This document provides an agenda and overview for a N1QL workshop on indexing and query tuning in Couchbase 4.0. The agenda includes sections on view index, global secondary index (GSI), multi-index scan, hands-on N1QL, query tuning, index selection hints, key-value access, joins, and more hands-on N1QL. The overview sections explain indexing in Couchbase including the primary index, secondary indexes, composite indexes, index intersection for multi-index scans, and the query execution flow involving parsing, planning, scanning indexes, and fetching documents.
Tuning for Performance: indexes & QueriesKeshav Murthy
There are three things important in databases: performance, performance, performance. From a simple query to fetch a document to a query joining millions of documents, designing the right data models and indexes is important. There are many indices you can create, and many options you can choose for each index. This talk will help you understand tuning N1QL query, exploiting various types of indices, analyzing the system behavior, and sizing them correctly.
Understanding N1QL Optimizer to Tune QueriesKeshav Murthy
Every flight has a flight plan. Every query has a query plan. You must have seen its text form, called EXPLAIN PLAN. Query optimizer is responsible for creating this query plan for every query, and it tries to create an optimal plan for every query. In Couchbase, the query optimizer has to choose the most optimal index for the query, decide on the predicates to push down to index scans, create appropriate spans (scan ranges) for each index, understand the sort (ORDER BY) and pagination (OFFSET, LIMIT) requirements, and create the plan accordingly. When you think there is a better plan, you can hint the optimizer with USE INDEX. This talk will teach you how the optimizer selects the indices, index scan methods, and joins. It will teach you the analysis of the optimizer behavior using EXPLAIN plan and how to change the choices optimizer makes.
From SQL to NoSQL: Structured Querying for JSONKeshav Murthy
Can SQL be used to query JSON? SQL is the universally known structured query language, used for well defined, uniformly structured data; while JSON is the lingua franca of flexible data management, used to define complex, variably structured data objects.
Yes! SQL can most-definitely be used to query JSON with Couchbase's SQL query language for JSON called N1QL (verbalized as Nickel.)
In this session, we will explore how N1QL extends SQL to provide the flexibility and agility inherent in JSON while leveraging the universality of SQL as a query language.
We will discuss utilizing SQL to query complex JSON objects that include arrays, sets and nested objects.
You will learn about the powerful query expressiveness of N1QL, including the latest features that have been added to the language. We will cover how using N1QL can solve your real-world application challenges, based on the actual queries of Couchbase end-users.
MongoDB .local Chicago 2019: Practical Data Modeling for MongoDB: TutorialMongoDB
For 30 years, developers have been taught that relational data modeling was THE way to model, but as more companies adopt MongoDB as their data platform, the approaches that work well in relational design actually work against you in a document model design. In this talk, we will discuss how to conceptually approach modeling data with MongoDB, focusing on practical foundational techniques, paired with tips and tricks, and wrapping with discussing design patterns to solve common real world problems.
MongoDB .local Munich 2019: Still Haven't Found What You Are Looking For? Use...MongoDB
Come and hear more about our new full-text search operator for MongoDB Atlas. This is a significant enhancement to MongoDB search features and is the easiest and most powerful full-text search solution for databases on MongoDB Atlas.
This talk is important for anyone who has implemented search or is considering a search feature in their MongoDB application.
You will see a demo of $searchBeta, learn about how it works, discover specific features to help you deliver relevant search results, and learn how you can start using full-text search in your application today.
Bringing SQL to NoSQL: Rich, Declarative Query for NoSQLKeshav Murthy
Abstract
NoSQL databases bring the benefits of schema flexibility and
elastic scaling to the enterprise. Until recently, these benefits have
come at the expense of giving up rich declarative querying as
represented by SQL.
In today’s world of agile business, developers and organizations need
the benefits of both NoSQL and SQL in a single platform. NoSQL
(document) databases provide schema flexibility; fast lookup; and
elastic scaling. SQL-based querying provides expressive data access
and transformation; separation of querying from modeling and storage;
and a unified interface for applications, tools, and users.
Developers need to deliver applications that can easily evolve,
perform, and scale. Otherwise, the cost, effort, and delay in keeping
up with changing business needs will become significant disadvantages.
Organizations need sophisticated and rapid access to their operational data, in
order to maintain insight into their business. This access should
support both pre-defined and ad-hoc querying, and should integrate
with standard analytical tools.
This talk will cover how to build applications that combine the
benefits of NoSQL and SQL to deliver agility, performance, and
scalability. It includes:
- N1QL, which extends SQL to JSON
- JSON data modeling
- Indexing and performance
- Transparent scaling
- Integration and ecosystem
You will walk away with an understanding of the design patterns and
best practices for effective utilization of NoSQL document
databases - all using open-source technologies.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
Tuning for Performance: indexes & QueriesKeshav Murthy
There are three things important in databases: performance, performance, performance. From a simple query to fetch a document to a query joining millions of documents, designing the right data models and indexes is important. There are many indices you can create, and many options you can choose for each index. This talk will help you understand tuning N1QL query, exploiting various types of indices, analyzing the system behavior, and sizing them correctly.
Understanding N1QL Optimizer to Tune QueriesKeshav Murthy
Every flight has a flight plan. Every query has a query plan. You must have seen its text form, called EXPLAIN PLAN. Query optimizer is responsible for creating this query plan for every query, and it tries to create an optimal plan for every query. In Couchbase, the query optimizer has to choose the most optimal index for the query, decide on the predicates to push down to index scans, create appropriate spans (scan ranges) for each index, understand the sort (ORDER BY) and pagination (OFFSET, LIMIT) requirements, and create the plan accordingly. When you think there is a better plan, you can hint the optimizer with USE INDEX. This talk will teach you how the optimizer selects the indices, index scan methods, and joins. It will teach you the analysis of the optimizer behavior using EXPLAIN plan and how to change the choices optimizer makes.
From SQL to NoSQL: Structured Querying for JSONKeshav Murthy
Can SQL be used to query JSON? SQL is the universally known structured query language, used for well defined, uniformly structured data; while JSON is the lingua franca of flexible data management, used to define complex, variably structured data objects.
Yes! SQL can most-definitely be used to query JSON with Couchbase's SQL query language for JSON called N1QL (verbalized as Nickel.)
In this session, we will explore how N1QL extends SQL to provide the flexibility and agility inherent in JSON while leveraging the universality of SQL as a query language.
We will discuss utilizing SQL to query complex JSON objects that include arrays, sets and nested objects.
You will learn about the powerful query expressiveness of N1QL, including the latest features that have been added to the language. We will cover how using N1QL can solve your real-world application challenges, based on the actual queries of Couchbase end-users.
MongoDB .local Chicago 2019: Practical Data Modeling for MongoDB: TutorialMongoDB
For 30 years, developers have been taught that relational data modeling was THE way to model, but as more companies adopt MongoDB as their data platform, the approaches that work well in relational design actually work against you in a document model design. In this talk, we will discuss how to conceptually approach modeling data with MongoDB, focusing on practical foundational techniques, paired with tips and tricks, and wrapping with discussing design patterns to solve common real world problems.
MongoDB .local Munich 2019: Still Haven't Found What You Are Looking For? Use...MongoDB
Come and hear more about our new full-text search operator for MongoDB Atlas. This is a significant enhancement to MongoDB search features and is the easiest and most powerful full-text search solution for databases on MongoDB Atlas.
This talk is important for anyone who has implemented search or is considering a search feature in their MongoDB application.
You will see a demo of $searchBeta, learn about how it works, discover specific features to help you deliver relevant search results, and learn how you can start using full-text search in your application today.
Bringing SQL to NoSQL: Rich, Declarative Query for NoSQLKeshav Murthy
Abstract
NoSQL databases bring the benefits of schema flexibility and
elastic scaling to the enterprise. Until recently, these benefits have
come at the expense of giving up rich declarative querying as
represented by SQL.
In today’s world of agile business, developers and organizations need
the benefits of both NoSQL and SQL in a single platform. NoSQL
(document) databases provide schema flexibility; fast lookup; and
elastic scaling. SQL-based querying provides expressive data access
and transformation; separation of querying from modeling and storage;
and a unified interface for applications, tools, and users.
Developers need to deliver applications that can easily evolve,
perform, and scale. Otherwise, the cost, effort, and delay in keeping
up with changing business needs will become significant disadvantages.
Organizations need sophisticated and rapid access to their operational data, in
order to maintain insight into their business. This access should
support both pre-defined and ad-hoc querying, and should integrate
with standard analytical tools.
This talk will cover how to build applications that combine the
benefits of NoSQL and SQL to deliver agility, performance, and
scalability. It includes:
- N1QL, which extends SQL to JSON
- JSON data modeling
- Indexing and performance
- Transparent scaling
- Integration and ecosystem
You will walk away with an understanding of the design patterns and
best practices for effective utilization of NoSQL document
databases - all using open-source technologies.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
MongoDB .local Munich 2019: A Complete Methodology to Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
Doing Joins in MongoDB: Best Practices for Using $lookupMongoDB
Speaker: Austin Zellner, Solutions Architect, MongoDB
Level: 200 (Intermediate)
Track: Data Analytics
$lookup is a pipeline stage in the aggregation framework that performs a left outer join. In this session, you will learn how to leverage $lookup in your applications and best practices for implementing features with $lookup.
What You Will Learn:
- Fundamentals of $lookup and its syntax.
- How to use $lookup stages in your aggregation pipelines.
- Best practices for using $lookup to implement application features.
MongoDB .local Chicago 2019: Still Haven't Found What You Are Looking For? Us...MongoDB
Atlas Search provides full-text search capabilities for MongoDB collections hosted on MongoDB Atlas. It uses Apache Lucene under the hood to index text fields and support complex search queries. Key features include configurable indexes, flexible scoring, and highlighting search results. The architecture involves a separate mongot process that handles indexing and queries using the Lucene query language, integrated seamlessly with MongoDB queries via the $searchBeta aggregation stage. Future roadmap items include expanded data type support and improved query operators.
Move Fast with MongoDB Cloud Database - Atlas.
The workshop covered:
Deploying a MongoDB cluster in minutes
Query and manage data in MongoDB
Executing continuous backups and point-in-time restores, ensuring that you can meet any restore point objectives
View historical metrics in optimized dashboards, see what’s happening in your database live, configure alerts, and receive automated index suggestions to improve the performance of your cluster
Using MongoDB Charts and create visual representations of your data
MongoDB .local Munich 2019: Managing a Heterogeneous Stack with MongoDB & SQLMongoDB
Data administrators face the challenge of integrating disparate data technologies into a cohesive and performant data platform. This is especially true when using diverse query languages and protocols. This session will focus on how to integrate SQL-aware applications into a MongoDB data platform.
This document provides a high-level summary of MongoDB and its features. It begins with an overview of MongoDB, including its employees, customers, offices, and public status. It then discusses MongoDB's document model and how it allows for flexible, schema-less structures. It also covers MongoDB's rich query language and secondary indexing capabilities. Other sections summarize MongoDB's availability and workload isolation with replica sets, its scalability features including sharding and data locality, its security features, and management tools like Ops Manager and Compass. The document also briefly discusses MongoDB's integration with BI tools and running MongoDB in the cloud with MongoDB Atlas.
MongoDB and Hadoop: Driving Business InsightsMongoDB
MongoDB and Hadoop can work together to solve big data problems facing today's enterprises. We will take an in-depth look at how the two technologies complement and enrich each other with complex analyses and greater intelligence. We will take a deep dive into the MongoDB Connector for Hadoop and how it can be applied to enable new business insights with MapReduce, Pig, and Hive, and demo a Spark application to drive product recommendations.
- IOOF uses MongoDB for state management in over 85% of its 250+ microservices due to its document storage, developer friendliness, performance at scale, and ability to break from monolithic database architectures.
- IOOF's data warehouse also utilizes MongoDB to store time series data from various microservices via change data capture (CDC) for improved analytics over relational databases.
- IOOF has three MongoDB replica sets for production, staging, and development environments, handling high read/write volumes through sharding and partitioning as needed for scale.
MongoDB Europe 2016 - Who’s Helping Themselves To Your Data? Demystifying Mon...MongoDB
The document discusses MongoDB's security features including authentication, authorization, encryption, and auditing. It emphasizes that MongoDB's security features have minimal dependencies and keep the path to secure success clear. The key features are authentication using passwords, LDAP, certificates or Kerberos; role-based authorization; encryption of data in transit using TLS and at rest using the encrypted storage engine; and auditing of operations to a configurable destination.
MongoDB .local Munich 2019: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business.
This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.
Tutorial: Building Your First App with MongoDB StitchMongoDB
MongoDB Stitch allows developers to easily access and integrate MongoDB databases with key services. It provides integrated rules, functions and SDKs to handle complex connection logic and orchestrate databases and third party services. Requests made through Stitch applications are parsed, services are orchestrated, rules are applied, and results are returned to clients. Stitch offers scalable hosted JavaScript functions and declarative access controls to securely manage data and service access.
This was presented by the MongoDB team at the Singapore VIP event on 24th Jan 2019.
The presentation covers-
What is MongoDB
Why MongoDB
MongoDB As a Service, Serverless Platform and Mobile
MongoDB Atlas: Database as a Service (Available on AWS, Azure and Google Cloud)
Usecases
This document discusses various indexing strategies in MongoDB to help scale applications. It covers the basics of indexes, including creating and tuning indexes. It also discusses different index types like geospatial indexes, text indexes, and how to use explain plans and profiling to evaluate queries. The document concludes with a section on scaling strategies like sharding to scale beyond a single server's resources.
MongoDB Europe 2016 - MongoDB 3.4 preview and introduction to MongoDB AtlasMongoDB
This document contains notes from a MongoDB conference presentation focused on improvements, extensions, and innovations to MongoDB. Key topics discussed include improvements to Wired Tiger storage engine and replica set election processes, extensions like document validation and $lookup features, and innovations like aggregation pipeline improvements and mixed storage engine sets. Demos were given on Compass UI tool and Atlas cloud database service. The presentation emphasized ongoing work to improve performance and capabilities, extend MongoDB to new domains, and innovate with features like zones and cloud-native services.
The integration between Spring Framework and MongoDB tends to be somewhat unknown. This presentation shows the different projects that compose Spring ecosystem, Springdata, Springboot, SpringIO etc and how to merge between the pure JAVA projects to massive enterprise systems that require the interaction of these systems together.
MongoDB Launchpad 2016: What’s New in the 3.4 ServerMongoDB
Asya Kamsky, a lead product manager at MongoDB, discussed improvements, extensions, and innovations in MongoDB. These included improvements to the Wired Tiger storage engine, replica set election process, and initial sync process. MongoDB was also extended with features like document validation, partial indexes, $lookup, read-only views, and faceted search. Innovations involved improvements to the aggregation pipeline, mixed storage engine sets, zones, and BI connectors.
This document summarizes a MongoDB webinar on advanced schema design patterns. It introduces common schema design patterns like attribute, subset, computed, and approximation patterns. It discusses how to use these patterns to address issues like large documents with many fields, working sets that don't fit in RAM, high CPU usage from repeated calculations, and changing schemas over time. The webinar provides examples of each pattern and encourages learning a common vocabulary for designing MongoDB schemas by applying these reusable patterns.
This document provides an introduction to schema design in MongoDB. It begins by explaining why schema design is important, even though MongoDB is often described as "schema-less". The goals of the session are to explain the document model, differences from relational databases, how schema design impacts performance, and thinking about schema design in a new way. Various schema design patterns are discussed, including embedding vs referencing for different types of data relationships. Examples of schema designs for a guitar collector application and healthcare application are provided. Tools like Atlas, Stitch, and Compass that are useful for MongoDB are also mentioned.
The document describes Nitro, a fast and scalable in-memory storage engine for global secondary indexes in NoSQL databases. Nitro uses lock-free data structures like skiplists to provide high concurrency. It creates immutable snapshots for indexes and handles multiple versions to avoid phantoms and provide stable scans. Nitro integrates with Couchbase to provide scalable indexing performance and reduced indexing latency. Evaluation shows Nitro achieves over 1 million operations per second for indexing and fast backup and recovery times.
MongoDB .local Munich 2019: A Complete Methodology to Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
Doing Joins in MongoDB: Best Practices for Using $lookupMongoDB
Speaker: Austin Zellner, Solutions Architect, MongoDB
Level: 200 (Intermediate)
Track: Data Analytics
$lookup is a pipeline stage in the aggregation framework that performs a left outer join. In this session, you will learn how to leverage $lookup in your applications and best practices for implementing features with $lookup.
What You Will Learn:
- Fundamentals of $lookup and its syntax.
- How to use $lookup stages in your aggregation pipelines.
- Best practices for using $lookup to implement application features.
MongoDB .local Chicago 2019: Still Haven't Found What You Are Looking For? Us...MongoDB
Atlas Search provides full-text search capabilities for MongoDB collections hosted on MongoDB Atlas. It uses Apache Lucene under the hood to index text fields and support complex search queries. Key features include configurable indexes, flexible scoring, and highlighting search results. The architecture involves a separate mongot process that handles indexing and queries using the Lucene query language, integrated seamlessly with MongoDB queries via the $searchBeta aggregation stage. Future roadmap items include expanded data type support and improved query operators.
Move Fast with MongoDB Cloud Database - Atlas.
The workshop covered:
Deploying a MongoDB cluster in minutes
Query and manage data in MongoDB
Executing continuous backups and point-in-time restores, ensuring that you can meet any restore point objectives
View historical metrics in optimized dashboards, see what’s happening in your database live, configure alerts, and receive automated index suggestions to improve the performance of your cluster
Using MongoDB Charts and create visual representations of your data
MongoDB .local Munich 2019: Managing a Heterogeneous Stack with MongoDB & SQLMongoDB
Data administrators face the challenge of integrating disparate data technologies into a cohesive and performant data platform. This is especially true when using diverse query languages and protocols. This session will focus on how to integrate SQL-aware applications into a MongoDB data platform.
This document provides a high-level summary of MongoDB and its features. It begins with an overview of MongoDB, including its employees, customers, offices, and public status. It then discusses MongoDB's document model and how it allows for flexible, schema-less structures. It also covers MongoDB's rich query language and secondary indexing capabilities. Other sections summarize MongoDB's availability and workload isolation with replica sets, its scalability features including sharding and data locality, its security features, and management tools like Ops Manager and Compass. The document also briefly discusses MongoDB's integration with BI tools and running MongoDB in the cloud with MongoDB Atlas.
MongoDB and Hadoop: Driving Business InsightsMongoDB
MongoDB and Hadoop can work together to solve big data problems facing today's enterprises. We will take an in-depth look at how the two technologies complement and enrich each other with complex analyses and greater intelligence. We will take a deep dive into the MongoDB Connector for Hadoop and how it can be applied to enable new business insights with MapReduce, Pig, and Hive, and demo a Spark application to drive product recommendations.
- IOOF uses MongoDB for state management in over 85% of its 250+ microservices due to its document storage, developer friendliness, performance at scale, and ability to break from monolithic database architectures.
- IOOF's data warehouse also utilizes MongoDB to store time series data from various microservices via change data capture (CDC) for improved analytics over relational databases.
- IOOF has three MongoDB replica sets for production, staging, and development environments, handling high read/write volumes through sharding and partitioning as needed for scale.
MongoDB Europe 2016 - Who’s Helping Themselves To Your Data? Demystifying Mon...MongoDB
The document discusses MongoDB's security features including authentication, authorization, encryption, and auditing. It emphasizes that MongoDB's security features have minimal dependencies and keep the path to secure success clear. The key features are authentication using passwords, LDAP, certificates or Kerberos; role-based authorization; encryption of data in transit using TLS and at rest using the encrypted storage engine; and auditing of operations to a configurable destination.
MongoDB .local Munich 2019: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business.
This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.
Tutorial: Building Your First App with MongoDB StitchMongoDB
MongoDB Stitch allows developers to easily access and integrate MongoDB databases with key services. It provides integrated rules, functions and SDKs to handle complex connection logic and orchestrate databases and third party services. Requests made through Stitch applications are parsed, services are orchestrated, rules are applied, and results are returned to clients. Stitch offers scalable hosted JavaScript functions and declarative access controls to securely manage data and service access.
This was presented by the MongoDB team at the Singapore VIP event on 24th Jan 2019.
The presentation covers-
What is MongoDB
Why MongoDB
MongoDB As a Service, Serverless Platform and Mobile
MongoDB Atlas: Database as a Service (Available on AWS, Azure and Google Cloud)
Usecases
This document discusses various indexing strategies in MongoDB to help scale applications. It covers the basics of indexes, including creating and tuning indexes. It also discusses different index types like geospatial indexes, text indexes, and how to use explain plans and profiling to evaluate queries. The document concludes with a section on scaling strategies like sharding to scale beyond a single server's resources.
MongoDB Europe 2016 - MongoDB 3.4 preview and introduction to MongoDB AtlasMongoDB
This document contains notes from a MongoDB conference presentation focused on improvements, extensions, and innovations to MongoDB. Key topics discussed include improvements to Wired Tiger storage engine and replica set election processes, extensions like document validation and $lookup features, and innovations like aggregation pipeline improvements and mixed storage engine sets. Demos were given on Compass UI tool and Atlas cloud database service. The presentation emphasized ongoing work to improve performance and capabilities, extend MongoDB to new domains, and innovate with features like zones and cloud-native services.
The integration between Spring Framework and MongoDB tends to be somewhat unknown. This presentation shows the different projects that compose Spring ecosystem, Springdata, Springboot, SpringIO etc and how to merge between the pure JAVA projects to massive enterprise systems that require the interaction of these systems together.
MongoDB Launchpad 2016: What’s New in the 3.4 ServerMongoDB
Asya Kamsky, a lead product manager at MongoDB, discussed improvements, extensions, and innovations in MongoDB. These included improvements to the Wired Tiger storage engine, replica set election process, and initial sync process. MongoDB was also extended with features like document validation, partial indexes, $lookup, read-only views, and faceted search. Innovations involved improvements to the aggregation pipeline, mixed storage engine sets, zones, and BI connectors.
This document summarizes a MongoDB webinar on advanced schema design patterns. It introduces common schema design patterns like attribute, subset, computed, and approximation patterns. It discusses how to use these patterns to address issues like large documents with many fields, working sets that don't fit in RAM, high CPU usage from repeated calculations, and changing schemas over time. The webinar provides examples of each pattern and encourages learning a common vocabulary for designing MongoDB schemas by applying these reusable patterns.
This document provides an introduction to schema design in MongoDB. It begins by explaining why schema design is important, even though MongoDB is often described as "schema-less". The goals of the session are to explain the document model, differences from relational databases, how schema design impacts performance, and thinking about schema design in a new way. Various schema design patterns are discussed, including embedding vs referencing for different types of data relationships. Examples of schema designs for a guitar collector application and healthcare application are provided. Tools like Atlas, Stitch, and Compass that are useful for MongoDB are also mentioned.
The document describes Nitro, a fast and scalable in-memory storage engine for global secondary indexes in NoSQL databases. Nitro uses lock-free data structures like skiplists to provide high concurrency. It creates immutable snapshots for indexes and handles multiple versions to avoid phantoms and provide stable scans. Nitro integrates with Couchbase to provide scalable indexing performance and reduced indexing latency. Evaluation shows Nitro achieves over 1 million operations per second for indexing and fast backup and recovery times.
Variety is the spice of life, but it’s also the reality of big data. For this reason, JSON has now becoming lingua franca of data in the internet – for APIs, data exchange, data storage and data processing. In the business intelligence world, SQL is the language to analyze the data in other forms. Hence, the myriad of “SQL-on-Hadoop” projects. However, traditional SQL isn’t JSON/Parquet/etc. friendly. ETL into flattened tables is costly and not real time.
Apache Drill unifies SQL with variety of data forms on Hadoop. That enables interactive analytics using your favorite BI tool and visualization tool on you data simultaneously. In this talk, we’ll introduce Apache Drill and describe use cases.
- See more at: http://nosql2014.dataversity.net/sessionPop.cfm?confid=81&proposalid=6850#sthash.NhuLz6Dq.dpuf
This document introduces Couchbase 4.5 and Couchbase Mobile 1.2 and discusses several use cases for using Couchbase as a NoSQL database solution. It summarizes five common use cases: 1) high-availability caching to speed up database operations, 2) using Couchbase as a session store, 3) creating a globally distributed user profile store, 4) aggregating data from various sources, and 5) storing and accessing content and metadata.
Utilizing Arrays: Modeling, Querying and IndexingKeshav Murthy
Arrays can be simple; arrays can be complex. JSON arrays give you a method to collapse the data model while retaining structure flexibility. Arrays of scalars, objects, and arrays are common structures in a JSON data model. Once you have this, you need to write queries to update and retrieve the data you need efficiently. This talk will discuss modeling and querying arrays. Then, it will discuss using array indexes to help run those queries on arrays faster.
Rolling presentation during Couchbase Day. Including
Introduction to NoSQL
Why NoSQL?
Introduction to Couchbase
Couchbase Architecture
Single Node Operations
Cluster Operations
HA and DR
Availability and XDCR
Backup/Restore
Security
Developing with Couchbase
Couchbase SDKs
Couchbase Indexing
Couchbase GSI and Views
Indexing and Query
Couchbase Mobile
This document discusses migrating from relational databases to NoSQL databases. It outlines some common use cases for NoSQL databases like caching, session storage, and content management. It then discusses some considerations for NoSQL databases, such as how data is accessed, consistency models, and scaling. The document promotes Couchbase as a NoSQL option, highlighting its ability to scale out horizontally, provide high availability through replication, and easily add and remove nodes.
SDEC2011 Using Couchbase for social game scaling and speedKorea Sdec
A social game, by it's very nature, can spread very quickly to a large user population. Because the game is typically interactive, the speed of retrieving information needed for the user's interactions with the system is critical. When building their new game Animal Party, the developers at Tribal Crossing needed to get away from the complexity of sharding an SQL database. They also were looking for a solution to the administration cost associated with the operation of traditional data stores. When evaluating multiple different NoSQL solutions, they realized that Couchbase's Membase server meets most of their critical requirements in developing their game software. Simple to use, Couchbase's model allows Tribal Crossing to easily model their game interactions with the key/value data store. Fast read and write performance is required with interactive, social games, and they found that support in Membase as well. Elastic scalability is easily achieved by simply adding more nodes to the Couchbase cluster without any modifications required to the application. Relying on Couchbase's technology Tribal Crossing has been able to quickly build and scale Animal Party with a small team and no dedicated system administrators.
http://sdec.kr/
Accelerating analytics on the Sensor and IoT Data. Keshav Murthy
Informix Warehouse Accelerator (IWA) has helped traditional
data warehousing performance to improve dramatically. Now,
IWA accelerates analytics over the sensor data stored in relational and timeseries data.
1. MongoDB Stitch is a backend as a service that allows developers to easily work with data and integrate their apps with key services.
2. It provides integrated rules, pipelines, and services to handle complex workflows between databases and third party services.
3. Requests made to Stitch are parsed, rules are applied, databases and services are orchestrated, results are aggregated and returned to the client.
Queries need indexes to speed up and optimize resource utilization. What indexes to create and what rules to follow to create right indexes to optimize the workload? This presentation gives the rules for those.
This document provides an agenda for a presentation on integrating Apache Cassandra and Apache Spark. The presentation will cover RDBMS vs NoSQL databases, an overview of Cassandra including data model and queries, and Spark including RDDs and running Spark on Cassandra data. Examples will be shown of performing joins between Cassandra and Spark DataFrames for both simple and complex queries.
Today’s highly connected world is flooding businesses with big and fast-moving data. The ability to trawl this data ocean and identify actionable insights can deliver a competitive advantage to any organization. The WSO2 Analytics Platform enables businesses to do just that by providing batch, real-time, interactive and predictive analysis capabilities all in one place.
In this tutorial we will
* Plug in the WSO2 Analytics Platform to some common business use cases
* Showcase the numerous capabilities of the platform
* Demonstrate how to collect data, analyze, predict and communicate effectively
* Demonstrate how it can analyze integration, security and IoT scenarios
Stick around till the end and you will walk away with the necessary skills to create a winning data strategy for your organization to stay ahead of its competition.
Persisting data in NoSQL document databases, such as Couchbase, offers a lot more options and flexibility than relational databases (RDBMS) like SQL Server. These choices can be daunting at first, and involve trade-offs between concurrency, consistency, and performance.
The goal of this session will be to demystify NoSQL data modeling techniques for Couchbase. We will cover everything from a basic overview of data types and relationships all the way to how the Domain Driven Design approach to modeling can be applied to Couchbase.
New Approaches for Fraud Detection on Apache Kafka and KSQLconfluent
This document discusses new approaches for fraud detection using Apache Kafka and KSQL. It introduces KSQL, an open-source streaming SQL engine for Apache Kafka. KSQL can be used to perform streaming ETL, anomaly detection, and event monitoring using SQL-like queries on streaming data. The document demonstrates how to run KSQL locally or in a client-server configuration, and how Arcadia Data provides a visualization layer on top of KSQL to enable visual analytics on streaming data.
Simplifying & accelerating application development with MongoDB's intelligent...Maxime Beugnet
The document discusses MongoDB's Intelligent Operational Data Platform and how it allows developers to simplify application development. It highlights how MongoDB uses a document model which is more flexible than a relational database and allows for embedding of related data. MongoDB also provides features like multi-document transactions, full indexing capabilities, advanced aggregations, and change streams for building reactive applications in real-time.
Slidedeck presented at http://devternity.com/ around MongoDB internals. We review the usage patterns of MongoDB, the different storage engines and persistency models as well has the definition of documents and general data structures.
Stratio: Geospatial and bitemporal search in Cassandra with pluggable Lucene ...DataStax Academy
The document describes a method for indexing and searching bitemporal data in Cassandra using Lucene indexes. It proposes using four R-trees, with each R-tree represented using two DateRangePrefixTrees in Lucene to index the data by valid and transaction time ranges. Queries are transformed and distributed to search the appropriate R-trees and DateRangePrefixTrees to retrieve bitemporal data within the specified time ranges.
Geospatial and bitemporal search in cassandra with pluggable lucene indexAndrés de la Peña
Stratio presented its open source Lucene-based implementation of Cassandra’s secondary indexes at Cassandra Summit London 2014, which provided several search engine features. It used to be distributed as a fork of Apache Cassandra, which was a huge problem both for users and maintainers. Nowadays, due to some changes introduced at C* 2.1.6, we are proud to announce that it has become a plugin that can be attached to the official Apache Cassandra. With the plugin we have been able to provide C* with geospatial capabilities, making it possible to index geographical positions and perform bounding box and radial distance queries. This is achieved through Lucene’s geospatial module. Another feature we have provided with our plugin is the possibility of indexing bitemporal data models, which distinguish between system time and business time. This way, it is possible to make queries over C* such as “give me what system thought in a certain instant about what happened in another instant”. The implementation has been performed combining range prefix trees with the 4R-Tree approach exposed by Bliujūtė et al. Both full-text, geospatial and bitemporal queries can be combined with Apache Spark to avoid systematic full-scan, dramatically reducing the amount of data to be processed.
This is a run-through at a 200 level of the Microsoft Azure Big Data Analytics for the Cloud data platform based on the Cortana Intelligence Suite offerings.
BM Cloudant is a NoSQL Database-as-a-Service. Discover how you can outsource the data layer of your mobile or web application to Cloudant to provide high availability, scalability and tools to take you to the next level.
Time series Analytics - a deep dive into ADX Azure Data Explorer @Data Saturd...Riccardo Zamana
Riccardo Zamana presented on time series analytics using Azure Data Explorer (ADX). The presentation covered ADX basics, architecture, quickstart, ingestion techniques including LightIngest and One Click ingestion, query optimization techniques like materialized views and caching, and visualization using dashboards in Kusto and Grafana. The document provided code examples of queries, functions, and operators for time series analysis in ADX.
The document discusses Cisco's Hadoop as a service offering on their Intercloud platform. Some key points:
- Cisco provides managed Hadoop, including Cloudera's distribution, on optimized instances with local storage and object storage. This offers a scalable, reliable, and secure environment for Hadoop workloads.
- Use cases discussed include predictive maintenance using IoT data and analyzing customer journeys across multiple channels.
- A pilot test showed Cisco's platform could process over 100 million records from production data across various Hadoop jobs.
- Cisco also discusses their data virtualization product CiscoDV, which can integrate data across on-premises, cloud sources on Cisco and AWS.
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Speaker:Drew DiPalma
Learn more about MongoDB Stitch, our new Backend as a Service (BaaS) that makes it easy for developers to create and launch applications across mobile and web platforms. Stitch provides a REST API on top of MongoDB with read, write, and validation rules built-in and full integration with the services you love. This talk will cover the what, why, and how of MongoDB Stitch. We'll discuss everything from features to the architecture. You'll walk away knowing how Stitch can kickstart your new project or take your existing application to the next level.
Global Secondary Indexes in Couchbase Server 4.0 - JUNE 2015Cihan Biyikoglu
In this presentation, you will get to know
- Couchbase Server, Global Secondary Indexes (GSI) – the new high performance indexer for N1QL
- Look at GSI lifecycle & management in Couchbase Server
- Cover top best practices and tips with GSI in Couchbase Server
Slides: Moving from a Relational Model to NoSQLDATAVERSITY
Businesses are quickly moving to NoSQL databases to power their modern applications. However, a technology migration involves risk, especially if you have to change your data model. What if you could host a relatively unmodified RDBMS schema on your NoSQL database, then optimize it over time?
We’ll show you how Couchbase makes it easy to:
• Use SQL for JSON to query your data and create joins
• Optimize indexes and perform HashMap queries
• Build applications and analysis with NoSQL
SQL for JSON: Rich, Declarative Querying for NoSQL Databases and Applications Keshav Murthy
In today’s world of agile business, Java developers and organizations benefit when JSON-based NoSQL databases and SQL-based querying come together. NoSQL provides schema flexibility and elastic scaling. SQL provides expressive, independent data access. Java developers need to deliver apps that readily evolve, perform, and scale with changing business needs. Organizations need rapid access to their operational data, using standard analytical tools, for insight into their business. In this session, you will learn to build apps that combine NoSQL and SQL for agility, performance, and scalability. This includes
• JSON data modeling
• Indexing
• Tool integration
The N1QL is a developer favorite because it’s SQL for JSON. Developer’s life is going to get easier with the upcoming N1QL features. We have exciting features in many areas including language to performance, indexing to search, and tuning to transactions. This session will preview new the features for both new and advanced users.
Couchbase Tutorial: Big data Open Source Systems: VLDB2018Keshav Murthy
The document provides an agenda and introduction to Couchbase and N1QL. It discusses Couchbase architecture, data types, data manipulation statements, query operators like JOIN and UNNEST, indexing, and query execution flow in Couchbase. It compares SQL and N1QL, highlighting how N1QL extends SQL to query JSON data.
N1QL+GSI: Language and Performance Improvements in Couchbase 5.0 and 5.5Keshav Murthy
N1QL gives developers and enterprises an expressive, powerful, and complete language for querying, transforming, and manipulating JSON data. We’ll begin this session with a brief overview of N1QL and then explore some key enhancements we’ve made in the latest versions of Couchbase Server. Couchbase Server 5.0 has language and performance improvements for pagination, index exploitation, integration, index availability, and more. Couchbase Server 5.5 will offer even more language and performance features for N1QL and global secondary indexes (GSI), including ANSI joins, aggregate performance, index partitioning, auditing, and more. We’ll give you an overview of the new features as well as practical use case examples.
XLDB Lightning Talk: Databases for an Engaged World: Requirements and Design...Keshav Murthy
Traditional databases have been designed for system of record and analytics. Modern enterprises have orders of magnitude more interactions than transactions. Couchbase Server is a rethinking of the database for interactions and engagements called, Systems of Engagement. Memory today is much cheaper than disks were when traditional databases were designed back in the 1970's, and networks are much faster and much more reliable than ever before. Application agility is also an extremely important requirement. Today's Couchbase Server is a memory- and network-centric, shared-nothing, auto-partitioned, and distributed NoSQL database system that offers both key-based and secondary index-based data access paths as well as API- and query-based data access capabilities. This lightning talk gives you an overview of requirements posed by next-generation database applications and approach to implementation including “Multi Dimensional Scaling.
Couchbase 5.5: N1QL and Indexing featuresKeshav Murthy
This deck contains the high-level overview of N1QL and Indexing features in Couchbase 5.5. ANSI joins, hash join, index partitioning, grouping, aggregation performance, auditing, query performance features, infrastructure features.
The document discusses improvements to the N1QL query optimizer and execution engine in Couchbase Server 5.0. Key improvements include UnionScan to handle OR predicates using multiple indexes, IntersectScan terminating early for better performance, implicit covering array indexes, stable scans, efficiently pushing composite filters, pagination support, index column ordering, aggregate pushdown, and index projections.
Mindmap: Oracle to Couchbase for developersKeshav Murthy
This deck provides a high-level comparison between Oracle and Couchbase: Architecture, database objects, types, data model, SQL & N1QL statements, indexing, optimizer, transactions, SDK and deployment options.
N1QL = SQL + JSON. N1QL gives developers and enterprises an expressive, powerful, and complete language for querying, transforming, and manipulating JSON data. We begin with a brief overview. Couchbase 5.0 has language and performance improvements for pagination, index exploitation, integration, and more. We’ll walk through scenarios, features, and best practices.
N1QL supports select, join, project,nest,unnest operations on flexible schema documents represented in JSON.
Couchbase 4.5 enhances the data modeling and query flexibility.
When you have parent-child relationship, children documents point to parent document, you join from child to parent. Now, how would you join from parent to child when parent does not contain the reference to child? How would you improve performance on this? This presentation explain the syntax, execution of the query.
Introducing N1QL: New SQL Based Query Language for JSONKeshav Murthy
This session introduces N1QL and sets the stage for the rich selection of N1QL-related sessions at Couchbase Connect 2015. N1QL is SQL for JSON, extending the querying power of SQL with the modeling flexibility of JSON. In this session, you will get an introduction to the N1QL language, architecture, and ecosystem, and you will hear the benefits of N1QL for developers and for enterprises.
Enterprise Architect's view of Couchbase 4.0 with N1QLKeshav Murthy
Enterprise architects have to decide on the database platform that will meet various requirements: performance and scalability on one side, ease of data modeling, agile development on the other, elasticity and flexibility to handle change easily, and a database platform that integrates well with tools and within ecosystem. This presentation will highlight the challenges and approaches to solution using Couchbase with N1QL.
You know what iMEAN? Using MEAN stack for application dev on InformixKeshav Murthy
You know what iMEAN? Using MEAN stack for application dev on Informix. MongoDB, ExpressJS, AngularJS, NodeJS combine to form a MEAN stack for quick appdev. iMEAN is using the same stack to develop applications on Informix.
Informix SQL & NoSQL: Putting it all togetherKeshav Murthy
IBM Informix is a database management system that provides capabilities for handling different types of data including relational tables, JSON collections, and time series data. It uses a hybrid approach that allows seamless access to different data types using SQL and NoSQL APIs. The document discusses how Informix can be used to store and analyze IoT, mobile, and sensor data from devices and gateways in both on-premises and cloud environments. It also highlights the Informix Warehouse Accelerator for in-memory analytics and how Informix can be integrated with other IBM products and services like MongoDB, Bluemix, and Cognos.
Informix SQL & NoSQL -- for Chat with the labs on 4/22Keshav Murthy
This document discusses how Informix can be used for both SQL and NoSQL applications. It notes that applications now need to support mobile, big data, and social/digital collaboration. NoSQL databases like MongoDB are growing in popularity due to their ability to handle these new requirements. The document then outlines how Informix provides drivers and tools that allow existing MongoDB applications and data models to run directly on Informix, and also allows SQL applications to access and query JSON document data stored in Informix. It discusses features like indexing, querying, scaling, and hybrid access to both relational and JSON data from a single database platform.
NoSQL Deepdive - with Informix NoSQL. IOD 2013Keshav Murthy
Deepdive into IBM Informix NoSQL with Mongodb API, hybrid access. Informix now supports MongoDB API and stores JSON natively, thus supporting flexible schema. Informix NoSQL also supports sharding, enabling scale out. This presentation gives overview to a real application to technical details of this
Informix NoSQL & Hybrid SQL detailed deep diveKeshav Murthy
This document provides an overview of Informix NoSQL capabilities and use cases. It discusses key-value stores, column family stores, document databases, and graph databases supported by Informix NoSQL. Several business uses of Informix NoSQL are outlined, including session store, user profile store, content metadata store, mobile apps, third party data aggregation, caching, and ecommerce. The document also compares pricing of Informix and MongoDB editions over a three year period. Finally, it provides timelines for go-to-market strategies for DB2 JSON and Informix JSON capabilities.
Table for two? Hybrid approach to developing combined SQL, NoSQL applications...Keshav Murthy
Informix embraces the NoSQL by implementing flexible schema via JSON, sharding and MongoAPI. Additionally, data in sql tables and JSON collections can by accessed by any API and use the power of the RDBMS engine.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Data-parallel — Query latency scales up with cores
Memory-bound
View Indexes: Incremental Map/Reduce with customer JavaScript for complex indexing logic for online reporting and analytics
GSI (Global Secondary Indexes): Efficient indexes for secondary lookups and ad-hoc query processing
Projector and Router:
1 Projector and Router per node
1 stream of changes per buckets per supervisor
Supervisor
1 Supervisor per node
Many indexes per Supervisor
PrimaryScan
Equivalent of full table scan in RDBMS
Uses the primary index to scan from start to finish
Equivalent of full table scan in RDBMS