InfluxDB vs Apache Doris

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).

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.

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.

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.

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.

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.

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.

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.