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 Apache Doris and TimescaleDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Doris and TimescaleDB 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.
Apache Doris vs TimescaleDB Breakdown
Time Series Database
Doris can be deployed on-premises or in the cloud and is compatible with various data formats such as Parquet, ORC, and JSON.
TimescaleDB is built on top of PostgreSQL and inherits its architecture. It extends PostgreSQL with time-series-specific optimizations and functions, allowing it to manage time series data efficiently. It can be deployed as a single node, in a multi-node setup, or in the cloud as a managed service.
Timescale License (for TimescaleDB Community Edition); Apache 2.0 (for core PostgreSQL)
Interactive analytics, data warehousing, real-time data analysis, reporting, dashboarding
Monitoring, observability, IoT, real-time analytics, financial market data
Horizontally scalable with distributed storage and compute
Horizontally scalable through native support for partitioning, replication, and sharding. Offers multi-node capabilities for distributing data and queries across nodes.
Apache Doris Overview
Apache Doris is an MPP-based interactive SQL data warehousing system designed for reporting and analysis. It is known for its high performance, real-time analytics capabilities, and ease of use. Apache Doris integrates technologies from Google Mesa and Apache Impala. Unlike other SQL-on-Hadoop systems, Doris is designed to be a simple and tightly coupled system that does not rely on external dependencies. It aims to provide a streamlined and efficient solution for data warehousing and analytics.
TimescaleDB is an open source time series database built on top of PostgreSQL. It was created to address the challenges of managing time series data, such as scalability, query performance, and data retention policies. TimescaleDB was first released in 2017 and has since become a popular choice for storing and analyzing time series data due to its PostgreSQL compatibility, performance optimizations, and flexible data retention policies.
Apache Doris for Time Series Data
Apache Doris can be effectively used with time series data for real-time analytics and reporting. With its high performance and sub-second response time, Doris can handle massive amounts of time-stamped data and provide timely query results. It supports both high-concurrent point query scenarios and high-throughput complex analysis scenarios, making it suitable for analyzing time series data with varying levels of complexity.
TimescaleDB for Time Series Data
TimescaleDB is specifically designed for time series data, making it a natural choice for storing and querying such data. It provides several advantages for time series data management like horizontal scalability, columnar storage, and retention policy support. However, TimescaleDB may not be the best choice for all time series use cases. One example would be if an application requires very high write throughput or real-time analytics, other specialized time series databases like InfluxDB may be more suitable.
Apache Doris Key Concepts
- MPP (Massively Parallel Processing): Apache Doris leverages MPP architecture, which allows it to distribute data processing across multiple nodes, enabling parallel execution and scalability.
- SQL: Apache Doris supports SQL as the query language, providing a familiar and powerful interface for data analysis and reporting.
- Point Query: Point query refers to retrieving a specific data point or a small subset of data from the database.
- Complex Analysis: Apache Doris can handle complex analysis scenarios that involve processing large volumes of data and performing advanced computations and aggregations.
TimescaleDB Key Concepts
- Hypertable: A hypertable is a distributed table that is partitioned by time and possibly other dimensions, such as device ID or location. It is the primary abstraction for storing time series data in TimescaleDB and is designed to scale horizontally across multiple nodes.
- Chunk: A chunk is a partition of a hypertable, containing a subset of the hypertable’s data. Chunks are created automatically by TimescaleDB based on a specified time interval and can be individually compressed, indexed, and backed up for better performance and data management.
- Distributed Hypertables: For large-scale deployments, TimescaleDB supports distributed hypertables, which partition data across multiple nodes for improved query performance and fault tolerance.
Apache Doris Architecture
Apache Doris is based on MPP architecture, which enables it to distribute data and processing across multiple nodes for parallel execution. It is a standalone system and does not depend on other systems or frameworks. Apache Doris combines the technology of Google Mesa and Apache Impala to provide a simple and tightly coupled system for data warehousing and analytics. It leverages SQL as the query language and supports efficient data processing and query optimization techniques to ensure high performance and scalability.
TimescaleDB is an extension built on PostgreSQL, inheriting its relational data model and SQL support. However, TimescaleDB extends PostgreSQL with custom data structures and optimizations for time series data, such as hypertables and chunks.
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Apache Doris Features
Apache Doris is designed for high-performance data analytics, delivering sub-second query response times even with massive amounts of data.
Apache Doris enables real-time data analysis, allowing users to gain insights and make informed decisions based on up-to-date information.
Apache Doris can scale horizontally by adding more nodes to the cluster, allowing for increased data storage and processing capacity.
TimescaleDB automatically partitions time series data tables using hypertables and chunks, which simplifies data management and improves query performance.
Time series focused SQL functions
TimescaleDB provides several specialized SQL functions and operators for time series data application scenarios, such as time_bucket, first, and last, which simplify querying and aggregating time series data.
As mentioned earlier, TimescaleDB extends PostgreSQL’s query planner for writing and querying time series data, including optimizations like time-based indexing and chunk pruning.
Apache Doris Use Cases
Apache Doris is well-suited for real-time analytics scenarios where timely insights and analysis of large volumes of data are crucial. It enables businesses to monitor and analyze real-time data streams, make data-driven decisions, and detect patterns or anomalies in real time.
Reporting and Business Intelligence
Apache Doris can be used for generating reports and conducting business intelligence activities. It supports fast and efficient querying of data, allowing users to extract meaningful insights and visualize data for reporting and analysis purposes.
Apache Doris is suitable for building data warehousing solutions that require high-performance analytics and querying capabilities. It provides a scalable and efficient platform for storing, managing, and analyzing large volumes of data for reporting and decision-making.
TimescaleDB Use Cases
Monitoring and metrics
TimescaleDB is well-suited for storing and analyzing monitoring and metrics data, such as server performance metrics, application logs, and sensor data. Its hypertable structure and query optimizations make it easy to store, query, and visualize large volumes of time series data.
IoT data storage
TimescaleDB can be used to store and analyze IoT data, such as sensor readings and device status information. Its support for automatic partitioning and specialized SQL interfaces simplifies the management and querying of large-scale IoT datasets.
TimescaleDB is suitable for storing and analyzing financial data, such as stock prices, exchange rates, and trading volumes. Its query optimizations and specialized SQL functions make it easy to perform time-based aggregations and analyze trends in financial data.
Apache Doris Pricing Model
As an open-source project, Apache Doris is freely available for usage and does not require any licensing fees. Users can download the source code and set up Apache Doris on their own infrastructure without incurring any direct costs. However, it’s important to consider the operational costs associated with hosting and maintaining the database infrastructure.
TimescaleDB Pricing Model
TimescaleDB is available in two editions: TimescaleDB Open Source and TimescaleDB Cloud. The open-source edition is free to use and can be self-hosted, while the cloud edition is a managed service with a pay-as-you-go pricing model based on storage, compute, and data transfer usage. TimescaleDB Cloud offers various pricing tiers with different levels of resources and features, such as continuous backups and high availability.
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