Let’s start by comparing the data format of DynamoDB and Timestream.ĭynamoDB holds a flexible amount of attributes, which are identified by a unique key. Testing out Timestream required two changes: An additional Lambda function to replicate from DynamoDB to Timestream, and a new API that reads from Timestream. While most of the system is event-driven and can complete eventually, there are also userfacing dashboards that need fast responses.īelow you can see a picture of the current architecture, where a Lambda function pulls data into DynamoDB, another one creates notifications when a trading opportunity appears and an API Gateway that serves data for the user dashboards.Įach record in the database consists of two measurements (price and volume), has two dimensions (article number and location) and has a timestamp. My application monitors markets to notify customers of trading opportunities and registers about 500,000 market changes each day. If you’re not familiar with those two, just read them as “compute” and “api”. I will also mention Lambda and API Gateway. But even if you don’t, you can learn about both databases here. PrerequisitesĪs this article compares Timestream with DynamoDB, it’s good for you to have some experience with the latter. One example are fuel measurements of trucks, with truck types and number plates as dimensions. ![]() Each timestream record can be extended with dimensions that give more context on the measurement. ![]() Timeseries data is a sequence of data points stored in time order. Based on my experimentation this article compares Amazon Timestream with DynamoDB and shows what I learned. I tried it out with an existing application that uses timeseries data. November 2021: AWS has released multi-measure records, scheduled queries, and magnetic storage writes for Amazon Timestream.ĪWS recently announced that their Timestream database is now generally available. ![]() Amazon Timestream vs DynamoDB for Timeseries Data
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