M3 vs AWS Redshift

Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of M3 and AWS Redshift so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how M3 and AWS Redshift perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

M3 Overview

M3 is a distributed time series database written entirely in Go. It is designed to collect a high volume of monitoring time series data, distribute storage in a horizontally scalable manner, and efficiently leverage hardware resources. M3 was initially developed by Uber as a scalable remote storage backend for Prometheus and Graphite and later open-sourced for broader use.

AWS Redshift Overview

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It was launched in 2012 as part of the AWS suite of products. Redshift is designed for analytic workloads and integrates with various data loading and ETL tools, as well as business intelligence and reporting tools. It uses columnar storage to optimize storage costs and improve query performance.

M3 for Time Series Data

M3 is specifically designed for time-series data. It is a distributed and scalable time-series database optimized for handling large volumes of high-resolution data points, making it an ideal solution for storing, querying, and analyzing time-series data.

M3’s architecture focuses on providing fast and efficient querying capabilities, as well as high ingestion rates, which are essential for working with time-series data. Its horizontal scalability and high availability ensure that it can handle the demands of large-scale deployments and maintain performance as data volumes grow.

AWS Redshift for Time Series Data

AWS Redshift can be used for time series data workloads, although Redshift is optimized for more general data warehouse use cases. Users can utilize date and time-based functions to aggregate, filter, and transform time series data. Redshift also offers ‘time-series tables’ which allow data to be stored in tables based on a fixed retention period.

M3 Key Concepts

• Time Series Compression: M3 has the ability to compress time series data, resulting in significant memory and disk savings. It uses two compression algorithms, M3TSZ and protobuf encoding, to achieve efficient data compression.
• Sharding: M3 uses virtual shards that are assigned to physical nodes. Timeseries keys are hashed to a fixed set of virtual shards, making horizontal scaling and node management seamless.
• Consistency Levels: M3 provides variable consistency levels for read and write operations, as well as cluster connection operations. Write consistency levels include One (success of a single node), Majority (success of the majority of nodes), and All (success of all nodes). Read consistency level is One, which corresponds to reading from a single nod

AWS Redshift Key Concepts

• Cluster: A Redshift cluster is a set of nodes, which consists of a leader node and one or more compute nodes. The leader node manages communication with client applications and coordinates query execution among compute nodes.
• Compute Node: These nodes store data and execute queries in parallel. The number of compute nodes in a cluster affects its storage capacity and query performance.
• Columnar Storage: Redshift uses a columnar storage format, which stores data in columns rather than rows. This format improves query performance and reduces storage space requirements.
• Node slices: Compute nodes are divided into slices. Each slice is allocated an equal portion of the node’s memory and disk space, where it processes a portion of the loaded data.

M3 Architecture

M3 is designed to be horizontally scalable and handle high data throughput. It uses fileset files as the primary unit of long-term storage, storing compressed streams of time series values. These files are flushed to disk after a block time window becomes unreachable. M3 has a commit log, equivalent to the commit log or write-ahead-log in other databases, which ensures data integrity. Client Peer streaming is responsible for fetching blocks from peers for bootstrapping purposes. M3 also implements caching policies to optimize efficient reads by determining which flushed blocks are kept in memory.

AWS Redshift Architecture

Redshift’s architecture is based on a distributed and shared-nothing architecture. A cluster consists of a leader node and one or more compute nodes. The leader node is responsible for coordinating query execution, while compute nodes store data and execute queries in parallel. Data is stored in a columnar format, which improves query performance and reduces storage space requirements. Redshift uses Massively Parallel Processing (MPP) to distribute and execute queries across multiple nodes, allowing it to scale horizontally and provide high performance for large-scale data warehousing workloads.