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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. CrateIO vs InfluxDB

CrateIO vs InfluxDB

OverviewDecisionsComparisonAlternatives

Overview

InfluxDB
InfluxDB
Stacks1.0K
Followers1.2K
Votes175
CrateIO
CrateIO
Stacks19
Followers39
Votes7
GitHub Stars4.3K
Forks581

CrateIO vs InfluxDB: What are the differences?

Introduction:

When comparing CrateIO and InfluxDB, there are key differences that differentiate each database management system.

1. Data Model: CrateIO uses a shared-nothing architecture with horizontal scaling, storing data in a table format, much like traditional relational databases. In contrast, InfluxDB is specifically designed for time-series data, using a data model that efficiently handles time-stamped data points, making it ideal for IoT and monitoring applications.

2. Query Language: InfluxDB employs its own query language known as InfluxQL, which is tailored for time-series data manipulation and analysis. On the other hand, CrateIO supports SQL as its primary query language, providing users with a more familiar syntax for interacting with the database.

3. Consistency Model: CrateIO offers eventual consistency, meaning that data updates are eventually propagated across the cluster, which may result in temporary inconsistencies. In comparison, InfluxDB provides strong consistency by default, ensuring that all data reads return the most up-to-date information, making it suitable for applications requiring strict data consistency guarantees.

4. Use Cases: CrateIO is well-suited for applications that need to handle complex, relational data structures and perform ad-hoc queries across diverse datasets. InfluxDB excels in scenarios where time-series data plays a pivotal role, such as IoT sensor data storage, real-time monitoring, and anomaly detection.

5. Ecosystem: InfluxDB has a robust ecosystem with various integrations, plugins, and community support tailored specifically for time-series data handling. CrateIO, while versatile, may require additional configuration and adaptations for specialized use cases due to its broader focus on relational data management.

6. Scalability: Both CrateIO and InfluxDB support horizontal scaling by distributing data across multiple nodes. However, InfluxDB's sharding and clustering capabilities are finely tuned for time-series data, enabling optimized performance and storage efficiency as datasets grow exponentially.

In Summary, when choosing between CrateIO and InfluxDB, consider the data model, query language, consistency model, use cases, ecosystem, and scalability requirements to select the right database management system for your specific needs.

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Advice on InfluxDB, CrateIO

Deepak
Deepak

Sep 6, 2021

Needs adviceonJSONJSONInfluxDBInfluxDB

Hi all, I am trying to decide on a database for time-series data. The data could be tracking some simple series like statistics over time or could be a nested JSON (multi-level nested). I have been experimenting with InfluxDB for the former case of a simple list of variables over time. The continuous queries are powerful too. But for the latter case, where InfluxDB requires to flatten out a nested JSON before saving it into the database the complexity arises. The nested JSON could be objects or a list of objects and objects under objects in which a complete flattening doesn't leave the data in a state for the queries I'm thinking.

[ 
  { "timestamp": "2021-09-06T12:51:00Z",
    "name": "Name1",
    "books": [
        { "title": "Book1", "page": 100 },
        { "title": "Book2", "page": 280 },
    ]
  },
 { "timestamp": "2021-09-06T12:52:00Z",
   "name": "Name2",
   "books": [
       { "title": "Book1", "page": 320},
       { "title": "Book2", "page": 530 },
       { "title": "Book3", "page": 150 },
   ]
 }
]

Sample query: With a time range, for name xyz, find all the book title for which # of page < 400.

If I flatten it completely, it will result in fields like books_0_title, books_0_page, books_1_title, books_1_page, ... And by losing the nested context it will be hard to return one field (title) where some condition for another field (page) satisfies.

Appreciate any suggestions. Even a piece of generic advice on handling the time-series and choosing the database is welcome!

30.5k views30.5k
Comments
Anonymous
Anonymous

Apr 21, 2020

Needs advice

We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.

So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily

381k views381k
Comments
Andy
Andy

Freelance Developer at DGTEpro

Feb 25, 2022

Review

There's really not an awful lot of difference between the two, they have wildly different storage mechanisms but they each have their fairly similar benefits. If you want to learn something that might be a requisite skill for a job, I would also look at alternatives such as time based and column based systems like InfluxDB and the unbelievably fast and flexible ClickHouse. While they may seem like an unlikely fit for a personal bug tracker app, there's no reason not to use them. Since I got into InfluxDB people have been requesting it a lot and I'll be using ClickHouse for all large databases, probably forever. Expand your horizons beyond your competition's.

78.1k views78.1k
Comments

Detailed Comparison

InfluxDB
InfluxDB
CrateIO
CrateIO

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Crate is a distributed data store. Simply install Crate directly on your application servers and make the big centralized database a thing of the past. Crate takes care of synchronization, sharding, scaling, and replication even for mammoth data sets.

Time-Centric Functions;Scalable Metrics; Events;Native HTTP API;Powerful Query Language;Built-in Explorer
Familiar SQL syntax;Semi-structured data;High availability, resiliency, and scalability in a distributed design;Powerful Lucene based full-text search
Statistics
GitHub Stars
-
GitHub Stars
4.3K
GitHub Forks
-
GitHub Forks
581
Stacks
1.0K
Stacks
19
Followers
1.2K
Followers
39
Votes
175
Votes
7
Pros & Cons
Pros
  • 59
    Time-series data analysis
  • 30
    Easy setup, no dependencies
  • 24
    Fast, scalable & open source
  • 21
    Open source
  • 20
    Real-time analytics
Cons
  • 4
    Instability
  • 1
    HA or Clustering is only in paid version
  • 1
    Proprietary query language
Pros
  • 3
    Simplicity
  • 2
    Scale
  • 2
    Open source
Integrations
No integrations available
Docker
Docker

What are some alternatives to InfluxDB, CrateIO?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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