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 Amazon Timestream for LiveAnalytics and Apache Druid so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Amazon Timestream for LiveAnalytics and Apache Druid 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.

Amazon Timestream for LiveAnalytics vs Apache Druid Breakdown


 
Database Model

Time series database

Columnar database

Architecture

Timestream is a fully managed, serverless time series database service that is only available on AWS.

Druid can be deployed on-premises, in the cloud, or using a managed service

License

Closed source

Apache 2.0

Use Cases

Monitoring, observability, IoT, real-time analytics

Real-time analytics, OLAP, time series data, event-driven data, log analytics, ad tech, user behavior analytics

Scalability

Serverless and automatically scalable, handling ingestion, storage, and query workload without manual intervention

Horizontally scalable, supports distributed architectures for high availability and performance

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Amazon Timestream for LiveAnalytics Overview

Amazon Timestream for LiveAnalytics is a fully managed, serverless time series database service developed by Amazon Web Services (AWS). Launched in 2020, Amazon Timestream for LiveAnalytics is designed specifically for handling time series data, making it an ideal choice for IoT, monitoring, and analytics applications that require high ingestion rates, efficient storage, and fast querying capabilities. As a part of the AWS ecosystem, Timestream seamlessly integrates with other AWS services, simplifying the process of building and deploying time series applications in the cloud.

Apache Druid Overview

Apache Druid is an open-source, real-time analytics database designed for high-performance querying and data ingestion. Originally developed by Metamarkets in 2011 and later donated to the Apache Software Foundation in 2018, Druid has gained popularity for its ability to handle large volumes of data with low latency. With a unique architecture that combines elements of time series databases, search systems, and columnar storage, Druid is particularly well-suited for use cases involving event-driven data and interactive analytics.


Amazon Timestream for LiveAnalytics for Time Series Data

Amazon Timestream for LiveAnalytics is designed specifically for handling time series data, making it a suitable choice for a wide range of applications that require high ingestion rates, efficient storage, and fast querying capabilities. Its dual-tiered storage architecture, consisting of the Memory Store and Magnetic Store, allows Timestream to automatically manage data retention and optimize storage costs based on data age and access patterns. Additionally, Timestream supports SQL-like querying and integrates with popular analytics tools, making it easy for users to gain insights from their time series data.

Apache Druid for Time Series Data

Apache Druid is designed for real time analytics and can be a good fit for working with time series data that needs to be analyzed quickly after being written. Druid also offers integrations for storing historical data in cheaper object storage so historical time series data can also be analyzed using Druid.


Amazon Timestream for LiveAnalytics Key Concepts

  • Memory Store: In Amazon Timestream for LiveAnalytics, the Memory Store is a component that stores recent, mutable time series data in memory for fast querying and analysis.
  • Magnetic Store: The Magnetic Store in Amazon Timestream for LiveAnalytics is responsible for storing historical, immutable time series data on disk for cost-efficient, long-term storage.
  • Time-to-Live (TTL): Amazon Timestream for LiveAnalytics allows users to set a TTL on their time series data, which determines how long data is retained in the Memory Store before being moved to the Magnetic Store or deleted.

Apache Druid Key Concepts

  • Data Ingestion: The process of importing data into Druid from various sources, such as streaming or batch data sources.
  • Segments: The smallest unit of data storage in Druid, segments are immutable, partitioned, and compressed.
  • Data Rollup: The process of aggregating raw data during ingestion to reduce storage requirements and improve query performance.
  • Nodes: Druid’s architecture consists of different types of nodes, including Historical, Broker, Coordinator, and MiddleManager/Overlord, each with specific responsibilities.
  • Indexing Service: Druid’s indexing service manages the process of ingesting data, creating segments, and publishing them to deep storage.


Amazon Timestream for LiveAnalytics Architecture

Amazon Timestream for LiveAnalytics is built on a serverless, distributed architecture that supports SQL-like querying capabilities. Its data model is specifically tailored for time series data, using time-stamped records and a flexible schema that can accommodate varying data granularities and dimensions. The core components of Timestream’s architecture include the Memory Store and the Magnetic Store, which together manage data retention, storage, and querying. The Memory Store is optimized for fast querying of recent data, while the Magnetic Store provides cost-efficient, long-term storage for historical data.

Apache Druid Architecture

Apache Druid is a powerful distributed data store designed for real-time analytics on large datasets. Within its architecture, several core components play pivotal roles in ensuring its efficiency and scalability. Here is an overview of the core components that power Apache Druid.

  • Historical Nodes are fundamental to Druid’s data-serving capabilities. Their primary responsibility is to serve stored data to queries. To achieve this, they load segments from deep storage, retain them in memory, and then cater to the queries on these segments. When considering deployment and management, these nodes are typically stationed on machines endowed with significant memory and CPU resources. Their scalability is evident as they can be expanded horizontally simply by incorporating more nodes.
  • Broker Nodes act as the gatekeepers for incoming queries. Their main function is to channel these queries to the appropriate historical nodes or real-time nodes. Intriguingly, they are stateless, which means they can be scaled out to accommodate an increase in query concurrency.
  • Coordinator Nodes have a managerial role, overseeing the data distribution across historical nodes. Their decisions on which segments to load or drop are based on specific configurable rules. In terms of deployment, a Druid setup usually requires just one active coordinator node, with a backup node on standby for failover scenarios.
  • Overlord Nodes dictate the assignment of ingestion tasks, directing them to either middle manager or indexer nodes. Their deployment mirrors that of the coordinator nodes, with typically one active overlord and a backup for redundancy.
  • MiddleManager and Indexer Nodes are the workhorses of data ingestion in Druid. While MiddleManagers initiate short-lived tasks for data ingestion, indexers are designed for long-lived tasks. Given their intensive operations, these nodes demand high CPU and memory resources. Their scalability is flexible, allowing horizontal expansion based on the volume of data ingestion.
  • Deep Storage is a component that serves as Druid’s persistent storage unit. Druid integrates with various blob storage solutions like HDFS, S3, and Google Cloud Storage.
  • Metadata Storage is the repository for crucial metadata about segments, tasks, and configurations. Druid is compatible with popular databases for this purpose, including MySQL, PostgreSQL, and Derby.

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Amazon Timestream for LiveAnalytics Features

Serverless architecture

Amazon Timestream for LiveAnalytics serverless architecture eliminates the need for users to manage or provision infrastructure, making it easy to scale and reducing operational overhead.

Dual-tiered storage

Timestream’s dual-tiered storage architecture, consisting of the Memory Store and Magnetic Store, automatically manages data retention and optimizes storage costs based on data age and access patterns.

SQL-like querying

Amazon Timestream for LiveAnalytics supports SQL-like querying and integrates with popular analytics tools, making it easy for users to gain insights from their time series data.

Apache Druid Features

Data Ingestion

Apache Druid supports both real-time and batch data ingestion, allowing it to process data from various sources like Kafka, Hadoop, or local files. With built-in support for data partitioning, replication, and roll-up, Druid ensures high availability and efficient storage.

Scalability and Performance

Druid is designed to scale horizontally, providing support for large-scale deployments with minimal performance degradation. Its unique architecture allows for fast and efficient querying, making it suitable for use cases requiring low-latency analytics.

Columnar Storage

Druid stores data in a columnar format, enabling better compression and faster query performance compared to row-based storage systems. Columnar storage also allows Druid to optimize queries by only accessing relevant columns.

Time-optimized Indexing

Druid’s indexing service creates segments with time-based partitioning, optimizing data storage and retrieval for time-series data. This feature significantly improves query performance for time-based queries. Data Rollups

Druid’s data rollup feature aggregates raw data during ingestion, reducing storage requirements and improving query performance. This feature is particularly beneficial for use cases involving high-cardinality data or large volumes of similar data points.


Amazon Timestream for LiveAnalytics Use Cases

IoT device monitoring

Amazon Timestream for LiveAnalytic’s support for high ingestion rates and efficient storage makes it an ideal choice for monitoring and analyzing data from IoT devices, such as sensors and smart appliances.

Application performance monitoring

Timestream’s fast querying capabilities and ability to handle large volumes of time series data make it suitable for application performance monitoring, allowing users to track and analyze key performance indicators in real-time and identify bottlenecks or issues.

Infrastructure monitoring

Amazon Timestream for LiveAnalytics can be used to monitor and analyze infrastructure metrics, such as CPU utilization, memory usage, and network traffic, enabling organizations to optimize resource utilization, identify potential issues, and maintain a high level of performance for their critical systems.

Apache Druid Use Cases

Geospatial Analysis

Apache Druid provides support for geospatial data and queries, making it suitable for use cases that involve location-based data, such as tracking the movement of assets, analyzing user locations, or monitoring the distribution of events. Its ability to efficiently process large volumes of geospatial data enables users to gain insights and make data-driven decisions based on location information.

Machine Learning and AI

Druid’s high-performance data processing capabilities can be leveraged for preprocessing and feature extraction in machine learning and AI workflows. Its support for real-time data ingestion and low-latency querying make it suitable for use cases that require real-time predictions or insights, such as recommendation systems or predictive maintenance.

Real-Time Analytics

Apache Druid’s low-latency querying and real-time data ingestion capabilities make it an ideal solution for real-time analytics use cases, such as monitoring application performance, user behavior, or business metrics.


Amazon Timestream for LiveAnalytics Pricing Model

Amazon Timestream for LiveAnalyticsv offers a pay-as-you-go pricing model based on data ingestion, storage, and query execution. Ingestion costs are determined by the volume of data ingested into Timestream, while storage costs are based on the amount of data stored in the Memory Store and Magnetic Store. Query execution costs are calculated based on the amount of data scanned and processed during query execution. Timestream also offers a free tier for users to explore the service and build proof-of-concept applications without incurring costs.

Apache Druid Pricing Model

Apache Druid is an open source project, and as such, it can be self-hosted at no licensing cost. However, organizations that choose to self-host Druid will incur expenses related to infrastructure, management, and support when deploying and operating Druid in their environment. These costs will depend on the organization’s specific requirements and the chosen infrastructure, whether it’s on-premises or cloud-based.

For those who prefer a managed solution, there are cloud services available that offer Apache Druid as a managed service, such as Imply Cloud. With managed services, the provider handles infrastructure, management, and support, simplifying the deployment and operation of Druid. Pricing for these managed services will vary depending on the provider and the selected service tier, which may include factors such as data storage, query capacity, and data ingestion rates.