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

FoundationDB vs InfluxDB

OverviewDecisionsComparisonAlternatives

Overview

InfluxDB
InfluxDB
Stacks1.0K
Followers1.2K
Votes175
FoundationDB
FoundationDB
Stacks34
Followers79
Votes21

FoundationDB vs InfluxDB: What are the differences?

What is FoundationDB? Multi-model database with particularly strong fault tolerance, performance, and operational ease. FoundationDB is a NoSQL database with a shared nothing architecture. Designed around a "core" ordered key-value database, additional features and data models are supplied in layers. The key-value database, as well as all layers, supports full, cross-key and cross-server ACID transactions.

What is InfluxDB? An open-source distributed time series database with no external dependencies. 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..

FoundationDB and InfluxDB belong to "Databases" category of the tech stack.

Some of the features offered by FoundationDB are:

  • Multiple data models
  • Full, multi-key ACID transactions
  • No locking

On the other hand, InfluxDB provides the following key features:

  • Time-Centric Functions
  • Scalable Metrics
  • Events

"ACID transactions" is the top reason why over 2 developers like FoundationDB, while over 36 developers mention "Time-series data analysis" as the leading cause for choosing InfluxDB.

InfluxDB is an open source tool with 16.7K GitHub stars and 2.38K GitHub forks. Here's a link to InfluxDB's open source repository on GitHub.

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Advice on InfluxDB, FoundationDB

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
Karan
Karan

Senior Software Developer at Shyplite

Jan 13, 2022

Decided

So, we started using foundationDB for an OLAP system although the inbuilt tools for some core things like aggregation and filtering were negligible, with the high through put of the DB, we were able to handle it on the application. The system has been running pretty well for the past 6 months, although the data load isn’t very high yet, the performance is fairly promising

40.9k views40.9k
Comments

Detailed Comparison

InfluxDB
InfluxDB
FoundationDB
FoundationDB

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.

FoundationDB is a NoSQL database with a shared nothing architecture. Designed around a "core" ordered key-value database, additional features and data models are supplied in layers. The key-value database, as well as all layers, supports full, cross-key and cross-server ACID transactions.

Time-Centric Functions;Scalable Metrics; Events;Native HTTP API;Powerful Query Language;Built-in Explorer
Multiple data models;Full, multi-key ACID transactions;No locking;Bindings available in Python, Ruby, Node, PHP, Java, Go, and C
Statistics
Stacks
1.0K
Stacks
34
Followers
1.2K
Followers
79
Votes
175
Votes
21
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
    Proprietary query language
  • 1
    HA or Clustering is only in paid version
Pros
  • 6
    ACID transactions
  • 5
    Linear scalability
  • 3
    Great Foundation
  • 3
    Multi-model database
  • 3
    Key-Value Store

What are some alternatives to InfluxDB, FoundationDB?

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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