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Brokerages Anticipate MongoDB, Inc. (NASDAQ:MDB) to Announce -$0.10 Earnings Per Share

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Equities research analysts predict that MongoDB, Inc. (NASDAQ:MDBGet Rating) will announce earnings of ($0.10) per share for the current fiscal quarter, Zacks reports. Five analysts have made estimates for MongoDB’s earnings. The lowest EPS estimate is ($0.12) and the highest is ($0.08). MongoDB posted earnings of ($0.15) per share during the same quarter last year, which indicates a positive year over year growth rate of 33.3%. The business is scheduled to announce its next quarterly earnings results after the market closes on Monday, January 1st.

According to Zacks, analysts expect that MongoDB will report full-year earnings of ($0.39) per share for the current fiscal year, with EPS estimates ranging from ($0.43) to ($0.33). For the next year, analysts forecast that the company will report earnings of $0.04 per share, with EPS estimates ranging from ($0.11) to $0.19. Zacks’ EPS averages are a mean average based on a survey of analysts that that provide coverage for MongoDB.

MongoDB (NASDAQ:MDBGet Rating) last released its earnings results on Tuesday, March 8th. The company reported ($1.20) earnings per share (EPS) for the quarter, topping analysts’ consensus estimates of ($1.25) by $0.05. MongoDB had a negative return on equity of 66.70% and a negative net margin of 35.12%. The business had revenue of $266.50 million during the quarter, compared to the consensus estimate of $243.42 million. During the same period last year, the business posted ($1.01) earnings per share. The business’s revenue for the quarter was up 55.8% on a year-over-year basis.

MDB has been the subject of a number of recent research reports. Needham & Company LLC decreased their target price on MongoDB from $626.00 to $362.00 and set a “buy” rating for the company in a research report on Wednesday, March 9th. Canaccord Genuity Group reduced their price target on MongoDB from $560.00 to $400.00 in a research report on Wednesday, March 9th. Barclays reduced their price target on MongoDB from $410.00 to $330.00 and set an “overweight” rating for the company in a research report on Friday, May 20th. Morgan Stanley reduced their price target on MongoDB from $475.00 to $378.00 and set an “overweight” rating for the company in a research report on Thursday, May 19th. Finally, Oppenheimer reduced their price target on MongoDB from $510.00 to $410.00 in a research report on Wednesday, March 9th. One investment analyst has rated the stock with a sell rating, one has given a hold rating and sixteen have issued a buy rating to the company’s stock. Based on data from MarketBeat.com, the company currently has a consensus rating of “Buy” and an average price target of $483.83.

NASDAQ:MDB traded up $17.00 during trading hours on Monday, reaching $250.06. The stock had a trading volume of 83,065 shares, compared to its average volume of 1,431,503. The company has a debt-to-equity ratio of 1.70, a quick ratio of 4.02 and a current ratio of 4.02. The stock has a 50 day simple moving average of $349.73 and a 200 day simple moving average of $407.40. MongoDB has a 12-month low of $213.39 and a 12-month high of $590.00. The stock has a market cap of $16.90 billion, a P/E ratio of -52.76 and a beta of 0.98.

In other news, CEO Dev Ittycheria sold 35,000 shares of the business’s stock in a transaction dated Thursday, May 5th. The stock was sold at an average price of $321.10, for a total value of $11,238,500.00. Following the completion of the sale, the chief executive officer now owns 204,744 shares of the company’s stock, valued at approximately $65,743,298.40. The transaction was disclosed in a filing with the SEC, which is available at this hyperlink. Also, Director Dwight A. Merriman sold 14,000 shares of the company’s stock in a transaction that occurred on Monday, May 2nd. The shares were sold at an average price of $349.22, for a total value of $4,889,080.00. Following the transaction, the director now directly owns 1,323,384 shares of the company’s stock, valued at approximately $462,152,160.48. The disclosure for this sale can be found here. In the last three months, insiders sold 124,475 shares of company stock valued at $43,717,816. 5.70% of the stock is currently owned by corporate insiders.

A number of institutional investors and hedge funds have recently added to or reduced their stakes in MDB. Allspring Global Investments Holdings LLC purchased a new position in shares of MongoDB during the 4th quarter worth approximately $674,390,000. Norges Bank purchased a new position in shares of MongoDB during the 4th quarter worth approximately $277,934,000. TD Asset Management Inc. grew its holdings in shares of MongoDB by 153.9% during the 4th quarter. TD Asset Management Inc. now owns 525,000 shares of the company’s stock worth $277,909,000 after acquiring an additional 318,259 shares during the period. Jennison Associates LLC purchased a new position in shares of MongoDB during the 1st quarter worth approximately $113,395,000. Finally, 1832 Asset Management L.P. grew its stake in shares of MongoDB by 19.3% in the 1st quarter. 1832 Asset Management L.P. now owns 1,028,400 shares of the company’s stock worth $450,095,000 after buying an additional 166,400 shares during the last quarter. 88.70% of the stock is owned by institutional investors and hedge funds.

MongoDB Company Profile (Get Rating)

MongoDB, Inc provides general purpose database platform worldwide. The company offers MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premise, or in a hybrid environment; MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

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Zacks: Analysts Expect MongoDB, Inc. (NASDAQ:MDB) Will Post Earnings of -$0.10 Per Share

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Equities analysts predict that MongoDB, Inc. (NASDAQ:MDBGet Rating) will post earnings per share (EPS) of ($0.10) for the current quarter, according to Zacks. Five analysts have issued estimates for MongoDB’s earnings, with the lowest EPS estimate coming in at ($0.12) and the highest estimate coming in at ($0.08). MongoDB posted earnings per share of ($0.15) during the same quarter last year, which indicates a positive year over year growth rate of 33.3%. The firm is expected to announce its next quarterly earnings report after the market closes on Monday, January 1st.

According to Zacks, analysts expect that MongoDB will report full year earnings of ($0.39) per share for the current financial year, with EPS estimates ranging from ($0.43) to ($0.33). For the next financial year, analysts expect that the business will post earnings of $0.04 per share, with EPS estimates ranging from ($0.11) to $0.19. Zacks Investment Research’s EPS averages are a mean average based on a survey of analysts that that provide coverage for MongoDB.

MongoDB (NASDAQ:MDBGet Rating) last issued its quarterly earnings results on Tuesday, March 8th. The company reported ($1.20) earnings per share (EPS) for the quarter, topping the consensus estimate of ($1.25) by $0.05. MongoDB had a negative return on equity of 66.70% and a negative net margin of 35.12%. The firm had revenue of $266.50 million during the quarter, compared to analyst estimates of $243.42 million. During the same quarter in the previous year, the company posted ($1.01) earnings per share. The company’s revenue was up 55.8% compared to the same quarter last year.

MDB has been the subject of a number of recent research reports. Royal Bank of Canada initiated coverage on MongoDB in a research note on Tuesday, March 1st. They issued an “outperform” rating and a $505.00 target price for the company. Mizuho lowered their target price on MongoDB from $325.00 to $270.00 and set a “neutral” rating for the company in a research note on Wednesday, May 18th. Credit Suisse Group lowered their target price on MongoDB from $700.00 to $650.00 and set an “outperform” rating for the company in a research note on Wednesday, March 9th. Zacks Investment Research lowered MongoDB from a “hold” rating to a “sell” rating in a research note on Thursday, February 3rd. Finally, Stifel Nicolaus lowered their price target on MongoDB from $550.00 to $425.00 in a research report on Wednesday, March 9th. One research analyst has rated the stock with a sell rating, one has issued a hold rating and sixteen have given a buy rating to the stock. Based on data from MarketBeat, MongoDB presently has a consensus rating of “Buy” and an average target price of $483.83.

In related news, CEO Dev Ittycheria sold 35,000 shares of the company’s stock in a transaction that occurred on Friday, March 4th. The stock was sold at an average price of $309.78, for a total value of $10,842,300.00. The transaction was disclosed in a filing with the SEC, which is accessible through this hyperlink. Also, insider Thomas Bull sold 2,500 shares of the company’s stock in a transaction that occurred on Thursday, March 31st. The stock was sold at an average price of $444.14, for a total value of $1,110,350.00. Following the completion of the sale, the insider now owns 17,904 shares in the company, valued at approximately $7,951,882.56. The disclosure for this sale can be found here. Insiders have sold a total of 124,475 shares of company stock worth $43,717,816 in the last quarter. Company insiders own 5.70% of the company’s stock.

Institutional investors have recently bought and sold shares of the stock. Northern Trust Corp grew its stake in shares of MongoDB by 3.1% in the 4th quarter. Northern Trust Corp now owns 405,648 shares of the company’s stock worth $214,731,000 after buying an additional 12,223 shares during the last quarter. Mercer Global Advisors Inc. ADV purchased a new stake in shares of MongoDB in the 4th quarter worth approximately $200,000. TD Asset Management Inc. boosted its stake in MongoDB by 153.9% during the 4th quarter. TD Asset Management Inc. now owns 525,000 shares of the company’s stock valued at $277,909,000 after purchasing an additional 318,259 shares during the last quarter. GraniteShares Advisors LLC purchased a new stake in MongoDB during the 4th quarter valued at $165,000. Finally, Kornitzer Capital Management Inc. KS purchased a new stake in MongoDB during the 4th quarter valued at $675,000. Institutional investors own 88.70% of the company’s stock.

Shares of NASDAQ MDB traded up $17.00 during midday trading on Monday, reaching $250.06. The company had a trading volume of 83,065 shares, compared to its average volume of 1,431,503. MongoDB has a one year low of $213.39 and a one year high of $590.00. The company has a quick ratio of 4.02, a current ratio of 4.02 and a debt-to-equity ratio of 1.70. The firm’s fifty day moving average price is $349.73 and its 200-day moving average price is $407.40. The stock has a market cap of $16.90 billion, a PE ratio of -52.76 and a beta of 0.98.

MongoDB Company Profile (Get Rating)

MongoDB, Inc provides general purpose database platform worldwide. The company offers MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premise, or in a hybrid environment; MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

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Earnings History and Estimates for MongoDB (NASDAQ:MDB)



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Database Management Software Market 2022 Segments Analysis by Top Key Players

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Database Management Software Market Report Coverage: Key Growth Factors & Challenges, Segmentation & Regional Outlook, Top Industry Trends & Opportunities, Competition Analysis, COVID-19 Impact Analysis & Projected Recovery, and Market Sizing & Forecast.

Latest launched research on Global Database Management Software Market, it provides detailed analysis with presentable graphs, charts and tables. This report covers an in depth study of the Database Management Software Market size, growth, and share, trends, consumption, segments, application and Forecast 2030. With qualitative and quantitative analysis, we help you with thorough and comprehensive research on the global Database Management Software Market. This report has been prepared by experienced and knowledgeable market analysts and researchers. Each section of the research study is specially prepared to explore key aspects of the global Database Management Software Market. Buyers of the report will have access to accurate PESTLE, SWOT and other types of analysis on the global Database Management Software market. Moreover, it offers highly accurate estimations on the CAGR, market share, and market size of key regions and countries.

Major Key players profiled in the report include:
Microsoft, Oracle, SAP, IBM, Teradata, Software AG, Apple (FileMaker), Amazon Web Services, NetApp, ManageEngine, MongoDB, PostgreSQL, Neo4j, SolarWinds MSP, Zoho, Kohezion, BMC Software

Download Free Sample PDF including COVID19 Impact Analysis, full TOC, Tables and [email protected] https://www.mraccuracyreports.com/report-sample/328008

Don’t miss the trading opportunities on Database Management Software Market. Talk to our analyst and gain key industry insights that will help your business grow as you create PDF sample reports.

Segmental Analysis:
The report has classified the global Database Management Software market into segments including product type and application. Every segment is evaluated based on share and growth rate. Besides, the analysts have studied the potential regions that may prove rewarding for the Database Management Software manufcaturers in the coming years. The regional analysis includes reliable predictions on value and volume, there by helping market players to gain deep insights into the overall Database Management Software industry.

Market split by Type, can be divided into:
On-Premise, Cloud-Based.

Market split by Application, can be divided into:
Large Enterprises, Small and Medium-sized Enterprises (SMEs)

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The authors of the report have analyzed both developing and developed regions considered for the research and analysis of the global Database Management Software market. The regional analysis section of the report provides an extensive research study on different regional and country-wise Database Management Software industry to help players plan effective expansion strategies.

Regions Covered in the Global Database Management Software Market:
• North America (U.S., Canada)
• Europe (U.K., Germany, France, Italy)
• Asia Pacific (China, India, Japan, Singapore, Malaysia)
• Latin America (Brazil, Mexico)
• Middle East & Africa (Kuwait, Saudi Arabia Egypt)

Years Considered to Estimate the Market Size:
History Year: 2019-2020
Base Year: 2021
Estimated Year: 2022
Forecast Year: 2022-2030

What market dynamics does this report cover?
The report shares key insights on:

  • Current market size
  • Market forecast
  • Market opportunities
  • Key drivers and restraints
  • Regulatory scenario
  • Industry trend
  • New product approvals/launch
  • Promotion and marketing initiatives
  • Pricing analysis
  • Competitive landscape

It helps companies make strategic decisions

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MR Accuracy Reports is the number one publisher in the world and have published more than 2 million reports across globe. Fortune 500 companies are working with us. Also helping small players to know the market and focusing on consulting.

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Non-Native Database Management Systems Market 2022 Segments Analysis by Top Key Players

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New Jersey, United States,- The latest report published by MR Accuracy Reports indicates that the Non-Native Database Management Systems Market is likely to accelerate strongly in the coming years. Analysts have studied market drivers, restraints, risks, and opportunities in the global market. The Non-Native Database Management Systems Market report shows the likely direction of the market in the coming years along with its estimates. An accurate study aims to understand the market price. By analyzing the competitive landscape, the authors of the report have made excellent efforts to help readers understand the key business tactics that major companies are using to maintain market sustainability.

Key Players Mentioned in the Non-Native Database Management Systems Market Research Report: Amazon Athena, Navicat Premium, dbForge Studio, Apache, MongoDB Cloud Manager, DBeaver, Robomongo, Microsoft Azure, DbVisualizer, QUEST (Toad Edge), SQL Developer, Toad For Oracle, SQLyog, TablePlus

Get Full PDF Sample Copy of Report: (Including Full TOC, List of Tables & Figures, Chart) @ https://www.mraccuracyreports.com/report-sample/327931

The report includes company profiles of almost all major players in the Non-Native Database Management Systems market. The Company Profiles section provides valuable analysis of strengths and weaknesses, business trends, recent advances, mergers and acquisitions, expansion plans, global presence, market presence, and portfolios of products from major market players. This information can be used by players and other market participants to maximize their profitability and streamline their business strategies. Our competitive analysis also provides vital information that will help new entrants identify barriers to entry and gauge the level of competitiveness in the Non-Native Database Management Systems market.

Non-Native Database Management Systems Market

Cloud Based, Web Based.

Application as below

Large Enterprises, SMEs

The global market for Non-Native Database Management Systems is segmented on the basis of product, type. All of these segments have been studied individually. The detailed investigation allows assessment of the factors influencing the Non-Native Database Management Systems Market. Experts have analyzed the nature of development, investments in research and development, changing consumption patterns, and growing number of applications. In addition, analysts have also evaluated the changing economics around the Non-Native Database Management Systems Market that are likely affect its course.

The regional analysis section of the report allows players to concentrate on high-growth regions and countries that could help them to expand their presence in the Non-Native Database Management Systems market. Apart from extending their footprint in the Non-Native Database Management Systems market, the regional analysis helps players to increase their sales while having a better understanding of customer behavior in specific regions and countries. The report provides CAGR, revenue, production, consumption, and other important statistics and figures related to the global as well as regional markets. It shows how different type, application, and regional segments are progressing in the Non-Native Database Management Systems market in terms of growth.

Non-Native Database Management Systems Market Report Scope

ESTIMATED YEAR 2022

BASE YEAR 2021

FORECAST YEAR 2029

HISTORICAL YEAR 2020

UNIT Value (USD Million/Billion)

The Non-Native Database Management Systems report provides information about the market area, which is further subdivided into sub-regions and countries/regions. In addition to the market share in each country and sub-region, this chapter of this report also contains information on profit opportunities. This chapter of the report mentions the market share and growth rate of each region, country and sub-region during the estimated period. 

  • North America (USA and Canada)
  • Europe (UK, Germany, France and the rest of Europe)
  • Asia Pacific (China, Japan, India, and the rest of the Asia Pacific region)
  • Latin America (Brazil, Mexico, and the rest of Latin America)
  • Middle East and Africa (GCC and rest of the Middle East and Africa)

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Key questions answered in the report:

  1. Which are the five top players of the Non-Native Database Management Systems market?
  2. How will the Non-Native Database Management Systems market change in the next five years?
  3. Which product and application will take a lion’s share of the Non-Native Database Management Systems market?
  4. What are the drivers and restraints of the Non-Native Database Management Systems market?
  5. Which regional market will show the highest growth?
  6. What will be the CAGR and size of the Non-Native Database Management Systems market throughout the forecast period?

Note – To provide a more accurate market forecast, all our reports will be updated prior to delivery considering the impact of COVID-19.

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Open-Source Database Software Market Size, Scope and Forecast – Industrial IT

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New Jersey, United States – The latest study released on the “Open-Source Database Software Market” with market Size, Share, Trend, and Forecast up to 2029.” The market study covers significant research data and development resource documents for managers, analysts, industry experts, and other competitors. This report survey helps market participants make better judgments and understand market growth drivers, opportunities, and upcoming challenges. It investigates the growth of present and emerging categories and the revenue performance of the market industry.

The Open-Source Database Software market analysis is provided for the international markets including development trends, analysis of competitive landscapes, and development status for key regions. The report provides a basic overview of the industry including definitions, classifications, applications, and industry chain structure. It covers information on profit models, competition spectrum, and associated vendor strategies illustrated by major players and market participants. The report comprises in-depth analysis and a comprehensive analysis of the future trends and developments in the market.

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The competitive landscape of the Open-Source Database Software market is broadly studied in the report with a large focus on recent developments, and key growth strategies. The Open-Source Database Software market threw light on their crucial business aspects such as production, areas of operation, and product/services portfolio. The report discusses the growth of the global as well as regional markets.

Key Players Mentioned in the Open-Source Database Software Market Research Report:

Titan, SQLite, Neo4j, MariaDB, Apache Hive, Couchbase, MongoDB, Redis, MySQL.

The Open-Source Database Software Market is highly fragmented and is characterized by key vendors and other prominent vendors. Key vendors are increasingly focusing on creating awareness about the Open-Source Database Software market courses and their benefits. Global vendors are trying to stabilize themselves in the market, whereas, regional vendors are focusing on product/service offerings to establish themselves in the market. Vendors are providing a diversified range of product lines intensifying the competitive scenario.

Open-Source Database Software Market Segmentation:  

Open-Source Database Software Market, By Product

• Cloud Based
• On Premises

Open-Source Database Software Market, By Application

• Small and Medium Enterprises (SMEs)
• Large Enterprises

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Open-Source Database Software Market Report Scope 

ATTRIBUTES DETAILS
ESTIMATED YEAR 2022
BASE YEAR 2021
FORECAST YEAR 2029
HISTORICAL YEAR 2020
UNIT Value (USD Million/Billion)
SEGMENTS COVERED Types, Applications, End-Users, and more.
REPORT COVERAGE Revenue Forecast, Company Ranking, Competitive Landscape, Growth Factors, and Trends
BY REGION North America, Europe, Asia Pacific, Latin America, Middle East and Africa
CUSTOMIZATION SCOPE Free report customization (equivalent up to 4 analysts working days) with purchase. Addition or alteration to country, regional & segment scope.

Key questions answered in the report: 

1. Which are the five top players of the Open-Source Database Software market?

2. How will the Open-Source Database Software market change in the next five years?

3. Which product and application will take a lion’s share of the Open-Source Database Software market?

4. What are the drivers and restraints of the Open-Source Database Software market?

5. Which regional market will show the highest growth?

6. What will be the CAGR and size of the Open-Source Database Software market throughout the forecast period?

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Visualize Open-Source Database Software Market using Verified Market Intelligence:- 

Verified Market Intelligence is our BI-enabled platform for narrative storytelling of this market. VMI offers in-depth forecasted trends and accurate Insights on over 20,000+ emerging & niche markets, helping you make critical revenue-impacting decisions for a brilliant future. 

VMI provides a holistic overview and global competitive landscape with respect to Region, Country, and Segment, and Key players of your market. Present your Market Report & findings with an inbuilt presentation feature saving over 70% of your time and resources for Investor, Sales & Marketing, R&D, and Product Development pitches. VMI enables data delivery In Excel and Interactive PDF formats with over 15+ Key Market Indicators for your market. 

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About Us: Verified Market Research® 

Verified Market Research® is a leading Global Research and Consulting firm that has been providing advanced analytical research solutions, custom consulting and in-depth data analysis for 10+ years to individuals and companies alike that are looking for accurate, reliable and up to date research data and technical consulting. We offer insights into strategic and growth analyses, Data necessary to achieve corporate goals and help make critical revenue decisions. 

Our research studies help our clients make superior data-driven decisions, understand market forecast, capitalize on future opportunities and optimize efficiency by working as their partner to deliver accurate and valuable information. The industries we cover span over a large spectrum including Technology, Chemicals, Manufacturing, Energy, Food and Beverages, Automotive, Robotics, Packaging, Construction, Mining & Gas. Etc. 

We, at Verified Market Research, assist in understanding holistic market indicating factors and most current and future market trends. Our analysts, with their high expertise in data gathering and governance, utilize industry techniques to collate and examine data at all stages. They are trained to combine modern data collection techniques, superior research methodology, subject expertise and years of collective experience to produce informative and accurate research. 

Having serviced over 5000+ clients, we have provided reliable market research services to more than 100 Global Fortune 500 companies such as Amazon, Dell, IBM, Shell, Exxon Mobil, General Electric, Siemens, Microsoft, Sony and Hitachi. We have co-consulted with some of the world’s leading consulting firms like McKinsey & Company, Boston Consulting Group, Bain and Company for custom research and consulting projects for businesses worldwide. 

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Global NoSQL Databases Software Market 2022 to 2031 Analysis

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Global NoSQL Databases Software Market

The NoSQL Databases Software Market accounted for US$ XX Million/Billion in the year 2022 and is expected to grow at a CAGR of XX% during the forecast period 2022 – 2030, to account for US$ XX Million/Billion in the year 2030.

Global NoSQL Databases Software Market is segmented by region into North America, Europe, Asia-Pacific, Middle East and Africa, South and Central America. The North America regional market is expected to grow with CAGR of XX.X% and reach US$ XX Million/Billion 2015 from US$ XX Million/Billion in 2022.

The key players profiled in the NoSQL Databases Software Market research study includes top:

MongoDB, Amazon, ArangoDB, Azure Cosmos DB, Couchbase, MarkLogic, RethinkDB, CouchDB, SQL-RD, OrientDB, RavenDB, Redis

By Type
– Cloud Based
– Web Based
By Application
– Large Enterprises
– SMEs

Request Sample Copy of NoSQL Databases Software Market research report at – marketreports.info/sample/45693/NoSQL-Databases-Software

Since, the key findings in the NoSQL Databases Software Market research reports highlight crucial progressive industry trends, it allows the companies across the value chain to develop effective long-term strategies. The clients get to understand a clear picture of the competitors and can develop strategies and modify business expansion plans accordingly. The NoSQL Databases Software Market research reports cover thousands of global players that based on several parameters, such as company revenue, product portfolio, and geographic presence.

marketreports.info adheres to the codes of practice of the Market Research Society and Strategic and Competitive Intelligence Professionals. The following methodology has been followed for the collection and analysis of data presented in this report:

Coverage:

The objective of updating “marketreports.info” coverage is to ensure that it represents the most up-to-date vision of the industry possible. The estimated revenues of all major companies, including private and governmental, are gathered and used to prioritize coverage. Companies which are making the news, or which are of particular interest due to their innovative approach, are prioritized.

Secondary Research:

The NoSQL Databases Software research process begins with exhaustive secondary research using internal and external sources to obtain qualitative and quantitative information relating to each NoSQL Databases Software Market. The secondary research sources that are typically referred to include, but are not limited to:

  • NoSQL Databases Software related Company Websites, Annual Reports, Financial Reports, Broker Reports and Investor Presentations
  • NoSQL Databases Software Industry Trade Journals and Other Literature
  • NoSQL Databases Software related National Government Documents, Statistical Databases and Market Reports
  • NoSQL Databases Software related News Articles, Press Releases and Web-Casts Specific to the Companies Operating in the Market

NOTE: All the financials considered in Company Profile’s section have been standardized to US$. This has been achieved after converting the financials (for those not in US$) with respective currency exchange rates of the particular year.

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Primary Research:

“marketreports.info” conducts hundreds of primary interviews a year with industry participants and commentators in order to validate its data and analysis. A typical NoSQL Databases Software research interview fulfils the following functions:

  • Provides First-Hand Information on NoSQL Databases Software Market Size, Market Trends, Growth Trends, Competitive Landscape and Future Outlook
  • NoSQL Databases Software Market Validates and Strengthens Secondary Research Findings
  • NoSQL Databases Software Market Further Develops the Analysis Team’s Expertise and Market Understanding

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Primary research involves email interactions and telephone interviews for each NoSQL Databases Software market, category, segment and sub -segment across geographies. The participants who typically take part in such a process include, but are not limited to:

  1. NoSQL Databases Software Industry Participants: VPs, Business Development Managers, Market Intelligence Managers and National Sales Managers
  2. NoSQL Databases Software Outside Experts: Valuation Experts, Research Analysts and Key Opinion Leaders Specializing in the Industry

About Us

marketreports.info is a global market research and consulting service provider specialized in offering wide range of business solutions to their clients including market research reports, primary and secondary research, demand forecasting services, focus group analysis and other services. We understand that how data is important in today’s competitive environment and thus, we have collaborated with industry’s leading research providers who works continuously to meet the ever-growing demand for market research reports throughout the year.

Contact Us:

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Global NoSQL Databases Software Market 2022 to 2031 Analysis

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Posted on mongodb google news. Visit mongodb google news

Global NoSQL Databases Software Market

The NoSQL Databases Software Market accounted for US$ XX Million/Billion in the year 2022 and is expected to grow at a CAGR of XX% during the forecast period 2022 – 2030, to account for US$ XX Million/Billion in the year 2030.

Global NoSQL Databases Software Market is segmented by region into North America, Europe, Asia-Pacific, Middle East and Africa, South and Central America. The North America regional market is expected to grow with CAGR of XX.X% and reach US$ XX Million/Billion 2015 from US$ XX Million/Billion in 2022.

The key players profiled in the NoSQL Databases Software Market research study includes top:

MongoDB, Amazon, ArangoDB, Azure Cosmos DB, Couchbase, MarkLogic, RethinkDB, CouchDB, SQL-RD, OrientDB, RavenDB, Redis

By Type
– Cloud Based
– Web Based
By Application
– Large Enterprises
– SMEs

Request Sample Copy of NoSQL Databases Software Market research report at – marketreports.info/sample/45693/NoSQL-Databases-Software

Since, the key findings in the NoSQL Databases Software Market research reports highlight crucial progressive industry trends, it allows the companies across the value chain to develop effective long-term strategies. The clients get to understand a clear picture of the competitors and can develop strategies and modify business expansion plans accordingly. The NoSQL Databases Software Market research reports cover thousands of global players that based on several parameters, such as company revenue, product portfolio, and geographic presence.

marketreports.info adheres to the codes of practice of the Market Research Society and Strategic and Competitive Intelligence Professionals. The following methodology has been followed for the collection and analysis of data presented in this report:

Coverage:

The objective of updating “marketreports.info” coverage is to ensure that it represents the most up-to-date vision of the industry possible. The estimated revenues of all major companies, including private and governmental, are gathered and used to prioritize coverage. Companies which are making the news, or which are of particular interest due to their innovative approach, are prioritized.

Secondary Research:

The NoSQL Databases Software research process begins with exhaustive secondary research using internal and external sources to obtain qualitative and quantitative information relating to each NoSQL Databases Software Market. The secondary research sources that are typically referred to include, but are not limited to:

  • NoSQL Databases Software related Company Websites, Annual Reports, Financial Reports, Broker Reports and Investor Presentations
  • NoSQL Databases Software Industry Trade Journals and Other Literature
  • NoSQL Databases Software related National Government Documents, Statistical Databases and Market Reports
  • NoSQL Databases Software related News Articles, Press Releases and Web-Casts Specific to the Companies Operating in the Market

NOTE: All the financials considered in Company Profile’s section have been standardized to US$. This has been achieved after converting the financials (for those not in US$) with respective currency exchange rates of the particular year.

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Primary Research:

“marketreports.info” conducts hundreds of primary interviews a year with industry participants and commentators in order to validate its data and analysis. A typical NoSQL Databases Software research interview fulfils the following functions:

  • Provides First-Hand Information on NoSQL Databases Software Market Size, Market Trends, Growth Trends, Competitive Landscape and Future Outlook
  • NoSQL Databases Software Market Validates and Strengthens Secondary Research Findings
  • NoSQL Databases Software Market Further Develops the Analysis Team’s Expertise and Market Understanding

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Primary research involves email interactions and telephone interviews for each NoSQL Databases Software market, category, segment and sub -segment across geographies. The participants who typically take part in such a process include, but are not limited to:

  1. NoSQL Databases Software Industry Participants: VPs, Business Development Managers, Market Intelligence Managers and National Sales Managers
  2. NoSQL Databases Software Outside Experts: Valuation Experts, Research Analysts and Key Opinion Leaders Specializing in the Industry

About Us

marketreports.info is a global market research and consulting service provider specialized in offering wide range of business solutions to their clients including market research reports, primary and secondary research, demand forecasting services, focus group analysis and other services. We understand that how data is important in today’s competitive environment and thus, we have collaborated with industry’s leading research providers who works continuously to meet the ever-growing demand for market research reports throughout the year.

Contact Us:

Carl Allison (Head of Business Development)

Tiensestraat 32/0302,3000 Leuven, Belgium.

Market Reports

phone: +44 141 628 5998

Email: sales@marketreports.info

Website: www.marketreports.info

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COVID-19 Impact on NoSQL Market Share, Size, Trends and Growth 2022 to 2031

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Posted on nosqlgooglealerts. Visit nosqlgooglealerts

Global NoSQL Market

A new Market Research from Marketreports.info, the Global NoSQL Market, is expected to show tremendous growth in the coming years. Analysts also analyzed the ongoing trends in NoSQL and the opportunities for growth in the industries. These shareholders include the following manufacturers of Outdoors Advertising: IBM Corporation, Aerospike Inc, MarkLogic Corporation, Hibernate, MariaDB, Oracle Database, Neo technology, MongoDB, Basho Technologies, Couchbase, PostgreSQL. The Worldwide NoSQL Market Research Report provides a picture of the competitive landscape of the international market. The report conveys the details resulting from the analysis of the focused market. Initially, the NoSQL Market report shares key aspects of the industry with the details of the impact and NoSQL industry experts maintain a consistent survey with innovative trends, Market share and cost.

Request Sample of NoSQL@: marketreports.info/sample/45470/NoSQL

The main sources are mainly industry experts in the core and related industries and manufacturers involved in all sectors of the industry supply chain. The bottom-up approach is used to plan the market size of NoSQL based on end-user industry and region in terms of value/volume. With the help of data, we support the primary market through the three-dimensional survey procedure and the first interview and data verification through expert telephone, determine the individual market share and size, and confirm with this study.

Top Companies covered in the report: IBM Corporation, Aerospike Inc, MarkLogic Corporation, Hibernate, MariaDB, Oracle Database, Neo technology, MongoDB, Basho Technologies, Couchbase, PostgreSQL

By Type
– Key-Value Store
– Document Databases
– Column Based Stores
– Graph Database
By Application
– Retail
– Online Game Development
– IT
– Social Network Development
– Web Applications Management
– Others

Regional Analysis:
• North America 
• Europe 
• Asia Pacific 
• Latin America
• Middle East and Africa 

Key Research: 
The main sources are industry experts from the NoSQL industry, including management organizations, processing organizations, and analytical services providers that address the value chain of industry organizations. We interviewed all major sources to collect and certify qualitative and quantitative information and to determine future prospects. The qualities of this study in the industry experts industry, such as CEO, vice president, marketing director, technology and innovation director, founder and key executives of key core companies and institutions in major biomass waste containers around the world in the extensive primary research conducted for this study We interviewed to acquire and verify both sides and quantitative aspects.

The research provides answers to the following key questions: 
1) Who are the key Top Competitors in the NoSQL Market? 
Following are list of players: IBM Corporation, Aerospike Inc, MarkLogic Corporation, Hibernate, MariaDB, Oracle Database, Neo technology, MongoDB, Basho Technologies, Couchbase, PostgreSQL

2) What is the expected Market size and growth rate of the Outdoors Advertising market for the period 2022-2030? 
** The Values marked with XX is confidential data. To know more about CAGR figures fill in your information so that our industry experts can get in touch with you.

3) Which Are The Main Key Regions Cover in NoSQL Reports? 
Geographically, this report is segmented into several key Regions, consumption, revenue (million USD), and market share and growth rate of NoSQL in these regions, from 2022 to 2030 (forecast), covering North America, Europe, Asia-Pacific, MEA and rest of the world.

Ask for discounts @ marketreports.info/discount/45470/NoSQL

Table of Contents 
Global NoSQL Market Research Report 2022-2030, by Manufacturers, Regions, Types and Applications

1 Study Coverage
1.1 NoSQL Product
1.2 Key Market Segments in This Study
1.3 Key Manufacturers Covered
1.4 NoSQL Market by Type
1.5 NoSQL Market by Application
1.6 Study Objectives
1.7 Years Considered

2 Executive Summary
2.1 Global NoSQL Production
2.2 NoSQL Growth Rate (CAGR) 2022-2030
2.3 Analysis of Competitive Landscape
2.4 Market Drivers, Trends and Issues

3 NoSQL Market Size by Manufacturers
3.1 NoSQL Production by Manufacturers
3.2 NoSQL Revenue by Manufacturers
3.3 NoSQL Price by Manufacturers
3.4 Mergers & Acquisitions, Expansion Plans

4 NoSQL Production by Regions
4.1 Global NoSQL Production by Regions
4.2 United States
4.3 Europe
4.4 China
4.5 Japan
4.6 Rest of the world

5 NoSQL Consumption by Regions
5.1 Global NoSQL Consumption by Regions
5.2 North America
5.2 Mexico
5.3 Europe
5.4 Asia Pacific
5.5 Central & South America
5.6 Middle East and Africa

6 Market Size by Type
6.1 Global NoSQL Breakdown Data by Type
6.2 Global NoSQL Revenue by Type
6.3 NoSQL Price by Type

7 NoSQL Market Size by Application
7.1 Overview
7.2 Global NoSQL Breakdown Dada by Application

8 Manufacturers Profiles

9 Production Forecasts
9.1 NoSQL Production and Revenue Forecast
9.2 NoSQL Production and Revenue Forecast by Regions
9.3 NoSQL Key Producers Forecast

9.4 Forecast by Type

10 Consumption Forecast
10.1 Consumption Forecast by Application
10.2 NoSQL Consumption Forecast by Regions
10.3 North America Market Consumption Forecast
10.4 Europe Market Consumption Forecast
10.5 Asia Pacific Market Consumption Forecast
10.6 Central & South America Market Consumption Forecast
10.7 Middle East and Africa Market Consumption Forecast

11 Upstream, Industry Chain and Downstream Customers Analysis
11.1 Analysis of NoSQL Upstream Market
11.2 NoSQL Industry Chain Analysis
11.3 Marketing & Distribution
11.4 NoSQL Distributors
11.5 NoSQL Customers

12 Opportunities & Challenges, Threat and Affecting Factors
12.1 Market Opportunities
12.2 Market Challenges
12.3 Porter’s Five Forces Analysis

13 Key Findings

14 Appendix
14.1 Research Methodology
14.2 Author Details
14.3 Disclaimer

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Note: Regional Breakdown & Sectional purchase Available We provide Pie chats Best Customize Reports as per specific Requirements. 

About Us

Marketreports.info is a global market research and consulting service provider specialized in offering wide range of business solutions to their clients including market research reports, primary and secondary research, demand forecasting services, focus group analysis and other services. We understand that how data is important in today’s competitive environment and thus, we have collaborated with industry’s leading research providers who works continuously to meet the ever-growing demand for market research reports throughout the year.

Contact Us:

Carl Allison (Head of Business Development)

Tiensestraat 32/0302,3000 Leuven, Belgium.

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Mobile BackEnd Services Market Innovative Strategy by 2031

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Posted on mongodb google news. Visit mongodb google news

Global Mobile BackEnd Services Market

Mobile BackEnd Services Market study by “Marketreports.info” provides details about the market dynamics affecting the Mobile BackEnd Services market, Market scope, Market segmentation and overlays shadow upon the leading market players highlighting the favorable competitive landscape and trends prevailing over the years.

An exclusive Mobile BackEnd Services Market research report provides depth analysis of the market dynamics across five regions such as North America, Europe, South America, Asia-Pacific, Middle East and Africa. The segmentation of the Mobile BackEnd Services market by type, application, and region was done based on the thorough market analysis and validation through extensive primary inputs from industry experts, key opinion leaders of companies, and stakeholders) and secondary research (global/regional associations, trade journals, technical white papers, company’s website, annual report SEC filing, and paid databases). Further, the Mobile BackEnd Services market has been estimated by utilizing various research methodologies and internal statistical model.

Mobile BackEnd Services Market report also provide a thorough understanding of the cutting-edge competitive analysis of the emerging market trends along with the drivers, restraints, challenges, and opportunities in the Mobile BackEnd Services Market to offer worthwhile insights and current scenario for making right decision. The Mobile BackEnd Services report covers the prominent players in the market with detailed SWOT analysis, financial overview, and key developments of the products/services from the past three years. Moreover, the Mobile BackEnd Services report also offers a 360º outlook of the market through the competitive landscape of the global Mobile BackEnd Services industry player and helps the companies to garner Mobile BackEnd Services Market revenue by understanding the strategic growth approaches.

Download instant copy of the sample on Mobile BackEnd Services market @marketreports.info/sample/45314/Mobile-BackEnd-Services

Leading Mobile BackEnd Services Market Players are as followed:

Salesforce, Built.io, Rackspace, Parse, AWS, Azure, MongoDB Stitch, AnyPresence, Kinvey, Apache

By Type
– Cloud Based
– Web Based
By Application
– Large Enterprises
– SMEs

Mobile BackEnd Services Market – Global Analysis to 2022 is an exclusive and in-depth study which provides a comprehensive view of the Mobile BackEnd Services market includes the current trend and future amplitude of the market with respect to the products/services. The Mobile BackEnd Services report provides an overview of the Mobile BackEnd Services Market with the detailed segmentation by type, application, and region through in-depth traction analysis of the overall virtual reality industry. This Mobile BackEnd Services report provides qualified research on the Mobile BackEnd Services market to evaluate the key players by calibrating all the relevant products/services to understand the positioning of the major players in Mobile BackEnd Services Market.

The Mobile BackEnd Services report is a combination of qualitative and quantitative analysis of the virtual reality industry. The global Mobile BackEnd Services market majorly considers five major regions, namely, North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South & Central America (SACM). The Mobile BackEnd Services report also focuses on the exhaustive PEST analysis and extensive market dynamics during the forecast period.

Purchase a instant copy of Mobile BackEnd Services report @marketreports.info/checkout?buynow=45314/Mobile-BackEnd-Services

Reason to Buy

  • Save and reduce time carrying out entry-level research by identifying the growth, size, leading players and segments in the global Mobile BackEnd Services Market.
  • Highlights key business priorities in order to guide the Mobile BackEnd Services related companies to reform their business strategies and establish themselves in the wide geography.
  • The key findings and recommendations highlight crucial progressive industry trends in the Mobile BackEnd Services Market, thereby allowing players to develop effective long term strategies in order to garner their market revenue. 
  • Develop/modify business expansion plans by using substantial growth offering developed and emerging [name] markets.
  • Scrutinize in-depth global Mobile BackEnd Services market trends and outlook coupled with the factors driving the market, as well as those restraining the growth at a certain extent.
  • Enhance the decision-making process by understanding the strategies that underpin commercial interest with respect to products, segmentation and Mobile BackEnd Services industry verticals.

About Us

Marketreports.info is a global market research and consulting service provider specialized in offering wide range of business solutions to their clients including market research reports, primary and secondary research, demand forecasting services, focus group analysis and other services. We understand that how data is important in today’s competitive environment and thus, we have collaborated with industry’s leading research providers who works continuously to meet the ever-growing demand for market research reports throughout the year.

Contact Us:

Carl Allison (Head of Business Development)

Tiensestraat 32/0302,3000 Leuven, Belgium.

Market Reports

phone: +44 141 628 5998

Email: sales@marketreports.info

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Podcast: Handling High Demand Ticket On-Sales with Anderson Parra and Vitor Pellegrino

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MMS Anderson Parra Vitor Pellegrino

Article originally posted on InfoQ. Visit InfoQ

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Transcript

Introductions [00:05]

Roland Meertens: Welcome, everybody, to the InfoQ podcast. My name is Roland Meertens, and today I am interviewing Anderson Parra and Vitor Pellegrino. Anderson is a senior software engineer at SeatGeek, and Vitor is the director of engineering at SeatGeek. I am speaking to them in person at a venue of QCon London. And in this podcast, we are discussing how SeatGeek is handling high-demand ticket sales, and how they deal with spikes in their traffic.

The day before we recorded the podcast, they discussed this topic in their presentation at QCon. And in this podcast, we are going to dive a bit deeper into some of the future ideas they want to work on. At the moment of recording, you can still register for QCon Plus, if you want to see their talk live, and you can ask them any questions yourself.

Now on to the podcast. Welcome, Vitor and Ander to the InfoQ podcast. We are here at QCon London. We just had our lunch, and how is your day so far?

Vitor Pellegrino: Well, it’s been great. So second day of the event, we had amazing talks. I mean, it’s been very good to be back in person in a live event. So yes, lots of good energy so far.

Roland Meertens: Good to hear. You actually had your talk yesterday. Can you maybe tell a bit about who are you guys actually? Where are you working?

Vitor Pellegrino: My name is Vitor Pellegrino. I run the cloud platform teams at SeatGeek, so we are responsible for all the components of the platform that other SeatGeek engineers use. So think about like compute, storage, networking, but also capabilities for the whole company, such as SRE instant response.

Anderson Parra: I’m Anderson, Ander. I work as a senior software engineer at SeatGeek. I work on that app platform team and we’re responsible to build the virtual waiting room. That was the topic of our presentation yesterday. Today is less stressful, enjoy the rest of the conference, then yes, it was good. We received good feedbacks there.

The problem of ticket on-sales [01:54]

Roland Meertens: So what are you guys currently working on? What was your talk about? What are the challenges you’re having?

Vitor Pellegrino: At SeatGeek, we handle very large on-sales, so for the folks that do not know that space, so on-sales are typically large events, that they’re also met with significant market push. They usually happen in specific times. I mean, you can imagine you want to attend a soccer match from Liverpool, and then you want to buy tickets for it. So maybe the club is going to announce, “Okay, at 2:00 PM, Wednesday, everybody’s going to be able to finally buy their tickets.” so there is a lot of demand, and that’s part of an everyday thing for us, so we have to build systems that are capable of handling that. And our talk was defining that problem space, talking a little bit more about how we reason about that problem, when we are actually developing systems. So as I said, that’s an everyday thing for us, so our systems must also handle that as if it were an everyday thing for them, as well.

And then we dive deeper in what Ander is talking about, the Vroom, that’s how we call virtual waiting room, Vroom. We also like to give funny names for stuff, I guess. And I think that is a core component that allows us to handle those situations, those on-sale situations.

Roland Meertens: To summarize it, if I want to buy a ticket for event, I go to your webpage. Of course, 1000s or 10,000s of people do this all at the same time, so I assume that normal auto scaling techniques are not applying here anymore. So everyone enters the virtual waiting room. And I think what you especially mentioned really nicely in your talk was how do you kind of prioritize people? I think you were talking about fairness, right?

Vitor Pellegrino: Yes.

Roland Meertens: And maybe explain that for a bit.

Anderson Parra: As you mentioned, it was well-described it by Vitor yesterday. Sometimes the auto scale doesn’t help, when we receive the high traffic in one second. Then you try to avoid errors, then the idea that you need a queue to control the traffic to the infrastructure. But we are selling tickets, and the idea that everybody should have the opportunity to purchase tickets as fair as possible.

The way that you guarantee that, you manage the state of the queue, then everybody, when getting settled in the queue, is associated with timestamp. And based off this timestamp, you can sort, and then you are draining the queue, in first-in first-out approach. Then the idea that who arrived earlier should have the opportunity to try to purchase earlier.

Roland Meertens: So it’s really all about kind of replicating the experience of buying something in real life, and the person who comes there first gets the tickets?

Anderson Parra: Exactly. Yes. I mean, the queues are bad in the real world, in the virtual world as well. We know that. And we’re trying to drain the queue as fast as possible. Try to make the on-sale going as fast as possible. That’s a good on-sale, when people can purchase the tickets earlier, then there is no bad experience with some errors, and the idea that we’re controlling the traffic to avoid errors for the user.

Roland Meertens: I think you also mentioned a bit like what’s actually the limiting factor? Why can’t everyone buy the ticket at the same moment?

Vitor Pellegrino: Yes. We’re talking about something that actually exists in the real world, there are seats, and there is a lot of demand for the same seats, so there is a physical limitation there. So imagine that we have a specific seat, premium or not, doesn’t really matter, but one specific seat that you have 10 people trying to buy at the same time.

How do you tie break? So how do you actually resolve? Was the person that first saw the seat has a priority? Was the person that actually submitted the first successful card payment for it? Or the first one to reserve? So these are the kind of things that we need to design for, to your point. I mean, that’s why we cannot just allow everybody to buy at the same time.

Roland Meertens: So basically, everybody tries to go for the front row seats.

Vitor Pellegrino: Yes.

Roland Meertens: But of course, there’s a limited amount of front row seats.

Vitor Pellegrino: Yes.

Anderson Parra: For the business model, when those service starts, the race condition starts as well. Then there are a lot of people trying to purchase, sometimes the same seat, then the way that we’re operating, you can reserve the seat, and you have a time to finish the purchase. But a lot of bad things could happen that purchase phase, credit card could be denied, or we realize they’re too expensive, that you give up. Then the seat becomes available again. Then another user has the opportunity to try to purchase this ticket. Then you try to control the traffic, as well, you can try to maximize the change for people who get a ticket. Then you need time to finish the purchase sometimes, then the queue helps in the business side as well.

The thundering herd problem [06:15]

Roland Meertens: And kind of how many people are we talking about? What’s the scale of the system? I think you had some graphs where you showed the normal usage of your site versus those kind of thundering herd events.

Vitor Pellegrino: Usually our flat line is relatively stable, but it can go several orders of magnitude, two or three or sometimes four, depending on the event. I mean, you can expect that for a large stadium, let’s say, imagine your largest stadium ,as for an American football club or something, like you were talking about, several tens of thousands of users, and then you were going to have perhaps hundreds of people interested to buy each seat. So that is that magnitude we were talking about.

Roland Meertens: So you really have massive peaks.

Vitor Pellegrino: And I think one important thing, and that was something that I tried to stress, and was one of the key points for us proposing this talk in the first place, is it isn’t enough to just say, “Okay, I’m going to always sustain that kind of load at all times.” You need to be able to also reduce and shrink your infrastructure when you do not have those events.We could be always prepared to handle that kind of load, but that wouldn’t be economical. It wouldn’t make sense for us as a business.

Anderson Parra: We try to predict, when there is an on-sale, try to predict the traffic for that on-sale. Then the good part should have the queue we’re collecting metrics for the on-sales. And then we are using those metrics to predict the next seasons, to see, “Okay, I have the seasons of the on-sales. Then I collect metrics for that, and how is going to be the next one? How can I use the [inaudible 00:07:47] that I have seen trying to purchase the tickets, to predict the next event, in terms of traffic?”

Roland Meertens: And this is something which you do manually right now? Do you say, “Okay, these teams are really the top league teams, so they tend to sell out. And these teams are like the lower level teams, so they have a bit more time.” Or is this something you’re also trying to learn from the data?

Vitor Pellegrino: A lot of that is already automated. It’s still a next step for us to increase the amount of automation that we have. We have close relationship with our customers. So one thing that I forgot to mention in the beginning, like SeatGeek, I would say that most of our listeners are going to be following the consumer category, which means somebody trying to attend an event, but we also design for the folks offering those events in the same place. So these are our customers as well. So the enterprise customers, as we call them. So we work in conjunction with them, we help them with whenever they’re about to do one of these large on-sales, we’re typically in close contact with them. So we do have systems that understand when an on-sale is about to happen, but we’re increasing, even more, the amount of automation from that starting point.

Stateful versus stateless architectures [08:55]

Roland Meertens: So in terms of scaling, I think you guys were mentioning the stateful versus stateless architectures. Maybe you can talk a bit about that, what kind of decisions are you making? What kind of options do you have?

Anderson Parra: Well, that was the main topic that, when we started to build our virtual waiting room, is how we’re going to control the traffic, in terms of to reduce the latency. So the best way, I mean, in the ideal world, you could run in the edge part. Then, for example, we are using Fastly as our CDN provider. Then we can try to create a mechanism to control the traffic on the CDN, but that environment is completely stateless, completely state … Well, I’m going to talk about that. Then you have this idea that it’s stateless, and then if it’s stateless, you can have rate limit, but you cannot control the order. And as I mentioned, the order matters for it. You can like to create a fair approach, then we need to manage the state of the queue. Then you need the state, a stateful model, then we have traditional back ends, when you can start. In controlling the state of the queue in our database, we are using DynamoDB as our primary data store.

But also, we have a hybrid mode that we have part of our logical running in the CDN. And when I said that’s completely stateless environment, then that’s the chain that the CDNs are making right now. So there is some small data stores on the CDN, we’re taking advantage of that. Fastly offers, as far as adjunctionary, is a simple key value store that we are using as our primary cache.

Then we have the problem to sync two data stores. We have that data store running the CDN, and also with our primary data store on the back end. Then we have all the mechanisms to keep those data stores synced. Then we can try to take that advantage, when it’s possible, to run the logic on the CDN to remove the latency. And then, if you don’t need to send a request to the back end, then we avoid that and the CDN takes that part.

Roland Meertens: So I think that’s a good summary of your talk, or most of the things you taught in your talk. And so if people are listening, it will be online on InfoQ, so you can re-watch it. But I think, also, what I may want to do is go a bit deeper into some of the suggestions you had for the future. So what are the next steps with your system? Or what are the things you are thinking about, about scaling it even better, or even further?

Vitor Pellegrino: Yes, that’s something that we’re spending a lot of time. And as I mentioned in the call, as well, it’s a topic we’re actively working on. We don’t have all the answers yet, but one thing that is important for us, is really having the systems understanding which mode they are operating. So we have a lot of metrics. We made a lot of investments in observability. So every system, they provide logging, extensive logging, tracing, metrics. I think a lot of our listeners, they probably are used to, but we aren’t able, yet, to say, “Okay, I want to see how my system behaved outside of an on-sale, versus how it behaved during an on-sale.”

We can infer that, we can see that the graphic is pretty obvious, but I would like to be able to say, “Okay, that was the latency of this endpoint, when we were under an on-sale. Oh, that’s the amount of request that happens throughout my entire system for this particular on-sale,” not only in the front end, but actually all the stuff that happened, to be able to categorize each one of the requests and say, “That’s an on-sale request.”

Roland Meertens: I can imagine that in this case, if you’re P99, it’s kind of irrelevant, because it’s really about, 99% of the time, you’re not having an on-sale.

Vitor Pellegrino: Exactly, yes. A problem that we have, I mean, going even further, we talk a lot about SLOs. It’s a very common thing that I see. For us, SLOs are very difficult to be used, as we normally see in the industry, precisely for that. If I have an error budget of, for the sake of the argument, 100 errors, if I have 100 errors outside of an on-sale, that’s not a big deal. But if I have two during an on-sale, might be disruptive enough. So how can I think about SLOs for a specific time of the day? So I’m not interested to know how many errors I had in the last 30 days. I’m far more interested to know how many errors I had in on-sales in the past, I don’t know, 30 days, right?

Roland Meertens: I think, especially for users, it’s always, when you want to buy those tickets, you want to buy it effortless.

Vitor Pellegrino: Yes.

Roland Meertens: And I think I have cases where you went from place 10,000 in the queue to place 300,000. You’re like, “What’s going on?”

Vitor Pellegrino: Yes.

Anderson Parra: The on-sale is the critical window for us. I mean, what Vitor said makes total sense, in terms of, if you have the critical window, when everybody was looking to you to try to do an action in the product, to purchase the tickets, that’s the moment that you need to avoid error, that you need to care more about our system. And then you need to know that the on-sale is there for the size of the [inaudible 00:13:37], for what’s the impact of the error? How many people are going to be affected?

And with all that information, you can try to react for that. And you have process, we are training people that they can try to render errors better, because it’s really hard to say that it is completely error free. Then, it’s common that you can have errors in the applications, but I think the most important thing is, if you have an error, how can you render that? And what’s the lessons in learning that you can have from the error, to try to prevent, because you are always in that process, in the continuous improvement. Then you can try it, “Okay, I’ve seen an error. I prevent that. Then, what’s the next one?”

Roland Meertens: So how does it work? Do you lock a lot of data, also, during the on-sale, when you’re actually putting tickets on sale? Or I can also imagine that at some point, you want the most bare-boned structure as possible, to actually handle everything, right?

Vitor Pellegrino: Yes. I mean, we log … And that’s the whole point. Right now, we make no distinction whether we are in either mode. And that’s something that we would like to change in the future. I mean, we’re going to still log everything, but we would like to categorize. I mean, maybe a way to think is just, “Okay. Well, I want to place things into different logical buckets. And I want to be able to reason about either bucket differently.”

So another thing that is important for us is understanding our non-functional trade-offs. So I think, if I’m browsing right now, if I want to see what’s happening in London, for tonight, I actually would care much more about the site feeling very snappy, I’m getting access to what I need, latency is very important to me. But if I’m in the middle of an on-sale, and I’m already in that stressful situation, I care far more if I press the button, I actually get the ticket. I don’t mind as much if I have to wait 200, 300 milliseconds, or even a second, for the sake of the argument. So that’s the kind of stuff that we are building that knowledge inside of our application. So I’m spending a lot of time thinking about that. “Okay. How do we get teams to design their systems with that in mind?” So how can I perhaps pass that information using, I don’t know, a notification system that each microservice is able to understand.

Roland Meertens: So your non-functionals are really, indeed, changing them over three of these different bases.

Anderson Parra: I think the key point is automation. That’s something that we’re trying to make our systems a little bit more sophisticated, that they can understand what mode the system is running. If it’s on sale, how can you just trigger the alerts different, if you have an error, how can recover as best as possible on that moment? Then, when we’re not in on-sale, then it’s a less stressful situation, then you can say, “Okay, I have time to see what’s going on, and to provide a fix for that.” You know?

Automating the ticket sales process [16:17]

Roland Meertens: You were mentioning kind of trying to run the system, at some point, by robots, or running everything automatically. How is it currently? Is there a lot of manual work involved into putting each ticket online?

Anderson Parra: When we started the virtual waiting room, you have a lot of manual work to set up the protected zones, and to see the paths of the events that are going to be on sale. Nowadays, everything is automatically. Then, when the event is created, in terms of, okay, someone was designing the event, the stage, and how many tickets are going to be available, and say, “Okay, this is going to be on sale in a certain day.” Then the protected zone is created automatically. You have over than 2000 protected zones running in production. It means that all the events are protected by that queuing system, and then reduces completely, the mental work. And we are still working to reduce even more. The idea that, okay, we know that the time for the engineers are really important to do engineer things. And you’re trying to reduce that engineer using operating systems, you know?

And then, we can automate it. You can see that “Okay. If the CPU is going up, then you can take decisions,” like something is looking to the chart, to the graph, they spike in the CPU, and decide to reduce, for example, the edge rate of the protected zone. I mean, if it’s not manual work, you could do the automation as well, because you can try to understand what’s going on with the CPU. Then, you can take a decision, and this sees that. And that’s the idea, the next steps for the evolution of our own sales, we can try to reduce the number of people operating it, you know?

Roland Meertens: Yes. Because right now, a lot of people are still looking at how many people are buying tickets at the same time. So can we allow more in, or less in, right?

Vitor Pellegrino: Yes. That’s what Ander was mentioning with the exit rate. So right now, people have to make a decision like, “Okay, it seems like we’re able to sustain more loads, so instead of allowing fictional numbers, like 300 people every minute, let’s allow around 500, 1000.” Or maybe it’s going the other way around like, “Actually we’re not able to sustain as much. So let’s reduce that, to avoid a bad experience to everybody that is already buying.” So that’s the kind of stuff that we want to allow for much more automation. So the system, they’re able to adjust their thresholds automatically.

Roland Meertens: And I think you are also thinking about the alerting. How does it currently work? Do you wake people up at night to push more tickets?

Vitor Pellegrino: No, no. I mean, our customers define how they want to buy. So I think the thing that wakes up engineers at night is more when things, like most companies, when they don’t work as intended. But I think, in the future, we would like to adjust the priority of these alerts. Again, coming back to the overall theme about on-sale or not. So right now, if people are having any service disruption, we’re going to treat that the same. But I would like to be able to say, “Okay, if it’s an on-sale, it’s actually something that I can wake up, fully refreshed and take a look, with fresh eyes in the morning. But if that’s on sale, please wake me out of, I don’t know, whatever I’m doing.” So that’s the kind of things we’re looking to do.

Fraud detection [19:13]

Roland Meertens: I think the other thing you mentioned at some point during the talk was fraud detection, that someone could maybe, very quickly, buy a single ticket automatically. Is that fraud? Or someone, maybe, buying 100 tickets for the entire group of friends, is that fraud? How do you handle this at the moment?

Vitor Pellegrino: It’s a good point. That was within the topics of we need to think about these things, every on-sale. So we leverage a lot of machine learning and fraud detection systems, throughout the entire stack, so sometimes people will execute some actions, and then, post-factum, realize that they could have been problems, and we have systems to care for that.

We use a lot of different tooling around bot protection and all of that, but it comes with the question, if I am trying to buy a ticket, and I use, I don’t know, selenium to automate that task, where do we draw the line? Is 10 tickets okay? Is 1 ticket? Is 100 tickets? So that is the kind of things, I would say, we work very closely with our customers, and then we define, “Okay, that’s what we believe is an acceptable behavior.”

Roland Meertens: And the queue helps?

Anderson Parra: The queue helps on that part, because the way that you’re guaranteed controlling the traffic, then you try to identify real users, and bots, and remove bots from the traffic. Then you can try to guarantee that people that get into the protected zones, that has the opportunity to purchase the tickets, they are real users, but it’s hard. The same way that we’re working to prevent, people are working, also, to buy best. That always is that way.

Roland Meertens: So as you also mentioned machine learning, what are some of the best features to detect if someone is a bot or not?

Vitor Pellegrino: It’s a good question. I think we use systems that provide that, almost as a kind of standalone service. So they analyze the usual patterns, like how fast people will navigate through webpage, just one example. There are a lot of signals involved.

Anderson Parra: Well, there are systems that create fingerprints in the request. Fastly helps, as well, to create a stamp in the request, say, “Okay, that’s a bot, or not a bot.” And we don’t rely on only one bucket, because as I mentioned saying, people are trying to buy past that. Then you have the combination to try to identify that’s a bot traffic or not. Then we try to guarantee, as fair as possible, the user experience, for real users that are trying to purchase the tickets, because that’s the most important part.

Roland Meertens: At the end of the day, you want real people to sit there, and not this call person making money of your tickets.

Anderson Parra: Exactly.

Vitor Pellegrino: Exactly. Yes.

Edge processing [21:28]

Roland Meertens: And you were mentioning Fastly as your content delivery network, so how does edge processing, how does a content delivery network work here? Because I can imagine, that because the state of your database and available tickets changes so often, you can’t cache too much at the moment.

Anderson Parra: Well, the problem that if you are caching, then you need to have a way that purging the cache. Then, you are thinking in event orchestration, because if you cash in the CDN, then you can see the latest going down. But if there’s a change in the event, for example, then you need to have a way to purging the cache that was made in the CDN, for example. The way that we’re thinking about that, systems could react for the chains, true events, and then you can orchestrate, choreograph the events, in terms of, “Okay, if something changed in the even model, then I know that I need to change the protected zone.” That’s the queuing system. “And also I need to purging some cache.” Then, again, it’s connected with the automation part. We would like to keep our systems as smart as possible, in terms of reacting for chains without manual intervention.

But you have to have users of CDN, in the end, for caching as an example, and part of our virtual waiting room works there. Then, we have logic to validate visitor tokens, access tokens, because in the end, the virtual waiting room is the exchanger of visitor tokens to access tokens.

And also, we need to maintain a state of the protected zones in the edge adjunctionary. That’s the way that we can control how we’re going to route the traffic. It should go to the queue, it should go to the target, it should go to ad block page. And then we have that part of the logic running the CDN, and then you don’t need to communicate to our back end. That’s good, as well, in that case, that you can reduce the cost that we have with our back end, because we’re distributing how we are executing the computing in the different layers.

Storing data at the edge [23:13]

Roland Meertens: And I think you were also mentioning storing data at the edge. What are your ideas around that? Is it something you’re already doing, or is this something you’re planning to do?

Anderson Parra: That’s something new. Then this adjunctionary, Fastly, is something new we’re using, we are taking advantage of that. We can see in other companies, like AWS with the cloud front, they have the Lambda edge, and they’re using the DynamoDB as the edge data store, with the global tables, because of new edge running in different regions, then you can try to make the data available for all the regions.

For us, Fastly works quite well. Then, the times to replicate the data store is around 30 seconds. DynamoDB is around two minutes. Then, I know that Akamai is working in the data, in a data store as well as in the edge. But my opinion, that looks like we are going to have more logic running the edge, in terms of to reduce the latency. Not planning to complete the systems on the edge, but the idea that you can have our first layer, and try to avoid to file request to the back end, when it’s possible.

Vitor Pellegrino: This is something that I would have loved to have, I would say, about seven, eight years ago, when I was working for a company that had a very heavy usage of social graph. So you would have users, their followers, people that they interact with. One of the things that I could see, if I were to rebuild that system, is actually trying to figure out how can I get some of the key users, that have huge crowds that follow them, actually store some of that information already closed to the visitors, like in their edge locations.

So these are the kind of maybe hinting towards new technologies that we’re excited about too. That’s the kind of things that I think could be so useful to solve that use case, when we’re investigating also. So storage at edge is definitely something that unlocks a lot of possibilities for us.

Anderson Parra: Thinking in the idea of the edge, I think we are in the moment that we are going to expand what’s the edge in the end. We have the 5G right now. Then, we are going to have more device with nice connection, where you can sync with some back end systems, then the edge will not be only the CDN. The edge will be the gateway that was in the stage on that we need to open when we can check it.

Then we validate that, check that it’s valid, and open the gate for that person, because it’s a lot that you’re getting. Then, I think, going for that direction, that the edge will be everywhere. The idea for the internet of things is going on. Then finally, the problem with the connection is going to be figured out. Then we’re going to have massive data, and you can try to think, and how can you just improve our business, because you have the opportunity to run software connected everywhere.

Roland Meertens: And especially for what you said with Stadium, which you are basically proposing, you said, “Maybe the database is at the stadium, so that even if there would be an outage outside of the stadium, you can still keep running.”

Anderson Parra: Or imagine that you have the database in the gateway. You have a ticket, and you need to go to the gate seven. And imagine for that gate seven, you have all the tickets available to getting on that one. I mean, if the gate is working, then you don’t care if there’s an outage, but what’s the problem for that. You need to sync, right?

Then how can you sync for that event. Then if you can sync in the right moment, then you allow people to get in fast.

Roland Meertens: And of course, you don’t want people to check into two gate at exactly the same time.

Vitor Pellegrino: Yes. And also reporting that customers do, and all of that, I think the main thing that kind of technology unlocks, and again, hinting towards what we could be doing, we don’t only do ticketing. That is, I would say, our bread and butter, but we also help customers, enterprise customers I’m talking about here, manage their food stamps, manage their convenience stores inside a stadium. So we can see expanding the devices to actually where the work is happening. So closer to the users, like visiting a stadium, so that in-stadium experience is also important for us.

Roland Meertens: And maybe, as the last thing you were mentioning, the elasticity as all the layers of the infrastructure, what are your thoughts on that? What’s the future of that?

Vitor Pellegrino: I think that’s something we’re working on right now. Our architecture, as any other company, we have things that we built in a different time. And I don’t think we’re able to grow all the systems in a lockstep. So I would like to be able to get to a point where, let’s say there’s an on-sale, while users are in a queue, perhaps you can add more database computing, power, or increase. I don’t know, even storage. I don’t know, making up an example here, but I can take the idea about auto scaling throughout all the layers of my architecture. Perhaps I add different components as I need, activate other vendors to add extra redundancy. So sometimes people focus only on the compute part, and sometimes only from one component, and then forget to also scale the downstream components of it. And for us, that adds, tremendously, the amount of time that people are waiting, for all the auto scaling components to kick in.

So the whole flexibility in all layers, we would like to be able to say, “Okay, the same way that people nowadays do, to increase the amount of processing to…” Let’s say there is a backlog in a Kafka infrastructure, or a wrapped-end queue, I don’t know. There, you add more consumers. We would like to reason the same way like, “Oh, we have more people in the queue. Therefore, I want to scale all the vertical that is serving that on-sale at the same time, so everything is ready.” And then, we can then tie back to what we talked about, increase the amount of people that we allow in, because now we have more capacity. Waiting to have, “Okay, first my computer restart increased, then my second service,” and so on, that is too long for us.

Roland Meertens: I can also imagine that it’s a bit hard to… Like the front end is maybe easiest to scale, but the database will be way harder, right?

Vitor Pellegrino: Yes.

Anderson Parra: It’s hard. And that’s the reason that they’re monitoring everything. We’re trying to avoid blind spots, then you can use those metrics to identify bottlenecks, and sometimes the bottleneck is on the database. It means that you need to go to the whiteboard again and rethinking the solution, and provide a different one that can support the traffic. And that’s a constant improvement. I mean, there is no right answer. What works today, maybe tomorrow, with a different traffic, is not going to work. Then, for our side, that we are always [inaudible 00:29:22] the details, you have dashboards, then we know when something is going bad. And when something’s going bad, we are refactoring to support what’s going on.

The database part is just an example. I think what we know is a little bit hard to change that particular layer, in any architecture, but I think we can do a lot of progress already, just by increasing and scaling the dependencies closer. For instance, if I have a back end for front end, which leaves closer to my front end, that talks to 10 different services, if I can scale all of that at the same time, looking at the same amount of queue, perhaps I’m going to be able to increase the amount of people that I can let in at once, in an on-sale. And most importantly, once the on-sale is over, I am able to scale all of that back down, because our traffic follows that kind of movement, and we will like to keep the efficiency of our infrastructure as well.

Roland Meertens: In this case, the vertical scaling is easy, but scaling to the right vertical size is the problem.

Vitor Pellegrino: Yes, yes.

Roland Meertens: All right. Any other things you wanted to talk about?

Vitor Pellegrino: We’re very happy to be here. Maybe it’s something that, I don’t know, just to leave to the listeners, if they want to hear more about any of them, we’re open to have that kind of discussion. I think a lot of that is something that we’re still thinking of. We don’t claim to have all the answers, but something we’re very excited. And it’s great to be here in an event like this, lots of energy. I’ve been spending a lot of time in a breakout rooms, and between talks, talking to people. And then I’m just like, “I can wait to come back,” and then actually get a lot of these things in practice.

Roland Meertens: You can definitely talk to a lot of people who are struggling with the same problems, or have maybe already solved it.

Anderson Parra: And also try that in the have great problems, then we’re looking for engineers. If you like to working that kind of challenge as well, you’re more than welcome that you can talk about it.

Roland Meertens: Then thank you very much for being here.

Anderson Parra: Thank you.

Roland Meertens: I hope you enjoy the rest of the conference, and I’ll see you in breakout rooms. […]

Anderson Parra: Thank you.

Vitor Pellegrino: I appreciate it. See you.

Roland Meertens: So this was the interview with Anderson and Vitor. I really hope you enjoyed this in-person interview recorded at QCon London, and thank you very much for listening to the InfoQ podcast.

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