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    AWS RDS vs DynamoDB

    In this article, you can learn how SQL and NoSQL databases work with their strengths and weaknesses, and how to monitor them with Thundra.

    5 minutes read POSTED May, 2020 IN

    AWS RDS vs DynamoDB - SQL or NoSQL Databases?

    Written by Emrah Samdan

    Former VP of Product @Thundra

    Choosing the right database has one of the most direct effects on the cost and performance of your application. A serverless environment is no exception to this principle. By considering parameters like access patterns, schema (data model), and expected performance, it's possible to logically arrive at an optimal decision for your database type.

    Databases in general have two broad classifications: SQL relational or normalized databases and NoSQL or denormalized databases. A third classification has also arisen for specific use cases like modeling graphs, in-memory caching systems among others.

    But in this article, we'll explore certain aspects of SQL and NoSQL databases and take a look at the services offered by AWS for those respective types of databases.

    SQL Databases

    SQL, or relational databases, use Structured Query Language for working with data. It’s a powerful language and one of the most versatile options out there for handling data. You can make complicated queries for retrieval, which comes in quite handy if you are looking to perform data analysis aka OLAP (online analytical processing).

    SQL excels in handling multi-row transactions at scale. This type of database is the right choice if you don’t fully understand the access patterns your application may have over time and don’t expect exponential growth.

    However, SQL databases come with their share of drawbacks. The schema needs to be defined before you can start working with the database, requiring significant preparation and planning. Once the data model has been set up, you’ll need to maintain that structure throughout the existence of your application.

    Scalability can also be problematic as SQL systems have vertical scaling; adding more CPU and RAM to handle the load can become very expensive, and there is a limit to how much you can “scale up”. You can always add more servers, or “scale-out,” but that requires extensive effort and comes at a steep cost.

    NoSQL Databases

    NoSQL databases unlike SQL don't have a specific query language and champions the concept of working with denormalized data sets.

    Their types include:

    Document Based Key/Value Pairs Graph Data

    Column Oriented Data Structure

    The elimination of complex joins across tables by simply putting everything in one collection with a systematic access pattern is the highlight of NoSQL databases. Their high performance, scalability, and extensibility makes NoSQL a perfect match for serverless development.

    NoSQL databases are known for their ability to scale out to handle tremendous OLTP (online transactional processing) work loads and manage disparate data sets. Also, changing the schema won’t disrupt your currently working application.

    Dynamic schemas + high performance at peak = best database option, right? Not quite so. If you are unsure of what data patterns your application entails then it's probably best to avoid NoSQL or invest time into learning NoSQL first before shooting yourself in the foot.

    Think of it this way, a startup will probably benefit in getting out an MVP as soon as possible by choosing SQL databases because of the flexibility and general developer familiarity over NoSQL systems, but a NoSQL database can lead to future gains because of its very high scalability. The only hurdle is appropriate data modeling so as to service the future needs of data access effectively.

    Those who start with NoSQL from a SQL background without the proper understanding of access patterns may walk themselves into a corner or use NoSQL as if it was still SQL. This will also impact your application as it grows.

    With that being said, let's explore Amazon’s managed versions of SQL and NoSQL services: RDS and DynamoDB, and their performance with Serverless applications.

    AWS RDS vs DynamoDB


    Amazon Relational Database Service (RDS) comes with six engines to pick from, MySQL, PostgreSQL, MariaDB, Oracle, SQL Server and their own implementation, Aurora.

    As explained already, relational databases follow a set pattern of structured data modelling and are governed by the SQL language for data handling. So if your decision is to go with Amazon RDS, here are a few key areas to be aware of.


    Your Lambda functions are pay-per-use but you will pay the hourly pricing for RDS instances unless your choice of engine is Aurora Serverless.

    Managing Connections

    Managing the connection limits with Lambda is important. Lambda does terminate connections on container expiration but the MAX CONNECTIONS issue still looms when the application comes under stress.

    Utilizing RDS Proxy, lowering connection timeouts for RDS, implementing a caching strategy, limiting concurrent connections etc. are some ways to deal with this problem.

    Performance and High Availability

    Amazon’s Aurora database service (either MySQL or PostgreSQL) can prove to be a great option especially in terms of read performance (across regions too) owing to their ability to replicate quicker than RDS counterparts.

    स्रोत : blog.thundra.io

    Choosing a Database for Serverless Applications

    Learn the factors to consider when choosing a database with Serverless applications and AWS Lambda

    Guides & Tutorials

    Choosing a Database for Serverless Applications

    Alex DeBrie Jun 20, 2019

    When designing and building an application, one of the key considerations is which database to use. A poor decision here can cost you severely, either by requiring costly ongoing maintenance of a database or by forcing a sensitive data migration to another solution.

    In this post, we'll cover how to choose a database for your serverless application. With serverless applications, there are new elements to consider as compared to traditional, instance-based architectures.

    This post will start with some key high-level factors that impact your choice of a serverless database. Then, we'll look at a few key categories of databases to see how well they fit with the key factors we've identified.

    The table of contents of this post is as follows:

    Key Factors for Serverless Databases

    Data model needs Connection model


    Fully managed Pricing model

    Serverless Database Categories

    Server-based, relational databases

    Server-based, NoSQL databases

    DynamoDB Aurora Serverless

    Key Factors for Serverless Databases

    Before we get too far, let's consider the different factors you should consider when evaluating databases for a serverless application.

    I've listed five factors that I consider most important for choosing a database in a serverless world. The first factor, centered on your data model needs, applies to serverless and non-serverless applications alike. The remaining four factors are more focused on particular attributes of serverless architectures.

    After we review the five factors generally, we'll look at a few classes of databases to see how they rate on the five factors.

    Data model needs

    The first factor you should consider is the data model needs of your application and how well a database fits those needs.

    Amazon has been pushing the notion of purpose-built databases for a bit now. The idea here is that in the past, most applications were forced to use a relational database. Now, there are a variety of database types to choose from, and you should pick the one that fits your application best.

    I'll oversimplify a bit, but I see database options as three main classes:

    Relational / SQL / normalized: Traditional RDBMS systems that allow for significant query flexibility at the cost of top-end performance. Examples include MySQL, PostgreSQL,NoSQL / denormalized: More recent database options that optimize for read-time queries at the expense of denormalized data sets. Lots of variety here but include MongoDB, Cassandra, and DynamoDB.Hyper-specific use cases: Databases that are specialized for a specific purpose. This may include Elasticsearch for full-text search, Neo4J for modeling graphs, or Redis for in-memory operations.

    I don't see this grouping a lot, but I think it's a fruitful way to think about it.

    For some very specialized use cases, your choice is basically made for you. This is for anything in the third bucket -- you need graph traversal queries or full-text search and thus need to use a database specifically suited for that access pattern.

    Most applications can model their data in either of the first two buckets and the choice is more about flexibility vs. performance. If your data access patterns may change and you need the flexiblity, then go with a relational database. If you need hyper-scale and high performance, then go with a NoSQL database.

    Connection model

    The second factor to consider is the connection model of the database.

    This factor is a bit different than traditional, non-serverless applications. Most databases were built for a pre-serverless world. In this world, database clients were long-running applications on servers. Most databases want you to set up a persistent TCP connection to the database server and reuse this connection across multiple requests.

    There are some downsides to this persistent connection. First, setting up and tearing down the connection takes time. When you're using a long-running application, this doesn't matter as much. You can pay that upfront cost once and then get the benefits of a persistent connection across all of your subsequent requests.

    A second issue with the persistent connection is that each connection uses up resources on the client. Having too many open connections can hurt your database performance. Again, in the old world this was acceptable. You generally had a small, static number of application instances that were connecting to your database.

    In the serverless world, this has been turned upside down. We're in a world of hyper-ephemeral compute, where your compute instance can be created, used, and destroyed within moments. This makes it inefficient to create a persistent database connection with every request, as you're paying the connection cost for something that may not be used again.

    Further, the autoscaling attributes of serverless compute means that your application can scale up to thousands of compute instances within seconds. With certain databases, this can be a problem as you quickly reach the database connection limits.

    स्रोत : www.serverless.com

    Understanding database options for your serverless web applications

    Web developers commonly use relational databases in building their applications. When migrating to serverless architectures, a web developer can continue to use databases like RDS, or take advantage of other options available.

    AWS Compute Blog

    AWS Compute Blog Understanding database options for your serverless web applications

    Many web developers use relational databases to store and manage data in their web applications. As you migrate to a serverless approach for web development, there are also other options available. These can help improve the scale, performance, and cost-effectiveness of your workloads. In this blog post, I highlight use-cases for different serverless database services, and common patterns useful for web applications.

    Using Amazon RDS with serverless web applications

    You can access Amazon RDS directly from your AWS Lambda functions. The RDS database, such as Amazon Aurora, is configured within the customer VPC. The Lambda function must be configured with access to the same VPC:

    There are special considerations for this design in busy serverless applications. It’s common for popular web applications to experience “spiky” usage, where traffic volumes shift rapidly and unpredictably. Serverless services such as AWS Lambda and Amazon API Gateway are designed to automatically scale to meet these traffic increases.

    However, relational databases are connection-based, so they are intended to work with a few long-lived clients, such as web servers. By contrast, Lambda functions are ephemeral and short-lived, so their database connections are numerous and brief. If Lambda scales up to hundreds or thousands of instances, you may overwhelm downstream relational databases with connection requests. This is typically only an issue for moderately busy applications. If you are using a Lambda function for low-volume tasks, such as running daily SQL reports, you do not experience this behavior.

    The Amazon RDS Proxy service is built to solve the high-volume use-case. It pools the connections between the Lambda service and the downstream RDS database. This means that a scaling Lambda function is able to reuse connections via the proxy. As a result, the relational database is not overwhelmed with connections requests from individual Lambda functions. This does not require code changes in many cases. You only need to replace the database endpoint with the proxy endpoint in your Lambda function.

    As a result, if you need to use a relational database in a high-volume web application, you can use RDS Proxy with minimal changes required.

    Using Amazon DynamoDB as a high-performance operational database

    Amazon DynamoDB is a high-performance key-value and document database that operates with single-digit millisecond response times at any scale. This is a NoSQL database that is a natural fit for many serverless workloads, especially web applications. It can operate equally well for low and high usage workloads. Unlike relational databases, the performance of a well-architected DynamoDB table is not adversely affected by heavy usage or large amounts of data storage.

    For web applications, DynamoDB tables are ideal for storing common user configuration and application data. When integrated with Amazon Cognito, you can restrict row-level access to the current user context. This makes it a frequent choice for multi-tenant web applications that host data for many users.

    DynamoDB tables can be useful for lookups of key-based information, in addition to geo-spatial queries in many cases. DynamoDB is not connection-based, so this integration works even if a Lambda function scales up to hundreds or thousands of concurrent executions. You can query directly from Lambda with minimal code:

    const AWS = require('aws-sdk')

    AWS.config.region = process.env.AWS_REGION

    const documentClient = new AWS.DynamoDB.DocumentClient()

    // Construct params const params = {

    TableName: 'myDDBtable',

    Item: {

    partitionKey: 'user-123',

    sortKey: Date.now(),

    name: 'Alice', cartItems: 3 } }

    // Store in DynamoDB

    const result = await documentClient.put(params).promise()


    Using advanced patterns in DynamoDB, it’s possible to build equivalent features frequently found in relational schemas. For example, one-to-many tables, many-to-many tables, and ACID transactions can all be modeled in a single DynamoDB table.

    Combining DynamoDB with RDS

    While DynamoDB remains highly performant for high volumes of traffic, you need to understand data access patterns for your application before designing the schema. There are times where you need to perform ad hoc queries, or where downstream application users must use SQL-based tools to interact with databases.

    In this case, combining both DynamoDB and RDS in your architecture can provide a resilient and flexible solution. For example, for a high-volume transactional web application, you can use DynamoDB to ingest data from your frontend application. For ad hoc SQL-based analytics, you could also use Amazon Aurora.

    By using DynamoDB streams, you can process updates to a DynamoDB table using a Lambda function. In a simple case, this function can update tables in RDS, keeping the two databases synchronized. For example, when sales transactions are saved in DynamoDB, a Lambda function can post the sales information to transaction tables in Aurora.

    स्रोत : aws.amazon.com

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