TB of data per day, per store. The problem is that this data has to move through several systems to eventually reach the transactional data store, where it is then extracted, transformed and loaded (ETL’d) into the analytics data store. And only after it goes through rigorous data security and compliance is it made available to users.
This fragmented, siloed approach to generating insights often leads to users experiencing:
Applications that aren’t fast or interactive enoughA lack of fresh, usable dataThe inability to scale as businesses hit data bottlenecks and fail to deliver on SLAsRising complexities and costs due to constant data movement
SingleStoreDB: The real-time, distributed SQL database
Unifying transactions and analytics with no data movement, SingleStoreDB is a fully distributed SQL database that scales and is built for the cloud. Unlike traditional systems that require several databases and constant movement, our unique three-tier architecture and Universal Storage capabilities effortlessly scale with your data requirements.
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Webinar Recap: Anuvu Speed Tests MariaDB, Snowflake & SingleStoreDB
Anuvu, one of the leading global airline telecommunications providers, had a problem. They were struggling with a patchwork of redundant databases to power their applications and analytics.
Like several other organizations, they had multiple instances of MySQL, MariaDB and other analytical engines, like Snowflake and Redshift.
As a result, Anuvu struggled with operational complexity, data errors and inconsistencies, redundant processes, skyrocketing costs and lagging speed. The company took a long, hard look at their data infrastructure — and decided to take action in simplifying their solutions.
In our webinar, “Anuvu Speed Tests MariaDB, Snowflake & SingleStoreDB,” we dive deeper into Anuvu’s journey toward database modernization and consolidation, including how they cut TCO by 10x and saw a 20x increase in performance.
Here’s a look at what we discussed when we were joined by Orlando Jimenez, Senior Infrastructure Engineer at Anuvu.
The Anuvu Database Journey
Anuvu started building their tech stack with MySQL and MariaDB, initially choosing the open-source databases for their unlimited record storage, free community version and MyISAM benchmark performance. As Jimenez goes on to explain, one of Anuvu’s main analytics processes (done using Java), took 16 hours to compile one days’ worth of data.
In an effort to reduce this time, Anuvu decided to run analytics as a stored procedure — and saw the time it took to compile the data drop from 16 to 3 hours. After that, they made the decision to run all analytics inside the database, adding Snowflake and Redshift to their stack.
While this worked for Anuvu for some time, weaknesses eventually emerged. These included:
Source data that didn’t write to all databases. Certain databases only received data generated for a specific client, which led to disparity between sources.Stored procedures started the same, but had to be updated based on specific client needs. These changes made each stored procedure unique — only making it more difficult to fix data errors.Table locks occurred with INSERT statements within MariaDB, leading to data availability issues.
Overall, these issues led to inconsistent datasets, asynchronous processes and poor performance that impacted customer experiences.
Searching for a Single Source of Truth
It became clear to the Anuvu team that disparate systems were no longer serving them well. “We needed a single database with all the processes and all the data — and then we can distribute and ensure the data is here, it’s correct and all the processes are right, “ says Jimenez.
To make this centralized model viable, Anuvu had a strict set of requirements that needed to be met. The database had to:
Easily migrate 500+ stored proceduresStore 500G+ tables, and outperform MySQL query capabilitiesHave analytical capabilities (columnstore)Easily scale as data grew, with available hard disk and memoryProvide a single, unified data repository for fast response times
The company compiled a list, with Amazon Redshift, Snowflake and Hadoop as candidates. But in weighing the pros and cons of each, they were led to SingleStoreDB.
“We started playing with it, and found out it was fast,” says Jimenez. Additionally, Anuvu chose SingleStoreDB for its MySQL compatibility, rowstore engine and columnstore capabilities.
Database Race: Speed Testing SingleStoreDB, Snowflake and MariaDB
After seeing what SingleStoreDB was capable of, Anuvu kicked off speed testing. “We started serious testing to make sure that it was not a mirage that we were seeing — that the solution was actually viable,” says Jimenez.
Using an existing client's query needs, Anuvu set up the testing environment for SingleStoreDB, Snowflake and MariaDB.
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Webinar Recap: Turbocharging MySQL JSON Data With SingleStoreDB
It’s no secret MySQL is one of the most popular open-source databases available on the market, with companies like Airbnb, Pinterest and Netflix using it in their tech stacks.
Additionally, JSON is a widely used, lightweight format for storing and transporting data in MySQL. Businesses at the start of their real-time application and analytics journey often turn to MySQL for its ease of use and familiarity.
But as a single-node architecture with limited scalability, MySQL users eventually hit a stall when handling real-time analytics on rapidly changing data — which often results in companies adding more databases into their tech stack, tacking on added complexities and costs.
But, what if there is a way to turbocharge your applications and analytics in MySQL, while still retaining the familiar syntax and structures of SQL and JSON?
In our webinar, “Turbocharging MySQL JSON Data with SingleStoreDB, Senior Technical Evangelist Akmal Chaudhri builds an example inventory system using SingleStoreDB, dives into the benefits and challenges of MySQL architectures and demonstrates how to store, retrieve and query JSON data using SingleStoreDB.
Here’s a quick recap of what else we cover in the webinar.
How Does SingleStoreDB Work for MySQL Users?
“SingleStoreDB has the MySQL compatibility, “ shares Chaudhri. “It uses the MySQL wire protocol — so if you’re familiar with MySQL or MariaDB, for example, various drivers and tools will work out of the box.”
Given its familiar SQL syntax, SingleStoreDB makes it incredibly easy to migrate from any flavor of MySQL, including AWS RDS, Google Cloud SQL, Azure MySQL and others. Developers who’ve hit a stall in their ability to scale their MySQL data architecture aren’t tasked with learning a completely new technology — or undertaking a massive migration project.
In addition to its SQL compatibility, SingleStoreDB also includes features that empower uses to load data at faster speeds than standard, single-node databases.
“SingleStore has this capability called Pipelines, a very useful feature that allows you to ingest data at scale in parallel,” explains Chaudhri. “That could be residing, for example, in a S3 bucket; it might come from a Kafka cluster.”
You can see more on how Pipelines work in our webinar, Introduction to SingleStoreDB — also hosted by Chaudhri.
JSON Support on SingleStoreDB
“With the JSON support that SingleStoreDB provides, there’s about 15-plus functions,” says Chaudhri.
These functions include (but aren’t limited to):
BuildExtractDeleteDisplay
You can get a full view of functions supported in SingeStoreDB in our JSON Functions documentation. As Chaudri goes on to explain, moving JSON data into SingleStoreDB is a simple, straightforward process — and can be done in one of three recommended ways:
MySQL CLI using LOAD DATA. Using this method, you can specify the format of the data that you’re reading in. This function currently supports JSON, Avro and CSV data types.SingleStoreDB Pipeline. As mentioned earlier, using Pipelines is particularly useful if you’re looking to ingest large quantities of data, and you want to do it in real time. This function currently supports JSON, AVRO, Parquet and CSV data types.SingleStoreDB Portal UI. This option is currently supported for S3 and available for all three major clouds — AWS, Google (GCP) and Microsoft Azure. While Chaudri demonstrates this method later in the webinar, you can read more about how to load CSV/JSON Files into SingleStoreDB with the portal UI from software engineer Marta Vasconcelos.
Ready to Turbocharge Your MySQL JSON Data? Watch the Webinar On Demand
Get a front-row view on our hypothetical inventory system built on MySQL using JSON data — and how effortless it is to store, retrieve and query that data using SingleStoreDB.
Watch “Turbocharging MySQL JSON Data With SingleStoreDB” on demand today.
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Why SingleStoreDB for Real-Time Analytics & Applications
How do you recognize poor data experiences?
You may recognize them when your application or dashboard doesn’t refresh properly. Or your supply chain application reflects data from a few days, instead of a minute ago. Or your ride-sharing app doesn’t reflect accurate pricing. Or you’ve missed identifying a fraudulent payment. The truth is, poor data experiences don’t just impact you — they impact your customers, the end users you’re working diligently to serve.
Today’s users don’t just demand data, they demand real-time visibility and insights. They demand the immediacy that empowers them to make fast, informed decisions that keep their worlds moving.
‘Real time’ doesn’t happen through sheer will. It’s the outcome of a foundation — a data architecture designed to drive instant analytics. Yet, legacy platforms can’t handle fast-moving and fast-changing data streams. They simply weren’t designed to keep up. And, too many databases cause a fragmented, overly complex look at analytics.
To truly deliver on the promise of real time, these workloads demand a key set of requirements from the underlying data platform:
Streaming Data Ingestion. Data needs to be continuously ingested from diverse sources as it's generated, and be immediately available for indexing and querying. Batch data loading or ETL isn't good enough.Low Latency Analytical Queries. The data platform needs to be able to deliver on stringent SLAs for latency (100s of ms or less) on complex analytical queries serving interactive applications, dashboards and APIsFlexible Indexing. The platform should offer the ability to enable low-latency data access in a variety of scenarios including selective queries, full-text search queries, geospatial queries and more.High Concurrency. Apps are often customer facing and experience spikes in usage — and your data architecture should support millions of real-time queries across tens of thousands of users.
The Challenge: Building Real-Time Applications & Analytics
Challenge 1: Single-node databases
Businesses at the start of their real-time application journey often start with single-node, open-source databases like MySQL, PostgreSQL, MongoDB, MariaDB and SQL Server. While these databases initially provide a good foundation, users quickly hit a single-node stall . That is, these legacy databases aren’t optimized for analytics — much less real-time, interactive analytics required to power real-time applications.
Read more: Full House: Developers Share 3 Signs You’ve Outgrown Your Open-Source Database
Challenge 2: Data Warehouses
Today’s data warehouses power business intelligence (BI) and reporting workloads that enable organizations to quickly aggregate and analyze large amounts of data from multiple sources to drive insights. But data warehouses aren’t optimized for low-latency analytics or large numbers of concurrent users — especially when dealing with fast-moving, streaming data from diverse sources that power modern applications and drive insights in real time.
Read more: Data Warehouse Augmentation With SingleStoreDB
A new approach to powering real-time analytical applications requires reducing time-to-insights for fast analytics on dynamic data for complex queries, all within sub-seconds.
The Solution: SingleStoreDB, The Database for Real-Time Applications & Analytics
The formula for a real-time system is simple: It needs to be fast, unified, scalable and resilient.
Built with a unique three-tiered storage architecture and designed for millisecond response times, SingleStoreDB is the world’s fastest distributed SQL database for real-time analytical applications. By combining transactional and analytical workloads within a multi-model structure in a single engine, SingleStoreDB eliminates performance bottlenecks and unnecessary data movement. We’re the crossroads where highly performant meets highly powerful.
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Webinar Recap: Accelerating Real-Time IoT Analytics With IBM Cognos & SingleStoreDB
From energy grids to smart meters, IoT systems access millions of devices that generate large amounts of streaming data. And for some equipment, a single event can play a critical role in understanding the health of a machine or system in real time.Accelerating Real-Time IoT AnalyticsFor IoT systems that keep oil rigs running smoothly and lights on in residential areas, real-time analytics are crucial to identifying potentially harmful anomalies — creating alarms by reading meter data, and classifying unusual spikes or activity as warnings.Even more, the right technology powering these systems can illuminate faulty grids or severed lines, reducing response times to action and ensuring systems don’t stay down for excessive periods.Our webinar in partnership with IBM, “Accelerating Real-Time IoT Analytics With IBM Cognos & SingleStoreDB” explores how real-time data and analytics from energy grids, smart meters and other devices help enable a safe, sustainable environment that relies on the flawless functioning of its IoT systems.Here’s a look at some of what Sugandan Barathy, Partner Solutions Manager at SingleStore and Robery Borovsky, Senior Data & AI Technical Specialist at IBM discussed.Real-Time Analytics & IBM Data FabricThree key requirements for real-time IoT analytics‘Real time’ doesn’t mean 1, 5 or even 10 minutes from now — it means immediately and with accuracy. For analytics and applications to truly function in real time, your technology needs:Ultra-Fast Ingest. Parallel, high-scale streaming data ingest that is capable of running millions of events per second — with immediate availability.Super-Low Latency. Blazing fast queries with sub-second latencies (no waiting minutes or hours for data), and immediate consistency.High Concurrency. Unparalleled scalability with millions of real-time queries across tens of thousands of users.How SingleStoreDB turbocharges IBM Data Fabric“Data fabric is truly a set of tools to democratize data access at scale,” says Barathy. This data fabric has three main properties:Data access, which may be physical or logical — meaning it resides in disks or disparate systems.Data governance, which involves data cataloging and automationData Security, which oversees access, data privacy and usage policies.“SingleStore connects to the data fabric using IBM Cloud Pak for Data,” Barathy goes on to explain. We have built a native connector that’s GA, that’s live, as of August 2022… what that helps you to do is it helps you to bring AI to your data.”
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SingleStore Hackathon 2022
Looking for the fastest way to contribute to the 2022 SingleStore Hackathon? Well, you’re in the right place.The SingleStore Hackathon Is Now OpenHere is a step-by-step guide on how to get started — and how you can get yourself in the running for $30K in cash prizes, the opportunity to meet with the SingleStore Chief Technology Office (CTO) and staff, promotion within the SingleStore community and exclusive SingleStore swag.Getting StartedHead over to our Hackathon website to register by clicking the “Join Hackathon” button. You’ll need to sign up to create a free Devpost account, or log in with an existing account, to complete your registration. This will enable you to receive important updates and to create your submission.Signup for a free Hackathon account on SingleStore that will launch in the Cloud with a fully managed database online in minutes. This includes $500 of FREE credits, fully managed service hosted for you on AWS, GCP, or Azure, full developer and administrative access, unlimited databases and users, and full security certifications and industry compliance.Create a working software application using SingleStoreDB that fits into one of the five following Hackathon categories:Multi-model. Build a multi-model application that supports JSON, geospatial, time-series, or full-text search.Hybrid Transaction/Analytical Processing (HTAP). Build an application that requires a high-volume of transactions along with analytical queries on big data and/or streaming data.Wasm/WebAssembly. Implement any calculation or function that does not exist in SingleStoreDB today by writing a new Wasm UDF. Alternatively, create a spaceship strategy that competes in the Wasm Space Program. For either, write your code in Rust, C++, or C, and execute it in SingleStoreDB.Machine Learning (ML) / Artificial Intelligence (AI). Train and/or execute a ML or AI model entirely within SingleStoreDB.Database Migration. Migrate any application to SingleStoreDB.Submission RequirementsInclude a link to the application code on GitHub . The code repository may be public or private. If the repository is private, access must be given in the testing instructions provided with your submission. Include all deployment files and testing instructions needed for testing your application.Create a video that includes footage explaining your application’s features and functionality through a comprehensive demonstration.Include a short overview (100 word limit) and how we can contact you to award any prizes won — LinkedIn profile, Github profile and Twitter accounts are preferred.Complete and enter all of the required fields on the “Enter a Submission” page of the Hackathon Website (each a “Submission”) during the Submission Period, and follow all requirements listed on the site.Developer ResourcesHere’s all the information developers need to know to create applications using SingleStoreDB — from getting started to migrating applications, to connecting with various application development languages and tools:Connect to application development tools such as C/C++, Java, Ruby, ODBC/JDBC, Perl, Python and many more.Use SingleStore's Data API to develop custom applications and build seamless integrations with applications.Use Management API to create and manage workspaces.View samples of concurrent multi-inserts for Bash, C, C# / .NET Core, Java, Node.js and Python.Perform operations based on features like time series data analysis, full-text search, geospatial features,and window functions.View the SQL command reference.Extend SingleStoreDB with SPs, UDFs, TVFs and UDAFs.Hackathon ResourcesMulti-model: Useful resources for building a multi-model application with SingleStoreDB.ArticlesDocumentationGithubJSONJSONTime SeriesTime SeriesReal-Time Digital Marketing DemoGeospatialGeospatialGlobal Package Logistics DemoFull-Text SearchFull-Text SearchKey-Value StoreHow to Build a Key-Value API on SingleStoreDBHybrid Transaction/Analytical Processing (HTAP): Useful resources for building a Hybrid Transaction/Analytical Processing (HTAP) application with SingleStoreDB.ArticlesDocumentationGithubWhat is HTAP?Universal StorageReal-Time Digital Marketing DemoPushing HTAP Databases Forward With SingleStoreDBChoosing a Storage TypeE-Sports Analytics DemoAn Engineer's Guide to Building a Data-Intensive DatabaseData Ingest With PipelinesGlobal Package Logistics DemoCloud-Based Analytics With SingleStoreDBWasm/Web Assembly: Useful resources for using Wasm within SingleStoreDB.ArticlesDocumentationGithubSingleStoreDB Wasm TutorialCode Engine — Powered by WasmWasm Space ProgramWasm ToolkitWRIT: Test Your WasmMachine Learning / AI: Useful resources for working with SingleStoreDB and AI/ML.ArticlesDocumentationGithubImage Classification Using SingleStoreDB, Keras and TensorflowOptimized Vector FunctionsNumeric Optimization Within SingleStoreQuickstart Guide to Using SingleStoreDB, MindsDB and Deepnote for Data ScienceCode Engine — Powered by WasmSingleStoreDB Python LibraryHow to Use SingleStore With Spark ML for Fraud Detection: Part 1, Part 2 & Part 3External Functions (over HTTP)SingleStoreDB Ibis LibraryUsing SingleStore as a ML Feature StoreSingleStoreDB Spark ConnectorUsing SingleStoreDB and Spark to Build a Movie Recommender SystemDatabase Migration: Useful resources for migrating an application to SingleStoreDBArticlesDocumentationGithubMigrating From MySQLLoad Data From Any Available Data SourceDocker Image for Running SingleStoreDBMigrating From PostgreSQLTutorial: Migrating From Another DatabaseTutorial: Data Warehouse AugmentationRegister for the SingleStoreDB 2022 Hackathon TodayOur global hackathon is officially open. Register today. We can’t wait to see what you build.Official Hackathon rules.
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Full House: Developers Share 3 Signs You’ve Outgrown Your Open Source Database
A developer’s life is anything but a sitcom. But if you’re building database functionality into an application or an operating environment, you eventually may run into a host of challenges with single-node open source databases that, if you weren’t tasked with fixing them, might seem comical.
In general, database performance problems relate to data ingestion, scaling, speed and not being able to easily store all the different kinds of data you want. In this blog you’ll get a personal view into what those problems look like, as developers share their experiences with the three signs of outgrowing popular open-source databases like MySQL, PostgreSQL and MariaDB. We’ve also included some tips on what to look for in a new database solution to ease the pain.
Sign 1: Application Performance Hits a Wall
Jack Ellis is co-founder of Fathom, Inc., a SaaS firm that believes website analytics should be simple, fast and privacy focused. Fathom delivers a simple, lightweight, privacy-first alternative to Google Analytics. Jack describes how his application’s performance suffered because he had maxed out MySQL:
"Despite keeping summary tables only (data rolled up by the hour), our [MySQL] database struggled to perform SUM and GROUP BY. And it was even worse with high cardinality data. One example was a customer who had 11,000,000 unique pages viewed on a single day. MySQL would take maybe 7 minutes to process a SUM/GROUP query for them, and our dashboard requests would just time-out. To work around this limitation, I had to build a dedicated cron job that pre-computed their dashboard data."
Read the impact story: Why Fathom Analytics Ditched MySQL, Redis and DynamoDB
Josh Blackburn is co-founder and head of technology at IEX Cloud, a data infrastructure and delivery platform for financial and alternative data sets that connects developers and financial data creators. Josh’s team builds high-performance APIs and real time streaming data services used by hundreds of thousands of applications and developers. He had hit a similar wall with MySQL running in Google Cloud:
"We average about 500,000 to 800,000 data ops per second, typically during market hours. These could be really tiny requests, but you can see our ingress and egress rates; we’re consuming a lot of data from multiple resources, but we’re also passing a lot of that out the door… In our case, we’ve got to keep up not just with the stock market, with real-time prices, but also with everyone coming in and needing all that data in real time."
Josh summed up his data ingestion challenge, “We were in a tight spot to find something that would scale and had better performance, especially on the ETL side, because we’re loading hundreds of gigs of data every day.”
Read the impact story: IEX Cloud Speeds Financial Data Distribution 15x With SingleStore
Sign 2: An Open-Source Database Doesn’t Support Your Business Needs
Gerry Morgan is lead developer at dailyVest, a fintech company using 401(k) participant data and analytics to improve the health and performance of retirement plans. Each month, over 7 million investors and plan participants can access digestible insights delivered via visual dashboards.
Data volumes are growing at 36% a year, fueled by billions of transactions, and Gerry found that dailyVest’s Azure SQL database couldn’t support business growth. He said:
"[We were] not just increasing resource requirements in our cloud environment, but also increasing costs [of Azure Cloud resources]… We were also seeing some performance degradation in Azure SQL. Not so much that our customers would have noticed, but we noticed there was some drop off in speed in our ingestion of data. We wanted to improve our ETL operation, but at the same time improve the customer experience — all customers will be happy if you make things faster, even if they haven’t noticed if things were particularly slow."
Read the impact story: dailyVest Empowers 401(k) Plans for 7 Million Plan Participants
Mohammed Radwan is head of engineering at Foodics, a restaurant management software company serving more than 22,000 establishments in 35 markets. The company processes more than 5 billion orders per year, offering dashboard analytics for business owners and managers. At first, Foodics used a combination of CitusDB for and MySQL to power the business, later swapping out MySQL for a commercial version of PostgreSQL.
Foodics ran into reliability problems with CitusDB, experiencing outages that lasted three hours at a time up to four times per month. Only 200 users could concurrently use the existing system. Foodics had 5,000 customers, but downtime and a lack of fast data were accelerating churn. Although the company had just received $20 million in Series B funding in 2021, the unreliable system limited growth and put future funding at risk. Mohammed said:
"Like many tech companies, we started with MySQL. It was compatible with what we had and was easy to use. It fulfilled its purpose for a while, but when we needed to grow and expand, MySQL couldn’t enable that."
When experiencing scaling issues with MySQL or other open source databases, developers often turn to sharding middleware or NoSQL. These approaches, however, can compromise the performance of ACID-compliant transactions and complex joins, particularly in high-volume production environments.
Sign 3: You’re dealing with database sprawl
Here, the writing on the wall is clear: if you need to incorporate multiple data types into your application or environment – such as time series, JSON, documents and other specialty data types – you are going to need to spin up specialty databases to contain them. These separate databases will need to be connected, maintained and upgraded, creating database sprawl — and exponential complexity.
If your single-node database supports only standard SQL numeric data, you will likely experience significant growing pains if you try to augment it to support multiple data types.
What should you look for in a replacement database?
Most single-node open source growing pains can be solved by a database that offers:
Streaming data ingestion overcomes open source databases’ inability to ingest, process and analyze streaming data necessary to power modern interactive SaaS applications and production environments.Low-latency query performance solves query performance problems as data or concurrency demands grow.Limitless scalability addresses the struggle that single-node architectures face when attempting to scale as business or user volumes grow.Robust analytical abilities to overcome open source databases’ basic to non-existent analytical capabilities — driving fast, interactive user experiences.Hybrid workload handling to eliminate the need for separate OLTP and OLAP systems; instead, these hybrid workloads can be handled in a single, unified system.
Your list may be much more granular. Jack at Fathom had a lengthy list of non-negotiables for any database he might consider to replace MySQL:
It must be ridiculously fastIt must grow with us. We don't want to be doing another migration any time soonIt must be a managed service. We are a small team, and if we start managing our database software, we've failed our customers. We're not database experts and would rather pay a premium price to have true professionals manage something as important as our customers' analytics dataIt must be highly available. Multi-AZ would be ideal, but high availability within a single availability zone is acceptable tooCost of ownership should be under $5,000/month. We didn't want to spend $5,000 off the mark, as this would be on top of our other AWS expenses, but we were prepared to pay for valueThe software must be matureCompanies much larger than us must already be using itSupport must be greatDocumentation must be well-written and easy to understand
For Mohammed, delivering 24/7 resource availability was paramount. He said:
"We can’t take time off or delay reports. The most important thing for us is concurrency. As we grow, we need to ensure that our customer base grows with us. We needed a database that allows for seamless reporting without worrying about how many customers are using it all at once."
After all the challenges Foodics had weathered, Mohammed needed a database that would offer:
The ability to place all analytics-related data in a single unified data storeA performant analytics engine with columnstore to democratize data accessReal time and near real-time analytics with very fast reads and quick ingestioA multi-tenant architecture to use a single database for all customers Support for a large and growing customer base in the tens of thousands 100 concurrent queries per second, or approximately 1% of Foodics’ customer base at the time, to support the large number of reports being generated The capability to process billions of orders and 5 million transactions per monthScale up and out capabilities to support Foodics’ accelerated growth strategy High availability with almost zero downtime
Developers Choose MySQL Wire-Compatible SingleStoreDB
Fathom, IEX Cloud, dailyVest and Foodics all chose SingleStoreDB, a real-time, distributed SQL database, to replace open source database technology. Mohammed’s reasons why are a common theme:
"We are a small team, so we did not want to spend time tuning a database. We wanted something that just worked out of the box. For this reason, we went with SingleStoreDB Cloud running on AWS. With SingleStore, we can just plug and play and do everything we need to empower our customers. It allows us to focus on what we are really here to do: serve our customers."
SingleStoreDB is MySQL wire-compatible, making it incredibly easy to migrate from any flavor of MySQL (including AWS RDS, Google Cloud SQL, Azure MySQL or others). It supports familiar SQL syntax, so developers don’t need to learn a completely new technology to get started.
Most developers can quickly complete their migration and get started with SingleStore in hours or a few days. To learn about migration, check out these resources:
Modernization of First-Generation Systems: Migrating From MySQL: Webinar that walks through the migration processNucleus Case Study: Another developer case study about replacing MariaDB with SingleStoreDB How to Migrate From MySQL to SingleStore; A technical overview of how migration works, complete with code snippets.
After Migration, a Bigger, Better House
All of the developers experienced major improvements in speed, performance, scalability and flexibility after they migrated to SingleStoreDB. Here’s how Jack tells Fathom’s “after” story:
We no longer need a dedicated data-export environment…We do our data exports by hitting SingleStore with a query that it will output to S3 for you typically within less than 30 seconds. It's incredible. This means we can export gigantic files to S3 with zero concern about memory. We would regularly run into data export errors for our bigger customers in the past, and I've spent many hours doing manual data exports for them. I cannot believe that is behind me. I'm tearing up just thinking about it.Our queries are unbelievably fast. A day after migrating, two of my friends reached out telling me how insanely fast Fathom was now, and we've had so much good feedback.We can update and delete hundreds of millions of rows in a single query. Previously, when we needed to delete a significant amount of data, we had to chunk up deletes into DELETE with LIMIT. But SingleStoreDB doesn't need a limit and handles it so nicelyWe used to have a backlog, as we used INSERT ON DUPLICATE KEY UPDATE for our summary tables… [W]e had to put sites into groups to run multiple cron jobs side by side, aggregating the data in isolated (by group) processes. But guess what? Cron jobs don't scale, and we were starting to see bigger pageview backlogs each day. Well, now we're in SingleStore, data is fully real time. So if you view a page on your website, it will appear in your Fathom dashboard with zero delays.Our new database is sharded and can filter across any field we desire. This will support our brand new, Version 3 interface, which allows filtering over EVERYTHING.We are working with a team that supports us. I often feel like I'm being cheeky with my questions, but they're always so happy to help. We're excited about this relationship.SingleStoreDB has plans up to $119,000/month, which is hilarious. That plan comes with 5TB of RAM and 640 vCPU. I don't think we'll get there any time soon, but it feels good to see they're comfortable supporting that kind of scale. They're an exciting company because they're seemingly targeting smaller companies like us, but they're ready to handle enterprise-scale too.And as for price, we're spending under $2,000/month, and we're over-provisioned, running at around 10% - 20% CPU most of the day.
Josh from IEX Cloud summed up, “SingleStore enables us to do monitoring and analysis in the same system that houses our historical data, and this creates enormous efficiencies for us. We’ve been able to consolidate multiple databases, run our platform faster, and speed the onboarding processes for new data sets.”
If you’ve outgrown your single-node open source database and are ready to move into a bigger, better house, try SingleStore for free today.
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Webinar Recap: Introduction to SingleStoreDB
Get to know SingleStoreDB, the fastest-growing distributed SQL database to power real-time applications and analytics.In today’s data-driven world, two things are certain: Customers want their data, and they want it fast. With a unified data engine for transactional and analytical workloads, SingleStoreDB powers those fast, real-time analytics and applications customers expect. How? SingleStoreDB is built with the key features and capabilities necessary to truly deliver up-to-the-minute insights — no matter the query complexity, size or number of users. It’s where fast, unified and resilient converge to make real time a reality.But let’s take a step back: What exactly is SingleStoreDB? What makes it unique, and why is it the right database for real-time analytics and applications? Our latest webinar, “Introduction to SingleStoreDB” takes a closer look at these things — and more. Here are the highlights.A Real-Time Analytics Database for Modern ApplicationsThe challengeWe see modern applications all around us today. “We all are now in the digital service economy,” says Domenic Ravita, VP of Product Marketing & Developer Relations at SingleStore. “We can have anything we want instantaneously…and that’s not just for consumer apps — this is how business is done.” These applications are prevalent in nearly every industry, from cybersecurity to IoT, fintech, eCommerce and more. To run efficiently, modern apps must also have the ability to:Access to real-time dataDeliver fast, interactive customer experiencesScale effortlesslyRun anywhere, anytimeYet modern, real-time applications also come with complexities and challenges — something organizations tend to solve for this by constantly adding (or ‘stitching’) various technologies and data stores together. This includes open-source databases like MySQL, PostgreSQL and MongoDB, as well as data warehouses like Snowflake. The problem? These individual data stores simply aren’t powerful enough to deliver the real-time experiences for your applications, APIs and dashboards. And, constantly adding new technologies to accommodate required functionalities ends up being extremely costly for businesses.
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Webinar Recap: Getting Started in SingleStoreDB
From IoT to fraud analytics, and cybersecurity to retail, today’s modern, data-intensive applications need access to fast analytics in real time.
Yet legacy data architectures — and single-node, open-source databases — aren’t equipped to handle the fast-moving data streams necessary for real-time analytics.
Up-to-the-minute insights require a database that powers low-latency access to large datasets. With a unified data engine for transactional and analytical workloads, SingleStoreDB powers real-time analytics and applications. Say hello to real time, unified, distributed SQL.
Led by Senior Technical Evangleist Akmal Chaudhri, “Getting Started With SingleStoreDB'' gives you an in-depth look at SingleStoreDB — including an introduction to real-time distributed SQL, the unique features and capabilities in SingleStoreDB, using connectors like Spark and Kafka, and how to get your OLAP & OLTP workloads up and running.
Here’s a look at the highlights:
Real-Time Analytics for Data-Intensive Applications
It’s no secret data volume and complexity are rising, and businesses need insights to drive real-time actions.
“It is a much more competitive world,” says Chaudhri. “Business pressures, de-regulation in many industries, there’s a lot of competitive pressures among organizations to be able to be innovative.” Combine that with how analytics have evolved and the demand for data, and you have a recipe for data intensity. The problem, however, is applications struggle to keep up. Sluggish event-to-insight response, increasing costs and complexity and growing demands for concurrency place a harsh spotlight on existing technology stacks that simply aren’t equipped to handle the five key requirements of data-intensive applications:
Data SizeSpeed of IngestionLatency RequirementsQuery ComplexityConcurrency
Data-Intensity Assessment: How Data Intensive Are Your Applications?
What Makes SingleStoreDB Unique?
SingleStoreDB is the #1 database designed for data-intensive applications. As a real-time, distributed SQL database, one of the key features that makes SingleStoreDB unique is Universal Storage, a patented, single table type for transactions and analytics. By combining rowstore and columnstore capabilities, SingleStoreDB enables both OLAP and OLTP workloads in a unified data engine — a move other database technologies are aiming to replicate.
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Engineering
Guest Engineer Showcase: Vedran Cindric
This edition of SingleStore’s engineer showcase puts a (guest) spotlight on Vedran Cindric, a Croatia-based founder and CEO. Hear how Vedran got his start in tech, his favorite tools, why he chose SingleStore, advice to developers navigating a crowded market and more.
Q: What’s your name, company and title?
A: My name is Vedran, full name Vedran Cindrić, and I'm the Founder and CEO of Treblle, an API observability platform.
Q: Where are you located?
A: I’m originally from a tiny town in Croatia but moved to the capital of Croatia, Zagreb. I came to study and stayed there for the past 12 years.
Q: Give us a bit of background on yourself: the project, app or product you're working on, and how you chose SingleStore.
A: I’ve always been interested in computers. I started early on, like really early — around when I was 5. Back then I used to play PC games from floppy discs, and slowly started to code because I wanted to fix and hack a few games. As time passed I got interested in front-end development, back then that only meant HTML, some CSS and literally a whiff of JS. I started building websites for fun and moved over to learning PHP and MySQL by the time I was 15. The combination of the two always amazed me because it allowed me to create something from start to finish without anybody's help.
The only thing I was missing was design, so I went to study IT with a focus on design. The first year of college I met my current co-founder Darko. After we finished studying together we opened a development agency and ran that for the past 10 years. We’ve done more than a 100 websites/apps/projects with startups, as well as big name brands. We spent a lot of time working on APIs and we got tired of manually writing documentation, spending hours on debugging calls, providing API integration support, monitoring, measuring…So we started building a tool that would do all of that for us. You all know that as Treblle today 😃.
At its essence, the idea behind Treblle was to allow anyone working with the API to be able to see requests as they are happening, in real time. Emphasis on real time. So today you add Treblle to your API using one of our many SDKs and out of the box you get things like: real-time API monitoring, logging an error tracking, auto-generated docs, API analytics, quality scoring, testing tools and more…
Since we built our entire platform using PHP, MySQL, and Laravel, we needed to solve many traditional scaling issues, the main one of course being MySQL. I’m a big fan of MySQL, even though we’ve spent so much time trying to scale it. That’s essentially how I discovered SingleStore. When I landed on the website and saw the illustration I knew it was for us.
Q: What is in the future for your project? Any other plans for using SingleStore?
A: We’re working on what we call “v2” of our product, which will launch in October of this year. It will include a lot of cool and real-time things that we can actually now use because we switched to SingleStore. The new version of the product will put an even greater focus on making everyday life easier for anyone working in the API lifecycle.
Given the performance of SingleStore we plan to build our own segmentation tooling that connects directly to our database — so we don’t need to pay or integrate any other third party tools for that.
Q: What is SingleStore's biggest strength, weakness, or something you wish was different?
A: It has to be the performance. When you’re dealing with a high volume of data, SingleStore beats any other cloud service we tried. It’s allowed us to add so many analytical features into our product because it’s so fast and performant. I do wish SingleStore had foreign key support just like MySQL does. That’s my number one thing. Again, we can live without it but it would be nice. Another thing would be that we can change column types for COLUMNSTORE tables.
Q: What is your favorite thing about SingleStore? What made you want to use it?
A: For me the most important part was that it had MySQL syntax and it scales much better than your traditional run-of-the-mill MySQL database. As soon as I saw that SingleStore can handle 10M+ TPS — and usually we would get 1M TPS on our databases — I was sold.
Q: Aside from SingleStore, do you have any favorite frameworks, languages or tools?
A: Our entire platform is built on top of Laravel, and we are really huge fans of Laravel at Treblle. We do use a lot of different tools that make our life easier on a daily basis, but for me Laravel and Laravel Vapor have allowed us to build Treblle so they have to be my favorite things.
Q: What advice would you offer developers as they navigate the crowded, often confusing database market?
A: The database is a key part of anyone's infrastructure and I think any developer should spend some time thinking about what their needs are today, and what they will be tomorrow. It’s hard but try to predict the things you’ll need and the volume you’ll need to handle, and test out as many providers as you can. See who fits the bill and go with them. Always test everything before making a decision and try to use as real of a dataset as possible.
Q: What is the best piece of developer advice you’ve ever received?
A: I’d have to highlight two pieces of advice that I think many developers overlook. Think first, code later would be the first. It sounds simple but is actually super powerful and interesting. As developers we like to start writing code as soon as possible, but sometimes you can save yourself a lot of time and money when you just think some things through before you start writing any code. Organize what you wanna do, almost write the code in your head and simply type it in your IDE.
The second one has to be about stepping back. Many times as a developer you’ll find yourself in a situation where you can't make something, fix something and things just don’t go your way. The best idea is just to step back, move away, go do something else and come back to it with a fresh pair of eyes as they say.
Q: What technology can you not live without?
There are many things I could write here but I owe a lot to PHP. It’s literally been putting food on my table for the past 15 years and helped me complete around 100 different projects with our agency, as well as launch our startup. What I love about it the most is that PHP grew as I grew — and became 10x the language it was when I started.
Get started with SingleStoreDB
SingleStoreDB is capable of processing massive data volumes and complex analytical queries in ultra-fast timeframes. Start adapting quickly, embracing diverse data and accelerating your innovations.
Try SingleStoreDB free today.
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MySQL Create Table
Want to learn more about the MySQL create table function? We're breaking down what it is, structure, best practices and more. MySQL Create Table — What Is It?To create a Relations (Tables) inside MySQL, the Create query is used with additional keyword Table, with the arguments consisting of column names and their respective data types.MySQL Create Table — Structure & CasesThe syntax for MySQL create table is the following:CREATE TABLE TableName (list of Column names and their attributes separated by comma);For instance, if you wanted to create an employee table with three columns, you’d do so with the following format:CREATE TABLE employee (Employee_ID int(8) Auto_increment, Employess_NAME VARCHAR(50) NOT NULL, Designation VARCHAR(10) NULL)From the MySQL statement above, we have the following to consider while using the CREATE Table statement:The keywords within the statement are separated by a space.Table names and column names cannot have escape characters, i.e., space, comma, quotes, slashes, etc.Column names are followed by data types, and enclosed parentheses contain the length — which determines the maximum value length the table will be able to handle for a particular column.Data types are followed by field/column attributes. These attributes specify various constraints on the field, such as NOT NULL which means column value cannot be empty for insertions or updates.Please see the following figure for further clarification of the MySQL `CREATE TABLE` statement.
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MySQL Server — A Good Choice for DBMS
With more than 100 million downloads, MySQL is the open-source relational database management system with the fastest growth.MySQL Server — A Good Choice for DBMSLooking for a better experience with your database management system? An efficient and easy-to-use database management system can save you a lot of time and money. A database management system allows you to manage and administer databases and thus, have a significant effect on daily business operations. A poor system can cause severe issues like activity lag and bad user experience, and you would surely want to avoid that. Here is why a MySQL server might be worth considering.MySQL ServerWith more than 100 million downloads, MySQL is the open-source relational database management system with the fastest growth. MySQL stands for “My Structured Query Language”. Many major websites, including Facebook, Wikipedia, Twitter, YouTube, Flickr and others presently utilize it as their preferred database for usage in online apps. The most-used standard language for accessing MySQL databases is SQL.MySQL server offers a database management system with querying, connection, good data structure capabilities and the ability to integrate with several platforms. In extremely demanding production applications, it can reliably and swiftly handle massive datasets. MySQL server also offers a variety of useful features, including connection, speed and security, which make it excellent for database access.Top Reasons to Choose MySQL ServerThere are various reasons you might want to consider a MySQL server. However, the most critical ones are:Open SourceThis implies that the MySQL server’s basic version can be installed and used by anybody, and that the source code can be altered and customized by outside parties. Advanced versions include tiered price structures that include more capacity, tools, and services.AvailabilityYou can rely on MySQL to ensure continuous uptime because of its steadfast dependability and unwavering availability. High-speed master/slave replication setups and specialized Cluster servers that allow fast failover are just a few of MySQL’s high-availability choices.CompatibilityOne of the core benefits of using a MySQL server is that it is highly compatible with diverse systems, languages and data models that include other DBMS alternatives, SQL and NoSQL databases, and cloud databases. MySQL also includes a wide range of database architecture and data modeling features (e.g., conceptual or logical data models). As a result, it becomes a straightforward and useful alternative for many enterprises, all while alleviating concerns about becoming “locked in” to the system.Management EaseAnother key feature of the MySQL server is that the average time from software download to installation completion for MySQL is less than 15 minutes, which is an amazing quick-start capacity. No matter the operating system — Microsoft Windows, Linux, or any other—this rule applies. Self-management features, including automated space expansion and dynamic configuration changes, can significantly ease your workload once deployed. As a DBA, you can manage, debug, and oversee the functioning of several MySQL servers from a single workstation, thanks to the comprehensive array of graphical administration and migration tools that MySQL offers.Final WordsMySQL server is undoubtedly the best choice for you to have a speedy DBMS that brings great value to your data and business operations. Its open-source availability, along with high compatibility and management ease, enhances its performance considerably. Besides, all these features combine to reduce the hassle of database management and daily data flow. You can simply begin working with the MySQL server by downloading the latest version and building and loading the server.One issue with MySQL, PostgreSQL and other legacy incumbent databases is that they often bottleneck with streaming ingest and have problems scaling– this issue creates a “price for performance” problem for high-growth data-intensive applications which makes the free open-source DB options inadequate. Enter: SingleStoreDB.SingleStoreDBSingleStoreDB is a real-time, distributed SQL database that unifies transactions and analytics in a single engine to drive low-latency access to large datasets, simplifying the development of fast, modern enterprise applications. SingleStoreDB provides support for large scale databases with analytics and takes care of most configuration, and also supports various distributions for deployment.SingleStore is MySQL wire compatible and offers the familiar syntax of SQL, but is based on modern underlying technology that allows infinitely higher speed and scale versus MySQL. This is one of the many reasons SingleStore is the #1, top-rated relational database on TrustRadius.ResourcesThree Common MySQL ErrorsMySQL Error: “too many connections”MySQL Error Command “error 1016 can’t open file”Connect With MySQLModernization of First-Generation Systems: Migrating from MariaDBUse cases of SingleStore and MariaDB
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Speed Up Database Queries MySQL
Today we will talk about improving the speed of queries in MySQL. To do so, we’ll use a demo database that provides the same MySQL main page with the following structure:
ENTITY RELATIONSHIP DIAGRAM
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How to Optimize MySQL Indexes for Better Performance
We're breaking down how to optimize MySQL indexes, and get better performance out of your server!How to Optimize MySQL Indexes for Better PerformanceIf you're using MySQL to host your website, you may have noticed that it can run slow at times, even when nothing significant is happening in the background. The most common reason for this is a poorly optimized MySQL database, which might result from an improperly structured database or some missing indexes. Fortunately, fixing this problem is pretty easy when you know what to do and which indexes to add or modify. Keep reading to learn more about how to optimize MySQL indexes and get better performance out of your server!What Is an Index?An index is an information shape that expedites information retrieval for a database desk on the cost of extra writes and garage area to keep the index information. Indexes are used to locate rows in a database table quickly. Without an index, MySQL must begin with the first row and then read through the entire table to find the relevant rows. The larger the table, the more time this takes.Types of IndexesThere are three types of indexes in MySQL:Primary. A primary key is a single column or combination of columns uniquely identifying a table row.Unique. A special key is a column or combination that ensures that no two rows in a table have the same value.Index. An index is a column or combination of columns that allows you to find data in a table quickly.Creating well-organized tables with appropriate indexes is the best way to get the most out of your database. However, there are many times when it's not practical or possible to create an index. In these cases, MySQL will automatically generate an index for you. Sometimes this automatic indexing can be less than optimal for performance depending on the type of query that should be run against the database.Which MySQL Index Should I Use?When choosing the right index type for your data, there are a few factors to consider. The first is the type of data you're working with. If you're working with a lot of numerical data, a numeric index might be the way to go. Similarly, a full-text index might better suit your needs if you have a lot of text data. A second factor to consider is the size of the data set. If you have a large amount of data, you'll want to choose an index that can scale well. Finally, you'll want to consider the performance of your queries. If you need fast query performance, you'll want to choose an index that can help speed up your questions.Maintaining Your IndexesIt's essential to keep your indexes up-to-date and relevant to your data. Here are some tips on how to optimize MySQL indexes for better performance:Know your dataThis is the primary and maximum critical step in optimizing your index. You need to know what data you have, how it's structured, and how it's accessed. Without this knowledge, it's impossible to create effective indexes.Resizing Your IndexesIf the index is too small, it will have to read from the disk more often, affecting performance. On the other hand, if the size is too large, it will use more memory than necessary. So how do you recognize which length is proper for you? There's no easy answer to this, but a good rule of thumb is that your index should be about the size of the data it contains.ConclusionIn conclusion, optimizing your MySQL indexes can be a complex and time-consuming task. However, it can significantly improve database performance, so it's worth it. Using the right tools and techniques will ensure optimal use of your indexes and improve the overall performance of your system.SingleStoreDBSingleStoreDB is a real-time, distributed SQL database that unifies transactions and analytics in a single engine to drive low-latency access to large datasets, simplifying the development of fast, modern enterprise applications. SingleStoreDB provides support for large scale databases with analytics and takes care of most configuration, and also supports various distributions for deployment.SingleStoreDB is MySQL wire compatible and offers the familiar syntax of SQL, but is based on modern underlying technology that allows infinitely higher speed and scale versus MySQL. This is one of the many reasons SingleStoreDB is the #1, top-rated relational database on TrustRadius.ResourcesConnect With MySQLHow to Migrate From MySQL to SingleStoreCreate MySQL Database: A Step-by-Step Guide
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Create MySQL Database: A Step-by-Step Guide
Here is a step-by-step guide for you to create a MySQL database — and how SingleStoreDB is a great complement to your open-source database. Create MySQL Database: A Step-by-Step GuideCreating a database in MySQL can be a challenging job if you are a newbie. To help, here is a step-by-step guide for you to create a MySQL database. This article will make the task easier and simpler for you.As a developer, you must be familiar with databases. If not, databases are an organized collection of data stored in the information system. Databases ensure that the data can be stored in a way that, when required, can be retrieved with ease and speed. Wondering how to create a MySQL database? There are two popular methods to create a MySQL database: command line interface (CLI) and MySQL Workbench.Taking the First StepThe first step to creating a MySQL database is downloading the MySQL server. MySQL provides an open-source version of MySQL with almost all the features you need. You can easily download the MYSQL server using this link.Choosing between CLI and MySQL WorkbenchThe next step in creating a database is choosing if you would like to work with CLI or MySQL Workbench. A lot of developers often prefer using CLI for creating and designing databases. CLI offers a typical look of a development environment that appeals to most developers. In CLI, you must enter all your commands in the command line prompt, or MySQL Command Line Client.However, MySQL Workbench can do the job if you are looking for a simpler and easier way to create a database. MySQL Workbench is a graphical and visual tool offered by MySQL for creating, managing and administering MySQL databases. Therefore, it can be the best choice for you if you are just starting out — since it provides all database handling features in one place.Logging Into MySQL ServerBefore beginning, you must log into the MySQL server as a user that has the privilege to create a database. Using the CLI, you can use the following command to log into the relevant user:mysql -u root -pWith MySQL Workbench, you can set up the connection with the MySQL server by clicking on MySQL connections. The following window will open up:
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MySQL Data Sharding
Learn more about MySQL data sharding — including what it is, specific sharding techniques, pros and cons of using sharding, and more.MySQL Data Sharding: What Is it?As data increases in MySQL, it’s not uncommon for schema performance to deteriorate. This deterioration is caused by:Increase in Throughput. As data volume increases, the size of indices also grows — and at a certain point, simple queries have unprecedented return times.Data Redundancy. One traditional optimization technique is to repeat data instead of using foreign keys, reducing return time for queries. But, it’s a poor practice that diminishes the purpose of relational databases.Bottlenecks. As data volume increases bottlenecks occur in batched tasks, backups or any intensive task associated with the database. This can result in an overall downgrade in performance and efficiency of the system.Storage Congestion. Single server optimization exponentially increases database size, which considerably impacts database performance.Sharding can be used to overcome these challenges. Data sharding is a technique where data is split into mutually exclusive segments, which is achieved by splitting tables into horizontal chunks. In a distributed environment, these chunks can be placed on partitions — and then nodes — which would balance the throughput.MySQL Data Sharding: What Are the Sharding Techniques?Before we discuss practical steps for sharding, let’s briefly take a look at different types of sharding: Hash Sharding. Hash functions are used for distribution of data partitions, and placement of data in those partitions.Range Sharding. In range sharding, a particular length is defined for a partition, which consists of a range of keys. Partitions do not need to be equal in length.Geo Sharding. In this data split, stored procedures are used to format data into its required form, and then distributed among partitions.In MySQL, sharding can be achieved with the following steps:Key Selection. This step can be deployed using either hash or range techniques (depending on the use case). Security-intensive applications often use hash functions.Schema Modifications. Depending on the Key section, the schema needs to be modified. This can be accomplished with ALTER commands.Distribution on Nodes. A scheme needs to be created at the application layer, which places data in the correct partition and retrieves it when required.MySQL Data Sharding: What Are the Pros & Cons?The follow are pros for MySQL data sharding:Sharding reduces throughput considerably when applied properly.Sharding can help reduce your storage footprint.Sharding allows node balancing — and if shards are optimally placed, users can have access to relevant data and the ability to handle complex queries.There are some instances where MySQL data sharding is not the best approach, and only presents further challenges:Establishing an analytics interface over a sharded database is very difficult due to limitations on JOINS and Aggregations.Sharded databases in MySQL are mostly ACID (Atomicity, Consistency, Isolation and Durability) compliant.MySQL does not provide automated sharding — sharding is normally implemented at the application layer. That means development teams are responsible for the entirety of sharding and maintenance. As such, MySQL is not suited in situations where sharding is required.SingleStoreDBSingleStoreDB is a real-time, distributed SQL database that unifies transactions and analytics in a single engine to drive low-latency access to large datasets, simplifying the development of fast, modern enterprise applications. SingleStoreDB provides support for large scale databases with analytics and takes care of most configuration, and also supports various distributions for deployment.SingleStore is MySQL wire compatible and offers the familiar syntax of SQL, but is based on modern underlying technology that allows infinitely higher speed and scale versus MySQL. This is one of the many reasons SingleStore is the #1, top-rated relational database on TrustRadius.Additional ResourcesSingleStore DocsSingleStoreDB ShardingSingleStoreDB Understanding Shard Key SelectionConnect with MySQL
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![The Data [r]evolution Is Coming](https://images.contentstack.io/v3/assets/bltac01ee6daa3a1e14/blte642b4223f7e2146/6433d90bcbf631109cafa97c/featured_the-data-revolution-is-coming.png?width=417)
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The Data [r]evolution Is Coming
Whether you’re a developer, engineer, IT or business leader, the ability to supercharge real-time customer experiences is more important than ever before.
On July 13, SingleStore is diving into all things real time with our Summer 2022 launch event, [r]evolution. This free, virtual event will highlight next-level innovations in SingleStoreDB — the #1 database for unifying transactions and analytics.
From first-look product demos to industry deep dives with leaders across fintech, cybersecurity, IoT and more, [r]evolution 2022 unlocks unprecedented access and insight to all things real-time data.
The event will kick off with a main session hosted by SingleStore CEO Raj Verma, and how we’ve crossed the real-time rubicon. You’ll also hear from engineers on our Launch Pad team as they demonstrate new features in SingleStoreDB including Workspaces, Code Engine powered by Wasm, Data APIs and more.
After the main session concludes, you’ll have the opportunity to choose your own adventure and join one of two breakout tracks. Here’s more info on what you’ll find in each:
Breakout Session A: Developers and Engineers
Host: Adam Prout, CTO at SingleStore
This session for developers, architects and engineers will take you on an under-the-hood guided tour of SingleStoreDB and its architectural design. This hour includes everything from building a database for real-time applications to a deeper dive at the newest product features in SingleStoreDB, and a partner showcase with MindsDB CEO Jorge Torres.
Breakout Session B: IT and Business Leaders
Host: Oliver Schabenberger, CIO at SingleStore
This session for IT and business leaders focuses on how to build a framework for measuring digital maturity and resilience. During this hour, you’ll get a look at data-intensive applications in action, customer and partner showcases with IBM, Siemens and Impact.com, and a fireside chat with John Foley, founder and editor of the Cloud Database Report.
Additionally, all attendees will be entered into a raffle for a chance to win a pair of Apple Airpods Pro, SingleStore swag and a grand prize summer vacation package!
Register today
Can’t make the live event? Register anyway, and we’ll share on-demand content with you as soon as it’s available.
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MySQL Error 2002 (hy000): can’t connect to local mysql server through socket
Getting a MySQL error 2002? We break down what this error means, and steps you can take it resolve it.MySQL Error 2002 (hy000) — What Is It?MySQL error 2002 refers to connection problems that arise either at the time of connection, or when a query is being executed. MySQL allows a dedicated server connection by using a socket. The official error reference is:MySQL Error 2002(CR_CONNECTION_ERROR) Can't connect to local MySQL server through socket '%s' (%d)MySQL Error 2002 (hy000) — What Are the Causes?The issue can be thrown due to various reasons, including:MySQL server crash. If the MySQL server crashes for any reason, the socket connection established in the handler will be broken, resulting in error 2002.Access issues. If authenticated user credentials are revoked post connection, the socket connection will be aborted.Version conflicts. Conflicts in configurations, versions and query formats could also lead to error 2002.MySQL Error 2002 (hy000) — SolutionsThe following are possible solutions for error 2002 (based on the situation):Validate the MySQL server status. Check if the MySQL server is functional — if not, be sure to start the server. On occasion, restarting the server also fixes the problem. If the issue persists, check the access and error log to uncover specific problems and take appropriate action.Fix any configuration conflicts. If the MySQL is working properly, check configuration for access control, MySQL may be blocking your database handlers access. Once configuration is fixed remember to restart MySQL server for changes to take effect.Connection errors, like MySQL error 2002 can break the flow of the entire service. However, by adhering to best practices and stability precautions, you can work to avoid the error.SingleStoreDBSingleStoreDB is a real-time, distributed SQL database that unifies transactions and analytics in a single engine to drive low-latency access to large datasets, simplifying the development of fast, modern enterprise applications. SingleStoreDB provides support for large scale databases with analytics and takes care of most configuration, and also supports various distributions for deployment.SingleStore is MySQL wire compatible and offers the familiar syntax of SQL, but is based on modern underlying technology that allows infinitely higher speed and scale versus MySQL. This is one of the many reasons SingleStore is the #1, top-rated relational database on TrustRadius.Additional ResourcesMySQL Error: 'the table is full'Connect with MySQLMySQL Error: Out of MemoryHow to Migrate From MySQL to SingleStore
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MongoDB vs. MySQL
Learn more about these open-source databases including advantages, disadvantages and uses.MongoDB vs. MySQL: What Are They?MongoDBMongoDB is a free and open-source NoSQL database. As the name ‘NoSQL’ suggests, it is a ‘non-relational/non SQL’ or ‘not only SQL’ database. Its approach to storing data is different from traditional relational databases where tables and rows have been replaced with JSON-like documents. The actual format is binary JSON or BSON.MongoDB uses collections instead of RDBMS tables, where each collection holds tens of JSON-like documents consisting of key-value pairs. It provides greater flexibility in storing data where the shape of the key-value pairs can be different from one document to another within the same collection. The scalability is easily achieved with MongoDB databases because the documents are self describing, and there is no need for costly schema migrations as in relational databases.MySQLMySQL is a widely known relational database management system(RDBMS) that is free, open source and owned by Oracle. It has almost the same set of features and behaviors as other relational database systems: the database is primarily based on the tables and rows, uses primary keys and foreign keys to maintain the relationships between tables and structured query language(SQL) is used to manipulate data.Whenever a consumer needs to fetch data rows, a SQL query has to be constructed by joining tables, and adding filters and conditions accordingly. Additionally, relational systems store data that adheres to a predefined database schema. It is a must to match the data objects with this schema to be stored in the relational database. It can be seen as a good safety measure but the flexibility is a trade-off. If there is a need to store data with a new format, the schema has to be changed or migrated which is complex and costly as the database size grows.MongoDB vs. MySQL: What Are Their Histories?
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MySQL Injection Attack
Find out more about what happens during a MySQL injection attack, where your database might vulnerable and what you can do to prevent it. MySQL Injection Attack: What Is It?An injection attack uses available paths to retrieve data from the database, and either hijack or attack the integrity of the data. Injection attacks are also used to scrap all privileged database information — like lists of users and their personal information. One of the most common ways for an injection attack to work is by using the flaws of the implementation and introducing a query inside the input. The code is then executed and the attacker can retrieve the target from the response. What Are The Ramifications of a MySQL Injection Attack?The following highlight a few critical ramifications of a MySQL injection attack:Query parameter possibilities. Attackers can utilize trial and error tactics to determine the possibilities of injection they can achieve — and if they can fully attack the database.Access hijacking. Access hijacking is done for numerous reasons, like exposing site vulnerabilities to general users, data theft and server hijacking.Critical data theft. One of the most common reasons for injection attacks is to steal secure, critical data including user profile information and financial data.Denial of service. Denial of Service (DOS) is the most commonly known services hack. Service is blocked for regular or subscribed users — which for some organizations, can lead to serious financial losses.Traps. Once a pattern has been established traps can be set for the system, allowing hackers to execute damaging queries at a later time.How to Prevent MySQL Injection AttacksYou can take the following steps in MySQL to secure your system against injection attacks:Input validation. Define a set of possible inputs in the implementation, and validate all inputs before executing a query.Input checking functions. Define a set of characters that are not allowed as a parameter, and use prepared statements wherever possible.Validate input sources. Only a set of pre-defined sources should be allowed to access the database — all others requests should be blocked.Access rights. A predefined access list should be maintained, and each access instance should be logged at the application layer.Security precautions. When setting up your database, be sure to configure it with proper security precautions in the production environment.SingleStoreDBSingleStoreDB is a real-time, distributed SQL database that unifies transactions and analytics in a single engine to drive low-latency access to large datasets, simplifying the development of fast, modern enterprise applications. SingleStoreDB provides support for large scale databases with analytics and takes care of most configuration, and also supports various distributions for deployment.SingleStoreDB is MySQL wire compatible and offers the familiar syntax of SQL, but is based on modern underlying technology that allows infinitely higher speed and scale versus MySQL. This is one of the many reasons SingleStore is the #1, top-rated relational database on TrustRadius.Data Security in SingleStoreDBSingleStore takes an all-encompassing approach to security, reliablity and trust. From industry-leading security certifications to full access controls, we protect the integrity of your — and your customers data. Read more about our comprehensive data security. Additional ResourcesSingleStoreDB: Using Prepared StatementsConnect with MySQLThree Common MySQL ErrorsMySQL Error 2002
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MySQL Optimize Table: How to Keep Your Database Running Smoothly
How do you keep MySQL running smoothly? Start with a MySQL optimize table. Read on to learn how to optimize your MySQL database. MySQL Optimize Table: How to Keep Your Database Running SmoothlyWhen it comes to web hosting, the two most important things are speed and stability — and optimizing your tables in MySQL can help you with both. Having an optimized database helps keep your website running smoothly (even when you're under heavy traffic) and ensures your customers don't have to wait longer than they should for their pages to load. Here's how to optimize your tables in MySQL.Why Do I Need To Optimize My Database?Your database is like a car. The more you use it, the more wear and tear it experiences. Over time, this can lead to performance issues. Just like you’d service your car to keep it running smoothly, you need to optimize your database periodically to keep it running at peak performance.There are two ways to optimize your MySQL databases:MySQLDump. MySQLDump dumps all the data in the selected table (or tables) into one large file. It does not reorganize or compress the data, but rather exports it in an easy-to-read format that can be imported back into MySQL using LOAD DATA INFILE or other commands.OPTIMIZE TABLE. OPTIMIZE TABLE runs a query on each table to find out which rows are least used. Those rows are then dropped from tables, allowing them to shrink in size and be scanned faster by MySQL when looking for specific information.Fixing Slow QueriesIf your MySQL database runs slowly, you can do a few things to speed it up — like optimizing your tables. This process can help improve performance by reorganizing how data is stored and accessed. To optimize a table, you can use the OPTIMIZE TABLE command. This will analyze the table and make changes to improve performance.You should also run this command regularly as part of your maintenance routine, keeping your database running smoothly and avoiding slowdown issues.Strategies for Reducing Disk StorageWhen it comes to reducing disk storage, the first step is understanding what is being stored on your disk.Once you know what is taking up space, you can take steps to reduce the amount of data stored.One way to reduce disk usage is by compressing data.Another strategy for reducing disk usage is by eliminating duplicate data.You can also reduce disk usage by partitioning data.Finally, you can also take advantage of caching and indexing to help reduce disk usage.By following these strategies, you can keep your MySQL database running smoothly and efficiently.Best Practices for MySQL TablesIn MySQL, you can use the OPTIMIZE TABLE statement to defragment tables. This can improve performance by making it easier for the server to find data.You should regularly check for fragmentation and optimize tables as needed. The frequency depends on how often the data changes.When optimizing a table, MySQL will create a new table copy with the same data without fragmentation.If you have a lot of data, this process can take some time and may impact performance while it's running. For this reason, it's best to schedule optimization during off-peak hours.Find and Clean Up Bad IndexesAs data is added, deleted and updated in a MySQL database, indexes can become fragmented. This can lead to performance issues as the database tries to search through indexes that are not organized properly. Optimizing tables by defragmenting indexes periodically is essential to keep your database running smoothly. You can use the OPTIMIZE TABLE command to do this.Final Tips for MySQL TablesIf you're using MySQL, make sure to optimize your tables regularly. Doing so can help keep your database running smoothly and avoid any potential performance issues.To optimize a table, use the OPTIMIZE TABLE command. This will help improve the performance of your database by defragmenting the data and index pages.Make sure to run the OPTIMIZE TABLE command on all of your tables, not just those that are frequently accessed. Doing so will help keep your entire database running smoothly.In addition to running the OPTIMIZE TABLE command, you should also consider running the ANALYZE TABLE command. This will help provide statistics about your tables that the optimizer can use to improve performance.
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Twitter Space Recap: Bringing the Heat on Data Intensity
There’s recently been a massive wave of activity and announcements in the database space. From new features to future product roadmaps, database providers are placing their stakes in the ground to be among the leaders in a real-time application market that continues to heat up.
In just the last few weeks, we’ve seen:
Google announce their OLTP database, AlloyDB. A fully managed, PostgreSQL-compatible database, AlloyDB aims to simplify enterprise-grade workloads with features like elastic storage and compute, intelligent caching and AI/ML capabilities.MongoDB reveal Column Store Indexes. Expected to be available later this year, this feature will allow users to create and maintain a purpose-built index that speeds up analytical queries without requiring document structure changes.Snowflake launch Unistore, their take on combining transactional and analytical workloads together in a single platform.
Well, that last one sounds awfully familiar…
With a single table type released in 2019 called Universal Storage, and hundreds of customers in production using it, SingleStoreDB is the #1 database for unified operational and analytical processing. We’ve long known that database unification can be done — and by carefully understanding the requirements of both transactional and analytical databases, have blazed a trail into the data-intensive era.
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