InfluxDB vs Amazon Timestream for LiveAnalytics
A detailed comparison
Compare InfluxDB and Amazon Timestream for LiveAnalytics for time series and OLAP workloads
Learn About Time Series DatabasesChoosing 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 InfluxDB and Amazon Timestream for LiveAnalytics so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how InfluxDB and Amazon Timestream for LiveAnalytics 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.
InfluxDB vs Amazon Timestream for LiveAnalytics Breakdown
Database Model | Time series database |
|
Architecture | Cloud native architecture that can be used as a managed cloud service or self-managed on your own hardware locally |
Timestream is a fully managed, serverless time series database service that is only available on AWS. |
License | MIT |
Closed source |
Use Cases | Monitoring, observability, IoT, real-time analytics |
IoT, DevOps, time series analytics |
Scalability | Horizontally scalable with decoupled storage and compute with InfluxDB 3.0 delivers up to 90% reduced storage costs( benchmarks ) |
Serverless and automatically scalable, handling ingestion, storage, and query workload without manual intervention |
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InfluxDB Overview
InfluxDB is a high-performance, time series database capable of storing any form of time series data, such as metrics, events, logs and traces. InfluxDB is developed by InfluxData and first released in 2013. InfluxDB is an open source database written in Go, with a focus on performance, scalability, and developer productivity. The database is optimized for handling time series data at scale, making it a popular choice for use cases involving monitoring performance metrics, IoT data, and real-time analytics.
InfluxDB 3.0 is the newest version of InfluxDB, currently available in InfluxDB Cloud Serverless and InfluxDB Cloud Dedicated. Built in Rust, a modern programming language designed for performance, safety, and memory management. InfluxDB also features a decoupled architecture that allows compute and storage to be scaled independently. InfluxDB 3.0 provides query support for both SQL and InfluxQL (custom SQL-like query language with added support for time-based functions).
Amazon Timestream for LiveAnalytics Overview
Timestream for LiveAnalytics is a fully managed, serverless time series database service developed by 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 for LiveAnalytics easily integrates with other AWS services, simplifying the process of building and deploying time series applications in the cloud. AWS also offers Timestream for InfluxDB which is a managed version of InfluxDB that is compatible with InfluxDB 2.x APIs and released in partnership with InfluxData.
InfluxDB for Time Series Data
InfluxDB is specifically designed for time series data, making it well-suited for applications that involve tracking and analyzing data points over time. It excels in scenarios where data is being written continuously at high volumes while users also require the ability to query that data quickly after ingest for monitoring and real time analytics use cases.
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 and efficient storage. Its dual-tiered storage architecture, consisting of the memory Store and magnetic Store, allows users to 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.
InfluxDB Key Concepts
- Columnar storage: InfluxDB stores data in a column-oriented format, using Parquet for persistent file storage and Apache Arrow as the in-memory representation of data. Columnar storage results in better data compression and faster queries for analytics workloads.
- Data Model: The InfluxDB data model will be familiar to anyone who has worked with other database systems. At the highest level are buckets, which are similar to what other systems call databases. InfluxDB measurements are synonymous with tables. Specific data points for a measurement contain tags and values. Tags are used as part of the primary key for querying data and should be used for identifying information used for filtering during queries. InfluxDB is schemaless so new fields can be added without requiring migrations or modifying a schema.
- Integrations: InfluxDB is built to be flexible and fit into your application’s architecture. One key aspect of this is the many ways InfluxDB makes it easy to read and write data. To start, all database functionality can be accessed via HTTP API or with the InfluxDB CLI. For writing data InfluxDB created Telegraf, a tool that can collect data from hundreds of different sources via plugins and write that data to InfluxDB. Client libraries are also available for the most popular programming languages to allow writing and querying data.
- Decoupled architecture: InfluxDB 3.0 features a decoupled architecture which allows query compute, data ingestion, and storage to be scaled independently. This allows InfluxDB to be fine-tuned for your use case and results in significant cost savings.
- Query Languages: InfluxDB can be queried using standard SQL or InfluxQL, an SQL dialect with a number of specialized functions useful for working with time series data.
- Retention Policies: InfluxDB allows you to define retention policies that determine how long data is stored before being automatically deleted. This is useful for managing the storage of high volume time series data.
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.
InfluxDB Architecture
At a high level, InfluxDB’s architecture is designed to optimize storage and query performance for time series data. The exact architecture of InfluxDB will vary slightly depending on the version and how you deploy InfluxDB.
InfluxDB 3.0’s architecture can be broken down into four key components that operate almost independently from each other, allowing for InfluxDB to be extremely flexible in terms of configuration. These components are are data ingest, data querying, data compaction, and garbage collection. Data is written via the ingesters with millisecond latency. This data can be queried almost immediately by the data queriers while in the background the compactor takes the newly written data files and combines them into larger files that will be sent to object storage. The garbage collector is responsible for data retention and space reclamations by scheduling soft and hard deletion of data.
They key part of InfluxDB’s architecture is the separation of the ingest and query components, which allows each to be scaled independently depending on the current write and query workload. The querier being able to seamlessly pull in recently written data from the ingesters as well as from object storage allows data to be stored cheaply without increasing query latency.
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.
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InfluxDB Features
High-performance storage and querying
InfluxDB is optimized for time series data, providing high-performance storage and querying capabilities. In terms of storage InfluxDB is able to scale effortlessly due to its decoupled architecture. Object storage is used to persist data and query nodes can be scaled independently to improve query performance and capacity.
Compared to previous versions of InfluxDB, the newly released InfluxDB 3.0 compresses data 4.5x more effectively and queries are 2.5-45x faster depending on the type of query.
Retention policies
InfluxDB allows users to define retention policies that automatically delete data points after a specified duration. This feature helps manage data storage costs and ensures that only relevant data is retained.
Data compression
InfluxDB’s storage engine automatically compacts data on disk, reducing storage requirements and improving query performance. With InfluxDB 3.0 data is stored using the Parquet file format to get even higher compression ratios on time series data.
Horizontal scaling and clustering
InfluxDB supports horizontal scaling and clustering, allowing users to distribute data across multiple nodes for increased performance and fault tolerance.
Data tiering
InfluxDB 3.0 is able to seamlessly move data from cheap object storage into faster storage for low latency queries without expensive SSD or high amounts of RAM utilization. This allows users to store data for longer at higher frequencies while still saving in storage costs.
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.
Timestream for InfluxDB
For workloads that require near real-time queries with single millisecond latency AWS recommends using Timestream for InfluxDB rather than LiveAnalytics. Timestream for InfluxDB also provides compatibility with InfluxDB APIs for users who want an AWS managed service without having to update their code.
InfluxDB Use Cases
Monitoring and alerting
InfluxDB is widely used for monitoring and alerting purposes, as it can efficiently store and process time series data generated by various systems, applications, and devices. With its high-performance query engine and integration with visualization tools like Grafana, users can create real-time dashboards and set up alerts based on specific conditions or thresholds.
IoT data storage and analysis
Due to its high write and query performance, InfluxDB is an ideal choice for storing and analyzing IoT data generated by sensors, devices, and applications. Users can leverage InfluxDB’s scalability and retention policies to manage large volumes of time series data, and use its powerful query languages to gain insights into the IoT ecosystem.
Real-time analytics
InfluxDB’s performance and flexibility make it suitable for real-time analytics use cases, such as tracking user behavior, monitoring application performance, and analyzing financial data. With its support for InfluxQL and SQL, users can perform complex data analysis and aggregation in real-time, enabling them to make data-driven decisions.
Amazon Timestream for LiveAnalytics Use Cases
IoT applications
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.
Devops
LiveAnalytics can be used for general DevOps workloads like monitoring application health and utilization. For use cases that require real time monitoring with the lowest latency possible, AWS recommends using Timestream for InfluxDB.
Analytics
Amazon Timestream for LiveAnalytics can be used to track analytics data like web and application data. The built-in time series analytics functions can then be used to aggregate and analyze data to get valuable insights with increased developer productivity.
InfluxDB Pricing Model
InfluxDB offers several pricing options, including a free open source version, a cloud-based offering, and an enterprise edition for on-premises deployment:
- InfluxDB Cloud Serverless: InfluxDB Cloud Serverless is a managed, cloud-based offering with a pay-as-you-go pricing model. It provides additional features, such as monitoring, alerting, and data visualization. InfluxDB Cloud is available across all major cloud providers.
- InfluxDB Cloud Dedicated - This is a managed cloud solution that provides an isolated InfluxDB instance on dedicated hardware for use cases that require isolation or benefit from being able to specify and fine-tune hardware configuration.
- InfluxDB Enterprise: On-prem solution with enterprise features for security and support for clustering and other horizontal scaling options.
- InfluxDB Open Source: The open source version of InfluxDB is free to use and provides the core functionality of the database.
Amazon Timestream for LiveAnalytics Pricing Model
Amazon Timestream for LiveAnalytics 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.
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