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Table of contents
The Architecture of Amazon Timestream
Write architecture
Characteristics of the Write Architecture
Query architecture
Characteristics of Query Architecture
Storage Architecture
Characteristics of Storage Architecture
Time-Series Data
Time-series Use cases
Characteristics of Timestream
Frequently Asked Questions
What is amazon timestream?
What are the advantages of amazon timestream? 
What are the disadvantages of amazon timestream?
What is a quorum?
Last Updated: Mar 27, 2024

Amazon Timestream

Author Saumya Gupta
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Amazon Timestream is a purpose-built time series Database that allows you to store and analyse trillions of data points every day in a fast, scalable and fully managed environment. Built-in time-series analytics tools in Amazon Timestream let you find trends and patterns in your data in near real-time. Timestream is a serverless application that automatically adjusts capacity and performance by scaling up or down.

This blog will discuss the Amazon Timestream in detail, how it supports AWS (Amazon Web Services), and relevant information.


                                                                    Source: Youtube

Must read, Amazon Hirepro


Amazon Timestream is a serverless time series database service that can scale to handle trillions of events per day for as low as 1/10th the cost of relational databases and up to 1000x faster.



We will see how easy it is to get started with Amazon Timestream and interact with AWS SDK. To ensure the security of our time-series data(both in transit and at rest), Timestream supports encryption using the Amazon Key Management Service (or KMS) key of your choice or an automatically created default account key. Encryption enables by default and cannot be turned off.

All data written to the Timestream initially resides in its write-optimized memory store and automatically migrates to the read-optimized magnetic store after aging past the "memory store retention" period. Depending on your needs, you can also have Timestream delete your older data automatically or retain it indefinitely.

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The Architecture of Amazon Timestream

The architecture of amazon timestream is a bit complex and tricky, so bear with us for some time to understand the timestream architecture thoroughly.

Here is a picture below for a better understanding of Amazon Timestream.


                                                                    Source: Amazon AWS

Generally, we have only one part of the architecture in all the systems, but here in amazon timestream, the architecture is divided into four parts. Each of these parts plays a significant role in the fully automated architecture of Amazon Timestream.  

The four types of amazon timestream architecture are given below:

  1. Write architecture
  2. Query architecture
  3. Storage architecture
  4. Cellular architecture

Write architecture

Timestream bears writing data directly into the magnetic disk or any other magnetic store. For example, the apps generate lower throughput (work per unit time) and have late arrival data.

Now, what is late arrival data?

As the name suggests, late arrival data is the data that arrives lately and is stored lately means that Timestamp data is earlier than the current time.

Characteristics of the Write Architecture

The working of write architecture are given as follows:

  1. Data written into the magnetic disk is duplicated over three AZs and is quorum-based, similar to the high throughput writes in the memory store.
  2. Timestream automatically indexes and partitions data before writing it to storage, whether in memory or on a magnetic disk. 
  3. Hundreds, thousands, or even millions of partitions in a single Timestream table.
  4. Individual partitions do not communicate with one another directly and do not share data (shared-nothing architecture). Instead, a highly available partition tracking and indexing service keep track of a table's partitioning.
  5. This time, it will create another layer of separation of concerns to reduce the impact of system failures and the likelihood of associated shortcomings.

Query architecture

As timestream queries are written in a SQL grammar with extensions for time series-specific support (time series-specific data types and functions), developers familiar with SQL will find the learning curve relatively short.

After that, an adaptive, distributed query engine employs metadata from the tile tracking and indexing service to easily access and aggregate data across data stores when the query is submitted. This creates a positive client experience by reducing many Rube Goldberg complications to a simple and familiar database abstraction.                 

Characteristics of Query Architecture

The characteristics of query architecture are given below:

  1. Queries are executed by a specialized group of workers, with the number of workers assigned to each query determined by the query's complexity and storage capacity.
  2. Massive parallelism is used on both the system's query execution and storage fleets to achieve performance for sophisticated queries over big data sets.
  3. One of Timestream's greatest assets is its capacity to swiftly and efficiently analyze large amounts of data. 
  4. Thousands of machines may be working on a single query that runs over terabytes or even petabytes of data simultaneously.

Storage Architecture

Data is arranged in Timestream in chronological order and across time based on context attributes written with the data. When massively scaling a time series system, having a partitioning mechanism that divides "space" and "time" is critical. Because most time-series data is written at or near the present time, this is the case. 

As a result, simply partitioning by time does not effectively distribute write traffic or allow for effective data pruning at query time. This is critical for large-scale time series processing, and it has enabled Timestream to scale orders of magnitude greater than the best serverless systems available today.

Because they represent sections of a two-dimensional space planned to be of similar size, the resulting partitions are referred to as "Tiles." Timestream tables begin as a single division (tile) and are later divided in the spatial dimension as throughput demands. Tiles divide in the time dimension when they reach a particular size, allowing for increased read parallelism as the data size expands.

Timestream is a database that manages the lifecycle of time series data automatically. Timestream supports establishing table-level rules to move data between stores automatically, and it offers two data stores: an in-memory store and a cost-effective magnetic store.

Characteristics of Storage Architecture

  1. Incoming high-throughput data writes are sent to the memory store, which optimizes data for writing and reads conducted around the current time for the dashboard and alerting inquiries. 
  2. Allowing data to automatically migrate from the memory store to the magnetic store to optimize cost once the main time frame for writes, alerts and dashboarding needs has passed.
  3. Timestream allows you to specify a data retention policy on the memory store. Data is directly written into the magnetic store for late arrival data.
  4. The data is restructured into a highly optimized format for big volume data reads once it is available in the magnetic store (due to the expiration of the memory store retention period or direct writes into the magnetic store). 
  5. The magnetic store also features a data retention policy that can be set if the data reaches a point where it is no longer helpful. 
  6. The data is automatically deleted when the period selected for the magnetic storage retention policy is exceeded. 
  7. Except for minor settings, Timestream manages the data lifetime seamlessly behind the scenes.


A timestamp is a computer's record of the current time of an event. The timestamp is used for the values that contain both time and date parts.

The timestamp is a prevalent attribute in various analog cameras. But nowadays, people are more inclined toward DSLRs and smartphones, due to which there is a massive decline in analog cameras.

We can use various names for timestamps like CheckSumtime-tCRLF, mtime, and local time.

A digital timestamp traces a PDF signature with the time and date as proof of authenticity.

Time-Series Data

Time Series data is a sequence of data points recorded over time intervals to measure events that change over time. Time Series Data is about measuring some value, whether that value is the car's speed or the price of a stock.

Everything that goes into a time-series database like amazon timestream will have an associated timestamp. It is about measuring these data points as they vary over time.

Time-series Use cases

Following are the use-cases of the time stream

1. IoT applications: collect motion or temperature data from the device sensors, interpolate to identify time ranges without motion, or alert consumers to take actions such as turning off the light to save energy.

2. DevOps Analysis: collect and analyze performance and health metrics such as CPU/memory utilization network data and IOPS to monitor health and optimize instance usage.

3. App Analytics: Easily store and analyze clickstream data at scale to understand the customer journey–The user activity across your application over a while.

Characteristics of Timestream


                                                                                             Source: AWS

The characteristics of the time stream are as follows:

1. You can't delete data, and you can't update data in the Timestream, so all data that is sent to Timestream is an append-only table. The data is not removed until it hits the end of the retention window.

2. All records require a Timestamp, one or more dimensions, a measurement name, and a value. 

3. Records cannot be updated or deleted. Records are only removed when they extend the retention limit within the magnetic tier (infinite storage is an option).

4. Multiple measures are logically represented as multiple individual records (one step per record).

5. Automatically scales to handle high-speed real-time data ingestion.

Frequently Asked Questions

What is amazon timestream?

Amazon Timestream is a time-series database that is very fast, secure, and serverless.

What are the advantages of amazon timestream? 

  1. Readily available  
  2. Flexibility and durability  
  3. Data access is effortless 
  4. Highly encrypted
  5. Cheaper than many databases. 
  6. Very fast.

What are the disadvantages of amazon timestream?

  1. Duplicate values for the same dimension. 
  2. Timestamps outside the memory retention store. 
  3. Timestream is a NoSQL database.

What is a quorum?

A quorum is a NoSQL approach used to achieve consistency in data, i.e., read nodes+write nodes> the total number of nodes).


In this article, we extensively discuss Amazon Timestream and its purpose. We have also discussed the characteristics, advantages, and disadvantages of Timestream.

We hope that this blog has helped you enhance your knowledge regarding Amazon Timestream and if you would like to grasp more, check out our articles on Coding Ninjas.

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